Obesity最新文献

{"title":"Mapping the landscape of childhood obesity: genomic insights and socioeconomic status in Indian school-going children","authors":"Janaki M. Nair,&nbsp;Ganesh Chauhan,&nbsp;Gauri Prasad,&nbsp;Khushdeep Bandesh,&nbsp;Anil K. Giri,&nbsp;Shraddha Chakraborty,&nbsp;Raman K. Marwaha,&nbsp;Sandeep Mathur,&nbsp;Devapriya Choudhury,&nbsp;Nikhil Tandon,&nbsp;Analabha Basu,&nbsp;Dwaipayan Bharadwaj","doi":"10.1002/oby.24248","DOIUrl":"10.1002/oby.24248","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Childhood obesity (OB) is influenced by complex gene–environmental interaction. While genetics of adult OB have been extensively studied, polygenic childhood OB in non-European populations is still underexplored. Furthermore, in a developing nation such as India, how the environmental component strongly modulated by the socioeconomic status (SES) shapes the genetic susceptibility is crucial to understand.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A two-staged genome-wide association study (GWAS; <i>N</i> = 5673) and an independent exome-wide association study (ExWAS; <i>N</i> = 4963) were performed using a generalized linear model assuming additive effect to identify the common and rare genetic variants respectively associated with childhood OB. Rare-variant burden testing was also performed. We used the gene expression profiles and regulatory data from public databases to explain the novel associations. The implications of SES as a potential modifier of genetic susceptibility were evaluated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>GWAS identified novel associations in <i>TCF7L2</i>, <i>IMMP2L</i>, <i>IPMK</i>, <i>CDC5L</i>, <i>SNTG1</i>, and <i>MX1</i>, whereas ExWAS uncovered <i>CNTN4</i>, <i>COQ4</i>, <i>TNFRSF10D</i>, <i>FLG-AS1</i>, and <i>BMP3</i>. Both GWAS and ExWAS validated known associations in <i>FTO</i> and <i>MC4R</i>. Furthermore, rare-variant testing highlighted the role of 101 genes. We also observed that SES can modulate the inherent susceptibility to OB.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our study identified genetic variants associated with childhood OB and highlighted the gene–environmental interaction in childhood OB.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 4","pages":"754-765"},"PeriodicalIF":4.2,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143506716","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":"Predictability of genetic risk score for insulin resistance is influenced by both BMI and race","authors":"Marian L. Yurchishin,&nbsp;Lauren A. Fowler,&nbsp;Amy M. Goss,&nbsp;William T. Garvey,&nbsp;Barbara A. Gower","doi":"10.1002/oby.24238","DOIUrl":"10.1002/oby.24238","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The study objective was to determine whether associations between a genetic risk score (GRS) for insulin resistance (IR) and measures of insulin sensitivity differ by race and/or BMI status in African American (AA) and European American (EA) adults without diabetes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Fifty-three AA and 54 EA participants were classified into “high” or “low” BMI groups using the sample median (25.9 kg/m²) as the cut point. The GRS was derived from 52 previously identified genetic variants. Skeletal muscle insulin sensitivity was measured with the hyperinsulinemic-euglycemic clamp. The homeostasis model assessment of insulin resistance (HOMA-IR) and the Matsuda index of insulin sensitivity were calculated from oral glucose tolerance test values to determine hepatic and whole-body insulin sensitivity, respectively. Linear regression models, stratified by race, assessed interactions between BMI status and GRS on measures of insulin sensitivity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In EA participants, associations of GRS with HOMA-IR and the Matsuda index differed by BMI status, where the GRS was associated with IR in the high-BMI group only. In AA participants, associations from the clamp differed by BMI status, but an association was observed only in the low-BMI group.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>These results highlight the heterogeneity of IR and support the hypothesis that the relationship between genetic predisposition for IR and obesity is race- and tissue-specific.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 4","pages":"788-795"},"PeriodicalIF":4.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/oby.24238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495124","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":"Zoledronic acid increases spine bone mass and prevents hip bone loss after bariatric surgery: a randomized placebo-controlled study","authors":"Søren Gam,&nbsp;Simon Lysdahlgaard,&nbsp;Bibi Gram,&nbsp;Martin Weber Kusk,&nbsp;Anne Pernille Hermann,&nbsp;Claus Bogh Juhl,&nbsp;Stinus Gadegaard Hansen","doi":"10.1002/oby.24214","DOIUrl":"10.1002/oby.24214","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The aim of this study was to investigate the effects of zoledronic acid for the prevention of bone loss after bariatric surgery.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In this randomized, double-blinded study, 59 patients undergoing Roux-en-Y gastric bypass or sleeve gastrectomy (mean [SD], age: 48.9 [6.3] years, BMI: 42.3 [5.3], 73% female) were randomly assigned (1:1) to receive either zoledronic acid (5 mg; intervention [INT]) or placebo (control [CON]) preoperatively. The primary endpoint was the change in spine volumetric bone mineral density (vBMD) at 12 months after surgery. Secondary outcomes included changes in hip and femoral neck vBMD and areal BMD.