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Case study: a patient with uncontrolled type 2 diabetes and complex comorbidities whose diabetes care is managed by an advanced practice nurse.

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Geralyn Spollett; Case Study: A Patient With Uncontrolled Type 2 Diabetes and Complex Comorbidities Whose Diabetes Care Is Managed by an Advanced Practice Nurse. Diabetes Spectr 1 January 2003; 16 (1): 32–36. https://doi.org/10.2337/diaspect.16.1.32

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The specialized role of nursing in the care and education of people with diabetes has been in existence for more than 30 years. Diabetes education carried out by nurses has moved beyond the hospital bedside into a variety of health care settings. Among the disciplines involved in diabetes education, nursing has played a pivotal role in the diabetes team management concept. This was well illustrated in the Diabetes Control and Complications Trial (DCCT) by the effectiveness of nurse managers in coordinating and delivering diabetes self-management education. These nurse managers not only performed administrative tasks crucial to the outcomes of the DCCT, but also participated directly in patient care. 1  

The emergence and subsequent growth of advanced practice in nursing during the past 20 years has expanded the direct care component, incorporating aspects of both nursing and medical care while maintaining the teaching and counseling roles. Both the clinical nurse specialist (CNS) and nurse practitioner (NP) models, when applied to chronic disease management, create enhanced patient-provider relationships in which self-care education and counseling is provided within the context of disease state management. Clement 2 commented in a review of diabetes self-management education issues that unless ongoing management is part of an education program, knowledge may increase but most clinical outcomes only minimally improve. Advanced practice nurses by the very nature of their scope of practice effectively combine both education and management into their delivery of care.

Operating beyond the role of educator, advanced practice nurses holistically assess patients’ needs with the understanding of patients’ primary role in the improvement and maintenance of their own health and wellness. In conducting assessments, advanced practice nurses carefully explore patients’ medical history and perform focused physical exams. At the completion of assessments, advanced practice nurses, in conjunction with patients, identify management goals and determine appropriate plans of care. A review of patients’ self-care management skills and application/adaptation to lifestyle is incorporated in initial histories, physical exams, and plans of care.

Many advanced practice nurses (NPs, CNSs, nurse midwives, and nurse anesthetists) may prescribe and adjust medication through prescriptive authority granted to them by their state nursing regulatory body. Currently, all 50 states have some form of prescriptive authority for advanced practice nurses. 3 The ability to prescribe and adjust medication is a valuable asset in caring for individuals with diabetes. It is a crucial component in the care of people with type 1 diabetes, and it becomes increasingly important in the care of patients with type 2 diabetes who have a constellation of comorbidities, all of which must be managed for successful disease outcomes.

Many studies have documented the effectiveness of advanced practice nurses in managing common primary care issues. 4 NP care has been associated with a high level of satisfaction among health services consumers. In diabetes, the role of advanced practice nurses has significantly contributed to improved outcomes in the management of type 2 diabetes, 5 in specialized diabetes foot care programs, 6 in the management of diabetes in pregnancy, 7 and in the care of pediatric type 1 diabetic patients and their parents. 8 , 9 Furthermore, NPs have also been effective providers of diabetes care among disadvantaged urban African-American patients. 10 Primary management of these patients by NPs led to improved metabolic control regardless of whether weight loss was achieved.

The following case study illustrates the clinical role of advanced practice nurses in the management of a patient with type 2 diabetes.

A.B. is a retired 69-year-old man with a 5-year history of type 2 diabetes. Although he was diagnosed in 1997, he had symptoms indicating hyperglycemia for 2 years before diagnosis. He had fasting blood glucose records indicating values of 118–127 mg/dl, which were described to him as indicative of “borderline diabetes.” He also remembered past episodes of nocturia associated with large pasta meals and Italian pastries. At the time of initial diagnosis, he was advised to lose weight (“at least 10 lb.”), but no further action was taken.

Referred by his family physician to the diabetes specialty clinic, A.B. presents with recent weight gain, suboptimal diabetes control, and foot pain. He has been trying to lose weight and increase his exercise for the past 6 months without success. He had been started on glyburide (Diabeta), 2.5 mg every morning, but had stopped taking it because of dizziness, often accompanied by sweating and a feeling of mild agitation, in the late afternoon.

A.B. also takes atorvastatin (Lipitor), 10 mg daily, for hypercholesterolemia (elevated LDL cholesterol, low HDL cholesterol, and elevated triglycerides). He has tolerated this medication and adheres to the daily schedule. During the past 6 months, he has also taken chromium picolinate, gymnema sylvestre, and a “pancreas elixir” in an attempt to improve his diabetes control. He stopped these supplements when he did not see any positive results.

He does not test his blood glucose levels at home and expresses doubt that this procedure would help him improve his diabetes control. “What would knowing the numbers do for me?,” he asks. “The doctor already knows the sugars are high.”

A.B. states that he has “never been sick a day in my life.” He recently sold his business and has become very active in a variety of volunteer organizations. He lives with his wife of 48 years and has two married children. Although both his mother and father had type 2 diabetes, A.B. has limited knowledge regarding diabetes self-care management and states that he does not understand why he has diabetes since he never eats sugar. In the past, his wife has encouraged him to treat his diabetes with herbal remedies and weight-loss supplements, and she frequently scans the Internet for the latest diabetes remedies.

During the past year, A.B. has gained 22 lb. Since retiring, he has been more physically active, playing golf once a week and gardening, but he has been unable to lose more than 2–3 lb. He has never seen a dietitian and has not been instructed in self-monitoring of blood glucose (SMBG).

A.B.’s diet history reveals excessive carbohydrate intake in the form of bread and pasta. His normal dinners consist of 2 cups of cooked pasta with homemade sauce and three to four slices of Italian bread. During the day, he often has “a slice or two” of bread with butter or olive oil. He also eats eight to ten pieces of fresh fruit per day at meals and as snacks. He prefers chicken and fish, but it is usually served with a tomato or cream sauce accompanied by pasta. His wife has offered to make him plain grilled meats, but he finds them “tasteless.” He drinks 8 oz. of red wine with dinner each evening. He stopped smoking more than 10 years ago, he reports, “when the cost of cigarettes topped a buck-fifty.”

The medical documents that A.B. brings to this appointment indicate that his hemoglobin A 1c (A1C) has never been <8%. His blood pressure has been measured at 150/70, 148/92, and 166/88 mmHg on separate occasions during the past year at the local senior center screening clinic. Although he was told that his blood pressure was “up a little,” he was not aware of the need to keep his blood pressure ≤130/80 mmHg for both cardiovascular and renal health. 11  

A.B. has never had a foot exam as part of his primary care exams, nor has he been instructed in preventive foot care. However, his medical records also indicate that he has had no surgeries or hospitalizations, his immunizations are up to date, and, in general, he has been remarkably healthy for many years.

Physical Exam

A physical examination reveals the following:

Weight: 178 lb; height: 5′2″; body mass index (BMI): 32.6 kg/m 2

Fasting capillary glucose: 166 mg/dl

Blood pressure: lying, right arm 154/96 mmHg; sitting, right arm 140/90 mmHg

Pulse: 88 bpm; respirations 20 per minute

Eyes: corrective lenses, pupils equal and reactive to light and accommodation, Fundi-clear, no arteriolovenous nicking, no retinopathy

Thyroid: nonpalpable

Lungs: clear to auscultation

Heart: Rate and rhythm regular, no murmurs or gallops

Vascular assessment: no carotid bruits; femoral, popliteal, and dorsalis pedis pulses 2+ bilaterally

Neurological assessment: diminished vibratory sense to the forefoot, absent ankle reflexes, monofilament (5.07 Semmes-Weinstein) felt only above the ankle

Lab Results

Results of laboratory tests (drawn 5 days before the office visit) are as follows:

Glucose (fasting): 178 mg/dl (normal range: 65–109 mg/dl)

Creatinine: 1.0 mg/dl (normal range: 0.5–1.4 mg/dl)

Blood urea nitrogen: 18 mg/dl (normal range: 7–30 mg/dl)

Sodium: 141 mg/dl (normal range: 135–146 mg/dl)

Potassium: 4.3 mg/dl (normal range: 3.5–5.3 mg/dl)

Lipid panel

    • Total cholesterol: 162 mg/dl (normal: <200 mg/dl)

    • HDL cholesterol: 43 mg/dl (normal: ≥40 mg/dl)

    • LDL cholesterol (calculated): 84 mg/dl (normal: <100 mg/dl)

    • Triglycerides: 177 mg/dl (normal: <150 mg/dl)

    • Cholesterol-to-HDL ratio: 3.8 (normal: <5.0)

AST: 14 IU/l (normal: 0–40 IU/l)

ALT: 19 IU/l (normal: 5–40 IU/l)

Alkaline phosphotase: 56 IU/l (normal: 35–125 IU/l)

A1C: 8.1% (normal: 4–6%)

Urine microalbumin: 45 mg (normal: <30 mg)

Based on A.B.’s medical history, records, physical exam, and lab results, he is assessed as follows:

Uncontrolled type 2 diabetes (A1C >7%)

Obesity (BMI 32.4 kg/m 2 )

Hyperlipidemia (controlled with atorvastatin)

Peripheral neuropathy (distal and symmetrical by exam)

Hypertension (by previous chart data and exam)

Elevated urine microalbumin level

Self-care management/lifestyle deficits

    • Limited exercise

    • High carbohydrate intake

    • No SMBG program

Poor understanding of diabetes

A.B. presented with uncontrolled type 2 diabetes and a complex set of comorbidities, all of which needed treatment. The first task of the NP who provided his care was to select the most pressing health care issues and prioritize his medical care to address them. Although A.B. stated that his need to lose weight was his chief reason for seeking diabetes specialty care, his elevated glucose levels and his hypertension also needed to be addressed at the initial visit.

The patient and his wife agreed that a referral to a dietitian was their first priority. A.B. acknowledged that he had little dietary information to help him achieve weight loss and that his current weight was unhealthy and “embarrassing.” He recognized that his glucose control was affected by large portions of bread and pasta and agreed to start improving dietary control by reducing his portion size by one-third during the week before his dietary consultation. Weight loss would also be an important first step in reducing his blood pressure.

The NP contacted the registered dietitian (RD) by telephone and referred the patient for a medical nutrition therapy assessment with a focus on weight loss and improved diabetes control. A.B.’s appointment was scheduled for the following week. The RD requested that during the intervening week, the patient keep a food journal recording his food intake at meals and snacks. She asked that the patient also try to estimate portion sizes.

Although his physical activity had increased since his retirement, it was fairly sporadic and weather-dependent. After further discussion, he realized that a week or more would often pass without any significant form of exercise and that most of his exercise was seasonal. Whatever weight he had lost during the summer was regained in the winter, when he was again quite sedentary.

A.B.’s wife suggested that the two of them could walk each morning after breakfast. She also felt that a treadmill at home would be the best solution for getting sufficient exercise in inclement weather. After a short discussion about the positive effect exercise can have on glucose control, the patient and his wife agreed to walk 15–20 minutes each day between 9:00 and 10:00 a.m.

A first-line medication for this patient had to be targeted to improving glucose control without contributing to weight gain. Thiazolidinediones (i.e., rosiglitizone [Avandia] or pioglitizone [Actos]) effectively address insulin resistance but have been associated with weight gain. 12 A sulfonylurea or meglitinide (i.e., repaglinide [Prandin]) can reduce postprandial elevations caused by increased carbohydrate intake, but they are also associated with some weight gain. 12 When glyburide was previously prescribed, the patient exhibited signs and symptoms of hypoglycemia (unconfirmed by SMBG). α-Glucosidase inhibitors (i.e., acarbose [Precose]) can help with postprandial hyperglycemia rise by blunting the effect of the entry of carbohydrate-related glucose into the system. However, acarbose requires slow titration, has multiple gastrointestinal (GI) side effects, and reduces A1C by only 0.5–0.9%. 13 Acarbose may be considered as a second-line therapy for A.B. but would not fully address his elevated A1C results. Metformin (Glucophage), which reduces hepatic glucose production and improves insulin resistance, is not associated with hypoglycemia and can lower A1C results by 1%. Although GI side effects can occur, they are usually self-limiting and can be further reduced by slow titration to dose efficacy. 14  

After reviewing these options and discussing the need for improved glycemic control, the NP prescribed metformin, 500 mg twice a day. Possible GI side effects and the need to avoid alcohol were of concern to A.B., but he agreed that medication was necessary and that metformin was his best option. The NP advised him to take the medication with food to reduce GI side effects.

The NP also discussed with the patient a titration schedule that increased the dosage to 1,000 mg twice a day over a 4-week period. She wrote out this plan, including a date and time for telephone contact and medication evaluation, and gave it to the patient.

During the visit, A.B. and his wife learned to use a glucose meter that features a simple two-step procedure. The patient agreed to use the meter twice a day, at breakfast and dinner, while the metformin dose was being titrated. He understood the need for glucose readings to guide the choice of medication and to evaluate the effects of his dietary changes, but he felt that it would not be “a forever thing.”

The NP reviewed glycemic goals with the patient and his wife and assisted them in deciding on initial short-term goals for weight loss, exercise, and medication. Glucose monitoring would serve as a guide and assist the patient in modifying his lifestyle.

A.B. drew the line at starting an antihypertensive medication—the angiotensin-converting enzyme (ACE) inhibitor enalapril (Vasotec), 5 mg daily. He stated that one new medication at a time was enough and that “too many medications would make a sick man out of me.” His perception of the state of his health as being represented by the number of medications prescribed for him gave the advanced practice nurse an important insight into the patient’s health belief system. The patient’s wife also believed that a “natural solution” was better than medication for treating blood pressure.

Although the use of an ACE inhibitor was indicated both by the level of hypertension and by the presence of microalbuminuria, the decision to wait until the next office visit to further evaluate the need for antihypertensive medication afforded the patient and his wife time to consider the importance of adding this pharmacotherapy. They were quite willing to read any materials that addressed the prevention of diabetes complications. However, both the patient and his wife voiced a strong desire to focus their energies on changes in food and physical activity. The NP expressed support for their decision. Because A.B. was obese, weight loss would be beneficial for many of his health issues.

Because he has a sedentary lifestyle, is >35 years old, has hypertension and peripheral neuropathy, and is being treated for hypercholestrolemia, the NP performed an electrocardiogram in the office and referred the patient for an exercise tolerance test. 11 In doing this, the NP acknowledged and respected the mutually set goals, but also provided appropriate pre-exercise screening for the patient’s protection and safety.

In her role as diabetes educator, the NP taught A.B. and his wife the importance of foot care, demonstrating to the patient his inability to feel the light touch of the monofilament. She explained that the loss of protective sensation from peripheral neuropathy means that he will need to be more vigilant in checking his feet for any skin lesions caused by poorly fitting footwear worn during exercise.

At the conclusion of the visit, the NP assured A.B. that she would share the plan of care they had developed with his primary care physician, collaborating with him and discussing the findings of any diagnostic tests and procedures. She would also work in partnership with the RD to reinforce medical nutrition therapies and improve his glucose control. In this way, the NP would facilitate the continuity of care and keep vital pathways of communication open.

Advanced practice nurses are ideally suited to play an integral role in the education and medical management of people with diabetes. 15 The combination of clinical skills and expertise in teaching and counseling enhances the delivery of care in a manner that is both cost-reducing and effective. Inherent in the role of advanced practice nurses is the understanding of shared responsibility for health care outcomes. This partnering of nurse with patient not only improves care but strengthens the patient’s role as self-manager.

Geralyn Spollett, MSN, C-ANP, CDE, is associate director and an adult nurse practitioner at the Yale Diabetes Center, Department of Endocrinology and Metabolism, at Yale University in New Haven, Conn. She is an associate editor of Diabetes Spectrum.

Note of disclosure: Ms. Spollett has received honoraria for speaking engagements from Novo Nordisk Pharmaceuticals, Inc., and Aventis and has been a paid consultant for Aventis. Both companies produce products and devices for the treatment of diabetes.

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INTRODUCTION

This topic will review the clinical presentation, diagnosis, and initial evaluation of diabetes in nonpregnant adults. Screening for and prevention of diabetes, the etiologic classification of diabetes mellitus, the treatment of diabetes, as well as diabetes during pregnancy are discussed separately.

● (See "Screening for type 2 diabetes mellitus" .)

● (See "Prevention of type 2 diabetes mellitus" and "Type 1 diabetes mellitus: Prevention and disease-modifying therapy" .)

● (See "Classification of diabetes mellitus and genetic diabetic syndromes" .)

• Patient Care & Health Information
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• Type 2 diabetes

Type 2 diabetes is a condition that happens because of a problem in the way the body regulates and uses sugar as a fuel. That sugar also is called glucose. This long-term condition results in too much sugar circulating in the blood. Eventually, high blood sugar levels can lead to disorders of the circulatory, nervous and immune systems.

In type 2 diabetes, there are primarily two problems. The pancreas does not produce enough insulin — a hormone that regulates the movement of sugar into the cells. And cells respond poorly to insulin and take in less sugar.

Type 2 diabetes used to be known as adult-onset diabetes, but both type 1 and type 2 diabetes can begin during childhood and adulthood. Type 2 is more common in older adults. But the increase in the number of children with obesity has led to more cases of type 2 diabetes in younger people.

There's no cure for type 2 diabetes. Losing weight, eating well and exercising can help manage the disease. If diet and exercise aren't enough to control blood sugar, diabetes medications or insulin therapy may be recommended.

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Symptoms of type 2 diabetes often develop slowly. In fact, you can be living with type 2 diabetes for years and not know it. When symptoms are present, they may include:

• Increased thirst.
• Frequent urination.
• Increased hunger.
• Unintended weight loss.
• Blurred vision.
• Slow-healing sores.
• Frequent infections.
• Numbness or tingling in the hands or feet.
• Areas of darkened skin, usually in the armpits and neck.

When to see a doctor

See your health care provider if you notice any symptoms of type 2 diabetes.

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Type 2 diabetes is mainly the result of two problems:

• Cells in muscle, fat and the liver become resistant to insulin As a result, the cells don't take in enough sugar.
• The pancreas can't make enough insulin to keep blood sugar levels within a healthy range.

Exactly why this happens is not known. Being overweight and inactive are key contributing factors.

How insulin works

Insulin is a hormone that comes from the pancreas — a gland located behind and below the stomach. Insulin controls how the body uses sugar in the following ways:

• Sugar in the bloodstream triggers the pancreas to release insulin.
• Insulin circulates in the bloodstream, enabling sugar to enter the cells.
• The amount of sugar in the bloodstream drops.
• In response to this drop, the pancreas releases less insulin.

The role of glucose

Glucose — a sugar — is a main source of energy for the cells that make up muscles and other tissues. The use and regulation of glucose includes the following:

• Glucose comes from two major sources: food and the liver.
• Glucose is absorbed into the bloodstream, where it enters cells with the help of insulin.
• The liver stores and makes glucose.
• When glucose levels are low, the liver breaks down stored glycogen into glucose to keep the body's glucose level within a healthy range.

In type 2 diabetes, this process doesn't work well. Instead of moving into the cells, sugar builds up in the blood. As blood sugar levels rise, the pancreas releases more insulin. Eventually the cells in the pancreas that make insulin become damaged and can't make enough insulin to meet the body's needs.

