Clinical Practice Guideline for the Treatment of Obesity and Overweight in Children and Adolescents

Case Examples

Girls camping

The role of psychologists and other behavioral health providers

Multicomponent behavioral treatment for obesity and overweight is often best provided by a team of healthcare professionals. A team may include a psychologist, physician, dietician, exercise specialist, nurse practitioner, or other professional. Which types of professionals should ideally be involved, and to what degree, depends on the needs and characteristics of the child or adolescent.

The following case examples focus on the role of psychologists (or other behavioral health providers), particularly at the early stages of treatment, rather than illustrating all aspects or stages of multicomponent behavioral treatment. These cases point to the need to consider such factors as the patient’s age, gender, socioeconomic status, ethnicity, and culture. Further, they demonstrate the relevance of psychosocial factors – such as the patient’s motivation, social support, family situation, and psychological symptoms (e.g., depression, anxiety, and executive function difficulties) – for understanding and addressing obesity and overweight.

These case examples were developed by Eleanor Mackey, PhD and Laura Kurzius, PhD of Children’s National Health System in Washington, DC. Each example describes an amalgamation of several patient presentations. None of these cases represents a specific patient.

Carmen, 6-year-old Latina girl

Carmen lived with her parents and grandmother. She was referred to a multidisciplinary weight management program due to concerns about her body mass index (BMI), which was at the 99th percentile for her age and gender.

Jason, 15-year-old white male

Jason lived with his mother and niece. He expressed a desire to be a healthier weight, but was having difficulty with managing his weight and had not been successful in a general weight management program.

Marcus, 11-year-old African-American boy

Marcus lived with his mother and two younger brothers and attended middle school. His body mass index (BMI) was at the 99th percentile for his age and gender. He had a very close relationship with his mother and did well in school, but he also needed additional support in completing tasks because of his diagnosis of ADHD.

June, 18-year-old biracial female

June, who lived with her parents, was a senior in high school with sporadic attendance. June was referred for psychotherapy and multicomponent behavioral weight treatment, with specific concerns focused on her difficulty making healthy eating choices and her low motivation to engage in physical activity.

CASE REPORT article

Clinical challenge: patient with severe obesity bmi 46 kg/m 2.

\nGitanjali Srivastava

  • Section of Endocrinology, Diabetes, Nutrition and Weight Management, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States

Obesity causes and exacerbates many disease processes and affects every organ system. Thus it is not surprising that clinical providers are often overwhelmed with the multitude of symptomatology upon initial presentation in patients with obesity. However, despite a “complicated medical history,” a systematic, organized approach in obesity medicine utilizes a personalized-tailored treatment strategy coupled with understanding of the disease state, presence of comorbidities, contraindications, side effects, and patient preferences. Here, we present the case of a young patient with Class 3b severe obesity, several obesity-related complications, and extensive psychological history. Through synergistic and additive treatments (behavioral/nutritional therapy combined with anti-obesity pharmacotherapy and concurrent enrollment in our bariatric surgery program), the patient was able to achieve significant −30.5% total body weight loss with improvement of metabolic parameters. Though these results are not typical of all patients, we must emphasize the need to encompass all available anti-obesity therapies (lifestyle, pharmacotherapy, medical devices, bariatric surgery in monotherapy or combination) in cases of refractory or severe obesity, as we do similarly for other disease modalities such as refractory hypertension or poorly controlled Type 2 diabetes that requires robust escalation in therapy.

Clinical Challenge

A 31 year old patient with a past medical history of Class 3 obesity BMI 46 kg/m 2 , Type 2 diabetes mellitus (A1c <5.7%, well controlled on metformin), polycystic ovarian syndrome, non-alcoholic steatosis of the liver, pulmonary and neurosarcoidosis on infliximab and methotrexate, and chronic worsening pain presents for weight management evaluation. She had a history of opioid use disorder due to the chronic pain, though in remission. She had been on several weight-promoting pain medications for symptom control, including gabapentin, duloxetine and nortriptyline. Contributing factors over the years to her weight gain also included her diagnosis of Bipolar Disorder with antipsychotic medication-induced weight gain (previously trialed aripiprazole, responded to lurasidone with decreasing efficiency, and now finally stable on paliperidone though weight gain promoting). Her highest adult weight was her current weight of 295 pounds with a lowest adult weight of 140 lbs. that pre-dated her Bipolar and sarcoidosis diagnoses several years ago. She had stable eating patterns, and often chose healthy meals such as hummus, vegetables, Greek salads, and lean meats, though had a weakness for sweet cravings. She engaged in structured gym exercise for 30 minutes three times per week despite the chronic pain. Recent stressors included her close aunt who had been diagnosed with cancer. She also suffered from insomnia and had been evaluated closely with sleep therapists and sleep hygiene specialists. Her polysomnogram was negative for sleep apnea.

What Would You Do Next?

A. Offer more aggressive intensive lifestyle therapy intervention

B. Trial of anti-obesity medication if option A above becomes ineffective

C. Metabolic and bariatric surgery only as anti-obesity medication would be contraindicated given her history of opioid use

D. Trial of anti-obesity medication for 3 months with concurrent referral to bariatric surgery

The patient depicted in the case has chronic, debilitating severe obesity classification with several inflammatory obesity-related comorbidities and other contributing etiology to her weight gain.

In regards to lifestyle intervention, the patient was started on a healthy low fat high fiber diet with increased consumption of vegetables, while minimizing intake of processed foods, added sugar, trans fats, and refined flours ( 1 ). Nutrient-dense whole foods prepared at home were encouraged. Acceptable macronutrient distribution range is 45–65% carbohydrates, 20–35% total fat of which <10% should be polyunsaturated fats, and 10–35% protein and amino acids 1 . However, obesity-related comorbidities such as type 2 diabetes mellitus, polycystic ovarian syndrome, and non-alcoholic steatosis of the liver suggesting features of insulin resistance need to be taken into consideration when implementing dietary modifications specific to this case. The patient's daily carbohydrate intake should be reduced to 40–50% to combat insulin resistance. Several studies have shown improvement in metabolic parameters and more rapid weight loss when a low carbohydrate diet was implemented initially in the first 3–6 months ( 2 , 3 ). At presentation, the patient's calculated daily protein intake was <20% of total daily intake and increasing her protein intake to 30% reduced her sweet cravings and increased satiety. In addition, she would benefit from at least 150 min per week of structured moderately intensive exercise as tolerated as recommended by The American College of Sports Medicine ( 4 ). Of note, the patient is also under significant stressors. Stress has been very strongly linked to hyperphagia, binging, and obesity ( 5 , 6 ). Stress management would also provide long-term strategies for emotional/stress eating should they arise. Her sleep has been adequately addressed by a specialist multidisciplinary team. Further, the patient was already under intense behavioral therapy given her underlying psychiatric illness. Early behavioral therapy intervention should be strongly considered in patients with adverse psychological factors, eating disorders and underlying psychiatric conditions that would otherwise impede their overall progress toward health goals. However, it may be difficult to promote more aggressive lifestyle intervention alone, especially in a patient with an advanced obesity disease staging who is already making strides to eat healthy and undergoing behavioral therapy.

Furthermore, the patient also meets criteria for initiation of anti-obesity pharmacotherapy (AOM): BMI >27 kg/m 2 plus the presence of one obesity-related comorbidity and/or BMI >30 kg/m 2 in conjunction with lifestyle intervention ( 7 , 8 ). Though the patient has a history of opioid use disorder, it is in remission and there is no active contraindication to AOM. The patient also does not have underlying heart disease, end-stage-renal disease, or acute angle glaucoma that would negate use of several AOM such as phentermine/topiramate, lorcaserin, and naltrexone/bupropion. Liraglutide 3.0 mg would be a first option given its double benefits in patients with severe obesity and diabetes ( 7 ) and other obesity-related comorbidities such as fatty liver ( 9 ) and polycystic ovarian disease ( 10 ). The medication is also generally well-tolerated and safe. Because anti-obesity medications can exert central effects in a patient with Bipolar Disorder, close monitoring and communication with the patient's psychiatrist would be critical. Because her BMI is already very elevated, clinically, both lifestyle changes and pharmacological treatment would be implemented together, rather than separately. Moreover, based on her current body mass index alone of 40 kg/m 2 , the patient meets National Institutes of Health consensus criteria for metabolic and bariatric surgery ( 11 ): BMI 35 kg/m 2 in the presence of at least one obesity-related comorbidity or BMI 40 kg/m 2 . Therefore, it would be prudent to discuss bariatric surgery in this patient given her disease severity.

The correct answer is D. The patient was actually started on AOM with concurrent referral to the institution's bariatric surgery program. Since the patient's insurance did not provide coverage for liraglutide 3.0 mg, she was alternatively prescribed a combination anti-obesity medication therapy (phentermine/topiramate) after discussion with her psychiatrist and other specialists. AOM were instrumental in improving the patient's overall hunger drive, cravings, and satiety. Despite being the best option for her at presentation, the patient was unwilling to undergo the bariatric procedure. Oftentimes, this may be the case in many patients until they consent to surgical intervention or have weight regain on non-surgical therapy. Future guidelines may need to be more definitive about earlier referral to bariatric surgery.

The patient continued AOM long-term, having lost 90 pounds over a 2 year time period ( Figure 1 ). Her BMI now is 28.7 kg/m 2 , weight 205 lbs. (reversed from Class 3 obesity, BMI 46 kg/m 2 , weight 295 lbs.) with improvement in quality of life and obesity-related comorbidities. Liver transaminases that were previously elevated in the context of fatty liver disease normalized along with return of regular menstrual cycles. In the process of losing weight with related attenuation in disease comorbidity and metabolic profile improvement, the patient's neurosarcoidosis continued to show remarkable recovery with stabilization of her mental health conditions and disability. Her specialists reported that this was the best she had been in many years. The patient lost −30.5% of her total body weight, which is typical weight loss achieved by metabolic and bariatric surgery means, through non-surgical intervention.

www.frontiersin.org

Figure 1 . Patient's weight graph derived from the electronic health record. The patient lost a total of 90 lbs. over a 2 year time period with adjunctive anti-obesity pharmacotherapy (phentermine/topiramate) in combination with behavioral and lifestyle intervention.

Though these results may not be usual for all patients, it is important to note that all treatment modalities (behavioral, lifestyle, pharmacological, and/or surgical whether as monotherapy or in combination) must be utilized for patients suffering with severe obesity and its devastating consequences on overall health and quality of life. Many of these patients present with complicated disease states and multiple comorbidities. Thus, important health targets include not only weight loss but treatment-enhanced double benefits leading to improvement of comorbidities.

Data Availability Statement

All datasets for this study were directly generated from the patient's electronic health record and are available upon request.

Informed Consent

Written informed consent to publish this case report was obtained from the patient.

Author Contributions

GS and CA contributed and edited the contents of this manuscript.

No external funding was provided for the creation of this manuscript.

Conflict of Interest

GS served as a consultant for Johnson and Johnson and advisor for Rhythm Pharmaceuticals. CA reports grants from Aspire Bariatrics, Myos, the Vela Foundation, the Dr. Robert C. and Veronica Atkins Foundation, Coherence Lab, Energesis, NIH, and PCORI, grants and personal fees from Orexigen, GI Dynamics, Takeda, personal fees from Nutrisystem, Zafgen, Sanofi-Aventis, NovoNordisk, Scientific Intake, Xeno Biosciences, Rhythm Pharmaceuticals, Eisai, EnteroMedics, Bariatrix Nutrition, and other from Science-Smart LLC, outside the submitted work.

Acknowledgments

We would like to thank the patient for permission to publish.

1. ^ http://www.nationalacademies.org/hmd/~/media/Files/ActivityFiles/Nutrition/DRI-Tables/8_MacronutrientSummary.pdf?la=en (accessed April 2, 2019).

1. Gardner CD, Trepanowski JF, Del Gobbo LC, Hauser ME, Rigdon J, Ioannidis JPA, et al. Effect of low-fat vs low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion: the DIETFITS randomized clinical trial. JAMA . (2018) 319:667–79. doi: 10.1001/jama.2018.0245

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2. Meng Y, Bai H, Wang S, Li Z, Wang Q, Chen L. Efficacy of low carbohydrate diet for type 2 diabetes mellitus management: a systematic review and meta-analysis of randomized controlled trials. Diabetes Res Clin Pract. (2017) 131:124–31. doi: 10.1016/j.diabres.2017.07.006

3. Jang EC, Jun DW, Lee SM, Cho YK, Ahn SB. Comparison of efficacy of low-carbohydrate and low-fat diet education programs in non-alcoholic fatty liver disease: A randomized controlled study. Hepatol Res. (2018) 48:E22–9. doi: 10.1111/hepr.12918

4. Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee IM, et al. American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc. (2011) 43:1334–59. doi: 10.1249/MSS.0b013e318213fefb

5. Manna P, Jain SK. Obesity, oxidative stress, adipose tissue dysfunction, and the associated health risks: causes and therapeutic strategies. Metab Syndr Relat Disord. (2015) 13:423–44. doi: 10.1089/met.2015.0095

6. Razzoli M, Pearson C, Crow S, Bartolomucci A. Stress, overeating, and obesity: Insights from human studies and preclinical models. Neurosci Biobehav Rev. (2017) 76:154–62. doi: 10.1016/j.neubiorev.2017.01.026

7. Srivastava G, Apovian CM. Current pharmacotherapy for obesity. Nat Rev Endocrinol. (2018) 14:12–24. doi: 10.1038/nrendo.2017.122

8. Apovian CM, Aronne LJ, Bessesen DH, McDonnell ME, Murad MH, Pagotto U, et al. Pharmacological management of obesity: an endocrine Society clinical practice guideline. J Clin Endocrinol Metab. (2015) 100:342–62. doi: 10.1210/jc.2014-3415

9. Khoo J, Hsiang JC, Taneja R, Koo SH, Soon GH, Kam CJ, et al. Randomized trial comparing effects of weight loss by liraglutide with lifestyle modification in non-alcoholic fatty liver disease. Liver Int. (2019) 39:941–9. doi: 10.1111/liv.14065

10. Nylander M, Frossing S, Clausen HV, Kistorp C, Faber J, Skouby SO. Effects of liraglutide on ovarian dysfunction in polycystic ovary syndrome: a randomized clinical trial. Reprod Biomed Online. (2017) 35:121–7. doi: 10.1016/j.rbmo.2017.03.023

11. Clinical guidelines on the identification evaluation and treatment of overweight and obesity in adults–the evidence report. National Institutes of Health. Obes Res. (1998) 6(Suppl.2):51S–209S.

