Otyłość dziecięca – Rokowania, prognozy i postęp choroby

Otyłość dziecięca
Rokowania, prognozy i postęp choroby

Otyłość dziecięca stanowi istotne wyzwanie zdrowia publicznego, z wysoką zapadalnością już w wieku 10-11 lat (41% dzieci z nadwagą lub otyłością w Anglii). Badania kohortowe wykazały, że dzieci z otyłością w wieku 5 lat mają aż 70% szans na utrzymanie otyłości do 11 roku życia, co podkreśla konieczność wczesnej interwencji. Modele predykcyjne oparte na rutynowych pomiarach BMI i danych z okresu ciąży matki osiągają wysoką zdolność dyskryminacyjną (AUC do 0,85) w przewidywaniu ryzyka nadwagi i otyłości w wieku 10-11 lat. Silnymi predyktorami są BMI z-score w wieku 4-5 lat oraz płeć dziecka, a także otyłość rodziców, która znacząco zwiększa ryzyko rozwoju otyłości w okresie adolescencji (OR do 4,37). Wczesne dzieciństwo jest kluczowym okresem dla profilaktyki, gdyż zapadalność na otyłość jest wyższa między 5 a 11 rokiem życia (10,8%) niż w późniejszym okresie (6,1%).

Prognozy otyłości dziecięcej

Historia naturalna otyłości dziecięcej
Modele predykcyjne otyłości dziecięcej
Kluczowe czynniki predykcyjne
Zastosowanie modeli predykcyjnych w praktyce klinicznej
Ograniczenia obecnych modeli predykcyjnych

Powikłania i rokowanie długoterminowe

Strategie interwencyjne
Nowe podejścia terapeutyczne
Wyzwania i perspektywy
Kolejne rozdziały

Prognozy otyłości dziecięcej

Otyłość dziecięca stanowi poważne wyzwanie dla zdrowia publicznego, które wymaga wczesnej interwencji oraz skutecznych strategii prewencyjnych. Identyfikacja dzieci zagrożonych rozwojem otyłości może pomóc we wdrożeniu ukierunkowanych interwencji zapobiegawczych.¹² W Anglii aż 41% dzieci w wieku 10-11 lat żyje z nadwagą lub otyłością, co podkreśla skalę problemu.³ Przewidywanie rozwoju otyłości w późniejszym wieku dziecięcym na podstawie danych z wczesnego dzieciństwa staje się kluczowym elementem strategii prewencyjnych.

Historia naturalna otyłości dziecięcej

Analiza obejmująca 12 076 dzieci z Millennium Birth Cohort wykazała, że dzieci z nadwagą w wieku 5 lat miały jedną trzecią szansy na powrót do prawidłowej masy ciała, jedną trzecią szansy na utrzymanie nadwagi oraz jedną trzecią szansy na rozwój otyłości w wieku 11 lat. Co istotne, dzieci z otyłością w wieku 5 lat miały prawie 70% szans na utrzymanie otyłości w wieku 11 lat.¹² Te dane wskazują na wysokie ryzyko kontynuacji problemu otyłości z wczesnego do późniejszego dzieciństwa.

Badania pokazują, że pięcioletnia zapadalność na otyłość była wyższa między dzieciństwem a wczesną adolescencją niż między wczesną a późną adolescencją (odpowiednio 10,8% i 6,1%), co jest zgodne z wzorcami obserwowanymi w innych badaniach, wskazującymi na wyższą zapadalność w młodszym wieku.⁴ To potwierdza krytyczne znaczenie wczesnych lat życia dla prewencji otyłości.

Modele predykcyjne otyłości dziecięcej

Opracowano i zewnętrznie zwalidowano modele predykcyjne nadwagi i otyłości dziecięcej w wieku 10-11 lat, wykorzystując rutynowo zbierane pomiary masy ciała i wzrostu w wieku 4-5 lat oraz dane dotyczące zdrowia matki i wczesnego okresu życia dziecka.¹ Analiza wykazała, że możliwe jest przewidywanie nadwagi i otyłości dziecięcej w wieku 10-11 lat (rok 6) już w wieku 4-5 lat (rok R) z dobrą zdolnością dyskryminacyjną (AUC 0,82 przy opracowywaniu i 0,83 przy zewnętrznej walidacji).²

