Pub Date : 2026-03-23DOI: 10.1001/jamapediatrics.2026.0343
Resthie R Putri,Pernilla Danielsson,Emilia Hagman,Claude Marcus
ImportanceMetabolically healthy obesity (MHO) in children has been considered a low-risk phenotype, potentially not requiring treatment. However, their long-term cardiometabolic outcomes remain unclear.ObjectiveTo compare the occurrence of type 2 diabetes, hypertension, dyslipidemia, and mortality up to young adulthood in children with metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), and general population peers, and to investigate the association between obesity treatment response and disease risk.Design, Setting, and ParticipantsThis was a prospective cohort study including children undergoing obesity treatment recorded in the Swedish Childhood Obesity Treatment Register (BORIS) between 1997 and 2020 and their general population comparators, linked with national registers. Children in the cohort with obesity were aged 7 to 17 years at obesity treatment initiation and had complete cardiometabolic data. General population comparators were matched (ratio 1:5) based on sex, birth year, and residential area. Study data were analyzed from February to March 2025.ExposuresExposures included metabolically healthy obesity (MHO), defined as the absence of high blood pressure, impaired fasting glycemia, elevated transaminases, elevated triglycerides, and low high-density lipoprotein cholesterol; otherwise, children were categorized as having metabolically unhealthy obesity (MUO).Main Outcomes and MeasuresType 2 diabetes, hypertension, dyslipidemia, and mortality up to age 30 years.ResultsA total of 7275 children (median [first quartile {Q1}-third quartile {Q3}] age, 11.1 [9.1-13.5] years; 4004 male [55.0%]) were included, along with 35 636 general population comparators (median [Q1-Q3] age, 11.1 [9.1-13.5] years; 19 596 male [55.0%]). MHO at baseline was present in 3626 children (49.8%; median [Q1-Q3] age, 10.6 [8.8-12.8] years; 1981 male [54.6%]), and MUO was present in 3649 children (50.2%; median [Q1-Q3] age, 11.6 [9.4-14.0] years; 2023 male [55.4%]). By age 30 years, cumulative incidences were as follows: type 2 diabetes (MHO, 9.1%; MUO, 16.8%; general population, 0.5%), hypertension (MHO, 10.8%; MUO, 18.3%; general population, 3.7%), and dyslipidemia (MHO, 5.3%; MUO, 12.7%; general population, 0.9%). A reduction of at least 0.25 body mass index (BMI) z score was associated with reduced incidence rate ratio (IRR) of type 2 diabetes (IRR, 0.22; 95% CI, 0.14-0.35), hypertension (IRR, 0.56; 95% CI, 0.34-0.93), and dyslipidemia (IRR, 0.28; 95% CI, 0.14-0.57), with similar risk reduction for MHO and MUO.Conclusions and RelevanceResults of this cohort study reveal that a reduction in BMI z score of at least 0.25 was associated with similar risk reductions for both MHO and MUO. Children with MHO face a substantially increased cardiometabolic disease risk already as young adults compared with the general population. Hence, obesity treatment should be recommended for all children with obesity, regardless of initial metabolic status.