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Keywords: anti-obesity medications, weight loss drugs, combination therapy, bariatric surgery, lifestyle intervention

Citation: Srivastava G and Apovian CM (2019) Clinical Challenge: Patient With Severe Obesity BMI 46 kg/m 2 . Front. Endocrinol. 10:635. doi: 10.3389/fendo.2019.00635

Received: 30 April 2019; Accepted: 03 September 2019; Published: 02 October 2019.

Reviewed by:

Copyright © 2019 Srivastava and Apovian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gitanjali Srivastava, geet5sri@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Case study: a patient with diabetes and weight-loss surgery.

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Sue Cummings; Case Study: A Patient With Diabetes and Weight-Loss Surgery. Diabetes Spectr 1 July 2007; 20 (3): 173–176. https://doi.org/10.2337/diaspect.20.3.173

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A.W. is a 65-year-old man with type 2 diabetes who was referred by his primary care physician to the weight center for an evaluation of his obesity and recommendations for treatment options, including weight-loss surgery. The weight center has a team of obesity specialists, including an internist, a registered dietitian (RD), and a psychologist, who perform a comprehensive initial evaluation and make recommendations for obesity treatment. A.W. presented to the weight center team reluctant to consider weight-loss surgery;he is a radiologist and has seen patients who have had complications from bariatric surgery.

Pertinent medical history. A.W.'s current medications include 30 and 70 units of NPH insulin before breakfast and before or after dinner, respectively, 850 mg of metformin twice daily, atorvastatin,lisinopril, nifedipine, allopurinol, aspirin, and an over-the-counter vitamin B 12 supplement. He has sleep apnea but is not using his continuous positive airway pressure machine. He reports that his morning blood glucose levels are 100–130 mg/dl, his hemoglobin A 1c (A1C) level is 6.1%, which is within normal limits, his triglyceride level is 201 mg/dl, and serum insulin is 19 ulU/ml. He weighs 343 lb and is 72 inches tall, giving him a BMI of 46.6 kg/m 2 .

Weight history. A.W. developed obesity as a child and reports having gained weight every decade. He is at his highest adult weight with no indication that medications or medical complications contributed to his obesity. His family history is positive for obesity; his father and one sister are also obese.

Dieting history. A.W. has participated in both commercial and medical weight-loss programs but has regained any weight lost within months of discontinuing the programs. He has seen an RD for weight loss in the past and has also participated in a hospital-based, dietitian-led, group weight-loss program in which he lost some weight but regained it all. He has tried many self-directed diets, but has had no significant weight losses with these.

Food intake. A.W. eats three meals a day. Dinner, his largest meal of the day, is at 7:30 p . m . He usually does not plan a mid-afternoon snack but will eat food if it is left over from work meetings. He also eats an evening snack to avoid hypoglycemia. He reports eating in restaurants two or three times a week but says his fast-food consumption is limited to an occasional breakfast sandwich from Dunkin'Donuts. His alcohol intake consists of only an occasional glass of wine. He reports binge eating (described as eating an entire large package of cookies or a large amount of food at work lunches even if he is not hungry) about once a month, and says it is triggered by stress.

Social history. Recently divorced, A.W. is feeling depressed about his life situation and has financial problems and stressful changes occurring at work. He recently started living with his girlfriend, who does all of the cooking and grocery shopping for their household.

Motivation for weight loss. A.W. says he is concerned about his health and wants to get his life back under control. His girlfriend, who is thin and a healthy eater, has also been concerned about his weight. His primary care physician has been encouraging him to explore weight-loss surgery; he is now willing to learn more about surgical options. He says that if the weight center team's primary recommendation is for weight-loss surgery,he will consider it.

Does A.W. have contraindications to weight-loss surgery, and, if not, does he meet the criteria for weight-loss surgery?

What type of weight-loss surgery would be best for A.W.?

Roles of the obesity specialist team members

The role of the physician as an obesity specialist is to identify and evaluate obesity-related comorbidities and to exclude medically treatable causes of obesity. The physician assesses any need to adjust medications and,if possible, determines if the patient is on a weight-promoting medication that may be switched to a less weight-promoting medication.

The psychologist evaluates weight-loss surgery candidates for a multitude of factors, including the impact of weight on functioning, current psychological symptoms and stressors, psychosocial history, eating disorders,patients' treatment preferences and expectations, motivation, interpersonal consequences of weight loss, and issues of adherence to medical therapies.

The RD conducts a nutritional evaluation, which incorporates anthropometric measurements including height (every 5 years), weight (using standardized techniques and involving scales in a private location that can measure patients who weigh > 350 lb), neck circumference (a screening tool for sleep apnea), and waist circumference for patients with a BMI < 35 kg/m 2 . Other assessments include family weight history,environmental influences, eating patterns, and the nutritional quality of the diet. A thorough weight and dieting history is taken, including age of onset of overweight or obesity, highest and lowest adult weight, usual weight, types of diets and/or previous weight-loss medications, and the amount of weight lost and regained with each attempt. 1  

Importance of type of obesity

Childhood- and adolescent-onset obesity lead to hyperplasic obesity (large numbers of fat cells); patients presenting with hyperplasic and hypertrophic obesity (large-sized fat cells), as opposed to patients with hypertrophic obesity alone, are less likely to be able to maintain a BMI < 25 kg/m 2 , because fat cells can only be shrunk and not eliminated. This is true even after weight-loss surgery and may contribute to the variability in weight loss outcomes after weight loss surgery. Less than 5% of patients lose 100% of their excess body weight. 2 , 3  

Criteria and contraindications for weight-loss surgery

In 1998, the “Clinical Guidelines on the Identification, Evaluation,and Treatment of Overweight and Obesity in Adults: The Evidence Report” 4   recommended that weight-loss surgery be considered an option for carefully selected patients:

with clinically severe obesity (BMI ≥ 40 kg/m 2 or ≥ 35 kg/m 2 with comorbid conditions);

when less invasive methods of weight loss have failed; and

the patient is at high risk for obesity-associated morbidity or mortality.

Contraindications for weight-loss surgery include end-stage lung disease,unstable cardiovascular disease, multi-organ failure, gastric verices,uncontrolled psychiatric disorders, ongoing substance abuse, and noncompliance with current regimens.

A.W. had no contraindications for surgery and met the criteria for surgery,with a BMI of 46.6 kg/m 2 . He had made numerous previous attempts at weight loss, and he had obesity-related comorbidities, including diabetes,sleep apnea, hypertension, and hypercholesterolemia.

Types of procedures

The roux-en-Y gastric bypass (RYGB) surgery is the most common weight-loss procedure performed in the United States. However, the laparoscopic adjustable gastric band (LAGB) procedure has been gaining popularity among surgeons. Both procedures are restrictive, with no malabsorption of macronutrients. There is,however, malabsorption of micronutrients with the RYGB resulting from the bypassing of a major portion of the stomach and duodenum. The bypassed portion of the stomach produces the intrinsic factor needed for the absorption of vitamin B 12 . The duodenum is where many of the fat-soluble vitamins, B vitamins, calcium, and iron are absorbed. Patients undergoing RYGB must agree to take daily vitamin and mineral supplementation and to have yearly monitoring of nutritional status for life.

Weight loss after RYGB and LAGB

The goal of weight-loss surgery is to achieve and maintain a healthier body weight. Mean weight loss 2 years after gastric bypass is ∼ 65% of excess weight loss (EWL), which is defined as the number of pounds lost divided by the pounds of overweight before surgery. 5   When reviewing studies of weight-loss procedures, it is important to know whether EWL or total body weight loss is being measured. EWL is about double the percentage of total body weight loss; a 65% EWL represents about 32% loss of total body weight.

Most of the weight loss occurs in the first 6 months after surgery, with a continuation of gradual loss throughout the first 18–24 months. Many patients will regain 10–15% of the lost weight; a small number of patients regain a significant portion of their lost weight. 6   Data on long-term weight maintenance after surgery indicate that if weight loss has been maintained for 5 years, there is a > 95% likelihood that the patient will keep the weight off over the long term.

The mean percentage of EWL for LAGB is 47.5%. 3   Although the LAGB is considered a lower-risk surgery, initial weight loss and health benefits from the procedure are also lower than those of RYGB.

Weight-loss surgery and diabetes

After gastric bypass surgery, there is evidence of resolution of type 2 diabetes in some individuals, which has led some to suggest that surgery is a cure. 7   Two published studies by Schauer et al. 8   and Sugarman et al. 9   reported resolution in 83 and 86% of patients, respectively. Sjoström et al. 10   published 2-and 10-year data from the Swedish Obese Subjects (SOS) study of 4,047 morbidly obese subjects who underwent bariatric surgery and matched control subjects. At the end of 2 years, the incidence of diabetes in subjects who underwent bariatric surgery was 1.0%, compared to 8.0% in the control subjects. At 10 years, the incidence was 7.0 and 24.0%, respectively.

The resolution of diabetes often occurs before marked weight loss is achieved, often days after the surgery. Resolution of diabetes is more prevalent after gastric bypass than after gastric banding (83.7% for gastric bypass and 47.9% for gastric banding). 5   The LAGB requires adjusting (filling the band through a port placed under the skin),usually five to six times per year. Meta-analysis of available data shows slower weight loss and less improvement in comorbidities including diabetes compared to RYGB. 5  

A.W. had diabetes; therefore, the weight center team recommended the RYGB procedure.

Case study follow-up

A.W. had strong medical indications for surgery and met all other criteria outlined in current guidelines. 4   He attended a surgical orientation session that described his surgical options,reviewed the procedures (including their risks and possible complications),and provided him the opportunity to ask questions. This orientation was led by an RD, with surgeons and post–weight-loss surgical patients available to answer questions. After attending the orientation, A.W. felt better informed about the surgery and motivated to pursue this treatment.

The weight center evaluation team referred him to the surgeon for surgical evaluation. The surgeon agreed with the recommendation for RYGB surgery, and presurgical appointments and the surgery date were set. The surgeon encouraged A.W. to try to lose weight before surgery. 11  

Immediately post-surgery. The surgery went well. A.W.'s blood glucose levels on postoperative day 2 were 156 mg/dl at 9:15 a . m . and 147 mg/dl at 11:15 a . m . He was discharged from the hospital on that day on no diabetes medications and encouraged to follow a Stage II clear and full liquid diet( Table 1 ). 12  

Diet Stages After RYBG Surgery

Diet Stages After RYBG Surgery

On postoperative day 10, he returned to the weight center. He reported consuming 16 oz of Lactaid milk mixed with sugar-free Carnation Instant Breakfast and 8 oz of light yogurt, spread out over three to six meals per day. In addition, he was consuming 24 oz per day of clear liquids containing no sugar, calories, or carbonation. A.W.'s diet was advanced to Stage III,which included soft foods consisting primarily of protein sources (diced,ground, moist meat, fish, or poultry; beans; and/or dairy) and well-cooked vegetables. He also attended a nutrition group every 3 weeks, at which the RD assisted him in advancing his diet.

Two months post-surgery. A.W. was recovering well; he denied nausea, vomiting, diarrhea, or constipation. He was eating without difficulty and reported feeling no hunger. His fasting and pre-dinner blood glucose levels were consistently < 120 mg/dl, with no diabetes medications. He continued on allopurinol and atorvastatin and was taking a chewable daily multivitamin and chewable calcium citrate (1,000 mg/day in divided doses) with vitamin D (400 units). His weight was 293 lb, down 50 lb since the surgery. A pathology report from a liver biopsy showed mild to moderate steatatosis without hepatitis.

One year post-surgery. A.W.'s weight was 265 lb, down 78 lb since the surgery, and his weight loss had significantly slowed, as expected. He was no longer taking nifedipine or lisinipril but was restarted at 5 mg daily to achieve a systolic blood pressure < 120 mmHg. His atorvastatin was stopped because his blood lipid levels were appropriate (total cholesterol 117 mg/dl, triglycerides 77 mg/dl, HDL cholesterol 55 mg/dl, and LDL cholesterol 47 mg/dl). His gastroesophageal reflux disease has been resolved, and he continued on allopurinol for gout but had had no flare-ups since surgery. Knee pain caused by osteoarthritis was well controlled without anti-inflammatory medications, and he had no evidence of sleep apnea. Annual medical follow-up and nutritional laboratory measurements will include electrolytes, glucose,A1C, albumin, total protein, complete blood count, ferritin, iron, total iron binding capacity, calcium, parathyroid hormone, vitamin D, magnesium, vitamins B 1 and B 12 , and folate, as well as thyroid, liver, and kidney function tests and lipid measurements.

In summary, A.W. significantly benefited from undergoing RYBP surgery. By 1 year post-surgery, his BMI had decreased from 46.6 to 35.8 kg/m 2 ,and he continues to lose weight at a rate of ∼ 2 lb per month. His diabetes, sleep apnea, and hypercholesterolemia were resolved and he was able to control his blood pressure with one medication.

