Rocío De la Peña-Armada, Giovana Longo-Silva, María Rodríguez-Martín, Hui-Wen Yang, Marta Garaulet
Objective
This longitudinal study aims to identify the key factors influencing the weight loss trajectory during a 24-week intervention and their relevance at different stages of the treatment.
Methods
We studied 1252 participants (age 18–65 years) of a cognitive behavioral program to lose weight. Body weight was measured weekly, and a range of variables at baseline and throughout the treatment period were assessed. Linear regression analyses were conducted to understand the factors involved in the weight loss trajectory across three treatment periods (0–6, 7–12, 13–24 weeks).
Results
The rate of weight loss progressively decreased from the 1st to the 3rd period (770–198 g/week). During the 1st period, the recent history of the individual, including baseline metabolic status, dietary habits, and motivation, was the highest-ranked determinant. In the 2nd period, the sole predictor was the long-term personal history of obesity, that is, the duration of the individual's lifetime spent with overweight/obesity. In the 3rd period, emotional eating behaviors and related barriers emerged as the two highest-ranked determinants of reduced rate of weight loss.
Conclusions
Findings suggest targeted interventions that address the specific challenges of each period of weight loss interventions. As treatment progresses, strategies focused on emotional-related behaviors may be crucial for sustaining weight loss.
{"title":"Key Determinants of Weight Loss Trajectory Across Different Periods of a Behavioral Weight Loss Intervention","authors":"Rocío De la Peña-Armada, Giovana Longo-Silva, María Rodríguez-Martín, Hui-Wen Yang, Marta Garaulet","doi":"10.1002/oby.70113","DOIUrl":"10.1002/oby.70113","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This longitudinal study aims to identify the key factors influencing the weight loss trajectory during a 24-week intervention and their relevance at different stages of the treatment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We studied 1252 participants (age 18–65 years) of a cognitive behavioral program to lose weight. Body weight was measured weekly, and a range of variables at baseline and throughout the treatment period were assessed. Linear regression analyses were conducted to understand the factors involved in the weight loss trajectory across three treatment periods (0–6, 7–12, 13–24 weeks).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The rate of weight loss progressively decreased from the 1st to the 3rd period (770–198 g/week). During the 1st period, the recent history of the individual, including baseline metabolic status, dietary habits, and motivation, was the highest-ranked determinant. In the 2nd period, the sole predictor was the long-term personal history of obesity, that is, the duration of the individual's lifetime spent with overweight/obesity. In the 3rd period, emotional eating behaviors and related barriers emerged as the two highest-ranked determinants of reduced rate of weight loss.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Findings suggest targeted interventions that address the specific challenges of each period of weight loss interventions. As treatment progresses, strategies focused on emotional-related behaviors may be crucial for sustaining weight loss.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Trial Registration</h3>\u0000 \u0000 <p>\u0000 ClinicalTrials.gov identifier NCT02829619</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"34 2","pages":"357-371"},"PeriodicalIF":4.7,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12850559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146013849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maurizio De Luca, Ricardo V. Cohen, Amanda Belluzzi, Giuseppe Navarra, Nicola Di Lorenzo, Tarissa B. Z. Petry, Paolo Sbraccia, Luca Busetto, Silvio Buscemi, Rocco Barazzoni, Benedetta Ragghianti, Edoardo Mannucci, Matteo Monami
Objective
This review compared antiobesity strategies—obesity management medications (OMM), endoscopic bariatric procedures (EBP), and metabolic bariatric surgery (MBS)—with lifestyle intervention, placebo, or no therapy (LSI/Pbo/NT).
Methods
This network meta-analysis included randomized clinical trials comparing OMM, EBP, and MBS versus LSI/Pbo/NT or active comparators in adults with obesity. MEDLINE and Embase were searched up to December 1, 2024. The primary endpoint was total body weight loss percentage (TBWL%), analyzed at 26–52, 53–104, 105–156, and ≥ 156 weeks. This study was registered with PROSPERO (CRD42024623707).