Risk factors

Factors that may increase the risk of type 2 diabetes include:

• Weight. Being overweight or obese is a main risk.
• Fat distribution. Storing fat mainly in the abdomen — rather than the hips and thighs — indicates a greater risk. The risk of type 2 diabetes is higher in men with a waist circumference above 40 inches (101.6 centimeters) and in women with a waist measurement above 35 inches (88.9 centimeters).
• Inactivity. The less active a person is, the greater the risk. Physical activity helps control weight, uses up glucose as energy and makes cells more sensitive to insulin.
• Family history. An individual's risk of type 2 diabetes increases if a parent or sibling has type 2 diabetes.
• Race and ethnicity. Although it's unclear why, people of certain races and ethnicities — including Black, Hispanic, Native American and Asian people, and Pacific Islanders — are more likely to develop type 2 diabetes than white people are.
• Blood lipid levels. An increased risk is associated with low levels of high-density lipoprotein (HDL) cholesterol — the "good" cholesterol — and high levels of triglycerides.
• Age. The risk of type 2 diabetes increases with age, especially after age 35.
• Prediabetes. Prediabetes is a condition in which the blood sugar level is higher than normal, but not high enough to be classified as diabetes. Left untreated, prediabetes often progresses to type 2 diabetes.
• Pregnancy-related risks. The risk of developing type 2 diabetes is higher in people who had gestational diabetes when they were pregnant and in those who gave birth to a baby weighing more than 9 pounds (4 kilograms).
• Polycystic ovary syndrome. Having polycystic ovary syndrome — a condition characterized by irregular menstrual periods, excess hair growth and obesity — increases the risk of diabetes.

Complications

Type 2 diabetes affects many major organs, including the heart, blood vessels, nerves, eyes and kidneys. Also, factors that increase the risk of diabetes are risk factors for other serious diseases. Managing diabetes and controlling blood sugar can lower the risk for these complications and other medical conditions, including:

• Heart and blood vessel disease. Diabetes is associated with an increased risk of heart disease, stroke, high blood pressure and narrowing of blood vessels, a condition called atherosclerosis.
• Nerve damage in limbs. This condition is called neuropathy. High blood sugar over time can damage or destroy nerves. That may result in tingling, numbness, burning, pain or eventual loss of feeling that usually begins at the tips of the toes or fingers and gradually spreads upward.
• Other nerve damage. Damage to nerves of the heart can contribute to irregular heart rhythms. Nerve damage in the digestive system can cause problems with nausea, vomiting, diarrhea or constipation. Nerve damage also may cause erectile dysfunction.
• Kidney disease. Diabetes may lead to chronic kidney disease or end-stage kidney disease that can't be reversed. That may require dialysis or a kidney transplant.
• Eye damage. Diabetes increases the risk of serious eye diseases, such as cataracts and glaucoma, and may damage the blood vessels of the retina, potentially leading to blindness.
• Skin conditions. Diabetes may raise the risk of some skin problems, including bacterial and fungal infections.
• Slow healing. Left untreated, cuts and blisters can become serious infections, which may heal poorly. Severe damage might require toe, foot or leg amputation.
• Hearing impairment. Hearing problems are more common in people with diabetes.
• Sleep apnea. Obstructive sleep apnea is common in people living with type 2 diabetes. Obesity may be the main contributing factor to both conditions.
• Dementia. Type 2 diabetes seems to increase the risk of Alzheimer's disease and other disorders that cause dementia. Poor control of blood sugar is linked to a more rapid decline in memory and other thinking skills.

Healthy lifestyle choices can help prevent type 2 diabetes. If you've received a diagnosis of prediabetes, lifestyle changes may slow or stop the progression to diabetes.

A healthy lifestyle includes:

• Eating healthy foods. Choose foods lower in fat and calories and higher in fiber. Focus on fruits, vegetables and whole grains.
• Getting active. Aim for 150 or more minutes a week of moderate to vigorous aerobic activity, such as a brisk walk, bicycling, running or swimming.
• Losing weight. If you are overweight, losing a modest amount of weight and keeping it off may delay the progression from prediabetes to type 2 diabetes. If you have prediabetes, losing 7% to 10% of your body weight may reduce the risk of diabetes.
• Avoiding long stretches of inactivity. Sitting still for long periods of time can increase the risk of type 2 diabetes. Try to get up every 30 minutes and move around for at least a few minutes.

For people with prediabetes, metformin (Fortamet, Glumetza, others), a diabetes medication, may be prescribed to reduce the risk of type 2 diabetes. This is usually prescribed for older adults who are obese and unable to lower blood sugar levels with lifestyle changes.

More Information

• Diabetes prevention: 5 tips for taking control
• Professional Practice Committee: Standards of Medical Care in Diabetes — 2020. Diabetes Care. 2020; doi:10.2337/dc20-Sppc.
• Diabetes mellitus. Merck Manual Professional Version. https://www.merckmanuals.com/professional/endocrine-and-metabolic-disorders/diabetes-mellitus-and-disorders-of-carbohydrate-metabolism/diabetes-mellitus-dm. Accessed Dec. 7, 2020.
• Melmed S, et al. Williams Textbook of Endocrinology. 14th ed. Elsevier; 2020. https://www.clinicalkey.com. Accessed Dec. 3, 2020.
• Diabetes overview. National Institute of Diabetes and Digestive and Kidney Diseases. https://www.niddk.nih.gov/health-information/diabetes/overview/all-content. Accessed Dec. 4, 2020.
• AskMayoExpert. Type 2 diabetes. Mayo Clinic; 2018.
• Feldman M, et al., eds. Surgical and endoscopic treatment of obesity. In: Sleisenger and Fordtran's Gastrointestinal and Liver Disease: Pathophysiology, Diagnosis, Management. 11th ed. Elsevier; 2021. https://www.clinicalkey.com. Accessed Oct. 20, 2020.
• Hypersmolar hyperglycemic state (HHS). Merck Manual Professional Version. https://www.merckmanuals.com/professional/endocrine-and-metabolic-disorders/diabetes-mellitus-and-disorders-of-carbohydrate-metabolism/hyperosmolar-hyperglycemic-state-hhs. Accessed Dec. 11, 2020.
• Diabetic ketoacidosis (DKA). Merck Manual Professional Version. https://www.merckmanuals.com/professional/endocrine-and-metabolic-disorders/diabetes-mellitus-and-disorders-of-carbohydrate-metabolism/diabetic-ketoacidosis-dka. Accessed Dec. 11, 2020.
• Hypoglycemia. Merck Manual Professional Version. https://www.merckmanuals.com/professional/endocrine-and-metabolic-disorders/diabetes-mellitus-and-disorders-of-carbohydrate-metabolism/hypoglycemia. Accessed Dec. 11, 2020.
• 6 things to know about diabetes and dietary supplements. National Center for Complementary and Integrative Health. https://www.nccih.nih.gov/health/tips/things-to-know-about-type-diabetes-and-dietary-supplements. Accessed Dec. 11, 2020.
• Type 2 diabetes and dietary supplements: What the science says. National Center for Complementary and Integrative Health. https://www.nccih.nih.gov/health/providers/digest/type-2-diabetes-and-dietary-supplements-science. Accessed Dec. 11, 2020.
• Preventing diabetes problems. National Institute of Diabetes and Digestive and Kidney Diseases. https://www.niddk.nih.gov/health-information/diabetes/overview/preventing-problems/all-content. Accessed Dec. 3, 2020.
• Schillie S, et al. Prevention of hepatitis B virus infection in the United States: Recommendations of the Advisory Committee on Immunization Practices. MMWR Recommendations and Reports. 2018; doi:10.15585/mmwr.rr6701a1.
• Caffeine: Does it affect blood sugar?
• GLP-1 agonists: Diabetes drugs and weight loss
• Hyperinsulinemia: Is it diabetes?
• Medications for type 2 diabetes

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• Diabetes & Primary Care
• Vol:23 | No:02

Interactive case study: Making a diagnosis of type 2 diabetes

• 12 Apr 2021

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Diabetes & Primary Care ’s series of interactive case studies is aimed at GPs, practice nurses and other professionals in primary and community care who would like to broaden their understanding of type 2 diabetes.

The three mini-case studies presented with this issue of the journal take you through what to consider in making an accurate diagnosis of type 2 diabetes.

The format uses typical clinical scenarios as tools for learning. Information is provided in short sections, with most ending in a question to answer before moving on to the next section.

Working through the case studies will improve your knowledge and problem-solving skills in type 2 diabetes by encouraging you to make evidence-based decisions in the context of individual cases.

Crucially, you are invited to respond to the questions by typing in your answers. In this way, you are actively involved in the learning process, which is a much more effective way to learn.

By actively engaging with these case histories, I hope you will feel more confident and empowered to manage such presentations effectively in the future.

Colin is a 51-year-old construction worker. A recent blood test shows an HbA 1c of 67 mmol/mol. Is this result enough to make a diagnosis of diabetes?

Rao, a 42-year-old accountant of Asian origin, is currently asymptomatic but has a strong family history of type 2 diabetes. Tests have revealed a fasting plasma glucose level of 6.7 mmol/L and an HbA 1c of 52 mmol/mol. How would you interpret these results?

43-year-old Rachael has complained of fatigue. She has a BMI of 28.4 kg/m 2 and her mother has type 2 diabetes. Rachael’s HbA 1c is 46 mmol/mol. How would you interpret her HbA 1c measurement?

By working through these interactive cases, you will consider the following issues and more:

• The criteria for the correct diagnosis of diabetes and non-diabetic hyperglycaemia.
• Which tests to use in different circumstances to determine a diagnosis.
• How to avoid making errors in classification of the type of diabetes being diagnosed.
• The appropriate steps to take following diagnosis.

How to diagnose and treat hypertension in adults with type 2 diabetes

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Diagnosing and treating hypertension in accordance with updated NICE guidelines.

24 Apr 2024

Quantifying the risk of worsening glycaemia, and how should healthcare professionals respond?

22 Apr 2024

Diagnosing and managing non-diabetic hyperglycaemia.

17 Apr 2024

The mortality benefits of smoking cessation may be greater and accrue more rapidly than previously understood.

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Open Access

Peer-reviewed

Research Article

Serum brain-derived neurotrophic factor levels in type 2 diabetes mellitus patients and its association with cognitive impairment: A meta-analysis

Contributed equally to this work with: Wan-li He, Fei-xia Chang

Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft

Affiliation Department of Medical Imaging Center, Gansu Provincial Maternal and Child Care Hospital (Gansu Provincial Central Hospital), Lanzhou, Gansu, China

Roles Conceptualization, Data curation, Investigation, Methodology, Writing – original draft

Roles Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing

Roles Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Roles Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Medical Imaging Center, Gansu Provincial Maternal and Child Care Hospital, Lanzhou, Gansu, China

Roles Data curation, Project administration, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Radiology, Gansu Provincial Hospital, Lanzhou, Gansu, China

• Wan-li He, 
• Fei-xia Chang, 
• Tao Wang, 
• Bi-xia Sun, 
• Rui-rong Chen, 
• Lian-ping Zhao

• Published: April 22, 2024
• https://doi.org/10.1371/journal.pone.0297785
• Peer Review
• Reader Comments

To compare the serum levels of brain-derived neurotrophic factor (BDNF) in type 2 diabetes mellitus (T2DM) patients with healthy controls (HC) and evaluate the BDNF levels in T2DM patients with/without cognitive impairment.

PubMed, EMBASE, and the Cochrane Library databases were searched for the published English literature on BDNF in T2DM patients from inception to December 2022. The BDNF data in the T2DM and HC groups were extracted, and the study quality was evaluated using the Agency for Healthcare Research and Quality. A meta-analysis of the pooled data was conducted using Review Manager 5.3 and Stata 12.0 software.

A total of 18 English articles fulfilled with inclusion criteria. The standard mean difference of the serum BDNF level was significantly lower in T2DM than that in the HC group (SMD: -2.04, z = 11.19, P <0.001). Besides, T2DM cognitive impairment group had a slightly lower serum BDNF level compared to the non-cognitive impairment group (SMD: -2.59, z = 1.87, P = 0.06).

BDNF might be involved in the neuropathophysiology of cerebral damage in T2DM, especially cognitive impairment in T2DM.

Citation: He W-l, Chang F-x, Wang T, Sun B-x, Chen R-r, Zhao L-p (2024) Serum brain-derived neurotrophic factor levels in type 2 diabetes mellitus patients and its association with cognitive impairment: A meta-analysis. PLoS ONE 19(4): e0297785. https://doi.org/10.1371/journal.pone.0297785

Editor: Purvi Purohit, All India Institute of Medical Sciences, INDIA

Received: July 25, 2023; Accepted: January 12, 2024; Published: April 22, 2024

Copyright: © 2024 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Type 2 diabetes mellitus (T2DM) is a chronic systemic metabolic disorder seriously affecting human health, which is triggered by genetic predisposition and environmental factors [ 1 ]. International Diabetes Federation estimates that T2DM occurs in over 400 million people and it is one of the largest epidemics worldwide [ 2 ]. T2DM manifesting through fasting and post-prandial hyperglycemia can induce various life-threatening co-morbidities and complications such as diabetic neuropathy and diabetic nephropathy [ 3 , 4 ]. Cognitive dysfunction is an important complication observed in type T2DM patients [ 5 ]. In addition, T2DM is an important risk factor implicated in cognitive deficits except aging and neurodegenerative disorder [ 6 ]. T2DM patients have a greater decline in cognitive function than those without T2DM [ 7 ]. Besides, it is reported that T2DM accelerates brain aging and cognitive decline [ 8 ]. T2DM is significantly associated with an increased risk of dementia and a large portion of T2DM patients with cognitive impairment eventually progress to dementia [ 9 , 10 ], which may represent a consequence of brain-specific insulin resistance and impaired glucose regulation [ 11 ]. However, the pathophysiological mechanisms of cerebral impairment in T2DM remain elucidated.

Brain-derived neurotrophic factor (BDNF), a member of the neurotrophic family of proteins, is most widely distributed in the central nervous system (CNS) [ 12 ]. It plays an important role in protecting neurons and synaptic activity [ 13 ]. BDNF was released from the brain to peripheral circulation [ 14 ], and there is a correlation between BDNF in serum and CNS, providing an alternative measure of BDNF changes [ 15 ]. Alternation of BDNF is observed in the pathophysiological basis of many neurodegenerative and psychiatric disorders [ 16 ], including Alzheimer’s disease and depression [ 17 , 18 ]. Furthermore, the serum BDNF is a useful biomarker for executive cognitive impairment in schizophrenia patients [ 19 , 20 ]. In addition, the BDNF Val66Met polymorphism may be a major factor in the susceptibility to cognitive impairment which affects the secretion of mature BDNF [ 21 ]. A meta-analysis suggests that BDNF Val66Met is associated with cognitive impairment in Parkinson’s disease [ 22 ], confirming that BDNF is a risk factor for this disorder [ 23 ]. Furthermore, BDNF is related to the regulation of glucose levels [ 24 ]. Exogenous BDNF reduces blood glucose concentrations and glycated hemoglobin in obese diabetic mice [ 25 ], which is consistent with the finding that there was a positive correlation between BDNF and insulin sensitivity [ 26 ]. Previous studies have revealed the relationship between serum BDNF and diabetic conditions in T2DM patients with controversial results [ 27 – 34 ]. However, the precise role of BDNF in the development of T2DM patients as well as in cognitive function remains unclear.

Therefore, our study aims to explore the alteration tendency of the serum BDNF levels in T2DM patients with or without cognitive impairment using meta-analysis with a comprehensive evaluation of relevant literature. The current study will provide a basic foundation for further investigating the neuropathophysiological mechanisms of cerebral damage in T2DM.

2.1. Literature search and selection

A systemic search strategy was used to identify the relevant studies published in PubMed, EMBASE, and the Cochrane Library from inception to December 2022. We applied a search strategy based on the combination of relevant terms. Two independent investigators acquired articles and sequentially screened their titles and abstracts for eligibility. Then, full texts of articles deemed potentially eligible were acquired. Any disagreement would be solved via discussion with the help of a third senior investigator. A screening guide was used to ensure that the selection criteria were constantly applied.

Inclusion criteria: (1) clinical cross-sectional studies concerning the quantitative values of serum BDNF level in T2DM patients; (2) sufficient data were available for mean and standard deviation analysis of BDNF level; (3) original research. Exclusion criteria: (1) review, abstracts only, letters, comments, guidelines, and case reports; (2) studies in vitro or in animal models; (3) duplicate publications; (4) incomplete data.

2.2. Quality evaluation and data extraction

Agency for Healthcare Research and Quality (AHRQ) was used to evaluate the quality of the included cross-sectional studies. The AHRQ included 8 items with a total score of 8 points. Two independent researchers assessed the quality of the literature and reached a consensus after consultation when necessary.

Calculate standard deviation from confidence interval:

(2) Calculate the standard deviation from an interquartile range:

(3) Calculate standard deviation by p -value:

2.3. Statistical analysis

All the meta-analyses were performed on Review Manager 5.3 and STATA12.0 with a significance level of P <0.05. To calculate the effect size for each study, the summary standard mean difference (SMD) and 95% confidence interval were applied to evaluate the serum BDNF values between T2DM and healthy control (HC), T2DM with or without cognitive impairment. Pooled SMD and corresponding 95% confidence interval were calculated using the inverse variances method. Heterogeneity was estimated using the Cochran Q ( P ) and the inconsistency index where a P value less than 0.05 and I 2 value greater than 50% indicated the presence of significant heterogeneity across the enrolled studies. If notable heterogeneity was observed, a random-effect model was applied and subgroup analyses were used to determine factors that contributed to the heterogeneity and to explore how those factors influenced the results. Subgroup analysis was stratified by the BDNF measuring instruments brand (China or USA; same brand in China or USA), ethnicity (Asian or European), and population [adults or the aged (years≥60)]. In addition, sensitivity analysis was performed to evaluate the reliability of included studies using STATA 12.0. The Egger’s test and the Begg’s test were applied to evaluate potential publication bias using STATA 12.0.

3.1. Search and selection results

The main search strategy is illustrated in Table 1 . Studies selection was managed using EndNote X7. A total of 678 records were initially identified, but only 501 records remained after the elimination of duplicates. Only 51 records were remaining after screening titles, and subsequently, 29 records remained after reading the abstract. After reading full texts, 11 articles with incomplete data were excluded and finally, 18 articles were enrolled. The flow diagram is shown in Fig 1 .

• PPT PowerPoint slide
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• TIFF original image

https://doi.org/10.1371/journal.pone.0297785.g001

https://doi.org/10.1371/journal.pone.0297785.t001

3.2. Characteristics and quality evaluation

Eighteen articles were included in the meta-analysis. The basic characteristics and quality evaluation of the studies are shown in Table 2 . Among them, 17 articles had T2DM and HC groups, and 3 articles divided the T2DM group into two subgroups according to the presence of cognitive impairment. Of the 18 articles included, 13 were done in China, 2 in Japan, and 1 in each of the following countries (USA, Italy, and Turkey). The sample’s mean age was >18 years in 15 articles and >60 years in 3 articles. The diagnostic criteria of T2DM as recommended by the World Health Organization were adopted in 11 articles; whereas the American Diabetes Association was employed in 1 article, but the remaining articles were not mentioned. Measurement of BDNF using ELISA in 17 articles, but 1 article was not mentioned. All of the 18 included studies were cross-sectional studies. Based on the quality evaluation of AHRQ, 11 studies scored 8, 4 studies scored 7, 1 study scored 5, and 1 study scored 4.

https://doi.org/10.1371/journal.pone.0297785.t002

3.3. Meta-analysis

We compared the BDNF level between T2DM and HC groups ( P < 0.001, I 2 = 99%), and between the T2DM with or without cognitive impairment groups ( P < 0.001, I 2 = 90%) using a random-effect model since the heterogeneity test showed the I 2 value >50%.

Seventeen articles contained 2966 T2DM cases and 3580 HCs. The serum BDNF level in the T2DM group was significantly lower than that in the HC group [SMD: -2.04, z = 11.19, P < 0.001] ( Fig 2A ) . The number of T2DM patients with or without cognitive impairment was 672 and 1913, respectively. The serum BDNF levels in T2DM with cognitive impairment group had a marginal difference from those without cognitive impairment [SMD: -2.59, z = 1.87, P = 0.06] ( Fig 2B ) .