Clinical Pearls

Individuals considering weight loss surgery require rigorous presurgical evaluation, education, and preparation, as well as a comprehensive long-term postoperative program of surgical, medical, nutritional, and psychological follow-up.

Individuals with diabetes should consider the RYBP procedure because the data on resolution or significant improvement of diabetes after this procedure are very strong, and such improvements occur immediately. Resolution in or improvement of diabetes with the LAGB procedure are more likely to occur only after excess weight has been lost.

Individuals with diabetes undergoing weight loss surgery should be closely monitored; an inpatient protocol should be written regarding insulin regimens and sliding-scale use of insulin if needed. Patients should be educated regarding self-monitoring of blood glucose and the signs and symptoms of hypoglycemia. They should be given instructions on stopping or reducing medications as blood glucose levels normalize.

Patient undergoing RYGB must have lifetime multivitamin supplementation,including vitamins B 1 , B 12 , and D, biotin, and iron, as well as a calcium citrate supplement containing vitamin D (1,000–1,500 mg calcium per day). Nutritional laboratory measurements should be conducted yearly and deficiencies repleted as indicated for the duration of the patient's life.

Sue Cummings, MS, RD, LDN, is the clinical programs coordinator at the MGH Weight Center in Boston, Mass.

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Population Health Science (1)

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11 Case Study: Can We Reduce Obesity by Encouraging People to Eat Healthy Food?

  • Published: June 2016
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In the United States, an estimated 17% of children age 2 to 19 years are considered obese; 32% are overweight. Worldwide, 12.9% to 23.8% of children are obese, and the prevalence is increasing. Preventing the onset of obesity remains a critical public health goal of the next decade. Population health science approaches to reducing the prevalence of obesity are presented: one that focuses on coaching individuals to change their behaviors related to food and exercise, and another that focuses on changing the food environment (ubiquitous exposure). An illustration is provided of how to conceptualize the limits of individual-level behavioral interventions on the population distributions of obesity incidence using basic assumptions and data simulation. The effect of individual motivation to prevent obesity is bounded by the prevalence of unhealthy environments in which children are living, which affects the number of incident obesity cases observed and the proportion attributable to individual determinants.

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  • Systematic Review
  • Published: 24 June 2024

Management for children and adolescents with overweight and obesity: a recommendations mapping

  • Chen Tian 1 , 2 , 3 ,
  • Meng Xu 1 , 2 , 3 ,
  • Honghao Lai 1 , 2 ,
  • Mingyao Sun 4 ,
  • Yao Lu 1 , 2 ,
  • Yong Wang 5 ,
  • Bo Tong 2 ,
  • Yiyun Wang 2 ,
  • Feiyang Na 6 ,
  • Jing Wang 7 ,
  • Qiong Li 8 &
  • Long Ge 1 , 2 , 3  

Pediatric Research ( 2024 ) Cite this article

19 Accesses

Metrics details

Childhood obesity is a global public health issue, and the status of clinical practice guidelines (CPGs) as instruction manuals for the management of childhood obesity remains unclear. This study aims to identify and apprise the methodological and reporting quality of CPGs focused on childhood obesity and provide an overview of key recommendations.

Databases and websites reporting guidelines were searched from January, 2018 to September, 2023. The methodological quality was graded using the AGREE II, and RIGHT was used to assess the reporting completeness.

Among the six included CPGs, two were rated as high quality and considered “Recommended” and three were reported no less than 80%. CPGs included 184 recommendations cover diagnosis, assessment and management of complications, interventions and prevention. The diagnostic criteria for children with obesity over 2 years of age are based on normative BMI percentiles, depending on sex and age. CPGs recommended the delivery of multi-component behavior-changed interventions included controlling diet and increasing physical activity. Pharmacological interventions and bariatric surgery are considered as complementary therapies.

CPGs for childhood obesity should emphasize the impact of psychological factors and consider the provision of interventions from multiple settings, and could consider the role of complementary alternative therapies.

Six guidelines have been published in the past 5 years focusing children obesity.

Recommendations covered diagnosis, multiple intervention and prevention.

Guidelines should focus on the role of complementary alternative therapies.

Guidelines should emphasize the impact of psychological factors.

Guidelines should consider the provision of interventions from multiple settings.

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  • Published: 24 June 2024

Global prevalence of obesity and overweight among medical students: a systematic review and meta-analysis

  • Arman Shafiee 1   na1 ,
  • Zahra Nakhaee 2   na1 ,
  • Razman Arabzadeh Bahri 3 ,
  • Mohammad Javad Amini 1 ,
  • Amirhossein Salehi 4 ,
  • Kyana Jafarabady 1 ,
  • Niloofar Seighali 1 ,
  • Pegah Rashidian 5 ,
  • Hanieh Fathi 1 ,
  • Fatemeh Esmaeilpur Abianeh 3 ,
  • Samira Parvizi Omran 3 ,
  • Mahmood Bakhtiyari 6 &
  • Amirhesam Alirezaei 4  

BMC Public Health volume  24 , Article number:  1673 ( 2024 ) Cite this article

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Metrics details

Obesity is a global health concern, and understanding its prevalence among medical students is crucial for shaping targeted interventions. This systematic review and meta-analysis aim to comprehensively assess the prevalence of obesity and overweight among medical students.

A systematic literature search was conducted across major databases, including PubMed, Scopus, and Web of Science, in order to identify relevant studies that evaluated obesity and overweight among medical students. Inclusion criteria encompassed published and peer-reviewed studies reporting the prevalence of obesity among medical students.

A total of 1245 studies were screened based on their titles and abstracts, and 99 studies comprised a total sample size of 47,455 medical students across diverse geographical regions were included in this study. The overall pooled prevalence of overweight among medical students was estimated at 18% (95% CI: 17%—20%), with obesity at 9% (95% CI: 7%—11%). The combined prevalence of excess weight (overweight and obesity) was calculated to be 24% (95% CI: 22%—27%). Meta-regression results indicated a significant correlation between study year and overweight/obesity prevalence ( p  < 0.05), with a trend towards increasing prevalence over time. Male medical students exhibited a higher pooled prevalence, increasing with the percentage of male participants.

This systematic review and meta-analysis provide a comprehensive overview of the prevalence of obesity among medical students globally. In summary, obesity and overweight present a substantial worldwide health concern, especially among susceptible groups such as medical students, whose prevalence is on the rise. It is crucial to grasp the extent and contributing factors of obesity among medical students to formulate precise interventions aimed at fostering healthier habits and alleviating the adverse impacts of obesity on both physical and mental health.

Peer Review reports

Introduction

In recent decades, obesity has emerged as a global health concern, and its prevalence is increasing dramatically worldwide [ 1 , 2 , 3 ]. Obesity is characterized by excessive accumulation of body fat within adipose tissue, which may lead to adverse health effects [ 4 ]. Globally, body mass index (BMI) is the most commonly used to classify overweight and obesity in adults and is defined as weight in kg/height in m 2 . Individuals with a BMI between 25 and 29.9 kg/m 2 are considered overweight, and Individuals with a BMI ≥ 30kg/m 2 are considered obese. Obesity is further classified into three severity levels: class I (BMI 30.0–34.9), class II (BMI 35.0–39.9), and class III (BMI ≥ 40.0) [ 5 ]. Several studies have identified obesity and overweight as risk factors for chronic and life-threatening illnesses, including diabetes [ 6 ], various cancers [ 7 , 8 ], cardiovascular disease [ 9 ], and hypertension [ 10 , 11 ]. The increasing prevalence of obesity and overweight, and its resulting mortality and morbidity, threaten people’s health in many countries. In addition, it causes destructive health conditions and financial burdens on people and society [ 12 , 13 ].

Obesity is a multifactorial pathology, and it has been suggested that the increasing prevalence can be attributed to lifestyle changes, particularly nutritional behavior and inadequate physical activity [ 14 , 15 , 16 ]. While the general population is affected by the obesity epidemic, certain subgroups, such as medical students, may be particularly vulnerable. Medical students, a population that should act as healthy role models, often face unique challenges that can contribute to unhealthy lifestyle habits, including long hours of studying, high levels of stress, and limited time for physical activity and self-care [ 17 ]. Shift work may have significant repercussions on the health of the worker and has been linked to unhealthy lifestyles [ 18 ]. A study demonstrated that those who work in shifts have a greater risk of being obese than regular 8-h workers [ 19 ]. Furthermore, medical students face a higher risk of developing psychological issues, such as feeding and eating disorders (FEDs) [ 20 , 21 , 22 ]. A study estimated that the prevalence of FEDs symptoms in medical students is approximately 17.35% [ 23 ]. Socioeconomic and psychological elements significantly affect dietary habits and physical inactivity [ 23 ]. Eating habits have a stronger impact on BMI than physical activity [ 24 ]. The dietary habits observed among medical students include irregular meals, skipping meals, insufficient intake of fruits and vegetables, high consumption of candies and alcohol, and excessive consumption of fried and fast foods [ 23 , 25 , 26 ]. Accordingly, exposure to these known and unknown factors may increase the risk of overweight and obesity among medical students.

Given the fact that obesity negatively impacts an individual’s physical and mental health [ 27 ], understanding the prevalence of obesity among medical students is crucial for identifying potential risk factors and developing targeted interventions to promote healthier lifestyles within this population. Several studies from different countries have reported the prevalence of obesity among medical students [ 28 , 29 , 30 ]. However, to the best of our knowledge, this study is the first systematic review and meta-analysis to explore the current state of obesity prevalence among medical students. Also, our study aims to take advantage of all available data on the topic to offer new insights into the prevalence and distribution of obesity within BMI subgroups in medical students.

The primary objective of this study is to investigate the prevalence of obesity and overweight among medical students globally. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist [ 31 ], our methodology encompasses key steps to ensure transparency and rigor in our research.

Research question

Our research seeks to ascertain the global prevalence of obesity and overweight among medical students, with a specific focus on studies employing body mass index (BMI) as the primary metric for the measurement of obesity and overweight.

Search strategy

We conducted a comprehensive search across various databases, including PubMed, Scopus, and Web of Science, from the inception to August 4th, 2023, to identify relevant studies. The search terms included variations of "medical students," "obesity," "overweight," and "BMI."

Eligibility criteria

The population, intervention, comparison, and outcome (PICO) framework was followed in this study and were as follows: Population (P): medical students; Intervention (I): none; Comparison (C): overweight, obese, or healthy medical students; and Outcome (O): prevalence of obesity or overweight among medical students. We included cross-sectional, descriptive, observational studies conducted globally that involved medical students. Studies were considered if they explored the prevalence of obesity and overweight, using BMI as the measurement tool. We excluded studies that did not meet these criteria or lacked essential information. No limitation was imposed regarding the original language of the identified articles or the gender of the evaluated medical students.

Study selection

Two independent reviewers screened the identified studies based on the title and abstract. Full-text assessments were performed to ensure the inclusion of relevant data. Any discrepancies in selection were resolved through discussion or consultation with a third reviewer.

Data extraction

We extracted pertinent information from the selected studies, including study design, geographic location, sample size, and prevalence rates of obesity and overweight among medical students. We prioritized data collected using the World Health Organization (WHO) criteria for obesity and overweight classification (BMI > 30 for obese, 25 < BMI ≤ 30 for overweight).

Quality assessment

The Newcastle–Ottawa Scale (NOS), which is a validated and easy-to-use scale, was used to assess the quality of the included articles (Supplemental Table 1). The NOS for cross-sectional studies contains seven items within three domains, including selection, comparability, and outcome, with an overall score of nine. The selection domain has four questions and a maximum score of five scores. The comparability domain has a maximum score of one. The outcome domain has two questions and a maximum score of three scores. A score of 7–9 indicates high quality, 4–6 indicates high risk, and 0–3 indicates very high risk of bias. Quality assessment was checked independently by two authors, and any disagreements were resolved by a third author.

Data synthesis

We synthesized the extracted data using a random effect meta-analysis, synthesizing the overall prevalence rates of obesity and overweight among medical students. Subgroup analyses were conducted based on geographic regions and study characteristics to explore potential variations. Publication bias was examined through doi plots and Peter's test, with statistical relevance set at a p -value below 0.1 [ 32 , 33 ]. All statistical operations and the production of graphs were conducted using STATA and R software(meta package) [ 34 ].

The systematic review and meta-analysis aimed to examine the prevalence of overweight, obesity, and overall excess weight among medical students. A comprehensive search of electronic databases identified 1,245 articles. After screening titles and abstracts, 254 articles underwent full-text review, with 99 studies meeting the inclusion criteria and included in the meta-analysis (Fig.  1 ).

figure 1

PRISMA flow diagram

Characteristics of included studies

The 100 included studies encompassed a total sample size of 47,455 medical students. These studies were conducted across diverse geographical regions, representing both developed and developing countries. The included studies were conducted in Bahrain, Bangladesh, Bosnia and Herzegovina, Cameron, China, Egypt, Saudi Arabia, Greece, India, Iran, Iraq, Lithuania, Malaysia, Mexico, Morocco, Nepal, Oman, Pakistan, Poland, Romania, Russia, Singapore, Slovakia, South Africa, Spain, Sudan, Syria, Thailand, Tunisia, Turkey, United Arab Emirates, the United States of America, and the United Kingdom. Predominantly, cross-sectional designs were employed, and data collection periods ranged from 1992 to 2023. However, most studies were published in recent years, ranging from 2018 to 2023.