{"title":"Long-Term Cardiometabolic Outcomes in Children With Metabolically Healthy and Unhealthy Obesity.","authors":"Resthie R Putri,Pernilla Danielsson,Emilia Hagman,Claude Marcus","doi":"10.1001/jamapediatrics.2026.0343","DOIUrl":"https://doi.org/10.1001/jamapediatrics.2026.0343","url":null,"abstract":"ImportanceMetabolically healthy obesity (MHO) in children has been considered a low-risk phenotype, potentially not requiring treatment. However, their long-term cardiometabolic outcomes remain unclear.ObjectiveTo compare the occurrence of type 2 diabetes, hypertension, dyslipidemia, and mortality up to young adulthood in children with metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), and general population peers, and to investigate the association between obesity treatment response and disease risk.Design, Setting, and ParticipantsThis was a prospective cohort study including children undergoing obesity treatment recorded in the Swedish Childhood Obesity Treatment Register (BORIS) between 1997 and 2020 and their general population comparators, linked with national registers. Children in the cohort with obesity were aged 7 to 17 years at obesity treatment initiation and had complete cardiometabolic data. General population comparators were matched (ratio 1:5) based on sex, birth year, and residential area. Study data were analyzed from February to March 2025.ExposuresExposures included metabolically healthy obesity (MHO), defined as the absence of high blood pressure, impaired fasting glycemia, elevated transaminases, elevated triglycerides, and low high-density lipoprotein cholesterol; otherwise, children were categorized as having metabolically unhealthy obesity (MUO).Main Outcomes and MeasuresType 2 diabetes, hypertension, dyslipidemia, and mortality up to age 30 years.ResultsA total of 7275 children (median [first quartile {Q1}-third quartile {Q3}] age, 11.1 [9.1-13.5] years; 4004 male [55.0%]) were included, along with 35 636 general population comparators (median [Q1-Q3] age, 11.1 [9.1-13.5] years; 19 596 male [55.0%]). MHO at baseline was present in 3626 children (49.8%; median [Q1-Q3] age, 10.6 [8.8-12.8] years; 1981 male [54.6%]), and MUO was present in 3649 children (50.2%; median [Q1-Q3] age, 11.6 [9.4-14.0] years; 2023 male [55.4%]). By age 30 years, cumulative incidences were as follows: type 2 diabetes (MHO, 9.1%; MUO, 16.8%; general population, 0.5%), hypertension (MHO, 10.8%; MUO, 18.3%; general population, 3.7%), and dyslipidemia (MHO, 5.3%; MUO, 12.7%; general population, 0.9%). A reduction of at least 0.25 body mass index (BMI) z score was associated with reduced incidence rate ratio (IRR) of type 2 diabetes (IRR, 0.22; 95% CI, 0.14-0.35), hypertension (IRR, 0.56; 95% CI, 0.34-0.93), and dyslipidemia (IRR, 0.28; 95% CI, 0.14-0.57), with similar risk reduction for MHO and MUO.Conclusions and RelevanceResults of this cohort study reveal that a reduction in BMI z score of at least 0.25 was associated with similar risk reductions for both MHO and MUO. Children with MHO face a substantially increased cardiometabolic disease risk already as young adults compared with the general population. Hence, obesity treatment should be recommended for all children with obesity, regardless of initial metabolic status.","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":"92 1","pages":""},"PeriodicalIF":26.1,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-23DOI: 10.1001/jamapediatrics.2026.0259
Cassie Burley,Carly E Milliren,Jessica A Lin,Tracy K Richmond
{"title":"COVID-19-Related Changes in Volume of Adolescents and Young Adults With Eating Disorders Requiring Hospitalization.","authors":"Cassie Burley,Carly E Milliren,Jessica A Lin,Tracy K Richmond","doi":"10.1001/jamapediatrics.2026.0259","DOIUrl":"https://doi.org/10.1001/jamapediatrics.2026.0259","url":null,"abstract":"","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":"15 1","pages":""},"PeriodicalIF":26.1,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-23DOI: 10.1001/jamapediatrics.2026.0387
Roberto Lorusso,Eddy Fan
{"title":"Role of Biomarkers in Acute Brain Injury During ECMO Support.","authors":"Roberto Lorusso,Eddy Fan","doi":"10.1001/jamapediatrics.2026.0387","DOIUrl":"https://doi.org/10.1001/jamapediatrics.2026.0387","url":null,"abstract":"","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":"17 1","pages":""},"PeriodicalIF":26.1,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-23DOI: 10.1001/jamapediatrics.2026.0335