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The estimated mean treatment effects of zoledronic acid on the spine and total hip were 6.8 mg/cm³ (95% CI 1.9–11.7; <i>p</i> = 0.003) and 5.0 mg/cm³ (95% CI: 1.4–8.5; <i>p</i> = 0.006), respectively. Bone mass in the spine increased by 2.6% in INT, whereas no changes were observed in CON. Additionally, bone loss in the total hip was prevented in INT compared with CON (vBMD: −0.6% vs. −3.6%; <i>p</i> = 0.006).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Zoledronic acid increases bone mass in the spine and prevents bone loss in the hip region after bariatric surgery compared with placebo.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 4","pages":"659-670"},"PeriodicalIF":4.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/oby.24214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470365","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":"Correction to “A meta-analysis comparing the effectiveness of alternate day fasting, the 5:2 diet, and time-restricted eating for weight loss”","authors":"","doi":"10.1002/oby.24266","DOIUrl":"10.1002/oby.24266","url":null,"abstract":"<p>\u0000 <span>Elortegui Pascual, P</span>, <span>Rolands, MR</span>, <span>Eldridge, AL</span>, <span>Kassis, A</span>, <span>Mainardi, F</span>, <span>Lê, KA</span>, <span>Karagounis, LG</span>, <span>Gut, P</span>, <span>Varady, KA</span>. <span>A meta-analysis comparing the effectiveness of alternate day fasting, the 5:2 diet, and time-restricted eating for weight loss</span>. <i>Obesity (Silver Spring)</i>. <span>2023</span>; <span>31</span>(<span>S1</span>): <span>9</span>–<span>21</span>. doi:10.1002/oby.23568\u0000 </p><p>In Figure 1, there is a miscount error in the “Records excluded based on title” box. “Records excluded based on title (<i>n</i> = 1748)” was incorrect. The number of articles should be <i>n</i> = 1747. The error occurred during the reporting of the number and does not affect the selection process or the validity of the final papers selected and analyzed.</p><p>We apologize for this error.</p>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 5","pages":"1011"},"PeriodicalIF":4.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/oby.24266","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470359","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":"Does deciding not to participate in a lifestyle intervention for children with obesity affect the long-term weight change?","authors":"Rasmus M. Jørgensen,&nbsp;Jane N. Østergaard,&nbsp;Mette Fogh,&nbsp;Rachael W. Taylor,&nbsp;Henrik Støvring,&nbsp;Jens M. Bruun","doi":"10.1002/oby.24231","DOIUrl":"10.1002/oby.24231","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The objective of this study was to investigate whether long-term weight change in children with obesity is affected after deciding not to participate in a lifestyle intervention.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This observational study included 713 children (aged 5–8 years) with obesity living in Aarhus, Denmark, of whom 157 decided not to participate in a community-based lifestyle intervention between 2010 and 2020, and 556 were never invited to participate (i.e., no-intervention group). Height and weight measurements were combined with socioeconomic information from national registers. A mixed-effects model with splines was used to model changes in BMI <i>z</i> score and stratification to investigate effect modifications.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We observed a median follow-up of 3.6 years (quartile [Q]₁;Q₃: 0.4;5.1) for the decided-not-to-participate group. No difference was observed in annual change in BMI <i>z</i> score between the decided-not-to-participate and no-intervention groups (0.00 per year, 95% CI: −0.03 to 0.03; <i>p</i> = 0.90). No effect modifications were observed between the two groups concerning highest completed household education (<i>p</i> = 0.59), household income (<i>p</i> = 0.72), or immigration status (<i>p</i> = 0.17).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Children deciding not to participate in an intervention did not increase their weight compared with children who were never invited, indicating that treatment could be briefly postponed until families are able to participate. Additionally, socioeconomic status or immigration background did not modify the weight change.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 7","pages":"1334-1343"},"PeriodicalIF":4.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/oby.24231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470361","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":"Global cancer burden attributable to excess body weight, 1990 to 2021, decomposed by population size, aging, and epidemiological change","authors":"Xiaoru Feng,&nbsp;Ruoqian Li,&nbsp;Hang Yi,&nbsp;Shuyi Chen,&nbsp;Meng Liu,&nbsp;You Wu","doi":"10.1002/oby.24219","DOIUrl":"10.1002/oby.24219","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The objective of this study was to estimate cancer burden attributable to excess body weight (EBW) and identify its main source.