(A) The different BDNF levels between T2DM and HC. (B) The different BDNF levels in T2DM patients with or without cognitive impairment. Abbreviations: BDNF, brain-derived neurotrophic factor; T2DM, type 2 diabetes mellitus; HC, healthy controls.

https://doi.org/10.1371/journal.pone.0297785.g002

3.4. Sensitivity analysis

Sensitivity analyses were conducted to evaluate the robustness of the findings by excluding 1 study at a time to assess if the results were driven by any one study. The significance of the meta-analysis outcome for T2DM and HC group changed when ruling out any one of 6/17 studies and the results also changed in T2DM with or without cognitive impairment group after ruling out 1/3 study, suggesting the results were unstable.

3.5. Subgroup analysis

Subgroup analysis based on the BDNF measuring instruments (either China or USA) exhibited that there were significant differences in BDNF values between T2DM and HC (China: P = 0.05; USA: P < 0.001; Total: P < 0.001), with large heterogeneity (China: P < 0.001 and I 2 = 100%; USA: P < 0.001 and I 2 = 99%; Total: P < 0.001 and I 2 = 99%) ( Fig 3 ) . Then, subgroup analysis was performed on the same instrument brand in China or the USA, respectively and similar results were observed (China: P <0.001; USA: P = 0.002; Total: P <0.001). The heterogeneity was only observed in the same brand from the USA, but not in the same brand from China [China: (P = 0.84 and I 2 = 0%; USA: P < 0.001 and I 2 = 98%; Total: P < 0.001 and I 2 = 96%)] ( Fig 4 ) . Subgroup analysis based on ethnicity and population distribution presented that the BDNF values were significantly different, except for the European (Asian: P < 0.001; European: P = 0.59; Total: P < 0.001). In addition, there was significant difference in the adults in T2DM and HC, except for the aged (adults: P < 0.001; the aged: P = 0.25; Total: P < 0.001), and there were no marked decrease in heterogeneity (Asian: P < 0.001 and I 2 = 99%; European: P < 0.001 and I 2 = 99%; Total: P < 0.001 and I 2 = 99%) (Adults: P < 0.001 and I 2 = 99%; the aged: P = 0.03 and I 2 = 80%; Total: P < 0.001 and I 2 = 99%) (Figs 5 and 6 ) .

https://doi.org/10.1371/journal.pone.0297785.g003

https://doi.org/10.1371/journal.pone.0297785.g004

https://doi.org/10.1371/journal.pone.0297785.g005

https://doi.org/10.1371/journal.pone.0297785.g006

3.6. Publication bias analysis

After Egger’s and Begg’s test, the studies of T2DM and HC group, T2DM with or without cognitive impairment group showed no significant publication bias ( P = 0.606, P = 0.672; P = 0.202, P = 1.000).

4. Discussion

Our study is the first meta-analysis to evaluate the levels of serum BDNF in T2DM patients and HCs and compare the levels between T2DM patients with or without cognitive impairment. We found that the serum BDNF levels were lower in T2DM compared with HC. Furthermore, the serum BDNF levels had a decreasing tendency in T2DM patients with cognitive impairment compared with those without cognitive impairment.

The BDNF plays a key role in the pathophysiology of T2DM due to improving glucose metabolism and insulin sensitivity [ 44 – 46 ]. Previous studies have reported that T2DM patients exhibited significantly lower levels of serum BDNF compared with normal controls [ 27 – 32 , 43 ], which is consistent with our research. Additionally, the cerebral output of BDNF is inhibited under hyperglycemia, logically decreased serum BDNF may be detected in the uncontrolled T2DM patients [ 14 ]. This is in line with the findings that there is an inverse correlation between serum BDNF levels and long-standing diabetes, in males and aged T2DM patients [ 43 ]. Interestingly, upregulated serum BDNF levels in T2DM patients were also reported [ 33 , 34 ]. Such discrepancy is possibly related to physical exercise, obesity, and a balanced diet in T2DM patients [ 47 – 50 ]. In addition, the serum BDNF levels are increased in T2DM patients who received metformin treatment [ 3 ]. This may also link to a compensatory mechanism of serum BDNF release in T2DM [ 33 ], which is supported by the findings that the upregulated serum BDNF levels control blood glucose in newly diagnosed T2DM patients, but this control ability might be lost in a long term T2DM patients [ 35 ]. Our explanation is further supported by a resting state fMRI report showing enhanced functional connectivity of the left hippocampus (a major source of BDNF) with the left inferior frontal gyrus in the early stage of T2DM, which might contribute to adaptive compensation of hippocampal function [ 51 ]. Taken together, the serum BDNF could be a useful biological marker to monitor the development of T2DM and the cerebral impairment in T2DM.

T2DM has reduced the number of new neurons in the hippocampus, and hippocampal neurogenesis plays an important role in learning and memory function throughout life [ 52 ]. The Hippocampal perhaps regulates BDNF to provide neuroprotection and control of synaptic interactions [ 53 – 56 ]. In the present meta-analysis, the serum BDNF levels presented a decreasing tendency in T2DM patients with cognitive impairment compared with those patients without cognitive impairment. Such downregulation was also observed in Alzheimer’s disease, showing that the serum BDNF levels may be involved in the progression of cognitive impairment [ 57 ]. Such findings showed that serum BDNF levels may be involved in the progression of cognitive impairment in patients with T2DM. Thus, the reduction of BDNF might contribute to the neuropathophysiology of brain damage in T2DM, especially relating to cognitive impairment in T2DM.

However, substantial heterogeneity existed in the present meta-analysis. The heterogeneity could be generated from related factors, including the different brands of instruments for measuring BDNF, times and methods of blood collection, population distributions, and ethnicities. Such heterogeneity has been eliminated in the subgroup analysis by comparing the data from the same brands of instruments.

There are some limitations in the study. Firstly, the meta-analysis mostly included Chinese Han populations, which may not reflect the entire population/race. Secondly, different diagnostic criteria for diabetes were applied which might also compromise the data analysis. Although internationally recognized scales were utilized, the lack of a standard protocol for cognitive impairment could lead to inconsistent results.

In conclusion, the present meta-analysis suggests that the decrease in serum BDNF levels in T2DM patients has resolved the inconsistencies in previous studies. The serum BDNF levels in T2DM patients with cognitive impairment had a downward trend compared with those patients without cognitive impairment. Moreover, the reduction of serum BDNF may be a vital neuropathophysiological mechanism of cognitive impairment in T2DM patients.

Supporting information

S1 raw data..

https://doi.org/10.1371/journal.pone.0297785.s001

S2 Raw data.

https://doi.org/10.1371/journal.pone.0297785.s002

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Diagnosis and Treatment

Criteria for diagnosis.

HbA1c ≥ 6.5%

Fasting plasma glucose (FPG) ≥ 126 mg/dL

2 hour plasma glucose ≥ 200 mg/dL during an oral glucose tolerance test (OGTT)

In a patient with classic symptoms of hyperglycemia, a random plasma glucose ≥ 200 mg/dL

Treatment of type 2 diabetes mellitus (T2DM) focuses on decreasing blood glucose, increasing insulin secretion, or countering insulin resistance. Treatment of symptoms, such as diabetic retinopathy, nephropathy, or neuropathy requires additional and involved patient education, medications, and therapies.

Lifestyle Modifications

Treatment of obesity and other symptoms of metabolic syndrome is essential. Exercise is an effective intervention because it reduces postprandial blood glucose levels, diminishes insulin requirements, lowers triglyceride and cholesterol levels, and increases the level of HDL cholesterol. Physical activity also aids in weight reduction. Diet modifications include restricted yet consistent caloric intake appropriate for ideal weight and activity level. Dietary counselling is through medical nutrition therapy (MNT) should focus on achieving biometric goals (McCance & Huether, 2014).

Getty Images; Shutterstock; Michael Phillips/Getty Images

Pharmacological Interventions

Oral hypoglycemic medications are usually needed for optimal management of T2DM. Insulin may be need in later stages due to functional loss of beta cells of the pancreas (McCance & Huether, 2014).

Bariatric surgery may be indicated for patients who are morbidly obese and unresponsive to diet and exercise interventions. Currently, powerful evidence exists that shows bariatric surgery improves glycemic control in up to 80% of individuals with T2DM even before there is any significant weight loss (McCance & Huether, 2014).

• Open access
• Published: 17 April 2024

Navigating outpatient care of patients with type 2 diabetes after hospital discharge - a qualitative longitudinal study

• Léa Solh Dost 1 , 2 ,
• Giacomo Gastaldi 3 ,
• Marcelo Dos Santos Mamed 4 , 5 &
• Marie P. Schneider 1 , 2  

BMC Health Services Research volume  24 , Article number:  476 ( 2024 ) Cite this article

257 Accesses

Metrics details

The transition from hospital to outpatient care is a particularly vulnerable period for patients as they move from regular health monitoring to self-management. This study aimed to map and investigate the journey of patients with polymorbidities, including type 2 diabetes (T2D), in the 2 months following hospital discharge and examine patients’ encounters with healthcare professionals (HCPs).

Patients discharged with T2D and at least two other comorbidities were recruited during hospitalization. This qualitative longitudinal study consisted of four semi-structured interviews per participant conducted from discharge up to 2 months after discharge. The interviews were based on a guide, transcribed verbatim, and thematically analyzed. Patient journeys through the healthcare system were represented using the patient journey mapping methodology.

Seventy-five interviews with 21 participants were conducted from October 2020 to July 2021. The participants had a median of 11 encounters (min–max: 6–28) with HCPs. The patient journey was categorized into six key steps: hospitalization, discharge, dispensing prescribed medications by the community pharmacist, follow-up calls, the first medical appointment, and outpatient care.

Conclusions

The outpatient journey in the 2 months following discharge is a complex and adaptive process. Despite the active role of numerous HCPs, navigation in outpatient care after discharge relies heavily on the involvement and responsibilities of patients. Preparation for discharge, post-hospitalization follow-up, and the first visit to the pharmacy and general practitioner are key moments for carefully considering patient care. Our findings underline the need for clarified roles and a standardized approach to discharge planning and post-discharge care in partnership with patients, family caregivers, and all stakeholders involved.

Peer Review reports

Care transition is defined as “the movement patients make between healthcare practitioners and settings as their condition and care needs change in the course of a chronic or acute illness” [ 1 ]. The transition from hospital to outpatient care is a particularly vulnerable period for patients as they move from a medical environment with regular health monitoring to self-management, where they must implement a large amount of information received during their hospital stay [ 2 , 3 , 4 , 5 , 6 ]. This transition period can be defined as “the post-hospital syndrome,” which corresponds to a transient period of vulnerability (e.g., 30 days) for various health problems, such as stress, immobility, confusion, and even cognitive decline in older adults, leading to complications [ 7 ]. Furthermore, discharged patients may experience a lack of care coordination, receive incomplete information, and inadequate follow-ups, leading to potential adverse events and hospital readmissions [ 8 , 9 , 10 ].

People with type 2 diabetes mellitus (T2D) represent a high proportion of hospitalized patients, and their condition and medications are associated with a higher rate of hospital readmission [ 11 , 12 , 13 ]. Moreover, T2D is generally associated with multiple comorbidities. This complex disease requires time-consuming self-management tasks such as polypharmacy, adaptations of medication dosages, diet, exercise, and medical follow-up, especially during care transition [ 14 , 15 , 16 ].

Various interventions and practices, such as enhanced patient education, discharge counseling, and timely follow-up, have been studied to improve care transition for patients with chronic diseases; however, they have shown mixed results in reducing costs and rehospitalization [ 17 , 18 , 19 , 20 ]. In addition, patient perspectives and patient-reported outcomes are rarely considered; however, their involvement and monitoring are essential for seamless and integrated care [ 21 , 22 ]. Care integration, an approach to strengthening healthcare systems in partnership with people, focuses on patient health needs, the quality of professional services, and interprofessional collaboration. This approach prevents care fragmentation for patients with complex needs [ 23 , 24 ]. Therefore, knowledge of healthcare system practices is essential to ensure integrated, coordinated, and high-quality care. Patient perspectives are critical, considering the lack of literature on how patients perceive their transition from hospital to autonomous care management [ 25 , 26 ].

Patients’ journeys during hospitalization have been described in the literature using various methods such as shadowing, personal diaries, and interviews; however, patients’ experiences after hospital discharge are rarely described [ 26 , 27 ]. Jackson et al. described the complexity of patient journeys in outpatient care after discharge using a multiple case study method to follow three patients with chronic obstructive pulmonary disease from hospitalization to 3 months post-discharge [ 26 ]. The literature does not provide an in-depth understanding of the experiences of patients with comorbidities during care transition upon hospital discharge. The assumption about the patient journey after discharge is that multiple and multi-professional encounters will ensure the transition of care from hospitalization to self-management, but often without care coordination.

This study aimed to investigate the healthcare trajectories of patients with comorbidities, including T2D, during the 2 months following hospital discharge and to examine patients’ encounters with healthcare professionals (HCPs).

While this article focuses on patients’ journeys to outpatient care, another article describes and analyzes patients’ medication management, knowledge, and adherence [ 28 ]. This study followed the Consolidated Criteria for Reporting Qualitative Research (COREQ).

Study design and population

A qualitative longitudinal research approach was adopted, with four individual semi-structured interviews over 2 months after discharge (approximately 3, 10, 30, and 60 days after discharge) that took place at home, by telephone, secured video call, or at the university at the participant’s convenience. Participants were recruited during hospitalization. The inclusion criteria were patients with T2D, with at least two other comorbidities, at least one medication change during hospitalization, hospitalization duration of at least 3 days, and those who returned home after discharge and self-managed their medications. A family caregiver could also participate in the interviews alongside to participants.

Researcher characteristics

All the researchers were trained in qualitative studies. The ward diabetologist and researcher (GG) who enrolled the patients in the study participated in most participants’ care during hospitalization. LS (Ph.D. student and community pharmacist) was unknown to participants and presented herself during hospitalization as a “researcher” rather than a pharmacist to avoid any risk of influencing participants’ answers. MS is a professor in pharmacy, whose research focuses on medication adherence in chronic diseases and aims at better understanding this behavior and its consequences for patients and the healthcare system. MDS is a researcher, linguist, and clinical psychologist, with a particular interest in patients living with chronic conditions such as diabetes and a strong experience in qualitative methodology and verbal data analysis.

Data collection

The interviews were based on four semi-structured interview guides based on existing frameworks and theories: the World Health Organization’s five dimensions for adherence, the Information-Motivation-Behavioral Skills model, and the Social Cognitive Theory [ 29 , 30 , 31 ]. For in-depth documentation of participants’ itinerary in the healthcare system, the interview guides included questions on the type, reason, and moment of the HCP’s encounters and patient relationships with HCPs. Interview guides are available in Supplementary File 1 . During the development phase of the study, the interview guides were reviewed for clarity and validity and adapted by two patient partners from the Geneva University Hospitals’ Patient Partner Platform for Research and Patient and Public Involvement. Thematic saturation was considered reached when no new code or theme emerged and new data repeated previously coded information [ 32 ]. Sociodemographic and clinical data were collected from hospital databases and patient questionnaires. The interviews were audio-recorded, anonymized, and transcribed verbatim.

Data analysis

The sociodemographic and clinical characteristics were descriptively analyzed. Transcriptions were double-coded until similar codes were obtained, and thematic analysis, as described by Braun and Clarke [ 33 , 34 ], was used in a systematic, iterative, and comparative manner. A patient journey mapping methodology was used to illustrate the trajectories of each participant and provide a comprehensive understanding of their experiences. Patient journey mapping is a visual method adapted from the marketing industry that is increasingly used in various health settings and contexts to illustrate and evaluate healthcare services and patient experiences [ 35 ]. In this analysis, we used the term “healthcare professionals” when more than one profession could be involved in participants’ healthcare. Otherwise, when a specific HCP was involved, we used the designated profession (e.g. physicians, pharmacists).

A. Participants description

Twenty-one participants were interviewed between October 2020 and September 2021, generating 75 interviews. All participants took part in Interview 1, 19 participants in Interview 2, 16 participants in Interview 3 and 19 participants in Interview 4, with a median duration of 41 minutes (IQR: 34-49) per interview. Interviews 1,2,3 and 4 took place respectively 5 days (IQR: 4-7), 14 days (13-20), 35 days (33-38), and 63 days (61-68) after discharge. Nine patients were newly diagnosed with T2D, and 12 had a previous diagnosis of T2D, two of whom were untreated. Further information on participants is described in Table 1 . The median number of comorbidities was six (range: 3–11), and participants newly diagnosed with diabetes tended to have fewer comorbidities (median: 4; range: 3–8). More detailed information regarding sociodemographic characteristics and medications has been published previously [ 28 ].

B. Journey mappings

Generic patient journey mapping, presented in Fig. 1 , summarizes the main and usual encounters participants had with their HCPs during the study period. Generic mapping results from all individual patient journey mappings from discharge to 2 months after discharge are available in Supplementary File 2 .

Generic patient journey mapping from hospitalization to two months after discharge

During the 2 months following discharge, the participants had a median number of 10 (range: 6–28) encounters with HCPs. The HCPs met by participants are represented in Fig. 2 . All participants visited their pharmacists at least once, and 16 of the 21 participants met their general practitioners (GPs) at least once. Five participants received home care assistance, four went to an outpatient cardiac rehabilitation program, and five were readmitted during the study period.

Healthcare professionals seen by participants during the study period. left: n=cumulative encounters; right: n=encountered at least once. Abbreviation: S.nurse: specialized nurse; Other physicians: ophthalmologists, neurologists, hematologists, immunologists, addictologists; other HCP: physiotherapists, dietitians, massage therapist

The first HCP encountered was at the community pharmacy on the same day or day after discharge, except for one participant who did not pick up her medication. The first medical appointment with a physician occurred between days 1 and 27 after discharge (median: 8; IQR: 6-14).

Participants newly diagnosed with diabetes had a closer follow-up after discharge than participants with a former diagnosis of T2D (median: 7; IQR: 6–10 vs median: 9; IQR: 5–19), fewer encounters with HCPs (median: 8; IQR: 7–10 vs. 11; IQR: 8–17), and fewer comorbidities (median: 4; IQR: 4–7 vs. 7; IQR: 5–9). Most participants newly diagnosed with T2D or receiving insulin treatment benefited from either a follow-up call, home visit by a nurse, or diabetes care appointment.

C. Qualitative analysis

Transcripts were analyzed longitudinally and categorized into six key steps based on the verbal data. These key steps, shown in Fig. 1 , represent the identified thematic categories and refer to the following elements: 1. Hospitalization, 2. Discharge, 3. Dispensing of prescribed medications at the pharmacy, 4. Possible follow-up call, 5. First medical appointment, and 6. Outpatient care.

Hospitalization: hospital constraints and care organization

Most participants thought they had benefited from adequate medical care by committed and attentive HCPs but highlighted different constraints and gaps. Some participants noted constraints related to the hospital environment, such as loss of autonomy during their stay, lack of privacy, and the large number of hospital staff encountered. This resulted in participants repeating the same information several times, causing frustration, misunderstanding and a lack of coordination for some participants:

“Twenty or thirty staff members come in during the day! So, it's hard to keep track of [what] is bein g said or done. The best thing for me [...] would be to have clear information from just one person.” Participant 8; interview 1 (P18.1)

Participants had different opinions on the hospital’s care organization. Some participants found that care coordination between the wards was well-organized. In contrast, others highlighted poor coordination and communication between the hospital wards, resulting in long waiting times, care fragmentation, and contradictory or unclear information. Some participants felt that they did not benefit from comprehensive and integrated care and that the hospital staff focused on the cause of their hospitalization, neglecting other comorbidities:

“They were not interested [in my diabetes and my sight]. I was there for the heart and that was where [my care] stopped.” P17.1

Patients’ involvement in decision-making regarding medical care varied. Some participants were involved in their care and took part in medical decisions. Written information, adequate communication, and health professionals’ interest in patients were highlighted by some participants:

“They took the information sheet and they explained everything to me. They didn't just come once; they came several times to explain everything to me.” P5.1

Other participants found the information difficult to understand, particularly because of their fatigue and because the information was provided orally.