Rebecca E Cooper,Amanda E Baker,Allysa D Quick,Lan Yu,Raul Gonzalez,Duncan B Clark,Dana L McMakin,Adriane M Soehner,Maria Jalbrzikowski,Meredith L Wallace
ImportanceSleep behavior markedly shifts in adolescence, increasing vulnerability to mental health disorders. Although sleep health is understood to be multidimensional, adolescent-specific sleep health dimensions have not been empirically validated and their relevance to transdiagnostic mental health outcomes is unknown.ObjectiveTo identify sleep health dimensions using Fitbit devices in a large sample of young adolescents and assess concurrent and prospective associations between sleep health dimensions and transdiagnostic mental health outcomes.Design, Setting, and ParticipantsMulticenter longitudinal cohort study using data from 3393 participants in the Adolescent Brain Cognitive Development (ABCD) Study (Data Release 5.1, collected 2018-2020), including early adolescents (ages 11-13 years) within the US. Exploratory factor analysis (EFA) was used to identify sleep health dimensions and confirmatory factor analysis (CFA) to confirm the factor structure in an independent subsample. Linear mixed-effects models were used to test concurrent and prospective associations between sleep dimensions and mental health outcomes at 1-year follow-up. Statistical analysis was conducted from January to November 2025.ExposuresObjective sleep data collected for up to 21 (range, 7-21) days, using wearable Fitbit devices.Main Outcomes and MeasuresTransdiagnostic mental health outcomes assessed via the Child Behavior Checklist and Brief Problem Monitor (internalizing and externalizing symptoms), Prodromal Questionnaire-Brief Child Version (psychoticlike symptoms), and 10-item Mania Scale (mania symptoms).ResultsThe 3393 participants (49% female; median age, 12 years) were split into EFA and CFA subsamples. Six sleep factors were identified using EFA: irregularity, timing, social jetlag, duration, weekend oversleep, and continuity. CFA confirmed this factor structure. All variables loaded strongly (≥0.64) onto at least 1 factor (factor 1 loadings, 0.64-0.98; factor 2, 0.96-0.98; factor 3, 0.95-0.97; factor 4, -0.86 to 1.01; factor 5, 0.68-0.93; factor 6, 0.82-0.94). Greater sleep irregularity was associated with transdiagnostic mental health symptoms cross-sectionally, but not prospectively (β, 0.06 [95% CI, 0.02-0.10] to 0.12 [95% CI, 0.08-0.16]). Shorter duration was associated with total, internalizing, externalizing, and attention symptoms cross-sectionally (β, -0.06 [95% CI, -0.10 to -0.01] to -0.11 [95% CI, -0.15 to -0.06]) and total, attention, and psychotic symptoms 1 year later.Conclusions and RelevanceIn this study, wearable Fitbit data provide empirical support for multidimensional frameworks of sleep health in adolescence. Although effect sizes were small, sleep irregularity and duration emerged as key dimensions with relevance to mental health. These findings establish a foundation for future investigations, including examining within-person patterns of the 6 dimensions, extending to older adolescence, investigating associations with other health outc
{"title":"Sleep Health Dimensions From Wearables and Transdiagnostic Mental Health in Young Adolescents.","authors":"Rebecca E Cooper,Amanda E Baker,Allysa D Quick,Lan Yu,Raul Gonzalez,Duncan B Clark,Dana L McMakin,Adriane M Soehner,Maria Jalbrzikowski,Meredith L Wallace","doi":"10.1001/jamapediatrics.2026.0335","DOIUrl":"https://doi.org/10.1001/jamapediatrics.2026.0335","url":null,"abstract":"ImportanceSleep behavior markedly shifts in adolescence, increasing vulnerability to mental health disorders. Although sleep health is understood to be multidimensional, adolescent-specific sleep health dimensions have not been empirically validated and their relevance to transdiagnostic mental health outcomes is unknown.ObjectiveTo identify sleep health dimensions using Fitbit devices in a large sample of young adolescents and assess concurrent and prospective associations between sleep health dimensions and transdiagnostic mental health outcomes.Design, Setting, and ParticipantsMulticenter longitudinal cohort study using data from 3393 participants in the Adolescent Brain Cognitive Development (ABCD) Study (Data Release 5.1, collected 2018-2020), including early adolescents (ages 