Results
Of 139 RCTs, 54 evaluated MBS (n = 61,961), 21 EBP (n = 2934), and 64 OMM (n = 5991). At 26–52 weeks, most treatments showed significant effects versus the reference. TBWL% exceeded 10% with most surgeries and tirzepatide. Long-term data were lacking for most OMM and all EBP. Most treatments maintained their efficacy over time, except greater curvature plication. EBP and MBS were generally associated with a higher SAE risk than OMM; BPD showed the highest long-term SAE incidence.
Conclusions
MBS appears superior in the long term (particularly for higher-efficacy procedures, such as RYGB, SG, SADI, and BPD). EBP, except ESG, was less effective than newer OMM. Semaglutide and tirzepatide showed no inferior short-term results in comparison with MBS.
{"title":"Efficacy and Safety of Pharmacological, Endoscopic, and Surgical Treatments for Obesity: A GRADE-Based Network Meta-Analysis","authors":"Maurizio De Luca, Ricardo V. Cohen, Amanda Belluzzi, Giuseppe Navarra, Nicola Di Lorenzo, Tarissa B. Z. Petry, Paolo Sbraccia, Luca Busetto, Silvio Buscemi, Rocco Barazzoni, Benedetta Ragghianti, Edoardo Mannucci, Matteo Monami","doi":"10.1002/oby.70083","DOIUrl":"10.1002/oby.70083","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This review compared antiobesity strategies—obesity management medications (OMM), endoscopic bariatric procedures (EBP), and metabolic bariatric surgery (MBS)—with lifestyle intervention, placebo, or no therapy (LSI/Pbo/NT).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This network meta-analysis included randomized clinical trials comparing OMM, EBP, and MBS versus LSI/Pbo/NT or active comparators in adults with obesity. MEDLINE and Embase were searched up to December 1, 2024. The primary endpoint was total body weight loss percentage (TBWL%), analyzed at 26–52, 53–104, 105–156, and ≥ 156 weeks. This study was registered with PROSPERO (CRD42024623707).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Of 139 RCTs, 54 evaluated MBS (<i>n</i> = 61,961), 21 EBP (<i>n</i> = 2934), and 64 OMM (<i>n</i> = 5991). At 26–52 weeks, most treatments showed significant effects versus the reference. TBWL% exceeded 10% with most surgeries and tirzepatide. Long-term data were lacking for most OMM and all EBP. Most treatments maintained their efficacy over time, except greater curvature plication. EBP and MBS were generally associated with a higher SAE risk than OMM; BPD showed the highest long-term SAE incidence.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>MBS appears superior in the long term (particularly for higher-efficacy procedures, such as RYGB, SG, SADI, and BPD). EBP, except ESG, was less effective than newer OMM. Semaglutide and tirzepatide showed no inferior short-term results in comparison with MBS.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"34 2","pages":"279-293"},"PeriodicalIF":4.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12850565/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145992564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Weight of Social Risk and Opportunities for Obesity Prevention","authors":"Stephanie P. Gilley, Rochelle L. Cason-Wilkerson","doi":"10.1002/oby.70138","DOIUrl":"10.1002/oby.70138","url":null,"abstract":"","PeriodicalId":215,"journal":{"name":"Obesity","volume":"34 2","pages":"277-278"},"PeriodicalIF":4.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145991688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The POWER of Sleep in Promoting Weight Loss Among Breast Cancer Survivors","authors":"Sarah Schmitz, Faris M. Zuraikat","doi":"10.1002/oby.70139","DOIUrl":"10.1002/oby.70139","url":null,"abstract":"","PeriodicalId":215,"journal":{"name":"Obesity","volume":"34 2","pages":"275-276"},"PeriodicalIF":4.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145914343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hunter G. Sellers, Zachary L. Brown, Noureen S. Khalil, Stephen B. Haigh, Mitchell A. Shivers, Tej V. Patel, Austin T. Joshua, Neal L. Weintraub, Scott A. Barman, Eric J. Belin de Chantemele, Sergei A. Kirov, David W. Stepp, David J. R. Fulton
Objective
This study utilized AAV gene delivery as an approach to induce and reverse hyperphagia in mice. We hypothesized that the delivery of orexigenic neuropeptides to the brain via AAV precipitates obesity and that the implementation of genetic switches to reverse transgene expression would elicit weight loss.
