Carolina Mendes Pessoa, Tâmara P. Taporoski, Felipe Beijamini, Shaina J. Alexandria, Jose E. Krieger, Malcolm von Schantz, Alexandre C. Pereira, Kristen L. Knutson
Objective
To examine whether (1) sleep architecture is associated with BMI groups in the absence of sleep apnea and (2) BMI group modified associations between sleep architecture and markers of glucose metabolism.
Methods
The Baependi Heart Study (BHS) is a family-based observational study of adults that assessed sleep using at-home polysomnography (PSG) and collected anthropometric and fasting blood measures. BMI was classified into: 18.5 to < 25, 25 to < 30, and ≥ 30 kg/m2. People with moderate–severe sleep apnea and taking diabetes-related medication were excluded. Cross-sectional associations were examined (n = 1014).
Results
Individuals with BMI ≥ 30 kg/m2 had less REM sleep (−7.8 min, p = 0.003) and the groups with BMI 25 to < 30 kg/m2 and ≥ 30 kg/m2 had higher apnea-hypopnea index than individuals with BMI 18.5 to < 25 kg/m2 (by 0.8 and 1.4 events per hour, respectively, p < =0.002). Wake after sleep onset (WASO) was associated with higher fasting glucose levels in participants with BMI ≥ 30 kg/m2 only; 10 min more WASO was associated with ~1% higher fasting glucose (p = 0.002).
Conclusions
After exclusion of participants with moderate–severe sleep apnea, there was no difference in non-REM sleep and only a small difference in REM sleep between BMI groups, suggesting that BMI does not substantially impair sleep unless sleep apnea is present.
{"title":"BMI, Sleep Architecture, and Glucose Metabolism: Insights From the Baependi Heart Study","authors":"Carolina Mendes Pessoa, Tâmara P. Taporoski, Felipe Beijamini, Shaina J. Alexandria, Jose E. Krieger, Malcolm von Schantz, Alexandre C. Pereira, Kristen L. Knutson","doi":"10.1002/oby.24359","DOIUrl":"10.1002/oby.24359","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine whether (1) sleep architecture is associated with BMI groups in the absence of sleep apnea and (2) BMI group modified associations between sleep architecture and markers of glucose metabolism.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The Baependi Heart Study (BHS) is a family-based observational study of adults that assessed sleep using at-home polysomnography (PSG) and collected anthropometric and fasting blood measures. BMI was classified into: 18.5 to < 25, 25 to < 30, and ≥ 30 kg/m<sup>2</sup>. People with moderate–severe sleep apnea and taking diabetes-related medication were excluded. Cross-sectional associations were examined (<i>n</i> = 1014).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Individuals with BMI ≥ 30 kg/m<sup>2</sup> had less REM sleep (−7.8 min, <i>p</i> = 0.003) and the groups with BMI 25 to < 30 kg/m<sup>2</sup> and ≥ 30 kg/m<sup>2</sup> had higher apnea-hypopnea index than individuals with BMI 18.5 to < 25 kg/m<sup>2</sup> (by 0.8 and 1.4 events per hour, respectively, <i>p</i> < =0.002). Wake after sleep onset (WASO) was associated with higher fasting glucose levels in participants with BMI ≥ 30 kg/m<sup>2</sup> only; 10 min more WASO was associated with ~1% higher fasting glucose (<i>p</i> = 0.002).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>After exclusion of participants with moderate–severe sleep apnea, there was no difference in non-REM sleep and only a small difference in REM sleep between BMI groups, suggesting that BMI does not substantially impair sleep unless sleep apnea is present.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 10","pages":"1849-1854"},"PeriodicalIF":4.7,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12404664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984504","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}
The cardiovascular–kidney–metabolic (CKM) syndrome lacks clear strategies for risk reduction.
Methods
This study utilized data from NHANES focusing on adults with CKM. Participants were categorized into early and advanced CKM. A multivariable logistic regression model evaluated the relationship between oxidative balance score (OBS) levels—total, dietary, and lifestyle—and advanced CKM. The mediating effects of cardiovascular indexes, atherogenic index of plasma (AIP), and cardiac metabolic index (CMI) were also analyzed. The SHapley Additive exPlanations (SHAP) in machine learning interpret the importance of indexes' components.
Results
Higher OBS levels were significantly associated with a lower risk of advanced CKM. Specifically, a total OBS ≥ 28 correlated with a 36% reduced risk compared to scores ≤ 16. AIP and CMI were linked to increased advanced CKM risk at levels > 0.56 and > 0.57, respectively. Mediation analysis showed AIP and CMI potentially mediated 0.68% and 4.08% of the OBS-advanced CKM risk association. SHAP highlighted the importance of carotene and niacin.
