Pub Date : 2026-01-01Epub Date: 2025-12-05DOI: 10.1016/j.obmed.2025.100674
Shania Liu , Genieve Wong , Pavneet Mavi , Stephanie C. Gysel , Daniel Burton , Neire Monteiro , Derek Durocher , Arya M. Sharma , Ross T. Tsuyuki
Type 2 diabetes and obesity are major public health concerns worldwide. This study examined reasons for stopping GLP-1 agonist therapy. Nearly half discontinued within six months. Key reasons included side effects, shortages, cost, perceived ineffectiveness, and life circumstances.
{"title":"Factors contributing to non-persistence of glucagon-like peptide-1 agonists: a cross-sectional study","authors":"Shania Liu , Genieve Wong , Pavneet Mavi , Stephanie C. Gysel , Daniel Burton , Neire Monteiro , Derek Durocher , Arya M. Sharma , Ross T. Tsuyuki","doi":"10.1016/j.obmed.2025.100674","DOIUrl":"10.1016/j.obmed.2025.100674","url":null,"abstract":"<div><div>Type 2 diabetes and obesity are major public health concerns worldwide. This study examined reasons for stopping GLP-1 agonist therapy. Nearly half discontinued within six months. Key reasons included side effects, shortages, cost, perceived ineffectiveness, and life circumstances.</div></div>","PeriodicalId":37876,"journal":{"name":"Obesity Medicine","volume":"59 ","pages":"Article 100674"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-26DOI: 10.1016/j.obmed.2025.100679
Tatiana Sampaio , Samuel Encarnação , Bruna Amaro , Joana Ribeiro , Luís Branquinho , António M. Monteiro , José E. Teixeira , Soukaina Hattabi , Andrew Sortwell , Luciano Bernardes Leite , Alexandra Malheiro , Pedro Rodrigues , Beat Knechtle , Pedro Flores , Pedro Forte
The escalating prevalence of obesity among adolescents has emerged as a critical global public health challenge. Machine learning techniques have been used to predict obesity in adolescents. This study aimed to develop and validate a robust obesity prediction model for adolescents using this hybrid approach, leveraging data from a diverse cross-sectional population-based study. The hybrid method combines statistical inference with non-linear machine learning to enhance prediction accuracy. Physical fitness data were collected from the FITescola® tests. Multiple tests were employed to evaluate physical fitness. Multiple Poisson's multiple regression method was applied to identify the most predictive variables set of the adolescent's body mass index (BMI) classification. The model's goodness-of-fit statistics indicate a strong fit, with a log-likelihood of −8068.6 and a Pseudo R-squared value of 0.8853, where the aerobic fitness (AF), upper limb strength (ULS) and lower limb flexibility (LLF) presented an inverse association with the adolescent's BMI. In contrast the adolescent's core strength presented a positive association with their body mass. The random forest regression showed that an average of 35 repetition on the yo-yo test predicted a healthy BMI percentile [predBMIperc = 0.31]. In addition, the model presented good validity [MAE = 0.36, MSE = 0.20, RMSE = 0.45, R2 = 0.54]. The model's strong fit and accurate random forest regression's predictions suggest that physical fitness components, such as aerobic fitness, upper limb strength, lower limb power, and core strength, play a significant role in obesity risk among adolescents.