Methods
We utilized capsid-modified AAV-PHP.eB and AAV-CAP.B10 to deliver AgRP, NPY, a leptin superantagonist, and ghrelin to the mouse brain. Cre-LoxP, TETOFF, and cumate expression systems were used to alter transgene expression.
Results
Delivery of three out of four orexigenic neuropeptides to the brain precipitated severe obesity. Cre-mediated excision of AgRP from the brain caused a return to baseline weight, confounded by tamoxifen-associated weight loss. Doxycycline-mediated suppression of AgRP in a TETOFF vector paused weight gain but did not elicit weight loss. Cumate induction of AgRP in the brain was unaffected by systemic administration, suggesting that cumate inadequately penetrates the blood–brain barrier.
Conclusions
Brain-targeted delivery of orexigenic peptides induces obesity in mice. This allows for temporally controlled, convenient, and robust preclinical models of obesity. The implementation of genetic switches enabled suppression/removal of AgRP expression, but we unexpectedly observed that removal of hyperphagic stimuli does not elicit robust weight loss.
{"title":"Modeling Obesity and Weight Loss in Mice Using Novel AAV Approaches","authors":"Hunter G. Sellers, Zachary L. Brown, Noureen S. Khalil, Stephen B. Haigh, Mitchell A. Shivers, Tej V. Patel, Austin T. Joshua, Neal L. Weintraub, Scott A. Barman, Eric J. Belin de Chantemele, Sergei A. Kirov, David W. Stepp, David J. R. Fulton","doi":"10.1002/oby.70081","DOIUrl":"10.1002/oby.70081","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study utilized AAV gene delivery as an approach to induce and reverse hyperphagia in mice. We hypothesized that the delivery of orexigenic neuropeptides to the brain via AAV precipitates obesity and that the implementation of genetic switches to reverse transgene expression would elicit weight loss.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We utilized capsid-modified AAV-PHP.eB and AAV-CAP.B10 to deliver AgRP, NPY, a leptin superantagonist, and ghrelin to the mouse brain. Cre-LoxP, TETOFF, and cumate expression systems were used to alter transgene expression.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Delivery of three out of four orexigenic neuropeptides to the brain precipitated severe obesity. Cre-mediated excision of AgRP from the brain caused a return to baseline weight, confounded by tamoxifen-associated weight loss. Doxycycline-mediated suppression of AgRP in a TETOFF vector paused weight gain but did not elicit weight loss. Cumate induction of AgRP in the brain was unaffected by systemic administration, suggesting that cumate inadequately penetrates the blood–brain barrier.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Brain-targeted delivery of orexigenic peptides induces obesity in mice. This allows for temporally controlled, convenient, and robust preclinical models of obesity. The implementation of genetic switches enabled suppression/removal of AgRP expression, but we unexpectedly observed that removal of hyperphagic stimuli does not elicit robust weight loss.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"34 2","pages":"417-427"},"PeriodicalIF":4.7,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12850578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145852091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pathogenic variants in five established leptin-melanocortin pathway genes (LEP, LEPR, MC4R, PCSK1, POMC) are associated with severe early-onset obesity and are targets for emerging treatments. However, these variants are rare in these patients, suggesting the involvement of additional genes interacting with this pathway.
Methods
Next-generation sequencing (NGS) analysis was performed in 395 patients with severe obesity, including 213 children (mean BMI: 56.3 kg/m2; BMI-z-score: 4.6). The analysis targeted 20 genes, including the 5 established genes. Rare genetic variants were assessed for pathogenicity using prediction algorithms, genetic databases, and literature review. Phenotypic data were retrospectively collected, focusing on obesity severity, age of onset, familial history, eating behavior disorder, neurodevelopmental and endocrine-associated diseases, and obesity complications.
Results
Pathogenic heterozygous variants were identified in 34 patients (8.6%), 18 of them harboring pathogenic variants in the 15 additional genes. In adults, early-onset obesity was more frequent in potentially pathogenic variants carriers than in non-carriers (83.3% vs. 55.0%, p = 0.04). No differences were observed in the other phenotypic characteristics.