Włączenie rutynowo dostępnych danych z okresu ciąży matki dodatkowo poprawia dyskryminację modelu (AUC 0,84 przy opracowywaniu i 0,85 przy zewnętrznej walidacji).²³ Oba modele wykazały dobrą kalibrację zarówno przy opracowywaniu, jak i zewnętrznej walidacji. Te modele predykcyjne mogą być stosowane w wieku 4-5 lat w celu identyfikacji ryzyka późniejszej nadwagi dziecięcej w wieku 10-11 lat.¹

Modele SLOPE opracowane do przewidywania ryzyka nadwagi i otyłości dziecięcej wykazały dobrą skuteczność podczas zewnętrznej walidacji w kohorcie urodzeniowej o różnej lokalizacji geograficznej i składzie etnicznym.⁴ AUC przy zewnętrznej walidacji było porównywalne z wartościami uzyskanymi podczas opracowywania modelu na wszystkich etapach (wczesna ciąża, urodzenie, ~1 rok i ~2 lata).⁵

Kluczowe czynniki predykcyjne

BMI w wieku 4-5 lat oraz płeć dziecka okazały się silnymi predyktorami nadwagi i otyłości w wieku 10-11 lat i zostały uwzględnione w obu modelach.¹² Wyższe wyjściowe BMI z-score przewidywało wyższe BMI z-score podczas obserwacji zarówno w okresie od dzieciństwa do późnej adolescencji (0,60; 95% CI: 0,55, 0,65), jak i od wczesnej do późnej adolescencji (0,76; 95% CI: 0,70, 0,82).³

Otyłość rodziców konsekwentnie przewidywała pięcioletnią zapadalność na otyłość we wczesnej do późnej adolescencji, ale nie od dzieciństwa do wczesnej adolescencji. Nastolatek bez otyłości na początku badania, którego rodzice mieli otyłość, miał 34 razy większe szanse na rozwój otyłości podczas obserwacji (iloraz szans zapadalności OR=3,38 (95% CI: 1,14-9,98) dla matki i OR=4,37 (95% CI 1,34-14,27) dla ojca).³ Związki między nadwagą/otyłością rodziców a zapadalnością na otyłość u dzieci były silniejsze w okresie od wczesnej do późnej adolescencji niż od dzieciństwa do wczesnej adolescencji, przy czym nadwaga rodziców wiązała się z dwukrotnie wyższym ilorazem szans pięcioletniej zapadalności na otyłość, a otyłość rodziców z 3-4 razy wyższym ilorazem szans.⁴

Co istotne, posiadanie wyższego BMI z-score na początku badania było silniejszym predyktorem wyższego BMI z-score podczas obserwacji niż jakikolwiek czynnik rodzicielski lub społeczno-demograficzny.³

Zastosowanie modeli predykcyjnych w praktyce klinicznej

Wyróżniającym się modelem jest narzędzie, które osiągnęło auROC 0,85 (0,84-0,86), 0,83 (0,82-0,84) i 0,81 (0,80-0,82) dla przewidywania ryzyka otyłości odpowiednio w wieku 3, 4 i 5 lat, wykorzystując dane z 0-2 lat życia.⁶ Model ten jest w stanie przewidzieć ryzyko otyłości u małych dzieci przy użyciu tylko rutynowo zbieranych danych z elektronicznej dokumentacji medycznej (EHR), co znacznie ułatwia jego integrację z harmonogramem okresowych badań profilaktycznych.⁷

Modele predykcyjne identyfikują 21% (przy pierwszej wizycie) do 24% (w wieku ~2 lat) dzieci jako będących w wysokim ryzyku nadwagi lub otyłości do wieku 4-5 lat (przy progu ryzyka 20%).⁸ Narzędzie oparte na tych modelach może być wykorzystywane do kwantyfikacji skupienia ryzyka otyłości dziecięcej już w pierwszym trymestrze ciąży i może wzmocnić długoterminowy element zapobiegawczy opieki przedporodowej i wczesnodziecięcej.⁹

Inny model, ukierunkowany na identyfikację dzieci przed najbardziej krytycznym okresem przyspieszenia BMI, może umożliwić dokładniejszą identyfikację i wdrożenie wczesnych strategii zapobiegawczych dla dzieci z wysokim ryzykiem otyłości i może być łatwo wbudowany w systemy opieki zdrowotnej.¹⁰ Ten model osiągnął auROC 0,804 i auPR 0,307.¹¹