{"title":"Machine learning prediction of adolescent obesity using physical fitness data","authors":"Tatiana Sampaio , Samuel Encarnação , Bruna Amaro , Joana Ribeiro , Luís Branquinho , António M. Monteiro , José E. Teixeira , Soukaina Hattabi , Andrew Sortwell , Luciano Bernardes Leite , Alexandra Malheiro , Pedro Rodrigues , Beat Knechtle , Pedro Flores , Pedro Forte","doi":"10.1016/j.obmed.2025.100679","DOIUrl":"10.1016/j.obmed.2025.100679","url":null,"abstract":"<div><div>The escalating prevalence of obesity among adolescents has emerged as a critical global public health challenge. Machine learning techniques have been used to predict obesity in adolescents. This study aimed to develop and validate a robust obesity prediction model for adolescents using this hybrid approach, leveraging data from a diverse cross-sectional population-based study. The hybrid method combines statistical inference with non-linear machine learning to enhance prediction accuracy. Physical fitness data were collected from the FITescola® tests. Multiple tests were employed to evaluate physical fitness. Multiple Poisson's multiple regression method was applied to identify the most predictive variables set of the adolescent's body mass index (BMI) classification. The model's goodness-of-fit statistics indicate a strong fit, with a log-likelihood of −8068.6 and a Pseudo R-squared value of 0.8853, where the aerobic fitness (AF), upper limb strength (ULS) and lower limb flexibility (LLF) presented an inverse association with the adolescent's BMI. In contrast the adolescent's core strength presented a positive association with their body mass. The random forest regression showed that an average of 35 repetition on the yo-yo test predicted a healthy BMI percentile [predBMIperc = 0.31]. In addition, the model presented good validity [MAE = 0.36, MSE = 0.20, RMSE = 0.45, R2 = 0.54]. The model's strong fit and accurate random forest regression's predictions suggest that physical fitness components, such as aerobic fitness, upper limb strength, lower limb power, and core strength, play a significant role in obesity risk among adolescents.</div></div>","PeriodicalId":37876,"journal":{"name":"Obesity Medicine","volume":"59 ","pages":"Article 100679"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-09-12DOI: 10.1016/j.obmed.2025.100642
Francisco J. Nachon Garcia , Gabriela E. Saldaña-Davila , Magdalena Valencia , Cesar Ochoa-Martínez
Background
The global rise in obesity, driven by hypercaloric diets and sedentary lifestyles, has intensified interest in novel dietary interventions. Very low-calorie ketogenic diets (VLCKDs) induce rapid weight loss but are typically high in fat. This study assessed the efficacy and safety of a very low-calorie, low-fat, ketogenic diet (VLCLFKD), also known as the Zélé method, versus a standard low-calorie diet (LCD) in adults with class I obesity.
Methods
In this 12-week, randomized, double-blind controlled trial (NCT06275347), 88 participants were allocated to VLCLFKD (n = 56) or LCD (n = 32), with 77 completing the protocol. The primary endpoint was weight change; secondary outcomes included body composition, fasting glucose, lipid profile, blood pressure, hepatic and renal function, and acid–base balance. All participants received weekly clinical and dietary support.
Results
VLCLFKD led to significantly greater weight loss (−12.4 ± 2.8 kg) than LCD (−7.0 ± 1.9 kg; p < 0.001). Fat mass reduction accounted for 82.1 % of total weight loss in the VLCLFKD group, compared to 38.4 % in the LCD group (p < 0.001), with markedly lower lean mass loss (11.9 % vs. 51.0 %). Significant improvements were observed in fasting glucose (−12.8 mg/dL), total cholesterol (−37.4 mg/dL), triglycerides (−67.4 mg/dL), and blood pressure normalization (88.1 % vs. 71.4 %). Renal and hepatic function and acid–base balance remained stable. No serious adverse events occurred.
Conclusion
The VLCLFKD (Zélé method) is a safe, fat-targeted, and metabolically advantageous strategy for class I obesity, delivering superior weight and metabolic outcomes compared with a conventional LCD while preserving lean mass.