Conclusions
This supports the relevance of expanded genetic testing in severe obesity. Early-onset obesity remains a key clinical feature to guide genetic investigation and identify patients who may benefit from early personalized care and targeted treatments.
{"title":"Targeted Next-Generation Sequencing of the Leptin-Melanocortin Pathway in Severe Obesity","authors":"Nathan Faccioli, Chrisitne Poitou, Mathieu Georget, Françoise Bertin, Ahlam Azar-Kolakez, Claire Carette, Pauline Faucher, Blandine Gatta-Cherifi, Julie Gonneau-Lejeune, Agnès Linglart, Karine Clément, Johanne Le Beyec-Le Bihan, Béatrice Dubern","doi":"10.1002/oby.70107","DOIUrl":"10.1002/oby.70107","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Pathogenic variants in five established leptin-melanocortin pathway genes (<i>LEP, LEPR, MC4R, PCSK1</i>, <i>POMC</i>) are associated with severe early-onset obesity and are targets for emerging treatments. However, these variants are rare in these patients, suggesting the involvement of additional genes interacting with this pathway.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Next-generation sequencing (NGS) analysis was performed in 395 patients with severe obesity, including 213 children (mean BMI: 56.3 kg/m<sup>2</sup>; BMI-<i>z</i>-score: 4.6). The analysis targeted 20 genes, including the 5 established genes. Rare genetic variants were assessed for pathogenicity using prediction algorithms, genetic databases, and literature review. Phenotypic data were retrospectively collected, focusing on obesity severity, age of onset, familial history, eating behavior disorder, neurodevelopmental and endocrine-associated diseases, and obesity complications.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Pathogenic heterozygous variants were identified in 34 patients (8.6%), 18 of them harboring pathogenic variants in the 15 additional genes. In adults, early-onset obesity was more frequent in potentially pathogenic variants carriers than in non-carriers (83.3% vs. 55.0%, <i>p</i> = 0.04). No differences were observed in the other phenotypic characteristics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This supports the relevance of expanded genetic testing in severe obesity. Early-onset obesity remains a key clinical feature to guide genetic investigation and identify patients who may benefit from early personalized care and targeted treatments.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"34 2","pages":"499-511"},"PeriodicalIF":4.7,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12850549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145835919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anna Ferrulli, Pamela Senesi, Andrea Sonaglioni, Daniele Cannavaro, Stefano Massarini, Concetta Macrì, Elisa Cipponeri, Ralph A. DeFronzo, Livio Luzi
Objective
This study compared the efficacy of a GLP-1 receptor agonist (GLP1-RA) (semaglutide, 0.5 mg/week), sodium-glucose cotransporter-2 inhibitors (SGLT2i), and repetitive transcranial magnetic stimulation (rTMS), a new emerging treatment for obesity, in reducing body weight (BW) after 1 year in patients with obesity and type 2 diabetes (T2D).
Methods
We included 40 patients with T2D treated with a SGLT2i, 37 patients with T2D treated with the GLP1-RA semaglutide, and 30 patients treated with rTMS in this retrospective comparative analysis. rTMS was administered three times per week for 5 weeks. All patients received dietary advice about moderate caloric restriction (−300 kcal/day).
Results
After 12 months the weight loss with rTMS (−8.2 ± 1.0 kg) was not significantly different from that with semaglutide (−5.7 ± 0.9 kg). Weight loss with SGLT2i (−2.0 ± 0.7 kg) was significantly less than with both semaglutide (p = 0.01) and rTMS (p < 0.0001). Individuals receiving SGLT2i therapy experienced weight regain from month 6 to month 12, while BW declined progressively in patients treated with semaglutide and rTMS.
Conclusions
Treatment with rTMS produced a comparable reduction in BW to that observed with the GLP1-RA semaglutide (at the dose of 0.5 mg/week) and represents a promising intervention for the treatment of obesity and T2D.