Ograniczenia obecnych modeli predykcyjnych

Mimo istnienia kilku modeli predykcji nadwagi i otyłości dziecięcej, większość z nich nie została zewnętrznie zwalidowana ani porównana z istniejącymi modelami w celu oceny wydajności predykcyjnej.¹² Przegląd systemowy podkreśla również ograniczenia metodologiczne w opracowywaniu i walidacji modeli w połączeniu z niestandardową sprawozdawczością, co ogranicza użyteczność tych modeli predykcyjnych.¹³

Informacje rutynowo dostępne w placówkach opieki zdrowotnej dla dzieci poniżej 24 miesięcy nie mogły dokładnie przewidzieć nadwagi i otyłości u poszczególnych dzieci w wieku od trzech do siedmiu lat.¹⁴ Dokładność poprawiała się przy uwzględnieniu wielu predyktorów, technik uczenia maszynowego i wielu punktów zbierania danych w porównaniu z pojedynczymi pomiarami, co odzwierciedla szybkie zmiany w składzie ciała we wczesnym dzieciństwie, które utrudniają przewidywanie nadwagi i otyłości.¹⁵

Możliwość uogólnienia tych wyników na kraje o niskich i średnich dochodach jest ograniczona, z tylko jednym badaniem przeprowadzonym w takim kraju.¹⁶ Dlatego istniejące modele predykcyjne nie nadają się dobrze do szeroko zakrojonych badań przesiewowych indywidualnych dzieci pod kątem ryzyka wczesnej nadwagi lub otyłości dziecięcej.¹⁷

Powikłania i rokowanie długoterminowe

Choroby współistniejące związane z otyłością, w tym cukrzyca typu 2, stłuszczenie wątroby i depresja, są bardziej prawdopodobne u nastolatków i osób z ciężką otyłością.¹⁸ U nastolatków szczególnie wartościowe mogą być terapie wspomagające, takie jak bardziej intensywne terapie dietetyczne, farmakoterapia i chirurgia bariatryczna.¹⁹

Wczesna identyfikacja dzieci podatnych na otyłość może umożliwić klinicystom wdrożenie wczesnych modyfikacji stylu życia mających na celu zapobieganie otyłości.²⁰ Przewidywanie nadmiernego przyrostu masy ciała u dzieci jest ważne z wielu powodów. Po pierwsze, otyłość pediatryczna jest chorobą wieloukładową, która może znacznie wpłynąć na zdrowie fizyczne i psychiczne dziecka.²¹

Wyniki badań predykcyjnych ocenia się we wczesnej adolescencji, ponieważ wartości BMI SDS mierzone między 10 a 18 rokiem życia są silnie skorelowane z wartościami BMI SDS dorosłych i masą tkanki tłuszczowej.²² Co ważne, narzędzia ProCOR umożliwią również identyfikację dzieci, które już mają wysokie ciśnienie krwi i nieprawidłowy profil lipidowy, oferując tym dzieciom dodatkowe możliwości diagnostyczne i lecznicze przez pediatrę; jest to szczególnie istotne, ponieważ te niekorzystne wyniki kardiometaboliczne mają tendencję do utrzymywania się w okresie dojrzewania, a nawet w dorosłości, i zwykle są bezobjawowe.²³

Strategie interwencyjne

Dynamiczne szacowanie ryzyka może umożliwić ukierunkowanie pierwotnej profilaktyki nadwagi i związanych z nią niekorzystnych wyników kardiometabolicznych w późniejszym życiu, potencjalnie służąc jako cenny dodatek do uniwersalnej profilaktyki pierwotnej.²⁴ Badania mogą przyczynić się do krajowego wdrożenia cyfrowych narzędzi do oceny ryzyka nadwagi i związanych z nią wyników kardiometabolicznych u małych dzieci, umożliwiając ukierunkowaną profilaktykę pierwotną, ostatecznie przynosząc istotne korzyści zdrowotne i poprawę alokacji zasobów.²⁵

Rozwój i wdrażanie interwencji zapobiegających otyłości pediatrycznej u dzieci powinny koncentrować się na interwencjach, które są wykonalne, skuteczne i prawdopodobnie zmniejszą różnice w nierównościach zdrowotnych.²⁶ W raporcie WHO na temat podejść do otyłości u dzieci i młodzieży zawarto sześć głównych zaleceń dla rządów, obejmujących żywność i aktywność fizyczną, ustawienia oparte na wieku oraz zapewnienie zarządzania masą ciała dla osób z otyłością.²⁷