Conclusions
This study is the first to identify an inverse association between higher OBS and advanced CKM risk, with cardiovascular indexes emerging as potential mediators, which lay the groundwork for future longitudinal and interventional studies to elucidate potential causal pathways.
{"title":"Cardiovascular Indexes and Oxidative Balance in Advanced Cardiovascular–Kidney–Metabolic Syndrome","authors":"Zhaoqi Yan, Xiangyi Pu, Xing Chang, Ruxiu Liu","doi":"10.1002/oby.24356","DOIUrl":"10.1002/oby.24356","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The cardiovascular–kidney–metabolic (CKM) syndrome lacks clear strategies for risk reduction.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This study utilized data from NHANES focusing on adults with CKM. Participants were categorized into early and advanced CKM. A multivariable logistic regression model evaluated the relationship between oxidative balance score (OBS) levels—total, dietary, and lifestyle—and advanced CKM. The mediating effects of cardiovascular indexes, atherogenic index of plasma (AIP), and cardiac metabolic index (CMI) were also analyzed. The SHapley Additive exPlanations (SHAP) in machine learning interpret the importance of indexes' components.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Higher OBS levels were significantly associated with a lower risk of advanced CKM. Specifically, a total OBS ≥ 28 correlated with a 36% reduced risk compared to scores ≤ 16. AIP and CMI were linked to increased advanced CKM risk at levels > 0.56 and > 0.57, respectively. Mediation analysis showed AIP and CMI potentially mediated 0.68% and 4.08% of the OBS-advanced CKM risk association. SHAP highlighted the importance of carotene and niacin.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study is the first to identify an inverse association between higher OBS and advanced CKM risk, with cardiovascular indexes emerging as potential mediators, which lay the groundwork for future longitudinal and interventional studies to elucidate potential causal pathways.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 9","pages":"1791-1801"},"PeriodicalIF":4.7,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746674","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}
Keeley J. Pratt, David B. Sarwer, Joseph A. Skelton
Adult weight management guidelines recommend offering antiobesity medications (AOMs) as an adjunct to lifestyle modification approaches. Consequently, prescriptions for AOMs are at an unprecedented high, with some estimates suggesting that one in eight US adults report ever taking an AOM. AOMs induce clinically meaningful weight loss for many; concerns about accessibility, cost, long-term efficacy and safety, and changes in eating behavior and psychosocial functioning remain unanswered. A question that has not been asked pertains to the family system. Family members' acceptability and perception of AOMs may dictate adherence, and established family and household routines and dynamics may influence individual behavior change during or after AOM use. This Perspective considers aspects of the family context with health behavior change and weight loss. The paper concludes with suggestions for future research to explore positive ripple effects and unintended consequences that AOMs could have on individual family members and family dynamics.
{"title":"Family Considerations in the Treatment of Adult Obesity With Antiobesity Medications","authors":"Keeley J. Pratt, David B. Sarwer, Joseph A. Skelton","doi":"10.1002/oby.24355","DOIUrl":"10.1002/oby.24355","url":null,"abstract":"<p>Adult weight management guidelines recommend offering antiobesity medications (AOMs) as an adjunct to lifestyle modification approaches. Consequently, prescriptions for AOMs are at an unprecedented high, with some estimates suggesting that one in eight US adults report ever taking an AOM. AOMs induce clinically meaningful weight loss for many; concerns about accessibility, cost, long-term efficacy and safety, and changes in eating behavior and psychosocial functioning remain unanswered. A question that has not been asked pertains to the family system. Family members' acceptability and perception of AOMs may dictate adherence, and established family and household routines and dynamics may influence individual behavior change during or after AOM use. This Perspective considers aspects of the family context with health behavior change and weight loss. The paper concludes with suggestions for future research to explore positive ripple effects and unintended consequences that AOMs could have on individual family members and family dynamics.</p>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 9","pages":"1619-1621"},"PeriodicalIF":4.7,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/oby.24355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736373","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}
Evgenia Gourgari, Gitanjali Srivastava, Aaron S. Kelly, Donna Mojdami, Dachuang Cao, Madhumita A. Murphy, Chrisanthi A. Karanikas, Clare J. Lee
Objective
People with early-onset obesity (diagnosed at age < 25 years) may present with more cardiometabolic abnormalities and obesity-related complications. This post hoc analysis assessed baseline characteristics and body weight (BW) changes with tirzepatide in people with early- versus later-onset obesity.
Methods
Participants (N = 3782) from SURMOUNT-1, SURMOUNT-3, and SURMOUNT-4 randomized to tirzepatide or placebo were included. Baseline characteristics and changes in BW and cardiometabolic risk factors at 72/88 weeks were assessed.