{"title":"Weight Loss With SGLT2 Inhibitors, Semaglutide, and Transcranial Magnetic Stimulation in Type 2 Diabetes and Obesity","authors":"Anna Ferrulli, Pamela Senesi, Andrea Sonaglioni, Daniele Cannavaro, Stefano Massarini, Concetta Macrì, Elisa Cipponeri, Ralph A. DeFronzo, Livio Luzi","doi":"10.1002/oby.70105","DOIUrl":"10.1002/oby.70105","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study compared the efficacy of a GLP-1 receptor agonist (GLP1-RA) (semaglutide, 0.5 mg/week), sodium-glucose cotransporter-2 inhibitors (SGLT2i), and repetitive transcranial magnetic stimulation (rTMS), a new emerging treatment for obesity, in reducing body weight (BW) after 1 year in patients with obesity and type 2 diabetes (T2D).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We included 40 patients with T2D treated with a SGLT2i, 37 patients with T2D treated with the GLP1-RA semaglutide, and 30 patients treated with rTMS in this retrospective comparative analysis. rTMS was administered three times per week for 5 weeks. All patients received dietary advice about moderate caloric restriction (−300 kcal/day).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>After 12 months the weight loss with rTMS (−8.2 ± 1.0 kg) was not significantly different from that with semaglutide (−5.7 ± 0.9 kg). Weight loss with SGLT2i (−2.0 ± 0.7 kg) was significantly less than with both semaglutide (<i>p</i> = 0.01) and rTMS (<i>p</i> < 0.0001). Individuals receiving SGLT2i therapy experienced weight regain from month 6 to month 12, while BW declined progressively in patients treated with semaglutide and rTMS.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Treatment with rTMS produced a comparable reduction in BW to that observed with the GLP1-RA semaglutide (at the dose of 0.5 mg/week) and represents a promising intervention for the treatment of obesity and T2D.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Trial Registration</h3>\u0000 \u0000 <p>\u0000 ClinicalTrials.gov identifier: NCT03009695</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"34 2","pages":"317-322"},"PeriodicalIF":4.7,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145835939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Benjamin Apple, Jingsha Chen, Tooraj Mirshahi, Christopher D. Still, Andrew S. Levey, Lesley A. Inker, Morgan E. Grams, Josef Coresh, Evan M. Zeitler, Alexander R. Chang
Objective
This study aimed to characterize bariatric surgery-induced changes in serum metabolites while accounting for changes in glomerular filtration rate (GFR) to understand the metabolic benefits to the kidney of bariatric surgery.
Methods
This was a prospective, single-center cohort of 27 adults with severe obesity who underwent bariatric surgery. Serum metabolomics and measured GFR (mGFR) were performed 1–3 months prior to and 6 months after surgery. In generalized estimating equation (GEE) models, we examined bariatric surgery- and mGFR-associated changes in serum metabolites. We used MetaboAnalyst to perform pathway analyses.
Results
Bariatric surgery was significantly associated with changes in 223 serum metabolites after adjustment. Following bariatric surgery, several pathways were downregulated (alpha-linoleic acid and linoleic acid, methionine, kynurenine, and alanine-glucose metabolism pathways; raw p < 0.05) or upregulated (phenylacetate, bile acid biosynthesis, taurine and hypo-taurine metabolism, porphyrin metabolism pathways; raw p < 0.05). Creatinine demonstrated a significant mGFR-independent decrease following surgery. The top metabolites significantly associated with mGFR included N,N,N-trimethyl-alanyl proline betaine (TMAP), followed by creatinine.
Conclusions
We discovered several GFR-independent metabolomic changes after bariatric surgery which may underlie its beneficial kidney effects including decreased inflammation, oxidation, and insulin resistance. Further studies are needed to investigate the potential mechanistic role of identified metabolites to clarify mechanisms of obesity-related kidney disease.