Discharge: an unclear process

The discharge process was unclear for patients who could not identify a specific related outpatient medical visit or a key step that summarized their hospital stay and prepared them for discharge:

“Well, there's no real preparation [for discharge]. I was waiting for them to give me the go-ahead so I could go home, that’s all...” P7.4

For some participants, outpatient care follow-up was organized before discharge by the hospital team (generally by making an appointment with the patient’s GP before discharge), whereas others had no post-discharge follow-up scheduled during their hospitalization. Approximately half of the participants refused follow-ups during their hospitalization, such as home care services provided by a nurse, or a rehabilitation hospital stay. The main reason for this refusal was that patients did not perceive the need for follow-up:

“It's true that I was offered a lot of services, which I turned down because I didn't realize how I would manage back at home.” P22.2

Dispensing prescribed medications by the community pharmacist: the first HCP seen after discharge

On behalf of half the participants, a family caregiver went to the usual community or hospital outpatient pharmacy to pick up the medications. The main reasons for delegation were tiredness or difficulty moving. In some cases, this missed encounter would have allowed participants to discuss newly prescribed medications with the pharmacist:

“[My husband] went to get the medication. And I thought afterward, […] that I could have asked [the pharmacist]: “But listen, what is this medication for?” I would have asked questions” P2.3

Participants who met their pharmacist after hospital discharge reported a range of pharmaceutical practices, such as checking the prescribed medication against medication history, providing information and explanations, and offering services such as the preparation of pillboxes. For some, the pharmacists’ work at discharge did not differ from regular prescriptions, whereas others found that they received further support and explanations:

“She took the prescription […] checked thoroughly everything and then she wrote how, when, and how much to take on each medication box. She managed it very well and I had good explanations.” P20.3

Some participants experienced problems with generic substitution, the unavailability of medications, or dispensing errors, complicating their journey through the healthcare system.

Possible follow-up call by HCP: an unsystematic practice

Some participants received a call from their GP or hospital physician a few days after discharge to check their health or answer questions. These calls reassured participants and their caregivers, who knew they had a point of contact in case of difficulty. Occasionally, participants received calls from their community pharmacists to ensure proper understanding and validate medication changes issued during hospitalization. Some participants did not receive any calls and were disappointed by the lack of follow-up:

“There is no follow-up! Nobody called me from the hospital to see how I was doing […]” P8.2

First medical appointment: a key step in the transition of care

The first medical appointment was made in advance by the hospital staff or the patient after discharge. For some participants, this first appointment did not differ from usual care. For most, it was a crucial appointment that allowed them to discuss their hospitalization and new medications and organize their follow-up care. Being cared for by a trusted HCP enabled some patients to feel safe, relieved, and well-cared for, as illustrated by the exchange between a patient and her daughter:

Daughter: When [my mom] came back from the GP, she felt much better [...] It was as if a cork had popped. Was it psychological? Patient: Maybe… I just felt better. D: Do you think it was the fact that she paid attention to you as a doctor? P: She took care of me. She did it in a delicate way. [silence] - P23.2

Some participants complained that their physicians did not receive the hospital discharge letter, making it difficult to discuss hospitalization and sometimes resulting in delayed care.

Outpatient care: a multifaceted experience

During the 2 months after hospital discharge, participants visited several physicians (Fig. 2 ), such as their GP and specialist physicians, for follow-ups, routine check-ups, medical examinations, and new prescriptions. Most participants went to their regular pharmacies to renew their prescriptions, for additional medication information, or for health advice.

Some participants had home care nurses providing various services, such as toileting, care, checks on vital functions, or preparing weekly pill boxes. While some participants were satisfied with this service, others complained that home nurses were unreliable about appointment times or that this service was unnecessary. Some participants were reluctant to use these services:

“The [homecare nurse] makes you feel like you're sick... It's a bit humiliating.” P22.2

Specialized nurses, mostly in diabetology, were appreciated by patients who had dedicated time to talk about different issues concerning diabetes and medication and adapted explanations to the patient’s knowledge. Participants who participated in cardiac rehabilitation said that being in a group and talking to people with the same health problems motivated them to undertake lifestyle and dietary changes:

“In the rehabilitation program, I’m part of a team [of healthcare professionals and patients], I have companions who have gone through the same thing as me, so I’m not by myself. That's better for motivation.” P16.2

 Navigating the outpatient healthcare system: the central role of patients

Managing medical appointments is time-consuming and complex for many participants. Some had difficulty knowing with whom to discuss and monitor their health problems. Others had difficulty scheduling medical appointments, especially with specialist physicians or during holidays. A few participants did not attend some of their appointments because of physical or mental vulnerabilities. Restrictions linked to the type of health insurance coverage made navigating the healthcare system difficult for some participants:

“Some medications weren't prescribed by my GP [...] but by the cardiologist. So, I must ask my GP for a delegation to see the cardiologist. And I have to do this for three or four specialists... Well, it’s a bit of a hassle […] it's not always easy or straightforward”. P11.2

Some participants had financial difficulties or constraints, such as expenses from their hospitalization, ambulance transportation, and medications not covered by their health insurance plans. This led to misunderstandings, stress, and anxiety, especially because some participants could not return to work or, to a lesser extent, because of their medical condition.

To ensure continuity of care, some participants were proactive in their case management, for example, by calling to confirm or obtain further information on an appointment or to ensure information transfer. Written convocations for upcoming medical appointments and tailored explanations helped the participants organize their care. Family caregivers were also key in taking participants to various consultations, reminding them, and managing their medical appointments.

 Information transfer: incomplete and missing information

Information transfer between and within settings was occasionally lacking. Even weeks after hospitalization, some documents were not transmitted to outpatient physicians, sometimes delaying medical care. Some participants reported receiving incomplete, unclear, or contradictory information from different HCPs, sometimes leading to doubts, seeking a second medical opinion, or personal searches for information. A few proactive participants ensured good information transmission by making a copy of the prescription or sending copies of their documents to physicians:

“My GP hasn't received anything from the hospital yet. I’ve sent him the PDF with the medication I take before our appointment […] Yes, It’s the patient that does all the job.” P10.3

 Interprofessional work: a practice highlighted by some participants

Several participants highlighted the interprofessional work they observed in the outpatient setting, especially because they had several comorbidities; therefore, several physicians followed their care:

“My case is very complex! For example, between the cardiologist and the diabetologist, they need to communicate closely because there could be consequences or interactions with the medications I take [for my heart and my diabetes].” P4.2

Health professionals referred their patients to the most appropriate provider for better follow-up (e.g., a nurse specializing in addictology referred a patient to a nurse specializing in diabetology for questions and follow-up on blood sugar levels). Interprofessional collaboration between physicians and pharmacists was noted by some participants, especially for prescription refills or ordering medications.

 Patient-HCPs relationships: the importance of trust

Trust in the care relationship was discussed by the participants regarding different HCPs, especially GPs and community pharmacists. Most participants highlighted the communication skills and active listening of healthcare providers. Knowing an HCP for several years helped build trust and ensure an updated medical history:

“I've trusted this pharmacist for 20 years. I can phone her or go to the pharmacy to ask any question[...] I feel supported.” P3.2

Some participants experienced poor encounters owing to a lack of attentive listening or adapted communication, especially when delivering bad news (new diagnoses or deterioration of health status). Professional competencies were an important aspect of the patient-HCP relationship, and some participants lost confidence in their physician or pharmacist because of inadequate medical or pharmaceutical care management or errors, such as the physician prescribing the wrong medication dosage, the pharmacist delivering the wrong pillbox or the general practitioner refusing to see a patient:

“I think I'll find another doctor… In fact, the day I was hospitalized, I called before to make an appointment with her and she refused to see me […] because I had a fever, and I hadn’t done a [COVID] test.” P6.2

Most participants underlined the importance of their GP because they were available, attentive to their health issues, and had a comprehensive view of their medications and health, especially after hospitalization:

“Fortunately, there are general practitioners, who know everything. With some specialists, the body is fragmented, but my GP knows the whole body.” P14.1

After hospitalization, the GP’s role changed for some participants who saw their GP infrequently but now played a central role.

 Community pharmacist: an indistinct role

Pharmacists and their teams were appreciated by most participants for their interpersonal competencies, such as kindness, availability, professional flexibility, and adaptability to patients’ needs to ensure medication continuity (e.g., extension of the prescription, home delivery, or extending time to pay for medications). The role of community pharmacists varied according to the participants. Some viewed pharmacists as simple salespeople:

“It's like a grocery store. [...] I go there, it's ordered, I take my medication, I pay and I leave.” P23.3

For others, the pharmacist provided medication and advice and was a timely source of information but did not play a central role in their care. For others, the pharmacist’s role is essential for medication monitoring and safety:

“I always go to the same pharmacy […] because I know I have protection: when [the pharmacist] enters the medications in his computer, if two medications are incompatible, he can verify. [...] There is this follow-up that I will not have if I go each time somewhere else.” P10.4

The patient journey mapping methodology, coupled with qualitative thematic analysis, enabled us to understand and shed light on the intricacies of the journey of polypharmacy patients with T2Din the healthcare system after discharge. This provided valuable insights into their experiences, challenges, and opportunities for improvement.

This study highlights the complex pathways of patients with comorbidities by considering the population of patients with T2D as an example. Our population included a wide variety of patients, both newly diagnosed and with known diabetes, hospitalized for T2D or other reasons. Navigating the healthcare system was influenced by the reason for hospitalization and diagnosis. For example, newly diagnosed participants with T2D had a closer follow-up after discharge, participants were more likely to undergo cardiac rehabilitation after infarction, and participants with a former T2D diagnosis were more complex, with more comorbidities and more HCP encounters. Our aim was not to compare these populations but to highlight particularities and differences in their health care and these qualitative data reveal the need for further studies to improve diabetes management during inpatient to outpatient care transition.

The variability in discharge practices and coordination with outpatient care highlights the lack of standardization during and after hospital discharge. Some participants had a planned appointment with their GP before discharge, others had a telephone call with a hospital or ambulatory physician, and some had no planned follow-up, causing confusion and stress. Although various local or national guidelines exist for managing patients discharged from the hospital [ 36 , 37 , 38 , 39 ], there are no standard practices regarding care coordination implemented in the setting of this study. The lack of local coordination has also been mentioned in other studies [ 5 , 40 , 41 ].

Our results also raise questions about the responsibility gap in the transition of care. Once discharged from the hospital, who is responsible for the patient until their first medical appointment? This responsibility is not clearly defined among hospital and outpatient care providers, with more than 25% of internal medicine residents indicating their responsibility for patients ending at discharge [ 42 , 43 ]. Importance should be given to clarifying when and who will take over the responsibility of guaranteeing patient safety and continuity of care and avoiding rehospitalization [ 44 ].

The first visit with the community pharmacist after discharge and the referring physician were the key encounters. While the role of the GP at hospital discharge is well-defined, the community pharmacist’s role lacks clarity, even though they are the first HCP encountered upon hospital discharge. A meta-analysis showed the added value of community pharmacists and how their active participation during care transition can reduce readmission [ 18 ]. A better definition of the pharmacist’s role and integration into care coordination could benefit patient safety during the transition and should be assessed in future studies.

Our findings showed that the time elapsed between discharge and the first medical appointment varied widely (from 1 to 27 days), correlating with findings in the literature showing that more than 80% of patients see their GP within 30 days [ 45 ]. Despite the first medical appointment being within the first month after discharge, some patients in our study reported a lack of support and follow-up during the first few days after discharge. Care coordination at discharge is critical, as close outpatient follow-up within the first 7–10 days can reduce hospital readmission rates [ 46 , 47 ]. Furthermore, trust and communication skills are fundamental components of the patient-HCP relationship, underlined in our results, particularly during the first medical appointment. Relational continuity, especially with a particular HCP who has comprehensive patient knowledge, is crucial when patients interact with multiple clinicians and navigate various settings [ 48 , 49 ].

Navigating the outpatient healthcare system after discharge was complex for most participants and relied heavily on patient involvement and responsibility. While some participants who received clear information felt more empowered and engaged in their care, others highlighted the difficulty in organizing their care during this vulnerable period. Such difficulties in case management have been described previously [ 50 , 51 ]. Moreover, services proposed by HCPs (e.g., home assistance) do not always correspond to patient needs and are sometimes refused. This highlights the tension between HCPs’ medical recommendations, priorities, and patient expectations. This tension between medical priorities and patient needs was felt during hospitalization and shaped the 2 months following discharge. HCPs need to assess patient needs and preferences during hospitalization and transition for follow-up services. They must also ensure that the offered services meet at least the most relevant of patients’ perceived needs to improve seamless care and patient safety [ 52 , 53 ].

Examples of a lack of communication and information transfer were described in our results at different levels among HCPs, between participants or family caregivers, and HCPs, and these findings correlate with the literature [ 3 , 54 , 55 , 56 ]. Although family caregivers play an important role in supporting patients in the healthcare system, they are also additional interlocutors, leading to missed opportunities for patient-pharmacist interactions when dispensing discharged medication. Therefore, it is paramount to integrate and involve family caregivers in shared decision-making and communicate with patients remotely when they are not present [ 57 ].

Opportunities to improve the discharge of patients returning home after discharge without home care are highlighted in this article. Our insights can serve as a valuable foundation for healthcare providers and policymakers seeking to optimize patient experience and quality of care in the post-discharge phase. Different professionals should be integrated into standard practice through guidelines to ensure improved collaboration from hospital discharge to outpatient care. During hospitalization:

an appointment should be scheduled with the referring physician shortly after discharge to guarantee continuity of care

a hospital discharge interview should be conducted in a systematic way to summarize and securely close the hospitalization

the community pharmacist should be informed before the patient’s discharge to prepare and reconcile medications before and after hospitalization

In outpatient care:

an in-person or phone encounter with the pharmacy team should be scheduled for the patient and/or caregivers at discharge

a contact point (phone number, email, or virtual chat assistant) or scheduled follow-up should be implemented to answer questions and redirect patients before they can meet with the referring physician

a long-term and active communication channel between HCPs should be established.

In other countries, several outpatient services are already available for patients discharged home to enhance continuity of care and patient safety after discharge. The telehealth-based Transitional Care Management Programme, a local initiative in a New York hospital, involves contacting discharged patients 24 to 48 hours after discharge to support understanding of discharge instructions, medication access, follow-up appointments, and social needs [ 58 ]. The Australian Government has introduced the Transition Care Program that provides short-term care for older people, including social work, nursing support, personal care, and allied health care [ 59 ]. In England, the NHS has introduced the Discharge Medicines Service (DMS) in community pharmacies, which aims to improve communication between hospitals and community pharmacies and to ensure that patients understand changes to their medications [ 60 ].

Limitations

This study has several limitations. First, the accuracy of the encounter dates with HCPs, as described by the participants, could not be verified using a second data source (e.g., medical or pharmacy records). Additionally, recall biases cannot be excluded, especially during interviews 3 and 4, which took place at longer intervals (20 days between interviews 2 and 3 and 30 days between interviews 3 and 4). Nevertheless, our findings express a patient's representation of their healthcare system navigation experience. Secondly, these results may not be generalizable to populations with other long-term diseases, even though we recruited patients with different reasons for hospitalization, including age, sex, and comorbidities. In addition, the study region is predominantly an urban area with a high density of HCPs, which may influence patient journeys in the healthcare system. Finally, we excluded patients whose medications were managed by HCPs because these patients might have had different experiences, difficulties, and needs. This exclusion criterion was chosen because our objective was to investigate patients’ medication self-management, as described in another article [ 28 ].

A patient’s journey in the 2 months following discharge is unique for each individual and constitutes a complex and adaptive process. Despite the active role of numerous HCPs, navigation in outpatient care after discharge relies heavily on the involvement and responsibilities of polypharmacy. The findings of this study highlight the need to standardize the approach for discharge planning and post-discharge care in partnership with patients and caregivers. Preparation for discharge, the first visit to the pharmacy, and the first appointment with the GP are key moments for all patients, along with the involvement of other medical and nurse specialists, as needed. Standardizing practices, clarifying responsibilities, integrating community pharmacists during the transition, empowering patients, and enhancing interprofessional communication and collaboration should be explored and implemented to achieve better patient outcomes and a more seamless healthcare journey for individuals transitioning from the hospital to the community.

Availability of data and materials

The qualitative codes in French and anonymized patient datasets are available from the corresponding author on reasonable request. Individual patient journeys are provided in the Supplementary Files.

Abbreviations

General practitioner

Healthcare professional

type 2 diabetes mellitus

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Acknowledgments

The authors would like to thank all the patients who took part in this study. We would also like to thank the Geneva University Hospitals Patients Partners +3P platform as well as Mrs Tourane Corbière and Mr Joël Mermoud, patient partners, who reviewed interview guides for clarity and significance.

Open access funding provided by University of Geneva This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Giacomo Gastaldi

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Marcelo Dos Santos Mamed

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Contributions

LS, GG, and MS conceptualized and designed the study. LS and GG screened and recruited participants. LS conducted the interviews. LS, GG, and MS performed data analysis and interpretation. LS drafted the manuscript and LS and MS worked on the different versions. MDS contributed its expertise and external opinion as a clinical psychologist and linguist. All authors read and approved the final manuscript.

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Supplementary Information

Additional file 1..

Interview guides.

Additional file 2.

Individual patient journey mappings from discharge to 2 months after discharge.

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Solh Dost, L., Gastaldi, G., Dos Santos Mamed, M. et al. Navigating outpatient care of patients with type 2 diabetes after hospital discharge - a qualitative longitudinal study. BMC Health Serv Res 24 , 476 (2024). https://doi.org/10.1186/s12913-024-10959-4

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A case study of type 2 diabetes self-management

1 Department of Biomedical Engineering, Texas A&M University, College Station, Texas, 77843-3120 USA

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Associated Data

It has been established that careful diabetes self-management is essential in avoiding chronic complications that compromise health. Disciplined diet control and regular exercise are the keys for the type 2 diabetes self-management. An ability to maintain one's blood glucose at a relatively flat level, not fluctuating wildly with meals and hypoglycemic medical intervention, would be the goal for self-management. Hemoglobin A1c (HbA1c or simply A1c) is a measure of a long-term blood plasma glucose average, a reliable index to reflect one's diabetic condition. A simple regimen that could reduce the elevated A1c levels without altering much of type 2 diabetic patients' daily routine denotes a successful self-management strategy.

A relatively simple model that relates the food impact on blood glucose excursions for type 2 diabetes was studied. Meal is treated as a bolus injection of glucose. Medical intervention of hypoglycaemic drug or injection, if any, is lumped with secreted insulin as a damping factor. Lunch was used for test meals. The recovery period of a blood glucose excursion returning to the pre-prandial level, the maximal reach, and the area under the excursion curve were used to characterize one's ability to regulate glucose metabolism. A case study is presented here to illustrate the possibility of devising an individual-based self-management regimen.

Results of the lunch study for a type 2 diabetic subject indicate that the recovery time of the post-prandial blood glucose level can be adjusted to 4 hours, which is comparable to the typical time interval for non-diabetics: 3 to 4 hours. A moderate lifestyle adjustment of light supper coupled with morning swimming of 20 laps in a 25 m pool for 40 minutes enabled the subject to reduce his A1c level from 6.7 to 6.0 in six months and to maintain this level for the subsequent six months.

Conclusions

The preliminary result of this case study is encouraging. An individual life-style adjustment can be structured from the extracted characteristics of the post-prandial blood glucose excursions. Additional studies are certainly required to draw general applicable guidelines for lifestyle adjustments of type 2 diabetic patients.