Results
In SURMOUNT-1, participants with early- versus later-onset obesity had longer mean obesity duration (20 ± 12 vs. 11 ± 8 years), higher BMI (40 ± 7 vs. 37 ± 6 kg/m2) and waist circumference (WC, 118 ± 16 vs. 112 ± 14 cm), and lower HbA1c (5.48% ± 0.4% vs. 5.60% ± 0.4%), triglycerides (median 120 vs. 130 mg/dL), and systolic blood pressure (SBP; 121 ± 13 vs. 125 ± 13 mmHg) at baseline (all p ≤ 0.004). At 72 weeks, improvements with tirzepatide in BW (−23% vs. −22%), WC (−22 vs. −19 cm), HbA1c (−0.51% vs. −0.52%), triglycerides (−32% vs. −31%), and SBP (−8 vs. −8 mmHg) were similar between subgroups. Similar improvements were observed in SURMOUNT-3 and SURMOUNT-4.
Conclusions
In this post hoc analysis, participants with early- versus later-onset obesity exhibited a mixed profile of metabolic health at baseline, including a higher degree of central adiposity and lower HbA1c and SBP. Improvements in BW and cardiometabolic markers with tirzepatide were similar between subgroups.
目的：早发性肥胖患者（年龄诊断）方法：纳入来自SURMOUNT-1、SURMOUNT-3和SURMOUNT-4随机分配至替西帕肽或安慰剂组的参与者（N = 3782）。在72/88周时评估基线特征和体重和心脏代谢危险因素的变化。结果：在SURMOUNT-1中，早发性肥胖与晚发性肥胖的参与者平均肥胖持续时间更长（20±12年vs 11±8年），BMI（40±7 vs 37±6 kg/m2）和腰围（WC, 118±16 vs 112±14 cm）更高，HbA1c（5.48%±0.4% vs 5.60%±0.4%）、甘油三酯（中位数120 vs 130 mg/dL）和收缩压(SBP；121±13对125±13 mmHg)（均p≤0.004）。在72周时，替西帕肽对BW （-23% vs. -22%）、WC （-22 vs. -19 cm）、HbA1c （-0.51% vs. -0.52%）、甘油三酯（-32% vs. -31%）和收缩压（-8 vs. -8 mmHg）的改善在亚组之间相似。在SURMOUNT-3和SURMOUNT-4中也观察到类似的改善。结论：在这项事后分析中，早发性肥胖与晚发性肥胖的参与者在基线时表现出混合的代谢健康状况，包括较高程度的中枢性肥胖和较低的HbA1c和收缩压。替西肽对体重和心脏代谢指标的改善在亚组之间相似。试验注册：ClinicalTrials.gov标识符：NCT04184622、NCT04657016、NCT04660643。
{"title":"Early-Onset Obesity and Tirzepatide Treatment: A Post Hoc Analysis of the SURMOUNT Clinical Trials","authors":"Evgenia Gourgari, Gitanjali Srivastava, Aaron S. Kelly, Donna Mojdami, Dachuang Cao, Madhumita A. Murphy, Chrisanthi A. Karanikas, Clare J. Lee","doi":"10.1002/oby.24348","DOIUrl":"10.1002/oby.24348","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>People with early-onset obesity (diagnosed at age < 25 years) may present with more cardiometabolic abnormalities and obesity-related complications. This post hoc analysis assessed baseline characteristics and body weight (BW) changes with tirzepatide in people with early- versus later-onset obesity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Participants (<i>N</i> = 3782) from SURMOUNT-1, SURMOUNT-3, and SURMOUNT-4 randomized to tirzepatide or placebo were included. Baseline characteristics and changes in BW and cardiometabolic risk factors at 72/88 weeks were assessed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In SURMOUNT-1, participants with early- versus later-onset obesity had longer mean obesity duration (20 ± 12 vs. 11 ± 8 years), higher BMI (40 ± 7 vs. 37 ± 6 kg/m<sup>2</sup>) and waist circumference (WC, 118 ± 16 vs. 112 ± 14 cm), and lower HbA1c (5.48% ± 0.4% vs. 5.60% ± 0.4%), triglycerides (median 120 vs. 130 mg/dL), and systolic blood pressure (SBP; 121 ± 13 vs. 125 ± 13 mmHg) at baseline (all <i>p</i> ≤ 0.004). At 72 weeks, improvements with tirzepatide in BW (−23% vs. −22%), WC (−22 vs. −19 cm), HbA1c (−0.51% vs. −0.52%), triglycerides (−32% vs. −31%), and SBP (−8 vs. −8 mmHg) were similar between subgroups. Similar improvements were observed in SURMOUNT-3 and SURMOUNT-4.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In this post hoc analysis, participants with early- versus later-onset obesity exhibited a mixed profile of metabolic health at baseline, including a higher degree of central adiposity and lower HbA1c and SBP. Improvements in BW and cardiometabolic markers with tirzepatide were similar between subgroups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Trial Registration</h3>\u0000 \u0000 <p>\u0000 ClinicalTrials.gov identifiers: NCT04184622, NCT04657016, NCT04660643</p>\u0000 \u0000 <div>\u0000 \u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </div>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 9","pages":"1668-1679"},"PeriodicalIF":4.7,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/oby.24348","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736372","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}
Ya Zhan, Na Zhao, Qi-Gang Zhao, Cheng-Xi Wu, Shan-Juan Yuan, Xiu-Juan Yu, Xiao Zheng, Chao-Jie Liu, Rong Hai, Zheng-Qing Liu, Yu-Fang Pei
Objective
This study investigated the shared genetic architecture between major depressive disorder (MDD) and waist-hip ratio (WHR) to provide insights into the biological mechanisms underlying their comorbidity.