{"title":"A randomized controlled trial comparing a very low-calorie low-fat ketogenic diet with a standard hypocaloric diet in adults with class I obesity","authors":"Francisco J. Nachon Garcia , Gabriela E. Saldaña-Davila , Magdalena Valencia , Cesar Ochoa-Martínez","doi":"10.1016/j.obmed.2025.100642","DOIUrl":"10.1016/j.obmed.2025.100642","url":null,"abstract":"<div><h3>Background</h3><div>The global rise in obesity, driven by hypercaloric diets and sedentary lifestyles, has intensified interest in novel dietary interventions. Very low-calorie ketogenic diets (VLCKDs) induce rapid weight loss but are typically high in fat. This study assessed the efficacy and safety of a very low-calorie, low-fat, ketogenic diet (VLCLFKD), also known as the Zélé method, versus a standard low-calorie diet (LCD) in adults with class I obesity.</div></div><div><h3>Methods</h3><div>In this 12-week, randomized, double-blind controlled trial (NCT06275347), 88 participants were allocated to VLCLFKD (n = 56) or LCD (n = 32), with 77 completing the protocol. The primary endpoint was weight change; secondary outcomes included body composition, fasting glucose, lipid profile, blood pressure, hepatic and renal function, and acid–base balance. All participants received weekly clinical and dietary support.</div></div><div><h3>Results</h3><div>VLCLFKD led to significantly greater weight loss (−12.4 ± 2.8 kg) than LCD (−7.0 ± 1.9 kg; p < 0.001). Fat mass reduction accounted for 82.1 % of total weight loss in the VLCLFKD group, compared to 38.4 % in the LCD group (p < 0.001), with markedly lower lean mass loss (11.9 % vs. 51.0 %). Significant improvements were observed in fasting glucose (−12.8 mg/dL), total cholesterol (−37.4 mg/dL), triglycerides (−67.4 mg/dL), and blood pressure normalization (88.1 % vs. 71.4 %). Renal and hepatic function and acid–base balance remained stable. No serious adverse events occurred.</div></div><div><h3>Conclusion</h3><div>The VLCLFKD (Zélé method) is a safe, fat-targeted, and metabolically advantageous strategy for class I obesity, delivering superior weight and metabolic outcomes compared with a conventional LCD while preserving lean mass.</div></div>","PeriodicalId":37876,"journal":{"name":"Obesity Medicine","volume":"58 ","pages":"Article 100642"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-11-10DOI: 10.1016/j.obmed.2025.100664
Sulagna Dutta , Pallav Sengupta
Polycystic ovary syndrome (PCOS) remains the most common endocrine disorder in reproductive-aged women, yet its diagnosis and classification are inconsistent. Conventional frameworks emphasize reproductive and androgenic features, while relying heavily on body mass index (BMI) as a proxy for adiposity. However, BMI fails to capture visceral fat distribution, which is a stronger determinant of insulin resistance, low-grade inflammation, and cardiovascular risk. Emerging evidence highlights waist-to-height ratio (WHtR) and waist circumference (WC) as superior markers of central adiposity and metabolic dysfunction in PCOS. Based on pooled data from recent studies, consolidated cut-offs for PCOS and insulin-resistant PCOS (PCOS-IR) were derived, allowing phenotype-based stratification according to adiposity patterns. These evidence-based thresholds distinguish the metabolic transition from general to insulin-resistant PCOS, providing practical reference points for clinical use. Based on these insights, we propose a hypothesis-based stratification of PCOS into three adiposity-driven phenotypes: lean PCOS with central adiposity, obese PCOS with predominant central adiposity, and obese PCOS with peripheral adiposity. This stratification framework integrates general and central adiposity measures to identify women at varying metabolic risk levels more accurately. Moving beyond BMI towards adiposity-driven classification is therefore a crucial step toward diagnostic precision in reproductive endocrinology.