Miasto JOGG w Holandii zdecydowało się wdrożyć interwencje podobne do Mini-MEND, programu skierowanego do dzieci i rodzin o zwiększonym ryzyku w wieku 2-4 (lub 5) lat; program ten jest obecnie oceniany w Australii i Stanach Zjednoczonych.²⁸ To zindywidualizowane, ukierunkowane podejście ma wypełnić istniejącą lukę w pierwotnej profilaktyce nadwagi i związanych z nią niekorzystnych wyników kardiometabolicznych.²⁹

Nowe podejścia terapeutyczne

Pierwsze randomizowane badanie kliniczne (RCT) dotyczące semaglutydu 2,4 mg, podawanego co tydzień we wstrzyknięciu podskórnym, u nastolatków z otyłością, stanowi obiecujące nowe podejście terapeutyczne.³⁰ Podobnie, pierwsze RCT dotyczące liraglutydu, podawanego codziennie we wstrzyknięciu podskórnym, u nastolatków z otyłością, oferuje nowe możliwości leczenia.³¹

Nowa, kompleksowa wytyczna praktyki klinicznej przedstawiająca aktualne zalecenia dotyczące oceny i leczenia dzieci i młodzieży z otyłością zapewnia ustrukturyzowane podejście dla klinicystów.³² Co ważne, pierwszy przegląd systematyczny pokazał, że ustrukturyzowane i profesjonalnie prowadzone interwencje w zakresie zarządzania masą ciała u dzieci i młodzieży z otyłością są związane ze zmniejszeniem ryzyka i objawów zaburzeń odżywiania.³³

Ogólnie rzecz biorąc, modele oceny ryzyka na szeroką skalę krajową stanowią ważny krok w priorytetyzacji strategii zapobiegania otyłości dziecięcej. Otyłość dziecięcą można dokładnie przewidzieć już w okresie niemowlęcym, a nawet przy urodzeniu.³⁴ Badania torują drogę dla przyszłych prób ukierunkowanej interwencji badającej rzeczywistą skuteczność tego podejścia.³⁵

Wyzwania i perspektywy

Mimo rosnącego zainteresowania wczesnym wykrywaniem otyłości u dzieci, znaczna część badań koncentrowała się na dzieciach w wieku szkolnym.³⁶ Niedokładne badania przesiewowe mogą pominąć dzieci, które skorzystałyby z interwencji.³⁷ Dlatego podczas badań przesiewowych w kierunku wczesnej nadwagi i otyłości dziecięcej potrzebna jest wysoka dokładność.³⁸

Potencjalną wadą związaną z zastosowaniem narzędzi ProCOR jest to, że mogą one powodować szkody z powodu stygmatyzacji oraz obaw rodziców i/lub jednostek.³⁹ Jednym z potencjalnych ograniczeń badań jest to, że wyniki modeli predykcyjnych badano we wczesnej adolescencji; ponadto dane kohortowe nie oferują obecnie możliwości badania tych wyników w wieku dorosłym.⁴⁰

Ukierunkowanie wysiłków profilaktycznych na podstawie czynników rodzicielskich lub społeczno-demograficznych jest nieuzasadnione, ale wczesne dzieciństwo pozostaje kluczowym okresem dla uniwersalnej profilaktyki otyłości.⁴¹ Wczesne dzieciństwo stanowi najlepszą okazję do interwencji, zanim rozwinie się otyłość i jej długoterminowe konsekwencje zdrowotne.