Methods
Using large-scale genome-wide association study summary statistics, we performed cross-trait genetic correlation and pleiotropic variant discovery analyses and bidirectional Mendelian randomization analysis, as well as drug target prioritization analysis.
Results
We identified significant genetic correlation between MDD and WHR (rg = 0.11, p = 4.18 × 10−5). Cross-trait analysis identified 26 pleiotropic loci, including a novel variant (rs2855812, intronic to MICB). Colocalization analysis confirmed six pleiotropic loci. Forward Mendelian randomization analysis demonstrated MDD is associated with increased WHR (β = 0.079, 95% CI: 0.014–0.143; p = 0.017), with no reverse causation. Drug prioritization identified agents able to be repurposed targeting MICB, PSORS1C1, and C6orf15. Enrichment analyses highlighted immune pathways.
Conclusions
Our findings establish pleiotropy between MDD and WHR, implicating dysregulated immunometabolic pathways as shared mechanisms. Prioritized drug targets represent translatable opportunities for comorbidity management.
{"title":"Investigating shared genetic architecture between major depressive disorder and central obesity","authors":"Ya Zhan, Na Zhao, Qi-Gang Zhao, Cheng-Xi Wu, Shan-Juan Yuan, Xiu-Juan Yu, Xiao Zheng, Chao-Jie Liu, Rong Hai, Zheng-Qing Liu, Yu-Fang Pei","doi":"10.1002/oby.24333","DOIUrl":"10.1002/oby.24333","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study investigated the shared genetic architecture between major depressive disorder (MDD) and waist-hip ratio (WHR) to provide insights into the biological mechanisms underlying their comorbidity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using large-scale genome-wide association study summary statistics, we performed cross-trait genetic correlation and pleiotropic variant discovery analyses and bidirectional Mendelian randomization analysis, as well as drug target prioritization analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We identified significant genetic correlation between MDD and WHR (<i>r</i>\u0000 <sub>g</sub> = 0.11, <i>p</i> = 4.18 × 10<sup>−5</sup>). Cross-trait analysis identified 26 pleiotropic loci, including a novel variant (rs2855812, intronic to <i>MICB</i>). Colocalization analysis confirmed six pleiotropic loci. Forward Mendelian randomization analysis demonstrated MDD is associated with increased WHR (<i>β</i> = 0.079, 95% CI: 0.014–0.143; <i>p</i> = 0.017), with no reverse causation. Drug prioritization identified agents able to be repurposed targeting <i>MICB</i>, <i>PSORS1C1</i>, and <i>C6orf15</i>. Enrichment analyses highlighted immune pathways.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings establish pleiotropy between MDD and WHR, implicating dysregulated immunometabolic pathways as shared mechanisms. Prioritized drug targets represent translatable opportunities for comorbidity management.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 9","pages":"1745-1755"},"PeriodicalIF":4.7,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736374","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}
Dabin Yeum, Shuxian Hua, Gita Thapaliya, Sarah Ann Duck, Susan J. Melhorn, Christian L. Roth, Ellen A. Schur, Susan Carnell, Leticia E. Sewaybricker
Objective
Ultraprocessed foods (UPF) are often energy-dense and nutrient-poor, and excess consumption can increase the risk of child obesity. Eating behaviors in childhood may influence future dietary patterns. This study examined the relationships of baseline eating behaviors with baseline UPF intake and 12-month changes in UPF intake in children.
Methods
Children aged 9–11 years were recruited (N = 101; 53% female). Parents reported on children's eating behaviors via the Child Eating Behavior Questionnaire (CEBQ). Children's dietary intake was captured with three automated dietary recalls. Daily average kilocalories of unprocessed/minimally processed foods (MPF) and UPF were extracted using the NOVA Food Classification System to calculate a UPF/MPF ratio.