Prevalence of overweight, obesity, and excess weight

The overall pooled prevalence of overweight among medical students was estimated to be 0.18 (95% CI: 0.17 – 0.20), while the pooled prevalence of obesity was 0.09 (95% CI: 0.07 – 0.11). The combined prevalence of excess weight (overweight and obesity) was calculated to be 0.24 (95% CI: 0.22 – 0.27) (Fig.  2 ).

figure 2

Results of meta-analysis for the prevalence of ( a ) excess weight (overweight/obesity); ( b ) overweight; and ( c ) obesity among medical students

Meta-regression analysis

A meta-regression was conducted to explore potential sources of heterogeneity across studies. Variables such as study year, percentage of male participants, and mean age of population were considered. The results indicated that the study year significantly correlated with overweight/obesity prevalence ( p  < 0.05) (Fig.  3 ), with a trend towards increasing prevalence over time. Male medical students exhibited a higher pooled prevalence of overweight/obesity, as the prevalence increased with the increased percentage of male participants. No significant associations were observed between the mean age of the population and the aforementioned outcomes (Supplementary Table 2).

figure 3

Scatter plot of meta-regression analysis for the association between ( a ) percentage of male participants, and ( b ) study year with the prevalence of overweight/obesity among medical students. Bubble size represents the weight of the study

Publication bias

Doi plot and Peter’s regression test showed possible publication bias across the included studies for the primary outcome (Fig.  4 ) ( p -value < 0.001).

figure 4

Doi plot for prevalence of excess weight (overweight and obesity) among medical students

Obesity has become one of the greatest health burdens of our era. As the World Health Organization states, around 2 billion people worldwide were reported to be overweight in 2016, of which more than 650 million people were considered to be obese, something around 13% of the whole population [ 35 ]. Globally speaking, 37% of men and 38% of women are considered to be overweight with a BMI greater than 25 kg/m2 [ 36 ]. Around 50% of obese people are distributed in only 10 countries, including the United States, China, India, Russia, Brazil, Mexico, Egypt, Germany, Pakistan, and Indonesia. In Europe, there is an upward trend towards obesity, and 17% of adults are obese [ 37 ]. As it has been long noticed before, obesity is not only an appearance complication but can also be a risk factor for health conditions of great significance, such as hypertensive diseases, dyslipidemia, obstructive sleep apnea, cancers, and etc. [ 11 , 38 , 39 ]

In the present systematic review and meta-analysis, we aimed to inquire into the prevalence of overweight, obesity, and overall excess weight among medical students. Overall, 254 studies were fully reviewed, of which 99 articles met the inclusion criteria and were used in this study. The sample consisted of 48,683 medical students coming from diverse backgrounds, representing both high and low-income countries. Data extraction was performed on relevant studies since 1992 to 2023. The total pooled prevalence of overweight among medical students was estimated to be 18.5% (95% CI: 16.5%—20.5%), while the pooled prevalence of obesity was 9% (95% CI: 7%—11%). The combined prevalence of excess weight (overweight and obesity) was calculated to be 24% (95% CI: 21%—26%). Moreover, the results specified that there is an obvious association between the year the study was conducted and the prevalence of overweight/obesity, meaning as time passes, the prevalence grows. Furthermore, it was indicated that male medical students had a slightly higher pooled prevalence of overweight/obesity.

Medical education is known to be one of the most demanding academic subjects there are. Education programs are usually too time-consuming, and plenty of medical students tend to ignore the importance of healthy nutrition and physical activity. In a study by Shah T. et al. (2014), 34% of medical students consumed fast food because healthy homemade food was just not available [ 40 ]. In another study by Savić S. et al. (2020), a major part of medical students did not have any form of physical activity throughout the week (64.3% of the study population) [ 41 ]. It has also been stated in another paper that university students with BMIs in normal ranges tend to participate more regularly in physical activities than underweight or overweight students [ 42 ]. Since medical students are the next generation’s medical doctors and, therefore, future leaders of health care procedures, it is of utmost importance to find out if overweight and obesity can be an actual concern for the group.

Throughout the years, there have been a variety of studies focusing on the matter of excessive body weight in medical students. In the present study, we tried to gather such studies and assess and possibly compare their results. In a cross-sectional study by Bazmi Inam, S. N. (2008), overall, 112 out of 241 students (46.5%) in the study were reported to be overweight or obese (BMI > 25) [ 43 ]. A different research by Gopalakrishnan S. et al. (2012) showed that of the 169 medical students who participated in the study, respectively 21.3% and 26.6% were discovered to be obese and overweight, of whom above 50% didn’t exercise regularly, 60.4% did not consume the necessary portions of fruits and vegetables daily, and 68% had a positive family history of Diabetes Mellitus [ 44 ]. In another cross-sectional descriptive study done by Purohit G. et al. (2015), the prevalence of medical students with a BMI more than 25 in a 138-participiant sample was 35.5%. The study also stated that more than 90% of the participants were consuming fast food [ 45 ]. Smrithi Krishnamohan et al. designed a non-randomized controlled trial in a private medical college located in India to measure the efficacy of health education using social networking sites in promoting healthy lifestyles among medical students. The sample was selected from overweight/obese individuals, and all participants were divided into two groups: with (intervention arm) and without a Facebook account (control arm). Results showed a significant decrease in BMI among the control group. They came to the conclusion that except for the decrease in junk food intake, the use of Facebook as an effective tool to promote a healthy lifestyle, e.g., weight reduction, could not be proved confidently [ 46 ]. In a study by Bing Li et al., the association between body composition and physical fitness among Chinese medical students was assessed. A total of 2291 medical students were recruited to participate in this cross-sectional study. They concluded that higher fat mass was significantly associated with worse physical fitness among medical students [ 47 ]. Miloš Ž. Maksimović et al. carried out a cross-sectional study to assess the knowledge and approach of medical students towards cardiovascular disease (CVD) risk factors, e.g., obesity and overweight. They also compared 2nd year and last year’s medical student’s knowledge regarding the CVD risk factors. Results indicated that last year medical students were significantly more knowledgeable than those at the beginning of their studies. However, their total awareness of such risk factors needs serious improvement [ 48 ].

In order to further broaden our view, it is vital to compare the obesity statistics among medical students with those of non-medical students. In a study by Tokaç Er, N. et al. (2021), the overweight and obesity rates amongst 984 undergraduate students from Ankara University Faculty of Health Sciences were respectively 16.5% and 4.5% [ 49 ]. Jiang S. et al. (2018) conducted a study to assess the prevalence of overweight and obesity in a sample of 11,673 Chinese college students; results showed a 9.5% rate for overweight and obesity combined [ 50 ]. Further analyzing such studies and comparing them to similar studies in medical students might reveal a noticeable difference between the two groups.

The present study has strengths on several sides. First, we followed the PRISMA guidelines to ensure transparency and rigor in our research. Second, our search was as comprehensive as possible. We utilized three major databases (Pubmed, Scopus, and Web of Science) to cover all relevant articles. Third, every included article was quality assessed based on the Newcastle Ottawa assessment tool for cross-sectional studies. Fourth, in the meta-analysis phase, we carried out a subgroup analysis based on geographic regions and study characteristics to find any potential variations. Finally, based on our meta-regression analysis, we found out that as time passes, more medical students are prone to obesity, and also more male students are in danger of excess body weight than the female population.

Despite the mentioned strengths, our study had some noticeable limitations. First, in recent years, the COVID-19 pandemic has seriously affected everyone’s lifestyle and somehow transformed it into a more stressful one. Medical students are no exception in this matter. Thus, more evaluation of the possible impacts of the COVID-19 pandemic on medical students’ weight changes is of great interest. Another thing that could perhaps be classified as a limitation was the lack of nationality diversity among the included studies. Factors like diet and tendency to exercise can be poles apart in different parts of the world. That being so, a more nationally diverse set of studies can aid us in a better assessment of the medical students’ obesity rate. Most of the studies calculated BMI from self-reported weights and heights. It is crucial for studies to indicate the how they measured height and weight so that the actual assessment can be highlighted. Furthermore, we recognize that there can be a tendency in published papers to overrepresent their statistically significant findings. Moreover, the unpublished or grey literature that was not included in this review article can perhaps lead to an incomplete picture of obesity in medical students.

As always, there is room for further research; other groups of students who may be at risk of obesity can be targeted in the future. For instance, the same topic could be assessed among the populations of dental students and medical specialty residents. Furthermore, similar systematic reviews can be conducted to evaluate the prevalence of unhealthy diet and inadequate exercise among medical students as contributing factors for overweight and obesity.

In conclusion, we conducted a systematic review and meta-analysis to assess the prevalence of obesity and overweight among medical students and further understand the significance of obesity among them. Herein, we included a total of 99 articles. The results exhibited that the combined prevalence of excess weight (overweight and obesity) was calculated to be 24%. Owing to the fact that excess body weight can be the leading point of many health problems such as diabetes mellitus, hypertension, psychological disorders, and many more, perhaps counseling medical students to maintain healthier lifestyles can avoid plenty of such health issues [ 51 , 52 ].

Availability of data and materials

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors would like acknowledge the clinical research development unit of Imam Ali Hospital Karaj, Iran.

To conduct this study, none of the people related to the study received any funds or grants from any institution, individual or organization.

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Arman Shafiee and Zahra Nakhaee are co-first authors of this manuscript.

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Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran

Arman Shafiee, Mohammad Javad Amini, Kyana Jafarabady, Niloofar Seighali & Hanieh Fathi

Student Research Committee, School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran

Zahra Nakhaee

School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

Razman Arabzadeh Bahri, Fatemeh Esmaeilpur Abianeh & Samira Parvizi Omran

School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Amirhossein Salehi & Amirhesam Alirezaei

School of Medicine, Guilan University of Medical Sciences, Rasht, Iran

Pegah Rashidian

Non-Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran

Mahmood Bakhtiyari

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A.S, Z.N: Conceptualization, Project Administration, Data curation, Writing- Original Draft, Writing – Review & Editing, Visualization. M.B, K.J, M.A: Validation, Resources, Methodology, Software, Formal analysis, Writing – Original Draft. R.A; H.F, S.P, N.S, A.SA; P.R; A.A: Writing- Original Draft, Writing – Review & Editing. R.A, F.E: Data curation, Project Administration.

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Shafiee, A., Nakhaee, Z., Bahri, R.A. et al. Global prevalence of obesity and overweight among medical students: a systematic review and meta-analysis. BMC Public Health 24 , 1673 (2024). https://doi.org/10.1186/s12889-024-19184-4

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obesity case study

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ASSOCIATION OF GENERAL OBESITY WITH AN INCREASED RISK OF STROKE: HOSPITAL-BASED CASE CONTROL STUDY

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Obesity is one of the most prevalent conditions making a significant impact on public health worldwide. This study aims to evaluate the contribution of general and abdominal obesity to the risk of stroke based on a hospital-based case-control study conducted in a tertiary hospital in Riyadh, Saudi Arabia. The present study evaluates a total of 94 stroke patients and 188 stroke-free patients with age (±5years) and sex-matched controls and investigates associations between different markers of obesity (BMI, waist-to-hip ratio, waist circumference, and waist-to-height ratio) and evaluates the risk of stroke using logistic regression analysis adjusted for other risk factors. The results reveal that there is no significant difference in the BMI between the control and case group (p > 0.05). However, stroke patients from the case group have a significantly higher waist circumference, waist-to-hip ratio, and waist-to-height ratio as compared to non-stroke patients in the control group (p < 0.05). Central indices show the strongest correlation and better prediction of any obesity-related metric with the occurrence of stroke. Regardless of other vascular risk variables, WHR is the most accurate predictor of the obesity markers under investigation.

Article Details

Obesity drug used in Mounjaro and Zepbound may help treat dangerous sleep apnea

FILE - A woman demonstrates how she puts on her sleep apnea breathing device at her home in...

(AP) - A popular obesity drug may help treat a dangerous disorder in which people struggle to breathe while they sleep,  a new study  finds.

Tirzepatide, the medication in the weight-loss drug Zepbound and also the diabetes treatment Mounjaro, appeared to reduce the severity of sleep apnea along with reducing weight and improving blood pressure and other health measures in patients with obesity who took the drug for a year.

Eli Lilly and Co., the drug’s maker who paid for the research, has asked the Food and Drug Administration to expand use of the drug to treat moderate to severe sleep apnea, in which people stop and start breathing during sleep, a spokesperson said Friday. A decision is expected by the end of the year.

But an outside expert cautioned  in an editorial  that more research will be needed to tell if the drug can be used as “a sole treatment” for obstructive sleep apnea, which occurs when tissue in the throat relaxes and collapses during sleep, fully or partially blocking the airway. It affects an estimated 20 million Americans and can cause short-term issues such as snoring, brain fog and daytime sleepiness but also severe long-term issues such as heart disease, dementia and early death.

The research, published Friday in the New England Journal of Medicine and presented at a medical meeting, included nearly 500 people diagnosed with obesity and sleep apnea. Half of them used what’s typically known as a CPAP machine that feeds oxygen through a mask to keep airways open during sleep. The other group included people for whom a CPAP machine had failed or wasn’t tolerated.

The study found that patients in both groups who got weekly injections of tirzepatide reduced the number of episodes per hour in which their breathing slowed or stopped completely during sleep by about half to nearly 60%, compared to about 10% in people who got a dummy drug. Up to half of the patients taking tirzepatide reduced the apnea episodes enough to potentially resolve the disorder, compared with up to 16% of those using the placebo medication, according to the research.

On average, patients who took tirzepatide also lost between 18% and 20% of their body weight and showed improvements in blood pressure and a condition in which blood oxygen drops during sleep. Patients also reported better sleep and fewer sleep disturbances, the study found.

The new research shows that tirzepatide is “a more effective knife in the drawer,” for treating sleep apnea, said lead author Dr. Atul Malhotra, a sleep medicine specialist at the University of California, San Diego.