It is well established that diabetes can lead to acute and chronic complications, compromising the health and quality of life. Results from various studies [ 1 ] have demonstrated that improved control of blood glucose in type 2 diabetes reduces related complications. Type 2 diabetes results from the metabolic problem that is related to certain tissue resistance to insulin action and to the inability of the pancreas to appropriately regulate the quantity of insulin for glucose metabolism. These metabolic abnormalities lead to the many complications of diabetes. Type 2 diabetes historically occurs predominantly in adults aged 40 and over. A recent trend, however, indicates that children and adolescents of minority ethnic groups, especially in African Americans and American Indians, are increasingly susceptible to type 2 diabetes [ 2 ]. With the prevalence of type 2 diabetes and its associated risk for serious complications, issues related to proactive self-management become an urgent concern.

Dietary management is frequently referred as the cornerstone, or the initial step, in treating of type 2 diabetes mellitus. Foods containing carbohydrates play an important role in the diet. The glycemic Index (GI) ranks foods according to their post-prandial glycemic responses. The GI was introduced more than twenty years ago and has been widely adopted in diabetes management in Australia, New Zealand, Canada, the United Kingdoms, and France [ 3 ]. The World Health Organization states that it is important to consider the GI in constructing a healthful diet because low GI foods help control blood sugar levels by producing minimal fluctuations in blood glucose [ 4 ]. For diabetic patients, choosing low GI foods is particularly important because consumption of high GI foods often results in far more exaggerated glycemic responses, creating a need for drug or insulin therapy [ 3 , 5 ].

Most published GI lists are for single food items only. A GI is a numerical measure of how a carbohydrate would increase one's blood glucose level over a period of two (for normal) or three hours (for diabetic patients) after eating [ 6 , 7 ]. The area of elevated blood glucose level from the baseline (the pre-prandial measure) is expressed as a percent of the area for the same amount of a reference carbohydrate such as a pure glucose or a white bread (usually 50 g) [ 8 , 9 ]. To plan a complete meal using the weighted mean [ 6 ] for various food items is not only tedious, but also impractical.

Diet exchange lists are usually recommended for diabetic patients to use in formulating a sensible meal plan. However, an exchange list is not always convenient to use. Moreover, there is a lack of ethnic diet exchange lists. For a member of an ethnic minority to follow a diet exchange list, he or she must prepare his or her own meal away from the rest of the family. Nutall and Chasuk [ 10 ] have stressed that dietary recommendations for type 2 diabetes should be flexible and highly individualized, yet most of the prepared meal programs and exchange-list diets for diabetes have not had individualization in mind nor are they designed for ethnic minorities.

When diet alone cannot effectively control the type 2 diabetic conditions, medical interventions, such as insulin injections or dispensing hypoglycaemic pills, are usually the next step of managing type 2 diabetes mellitus. Medical interventions notoriously exacerbate the fluctuation of blood glucose excursions. Even with the smallest dosage of hypoglycaemic drug (5 mg glucotrol or glyburide) once in the morning, the subject of this study still experienced frequent acute hypoglycaemias. Besides, his A1c levels hovered around 6.5 levels for many years following his physician's advice of taking 5 mg glucotrol per day. It became obvious that a properly designed drug dispensing regimen was needed to avoid hypoglycaemic bouts and effectively reduce A1c levels.

Fasting blood glucose measurements are not consistent indicators, fluctuating widely from a low of 70 mg/dL to a high of 200 mg/dL (with most frequent range lay between 90 to 150 mg/dL) that were experienced by this type 2 diabetic subject prior to the model-based lifestyle adjustment. Initially, the subject tried to adjust lifestyle based on fasting glucose measurements, but it was not successful. His A1c measurements crept from 6.3 to 6.7 in a year. As glucose binds irreversibly to haemoglobin molecules within red blood cells, the amount of glucose that is bound to haemoglobin is directly tied to the concentration of glucose in the blood. The average life span of erythrocytes is about 120 days [ 11 ], measuring the amount of glucose bound to haemoglobin – by the A1c measurement – can provide an estimate of average blood sugar level during the 3 to 4 months period. It is obvious that A1c is a more reliable indicator than fasting glucose measurements for an effective blood glucose control self-management.

It has been established that exercise can effectively alleviate diabetic conditions. Although no rigorous investigation has been performed here, nor is the focus of this current study, a forty-minute exercise of swimming, or weight lifting, or jogging, or any combination of these, prior to a meal or 3 to 4 hours after a meal, can significantly depress the volunteer's post-prandial blood glucose levels. However, it is impractical to substitute hypoglycemic pills with a multiple daily exercise schedule. A sensible lifestyle adjustment is required to manage the diabetic conditions without altering much of daily routines.

Post-prandial blood glucose excursions (time series) for type 2 diabetes vary widely depending on the variety and the amount of food consumed. It also depends on long and short term physical conditions (exercise routines and stress levels such as insomnia) to a lesser scale. The recovery periods of blood glucose excursions returning to the pre-prandial level (or baseline) for diabetics are generally longer than those for non-diabetics. Although a simple glucose-insulin interaction compartmental model exists [ 12 ], not all the model parameters are readily interpretable. In addition, no case study is given to illustrate its potential applications. Compartmental models can provide first-order approximations that may be sufficient for specific goals. Simple models may not duplicate real phenomena but may reveal enough clues for which alternative approaches or experimental designs may come to light.

A biophysically-based model of impulse-force-generated heavily damped oscillatory system is used here to capture the post-prandial blood glucose characteristics of type 2 diabetes. The model follows the general approach of glucose-insulin interaction model (bolus injection of glucose) with a few modifications, for which parameters can readily be interpreted and a case study is presented for exploring its potential applications. Rather than using single food items for their published GI values, or its cumbersome weighted mean of multiple ingredients in a meal, normally consumed lunch for the subject was used for the test meal. Based on the preliminary results obtained from the model, a moderate lifestyle adjustment was devised for the subject: swimming 20 laps for 40 minutes in a 25 m pool in the morning and dispensing 1/4 of 5 mg glyburide 1/2 to 1 hour before lunch and dinner – that enables him to reduce 10% of his A1c level in six months and maintain the desirable lower level for the subsequent six months.

The subject is a mid-sixty healthy male of 180 lbs with 5'10" frame, leading a productive professional life. He has been diagnosed with type 2 diabetes for more than 30 years. Initially, he was on diet regimen for nearly twenty years and then was instructed by his physician to dispense 5 mg glucotrol once every morning. He experienced frequent acute hypoglycemia that led him to discuss a possible self-managed regimen with his family physician.

Lunch was chosen as the test meal for having sufficient time to take post-prandial measurements. The test meals were 15 sets of lunches that consisted either (1) 10 to 12 oz of steamed rice, stir-fried vegetables with 4 oz canned tuna (or steamed cod), or (2) 10 to 12 oz spaghetti with 6 medium sized meat balls (from Sam's family package). Five sets of data each were collected from: (i) without taking hypoglycemic pills before test meals; (ii) 1/4 size of 5 mg glyburide pills were dispensed pre-prandially right before the meal and (iii) 1/4 size of 5 mg glyburide pills were dispensed pre-prandially an hour before the test meals. One pre- and 8 to 12 post-prandial blood glucose measurements were taken at 30-minute intervals starting at the beginning of a meal (meal is usually consumed in 15 minutes): (i) for 6 hours, (ii) for 5 hours, and (iii) for 4 hours. In addition, for case (iii) two reference measurements were taken with one right before dispensing the pill and one an hour after completion of the 8 post prandial measurements, i.e ., at hour 5, for a total of 11 readings.

The purpose of the first set of measurements was to establish the baseline for this diabetic subject: the recovery period of post-prandial blood glucose excursion without medication. The second and the third sets of the trials were designed to quantitatively measure the hypoglycemic drug effects and the most optimal time frame to administer the pills. Raw data were averaged and the corresponding standard deviations were also calculated for 5 replicates at given times. The averaged data were then used for modeling analysis.

Model formulation

The post-prandial blood glucose excursion can be considered as a hormone regulated resilient system. The food intake is treated as a bolus injection of glucose, and thus the impulse force f ( t ); effects of exercises and hypoglycemic medication are lumped as the damping factor, β . The differential equation of such an oscillatory system, that is used to describe post-prandial blood glucose excursions, can be found in many physics texts:

where x represents blood glucose level over the baseline at time t , ω 0 is the system natural frequency [ 12 ]. The pre-prandial blood glucose levels are generally fluctuating with relatively insignificant magnitudes thus can be approximated as a flat level. If the impulse force f ( t ) takes the form of the Dirac delta function, F δ ( t -0) with F being a food intake dependent parameter, the solution of Eq. (1) is

Parametric estimation

For a given blood glucose excursion, data was taken every 30 minute interval from the time a meal was initially consumed, from which the excursion peak ( MR ), x max , and the corresponding time τ to reach MR can both be estimated. Setting dx / dt = 0 in Eq. (2), the time τ can be expressed as:

Substituting Eq. (3) into Eq.(2), we have

The area under an excursion curve, AUC , can also be obtained:

where T = 2 π / ω is the period of oscillation. The reason for setting the upper integral limit to T /2 is because the damping factor β effectively depresses the glucose excursion levels x near zero for t > T /2, i.e ., it ripples about pre-prandial level. The time T /2 is therefore defined as the recovery period ( RP ). For type 2 diabetic patients who are not in a properly structured regimen, the recovery periods are often longer than 5 hours, by which time the next meal arrives and induces another blood glucose upswing.

Equations (3) – (5) can be used to estimate the three parameters, F , ω and β , from the measurable quantities of τ , x max , and AUC . The procedure is briefly described below:

1. Assign T as twice the roughly estimated recovery period in hours, which can be obtained from the raw data and thus ω = 2 π / T .

4. Fine tune these three parameters by using MATLAB function fminsearch to minimize [ AUC data - AUC ( F , β , ω )] 2 , where AUC data is calculated from the averaged data points by the trapezoidal rule and AUC ( F , β , ω ) is calculated from Eq. (5).

5. These three parameters can further be fine-tuned by fminsearch (sum of squared errors between the averaged data points and the model predicted values).

Two MATLAB user defined functions: GlucoseModel (for No pill and Pill at meal) and GlucoseModel1 (for Pill one hour prior) to estimate these model parameters and calculating the relevant diabetic characteristic measures: τ , x max , AUC are listed in the Additional files 1 and 2 , respectively.

Table ​ Table1 1 lists the fine-tuned values of model parameters: F , ω , β , and those characteristic parameters: RP , τ , x max , and AUC , the latter three are calculated from Eqs. (3) to (5). Also included in Table ​ Table1 1 are the fitting statistics R 2 values that indicate how well model curves fit the data.

Model and characteristic parameters for the post-prandial blood glucose excursion

The parametric value of F is the result of food impact, or the rate of glucose being absorbed into the blood stream. The interpretation of F is rather difficult as the liver acts as a storage compartment for glucose [ 12 ]. Liver regulates blood plasma glucose levels; if it is too high, the excess will be stored in the liver, and the reverse process will take place if the plasma glucose is too low. Although all three model parameters: F , ω , and β are more or less influenced by the liver function, the impact on F deems more pronounced as it has a direct impact on the glucose levels in the blood stream. As the function of the liver is not included in the current model, the estimated F values can only be loosely inferred as a function of insulin level, F increases as hypoglycemic drug depresses the blood glucose levels that in turn increases the absorption rate of glucose into the blood stream as in the case of 1/4 pill taken right before the meal. When the drug is taken an hour before the meal, the liver may have sufficient time to regulate blood glucose levels that additional glucose absorption becomes less intensive.

Ratio of characteristic parameters for the post-prandial blood glucose excursion

No pill trial

Parametric values for no-pill trial reveal that glucose absorption rate is generally slower (low F value) in comparison with the other two cases. The exceedingly long RP of nearly 7 hours is undesirable: as it implies that the next meal time arrives before the blood glucose level could return to the baseline, i.e ., an elevated blood glucose level would be sustained for a prolonged period of time. The high RP and AUC are unmistakably the characteristics for type 2 diabetes. Figure ​ Figure1 1 compares the model and the data with the corresponding standard deviation bars. Model curves are extended for an additional hour beyond the last data point (and in all the figures herewith) to denote the trend of blood glucose excursion.

Post-prandial glucose excursion: no pill trial

1/4 of 5 mg glyburide taken right before the meal

The blood glucose characteristics are significantly improved with a 1/4 size of 5 mg glyburide taken right before lunch. Increased ω and β values translate to significantly lower RP and AUC with virtually unchanged x max . Although the mean RP is less than 5 hours, it is still a bit too long in comparison with the non-diabetics [ 12 ] (~ 4 hours). A higher F value than the one for no-pill trial may partly due to the liver intervention. Figure ​ Figure2 2 compares the model and the data. From the figure one can tell that hypoglycemic drug has an effective delayed effect of about two hours as the rising portion of the model is almost identical to the one for no-pill trial with both x max are about 60, which may be the result of liver function that with initial stimulation of hypoglycemic drug, liver may also release glucose. As the hypoglycemic drug effect persists, the liver ceases to interfere.

Post-prandial glucose excursion: 1/4 pill right before the meal

1/4 of 5 mg glyburide taken an hour before the meal

From the personal experience of the participating subject, the hypoglycemia usually occurs 3 to 4 hours after taking the pill. The trial described in the previous section also reveals that no significant hypoglycemic drug effect is detected in the initial two hours. In order to learn the drug impact on an empty stomach, an additional glucose measurement was made prior to taking the hypoglycemic pill at -1 hour. Another measurement was also taken an hour after the blood glucose excursion returned to the baseline ( i.e ., at hour 5). This is meant to check if the blood glucose would remain near the baseline level. The drop of blood glucose levels between -1 and 0 hours are roughly 10 mg/dL, which can be contributed to the mild liver intervention. No net hypoglycemic drug effect is taking place before the meal as evidenced from the initial rise of the blood excursion curve as shown in Fig. ​ Fig.3 3 (in comparison with Fig. ​ Fig.2), 2 ), where only data between hour 0 and hour 4 were used to generate the model curve. Indeed, all parametric values are improved significantly: both PR and x max are decreased by 20% and their combination that reflected in AUC dropped nearly 35% in comparison to those for pill taken at meal trial as shown in Table ​ Table1. 1 . The food impact parameter F decreased a little from the one for pill at meal trial, which may indicate an hour after dispensing the pill, a quasi-equilibrium state has been reached among the liver function, hypoglycemic drug effects, and the bolus injection of glucose. The system frequency ω increased for more than 25%, which gives a shorter RP that compares favorably with non-diabetics. The drop of damping factor β may be the result of low F , as both τ and x max are already significantly reduced that further strengthening of β becomes unnecessary. The hour 5 measurements confirm that although the model curve shows a decreasing trend, upon returning to the base level the blood glucose excursions practically stabilizes. In addition, the volunteer patient did not experience any hypoglycemia even two to three hours after the final post-prandial measurement.

Post-prandial glucose excursion: 1/4 pill an hour before the meal

This simple impulse-forced model provides a means to shape a self-management regimen for the type 2 diabetic subject: a moderate meal coupled with minimal amount of medical intervention has effectively modulated the blood glucose excursion by reducing its recovery periods and fluctuation amplitudes. Based on the model, the type 2 diabetic subject was able to adjust a lifestyle that include (a) 40 minute swimming in a 25 m pool in the morning, (b) a fruit of mid-size apple or its equivalent and a cup of coffee with cream for breakfast without taking hypoglycaemic pill, (c) moderate lunch with 1/4 size of 5 mg glyburide taken 1/2 to 1 hour before the meal, (d) moderate early dinner, 4 hours prior to bed time, with 1/4 size of 5 mg glyburide taken 1/2 to 1 hour before the meal, (e) snack a mid-size banana, or a small bag (3.5 oz) of peanuts, or 6 crackers when needed in between meals. With this regimen, he was able to reduce his A1c level from 6.7 to 6.0 in 6 months and maintained at this level for the subsequent 6 months. Moreover, he has not had any hypoglycaemic bouts ever since he particitipated in this study more than two years ago.

Elevated blood glucose excursions during the night would boost the A1c levels. To keep a low average fluctuation of blood glucose excursion amplitudes, the evening meal is crucial. In order to avoid hypoglycaemia during the sleep, an early dinner is advised. The subject has been able to keep post-prandial blood glucose levels within 200 mg/dL with the mean fasting reading of 90 ± 20 mg/dL. Occasionally he consumes a can of beer or sugar free deserts. Although no rigorous study has been performed, a forty-minute exercise of swimming, or weight lifting, or jogging, or any combination of these is roughly equivalent to the effect of 1/4 size of 5 mg glyburide. Nonetheless, it is impractical to exercise more than once a day, thus the subject takes 2.5 mg of hypoglycemic pill a day instead. His physician originally prescribed him to take one 5 mg hypoglycemic pill daily. That was more that 10 years ago. The regimen did not work very well as he experienced hypoglycaemic bouts often. This model-based regimen not only reduced A1c level but entirely eliminated hypoglycaemic symptoms. In addition, one fasting blood glucose measurement in the morning is sufficient for him to maintain a healthy daily routine of exercise, consuming meals/snacks and leading a productive life with mental and physical activities.

Lifestyle adjustments are the best regimens for many chronicle ailments such as diabetes, hypertension, high cholesterol levels, etc . Although this model-based self-management regimen for the type 2 diabetic subject is only a case study, it certainly provides a general guideline for an applicable life-style adjustment. Currently not all the model parameters are entirely clear, additional data are required to draw a meaningful general conclusion. A pilot project of testing this regimen on six type 2 diabetic patients in a regional nursing home is proposed for the next phase of study.

Although derived characteristic parameters: RP and AUC (to a lesser degree, τ and x max ), carry clear meaning that can be used to characterize type 2 diabetic subjects from non-diabetics, the implications of model parameters, F , ω and β are not as translucent. With additional data, one may be able to draw plausible conclusions about (a) how F is influenced by food intakes, drug (delaying) effects, and liver (regulatory) functions; and (b) how ω and β behave, whether they are independent of F and of each other, or all three somewhat mutually dependent. Better understanding of these parameters would definitely enhance the self-management for type 2 diabetes.

This model-based lifestyle adjustment has another advantage: it can be used to manage each individual needs. Nutall and Chasuk [ 10 ] have stressed that dietary recommendation for type 2 diabetes should be flexible and highly individualized; most of prepared meal programs and exchange-list diets for diabetes have not had individualization in mind nor are they designed for ethnic minorities. Once we have a comprehensive understanding of these parameters, it is possible to tailor individual lifestyle adjustment accordingly.

For those individuals who are interested in self-managing the type 2 diabetes, the general advice is: avoiding big meals, may snack moderately between meals, eat an early dinner – about 4 hours before bedtime, and exercise regularly. If one is interested in "normal" meal effects on one's post-prandial blood glucose excursion, taking a pre-prandial blood glucose measurement prior to a typical lunch and 8 to 10 post-prandial measurements at half-hour intervals for 5 or more replicates and follow the procedure described here to obtain these characteristic parameters RP , τ , x max , and AUC . Applying a small dosage of medical intervention prior to a meal can keep the blood glucose at a relatively flat level and depress the overnight blood glucose excursion; however, this practice needs the approval from one's family physician and is not recommended here.

Authors' contributions

Sole authorship: data collection/analysis, model building, parameter estimation/interpretation, and the design of life-style adjustment regimen for the participating subject.

Supplementary Material

MATLAB user defined function: GlucoseModel (for No pill and Pill at meal) to estimate model parameters: F , β , ω and to calculate the relevant diabetic characteristic measures: τ , x max , AUC .

MATLAB user defined function: GlucoseModel1 (for Pill one-hour prior) to estimate model parameters: F , β , ω and to calculate the relevant diabetic characteristic measures: τ , x max , AUC .

Acknowledgements

The author wishes to express his appreciation to Ms. Katherine Jakubik for her editing efforts, to Professor Jame B. Bassingthwaighte and two other anonymous reviewers for their critical comments to an earlier version of this manuscript.

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Physical and Mental Health Characteristics of Hospitalized COVID-19 Patients with and without Type 2 Diabetes Mellitus in Turkey

Affiliations.