{"title":"Stratification of polycystic ovary syndrome by central adiposity phenotypes: Toward diagnostic precision","authors":"Sulagna Dutta , Pallav Sengupta","doi":"10.1016/j.obmed.2025.100664","DOIUrl":"10.1016/j.obmed.2025.100664","url":null,"abstract":"<div><div>Polycystic ovary syndrome (PCOS) remains the most common endocrine disorder in reproductive-aged women, yet its diagnosis and classification are inconsistent. Conventional frameworks emphasize reproductive and androgenic features, while relying heavily on body mass index (BMI) as a proxy for adiposity. However, BMI fails to capture visceral fat distribution, which is a stronger determinant of insulin resistance, low-grade inflammation, and cardiovascular risk. Emerging evidence highlights waist-to-height ratio (WHtR) and waist circumference (WC) as superior markers of central adiposity and metabolic dysfunction in PCOS. Based on pooled data from recent studies, consolidated cut-offs for PCOS and insulin-resistant PCOS (PCOS-IR) were derived, allowing phenotype-based stratification according to adiposity patterns. These evidence-based thresholds distinguish the metabolic transition from general to insulin-resistant PCOS, providing practical reference points for clinical use. Based on these insights, we propose a hypothesis-based stratification of PCOS into three adiposity-driven phenotypes: lean PCOS with central adiposity, obese PCOS with predominant central adiposity, and obese PCOS with peripheral adiposity. This stratification framework integrates general and central adiposity measures to identify women at varying metabolic risk levels more accurately. Moving beyond BMI towards adiposity-driven classification is therefore a crucial step toward diagnostic precision in reproductive endocrinology.</div></div>","PeriodicalId":37876,"journal":{"name":"Obesity Medicine","volume":"58 ","pages":"Article 100664"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-10-28DOI: 10.1016/j.obmed.2025.100660
Isar Sharma , Ritu Mahajan , Nisha Kapoor
Obesity, a widespread health concern, presents a clinical paradox: not everyone carrying excess weight develops the same metabolic complications. Some individuals remain metabolically healthy (MHO), while others progress to metabolically unhealthy obesity (MUO), facing severe risks like diabetes and heart disease. This critical review assesses the genetic underpinnings of this crucial distinction, focusing on the leptin receptor (LEPR), a key component in the regulation of our body's energy balance. Leptin, a hormone from fat cells, signals satiety through the LEPR. When genetic variations (polymorphisms) in LEPR disrupt this delicate signalling, it can lead to leptin resistance, a state contributing to weight gain and metabolic dysfunction. This review critically evaluates the evidence for how specific LEPR polymorphisms may influence the phenotypic divergence between MHO and MUO, highlighting the complex and population-specific nature of these associations. While variants like Q223R show inconsistent associations, others, such as K656N (rs8179183) and rs3790435, appear to directly contribute to the MUO phenotype by affecting fat distribution and inflammatory pathways. Understanding these genetic influences is paramount, as it shifts our view of obesity from a monolithic condition to a spectrum, revealing how individual genetic predispositions can dictate metabolic resilience or vulnerability. This work is crucial for developing more precise risk assessments and personalized interventions, ultimately paving the way for more effective strategies to promote metabolic health and mitigate the diverse impacts of obesity.
{"title":"Leptin receptor polymorphisms: Unravelling the genetic modulators of metabolically healthy and unhealthy obesity","authors":"Isar Sharma , Ritu Mahajan , Nisha Kapoor","doi":"10.1016/j.obmed.2025.100660","DOIUrl":"10.1016/j.obmed.2025.100660","url":null,"abstract":"<div><div>Obesity, a widespread health concern, presents a clinical paradox: not everyone carrying excess weight develops the same metabolic complications. Some individuals remain metabolically healthy (MHO), while others progress to metabolically unhealthy obesity (MUO), facing severe risks like diabetes and heart disease. This critical review assesses the genetic underpinnings of this crucial distinction, focusing on the leptin receptor (LEPR), a key component in the regulation of our body's energy balance. Leptin, a hormone from fat cells, signals satiety through the LEPR. When genetic variations (polymorphisms) in LEPR disrupt this delicate signalling, it can lead to leptin resistance, a state contributing to weight gain and metabolic dysfunction. This review critically evaluates the evidence for how specific LEPR polymorphisms may influence the phenotypic divergence between MHO and MUO, highlighting the complex and population-specific nature of these associations. While variants like Q223R show inconsistent associations, others, such as K656N (rs8179183) and rs3790435, appear to directly contribute to the MUO phenotype by affecting fat distribution and inflammatory pathways. Understanding these genetic influences is paramount, as it shifts our view of obesity from a monolithic condition to a spectrum, revealing how individual genetic predispositions can dictate metabolic resilience or vulnerability. This work is crucial for developing more precise risk assessments and personalized interventions, ultimately paving the way for more effective strategies to promote metabolic health and mitigate the diverse impacts of obesity.</div></div>","PeriodicalId":37876,"journal":{"name":"Obesity Medicine","volume":"58 ","pages":"Article 100660"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-10-25DOI: 10.1016/j.obmed.2025.100658
Mona Abdel-Bary , Andrea Brody , Jenna Schmitt , Karen Prieto , Amy Wetzel , Yen-Yi Juo
Introduction
Nearly half of Glucagon-Like Peptide-1 Receptor Agonists (GLP1RA) usage were discontinued within two years. We seek to investigate the reasons and consequences of unplanned GLP1RA discontinuation.