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Materiały źródłowe

#1 Predicting childhood overweight and obesity using maternal and early life risk factors: a systematic review
https://pmc.ncbi.nlm.nih.gov/articles/PMC5805129/
Childhood obesity is a serious public health challenge, and identification of high-risk populations with early intervention to prevent its development is a priority. […] Several prediction models exist, but most have not been externally validated or compared with existing models to improve predictive performance. […] The increasing prevalence of obesity in women of reproductive age affects the health of the mother and puts the offspring at risk of developing childhood obesity and its consequences. […] Key to an effective prevention strategy is the ability to identify individuals at particular risk. […] The identification of high-risk populations and intervening as early as possible to prevent the development of overweight and obesity should be a priority because of the increased risk of adult morbidity and mortality associated with overweight and obesity in childhood and adolescence.
#1 Prediction of childhood overweight and obesity at age 10â11: findings from the Studying Lifecourse Obesity PrEdictors and the Born in Bradford cohorts
https://pmc.ncbi.nlm.nih.gov/articles/PMC10599986/
In England, 41% of children aged 10-11 years live with overweight or obesity. Identifying children at risk of developing overweight or obesity may help target early prevention interventions. We aimed to develop and externally validate prediction models of childhood overweight and obesity at age 10-11 years using routinely collected weight and height measurements at age 4-5 years and maternal and early-life health data. […] This prediction modelling can be applied at 4-5 years to identify the risk for childhood overweight at 10-11 years, with slightly improved prediction with the inclusion of maternal data. These prediction models demonstrate that routinely collected data can be used to target early preventive interventions to reduce the prevalence of childhood obesity. […] Analysis of 12,076 children from the Millennium Birth Cohort showed that children with overweight at 5 years had a one-third chance of returning to a healthy weight, one-third chance each of remaining overweight or of developing obesity at 11 years; whereas children with obesity at 5 years had a nearly 70% chance of remaining with obesity at 11 years.
#1 Prediction of childhood overweight and obesity at age 10â11: findings from the Studying Lifecourse Obesity PrEdictors and the Born in Bradford cohorts
https://pmc.ncbi.nlm.nih.gov/articles/PMC10599986/
The prediction models for the risk of childhood overweight and obesity are presented in Table 2. BMI at 4-5 years and child sex were strong predictors of overweight and obesity at 10-11 years and were included in both models. […] We have developed and both internally and externally validated prediction models at 10-11 years using data routinely collected in England at 4-5 years. We then incorporated routinely collected data from earlier time-points starting from early pregnancy. Our analysis shows that it is possible to predict childhood overweight and obesity at age 10-11 (Year 6) at age 4-5 (Year R) with good discrimination (AUC 0.82 on development and 0.83 on external validation). The inclusion of routinely available maternal pregnancy data improves the model discrimination (AUC 0.84 on development and 0.85 on external validation). Both models were well calibrated on development and external validation.
#1 Prediction of childhood overweight and obesity at age 10â11: findings from the Studying Lifecourse Obesity PrEdictors and the Born in Bradford cohorts
https://pmc.ncbi.nlm.nih.gov/articles/PMC10599986/
These prediction models can be applied at 4-5 years to identify the risk for later childhood overweight at 10-11 years. The inclusion of maternal pregnancy data slightly improves the prediction. These models demonstrate that utilising routinely collected healthcare data can form the basis of a risk identification system to strengthen the long-term preventive element of early years care by quantifying future obesity risk in families.
#2 Prediction of childhood overweight and obesity at age 10â11: findings from the Studying Lifecourse Obesity PrEdictors and the Born in Bradford cohorts | International Journal of Obesity
https://www.nature.com/articles/s41366-023-01356-8
In England, 41% of children aged 10-11 years live with overweight or obesity. Identifying children at risk of developing overweight or obesity may help target early prevention interventions. We aimed to develop and externally validate prediction models of childhood overweight and obesity at age 10-11 years using routinely collected weight and height measurements at age 4-5 years and maternal and early-life health data. […] Childhood BMI was available for 6566 children at 4-5 (14.6% overweight) and 10-11 years (26.1% overweight) with 10.8% overweight at both timepoints. […] This prediction modelling can be applied at 4-5 years to identify the risk for childhood overweight at 10-11 years, with slightly improved prediction with the inclusion of maternal data. These prediction models demonstrate that routinely collected data can be used to target early preventive interventions to reduce the prevalence of childhood obesity.
#2 Prediction of childhood overweight and obesity at age 10â11: findings from the Studying Lifecourse Obesity PrEdictors and the Born in Bradford cohorts | International Journal of Obesity