• 1 Department of Public Health, Medipol International School of Medicine, Istanbul Medipol University, Istanbul 34810, Turkey.
• 2 Department of Evidence for Population Health Unit, School of Epidemiology and Health Sciences, The University of Manchester, Manchester M13 9PR, UK.
• 3 Department of Biostatistics & Medical Informatics, Cerrahpaşa Faculty of Medicine, Istanbul University-Cerrahpaşa, Istanbul 34320, Turkey.
• 4 Department of Endocrinology, Medipol International School of Medicine, Istanbul Medipol University, Istanbul 34810, Turkey.
• 5 Qatar Diabetic Association and Qatar Foundation for Research, Doha P.O. Box 752, Qatar.
• 6 Department of Clinical & Experimental Medicine, University of Foggia, 71122 Foggia, Italy.
• PMID: 38672026
• PMCID: PMC11048631
• DOI: 10.3390/brainsci14040377

The aim of this study was to assess the rates of depression, anxiety, and stress and quality of sleeping among COVID-19 patients with and without type 2 diabetes mellitus (T2DM). A case and control design has been employed, involving patients affected by COVID-19 infection (884 with T2DM vs. 884 controls without T2DM) and hospitalized in Istanbul (Turkey) from January to December 2021. A multivariate stepwise regression approach was used to test the associations between sociodemographic, metabolic, serum markers, mental health scores, and T2DM/COVID-19 patients' clinical presentation. A statistically significant difference between T2DM and non-T2DM was found with respect to age, gender, BMI (body mass index), smoking, physical exercise, and physical comorbidities as well as levels of depression, anxiety, stress, and sleeping disorders (0.0003 ≤ all p = 0.025). With regard to serum biomarkers, vitamin D and ferritin were identified as useful parameters of reduction of glycated hemoglobin as well as COVID-19 infection among T2DM patients. This study detected that 25% of patients with COVID-19 and T2DM experienced mental distress, with sleeping disturbances and lifestyle changes markedly impacting their clinical outcome alongside metabolic and serum parameters.

Keywords: COVID-19; diabetes; lifestyle; mental health; sleeping disorders.

Grants and funding

• Open access
• Published: 25 April 2024

Prevalence of and factors associated with suboptimal glycemic control among patients with type 2 diabetes mellitus attending public hospitals in the Greater Male’ Region, Maldives: a hospital-based cross-sectional study

• Jeehana Shareef 1 ,
• Tawatchai Apidechkul 1 , 2 &
• Peeradone Srichan 1 , 2  

BMC Public Health volume  24 , Article number:  1166 ( 2024 ) Cite this article

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Suboptimal glycemic control of type 2 diabetes mellitus (T2DM) which is defined as having HbA1c greater than 7% is a major public health problem in several countries, including the Maldives. The study aimed to estimate the prevalence and determine factors associated with suboptimal glycemic control among T2DM patients.

A hospital-based cross-sectional was applied to collect data from T2DM patients who attended public hospitals in the Greater Male’ Region, Maldives where were one of the highest reports of T2DM and suboptimal glycemic control cases in the country between January to March 2023 by a validated questionnaire and anthropometric measurements. Five (5) ml blood specimens were collected to measure the glycated hemoglobin (HbA1c) level. Univariable and multivariable logistic regressions were employed to determine factors associated with suboptimal glycemic control of T2DM at a significant level of α = 0.05.

A total of 341 participants were recruited for the study: 65.7% were female, 42.5% were aged 40–60 years, and 42.2% were married. The overall prevalence of suboptimal glycemic control was 50.7%. Ten variables were found to be associated with suboptimal glycemic control in multivariable logistic regression. Those aged 40–60 years (AOR = 3.35, 95% CI = 1.78–6.30), being single (AOR = 2.53, 95% CI = 1.21–5.30), preparation of food using more than three tablespoons of cooking oil (AOR = 2.78, 95% CI = 1.46–5.28), preparation of food with more than three tablespoons of sugar (AOR = 2.55, 95% CI = 1.31–4.93), no exercise (AOR = 2.04, 95% CI = 1.15–3.61), DM diagnosed with more than twenty years prior (AOR = 2.59, 95% CI = 1.34–4.99), obese body mass index (BMI) (AOR = 3.82, 95% CI = 1.75–8.32), high total cholesterol (AOR = 2.43, 95% CI = 1.36–4.35), high triglycerides (AOR = 3.43, 95% CI = 1.93–6.11), and high-level stress (AOR = 2.97, 95% CI = 1.48–5.93) were having a greater odds of having suboptimal glycemic control than those who did not have these characteristics.

A large proportion of T2DM patients in the Greater Male’ Region fail to control their blood glucose. Effective public health interventions should be introduced, especially interventions focused on reducing cooking oil and sugar in daily cooking practices, encouraging regular exercise, and maintaining cholesterol levels, particularly for those diagnosed with diabetes mellitus for more than 20 years prior.

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Introduction

Diabetes mellitus (DM) is a serious public health concern in low and middle-income countries, especially in type 2 diabetes mellitus (T2DM) patients with suboptimal glycemic control [ 1 ], which is defined as HbA1c ≥ 7% [ 2 ]. According to the World Health Organization (WHO), T2DM affected 422 million people worldwide, causing 1.5 million deaths annually [ 3 ], and an extra 3 million T2DM deaths occurred due to suboptimal glycemic control in 2019 [ 1 ]. Aside from T2DM deaths, uncontrolled long-term hyperglycemia can result in the development of macrovascular and microvascular complications, like diabetic nephropathy, neuropathy, cardiovascular disease, and lower limb amputation [ 1 ]. It also significantly burdens public health and socioeconomic development in all countries, which requires substantial financial resources for treatment and care, particularly in low-income countries where screening, diagnosis, and treatment are limited [ 4 , 5 , 6 , 7 ], including the Maldives. Its complications can diminish patients and their family’s quality of life [ 4 , 5 ].

In the same vein, the country is experiencing a rapid increase in non-communicable diseases (NCDs) due to economic development and globalization. The Maldivians have transitioned from an active lifestyle to a sedentary one, consuming processed foods, high-calorie foods, saturated fats, and sugar. Additionally, most of the Maldivian islands are small and many residents depend on motorbikes rather than walking or cycling, particularly in the Greater Male’ Region. This has increased NCDs, and the primary cause of morbidity and mortality in the country is accounting for up to 81.0% of all deaths [ 8 ]. Poor dietary habits, a higher body mass index (BMI), and elevated blood pressure were found to be the top five risk factors for T2DM with other NCD burdens in the country [ 9 ]. The availability and promotion of unhealthy foods increase T2DM prevalence [ 10 ], with a 6.7% prevalence among people aged 20–79 years [ 11 ]. In 2020, 23 DM deaths were attributed in the country, and the disease ranked as the sixth leading cause of NCD-related deaths [ 12 ]. Many T2DM patients are diagnosed and treated, but their ability to control their blood sugar level is often very poor [ 13 ].

Year by year, the number of T2DM cases is increasing in the country. According to the National Diabetes Center (NDC) at Indira Gandhi Memorial Hospital (IGMH), a total of 1,733 T2DM patients were registered in the NDC between October 2020 and December 2022, with 293 suboptimal glycemic control cases were attended in January 2023 [ 14 ]. This was an alarming figure for being a small, populated country. Since there is little or no information regarding suboptimal glycemic control among T2DM patients. Therefore, the study aimed to estimate the prevalence and to determine factors associated with suboptimal glycemic control among T2DM patients attending public hospitals in the Greater Male’ Region, Maldives.

Study design and study setting

A hospital-based cross-sectional study was employed to collect data from T2DM patients attending public hospitals in the Greater Male’ Region of the Maldives, including IGMH, Hulhumale’ Hospital, Vilimale’ Hospital, and Senahiya Hospital.

Study population and eligible population

Patients with T2DM who attended four selected public hospitals were enrolled in the study population. Those who had been diagnosed with T2DM for at least two years and who attended the IGMH National Diabetic Centre, as well as T2DM patients who attended other public hospitals (Hulhumale Hospital, Villimale Hospital, and Senahiya Hospital) between 24th January and 18th March 2023, met the inclusion criteria. However, those unable to provide the necessary information and pregnant women were excluded from the study.

Sample size

The sample size of this study was determined using the standard formula for a cross-sectional design [ 15 ]; n = [Z 2 α/2 P (1-P)] / d 2 , where the Z = value from the standard normal distribution corresponded to the desired confidence level (Z = 1.96 for 95% CI), P  = the expected true proportion ( P  = 0.72) [ 16 ], and d = precision (d = 0.05); after adding 10% for non-response and any other error in the study, 341 participants were used as the final sample size for analysis.

Research instruments

The researcher developed a questionnaire that was used to collect data. It entailed four parts. Part I, eight questions were used to collect sociodemographic information, such as age, gender, level of education, marital status, occupation, monthly income, and living status. Part II, twenty-four questions were used to collect information regarding diabetic self-care factors, such as self-monitoring blood glucose, regular follow-up visits, cigarette smoking, alcohol consumption, food consumption behavior, adherence to exercise, and knowledge about DM prevention and care. In this section, under knowledge about DM prevention and care, ten questions were asked to assess the three levels of knowledge. Part III, consisted of seven clinically relevant questions, i.e., duration of diabetes, hypertension, family history of DM, BMI or obesity, waist-to-hip ratio, lipid profile levels, and stress. The anthropometric measurements (such as height, weight, BMI, waist, and hip circumference), blood pressure, as well as a lipid profile were collected in this part A three mL blood sample was drawn to determine HbA1c level, and a five mL blood sample for lipid profile tests, including total cholesterol, triglycerides, HDL-C, and LDL-C. Under stress, five questions from the stress test (ST-5) [ 17 ], were used to determine the stress level. In the last part, questions included the types of antidiabetic medication taken as well as the behavior of taking medication.

Validated questionnaire

The validity and reliability of the questionnaire were assessed using various methods. The item-objective congruence (IOC) technique [ 18 ] was used to assess the validity of the questionnaire. Using this method, three experts evaluated the congruence between each question in terms of how well those questions reflect the content and objectives of the study. Each expert provided a score for each item: “1” means that the question is relevant to the content and objectives of the study, which means that the question does not require any improvement. “0” means that the question is relevant to the content and objectives of the study but needs to be improved before use in the study. “-1” means that the question does not reflect the content and objectives of the study and requires improvement before use. Before interpretation, the average scores were calculated by adding and dividing the scores of three experts. If the average score for the question was less than 0.5, it was eliminated from the final questionnaire. If the questions with scores between 0.5 and 0.7 were to be included in the final questionnaire, they needed to be improved before use. Questions with a score of 0.70 or above were considered for inclusion in the final questionnaire.

Before using the questionnaire in the field, all the questions were tested for reliability with 30 people whose characteristics were similar to those of the study subjects. The pilot testing was conducted on T2DM patients who attended the NDC at IGMH in Male’, Maldives. During this process, the feasibility, proper words or sentences, and order of the questions were assessed. The questionnaire’s knowledge-related questions were subjected to a reliability test (Cronbach’s alpha) with a result of 0.86.

Body mass index (BMI) is classified based on the WHO Asian BMI classification standard. BMI is calculated using the formula: a person’s weight in kilograms divided by a height in meters squared (kg/m 2 ). It is divided into five categories: underweight (<18.5 kg/m 2 ), normal weight (18.5–22.9 kg/m 2 ), overweight (23.3–24.9 kg/m 2 ), obese 1 (25–29.9 kg/m 2 ), and obese 11 (≥ 30 kg/m 2 ) [ 19 ]. A waist-to-hip ratio (WHR) ≥ 94.0 centimeters for males and ≥ 80.0 centimeters for females was classified as an unhealthy waist-to-hip ratio [ 20 ]. WHR was calculated as waist circumference in centimeters divided by hip circumference in centimeters. Patients with a systolic blood pressure of ≥ 140 mmHg and/or diastolic blood pressure of ≥ 90 mmHg, or who use antihypertensive medication regardless of their current blood pressure, were classified as hypertensive [ 21 ].

To measure the level of stress among participants, the ST-5 was used, and stress was classified into three categories: low (≤4 scores), moderate (5–7 scores), and high (≥8 scores) [ 17 ]. Glycated hemoglobin (HbA1c) levels were classified according to the American Diabetes Association standard, with an HbA1c level of > 7.0% defining suboptimal glycemic control [ 2 ] Abnormal lipid profiles were defined as total cholesterol (TC) levels > 200.0 mg/dL [ 22 ]. High-density lipoprotein cholesterol (HDL-C) was divided into two main groups based on WHO recommendations: low (<40.0 mg/dL) and normal (≥ 40.0 mg/dL) [ 20 ], HDL-C abnormal levels for men (< 40.0 mg/dL) and women (< 50.0 mg/dL) [ 20 ]. Low-density lipoprotein cholesterol was classified by WHO recommendations. They are classified into two main groups: normal (<100.0 mg/dL) and high (≥ 130.0 mg/dL) [ 20 ]. The triglyceride levels were divided into two groups, which were based on WHO guidelines, which included: optimal (<150 mg/dL) and high (≥ 150 mg/dL) [ 22 ].

Data gathering procedures

The IGMH and the other three designated public hospital directors and chiefs were contacted, an appointment was made, and a brief meeting was held to explain the research objectives and data collection procedures. All T2DM patients who attended the clinic on the day of data collection were invited to participate in the study voluntarily. Those who agreed to participate the study were informed about the study objectives, the data collection and blood sample-taking procedures. Before the beginning of the study, participants were informed and asked to sign a written consent form. Then, the participant’s height, weight, and waist circumference were measured as part of the physical examination. Blood pressure was assessed using the Omron Automatic Inflation Blood Pressure Monitor. Qualified and experienced nurses assessed anthropometric measurements (such as height, weight, and waist circumference) and blood pressure. Next, participants were asked to fill out the questionnaire or self-administered to complete the questionnaire. Participants who could not sign the written consent form were asked to use their fingerprints. The researcher helped them complete the questionnaire for those who couldn’t fill it out by themselves. As part of gathering information from the study subjects, blood specimens were collected from those who hadn’t done the recommended blood tests one week before the date of data collection. In these blood specimen tests, patients were asked to fast (nothing to eat or drink) for at least 12 h to determine clinical laboratory tests such as HbA1c, and lipid profiles. Blood samples were obtained after fasting was completed. A medical technician who has a valid license drew blood samples from each participant. All blood specimens were sent to the same hospital medical laboratory on the same day for analysis.

Statistical analysis

The data were entered into an Excel sheet, coded, cleaned, managed, and then exported into the SPSS IBM SPSS Statistics software, version 20.0 (SPSS, Chicago, IL) for analysis. Descriptive statistics were used to describe the general characteristics of the participants. While percentages were used to describe categorical data, continuous data were described using the mean and standard deviation (SD) for a normal distribution and the median and interquartile range (IQR) for a skewed distribution. The Chi-square was used to determine whether there was any statistically significant association between independent variables and the outcome variable. Logistic regression was applied to find the risk factors for suboptimal glycemic control at a significance level of a = 0.05. The “stepwise method” method was used as a selection variable in the model. In all phases, the Cox-Snell R 2 , Nagelkerke R 2 , and Hosmer-Lemshow were employed to assess the fit of the model. The variables shown to be significant in the univariable logistic model must be included in the multivariable model. The final estimation models were interpreted after fitting all significant variables in the model.

General characteristics of the participants

A total of 341 T2DM patients were enrolled from 4 public hospitals: 200 T2DM cases (58.7%) from Indira Gandhi Memorial Hospital, 56 cases (16.4%) from Hulhumale’ Hospital, 55 cases (16.1%) from Villimale Hospital, and 30 T2DM cases (8.8%) from Senahiya Hospital. Of these, 173 had suboptimal glycemic control (50.7%), and 168 had controlled blood glucose (49.3%).

More than half of the participants, 65.7% were female, 42.5% were aged 40–60 years, 44.3% had attained informal education, and 42.2% were married. Nearly half (49.6%) were unemployed, 50.2% did not receive a monthly income, and 26.4% received less than 10,000 MVR per month. Slightly more than one-third (44.0%) had more than five members in their family, and 47.8% stayed with a spouse. In the alcohol use concern, 341 (100.00%) participants (Table  1 ).

Four (4) variables were detected in general characteristics, with statistically significant differences between suboptimal glycemic control group and controlled blood glucose groups: age ( p -value = 0.016), education ( p -value = 0.002), marital status ( p -value = 0.031), and occupation ( p -value = 0.031) (Table  1 ).

Prevalence of suboptimal glycemic control

The overall prevalence of suboptimal glycemic control was 50.7% (50.5% in females and 51.3% in males). The age group 40–60 had the highest prevalence of suboptimal glycemic control (58.6%) (Table  1 ).

Three variables were found to be associated with suboptimal glycemic control in the univariable analysis in the dimension of socio-demographic characteristics: age, education, and marital status (Table  2 ).

However, two variables were found to be associated with suboptimal glycemic control in multivariable logistic regression. Participants aged 40–60 years were 3.35 times (95% CI = 1.78–6.30) greater risk of having suboptimal glycemic control, respectively than those aged below 40 and above 60 years. Unmarried participants had 2.53 times (95% CI = 1.21–5.30) greater odds of getting suboptimal glycemic control than those who were married and ever married (Table  2 ).

Twelve variables were found to be associated with suboptimal glycemic control in the univariable analysis in the dimension of self-care: method of blood glucose checking, frequency of checking blood glucose, missed DM appointments, number of cigarettes smoked per day, number of meals had daily, food prepared with cooking oil, food prepared with sugar, coconut milk-prepared food consumed on every week, eating sugary foods daily, drinking tea with sugar, juice, and exercise (Table  3 ).

Three variables were found to be associated with suboptimal glycemic control in multivariable logistic regression. Those who prepared their favorite dish with more than three tablespoons of cooking oil were 2.78 times (95% CI = 1.46–5.28) more likely to have suboptimal glycemic control than those who used less than three tablespoons. Participants who added more than three tablespoons of sugar to their favorite dish had 2.55 times (95% CI = 1.31–4.93) greater odds of developing suboptimal glycemic control than those who added less than three tablespoons. Those who did not exercise regularly had 2.04 times (95% CI = 1.15–3.61) more likely to have suboptimal glycemic control than those who did (Table  3 ).

Eleven variables were found to be associated with suboptimal glycemic control in the univariable analysis in the dimension of clinical history with biomarkers and DM treatment-related experiences: duration of diabetes, family history of hypertension for the mother, BMI, waist-hip ratio, total cholesterol levels, LDL cholesterol, triglycerides, and stress, the type of diabetes medication taken, forgetting to take diabetes medication (weekly), and forgetting to take diabetes medication monthly (Table  4 ).

Five variables were found to be associated with suboptimal glycemic control in multivariable logistic regression. Participants diagnosed with DM more than twenty years prior had 2.59 times (95% CI = 1.34–4.99) greater odds of having suboptimal glycemic control, respectively, than those diagnosed with DM less than twenty years. Those with an obese BMI were 3.82 times (95% CI = 1.75–8.32) more likely to have suboptimal glycemic control than those with a normal BMI. Participants with high total cholesterol had 2.43 times (95% CI = 1.36–4.35) more likely to have suboptimal glycemic control than those with normal total cholesterol. Participants with high triglyceride had 3.43 times (95% CI = 1.93–6.11) greater odds of getting suboptimal glycemic control than those with optimal triglycerides, and those who had high-level stress had 2.97 times (95% CI = 1.48–5.93) greater chance of having suboptimal glycemic control than those who had low and moderate levels of stress (Table  4 ).

A large proportion of the T2DM patients in the Greater Male’s Region suffer from suboptimal glycemic control, particularly in older age, females, and single. While most people live with low socioeconomic status. Many cooking practices use high volumes of sugar and cooking oil, which leads to high BMI. Lack of regular exercise and high stress are also detected among T2DM patients, which are associated with suboptimal glycemic control.