Materials and methods
This is a retrospective cohort study of adults who discontinued GLP1RA obesity treatment between 2022 and 2024.
Results
A total of 130 patients fitted our inclusion criteria. At the time GLP1RA was discontinued, the mean (Standard Deviation) total weight loss was −2.26 (8.20)%, ranging from −24.52 % to 11.79 %. Over the next twelve months, 85 patients (65.38 %) gained weight, with mean (SD) weight gain percentage at 1, 3, 6, and 12 months being −1.53 (2.33)%, 2.75 (4.19)%, 3.46 (7.38)%, and 24.44 (10.06)%. Of the 75 patients that had previously lost weight, 37 patients (49.33 %) had exceeded their original weight within a year. A significantly higher proportion of patients without T2DM gained weight than those with T2DM (70.75 % vs 41.67 %, p = .007). Besides diabetes, weight again appeared to occur with no association to demographic, socioeconomic and comorbidity factors.
Conclusion
Real-world weight gain following GLP1RA discontinuation was common, but neither as rapid nor as consistent as predicted by RCT literature. Developing transitional management strategy is crucial for optimizing these patient's weight outcomes.
近一半胰高血糖素样肽-1受体激动剂（GLP1RA）的使用在两年内停止。我们试图调查计划外停用GLP1RA的原因和后果。材料和方法这是一项回顾性队列研究，研究对象是在2022年至2024年间停止GLP1RA肥胖治疗的成年人。结果共有130例患者符合我们的纳入标准。在停用GLP1RA时，平均（标准差）总体重减轻为- 2.26(8.20)%，范围为- 24.52%至11.79%。在接下来的12个月里，85名患者（65.38%）体重增加，在1、3、6和12个月的平均（SD）体重增加百分比分别为- 1.53(2.33)%、2.75(4.19)%、3.46(7.38)%和24.44(10.06)%。在75例减肥患者中，37例（49.33%）患者在一年内体重超过了原来的体重。非T2DM患者体重增加的比例明显高于T2DM患者（70.75% vs 41.67%, p = 0.007）。除糖尿病外，体重似乎与人口统计学、社会经济和合并症因素无关。结论：GLP1RA停药后体重增加是常见的，但不像RCT文献预测的那样迅速和一致。制定过渡性管理策略对于优化这些患者的体重结果至关重要。
{"title":"Real-world weight change pattern after glucagon-like peptide-1 receptor agonist discontinuation: A 1-year observational study","authors":"Mona Abdel-Bary , Andrea Brody , Jenna Schmitt , Karen Prieto , Amy Wetzel , Yen-Yi Juo","doi":"10.1016/j.obmed.2025.100658","DOIUrl":"10.1016/j.obmed.2025.100658","url":null,"abstract":"<div><h3>Introduction</h3><div>Nearly half of Glucagon-Like Peptide-1 Receptor Agonists (GLP1RA) usage were discontinued within two years. We seek to investigate the reasons and consequences of unplanned GLP1RA discontinuation.</div></div><div><h3>Materials and methods</h3><div>This is a retrospective cohort study of adults who discontinued GLP1RA obesity treatment between 2022 and 2024.</div></div><div><h3>Results</h3><div>A total of 130 patients fitted our inclusion criteria. At the time GLP1RA was discontinued, the mean (Standard Deviation) total weight loss was −2.26 (8.20)%, ranging from −24.52 % to 11.79 %. Over the next twelve months, 85 patients (65.38 %) gained weight, with mean (SD) weight gain percentage at 1, 3, 6, and 12 months being −1.53 (2.33)%, 2.75 (4.19)%, 3.46 (7.38)%, and 24.44 (10.06)%. Of the 75 patients that had previously lost weight, 37 patients (49.33 %) had exceeded their original weight within a year. A significantly higher proportion of patients without T2DM gained weight than those with T2DM (70.75 % vs 41.67 %, p = .007). Besides diabetes, weight again appeared to occur with no association to demographic, socioeconomic and comorbidity factors.