11-13 years) within the US. Exploratory factor analysis (EFA) was used to identify sleep health dimensions and confirmatory factor analysis (CFA) to confirm the factor structure in an independent subsample. Linear mixed-effects models were used to test concurrent and prospective associations between sleep dimensions and mental health outcomes at 1-year follow-up. Statistical analysis was conducted from January to November 2025.ExposuresObjective sleep data collected for up to 21 (range, 7-21) days, using wearable Fitbit devices.Main Outcomes and MeasuresTransdiagnostic mental health outcomes assessed via the Child Behavior Checklist and Brief Problem Monitor (internalizing and externalizing symptoms), Prodromal Questionnaire-Brief Child Version (psychoticlike symptoms), and 10-item Mania Scale (mania symptoms).ResultsThe 3393 participants (49% female; median age, 12 years) were split into EFA and CFA subsamples. Six sleep factors were identified using EFA: irregularity, timing, social jetlag, duration, weekend oversleep, and continuity. CFA confirmed this factor structure. All variables loaded strongly (≥0.64) onto at least 1 factor (factor 1 loadings, 0.64-0.98; factor 2, 0.96-0.98; factor 3, 0.95-0.97; factor 4, -0.86 to 1.01; factor 5, 0.68-0.93; factor 6, 0.82-0.94). Greater sleep irregularity was associated with transdiagnostic mental health symptoms cross-sectionally, but not prospectively (β, 0.06 [95% CI, 0.02-0.10] to 0.12 [95% CI, 0.08-0.16]). Shorter duration was associated with total, internalizing, externalizing, and attention symptoms cross-sectionally (β, -0.06 [95% CI, -0.10 to -0.01] to -0.11 [95% CI, -0.15 to -0.06]) and total, attention, and psychotic symptoms 1 year later.Conclusions and RelevanceIn this study, wearable Fitbit data provide empirical support for multidimensional frameworks of sleep health in adolescence. Although effect sizes were small, sleep irregularity and duration emerged as key dimensions with relevance to mental health. These findings establish a foundation for future investigations, including examining within-person patterns of the 6 dimensions, extending to older adolescence, investigating associations with other health outc","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":"37 1","pages":""},"PeriodicalIF":26.1,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-16DOI: 10.1001/jamapediatrics.2026.0289
Dimitri A Christakis
{"title":"JAMA Pediatrics-The Year in Review, 2025.","authors":"Dimitri A Christakis","doi":"10.1001/jamapediatrics.2026.0289","DOIUrl":"https://doi.org/10.1001/jamapediatrics.2026.0289","url":null,"abstract":"","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":" ","pages":""},"PeriodicalIF":18.0,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147467941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-16DOI: 10.1001/jamapediatrics.2026.0032
Cindy H. Liu, Tiffany Yip
This Viewpoint describes the engagement of generative artificial intelligence (AI) as a relational partner among adolescents and how to set research priorities in order to pay immediate attention to the developmental risks and benefits.
{"title":"Generative AI in Adolescence—A Developmental Framework","authors":"Cindy H. Liu, Tiffany Yip","doi":"10.1001/jamapediatrics.2026.0032","DOIUrl":"https://doi.org/10.1001/jamapediatrics.2026.0032","url":null,"abstract":"This Viewpoint describes the engagement of generative artificial intelligence (AI) as a relational partner among adolescents and how to set research priorities in order to pay immediate attention to the developmental risks and benefits.","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":"126 1","pages":""},"PeriodicalIF":26.1,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147461803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-16DOI: 10.1001/jamapediatrics.2026.0272
Robert T Ammerman
{"title":"Coding Error in Dataset of Study of Behavior Problems in Low-Income Young Children Screened in Pediatric Primary Care.","authors":"Robert T Ammerman","doi":"10.1001/jamapediatrics.2026.0272","DOIUrl":"https://doi.org/10.1001/jamapediatrics.2026.0272","url":null,"abstract":"","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":"26 1","pages":""},"PeriodicalIF":26.1,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147465493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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