Results
Mean age and BMI z-score were 10.5 years (SD 0.88) and 0.97 (SD 1.07), respectively. Linear regressions controlling for sex and pubertal stage found that CEBQ-food fussiness was associated with a higher UPF/MPF intake ratio (p = 0.02) at baseline and an increase in UPF/MPF intake ratio (p = 0.04) over 1 year. CEBQ-enjoyment of food was associated with a decrease in UPF/MPF intake ratio (p = 0.03) over 1 year.
Conclusions
Child eating behaviors predict changes in UPF intake over 1 year. These results advocate for longitudinal research to examine dynamic relationships between eating behaviors, UPF intake, and body weight.
{"title":"The Impact of Eating Behaviors on Ultraprocessed Food Consumption Over 12 Months in Children","authors":"Dabin Yeum, Shuxian Hua, Gita Thapaliya, Sarah Ann Duck, Susan J. Melhorn, Christian L. Roth, Ellen A. Schur, Susan Carnell, Leticia E. Sewaybricker","doi":"10.1002/oby.24361","DOIUrl":"10.1002/oby.24361","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Ultraprocessed foods (UPF) are often energy-dense and nutrient-poor, and excess consumption can increase the risk of child obesity. Eating behaviors in childhood may influence future dietary patterns. This study examined the relationships of baseline eating behaviors with baseline UPF intake and 12-month changes in UPF intake in children.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Children aged 9–11 years were recruited (<i>N</i> = 101; 53% female). Parents reported on children's eating behaviors via the Child Eating Behavior Questionnaire (CEBQ). Children's dietary intake was captured with three automated dietary recalls. Daily average kilocalories of unprocessed/minimally processed foods (MPF) and UPF were extracted using the NOVA Food Classification System to calculate a UPF/MPF ratio.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Mean age and BMI <i>z</i>-score were 10.5 years (SD 0.88) and 0.97 (SD 1.07), respectively. Linear regressions controlling for sex and pubertal stage found that CEBQ-food fussiness was associated with a higher UPF/MPF intake ratio (<i>p</i> = 0.02) at baseline and an increase in UPF/MPF intake ratio (<i>p</i> = 0.04) over 1 year. CEBQ-enjoyment of food was associated with a decrease in UPF/MPF intake ratio (<i>p</i> = 0.03) over 1 year.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Child eating behaviors predict changes in UPF intake over 1 year. These results advocate for longitudinal research to examine dynamic relationships between eating behaviors, UPF intake, and body weight.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 9","pages":"1704-1712"},"PeriodicalIF":4.7,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736375","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}
Hyo Young Choi, Luhang Han, Alison G. Paquette, James MacDonald, Theo Bammler, Christine Loftus, Daniel A. Enquobahrie, Kaja Z. LeWinn, Nicole R. Bush, Catherine J. Karr, Sheela Sathyanarayana, Qi Zhao
Objective
This study aimed to investigate the association of placental gene expression with early childhood growth trajectories and obesity risk.
Methods
We analyzed 794 children from the Conditions Affecting Neurocognitive Development and Learning in Early Childhood study. Placental samples collected at delivery underwent RNA sequencing to obtain transcriptome data. BMI trajectories from birth to 4 years (rising-high-, moderate-, and low-BMI) and overweight/obesity at 4 years were the childhood outcomes of interest. Differentially expressed genes (DEGs) associated with the outcomes were identified using DESeq2. Pathway enrichment analysis was performed on DEGs. Their causal relationships with outcomes were explored using the Mendelian randomization (MR) approach.
Results
We identified 22 and 23 DEGs associated with BMI trajectories and overweight/obesity, respectively, with false discovery rates (FDR) < 0.05. Pathway analysis of these DEGs identified 26 biological pathways, primarily related to the immune system. MR analysis suggested that one (SSX2B) and eight DEGs (e.g., HSPA1A, DNAJB1) might be causally associated with the BMI trajectories and overweight/obesity (FDR < 0.05), respectively.
Conclusions
This study identified placental gene expression associated with early childhood growth outcomes. These findings suggest the potential important role of placental immune system genes in the development of childhood obesity.