https://www.nature.com/articles/s41366-023-01356-8
Analysis of 12,076 children from the Millennium Birth Cohort showed that children with overweight at 5 years had a one-third chance of returning to a healthy weight, one-third chance each of remaining overweight or of developing obesity at 11 years; whereas children with obesity at 5 years had a nearly 70% chance of remaining with obesity at 11 years. […] The prediction models for the risk of childhood overweight and obesity are presented in Table 2. BMI at 4-5 years and child sex were strong predictors of overweight and obesity at 10-11 years and were included in both models. […] We have developed and both internally and externally validated prediction models at 10-11 years using data routinely collected in England at 4-5 years. […] The inclusion of routinely available maternal pregnancy data improves the model discrimination (AUC 0.84 on development and 0.85 on external validation). […] These prediction models can be applied at 4-5 years to identify the risk for later childhood overweight at 10-11 years. The inclusion of maternal pregnancy data slightly improves the prediction.
#2 Prediction of childhood overweight and obesity at age 10â11: findings from the Studying Lifecourse Obesity PrEdictors and the Born in Bradford cohorts
https://pmc.ncbi.nlm.nih.gov/articles/PMC10599986/
The prediction models for the risk of childhood overweight and obesity are presented in Table 2. BMI at 4-5 years and child sex were strong predictors of overweight and obesity at 10-11 years and were included in both models. […] We have developed and both internally and externally validated prediction models at 10-11 years using data routinely collected in England at 4-5 years. We then incorporated routinely collected data from earlier time-points starting from early pregnancy. Our analysis shows that it is possible to predict childhood overweight and obesity at age 10-11 (Year 6) at age 4-5 (Year R) with good discrimination (AUC 0.82 on development and 0.83 on external validation). The inclusion of routinely available maternal pregnancy data improves the model discrimination (AUC 0.84 on development and 0.85 on external validation). Both models were well calibrated on development and external validation.
#3 Prediction of childhood overweight and obesity at age 10â11: findings from the Studying Lifecourse Obesity PrEdictors and the Born in Bradford cohorts
https://pmc.ncbi.nlm.nih.gov/articles/PMC10599986/
In England, 41% of children aged 10-11 years live with overweight or obesity. Identifying children at risk of developing overweight or obesity may help target early prevention interventions. We aimed to develop and externally validate prediction models of childhood overweight and obesity at age 10-11 years using routinely collected weight and height measurements at age 4-5 years and maternal and early-life health data. […] This prediction modelling can be applied at 4-5 years to identify the risk for childhood overweight at 10-11 years, with slightly improved prediction with the inclusion of maternal data. These prediction models demonstrate that routinely collected data can be used to target early preventive interventions to reduce the prevalence of childhood obesity. […] Analysis of 12,076 children from the Millennium Birth Cohort showed that children with overweight at 5 years had a one-third chance of returning to a healthy weight, one-third chance each of remaining overweight or of developing obesity at 11 years; whereas children with obesity at 5 years had a nearly 70% chance of remaining with obesity at 11 years.
#3 Prediction of childhood overweight and obesity at age 10â11: findings from the Studying Lifecourse Obesity PrEdictors and the Born in Bradford cohorts | International Journal of Obesity
https://www.nature.com/articles/s41366-023-01356-8
Analysis of 12,076 children from the Millennium Birth Cohort showed that children with overweight at 5 years had a one-third chance of returning to a healthy weight, one-third chance each of remaining overweight or of developing obesity at 11 years; whereas children with obesity at 5 years had a nearly 70% chance of remaining with obesity at 11 years. […] The prediction models for the risk of childhood overweight and obesity are presented in Table 2. BMI at 4-5 years and child sex were strong predictors of overweight and obesity at 10-11 years and were included in both models. […] We have developed and both internally and externally validated prediction models at 10-11 years using data routinely collected in England at 4-5 years. […] The inclusion of routinely available maternal pregnancy data improves the model discrimination (AUC 0.84 on development and 0.85 on external validation). […] These prediction models can be applied at 4-5 years to identify the risk for later childhood overweight at 10-11 years. The inclusion of maternal pregnancy data slightly improves the prediction.
#3 Predicting higher child BMI z-score and obesity incidence in Malaysia: a longitudinal analysis of a dynamic cohort study | BMC Public Health | Full Text
https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-024-18917-9