The prevalence of suboptimal glycemic control among T2DM patients attending public hospitals was extremely high (50.7%), which was in line with a study conducted in Brazil (47.3%) [ 5 ]. However, this proportion is shown to be higher in studies conducted in northern Thailand (54.8%) [ 23 ], Ethiopia 71.9% [ 24 ], India (64.1%) [ 25 ], Bangladesh (71.8%) [ 16 ], and Saudi Arabia (75.9%) [ 26 ]. These variations could be attributable to rapid urbanization, cultural attitudes and beliefs, behavioral and clinical characteristics, availability of health services, income, lack of uniform guidelines, and a lack of patient awareness regarding diabetes prevention and care.

In this study, age was identified as an associated factor that contributes to suboptimal glycemic control. Participants aged 40–60 years had a greater chance of having suboptimal glycemic control than those in the age groups below 40 and above 60 years. This coincided with a study conducted in Western Ethiopia [ 27 ], which reported that T2DM patients between the ages of 41 and 60 were more likely to develop suboptimal glycemic control than those in the age groups below 40 and above 60. However, a study conducted in Eastern Sudan [ 28 ] did not detect any association between age and suboptimal glycemic control. Contrary to the findings of this study, a study conducted in Ethiopia [ 21 ] revealed that T2DM patients over the age of 50 had a greater risk of having suboptimal glycemic control compared to those below the age of 50. The possible reason for suboptimal glycemic control among people in the Greater Male’ Region could be that this age group is a working age group. They may have a busy daily life, which results in difficulty seeking health care, exercising, or adhering to medical recommendations and makes it difficult to control their blood glucose levels.

Being single was detected as another contributor associated with suboptimal glycemic control in this study. Unmarried participants had greater odds of experiencing suboptimal glycemic control than those who were married and ever married. This is in line with results obtained from studies conducted in Northwestern Nigeria [ 28 ], Eastern Sudan [ 29 ], and Ethiopia [ 21 ], which reported that being unmarried was at greater risk of having suboptimal glycemic control than being married. However, a study conducted in northeast Ethiopia showed no significant association between marital status and glycemic control [ 30 ]. In contrast, a study conducted in northern Thailand found that married T2DM patients had greater odds of having suboptimal glycemic control compared to their unmarried counterparts [ 23 ]. Perhaps it was assumed that unmarried patients might not receive adequate support from their families in terms of clinic attendance, adherence to a healthy diet, and medication as directed. Maybe this could be the reason they were not achieving glycemic levels. Even though in our study, marriage status was not found to be associated with suboptimal glycemic control, a study in Ethiopia [ 31 ] reported that it was a protective factor to the suboptimal glycemic control. Another study [ 32 ] conducted in Oman reported that a single marital status was associated with suboptimal glycemic control. It is important to investigate the associations between social determinants and suboptimal glycemic control in any social context for further considering effective public health intervention.

The present study showed that cooking oil beyond the recommended daily was associated with suboptimal glycemic control. Participants who prepared their favorite dish using more than three tablespoons of cooking oil were more likely to have suboptimal glycemic control than those who used less than three tablespoons of cooking oil. Thus, this factor tends to play a significant role in developing suboptimal glycemic control and coronary heart disease. A scoping review reported that people with DM should limit their daily intake of cooking oil to a maximum of three teaspoons to manage their diabetes condition effectively [ 31 ]. The possible reason for the suboptimal glycemic control observed among people in the Greater Male’ Region could be that deep-frying oily foods is a more common practice in Maldivian culture, and palm oil is the most common oil used for deep frying.

In the same way, this study also found that using excessive amounts of sugar in daily cooking practices was associated with suboptimal glycemic control. Those who added more than three tablespoons of sugar to their favorite dish had a greater risk of developing suboptimal glycemic control than those who added less than three. This finding was consistent with the study conducted in Eastern Sudan, which reported that adding sugar to beverages increased the risk of poor glycemic control [ 29 ]. High added sugar intake lowers the hepatic insulin sensitivity index and increases hepatic lipogenesis and visceral fat, boosting blood insulin levels in DM patients [ 32 ]. Furthermore, the Maldivian population has observed an increase in the consumption of sugary foods and drinks with added sugar in recent decades [ 8 ]. Traditional Maldivian sweets, drinks, pudding, cakes, pastries, baked foods, and areca nut products contain high-added sugar.

This study detected exercise as a predictor associated with suboptimal glycemic control. Participants who did not exercise regularly had greater odds of having suboptimal glycemic control than those who exercised regularly. This finding was supported by studies conducted in Ethiopia [ 21 ], Northeast Nigeria [ 33 ], Yemen [ 34 ], Uganda [ 35 ], and Saudi Arabia [ 26 ], which reported that those who were not engaged in physical activity had a greater risk of developing suboptimal glycemic control than those who did exercise. Exercising may lower blood glucose levels because active muscles absorb more glucose than resting muscles, which enhances insulin receptors and sensitivity [ 36 ]. One possible reason people in the Greater Male’ Region avoid exercise may be due to a lack of time and always being occupied with daily work to support their family.

The duration of diabetes was identified as a significant positive factor in this study. Those diagnosed with DM for more than twenty years prior had greater odds of developing suboptimal glycemic status compared to those who had been diagnosed with DM for less than twenty years. This result was confirmed by studies in Ethiopia [ 37 ], Saudi Arabia [ 38 ], northern Thailand [ 23 ], Nepal [ 39 ], and India [ 40 ], which discovered that people who were diagnosed with diabetes more than ten years ago were more likely to have poor glycemic control than those diagnosed with diabetes less than ten years. A prolonged period of T2DM is often accompanied by a gradual reduction of insulin production due to pancreatic β-cell failure, which in turn increases insulin resistance, making it more difficult to manage blood glucose [ 27 ]. This could be the reason for T2DM patients frequently having suboptimal glycemic control.

Moreover, participants with an obese BMI were more likely to have suboptimal glycemic control than those with normal BMI. This finding was consistent with findings from studies conducted in Ethiopia [ 6 ], Saudi Arabia [ 38 ], and India [ 40 ], which reported that those with an obese BMI had a greater likelihood of developing suboptimal status compared to those with a normal body weight. Obesity causes an increase in the release of Non-Esterified Fatty Acids from adipose tissue, which has been associated with insulin resistance [ 36 ]. This might be a possibility for obese diabetics who have poor glycemic control.

TC was discovered to be an important modifiable risk factor associated with suboptimal glycemic control. Participants with high total cholesterol levels were at greater risk of developing suboptimal glycemic control than those with normal total cholesterol. This finding was similar to the study conducted in Southwest Ethiopia [ 36 ], and Oman [ 41 ] which reported that high total cholesterol had more likelihood of developing suboptimal glycemic control. A possible justification may be the relationship between glycemic control and its influences on total cholesterol in T2DM patients.

Participants with elevated triglyceride levels had a higher risk of developing suboptimal glycemic status than those with optimal triglyceride levels. This was confirmed by a study conducted in India [ 40 ], which revealed that DM patients frequently had lipid problems and dyslipidemia was associated with suboptimal glycemic control, especially those with triglycerides > 150 mg/dL. This might occur due to the persistent fatty acid entry into the β cell, resulting in pancreatic β cell dysfunction, which leads to insulin resistance and makes it difficult to manage blood glucose levels [ 21 ].

Finally, the results of this study revealed that stress had a significant association with suboptimal glycemic control. Participants who experienced high stress levels had a greater chance of having suboptimal glycemic control than those who experienced moderate or low stress. A study conducted in Iran [ 42 ] showed that stress management reduces HbA1c levels among T2DM patients. It is more common among DM patients and has a dual function in its association with DM, like cause and effect. Stress increases HbA1c, whereas diabetes and its complications increase stress in people with T2DM, particularly physical and emotional stress [ 42 ].

Throughout the study, some limitations were identified that may have impacted the analysis and interpretation of the findings. First, the design of this study might not be able to apply to identify the causal relationship between independent variables and suboptimal glycemic control due to assessing both exposures and outcomes at the same time. Second, due to the inability to obtain T2DM statistics from designated hospitals, it was challenging to estimate the sample size for each hospital in this study. Third, some questions asked about participants’ experiences might cause recall bias. Lastly, the study settings were hospitals, then generalizing the findings to the population is limited.

Conclusions

A large proportion of T2DM patients in the Greater Male’ Region fail to control their blood glucose. Effective public health interventions should be introduced, especially interventions focused on reducing cooking oil and sugar in daily cooking practices, encouraging regular exercise, and maintaining cholesterol levels, particularly for those diagnosed with T2DM for more than 20 years prior. Policymakers at all levels should be informed of the information to create a proper approach for further national policy development and implementation.

Availability of data and materials

Supported data for the study findings are available in supplement files.

Abbreviations

Body mass index

• Diabetes mellitus

Item objective congruence

Noncommunicable diseases

National Diabetes Center

Type 2 diabetes mellitus

World Health Organization

Waist hip ratio

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Acknowledgements

The authors would like to thank the Thailand International Cooperation Agency (TICA), Mae Fah Luang University, and The Center of Excellence for the Hill tribe Health Research in support grant. The authors are also grateful to all healthcare workers who worked in the study setting for their assistance in obtaining access and recruiting participants. Finally, we thank all the participants for their participation in the study.

This study was supported by Thailand International Cooperation Agency (TICA) and Mae Fah Luang University, Thailand (No.3-2022). However, the funders have no role, and involvement in the study.

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JS, TA, and PS designed the study, analyzed the data, drafted the manuscript, and approved the final version of the manuscript. JS contacted the hospitals, and collected the data. All authors approved the final version of the manuscript.

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Shareef, J., Apidechkul, T. & Srichan, P. Prevalence of and factors associated with suboptimal glycemic control among patients with type 2 diabetes mellitus attending public hospitals in the Greater Male’ Region, Maldives: a hospital-based cross-sectional study. BMC Public Health 24 , 1166 (2024). https://doi.org/10.1186/s12889-024-18693-6

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Liraglutide and not lifestyle intervention reduces soluble CD163 after comparable weight loss in obese participants with prediabetes or type 2 diabetes mellitus

• Helene Grannes 1 , 2 ,
• Thor Ueland 1 , 2 , 3 ,
• Paola Simeone 4 ,
• Rossella Liani 4 ,
• Maria Teresa Guagnano 4 ,
• Pål Aukrust 1 , 2 , 5 ,
• Annika E. Michelsen 1 , 2 ,
• Kåre Birkeland 2 , 6 ,
• Augusto di Castelnuovo 7 ,
• Francesco Cipollone 4 ,
• Agostino Consoli 4 ,
• Bente Halvorsen 1 , 2 ,
• Ida Gregersen 1   na1 &
• Francesca Santilli 4   na1  

Cardiovascular Diabetology volume  23 , Article number:  146 ( 2024 ) Cite this article

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The GLP-1 receptor agonist liraglutide is used to treat hyperglycemia in type 2 diabetes but is also known to induce weight loss, preserve the beta cell and reduce cardiovascular risk. The mechanisms underlying these effects are however still not completely known. Herein we explore the effect of liraglutide on markers of immune cell activity in a population of obese individuals with prediabetes or newly diagnosed type 2 diabetes mellitus.

Plasma levels of the monocyte/macrophage markers, soluble (s)CD163 and sCD14, the neutrophil markers myeloperoxidase (MPO) and neutrophil gelatinase‐associated lipocalin (NGAL),the T-cell markers sCD25 and T-cell immunoglobulin mucin domain-3 (sTIM-3) and the inflammatory marker TNF superfamily (TNFSF) member 14 (LIGHT/TNFSF14) were measured by enzyme-linked immunosorbent assays in obese individuals with prediabetes or diabetes diagnosed within the last 12 months, prior to and after comparable weight loss achieved with lifestyle changes (n = 20) or liraglutide treatment (n = 20), and in healthy subjects (n = 13).

At baseline, plasma levels of the macrophage marker sCD163, and the inflammatory marker LIGHT were higher in cases as compared to controls. Plasma levels of sCD14, NGAL, sTIM-3 and sCD25 did not differ at baseline between patients and controls. After weight reduction following lifestyle intervention or liraglutide treatment, sCD163 decreased significantly in the liraglutide group vs. lifestyle (between-group difference p = 0.023, adjusted for visceral adipose tissue and triglycerides basal values). MPO and LIGHT decreased significantly only in the liraglutide group (between group difference not significant). Plasma levels of MPO and in particular sCD163 correlated with markers of metabolic dysfunction and inflammation. After weight loss, only sCD163 showed a trend for decreased levels during OGTT, both in the whole cohort as in those of liraglutide vs lifestyle group.

Weight loss following treatment with liraglutide was associated with reduced circulating levels of sCD163 when compared to the same extent of weight loss after lifestyle changes. This might contribute to reduced cardiometabolic risk in individuals receiving treatment with liraglutide.

Introduction

Liraglutide is an analogue of glucagon-like peptide-1 (GLP-1) widely used in the treatment of type 2 diabetes (T2DM) and has been shown to reduce body weight [ 1 ]. Further, liraglutide is reported to lower cardiovascular and total mortality in patients with T2DM and high cardiovascular risk [ 2 ], but the mechanisms are not fully understood. Macrophages, neutrophils and T-cells express the GLP-1 receptor, and thus GLP-1 and its analogues have the potential to affect a wide spectrum of immune cells [ 3 , 4 , 5 ]. Liraglutide affects immune cells in obesity mouse models as well as in human and mouse cells in vitro [ 6 , 7 , 8 ]. In clinical trials, liraglutide reduces levels of various inflammatory cytokines such as tumor necrosis factor (TNF) and interleukin (IL)-6 in overweight and obese individuals with T2DM [ 9 , 10 , 11 ]. We have previously shown that the inflammatory marker soluble suppression of tumorigenesis-2 (sST2) was decreased after weight loss induced by liraglutide treatment, but not after comparable weight loss due to lifestyle changes [ 12 ]. Interestingly, in the same cohort, liraglutide achieved a more pronounced reduction in visceral adipose tissue (VAT) and improvement in beta cell function, independently of weight loss [ 13 ]. Monocyte/macrophages are known to be involved in both adipose tissue and islet inflammation, however, the effect of liraglutide treatment on immune cells is still not fully clarified, and it is uncertain whether the anti-inflammatory effects of liraglutide is mainly related to weight reduction.

In the present study, we challenged the idea that immune cells such as monocyte/macrophages, Tcells or neutrophils may be interesting cellular targets of liraglutide, thus contributing to alterations in systemic, adipose tissue and islet cell inflammation. Analyzing plasma from a previous study, comparing liraglutide and lifestyle intervention [ 13 ], we explored the regulation of markers reflecting activation of neutrophils: myeloperoxidase (MPO) and neutrophil gelatinase‐associated lipocalin (NGAL), T-cells: soluble (s)CD25 and T-cell immunoglobulin mucin domain-3 (sTIM-3) and monocyte/macrophages: sCD14 and sCD163, before and after similar weight loss induced by liraglutide treatment or lifestyle-changes only.

Subjects and study design

The current study was a post hoc analysis using stored serum and plasma samples from a randomized, controlled, parallel-arm study designed to assess the effects of an equal degree of weight loss, achieved by either lifestyle changes or liraglutide, on cardiometabolic variables in obese subjects with impaired glucose tolerance (IGT) and/or impaired fasting glucose (IFG) or early T2DM. The protocol and patient characteristics have been previously described [ 13 ]. In brief, 62 obese patients with prediabetes (IFG and IGT) or early T2DM were enrolled at the Obesity and Diabetes Clinics of Chieti University Hospital. In addition, 13 subjects, without obesity, diabetes mellitus or prediabetes and not on pharmacological treatment, were enrolled as controls. The patients were randomized 1:1 to receive liraglutide or lifestyle counseling. Study medication was supplied to the research pharmacy by Novo Nordisk as liraglutide 6.0 mg/mL in 3-mL prefilled pen injectors. Liraglutide treatment was administered daily by subcutaneous injection at bedtime with an initial dose of 0.6 mg/day (first week) and titrated over a 3-week period to doses of 1.2 mg daily (second week) and 1.8 mg daily (third week) based on the clinical response and side effects. The nonattainment of the 1.8 mg dose level did not constitute a withdrawal criterion. The participants in the liraglutide arm were encouraged to continue with their existing dietary and exercise habits in addition to liraglutide. The second arm consisted of an intensive lifestyle intervention. The lifestyle-arm participants received two initial days of education on the “Mediterranean diet” and food label education. The aim was a diet with an average of 30% lipids, minimum 15% protein, and maximum 10% of simple sugars, with focus on fibre rich foods, nuts, legumes, and fish rich in omega-3 fatty acids. In addition, they were recommended to consume 10 g of dark chocolate daily, as well as to reduce the intake of salt. Regarding physical activity they were recommended three hours of physical activity per week and two of the three hours were scheduled exercise with the intervention team. Both groups had regular check-ins with the intervention team, but the lifestyle group had in addition first weekly, then biweekly, and lastly monthly consultations with the team to discuss how they were doing with the lifestyle program, and to help keep the motivation up until the goal of 7% weight loss was achieved. Participants in both groups continued with their assigned treatment until they lost 7% of their initial body weight (calculated on the basis of body weight at baseline visit at the time of randomization). Six patients did not achieve this amount of weight loss within 15 months after randomization and were excluded, in addition to 16 participants that dropped out, leaving n = 20 in each treatment arm.

All study visits and procedures took place at the Clinical Research Center within Department of Medicine and Aging, Center for Advanced Studies and Technology (CAST), University of Chieti, Italy. Each patient provided written informed consent to participate, and the Protocol was approved by the Ethics Committee of the University of Chieti, and the Regional Ethical Committee in South-Eastern Norway approved the import in Norway of blood for laboratory assessments.

Blood sampling

Venous blood samples (EDTA platelet-poor plasma) were collected at inclusion in the study and after the achievement of the 7% weight loss goal. At both visits an oral glucose tolerance test (OGTT) was performed and blood samples were taken before (T0), and 60, 90 and 120 min after a 75 g glucose load. The β cell secretion during an OGTT was estimated by applying a minimal model of glucose-induced insulin secretion to the glucose and C-peptide curves of each subject, as previously described in detail [ 14 ]. In addition, we evaluated another OGTT-based measure of B-cell function: the insulin secretion-sensitivity index-2 (ISSI-2) (defined as the ratio of the area-under-the-insulin-curve to the area-under-the-glucose curve, multiplied by the Matsuda index) [ 15 ]. All samples were frozen at − 80 °C for subsequent biochemical measurements.

Biochemical measurements

Plasma levels of immune cell markers sCD163, sCD14, MPO, NGAL, sTIM-3 and sCD25 were measured by DuoSet enzyme-linked immunosorbent assays from R&D Systems (Stillwater, MN). The inflammatory marker LIGHT/tumor necrosis factor super family member 14 (TNFSF14) was measured with a Quantikine enzyme-linked immunosorbent assays from R&D Systems (Stillwater, MN). All were analyzed in a 384-format using a combination of a SELMA pipetting robot (Analytik Jena AG, Jena, Germany) and a BioTek dispenser/washer (BioTek Instruments, Winooski, VT). Absorption was read at 450 nm by using an EIA plate reader (BioTek Instruments) with wavelength correction set to 540 nm. Samples from all patients and controls were run on the same 384-well plate. Calculated limit of the detection (3*SD + OD of 0-standard) was 0.45 ng/mL (sCD163), 0.23 ng/mL (sCD14), 0.086 ng/mL (MPO), 0.035 ng/mL (NGAL), 0.0093 ng/mL (sTIM-3), 23 pg/mL (sCD25) and 10.4 pg/mL (LIGHT), respectively. The inter- and intra-assay coefficients of variation were < 10%.