In an accompanying editorial, Dr. Sanjay Patel, a sleep medicine specialist at the University of Pittsburgh, cautioned that whether tirzepatide can treat sleep apnea in real-world patients “remains unclear” because of the way improvement is measured. He also noted that cost and access remain obstacles to using tirzepatide and that the addition of the drug as a treatment could exacerbate racial and other disparities in addressing sleep apnea.

Dr. Paul Peppard, a sleep medicine researcher at the University of Wisconsin who was not involved in the study, said losing weight has long been recommended as a way to reduce the severity of sleep apnea by expanding lung capacity, reducing fat in the airways and improving oxygen usage. While diet and exercise can spur weight loss and reduce the consequences of the disorder, the ongoing obesity epidemic in the U.S. proves that shedding pounds is difficult for many people, he said. In such cases, medications such as tirzepatide can help.

“I expect that these drugs could be used as a tool to treat many of the established outcomes of obesity,” Peppard said.

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The frequent UCP2 -866G>A polymorphism protects against insulin resistance and is associated with obesity: a study of obesity and related metabolic traits among 17 636 Danes

Affiliation.

  • 1 Hagedorn Research Institute, Gentofte, Denmark.
  • PMID: 22349573
  • DOI: 10.1038/ijo.2012.22

Context: Uncoupling protein 2 (UCP2) is involved in regulating ATP synthesis, generation of reactive oxygen species and glucose-stimulated insulin secretion in β-cells. Polymorphisms in UCP2 may be associated with obesity and type 2 diabetes mellitus.

Objective: To determine the influence of a functional UCP2 promoter polymorphism (-866G>A, rs659366) on obesity, type 2 diabetes and intermediary metabolic traits. Furthermore, to include these and previously published data in a meta-analysis of this variant with respect to its impact on obesity and type 2 diabetes.

Design: We genotyped UCP2 rs659366 in a total of 17 636 Danish individuals and established case-control studies of obese and non-obese subjects and of type 2 diabetic and glucose-tolerant subjects. Meta-analyses were made in own data set and in publicly available data sets. Quantitative traits relevant for obesity and type 2 diabetes were analysed within separate study populations.

Results: We found no consistent associations between the UCP2 -866G-allele and obesity or type 2 diabetes. Yet, a meta-analysis of data from 12 984 subjects showed an association with obesity (GA vs GG odds ratio (OR) (95% confidence interval (CI)): 0.894(0.826-0.968) P=0.00562, and AA vs GG OR(95% CI): 0.892(0.800-0.996), P=0.0415. Moreover, a meta-analysis for type 2 diabetes of 15 107 individuals showed no association. The -866G-allele was associated with elevated fasting serum insulin levels (P=0.002) and HOMA insulin resistance index (P=0.0007). Insulin sensitivity measured during intravenous glucose tolerance test in young Caucasian subjects (n=377) was decreased in carriers of the GG genotype (P=0.05).

Conclusions: The UCP2 -866G-allele is associated with decreased insulin sensitivity in Danish subjects and is associated with obesity in a combined meta-analysis.

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  • Published: 28 June 2024

Diabetes surpasses obesity as a risk factor for low serum testosterone level

  • Samir H. Assaad Khalil   ORCID: orcid.org/0000-0003-1899-9361 1 ,
  • Paresh Dandona 2 ,
  • Nermin A. Osman 3 , 4 ,
  • Ramy Samir Assaad 5 ,
  • Basma Tayseer Abdalla Zaitoon 1 ,
  • Amal Abdulaziz Almas 1 , 6 &
  • Noha Gaber Amin 1  

Diabetology & Metabolic Syndrome volume  16 , Article number:  143 ( 2024 ) Cite this article

23 Accesses

Metrics details

Male obesity is one of the most associated factors with substandard testosterone levels. However, there is growing evidence linking low testosterone levels to insulin resistance and diabetic complications. We aimed to study the impact of diabetes mellitus on testosterone levels and to assess the correlation of various clinical and biochemical factors with hypogonadism.

Subjects and methods

This case-control study was conducted on 160 adult males categorized into four equal groups (40 each); Group A: lean men with T2DM, Group B: obese with T2DM, Group C: lean with normal glycemic profile, Group D: obese with normal glycemic profile. Serum total testosterone (TT), SHBG and HbA1c have been measured. Free testosterone (cFT) and HOMA-IR were calculated.

A significant negative correlation of serum TT and cFTwith BMI (r -0.16, p 0.04/ r -0.26, p  < 0.001, respectively) and with waist circumference (WC) (r -0.23, p 0.003 and r -0.3, p  < 0.001, respectively). A significant decrease in TT and cFT in the diabetes group versus the non-diabetes one ( p  < 0.001 for both). TT level was significantly lower in the diabetic lean group than in the non-diabetic lean ( p  < 0.001), and even significantly lower than in the non-diabetic obese ( p  < 0.001). TT level in the diabetic obese group was lower than in the non-diabetic obese ( p  < 0.001). The same for cFT level, lower in the diabetic lean group than in non-diabetic lean ( p  < 0.001) and lower in the diabetic obese than in the non-diabetic obese ( p  < 0.001). Concomitant significant reduction in SHBG in the diabetes group ( p  < 0.001). Linear regression analysis revealed that TT significantly correlated with HOMA-IR. HOMA-IR with WC, age and the duration of diabetes correlated significantly with cFT. In our model, HOMA-IR and HbA1c accounted for approximately 51.3% of TT variability (adjusted R-squared 0.513).

Conclusions

The impact of T2DM on serum testosterone levels was more significant than that of obesity. Our study showed a decrease in SHBG together with cFT among the diabetes group. Hypogonadism is significantly correlated to insulin resistance and poor glycemic control, which implies another perspective on the impact of suboptimal glycemic control on the development of hypogonadism.

Introduction

Type 2 diabetes is a significant global public health burden. More than 536 million adults are estimated to have diabetes, and this is expected to increase in alignment with the obesity surge. Egypt is among the top 10 countries regarding the number of people with diabetes, exceeding a prevalence of 18.4% among the age group between 20 and 79 years, according to the IDF data [ 1 ]. Moreover, the age-adjusted prevalence of type 2 diabetes among adults in Alexandria was evaluated to be 16.8% [ 2 ].

Type 2 diabetes is commonly associated with obesity, insulin resistance, increased hepatic glucose output, and relative insulin insufficiency. People with type 2 diabetes often show abnormalities consistent with metabolic syndrome [ 3 ].

The association of low free testosterone levels in the presence of subnormal LH and FSH levels in men with type 2 diabetes was first reported in 2004 [ 4 ]. Several studies have confirmed the association of HH (Hypogonadotropic Hypogonadism) with type 2 diabetes, estimated to be prevalent among 25–40% of men with type 2 diabetes. Moreover, type 2 diabetes is included among the conditions associated with HH, according to the Endocrine Society, suggesting screening patients with type 2 diabetes for hypogonadism [ 5 , 6 , 7 , 8 ]. Although data show that total testosterone and free testosterone concentrations were inversely related to BMI and age, the presence of low T concentration was present among 25% of non-obese patients, confirming that HH was independent of obesity in many cases. In addition, HH was independent of the level of glycemic control and the duration of hyperglycemia [ 4 ].

Several studies reported low testosterone to be an independent risk factor for the development of type 2 diabetes [ 9 , 10 , 11 , 12 ]. Additionally, there is growing evidence linking low testosterone levels to the presence of insulin resistance status [ 13 ], an independent risk factor for the progression of diabetes-related complications, whether microvascular or macrovascular [ 14 ].

Epidemiologic and genetic evidence has also pointed to the possible impact of sex hormone-binding globulin (SHBG) in developing insulin resistance, metabolic syndrome, and type 2 DM [ 15 , 16 , 17 ]. Low serum SHBG levels are linked to the status of insulin resistance and hyperinsulinemia [ 15 ], indicating that SHBG could be a future risk factor predicting the occurrence of type 2 DM [ 18 , 19 ].

The aim of this study was primarily to study the impact of diabetes mellitus on testosterone levels in men and to determine the clinical and biochemical correlates of hypogonadism. We aimed to compare testosterone levels (total and free) and sex hormone-binding globulin (SHBG) among patients with and without type 2 diabetes, in addition to predicting the significant factors affecting their levels among the studied population.

Materials and methods

Subjects and study design : This case-control study was conducted by matching other confounding factors on adult male subjects attending the Diabetes and Metabolism Outpatient Clinic of Alexandria Main University Hospital as a part of their routine diabetes care. The study protocol received authorisation from the ethical committee. Informed written consent was obtained from all the subjects recruited for the study after the purpose and nature of the study were explained to them. The study was carried out between October 2021 and September 30, 2022. The study enrolled 160 male subjects aged between 27 and 45 years; the study subjects were randomly classified into four equal groups:

Group A : 40 lean men with T2DM: BMI < 25 kg/m². Group B : 40 obese men with T2DM: BMI ≥ 30 kg/m². Group C : 40 lean men with normal glycemic profile: BMI < 25 kg/m². Group D : 40 obese men with normal glycemic profile: BMI ≥ 30 kg/m².

Group C and D subjects were chosen from accompaniers bringing in their DM relatives to the diabetes and metabolism outpatient clinic of Alexandria University Hospital. Patients with a known history of hypogonadism or a history of chronic debilitating diseases, such as severe hepatic impairment or renal failure, subjects with severe obstructive sleep apnea, and patients suffering from symptomatic depression were excluded from the study. We excluded participants with any previously diagnosed malignancy and those who have been prescribed medications for benign prostate hypertrophy or hypogonadism treatment that may impact testosterone levels such as SERMs (clomiphene, tamoxifen), aromatase inhibitors (e.g., letrozole), GnRH agonists, and 5-alpha reductase inhibitors (e.g., finasteride). Patients on insulin therapy were excluded from the study as this might interfere with HOMA- calculation.

We also excluded subjects who received testosterone therapy and over-the-counter health supplements comprising androgens, narcotics, or corticosteroids within the past three months.

Demographic parameters and anthropometric measures : Body weight and height were assessed, and body mass index (BMI) was calculated by dividing body weight in kilograms (Kg) by height in meters squared (m 2 ). Waist circumference (WC) was measured at the end of expiration in a standing position midway between the lower rib margin and the superior iliac spine.

Biochemical analysis : Morning (before 10 am) blood samples were withdrawn from all participants after 8–10 h of fasting. Plain vacutainer samples were centrifuged, and the separated sera were used for the spectrophotometric measurement of fasting glucose and albumin. Serum insulin levels were estimated using a chemiluminescence technique with advanced acridinium ester technology (ADVIA Centaur immunoassay System – Siemens). Homeostasis model assessment 2 (HOMA-IR 2) was calculated for the estimation of insulin resistance of all participants, using the formula fasting glucose (mg/dL) X fasting insulin (mU/L) / 405. Serum total testosterone (TT) measurement was performed using a competitive solid phase enzyme-linked immunosorbent assay (ELISA - DRG Diagnostics Gmbh, Germany). The quantification of SHBG in serum samples was also performed using the sandwich technique solid-phase ELISA (DRG Diagnostics Gmbh, Germany). Free testosterone (cFT) was calculated from SHBG and testosterone using Vermeulen and colleagues’ method. Blood collected into EDTA vacutainers was used to directly determine glycated haemoglobin (HbA1c). This was performed by ion exchange high-performance liquid chromatography (HPLC – Tosoh Bioscience G8, Japan).

Statistical analysis : All statistical analyses were conducted using R version 4.2.1 with the following packages: tidyverse, dplyr, Nagpur, static, psych, complot, Hmisc, pROC, and randomForest. Categorical variables were described in terms of numbers and percentages. Shapiro’s normality test determines the mean and standard deviation usage for normally distributed data or the median (min-max) and interquartile range (IQR) for skewed data. Mann-Whitney tests defined diabetic and non-diabetic groups in various numerical data sets. For bivariate analysis between categorical variables, the Chi-square test was applied. We applied for the Monte-Carlo test in case of the Chi-Square test assumption violation. The Spearman Rho Rank Correlation test was conducted to determine the direction and strength of association between continuous variables.

The corrected F-welch ANOVA was used to investigate differences in Albumin and cFT among the four groups: A (DM and lean), B (DM and obese), C (lean), and D (obese). In contrast, the Kruskal-Wallis test was used to evaluate the rest of the study parameters. Three logistic regression models were conducted to predict the significant factors affecting cFT, TT, and SHBG. The significant p -value was set to less than 0.05.

Table  1 summarizes the general characteristics of the study population. The bivariate analysis between subjects with and without diabetes is shown in Table  2 . The two groups had no significant difference regarding age, BMI, and WC. On the other hand, serum testosterone levels (total and free) and SHBG were statistically significantly higher in the non-diabetes group compared to the diabetes group. Figure  1 elucidates the difference regarding several studied parameters between the main diabetic and non-diabetic groups with the subgroup comparisons.

In the linear regression analysis, as shown in Table  3 , total testosterone was significantly correlated with HOMA-IR and HbA1c, two important diabetes-related biomarkers. Based on the adjusted R-squared value of 0.513, these two variables account for approximately 51.3% of testosterone variability. A lower testosterone level is associated with higher insulin resistance and less glycemic control, as suggested by the negative coefficients for HOMA-IR and HbA1c. This model has the following equation: Total testosterone = 805.742–39.32 (HOMA-IR) − 44.977 (HbA1c).

Several variables were associated with free testosterone as shown in figure  2 , including duration of diabetes, waist circumference, age, and HOMA-IR. These variables can explain approximately 45.1% of free testosterone variability based on adjusted R-squared. Diabetes duration, WC, age, and IR were all associated with lower levels of free testosterone based on the negative coefficients. Using this model, Free testosterone would be calculated as follows: 19.76135 − 0.33104 (Duration of DM) − 0.03849 (Waist circumference) − 0.1329 (Age) − 0.72943 (HOMA-IR).