{"title":"Placental Gene Expression Associated With Early Childhood Growth Trajectories and Obesity Risk","authors":"Hyo Young Choi, Luhang Han, Alison G. Paquette, James MacDonald, Theo Bammler, Christine Loftus, Daniel A. Enquobahrie, Kaja Z. LeWinn, Nicole R. Bush, Catherine J. Karr, Sheela Sathyanarayana, Qi Zhao","doi":"10.1002/oby.70000","DOIUrl":"10.1002/oby.70000","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study aimed to investigate the association of placental gene expression with early childhood growth trajectories and obesity risk.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We analyzed 794 children from the Conditions Affecting Neurocognitive Development and Learning in Early Childhood study. Placental samples collected at delivery underwent RNA sequencing to obtain transcriptome data. BMI trajectories from birth to 4 years (rising-high-, moderate-, and low-BMI) and overweight/obesity at 4 years were the childhood outcomes of interest. Differentially expressed genes (DEGs) associated with the outcomes were identified using DESeq2. Pathway enrichment analysis was performed on DEGs. Their causal relationships with outcomes were explored using the Mendelian randomization (MR) approach.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We identified 22 and 23 DEGs associated with BMI trajectories and overweight/obesity, respectively, with false discovery rates (FDR) < 0.05. Pathway analysis of these DEGs identified 26 biological pathways, primarily related to the immune system. MR analysis suggested that one (<i>SSX2B</i>) and eight DEGs (e.g., <i>HSPA1A</i>, <i>DNAJB1</i>) might be causally associated with the BMI trajectories and overweight/obesity (FDR < 0.05), respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study identified placental gene expression associated with early childhood growth outcomes. These findings suggest the potential important role of placental immune system genes in the development of childhood obesity.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 9","pages":"1713-1724"},"PeriodicalIF":4.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710415","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}
Md Akheruzzaman, Marleigh Hefner, Daniel Baller, Shane Clark, Zahra Feizy, Diana M. Thomas, Nikhil V. Dhurandhar
Objective
The objective of this study was to conduct a systematic review and meta-analysis of intervention trials that have determined the effect of unprocessed red meat (URM) intake on obesity-related outcomes.
Methods
The populations, interventions, controls, and outcomes (PICO) framework was used to create questions to search seven databases from July 29, 2020, to August 21, 2020. Two reviewers independently screened 5630 references. English-language intervention trials in adults testing the effect of URM on obesity-related outcomes were included. Twenty-four studies met selection criteria. A random-effects model was developed to calculate pooled effect sizes. The DerSimonian-Laird estimator was used to estimate the variance of the true effect sizes. An interactive dashboard was published to provide transparent analysis and data presentation.
Results
We found no significant effect of URM for BMI, body weight, or percent body fat based on unfiltered pooled effect sizes. Filtered pooled effect size analysis showed a slight adverse effect of URM for total cholesterol and low-density lipoprotein cholesterol.
Conclusions
Studies did not show an effect of URM on weight gain, obesity, or related metabolic conditions. This may help clinicians when considering the use of URM for patients. Longer studies may be needed for observing obesity development in case the effect of URM on weight gain is small and needs a much longer time to express.
目的：本研究的目的是对干预试验进行系统回顾和荟萃分析，这些试验确定了未加工红肉（URM）摄入对肥胖相关结果的影响。方法：采用PICO （population, interventions, control, and outcomes，人群、干预、对照和结果）框架创建问题，对2020年7月29日至2020年8月21日的7个数据库进行检索。两位审稿人独立筛选了5630篇参考文献。包括在成人中测试URM对肥胖相关结果影响的英语干预试验。24项研究符合选择标准。建立了一个随机效应模型来计算合并效应大小。使用dersimonan - laird估计器估计真实效应大小的方差。发布了一个交互式仪表板，以提供透明的分析和数据表示。结果：根据未过滤的合并效应大小，我们发现URM对BMI、体重或体脂百分比没有显著影响。过滤合并效应大小分析显示，URM对总胆固醇和低密度脂蛋白胆固醇有轻微的不利影响。结论：研究没有显示URM对体重增加、肥胖或相关代谢状况的影响。这可能有助于临床医生考虑对患者使用URM。如果URM对体重增加的影响较小，需要更长的时间来表达，则可能需要更长的研究时间来观察肥胖的发展。
{"title":"Effect of unprocessed red meat on obesity and related factors: A systematic review and meta-analysis","authors":"Md Akheruzzaman, Marleigh Hefner, Daniel Baller, Shane Clark, Zahra Feizy, Diana M. Thomas, Nikhil V. Dhurandhar","doi":"10.1002/oby.24322","DOIUrl":"10.1002/oby.24322","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The objective of this study was to conduct a systematic review and meta-analysis of intervention trials that have determined the effect of unprocessed red meat (URM) intake on obesity-related outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The populations, interventions, controls, and outcomes (PICO) framework was used to create questions to search seven databases from July 29, 2020, to August 21, 2020. Two reviewers independently screened 5630 references. English-language intervention trials in adults testing the effect of URM on obesity-related outcomes were included. Twenty-four studies met selection criteria. A random-effects model was developed to calculate pooled effect sizes. The DerSimonian-Laird estimator was used to estimate the variance of the true effect sizes. An interactive dashboard was published to provide transparent analysis and data presentation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We found no significant effect of URM for BMI, body weight, or percent body fat based on unfiltered pooled effect sizes. Filtered pooled effect size analysis showed a slight adverse effect of URM for total cholesterol and low-density lipoprotein cholesterol.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Studies did not show an effect of URM on weight gain, obesity, or related metabolic conditions. This may help clinicians when considering the use of URM for patients. Longer studies may be needed for observing obesity development in case the effect of URM on weight gain is small and needs a much longer time to express.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 9","pages":"1627-1636"},"PeriodicalIF":4.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/oby.24322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710414","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}