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We obtained relative risks from meta-analyses, cancer and population data from the Global Burden of Disease Study (GBD) 2021, and BMI prevalence data from the NCD Risk Factor Collaboration (NCD-RisC). We calculated the incidence of 11 cancers attributable to high BMI from 1990 to 2021, analyzed trends using joinpoint regression, and assessed cohort effects with the age-period-cohort model. Decomposition analysis was conducted by cancer-specific risk factors and by population size, aging, and epidemiological changes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The incidence of 11 EBW-related cancers has increased from 1990 to 2021. Later-born cohorts and older age groups had higher cancer incidence rates. High BMI was the top contributor to changes in cancer burden (15.96% of all disability-adjusted life years [DALYs]), particularly in high Sociodemographic Index (SDI) regions. Colorectal, esophageal, and liver cancer had the highest burden due to high BMI (1,349,622; 1,284,385; and 944,616 DALYs, respectively). Epidemiological changes in BMI contributed to the rising DALY burden, ranging from 7.88% for postmenopausal breast cancer to 49.20% for liver cancer.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The rising prevalence of EBW contributed to the global cancer burden, showing a significant birth cohort effect. High BMI was the top contributing factor to obesity-related cancers, surpassing other epidemiological risk factors.</p>\u0000 \u0000 <div>\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 3","pages":"567-577"},"PeriodicalIF":4.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470363","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":"Time-restricted eating, caloric reduction, and unrestricted eating effects on weight and metabolism: a randomized trial","authors":"Niki Oldenburg,&nbsp;Douglas G. Mashek,&nbsp;Lisa Harnack,&nbsp;Qi Wang,&nbsp;Emily N. C. Manoogian,&nbsp;Nicholas Evanoff,&nbsp;Donald R. Dengel,&nbsp;Abdisa Taddese,&nbsp;Brad P. Yentzer,&nbsp;Lesia Lysne,&nbsp;Alison Wong,&nbsp;Michelle Hanson,&nbsp;Julie D. Anderson,&nbsp;Alison Alvear,&nbsp;Nicole LaPage,&nbsp;Justin Ryder,&nbsp;Krista Varady,&nbsp;Zan Gao,&nbsp;Suryeon Ryu,&nbsp;Patrick J. Bolan,&nbsp;Bryan Bergman,&nbsp;Erika Helgeson,&nbsp;Satchidananda Panda,&nbsp;Lisa S. Chow","doi":"10.1002/oby.24252","DOIUrl":"10.1002/oby.24252","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Metabolic improvements may precede weight loss. We compared the effects of self-selected 8-h time-restricted eating (TRE), 15% caloric restriction (CR), and unrestricted eating (UE) on weight, body composition, caloric intake, glycemic measures, and metabolic flexibility.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In this 12-week randomized-controlled trial, we measured weight (primary outcome), body composition (dual-energy x-ray absorptiometry/magnetic resonance imaging), caloric intake (24-h recall), metabolic flexibility (indirect calorimetry during hyperinsulinemic-euglycemic clamp), and glycemic measures (hemoglobin A1c, hyperinsulinemic-euglycemic clamp, continuous glucose monitoring).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Of the 88 enrolled participants, 81 (92%) completed the trial (mean [SD], age, 43.2 [10.5] years, BMI, 36.2 [5.1] kg/m²; 54.5% female, 84.1% White). Final eating windows were 9.8 h (95% CI: 9.0 to 10.6) for TRE, 12.9 h (95% CI: 11.9 to 13.9) for CR, and 11.8 h (95% CI: 11.0 to 12.7) for UE. Compared with UE (<i>n</i> = 29), weight changes were −1.4 kg (95% CI: −4.5 to 1.7; <i>p</i> = 0.53) with TRE (<i>n</i> = 30) and −2.5 kg (95% CI: −5.8 to 0.8; <i>p</i> = 0.18) with CR (<i>n</i> = 29). TRE showed lower metabolic flexibility than CR (−0.041 [95% CI: −0.080 to −0.002]). Weight, body composition, caloric intake, and glycemic measures were similar among groups. Eating window reduction correlated with decreased caloric intake and visceral fat.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In a 12-week intervention, TRE did not lead to significant improvements in weight, average body composition, or glycemic or metabolic measures compared with CR or UE.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 4","pages":"671-684"},"PeriodicalIF":4.2,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/oby.24252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143461080","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":"Associations between obesity class and ambulatory blood pressure curves in African American women","authors":"Raphiel J. Murden,&nbsp;Nicole D. Fields,&nbsp;Zachary T. Martin,&nbsp;Benjamin B. Risk,&nbsp;Alvaro Alonso,&nbsp;Amita Manatunga,&nbsp;Christy L. Erving,&nbsp;Reneé Moore,&nbsp;Shivika Udaipuria,&nbsp;Arshed Quyyumi,&nbsp;Viola Vaccarino,&nbsp;Tené T. Lewis","doi":"10.1002/oby.24230","DOIUrl":"10.1002/oby.24230","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Studies of body size and blood pressure (BP) in African American women typically focus on obesity overall or collapse obesity classes II and III into a single subgroup, ignoring potential heterogeneity in associations across categories. Moreover, ambulatory BP outcomes are primarily analyzed as mean daytime and/or nighttime BP, without examination of circadian changes during the day-to-night transition or the full 24-h cycle.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Functional data analysis methods were used to examine whether obesity categories modified ambulatory monitoring-assessed BP circadian rhythm in a cohort of 407 African American women.