Regarding the SHBG, the linear regression analysis demonstrated a significant association between SHBG levels and several variables, including BMI, HbA1c, albumin, and total testosterone, as follows:

BMI: SHBG levels are expected to increase by 0.35 units for each unit increase in BMI.

HbA1c: For each unit increase in HbA1c, SHBG levels are expected to decrease by 3.43 units.

Albumin: SHBG levels are expected to increase for each albumin unit increase by 5.38 units.

Total testosterone: If the total testosterone level is less than 264, SHBG levels are expected to decrease by 10.49 units.

The adjusted R-squared value of 0.586 indicates approximately 58.6% of the variability in SHBG levels.

SHBG = 25.79 + 0.35(BMI) − 3.43(HbA1c) + 5.38(Albumin) − 10.49(TT (low)).

figure 1

Box Plot Matrix illustrates the difference between the main diabetic and non-diabetic groups with the subgroup comparisons

figure 2

Correlation Matrix using Spearman Rho Rank test to determine the direction and strength of association between the main parameters

In agreement with previous studies (20–22), the results from the current study demonstrated a significant negative correlation of total and free testosterone with BMI (r -0.16, p 0.04 and r -0.26, p  < 0.001, respectively).

However, results have shown particularly as well a significant negative correlation between total and free testosterone with waist circumference (WC) (r -0.23, p 0.003 and r -0.3, p  < 0.001, respectively). This finding was also reached in the Tromsø study [ 20 ], in which total testosterone was measured, and free testosterone was calculated in 1548 men and was analysed using anthropometric data. The age-adjusted correlation between WC and total and free testosterone was − 0.34 ( p  < 0.001) and − 0.09 ( p  < 0.001), respectively, which were stronger than that with BMI.

It is worth mentioning that central obesity and waist circumference as a marker for visceral fat have proved, without a doubt, to be independent risk factors for cardiovascular disease [ 21 ]. At the same time, lower levels of circulating testosterone have been reported to be positively associated with cardiovascular risk factors and atherosclerosis [ 22 , 23 ]. With the background of this data, together with the reported association of testosterone and WC, an incriminated role for testosterone levels in men in the pathogenic link of central obesity and WC with cardiovascular risk can be proposed. Consistent with findings from adult males, the association between obesity and hypogonadism were also reported among young pubertal and post-pubertal males by Morgi et al., where testosterone levels were 40–50% lower than young males with normal BMI. Interestingly, following post-bariatric weight loss of one-third of the body weight among severely obese adolescents in a prospective multicentric study, a significant increase in testosterone levels decreased again on weight regain [ 24 , 25 ].

However, the scope of our research was primarily to study the effect of diabetes mellitus on testosterone levels in men and to determine the clinical and biochemical parameters correlated to hypogonadism and the clinical predictors of low serum testosterone levels in men with type 2 diabetes. Therefore, the study participants were categorized into two groups: with and without diabetes. Each group consisted of 80 participants. The two groups included equal numbers of lean ( n  = 40, in each group) and obese ( n  = 40, in each group) participants. The two main groups (with and without DM) were statistically matched in terms of age (median 43 and 42 years respectively, p 0.21), BMI (median 27.45 and 27.45 Kg/m 2 respectively, p 0.67), and WC (median 94.5 and 95.5 cm respectively, p 0.82).

Despite the nullification of the leading known variables affecting testosterone level (age, BMI and WC) between the two groups, there was a statistically significant decrease in the measured serum total testosterone in the diabetes group versus the non-diabetes one (median 297.5 and 510 ng/dL respectively, p  < 0.001). The same has also been shown for the calculated free testosterone (cFT) (median 6.15 and 9.22 ng/dL, respectively, p  < 0.001).

Male obesity is supposed to be one of the most commonly associated with substandard serum levels of testosterone. Despite that, on statistically comparing TT and cFT levels between the different subgroups in our study, TT level was significantly lower in the Diabetic Lean group than in the Non-Diabetic Lean (median 302 and 505 ng/dL, respectively, p  < 0.001), and even significantly lower than in the Non-Diabetic Obese group (median 510 ng/dL, p  < 0.001). In the same context, the TT level in the Diabetic Obese group was significantly lower than in the Non-Diabetic Obese group (median 284.5 and 510 ng/dL, respectively, p  < 0.001). The same was true for the cFT level, which was significantly lower in the Diabetic Lean group than in Non-Diabetic Lean ( p  < 0.001) and was also significantly lower in the Diabetic Obese group than in the Non-Diabetic Obese group ( p  < 0.001).

These findings add to the accumulating evidence [ 26 , 27 , 28 , 29 , 30 ] that men with type 2 diabetes have a significantly greater prevalence of hypogonadism and impose diabetes per se to be one of the most associated conditions with decreased both total and free serum testosterone levels. This is slightly different from the meta-analysis conclusion by Grossmann et al. [ 27 ], reporting that the inverse association between testosterone and diabetes is stronger for total compared with free testosterone, which implies a role for SHBG given that total but not free testosterone changes in parallel with SHBG.

In our study, the decrease in the calculated free testosterone level was evident in the diabetes group in addition to the concomitant statistically significant reduction in the SHBG as well, in the diabetes group versus the non-diabetics (median 21.7 and 42.15 nmol/L respectively, p  < 0.001). SHBG proved to relate to insulin resistance, an adiposity indicator [ 31 ]. There was a significant decrease in patients with diabetes compared to subjects without diabetes [ 32 , 33 ]. Moreover, a lower level of SHBG was suggested as an independent predictor of incident type 2 diabetes mellitus in men [ 19 ]. One of the probable mechanisms by which elevated circulating SHBG protects from the development of type 2 DM is attributed to the regulation of fasting glycemia but without modification of the secretory function of insulin [ 17 ].

Dhindsa et al. suggested a hypogonadotropic mechanism for the low testosterone levels in diabetes, evidenced by the fact that LH and FSH levels were significantly lower in the 33% of hypogonadal patients of the 103 patients with diabetes enrolled in the study [ 4 ]. This aligns with the current study findings that total and free testosterone was significantly lower with diabetes and not peculiar TT as a function of lowered SHBG imposing a role for insulin resistance in type 2 diabetes as an implicated factor in the decreased total as well as free testosterone. These data were confirmed in our study, as we performed a linear regression analysis and emphasised that total testosterone was significantly correlated with HOMA-IR. Notably, the linear regression analysis of free testosterone also showed again that HOMA-IR with three other variables correlates significantly with cFT level, which was the WC (an important marker of insulin resistance), the age and the duration of diabetes.

The progressively evident causal relationship of insulin resistance with the decreased testosterone level seems bidirectional. This is believed as it has been reported that reduced levels of TT have been related to resistance to insulin and subsequent risk for T2DM development [ 34 , 35 ]. It is still not clinically apparent to which extent low serum testosterone levels causally lead to type 2 diabetes. Theoretically, complex interactions among the hypothalamic–pituitary–gonadal axis, a status of insulin resistance, can give rise to glucose intolerance associated with ongoing low-grade inflammation and consequently increase the risk of cardiovascular disease [ 36 ]. Moreover, performed trials, though short-term, showed that testosterone supplementation in men may improve insulin sensitivity and reduce inflammation [ 37 , 38 , 39 ]. Furthermore, data from real-world registry reported that long-term testosterone treatment for patients with type 2 diabetes and hypogonadism was associated with improvement in glycemic control and insulin sensitivity. Interestingly, diabetes remission was achieved in one-third of the patients recruited in this 11-year data registry [ 40 ].

Another interesting finding in our model is that HOMA-IR and HbA1c- two significant correlating variables with TT- accounted for approximately 51.3% of TT variability (adjusted R-squared value of 0.513). This implies another perspective of the effect of glycemic control on hypogonadism and erectile dysfunction among males with diabetes, added to the known microvascular pathogenesis that involves a pro-inflammatory status that results in the decreased availability and activity of NO. In agreement with our results, Kim et al., involving Korean male patients with diabetes, reported that 34.9% of the 464 enrolled subjects had testosterone deficiency [ 41 ]. The testosterone deficiency group showed significantly higher mean fasting plasma glucose and HbA1c levels than the control group ( P  = 0.007 and 0.038, respectively). The results showed a significant negative correlation between fasting plasma glucose levels ( r =-0.142, P  = 0.002) and HbA1c values ( r =-0.097, P  = 0.040) with serum testosterone levels in men with diabetes.

This study encounters recruitment of patients attending Alexandria University Hospital which is a tertiary center receiving patients from four governments. It is an observational case-control unicentric study based on admission in the pre-determined study period; it is recommended to be conducted in a higher evidence base with a larger sample size. However, post hoc power was estimated by the end of the analysis to be 83%. The main study limitation is that the results were only valid among our involved population, so the conclusions may not be applied to other populations.

This observational case-control study confirms that diabetes per se imposes a significant impact on both low total and low free testosterone, and SHBG. Unlike previous studies, our study investigated the levels of total testosterone, free testosterone and SHBG in both obese and lean patients with type 2 diabetes in comparison to healthy controls. Several factors – beyond BMI- were highly associated with low testosterone levels, mainly insulin resistance, visceral adiposity, poor glycemic control, and increased duration of diabetes. The impact of type 2 diabetes on serum testosterone levels is shown to be more significant than that of obesity. The significant correlation of hypogonadism to poor glycemic control implies another perspective on the impact of suboptimal glycemic control on hypogonadism complications of diabetes.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

glycosylated hemoglobin

body mass index

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We thank all patients who participated in the study.

The authors did not receive support from any organisation for the submitted work. No funding was received to assist with the preparation of this manuscript. No funding was received for conducting this study. No funds, grants, or other support was received.

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Samir H. Assaad Khalil, Basma Tayseer Abdalla Zaitoon, Amal Abdulaziz Almas & Noha Gaber Amin

Department of Endocrinology, Faculty of Medicine, University of Buffalo and the State University of New York (SUNY), NY, USA

Paresh Dandona

Department of Biomedical Informatics and Medical Statistics, Medical Research Institute, Alexandria University, Alexandria, Egypt

Nermin A. Osman

Data Science Institute, Imperial College London, London, UK

Department of Chemical Pathology, Medical Research Institute, Alexandria University, Alexandria, Egypt

Ramy Samir Assaad

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Khalil, S.H.A., Dandona, P., Osman, N.A. et al. Diabetes surpasses obesity as a risk factor for low serum testosterone level. Diabetol Metab Syndr 16 , 143 (2024). https://doi.org/10.1186/s13098-024-01373-1

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  • Testosterone

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

obesity case study

  • Introduction
  • Conclusions
  • Article Information

Participants continued to live in their home environment without any prescribed diet or physical activity during the 28 consecutive days of the study. Error bars are SEs of the mean. The vertical dashed line separates the two 2-week sleep periods.

A-D, Data are in ascending order of change in sleep duration for the control group and sleep extension group. E, Data were from 74 participants. All available data were used. The line represents the line of best fit from the linear regression model. One participant in the control group and 3 participants in the sleep extension group had missing data in change in sleep duration (ie, missing mean data in at least 1 of 2 study periods). One participant in the control group and 4 participants in the sleep extension group had missing data in change in energy intake. Overall, 1 participant in the control group and 5 participants in the sleep extension group had missing data in either change in sleep duration or change in energy intake.

Trial Protocol

eMethods. Participants, Inclusion and Exclusion Criteria

eReferences

eTable 1. Effect of Treatment on Actigraphy-Based Time in Bed and Sleep Duration on All Days, Workdays and Free Days

eTable 2. Effect of Treatment on Actigraphy-Based Outcomes

eTable 3. Baseline Characteristics of Participants With Complete vs Incomplete Data

eTable 4. Self-Reported Outcomes by Visual Analog Scales

Data Sharing Statement

  • Good Sleep, Better Life—Enhancing Health and Safety With Optimal Sleep JAMA Internal Medicine Invited Commentary April 1, 2022 Mark R. Rosekind, PhD; Rafael Pelayo, MD; Debra A. Babcock, MD

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Tasali E , Wroblewski K , Kahn E , Kilkus J , Schoeller DA. Effect of Sleep Extension on Objectively Assessed Energy Intake Among Adults With Overweight in Real-life Settings : A Randomized Clinical Trial . JAMA Intern Med. 2022;182(4):365–374. doi:10.1001/jamainternmed.2021.8098

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Effect of Sleep Extension on Objectively Assessed Energy Intake Among Adults With Overweight in Real-life Settings : A Randomized Clinical Trial

  • 1 Department of Medicine, The University of Chicago, Chicago, Illinois
  • 2 Department of Public Health Sciences, The University of Chicago, Chicago, Illinois
  • 3 Biotechnology Center, Department of Nutritional Sciences, University of Wisconsin–Madison, Madison
  • Invited Commentary Good Sleep, Better Life—Enhancing Health and Safety With Optimal Sleep Mark R. Rosekind, PhD; Rafael Pelayo, MD; Debra A. Babcock, MD JAMA Internal Medicine

Question   What is the effect of sleep extension on objectively assessed energy intake in adults with overweight in their usual home environment?

Findings   In this randomized clinical trial of 80 adults with overweight and habitual sleep less than 6.5 hours per night, those randomized to a 2-week sleep extension intervention significantly reduced their daily energy intake by approximately 270 kcal compared with the control group. Total energy expenditure did not significantly differ between the sleep extension and control groups, resulting in a negative energy balance with sleep extension.

Meaning   The findings suggest that improving and maintaining adequate sleep duration could reduce weight and be a viable intervention for obesity prevention and weight loss programs.

Importance   Short sleep duration has been recognized as a risk factor for obesity. Whether extending sleep duration may mitigate this risk remains unknown.