<p>Obesity is a multifactorial and highly heritable condition, influenced by the interplay between genetic predisposition and modifiable lifestyle behaviors. While the contribution of diet composition and physical activity to energy balance is well established, growing evidence highlights the role of circadian rhythms, particularly meal timing, in regulating metabolic health [<span>(1)</span>]. Disruptions in the synchrony between endogenous biological rhythms and external behavioral cues, such as the timing of food intake, have been associated with increased risk of obesity, insulin resistance, and related cardiometabolic disorders. However, the mechanisms and the extent to which timing of meal intake interacts with genetic susceptibility to influence obesity-related outcomes remain not fully clear.</p><p>In this issue of <i>Obesity</i>, a study by Rocío De la Peña-Armada et al. [<span>(2)</span>], conducted in the Obesity, Nutrigenetics, Timing, and Mediterranean (ONTIME) cohort, addresses this gap by examining the independent and interactive effects of meal timing and polygenic risk for body mass index (BMI) on weight-related outcomes. The authors included 1195 adults with overweight or obesity who were participating in a standardized, multimodal weight loss intervention in Spain. The intervention comprised dietary counseling, physical activity, and behavioral therapy but notably did not advise on meal timing, thereby allowing for natural variation in chrononutritional behavior.</p><p>Meal timing was assessed via self-reported usual times for breakfast and dinner, from which the midpoint of food intake was calculated and used as a marker of chrononutritional behavior [<span>(2)</span>]. Participants were classified as early or late eaters based on the tertiles of this midpoint. Genetic predisposition to obesity was quantified using a genome-wide polygenic score for BMI (PGS-BMI), generated through the polygenic scores of continuous shrinkage (PGS-CS) method, which modeled the effects of ~900,000 single-nucleotide polymorphisms (SNPs) using a Bayesian framework [<span>(3)</span>]. This approach offers improved prediction of complex traits over traditional polygenic risk scoring methods by accounting for the continuous shrinkage of SNP effects and linkage disequilibrium.</p><p>The study reports robust and clinically relevant findings [<span>(2)</span>]. Later meal timing was independently associated with higher baseline BMI, slower weight loss during intervention, and poorer long-term weight maintenance. Specifically, each 1-h delay in meal intake midpoint was associated with nearly a 1-kg/m<sup>2</sup> increase in BMI, a slower weight loss rate of 0.05 kg/week, and a 3% increase in weight regained after an average of 12 years. These associations persisted even after adjusting for potential confounders, such as total energy intake, macronutrient distribution, sleep duration, physical activity, and educational level. Notably, the authors observed
{"title":"Timing Matters: Early Eating Mitigates Genetic Susceptibility for Obesity","authors":"Divya Joshi, Marie Pigeyre","doi":"10.1002/oby.24350","DOIUrl":"10.1002/oby.24350","url":null,"abstract":"<p>Obesity is a multifactorial and highly heritable condition, influenced by the interplay between genetic predisposition and modifiable lifestyle behaviors. While the contribution of diet composition and physical activity to energy balance is well established, growing evidence highlights the role of circadian rhythms, particularly meal timing, in regulating metabolic health [<span>(1)</span>]. Disruptions in the synchrony between endogenous biological rhythms and external behavioral cues, such as the timing of food intake, have been associated with increased risk of obesity, insulin resistance, and related cardiometabolic disorders. However, the mechanisms and the extent to which timing of meal intake interacts with genetic susceptibility to influence obesity-related outcomes remain not fully clear.</p><p>In this issue of <i>Obesity</i>, a study by Rocío De la Peña-Armada et al. [<span>(2)</span>], conducted in the Obesity, Nutrigenetics, Timing, and Mediterranean (ONTIME) cohort, addresses this gap by examining the independent and interactive effects of meal timing and polygenic risk for body mass index (BMI) on weight-related outcomes. The authors included 1195 adults with overweight or obesity who were participating in a standardized, multimodal weight loss intervention in Spain. The intervention comprised dietary counseling, physical activity, and behavioral therapy but notably did not advise on meal timing, thereby allowing for natural variation in chrononutritional behavior.