</div></div><div><h3>Conclusion</h3><div>Real-world weight gain following GLP1RA discontinuation was common, but neither as rapid nor as consistent as predicted by RCT literature. Developing transitional management strategy is crucial for optimizing these patient's weight outcomes.</div></div>","PeriodicalId":37876,"journal":{"name":"Obesity Medicine","volume":"58 ","pages":"Article 100658"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-09-29DOI: 10.1016/j.obmed.2025.100656
Adnan Agha , Bachar Afandi , Waad Ibrahim Alhammadi , Hamad Talal Jumaa Mubarak Almaskari , Asma Alshamsi , Mohammed Saleem , Juma Al Kaabi
Background
Bariatric surgery has emerged as a potent intervention for patients with obesity and Type 2 Diabetes Mellitus (T2DM).
Objectives
To assess 5-year outcomes of bariatric surgery in Middle Eastern patients with T2DM and develop a predictive model for diabetes remission.
Setting
Tertiary care center, United Arab Emirates.
Methods
We conducted a retrospective cohort study of 238 T2DM patients (161 females, 67.6 %; mean age 46.8 ± 11.6 years) who underwent bariatric surgery from 2013 to 2019. Procedures included laparoscopic sleeve gastrectomy (LSG; n = 138, 58.0 %), Roux-en-Y gastric bypass (RYGB; n = 85, 35.7 %), and adjustable gastric banding (AGB; n = 15; 6.3 %). Clinical, biochemical and cardiometabolic parameters were followed for 5 years. Multivariate logistic regression analysis identified independent predictors of diabetes remission.
Results
Sustained total weight loss of 26.2 %, 29.2 %, and 27.2 % was achieved at 1, 3, and 5 years respectively. HbA1c decreased from 7.8 ± 1.5 % to 6.1 ± 1.0 % at 5 years (p < 0.001). By 5 years, 95.4 % of patients discontinued diabetes medications. Complete diabetes remission (HbA1c <6.0 % without medications) was achieved in 52.9 %, exceeding rates typically reported in randomized controlled trials of bariatric surgery (23–29 %) in Western populations. Multivariate analysis revealed baseline HbA1c <8 % (OR 2.84, 95 % CI 1.52–5.31, p = 0.001), diabetes duration <5 years (OR 3.21, 95 % CI 1.78–5.79, p < 0.001), and weight loss >20 % at 1 year (OR 4.15, 95 % CI 2.23–7.72, p < 0.001) as independent predictors of sustained remission (C-statistic = 0.842).
Conclusions
Bariatric surgery provides sustained improvements in weight loss and glycemic control in Middle Eastern T2DM patients over 5 years. The identification of key predictors enables better patient selection and personalized treatment strategies.