{"title":"Metabolomic Changes After Bariatric Surgery Adjusted for Glomerular Filtration Rate Suggest Mechanisms of Kidney Protection","authors":"Benjamin Apple, Jingsha Chen, Tooraj Mirshahi, Christopher D. Still, Andrew S. Levey, Lesley A. Inker, Morgan E. Grams, Josef Coresh, Evan M. Zeitler, Alexander R. Chang","doi":"10.1002/oby.70112","DOIUrl":"10.1002/oby.70112","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study aimed to characterize bariatric surgery-induced changes in serum metabolites while accounting for changes in glomerular filtration rate (GFR) to understand the metabolic benefits to the kidney of bariatric surgery.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This was a prospective, single-center cohort of 27 adults with severe obesity who underwent bariatric surgery. Serum metabolomics and measured GFR (mGFR) were performed 1–3 months prior to and 6 months after surgery. In generalized estimating equation (GEE) models, we examined bariatric surgery- and mGFR-associated changes in serum metabolites. We used MetaboAnalyst to perform pathway analyses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Bariatric surgery was significantly associated with changes in 223 serum metabolites after adjustment. Following bariatric surgery, several pathways were downregulated (alpha-linoleic acid and linoleic acid, methionine, kynurenine, and alanine-glucose metabolism pathways; raw <i>p</i> < 0.05) or upregulated (phenylacetate, bile acid biosynthesis, taurine and hypo-taurine metabolism, porphyrin metabolism pathways; raw <i>p</i> < 0.05). Creatinine demonstrated a significant mGFR-independent decrease following surgery. The top metabolites significantly associated with mGFR included N,N,N-trimethyl-alanyl proline betaine (TMAP), followed by creatinine.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We discovered several GFR-independent metabolomic changes after bariatric surgery which may underlie its beneficial kidney effects including decreased inflammation, oxidation, and insulin resistance. Further studies are needed to investigate the potential mechanistic role of identified metabolites to clarify mechanisms of obesity-related kidney disease.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"34 2","pages":"439-449"},"PeriodicalIF":4.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145812233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Samantha L. Pierce, Renee M. Porter, Lyudmyla Kompaniyets, Siran He, Alyson B. Goodman
Objective
This study examined associations between CDC's 2022 extended BMI percentiles (BMIp) and cardiometabolic biomarkers.
Methods
Using electronic medical record data, we included patients aged 6–17 years with BMI ≥ 85th percentile who had at least one of the following: total cholesterol, low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), triglycerides, alanine aminotransferase (ALT), and hemoglobin A1c (HbA1c). We calculated adjusted prevalence ratios (aPR) for (1) borderline or abnormal and (2) abnormal lab values by extended BMIp (85th–< 95th, 95th–< 98th, 98th–< 99th, 99th–< 99.9th, ≥ 99.9th).
Results
Compared to those with overweight (BMIp 85th–< 95th), patients in higher extended BMIp categories had a higher prevalence of borderline or abnormal HbA1c (aPR range: 1.46–2.79), ALT (aPR range: 1.58–2.59), triglycerides (aPR range: 1.24–1.46), total cholesterol (aPR range: 1.11–1.23), HDL (aPR range: 1.40–1.94), and LDL (aPR range: 1.23–1.55). Dose–response relationships were observed for multiple cardiometabolic biomarkers. Similar patterns were found for abnormal lab values.
Conclusions
Many US youth aged 6–17 with overweight and obesity had borderline or abnormal cardiometabolic biomarkers. Risks varied within obesity: higher extended BMIp were linked to a greater likelihood of borderline or abnormal lab values. Identifying extended BMIp thresholds tied to these risks could facilitate clinical practice, including identifying high-risk patients and informing escalation of care.