Objective   To determine the effects of a sleep extension intervention on objectively assessed energy intake, energy expenditure, and body weight in real-life settings among adults with overweight who habitually curtailed their sleep duration.

Design, Setting, and Participants   This single-center, randomized clinical trial was conducted from November 1, 2014, to October 30, 2020. Participants were adults aged 21 to 40 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) between 25.0 and 29.9 and had habitual sleep duration of less than 6.5 hours per night. Data were analyzed according to the intention-to-treat principle.

Interventions   After a 2-week habitual sleep period at baseline, participants were randomized to either an individualized sleep hygiene counseling session that was intended to extend their bedtime to 8.5 hours (sleep extension group) or to continue their habitual sleep (control group). All participants were instructed to continue daily routine activities at home without any prescribed diet or physical activity.

Main Outcomes and Measures   The primary outcome was change in energy intake from baseline, which was objectively assessed as the sum of total energy expenditure and change in body energy stores. Total energy expenditure was measured by the doubly labeled water method. Change in body energy stores was computed using regression of daily home weights and body composition changes from dual-energy x-ray absorptiometry. Sleep duration was monitored by actigraphy. Changes from baseline were compared between the 2 groups using intention-to-treat analysis.

Results   Data from 80 randomized participants (mean [SD] age, 29.8 [5.1] years; 41 men [51.3%]) were analyzed. Sleep duration was increased by approximately 1.2 hours per night (95% CI, 1.0 to 1.4 hours; P  < .001) in the sleep extension group vs the control group. The sleep extension group had a significant decrease in energy intake compared with the control group (−270 kcal/d; 95% CI, −393 to −147 kcal/d; P  < .001). The change in sleep duration was inversely correlated with the change in energy intake ( r  = −0.41; 95% CI, −0.59 to −0.20; P  < .001). No significant treatment effect in total energy expenditure was found, resulting in weight reduction in the sleep extension group vs the control group.

Conclusions and Relevance   This trial found that sleep extension reduced energy intake and resulted in a negative energy balance in real-life settings among adults with overweight who habitually curtailed their sleep duration. Improving and maintaining healthy sleep duration over longer periods could be part of obesity prevention and weight loss programs.

Trial Registration   ClinicalTrials.gov Identifier: NCT02253368

Obesity is a major public health concern. 1 The obesity epidemic appears to coincide with a pattern of sleeping less that has been observed in society over the past several decades. For example, one-third of the US population reported not getting the recommended 7 to 9 hours of sleep per night. 2 - 4 Substantial evidence suggests that sleeping less than 7 hours per night on a regular basis is associated with adverse health consequences. 5 Particularly, insufficient sleep duration has been increasingly recognized as an important risk factor for obesity. 6 , 7 Prospective epidemiologic studies suggest that short sleep duration is an important risk factor for weight gain. 8 - 10 However, it remains unknown whether extending sleep duration can be an effective strategy for preventing or reversing obesity. Although sleep hygiene education is encouraged by obesity experts, 11 most health professionals and patients do not implement obtaining adequate sleep duration as part of the strategies to combat the obesity epidemic. 12

At the population level, the association between energy flux and body weight implicates that increased energy intake is the main factor in higher body weights in modern society. 13 According to dynamic prediction models, a sustained increase in energy intake of even 100 kcal/d would result in a weight gain of about 4.5 kg over 3 years. 14 , 15 Factors that underlie the observed persistent increase in energy intake and mean weight gain at the population level need to be better understood. One such factor is insufficient sleep duration. Short-term experimental laboratory studies have found that sleep restriction in healthy individuals is associated with an increased mean energy intake of about 250 to 350 kcal/d with minimal to no change in energy expenditure. 16 - 19 However, these laboratory studies do not represent real life. The magnitude of sleep restriction was extreme in most cases, and energy intake was ascertained from a single or a few meals. In a real-life setting in which participants continue their normal daily activities, multiple interacting factors (eg, social interactions and free-living physical activity) can influence energy intake or expenditure and weight.

To date, it remains unknown whether and to what extent an intervention that is intended to increase sleep duration in a real-life setting affects energy balance and body weight. We conducted a randomized clinical trial (RCT) to determine the effects of a sleep extension intervention on objectively assessed energy intake, energy expenditure, and body weight in real-life settings among adults with overweight who habitually curtailed their sleep duration.

This single-center, parallel-group RCT was conducted from November 1, 2014, to October 30, 2020. The protocol was approved by The University of Chicago Institutional Review Board, and participants provided written informed consent. The study protocol is available in Supplement 1 . We followed the Consolidated Standards of Reporting Trials ( CONSORT ) reporting guideline.

Adult men and women aged 21 to 40 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) between 25.0 and 29.9 and a mean habitual sleep duration of less than 6.5 hours per night were eligible. Individuals were required to have stable self-reported sleep habits for the past 6 months. They were recruited from the community and completed an initial online survey followed by a face-to-face interview. Race and ethnicity data were self-reported at this time and included the following race and ethnicity categories: Asian, Black or African American, Hispanic, and White. Those who met the inclusion criteria underwent laboratory screening (polysomnography, oral glucose tolerance test, and blood tests) to determine eligibility. Habitual sleep duration was confirmed by a 1-week screening wrist actigraphy at home. Those who had obstructive sleep apnea confirmed by laboratory polysomnography (apnea-hypopnea index >5), insomnia or history of any other sleep disorder, or night shift and rotating shift work (current or in the past 2 years) were excluded. Detailed eligibility criteria are provided in the eMethods in Supplement 2 .

After a 2-week habitual sleep period at baseline, participants were randomized to either 2-week sleep extension (sleep extension group) or 2-week continued habitual sleep (control group) ( Figure 1 ). Participants continued their daily routine activities at home without any prescribed diet or physical activity.

To blind participants to the sleep extension intervention, we described the study in the recruitment materials as follows: “we will collect information about sleep habits and metabolism.” The sleep extension group was blinded to randomization until after the 2-week baseline assessments, and the control group was blinded until the end of the 4-week study. This approach allowed us to capture habitual sleep-wake patterns without influencing participants' usual behavior or creating selection bias with only participants interested in improving sleep habits. After study completion, all participants were provided with information about the health benefits of optimal sleep duration. Block randomization, stratified by sex, was performed using computer-generated random numbers. Before the trial, randomization assignments were prepared by a biostatistician (K.W.) using opaque, sealed, and numbered envelopes and were given to the research coordinator (E.K.).

Sleep-wake patterns were continuously monitored at home by wrist actigraphy throughout the 4-week study. Participants were asked to wear an accelerometer (motion)-based monitor (Actiwatch Spectrum Plus; Philips) and to press a built-in event marker button when they went to bed to sleep each night and when they got out of bed each morning. Sleep was automatically scored (Actiware, version 6.0.9; Philips) using validated algorithms as the sum of all epochs that were scored as sleep during the total time spent in bed. 20 , 21

During the 2-week baseline, all participants were instructed to continue their habitual sleep patterns at home. On the morning of day 15, participants met with study investigators (E.T. and E.K.) in the research center. Those who were randomized to the sleep extension group received individualized sleep hygiene counseling through a structured interview (E.T.) (eMethods in Supplement 2 ). 22 At the end of the interview, participants were provided with individualized recommendations to follow at home for 2 weeks, with the aim of extending their bedtime duration to 8.5 hours. On day 22, participants returned for a brief follow-up visit. Actigraphy data from the first intervention week were reviewed, and further sleep counseling was provided as needed.

To minimize any imbalance in contact with the investigators between the 2 groups, we asked participants in the control group to meet with the study investigators on days 15 and 22. Actigraphy data of these participants were downloaded, but the participants did not receive any specific sleep recommendations and were instructed to continue their daily routine and habitual sleep behaviors until the end of the study.

For each 2-week period, the energy intake was calculated from the sum of total energy expenditure and change in body energy stores using the principle of energy balance. 14 , 23 , 24 Total energy expenditure was measured by the doubly labeled water method. 25 - 29 For each 2-week period, the change in body energy stores was computed from the regression (slope, grams per day) of daily home weights and change in body composition (ie, fat mass and fat-free mass) using dual-energy x-ray absorptiometry. Participants were provided a cellular-enabled weight scale (BodyTrace; BodyTrace Inc) and instructed to take their nude weights twice every morning after awakening before eating or drinking. Weight values were hidden from the participants to minimize potential influence on behavior. Changes in body composition were converted to changes in energy stores using 9.5 kcal/g as the energy coefficient of fat mass and 1.0 kcal/g as the energy coefficient of fat-free mass. 30 Resting metabolic rate was measured by indirect calorimetry for 30 minutes after fasting and for 4 hours after eating a standardized breakfast. Thermic effect of the meal was calculated, which was previously described elsewhere. 31 Activity energy expenditure was calculated by subtracting the resting metabolic rate and thermic effect of the meal from the total energy expenditure. 31 , 32 Additional details are provided in the eMethods in Supplement 2 .

The primary outcome was change in energy intake from baseline. A total final sample size of 80 participants (40 per group) was originally planned and provided 80% power to detect a true difference in energy intake between groups of 207 kcal/d using a 2-sided α = .05 significance threshold (trial protocol in Supplement 1 ). An intention-to-treat analysis was conducted in Stata, version 16 (StataCorp LLC) using 2-tailed tests with statistical significance set at P  < .05. Categorical data are presented as counts and percentages. Continuous data are presented as means and SDs. Linear mixed-effects models were fit to determine the treatment differences between the groups. 33 Models included the randomization group, 2-week baseline period (period 1) vs 2-week intervention (period 2) and their interaction, and random effects for each participant. The treatment effect (95% CI) was estimated by the treatment group and period interaction, which is equivalent to testing the difference in change from baseline (period 2 minus period 1) in the sleep extension group vs the control group. To confirm the robustness of primary findings, we fit additional models using the analysis of covariance approach with the period 2 value as the dependent variable, treatment group as the independent variable, and period 1 value as covariates.

In secondary analyses, mixed models that adjusted for sex or menstrual cycle were also fit; these covariates were chosen because of the known influence of menstrual cycle on short-term changes in weight. A Pearson correlation coefficient was calculated to assess the relationships between the changes from baseline in sleep duration and the changes from baseline in energy intake. No adjustments were made to P values or CIs for multiple comparisons. Baseline characteristics of participants with complete data were compared with those of participants with incomplete data using unpaired, 2-tailed t tests and Fisher exact tests. No imputation for missing values was performed.

Of the 210 adults who provided consent and were assessed for eligibility, 81 were randomized (41 to the control group and 40 to the sleep extension group) initially ( Figure 1 ). One participant in the control group revealed adhering to a weight loss regimen and thus did not meet the study inclusion criteria and was deemed ineligible after randomization. 34 The 80 participants had a mean (SD) age of 29.8 (5.1) years and consisted of 41 men (51.3%) and 39 women (48.7%). Baseline characteristics of participants were similar between randomization groups ( Table 1 ). None of the participants were using any antihypertensive or lipid-lowering agents or any prescription medication that can affect sleep or metabolism.

Figure 2 illustrates the mean nightly sleep duration by actigraphy in each group throughout the 4-week study. Participants in the sleep extension group had a significant increase from baseline in mean sleep duration by actigraphy compared with those in the control group (1.2 hours; 95% CI, 1.0-1.4 hours; P  < .001). The findings were similar with regard to change in sleep duration when only participants' workdays (1.3 hours; 95% CI, 1.0-1.5 hours; P  < .001) or free days (1.1 hours; 95% CI, 0.7-1.5 hours; P  < .001) were considered (eTable 1 in Supplement 2 ). No difference was found in change in sleep efficiency (percentage of time spent asleep during time in bed) between the 2 groups (–0.6 hours; 95% CI, –2.1 to 1.0 hours; P  = .48), confirming the success of the intervention (eTable 2 in Supplement 2 ).

Energy intake was statistically significantly decreased in the sleep extension group compared with the control group (−270.4 kcal/d; 95% CI, −393.4 to −147.4 kcal/d; P  < .001). Figure 3 A through D illustrates the changes from baseline in energy intake and the changes from baseline in sleep duration in individual participants. There was a significant increase in energy intake from baseline in the control group (114.9 kcal/d; 95% CI, 29.6 to 200.2 kcal/d) and a significant decrease in energy intake from baseline in the sleep extension group (−155.5 kcal/d; 95% CI, −244.1 to −66.9 kcal/d) ( Table 2 ). Considering all participants, the change in sleep duration was inversely correlated with the change in energy intake ( r  = −0.41; 95% CI, −0.59 to −0.20; P  < .001) ( Figure 3 E). Each 1-hour increase in sleep duration was associated with a decrease in energy intake of approximately 162 kcal/d (−162.3 kcal/d; 95% CI, −246.8 to −77.7 kcal/d; P  < .001).

No statistically significant treatment effect was found in total energy expenditure or other measures of energy expenditure ( Table 2 ). Participants in the sleep extension group had a statistically significant reduction in weight compared with those in the control group (−0.87 kg; 95% CI, −1.39 to −0.35 kg; P  = .001). There was weight gain from baseline in the control group (0.39 kg; 95% CI, 0.02 to 0.76 kg) and weight reduction from baseline in the sleep extension group (−0.48 kg; 95% CI, −0.85 to −0.11 kg) ( Table 2 ).

The findings on energy intake, energy expenditure, and weight were similar after adjustment for the effects of sex or menstrual cycle. No statistically significant differences in baseline characteristics were found between the 75 participants (93.8%) who had complete data on energy intake (primary outcome) vs participants with missing data on energy intake. The proportion of participants with complete data on energy intake was not significantly different between the sleep extension and control groups (90.0% vs 97.5%; P  = .36). When all reported outcomes were considered, no significant differences (except for depressive symptoms) in baseline characteristics were found between participants with complete data and participants with incomplete or missing data (eTable 3 in Supplement 2 ). The proportion of participants with complete data on all reported outcomes was similar between the sleep extension and control groups (82.5% vs 85.0%; P  > .99).