背景：减肥手术已成为肥胖和2型糖尿病（T2DM）患者的有效干预措施。目的评估中东T2DM患者减肥手术的5年预后，并建立糖尿病缓解的预测模型。三级保健中心，阿联酋。方法对2013 - 2019年接受减肥手术的238例T2DM患者（女性161例，67.6%，平均年龄46.8±11.6岁）进行回顾性队列研究。手术包括腹腔镜袖胃切除术（LSG, n = 138, 58.0%）、Roux-en-Y胃旁路术（RYGB, n = 85, 35.7%）和可调节胃束带（AGB, n = 15, 6.3%）。临床、生化及心脏代谢指标随访5年。多因素logistic回归分析确定了糖尿病缓解的独立预测因素。结果1年、3年和5年的持续总体重减轻率分别为26.2%、29.2%和27.2%。5年后，HbA1c从7.8±1.5%降至6.1±1.0% （p < 0.001）。5年后，95.4%的患者停止了糖尿病药物治疗。52.9%的患者实现了糖尿病完全缓解（无药物治疗的HbA1c和lt达到6.0%），超过了西方人群中减肥手术随机对照试验中通常报道的比率（23 - 29%）。多因素分析显示，基线HbA1c <； 8% （OR 2.84, 95% CI 1.52-5.31, p = 0.001）、糖尿病病程<；5年（OR 3.21, 95% CI 1.78-5.79, p < 0.001）和1年体重减轻>； 20% （OR 4.15, 95% CI 2.23-7.72, p < 0.001）是持续缓解的独立预测因子（C-statistic = 0.842）。结论：在中东T2DM患者中，减肥手术提供了5年以上体重减轻和血糖控制的持续改善。关键预测因素的识别使更好的患者选择和个性化的治疗策略。
{"title":"Impact of bariatric surgery on Type 2 Diabetes Mellitus: A comprehensive 5-year analysis of weight loss, glycemic control, and cardiometabolic outcomes","authors":"Adnan Agha , Bachar Afandi , Waad Ibrahim Alhammadi , Hamad Talal Jumaa Mubarak Almaskari , Asma Alshamsi , Mohammed Saleem , Juma Al Kaabi","doi":"10.1016/j.obmed.2025.100656","DOIUrl":"10.1016/j.obmed.2025.100656","url":null,"abstract":"<div><h3>Background</h3><div>Bariatric surgery has emerged as a potent intervention for patients with obesity and Type 2 Diabetes Mellitus (T2DM).</div></div><div><h3>Objectives</h3><div>To assess 5-year outcomes of bariatric surgery in Middle Eastern patients with T2DM and develop a predictive model for diabetes remission.</div></div><div><h3>Setting</h3><div>Tertiary care center, United Arab Emirates.</div></div><div><h3>Methods</h3><div>We conducted a retrospective cohort study of 238 T2DM patients (161 females, 67.6 %; mean age 46.8 ± 11.6 years) who underwent bariatric surgery from 2013 to 2019. Procedures included laparoscopic sleeve gastrectomy (LSG; n = 138, 58.0 %), Roux-en-Y gastric bypass (RYGB; n = 85, 35.7 %), and adjustable gastric banding (AGB; n = 15; 6.3 %). Clinical, biochemical and cardiometabolic parameters were followed for 5 years. Multivariate logistic regression analysis identified independent predictors of diabetes remission.</div></div><div><h3>Results</h3><div>Sustained total weight loss of 26.2 %, 29.2 %, and 27.2 % was achieved at 1, 3, and 5 years respectively. HbA1c decreased from 7.8 ± 1.5 % to 6.1 ± 1.0 % at 5 years (p < 0.001). By 5 years, 95.4 % of patients discontinued diabetes medications. Complete diabetes remission (HbA1c <6.0 % without medications) was achieved in 52.9 %, exceeding rates typically reported in randomized controlled trials of bariatric surgery (23–29 %) in Western populations. Multivariate analysis revealed baseline HbA1c <8 % (OR 2.84, 95 % CI 1.52–5.31, p = 0.001), diabetes duration <5 years (OR 3.21, 95 % CI 1.78–5.79, p < 0.001), and weight loss >20 % at 1 year (OR 4.15, 95 % CI 2.23–7.72, p < 0.001) as independent predictors of sustained remission (C-statistic = 0.842).</div></div><div><h3>Conclusions</h3><div>Bariatric surgery provides sustained improvements in weight loss and glycemic control in Middle Eastern T2DM patients over 5 years. The identification of key predictors enables better patient selection and personalized treatment strategies.</div></div>","PeriodicalId":37876,"journal":{"name":"Obesity Medicine","volume":"58 ","pages":"Article 100656"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-10-30DOI: 10.1016/j.obmed.2025.100661
Tufayl Ahmed Mohammed Shekha, Swathi Suresh
{"title":"From trend to treatment: Regulating anti-obesity medications in India","authors":"Tufayl Ahmed Mohammed Shekha, Swathi Suresh","doi":"10.1016/j.obmed.2025.100661","DOIUrl":"10.1016/j.obmed.2025.100661","url":null,"abstract":"","PeriodicalId":37876,"journal":{"name":"Obesity Medicine","volume":"58 ","pages":"Article 100661"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-10-15DOI: 10.1016/j.obmed.2025.100657
Jennifer Teke , Maines Msiska , Oluronke Abisoye Adanini , Eghosasere Egbon , Augustus Osborne , David B. Olawade