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Age-adjusted systolic BP (SBP) was 4 mm Hg (95% CI: 0.4–8.4) higher among women with class I or II obesity than those with normal weight or overweight from 12:30 p.m. through 8:00 a.m. Age-adjusted differences in SBP among women with class III obesity versus those with normal weight or overweight were 6 mm Hg (95% CI: 0.7–10.8) during daytime hours and increased to 11 mm Hg (95% CI: 5.8–16.0) overnight. Compared with all other BMI categories, SBP of women with class III obesity declined more slowly from day to night.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Circadian BP among African American women was distinct among those with class III obesity compared with those with other body weight categories, suggesting that intervention efforts in African American women should target this group.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 3","pages":"589-598"},"PeriodicalIF":4.2,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/oby.24230","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143461079","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":"Adherence to self-monitoring and behavioral goals is associated with improved weight loss in an mHealth randomized-controlled trial","authors":"Lora E. Burke,&nbsp;Zhadyra Bizhanova,&nbsp;Molly B. Conroy,&nbsp;Jessica Cheng,&nbsp;Britney Beatrice,&nbsp;Jacob K. Kariuki,&nbsp;Bambang Parmanto,&nbsp;Susan M. Sereika","doi":"10.1002/oby.24234","DOIUrl":"10.1002/oby.24234","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The SMARTER mobile health (mHealth) weight-loss trial compared adherence to self-monitoring (SM) of diet, physical activity (PA), and weight and adherence to study-prescribed diet and PA goals between SM + feedback (SM + FB) and SM-only arms over 12 months.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Participants used digital tools to monitor their dietary intake, PA, and weight. We applied generalized linear mixed modeling to compare patterns of monthly adherence to SM and behavioral goals between groups over time and examine the association of adherence to SM and behavioral goals with ≥5% weight loss.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The sample (<i>N</i> = 502) was 80% female and 82% White, with a mean (SD) BMI of 33.7 (4.0) kg/m². Adherence to SM and fat, calorie, and PA goals declined nonlinearly over time, with the SM + FB group displaying less of a decline compared with the SM-only group. Higher adherence to diet, PA, and weight SM and to calorie and PA goals was associated with greater odds of achieving ≥5% weight loss. A higher monthly probability of achieving ≥5% weight loss was associated with greater adherence to diet, PA, and weight SM and to calorie and PA goals.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>These results suggest that future research should examine the mechanisms underlying tailored FB to improve the effect of FB intervention strategies that can lead to improved weight loss.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 3","pages":"478-489"},"PeriodicalIF":4.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/oby.24234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443136","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":"Trends in educational inequalities in obesity-attributable mortality in England and Wales, Finland, and Italy","authors":"Fanny Janssen,&nbsp;Rolando Gonzales Martinez,&nbsp;Nicolás Zengarini,&nbsp;Pekka Martikainen,&nbsp;Anton Kunst","doi":"10.1002/oby.24225","DOIUrl":"10.1002/oby.24225","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>We assessed trends in educational inequalities in obesity-attributable mortality (OAM) and their contribution to educational inequalities in all-cause mortality for people aged 30 years and older, in England and Wales (1991–2017), Finland (1978–2017), and Italy (1990–2018).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In our population-level study, we estimated the shares of all-cause mortality due to OAM by educational level (i.e., low, middle, and high) by applying the population-attributable fraction formula to harmonized obesity prevalence data by educational level, along with sex- and age-specific relative risks of dying from obesity. We obtained OAM rates by multiplying the shares with individually linked all-cause mortality data by educational level. We measured absolute inequalities in OAM and all-cause mortality by the slope index of inequality.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>OAM largely increased for the different sex- and education-specific populations and increased most strongly for those with low educational level up to 2010 to 2015. Educational inequalities in OAM initially increased but stabilized or declined from at least 2008 onward. Obesity contributed, on average, 15% to absolute educational inequalities in all-cause mortality in 1991 through 2017.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The mortality impact of the obesity epidemic by educational level changed over time. Although the observed change from increasing to declining or stable educational inequalities is encouraging, reducing OAM in all socioeconomic groups remains a challenge.</p>\u0000 </section>\u0000 </div>","PeriodicalId":215,"journal":{"name":"Obesity","volume":"33 3","pages":"578-588"},"PeriodicalIF":4.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/oby.24225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443137","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}