</p><p>Meal timing was assessed via self-reported usual times for breakfast and dinner, from which the midpoint of food intake was calculated and used as a marker of chrononutritional behavior [<span>(2)</span>]. Participants were classified as early or late eaters based on the tertiles of this midpoint. Genetic predisposition to obesity was quantified using a genome-wide polygenic score for BMI (PGS-BMI), generated through the polygenic scores of continuous shrinkage (PGS-CS) method, which modeled the effects of ~900,000 single-nucleotide polymorphisms (SNPs) using a Bayesian framework [<span>(3)</span>]. This approach offers improved prediction of complex traits over traditional polygenic risk scoring methods by accounting for the continuous shrinkage of SNP effects and linkage disequilibrium.</p><p>The study reports robust and clinically relevant findings [<span>(2)</span>]. Later meal timing was independently associated with higher baseline BMI, slower weight loss during intervention, and poorer long-term weight maintenance. Specifically, each 1-h delay in meal intake midpoint was associated with nearly a 1-kg/m<sup>2</sup> increase in BMI, a slower weight loss rate of 0.05 kg/week, and a 3% increase in weight regained after an average of 12 years. These associations persisted even after adjusting for potential confounders, such as total energy intake, macronutrient distribution, sleep duration, physical activity, and educational level. Notably, the authors observed","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 8","pages":"1414-1415"},"PeriodicalIF":4.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/oby.24350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710416","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}
Maryam Kebbe, Kaja Falkenhain, Robbie Beyl, Abby D. Altazan, Emily W. Flanagan, Chelsea L. Kracht, Hannah E. Cabre, Emily K. Woolf, Daniel S. Hsia, John W. Apolzan, Leanne M. Redman
Objective
To examine the effects of a pragmatic multicomponent eHealth intervention in pregnancy on body composition changes and subsequent associations with perinatal outcomes.
Methods
Pregnant individuals (n = 351) enrolled in Louisiana's Women, Infants, and Children program were randomly assigned to a multicomponent eHealth Intervention or Usual Care. Fat percentage, fat mass, and fat-free mass were assessed using bioelectrical impedance at trimester-specific study visits. Mixed models evaluated within- and between-group differences in body composition from early to late pregnancy: overall, by BMI, and by gestational weight gain (GWG) guideline attainment. Effects of body composition changes on perinatal outcomes was evaluated.
Results
Compared to Usual Care (n = 172), the Intervention Group (n = 179) had attenuated gains in fat mass, fat mass index, and fat percentage from early to late pregnancy overall, in individuals who had normal weight at enrollment, and in those who exceeded GWG guidelines (p < 0.05). No significant between-group differences in fat-free mass were observed. Fat mass change interacted with intervention effects on neonatal health outcomes (p = 0.01).
Conclusions
Lifestyle interventions during pregnancy may attenuate gestational fat mass gain, particularly among women with normal weight and those who exceed GWG guidelines, with potential implications for neonatal health outcomes.
{"title":"An eHealth Intervention in Pregnancy on Maternal Body Composition and Subsequent Perinatal Outcomes: A Randomized Trial","authors":"Maryam Kebbe, Kaja Falkenhain, Robbie Beyl, Abby D. Altazan, Emily W. Flanagan, Chelsea L. Kracht, Hannah E. Cabre, Emily K. Woolf, Daniel S. Hsia, John W. Apolzan, Leanne M. Redman","doi":"10.1002/oby.24357","DOIUrl":"10.1002/oby.24357","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine the effects of a pragmatic multicomponent eHealth intervention in pregnancy on body composition changes and subsequent associations with perinatal outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Pregnant individuals (<i>n</i> = 351) enrolled in Louisiana's Women, Infants, and Children program were randomly assigned to a multicomponent eHealth Intervention or Usual Care. Fat percentage, fat mass, and fat-free mass were assessed using bioelectrical impedance at trimester-specific study visits. Mixed models evaluated within- and between-group differences in body composition from early to late pregnancy: overall, by BMI, and by gestational weight gain (GWG) guideline attainment. Effects of body composition changes on perinatal outcomes was evaluated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Compared to Usual Care (<i>n</i> = 172), the Intervention Group (<i>n</i> = 179) had attenuated gains in fat mass, fat mass index, and fat percentage from early to late pregnancy overall, in individuals who had normal weight at enrollment, and in those who exceeded GWG guidelines (<i>p</i> < 0.05). No significant between-group differences in fat-free mass were observed. Fat mass change interacted with intervention effects on neonatal health outcomes (<i>p</i> = 0.01).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Lifestyle interventions during pregnancy may attenuate gestational fat mass gain, particularly among women with normal weight and those who exceed GWG guidelines, with potential implications for neonatal health outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Trial Registration</h3>\u0000 \u0000 <p>\u0000 ClinicalTrials.gov identifier: NCT04028843</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 9","pages":"1680-1693"},"PeriodicalIF":4.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710413","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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