Traditional obesity management approaches, including dietary interventions, physical activity programmes, pharmacotherapy, and behavioural therapies, face significant limitations in scalability, personalisation, and long-term adherence rates. The emergence of artificial intelligence (AI) technologies, particularly machine learning and deep learning algorithms, has opened new frontiers for transforming obesity prevention, diagnosis, and management strategies. This comprehensive narrative review synthesises current evidence on AI applications in obesity management, examining technological innovations from predictive risk models to personalised digital therapeutics. The review explores AI-based diagnostic tools utilising computer vision for body composition analysis, predictive algorithms identifying high-risk individuals using electronic health records, personalised behavioural interventions powered by reinforcement learning, and remote monitoring systems integrating wearable technologies with intelligent data analytics. Furthermore, it investigates clinical effectiveness of AI-driven digital therapeutics platforms and examines AI integration within clinical decision support systems. The analysis reveals significant benefits including enhanced scalability for population-level interventions, improved personalisation through real-time data integration, increased precision in risk stratification, and potential cost-effectiveness through optimised resource allocation. However, substantial challenges remain, including data privacy and security concerns, algorithmic bias that may exacerbate health disparities, limited large-scale clinical validation, declining user engagement over time, and complex regulatory and ethical considerations. Addressing these challenges through multidisciplinary collaboration, robust validation studies, and ethical frameworks will be critical for successfully integrating AI technologies into routine obesity care and achieving equitable health outcomes across diverse populations.
{"title":"Artificial intelligence for obesity management: A review of applications, opportunities, and challenges","authors":"Jennifer Teke , Maines Msiska , Oluronke Abisoye Adanini , Eghosasere Egbon , Augustus Osborne , David B. Olawade","doi":"10.1016/j.obmed.2025.100657","DOIUrl":"10.1016/j.obmed.2025.100657","url":null,"abstract":"<div><div>Traditional obesity management approaches, including dietary interventions, physical activity programmes, pharmacotherapy, and behavioural therapies, face significant limitations in scalability, personalisation, and long-term adherence rates. The emergence of artificial intelligence (AI) technologies, particularly machine learning and deep learning algorithms, has opened new frontiers for transforming obesity prevention, diagnosis, and management strategies. This comprehensive narrative review synthesises current evidence on AI applications in obesity management, examining technological innovations from predictive risk models to personalised digital therapeutics. The review explores AI-based diagnostic tools utilising computer vision for body composition analysis, predictive algorithms identifying high-risk individuals using electronic health records, personalised behavioural interventions powered by reinforcement learning, and remote monitoring systems integrating wearable technologies with intelligent data analytics. Furthermore, it investigates clinical effectiveness of AI-driven digital therapeutics platforms and examines AI integration within clinical decision support systems. The analysis reveals significant benefits including enhanced scalability for population-level interventions, improved personalisation through real-time data integration, increased precision in risk stratification, and potential cost-effectiveness through optimised resource allocation. However, substantial challenges remain, including data privacy and security concerns, algorithmic bias that may exacerbate health disparities, limited large-scale clinical validation, declining user engagement over time, and complex regulatory and ethical considerations. Addressing these challenges through multidisciplinary collaboration, robust validation studies, and ethical frameworks will be critical for successfully integrating AI technologies into routine obesity care and achieving equitable health outcomes across diverse populations.</div></div>","PeriodicalId":37876,"journal":{"name":"Obesity Medicine","volume":"58 ","pages":"Article 100657"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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