Statistical analysis

In a study with 20 patients in each treatment arm (liraglutide vs. lifestyle), we were able to detect, by the end of the treatment period, a genuine difference in the mean response between the experimental and control arms, equivalent to one standard deviation of a not pre-specified continuous outcome. This was achieved with a power of 0.9 and a Type I error probability of 0.05. Comparisons of variables between groups (liraglutide versus lifestyle versus controls) and between arms (liraglutide versus lifestyle advice) were performed by χ2 test or Mann–Whitney U test. Spearman rank correlation test was used to assess relationships among continuous variables. All tests were two-tailed. All calculations were carried out using SPSS (SPSS, Chicago, IL, USA).

Baseline characteristics

Clinical and biochemical baseline characteristics of the study subjects have been previously presented [ 12 , 13 , 16 ] and relevant variables are shown in Table  1 . Patients randomized to liraglutide treatment and lifestyle intervention were similar on most parameters, except for triglycerides (TG), waist circumference and visceral adipose tissue (VAT) being higher in the liraglutide arm. Compared to healthy controls, both patient groups had a lower age, higher BMI, lower CRP higher total- and LDL cholesterol, and lower HDL cholesterol. In the liraglutide group, 10 (50%) subjects had IFG or IGT and in the lifestyle group 13 (65%) subjects had IFG or IGT.

Baseline comparisons of soluble immune cell markers between participants with obesity and healthy controls

At baseline, plasma levels of the macrophage marker sCD163, but not sCD14, was higher in patients as compared to healthy subjects (Table  2 ). The neutrophil marker MPO was higher in the lifestyle group, compared to the liraglutide group, but when compared to controls they were not statistically different (Table  2 ). There were no significant differences in plasma levels of the neutrophil marker NGAL, or of the T-cell activation markers, sTIM-3 and sCD25, when comparing cases and controls at baseline (Table  2 ).

Baseline sCD163 and MPO correlate with markers of metabolic dysfunction and inflammation

In the study group as a whole, baseline levels of sCD163 correlated positively with several metabolic parameters such as BMI (rho = 0.432, p = 0.006), C-peptide (rho = 0.410, p = 0.009), insulin (rho = 0.340, p = 0.034), total cholesterol (rho = 0.358, p = 0.025), leptin (rho = 0.481, p = 0.002) and HOMA-IR (rho = 0.389, p = 0.014), and negatively with Matsuda index (rho = − 0.337, p = 0.042). Baseline levels of sCD163 also correlated positively with other markers related to inflammation, i.e., C-reactive protein (CRP, rho = 0.337, p = 0.042), IL-10 (rho = 0.559, p = 0.002), and total leukocyte counts (rho = 0.493, p = 0.001) as well as with non-alcoholic fatty liver disease (NAFLD) prior to intervention (rho = 0.356, p = 0.026, Additional file 1 : Table S1).

Baseline MPO showed a negative correlation with waist-to-hip ratio (WHR, rho = − 0.345, p = 0.031) and beta-index (rho = − 0.356, p = 0.024, Additional file 1 : Table S2).

Liraglutide treatment improved metabolic parameters compared to lifestyle intervention

All study participants except one attained the 1.8 mg dose level through-out the study period. The amount of weight loss was prespecified by the protocol to 7% of initial body weight and did not differ between the groups. Median time to predefined weight loss was 4.8 months and did not differ between the two treatment arms [ 13 ]. Concomitant therapy was unchanged during the follow-up. At the end of the intervention period (i.e. after achievement of the weight loss target) both groups experienced a reduction in several metabolic parameters, including BMI, HbA1c, fasting plasma insulin, disposition index, as well as CRP as a reliable marker of systemic inflammation, with the decrease in VAT and beta-index being more pronounced in the liraglutide arm (Table  3 ) [ 13 ]. The liraglutide arm showed decreased systolic blood pressure and total cholesterol, and improved beta cell function, as assessed by beta-index, and glucose tolerance, indicated by reduced fasting glucose and 1- and 2-h post load glucose levels (Table  3 ). On the other hand, the lifestyle intervention group showed a significant decrease in 2-h post load insulin levels, which was not significant in the liraglutide group (Table  3 ). There were, however, no associations between changes in the inflammatory markers (sCD163 and MPO) and changes in any of metabolic parameters (Additional file 1 : Table S4).

Liraglutide, but not lifestyle changes reduce levels of sCD163 and MPO

At the end of the intervention period, we observed a significant reduction in sCD163 levels in the liraglutide arm (∆ = 87, SD = 115, p = 0.001), but not in the lifestyle arm (∆ = 24, SD = 85), with a significant between-group difference in ∆sCD163 also when adjusted for basal VAT and basal triglycerides values (p = 0.026) (Fig.  1 A). In contrast, levels of sCD14 as an additional marker of monocyte/macrophage activation, were not affected by intervention in any of the two arms (Fig.  1 B). Weight loss induced a significant decrease in MPO levels in the group receiving liraglutide (p = 0.048) (Fig.  1 C), and not in the lifestyle intervention group, but the difference in decreases between arms was not statistically significant. For the other neutrophil markers (i.e., NGAL) and the T-cell markers (i.e., sTIM-3 and sCD25) no significant between arm difference was observed during the intervention. (Fig.  1 D–F).

Changes in plasma concentration of soluble immune markers during a liraglutide-(green bar) or lifestyle-induced (blue bar) weight loss intervention. A sCD163, ( B ) sCD14, ( C ) MPO, ( D ) NGAL, ( E ) sTIM-3 and ( F ) sCD25. LIFE: lifestyle intervention group, LIRA: Liraglutide intervention group, ns: not significant, NGAL: Neutrophil gelatinase‐associated lipocalin, MPO: Myeloperoxidase, sTIM-3: T-cell immunoglobulin mucin domain-3, ∆: Change from baseline (pre) to post-intervention

sCD163 levels were regulated during an oral glucose tolerance test, after weight loss intervention

A 75 g oral glucose load given before the intervention, did not show any change in levels of sCD163 over a period of 120 min (p = 0.835) and there were, as expected, no differences in response between the treatment groups at baseline (p = 0.860) (Fig.  2 A). After intervention, however, we observed a reduction in the levels of sCD163 in the whole study population, after the glucose load over time (p = 0.001). This effect was more pronounced in patients randomized to liraglutide compared to the lifestyle arm, although the difference between arms was not statistically significant (p = 0.081, Fig.  2 B). Interestingly, and in concordance with the baseline correlations between plasma glucose and sCD163, during the OGTT post intervention, the sCD163 AUC after glucose-load was significantly and directly related to the glucose AUC only in the lifestyle arm (data not shown), suggesting that liraglutide directly affects sCD163, independently of blood glucose rise. No change in any of the other variables studied was observed during OGTT neither before nor after weight loss.

Percentage change from baseline concentrations of sCD163 during an oral glucose tolerance test, comparing lifestyle and liraglutide intervention groups. ( A ) pre, and ( B ) post intervention

Liraglutide, but not lifestyle changes reduce levels of LIGHT

We have previously shown that the inflammatory cytokine LIGHT/TNFSF14 is increased in patients with T2DM and can induce beta cell death and impair insulin secretion [ 17 ]. Monocyte/macrophages are important cellular sources of LIGHT and based on the regulation of sCD163 in the liraglutide arm, we therefore analyzed LIGHT levels in the study group. At baseline, LIGHT was significantly increased in the patient cohort, as compared to controls (Fig.  3 A), and indeed, LIGHT showed a positive correlation with sCD163 (rho = 0.417, p = 0.001) in the study group as a whole. Further, LIGHT correlated with total leukocyte counts (rho = 0.395, p = 0.017) and with ISSI-2 at baseline (rho = − 0.321, p = 0.044) (Additional file 1 : Table S3). After intervention, LIGHT was reduced in the liraglutide group (p = 0.003), but not in the lifestyle group, the difference between treatment arms where however not statistically significant (Fig.  3 B, C ).

Plasma concentrations of LIGHT in participants before and after liraglutide or lifestyle-induced weight loss intervention. ( A) Comparing concentrations pre- and post-intervention of all participants, ( B ) Comparing pre- and post-intervention concentrations in liraglutide and lifestyle treatment groups, ( C ) Comparing change in concentrations from baseline between treatment groups. LIGHT: TNF superfamily (TNFSF) member 14, LIFE: Lifestyle treatment group, LIRA: Liraglutide treatment group, ∆: Change from baseline (pre) to post-intervention

Liraglutide is an acylated glucagon-like peptide-1 analogue with 97% amino acid homology with endogenous GLP-1 and greatly protracted action. It is widely used for the treatment of T2DM and administered by subcutaneous injection once daily [ 18 ]. GLP-1 analogues were initially developed to treat T2DM patients, in whom the effects upon glycemia and, also weight loss, were evident. Recently, the latter effect of these drugs has received much attention [ 19 ].

CD163 is a member of the scavenger receptor superfamily, categorized into class B, and its soluble form, sCD163, is regarded as a marker of activated M2 macrophages, and thus potentially a systemic marker reflecting a counteracting mechanism of pro-inflammatory activation of tissue-resident macrophages [ 20 ]. sCD163 is cleaved upon activation by a myriad of stimuli, and plasma levels are most likely due to a combination of CD163 expression, altered clearance rate and increased shedding [ 21 ]. Increased sCD163 has been shown in obesity [ 22 , 23 ], metabolic dysregulation and visceral adiposity [ 24 ], and is considered a marker of insulin resistance and future T2DM [ 25 , 26 ]. In keeping with this, we found that baseline sCD163 was associated with several markers of metabolic dysfunction (e.g., a negative association with insulin sensitivity, as measured by Matsuda index) and with markers of inflammation such as CRP. In a recent study, and in agreement with our data, serum levels of sCD163 were higher in patients with obesity and metabolic syndrome as compared to controls [ 27 ]. Furthermore, a decrease in serum concentrations of sCD163 and fewer inflammatory macrophages has been previously demonstrated in patients with T2DM treated for 6 months with liraglutide [ 10 ], but the present study is, as far as we know, the first to compare levels of immune cell markers after liraglutide treatment and lifestyle intervention with comparable weight loss.

The mechanisms for the sCD163-reducing effect of liraglutide are not known, but we have previously shown improved beta-cell function and reduced VAT after liraglutide treatment, and reduced macrophagic sCD163 might be connected to both these events [ 13 ]. Indeed, macrophages are the primary immune cells involved in obesity-associated islet inflammation in both mice and humans [ 28 , 29 ]. Along these lines, in the same cohort of obese subjects with prediabetes or early diabetes we showed that individuals treated with liraglutide experienced a larger reduction in VAT [ 10 ], as compared to individuals who achieved the same extent of weight loss receiving lifestyle intervention. As VAT is regarded as more inflammatory than the subcutaneous adipose tissue (SAT) compartment [ 30 ], macrophages from VAT may be a primary source of sCD163 and VAT reduction with liraglutide treatment may mirror reduced macrophage inflammation, as assessed by decreased circulating sCD163. In contrast, Pastel et al . showed that treatment with liraglutide, compared to dietary restriction-based weight loss, increased adipose tissue inflammation, measured as CD14, MCP-1, TNF and IL-6 gene expression and concluded that despite a stronger improvement of glycemic control, liraglutide was not effective in amelioration of obesity-associated adipose tissue dysfunction [ 31 ]. However, in that study, the degree of weight reduction between the groups was not equal and gene expression may not necessarily reflect the protein levels, which both could be significant confounding factors. Nonetheless, the reduction in sCD163 in the liraglutide arm and not in the lifestyle intervention arm with comparable weight loss, as well as lack of association between changes in sCD163 and changes in any of metabolic parameters such as BMI, HbA1c, fasting plasma insulin and Matsuda index, support an immune-modulating effect of this GLP-1 analog at least partly independent of the weight loss and related metabolic changes.

Obesity promotes local replication of islet-resident macrophages and recruits circulating monocytes [ 29 ]. Islet macrophages in obese mice have been shown to dampen beta cell insulin secretion and promote beta cell proliferation [ 28 ]. Thus, targeting islet macrophages is a potential therapeutic approach to modulate beta cell function and prevent development of T2DM. In this regard we previously reported, in patients with T2DM, increased circulating levels of the inflammatory mediator LIGHT/TNFSF14 largely derived from activated monocytes and platelets [ 17 ]. We further showed that receptors for LIGHT on islet cells are upregulated and can induce beta cell death and impair insulin secretion from human pancreatic islets in vitro [ 17 ], thus contributing to occurrence of overt diabetes and its progression. Interestingly, in the present cohort LIGHT and sCD163 were both increased in obese subjects vs. controls, directly related with each other at baseline, and both were significantly reduced after liraglutide treatment but not during life-style intervention. Thus, we can speculate that GLP-1RAs may act on circulating monocyte/islet macrophages, decrease the release of LIGHT, thus reducing the extent of systemic and local inflammation, leading to improved beta cell function, as assessed by beta-index [ 13 ].

MPO has also been shown to be causally linked to development of obesity and insulin resistance [ 32 ]. In humans, MPO is upregulated in obesity, independently of T2DM status [ 33 ]. In contrast to these findings, baseline MPO was herein not significantly increased in individuals with obesity, as compared to controls, and was negatively correlated with WHR. Nevertheless, the liraglutide group experienced a decrease in MPO levels during weight reduction which was not seen in the lifestyle intervention group. Thus, our data may suggest specific anti-inflammatory effects of liraglutide in T2DM, independent of weight reduction, potentially also involving attenuated neutrophil responses. In cardiovascular safety trials in T2DM patients, with most individuals presenting with cardiovascular disease and excess weight, GLP-1RAs decreased cardiovascular risk [ 34 ]. In the case of the LEADER study, major adverse cardiovascular events (MACE) decreased by 13 percent with liraglutide [ 2 ]. The mechanism behind the cardiovascular protection observed with human GLP-1RAs in T2DM is not fully known. Carotid plaque inflammation is mitigated in patients treated with GLP-1RAs [ 35 ], pointing to reduced inflammation as a possible mechanism underlying the reduction in cardiovascular risk. Gaining insight into the complex mechanisms through which GLP-1RAs produce their anti-inflammatory effects will improve our understanding of their therapeutic potential and facilitate the creation of new anti-inflammatory approaches [ 36 ]. Both MPO and LIGHT are associated with increased cardiovascular disease risk and in particular sCD163 is associated with both incidence and death from cardiovascular disease [ 37 , 38 , 39 , 40 , 41 ], and thus reduced levels of these could be one of the mechanisms contributing to the cardioprotective effects of liraglutide.

The present study has some limitations such as a relatively low number of participants. Despite the small sample size, we adjusted for VAT and triglycerides, values that were significantly different between arms at baseline, but we intentionally refrained from conducting further adjustments which may not yield reliable results and could potentially introduce spurious associations. Nonetheless, the lack of correction for several potentially confounding factors is a limitation of the present study. Univariate correlation analyses cannot be used to make assumptions about a causal relationship, and analyzing a large number of variables increases the likelihood of false positive correlations. Another limitation is that there were no measures of diet nor exercise compliance during the intervention.

Finally, our understanding of the inherent functions of the soluble immune cell markers is lacking. Strengths of our study includes the randomized design of the study and the comparable weight reduction between the intervention groups, excluding the confounding effect of different weight loss on the markers in study, as well as the detailed characterization of the metabolic features of participants.

Nevertheless, these data provide novel insight to the regulation of immune cell markers after liraglutide treatment, and a potential mechanism for the observed metabolic benefits seen with liraglutide as compared with lifestyle intervention for the treatment of T2DM and obesity. These data are also interesting in relation to the more widely use of GLP-1 analogs in obese patients to obtain weight reduction independent on the presence of diabetes. Moreover, reduced levels of sCD163, LIGHT and MPO could reflect the observed decrease in VAT, preservation of beta cell function as well as cardiovascular protection by liraglutide treatment, but this needs to be confirmed in larger studies and validation cohorts.

Availability of data and materials

The datasets analysed during the study are available from the project leader Francesca Santilli, MD, PhD, upon reasonable request (email: [email protected]).

Abbreviations

Glucagon-like peptide-1

Myeloperoxidase

TNF superfamily (TNFSF) member 14

Oral glucose tolerance test

Type 2 diabetes mellitus

Tumor necrosis factor

Impaired glucose tolerance

Impaired fasting glucose

Ethylenediaminetetraacetic acid

Triglycerides

Visceral adipose tissue

Subcutaneous adipose tissue

Body mass index

Low-density lipoprotein

High-density lipoprotein

Homeostatic model assessment insulin resistance

C-reactive protein

Non-alcoholic fatty liver disease

Waist-Hip Ratio

Cardiovascular disease

Monocyte chemoattractant protein-1

Interleukin 6

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Acknowledgements

A heartfelt thanks to all the participants that donated their time and health information to our research.

Open access funding provided by University of Oslo (incl Oslo University Hospital). The main study was funded by grant PRIN No. 2010JS3PMZ from the Italian Ministry of University and Research to Francesca Santilli. This sub-study was funded by South-Eastern Norway Regional Health Authority through grants 2019067 (recipient Bente Halvorsen) and 2020047 (recipient Ida Gregersen). The study funders were not involved in in study design, collection, analysis nor interpretation of the data.

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Ida Gregersen and Francesca Santilli shared senior authorship.

Authors and Affiliations

Research Institute for Internal Medicine, Oslo University Hospital Rikshospitalet, Sognsvannsveien 20, 0372, Oslo, Norway

Helene Grannes, Thor Ueland, Pål Aukrust, Annika E. Michelsen, Bente Halvorsen & Ida Gregersen

Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

Helene Grannes, Thor Ueland, Pål Aukrust, Annika E. Michelsen, Kåre Birkeland & Bente Halvorsen

Thrombosis Research and Expertise Centre, University of Tromsø, Tromsø, Norway

Thor Ueland

Department of Medicine and Aging, and Center for Advanced Studies and Technology (CAST), “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy

Paola Simeone, Rossella Liani, Maria Teresa Guagnano, Francesco Cipollone, Agostino Consoli & Francesca Santilli

Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital Rikshospitalet, Oslo, Norway

Pål Aukrust

Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway

Kåre Birkeland

Mediterranea Cardiocentro, Naples, Italy

Augusto di Castelnuovo

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Contributions

Concept and design: FS, AC, PA, BH. Acquisition, analysis, or interpretation of data: PS, RL, MTG, FC, AC, AM, TU, PA, KIB, IG, HG. Drafting of the manuscript HG, IG, FS, PS. Critical revision of the manuscript: TU, KIB, PA, FS, BH, AC. Statistical analysis and figures: PS, AdC. All authors read and approved the final manuscript. F.S. is the guarantor of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

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Correspondence to Ida Gregersen or Francesca Santilli .

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The trial was approved by the Italian Ethics Committee of the University of Chieti (Approval n. 10 (protocol 20131) 23.05.2013), and the import of human material to Norway of for laboratory analysis was approved through the Norwegian Regional Committee for Medical and Health Research Ethics, with reference number: 2018/2516. Each patient provided written informed consent before participation.

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Supplementary Information

Additional file 1: table s1..

Spearman correlations between sCD163 and clinical and biochemical parameters at baseline in the total study population that participated in a liraglutide or lifestyle-induced weight loss intervention. Table S2. Spearman correlations between MPO and clinical and biochemical parameters at baseline in the total study population that participated in a liraglutide or lifestyle-induced weight loss intervention. Table S3. Spearman correlations between LIGHT and clinical and biochemical parameters at baseline in the total study population that participated in a liraglutide or lifestyle-induced weight loss intervention. Table S4. Spearman correlations between changes in sCD163, MPO and LIGHT with changes in selected metabolic parameters in the total study population that participated in a liraglutide or lifestyle-induced weight loss intervention.

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Grannes, H., Ueland, T., Simeone, P. et al. Liraglutide and not lifestyle intervention reduces soluble CD163 after comparable weight loss in obese participants with prediabetes or type 2 diabetes mellitus. Cardiovasc Diabetol 23 , 146 (2024). https://doi.org/10.1186/s12933-024-02237-8

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Received : 20 March 2024

Accepted : 16 April 2024

Published : 29 April 2024

DOI : https://doi.org/10.1186/s12933-024-02237-8

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