{"title":"Examining Biomarkers for Dyslipidemia, Diabetes, and NAFLD by CDC's 2022 Extended BMI Percentiles in US Youth Aged 6–17 Years","authors":"Samantha L. Pierce, Renee M. Porter, Lyudmyla Kompaniyets, Siran He, Alyson B. Goodman","doi":"10.1002/oby.70077","DOIUrl":"10.1002/oby.70077","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study examined associations between CDC's 2022 extended BMI percentiles (BMIp) and cardiometabolic biomarkers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using electronic medical record data, we included patients aged 6–17 years with BMI ≥ 85th percentile who had at least one of the following: total cholesterol, low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), triglycerides, alanine aminotransferase (ALT), and hemoglobin A1c (HbA1c). We calculated adjusted prevalence ratios (aPR) for (1) borderline or abnormal and (2) abnormal lab values by extended BMIp (85th–< 95th, 95th–< 98th, 98th–< 99th, 99th–< 99.9th, ≥ 99.9th).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Compared to those with overweight (BMIp 85th–< 95th), patients in higher extended BMIp categories had a higher prevalence of borderline or abnormal HbA1c (aPR range: 1.46–2.79), ALT (aPR range: 1.58–2.59), triglycerides (aPR range: 1.24–1.46), total cholesterol (aPR range: 1.11–1.23), HDL (aPR range: 1.40–1.94), and LDL (aPR range: 1.23–1.55). Dose–response relationships were observed for multiple cardiometabolic biomarkers. Similar patterns were found for abnormal lab values.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Many US youth aged 6–17 with overweight and obesity had borderline or abnormal cardiometabolic biomarkers. Risks varied within obesity: higher extended BMIp were linked to a greater likelihood of borderline or abnormal lab values. Identifying extended BMIp thresholds tied to these risks could facilitate clinical practice, including identifying high-risk patients and informing escalation of care.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"34 2","pages":"450-459"},"PeriodicalIF":4.7,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12850602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zachary Arnold, Phu Dang, Nicholas Hollman, Zhamak Khorgami
Objective
This study aimed to characterize the underdocumentation of obesity by comparing the rates of obesity in databases based on measured BMI, BMI measured while inpatient, and obesity ICD-10 codes input by health providers for inpatients in the United States.
Methods
We compared three datasets to test the prevalence of obesity in the United States general population and the inpatient hospital in the United States. The 2017–2018 National Health and Nutrition Examination (NHANES) dataset was used to estimate the prevalence in the general population. For the inpatient hospitals, we used data from the 2017 National Inpatient Sample (NIS) and the 2017–2018 American College of Surgeons–National Surgical Quality Improvement Program (NSQIP) datasets.
Results
There were 5434 participants in the NHANES, 2,006,047 participants in the NSQIP, and 6,084,184 participants in the NIS databases for analysis. The overall prevalence/diagnosis of obesity was 41.9% for NHANES (nationwide survey), 44.5% for NSQIP (in-hospital measurements), and 15.4% for NIS (ICD codes retrieved from medical charts).
Conclusions
The prevalence of obesity in NIS (based on recorded obesity in medical charts) is significantly lower than NHANES and NSQIP (based on measured BMI) suggesting underrecognition or underdocumentation of obesity in the inpatient setting.
{"title":"Underdocumentation of Obesity in the National Inpatient Sample","authors":"Zachary Arnold, Phu Dang, Nicholas Hollman, Zhamak Khorgami","doi":"10.1002/oby.70111","DOIUrl":"10.1002/oby.70111","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study aimed to characterize the underdocumentation of obesity by comparing the rates of obesity in databases based on measured BMI, BMI measured while inpatient, and obesity ICD-10 codes input by health providers for inpatients in the United States.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We compared three datasets to test the prevalence of obesity in the United States general population and the inpatient hospital in the United States. The 2017–2018 National Health and Nutrition Examination (NHANES) dataset was used to estimate the prevalence in the general population. For the inpatient hospitals, we used data from the 2017 National Inpatient Sample (NIS) and the 2017–2018 American College of Surgeons–National Surgical Quality Improvement Program (NSQIP) datasets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>There were 5434 participants in the NHANES, 2,006,047 participants in the NSQIP, and 6,084,184 participants in the NIS databases for analysis. The overall prevalence/diagnosis of obesity was 41.9% for NHANES (nationwide survey), 44.5% for NSQIP (in-hospital measurements), and 15.4% for NIS (ICD codes retrieved from medical charts).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The prevalence of obesity in NIS (based on recorded obesity in medical charts) is significantly lower than NHANES and NSQIP (based on measured BMI) suggesting underrecognition or underdocumentation of obesity in the inpatient setting.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"34 2","pages":"329-333"},"PeriodicalIF":4.7,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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