In this RCT of adults with overweight who habitually curtailed their sleep duration, sleep extension reduced energy intake and resulted in a negative energy balance (ie, energy intake that is less than energy expenditure) in real-life settings. To our knowledge, this study provides the first evidence of the beneficial effects of extending sleep to a healthy duration on objectively assessed energy intake and body weight in participants who continued to live in their home environment. Modest lifestyle changes in energy intake or expenditure are increasingly promoted as viable interventions to reverse obesity.

According to the Hall dynamic prediction model, a decrease in energy intake of approximately 270 kcal/d, which we observed after short-term sleep extension, would predict an approximately 12-kg weight loss over 3 years if the effects were sustained over a long term. 14 , 15 However, this study cannot infer how long healthy sleep habits may be sustained. Nevertheless, these modeling predictions on weight change suggest that continued adequate sleep duration and beneficial effect on energy intake could translate into clinically meaningful weight loss and help reverse or prevent obesity. Thus, the findings of this study may have important public health implications for weight management and policy recommendations.

The findings of decreased energy intake, negative energy balance, and weight reduction resulting from sleep extension are in agreement with the findings of short-term laboratory sleep-restriction studies showing increased energy intake and weight gain 17 as well as the findings of prospective epidemiologic studies linking sleep restriction to obesity risk. 8 A recent meta-analysis of randomized controlled laboratory studies found that short-term sleep restriction over 1 to 14 days of duration in healthy individuals was associated with increases of mean energy intake by approximately 253 kcal/d, as assessed during a single meal. 17 Another meta-analysis of prospective cohort studies found that the risk of obesity increased by 9% for each 1-hour decrease in sleep duration. 8 We did not observe a statistically significant change in total energy expenditure by doubly labeled water method or mean daytime activity counts by actigraphy (eTable 2 in Supplement 2 ). Although some laboratory sleep-restriction studies reported an increase in total energy expenditure of approximately 92 to 111 kcal/d, using a whole-room calorimeter, 35 , 36 other studies observed no change. 16 , 37 We found a modest reduction in weight after sleep extension, and the composition of weight change was primarily in fat-free mass, which is consistent with the short-term changes in body composition. 38 , 39 If sleep is extended over longer periods, weight loss in the form of fat mass would likely increase over time. A few observations suggest that sleeping 7 to 8 hours per night is associated with greater success in weight loss interventions. 40 - 43

In this RCT, we found an overall increase in objective sleep duration of approximately 1.2 hours in participants who habitually slept less than 6.5 hours per night. The change in sleep duration from baseline varied between participants and from night to night in the real-life setting. Overall, the sleep extension group compared with the control group had significantly higher subjective scores in obtaining sufficient sleep, with more daytime energy and alertness and better mood (eTable 4 in Supplement 2 ). Similar to a previous study of sleep extension, 22 the present RCT used an individualized counseling approach. Another study used bedtime extension in habitual short sleepers in real-life conditions but obtained variable benefits on sleep, likely because of a lack of an individualized approach or appropriate blinding. 44 None of these previous studies objectively measured energy intake.

Future similarly rigorous intervention studies of longer duration and using objective assessments of energy balance under real-life conditions are warranted to elucidate the underlying mechanisms and to investigate whether sleep extension could be an effective, scalable strategy for reversing obesity in diverse populations. Along with a healthy diet and regular physical activity, healthy sleep habits should be integrated into public messages to help reduce the risk of obesity and related comorbidities.

This study has several strengths. The major strengths are the randomized design and the objective tracking of energy intake and sleep in real-life settings. Most epidemiologic studies linking short sleep duration to body weight relied on self-reported dietary intake. 45 We did not collect self-reported dietary data because this method is subject to bias and has been shown to be inaccurate compared with the doubly labeled water method. 46 , 47 Most experimental studies that measured energy intake used a single meal under unnatural laboratory conditions. We used a validated method to objectively track energy intake by the doubly labeled water method and change in energy stores. 23 , 48 , 49 In this trial, we objectively quantified energy intake after sleep extension while individuals continued their daily routine in their usual environment. Participant blinding and use of actigraphy allowed us to capture true habitual sleep patterns at baseline. 22 , 50 In addition, we excluded insomnia and sleep apnea.

This study also has several limitations. We enrolled adults with overweight and used selective eligibility criteria, which may limit generalizability to more diverse populations. The increase in energy intake and weight from baseline that we observed in the control group may have contributed to the significant treatment effects. However, in RCTs, performing a between-group comparison, rather than separate tests against baseline within the groups, is strongly recommended. 51 The study did not provide information on how long healthy sleep habits could be maintained over longer periods. 44 We did not systematically assess the factors that may have influenced sleep behavior, but limiting the use of electronic devices appeared to be a key intervention among the participants (eTable 4 in Supplement 2 ). The doubly labeled water method has a precision of 5%, which may translate into some degree of uncertainty in the energy intake calculations. Although whole-room calorimeters can measure energy expenditure with a higher precision of approximately 1% to 2%, they do not represent real-life measurement and are not feasible over longer periods. We did not assess the underlying biological mechanisms of food frequency and the circadian timing of food intake. Multiple interrelated factors could contribute to the finding of decreased energy intake after sleep extension. 6 , 52 Evidence from laboratory sleep restriction studies suggests that increased hunger, alterations in appetite-regulating hormones, and changes in brain regions related to reward-seeking behavior are potential mechanisms that promote overeating after sleep restriction. 6 , 45

This RCT found that short-term sleep extension reduced objectively measured energy intake and resulted in a negative energy balance in real-life settings in adults with overweight who habitually curtailed their sleep duration. The findings highlighted the importance of improving and maintaining adequate sleep duration as a public health target for obesity prevention and increasing awareness about the benefits of adequate sleep duration for healthy weight maintenance.

Accepted for Publication: November 14, 2021.

Published Online: February 7, 2022. doi:10.1001/jamainternmed.2021.8098

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Tasali E et al. JAMA Internal Medicine .

Corresponding Author: Esra Tasali, MD, Department of Medicine, The University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637 ( [email protected] ).

Author Contributions: Author Dr Tasali and Ms Wroblewski had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Tasali, Schoeller.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Tasali, Schoeller.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Tasali, Wroblewski.

Obtained funding: Tasali.

Administrative, technical, or material support: Tasali, Kahn, Kilkus, Schoeller.

Supervision: Tasali.

Other - research coordination duties: Kahn.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was funded by grants R01DK100426, CTSA-UL1 TR0002389, and UL1TR002389 from the National Institutes of Health and by the Diabetes Research and Training Center at The University of Chicago.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement : See Supplement 3 .

Additional Contributions: Timothy Shriver, MS, University of Wisconsin–Madison, assisted with doubly labeled water measurements. Maureen Costello, MS, The University of Chicago, assisted with dual-energy x-ray absorptiometry scans. Becky Tucker, BA, Harry Whitmore, RPSGT, and Kristin Hoddy, PhD, RD, The University of Chicago, assisted with data collection. We thank the nurses, dieticians, and technicians at the Clinical Research Center at The University of Chicago for their expert assistance in data collection. We also thank the staff of the Sleep Research Center at The University of Chicago for their support. These individuals received no additional compensation, outside of their usual salary, for their contributions. We thank the volunteers for participating in this study.

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  • v.317(7153); 1998 Jul 25

Evidence based case report

Helping an obese patient make informed choices.

Not long ago, a patient, whom I will call Mrs Bariatrico, asked me to prescribe a diet pill for her. Mrs Bariatrico is a middle class woman aged 48 years. She is 1.6 m tall and weighs 77.2 kg. Her body mass index is 30.2 and her waist to hip ratio is 1.0. Mrs Bariatrico is healthy and does not smoke. She told me she plans to enrol in a commercial diet programme and believes her ability to change her lifestyle is good. 1 Her main concern is cosmetic—she values “looking good” and considers weight loss an important outcome.

As her primary care provider, I had several concerns. I knew the health insurance system that serves Mrs Bariatrico has no formal weight loss programmes, and the cost of appetite suppressing drugs is not reimbursed. I had some doubts about my own ability to manage obesity and asked the following questions:

  • What are the actual health risks associated with obesity in a middle aged woman with few cardiovascular risk factors?
  • What are the expected benefits and hazards of weight loss?
  • What are Mrs Bariatrico’s treatment options and their expected benefits and adverse effects?

Risks of obesity

Obesity is a chronic condition associated with hyperlipidaemia, hypertension, non-insulin dependent diabetes, gall bladder disease, some cancers, sleep apnoea, and degenerative joint disease. 2 , 3 Assessing the magnitude of risk for these conditions is complicated by several elements: many patients have several interacting risks; measuring the impact of some risks requires large, long cohort studies; and there are several confounding factors such as smoking and the duration of obesity. Regardless of these cautions, studies suggest that people who are more than 20% overweight have prevalences of hyperlipidaemia, hypertension, and diabetes that are between 1.5 and 3.5 times higher than those in people whose weight is normal. 2 , 3 The morbidity risks increase steadily from a body mass index of 25-30 and more rapidly at higher index values. Mortality risks increase above body mass indices of 20-27. 4 , 5 Relevant to Mrs Bariatrico, values of 29.0-31.9 in non-smoking middle aged women are associated with a relative mortality risk of 1.7 (95% confidence interval. 1.4 to 2.2; reference body mass index <19). 4

Expected benefits and hazards

Randomised trials confirm several physiological benefits—including reductions in blood pressure and glucose and lipid concentrations—when weight is reduced by 10-15%. 2 Trials are neither large enough nor long enough to identify survival benefits. One observational study that lasted 12 years showed that an intentional weight loss of 0.5-9.0 kg in overweight women with disorders related to obesity was associated with a 20% reduction in all cause mortality (relative risk=0.80; 0.68 to 0.94). 6 Potential hazards of weight loss include increased risks of gall stones during rapid weight loss and loss of bone density. 2

Treatment options

A comprehensive systematic review from the Centre for Reviews and Dissemination evaluates treatment options appropriate for Mrs Bariatrico. 7 These include diet, exercise, and appetite suppressing drugs. A recent book describes many complementary therapies, including herbal remedies and chromium, but none have been adequately evaluated in controlled trials. 8

Diet and exercise

Randomised controlled trials show that diets allowing an intake of 1200 kcal/day coupled with behaviour modification result in an approximate weight loss of 8.5 kg at 20 weeks. 9 Providing patients with food and meal plans, focusing on restricting fat as well as calories, and encouraging daily self monitoring of weight may be particularly effective strategies. 7 Very low calorie diets of less than 800 kcal/day result in a weight loss of approximately 20 kg at 12 to 16 weeks. One half to two thirds of the weight loss is maintained at one year. 9 Adding regular aerobic exercise results in minimal additional weight loss (approximately 2.5 kg after six months) and limits the amount of weight regained. 10 Resistance exercise has little effect on weight but increases the lean body mass. 10

Appetite suppressants

Double blind randomised trials of longer than six months’ duration show that antidepressant serotonergic agents such as fluoxetine are not effective weight loss treatments. 7 , 11 Other serotonergic agents, dexfenfluramine and fenfluramine (a racemic mixture of d -fenfluramine and l -fenfluramine), are effective when combined with diet. 7 , 11 Five trials, in which 1029 patients participated, showed that the weight loss with dexfenfluramine was 2.5 to 8.7 kg greater than with placebo at six months; two trials showed losses of 2.6 and 4.2 kg at 12 months. 11 The combination of fenfluramine and phentermine (colloquially known as fen-phen) resulted in a loss of 9.7 kg after six months compared with placebo. Two new drug are sibutramine (serotonin and noradrenergic reuptake inhibitor) and orlistat (a fat absorption inhibitor). In one multicentre randomised trial, sibutramine showed a 2.8 kg loss compared with placebo at 12 months. 7 In a preliminary report from one centre of a multicentre trial comparing orlistat with placebo, weight reduction with orlistat was 3.1 kg more than with placebo at six months. 12 Trial data beyond 12 months of active treatment are not available for either of the two agents, and effects on mortality are not known.

Adverse effects that occur in more than 10% of patients taking dexfenfluramine include tiredness, diarrhoea, and dry mouth. Use of appetite suppressants (mostly dexfenfluramine) for more than three months is associated with pulmonary hypertension. 13 The risk is estimated at 23-46 cases per million per year or one in 22 000-44 000 patients taking appetite suppressing drugs. Highly publicised case series describe unusual heart valve deterioration in 60 otherwise healthy women taking newer agents. 14 , 15 Most were taking the combination of fenfluramine and phentermine, but six were taking either fenfluramine or dexfenfluramine alone. 14 , 15 In addition, a case series of 291 asymptomatic people taking these drugs showed that 92 had evidence of valvular disease, primarily aortic regurgitation. 16 This information prompted manufacturers to withdraw dexfenfluramine and fenfluramine from the market in September 1997.

The informed decision

I gave Mrs Bariatrico feedback on the health risks of obesity, listed the treatment options, and advised her about the expected effects. She viewed the health risks of obesity as relatively minor and reiterated her primary value of losing weight so she would “look and feel good.” She was surprised that the weight loss expected from diet pills was not greater and worried about possible serious adverse heart effects. She was determined to try a low fat, low calorie diet and daily exercise. I praised her willingness to tackle difficult lifestyle changes. On her way out the door, she turned, smiled at me, and requested a prescription for phentermine—one of the few remaining appetite suppressants available on the market.

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Object name is mulc18sp.f1.jpg

Wanting to “look and feel good” is often the spur to undertaking difficult lifestyle changes

Funding: None.

Conflict of interest: None.

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