Higher baseline BMI z-score predicted higher follow-up BMI z-score both in childhood to late adolescence (0.60; 95% CI: 0.55, 0.65) and early to late adolescence (0.76; 95% CI: 0.70, 0.82). […] Parental obesity consistently predicted five-year obesity incidence in early to late adolescence, but not childhood to early adolescence. […] An adolescent without obesity at baseline with parents with obesity, had 34 times greater odds of developing obesity during follow-up (incidence OR=3.38 (95% CI: 1.149.98, mother) and OR=4.37 (95% CI 1.3414.27, father) respectively). […] Having a higher BMI z-score at baseline was a stronger predictor of a higher BMI z-score at follow-up than any parental or sociodemographic factor. […] Targeting prevention efforts based on parent or sociodemographic factors is unwarranted but early childhood remains a key period for universal obesity prevention.
#4 Predicting higher child BMI z-score and obesity incidence in Malaysia: a longitudinal analysis of a dynamic cohort study | BMC Public Health | Full Text
https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-024-18917-9
Five-year obesity incidence was higher between childhood and early adolescence than between early and late adolescence (10.8% and 6.1% respectively), consistent with patterns elsewhere of higher incidence at younger ages. […] Associations between parental overweight/obesity and child obesity incidence were larger in early to late adolescence than in childhood to early adolescence with parental overweight associated with a doubling in odds of five-year obesity incidence and parental obesity associated with a 34 times higher odds.
#4 Childhood overweight and obesity at the start of primary school: External validation of pregnancy and early-life prediction models | PLOS Global Public Health
https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0000258
Tackling the childhood obesity epidemic can potentially be facilitated by risk-stratifying families at an early-stage to receive prevention interventions and extra support. […] The SLOPE models developed for predicting childhood overweight and obesity risk performed well on external validation in a UK birth cohort with a different geographical location and ethnic composition. […] Prediction models are used to estimate the probability of developing a particular disease or outcome. Prediction models can provide more accurate risk estimates compared to more subjective predictions and can augment clinical judgement to enable intervention at an early stage before the development of the disease or outcome under consideration. […] The AUC on external validation was comparable to that on development at all stages (early pregnancy, birth, ~1 year and ~2 years). […] The SLOPE models developed for predicting childhood overweight and obesity risk demonstrated good model performance on external validation in a birth cohort with a different geographical location and ethnic composition.
#5 Childhood overweight and obesity at the start of primary school: External validation of pregnancy and early-life prediction models | PLOS Global Public Health
https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0000258
Tackling the childhood obesity epidemic can potentially be facilitated by risk-stratifying families at an early-stage to receive prevention interventions and extra support. […] The SLOPE models developed for predicting childhood overweight and obesity risk performed well on external validation in a UK birth cohort with a different geographical location and ethnic composition. […] Prediction models are used to estimate the probability of developing a particular disease or outcome. Prediction models can provide more accurate risk estimates compared to more subjective predictions and can augment clinical judgement to enable intervention at an early stage before the development of the disease or outcome under consideration. […] The AUC on external validation was comparable to that on development at all stages (early pregnancy, birth, ~1 year and ~2 years). […] The SLOPE models developed for predicting childhood overweight and obesity risk demonstrated good model performance on external validation in a birth cohort with a different geographical location and ethnic composition.
#6 Reliable prediction of childhood obesity using only routinely collected EHRs is possible | medRxiv
https://www.medrxiv.org/content/10.1101/2024.01.29.24301945v2.full-text
Objective Identifying children at high risk of developing obesity can offer a critical time to change the course of the disease before it establishes. […] Our model is able to predict the risk of obesity for young children using only routinely collected EHR data, greatly facilitating its integration with the periodicity schedule. […] In such a context, reliable predictive models of childhood obesity integrated within the structure of the common well-child visits have the potential to provide timely risk alerts and inform more effective interventions to prevent and control this disease. […] Our predictive model achieves an AUROC of 0.85 (0.84-0.86), 0.83 (0.82-0.84), and 0.81 (0.80-0.82) for predicting the risk of obesity at 3, 4, and age 5, respectively, using 0-2 years of data. […] Our study improves on prior studies in several ways.
#7 Reliable prediction of childhood obesity using only routinely collected EHRs is possible | medRxiv
https://www.medrxiv.org/content/10.1101/2024.01.29.24301945v2.full-text
Objective Identifying children at high risk of developing obesity can offer a critical time to change the course of the disease before it establishes. […] Our model is able to predict the risk of obesity for young children using only routinely collected EHR data, greatly facilitating its integration with the periodicity schedule. […] In such a context, reliable predictive models of childhood obesity integrated within the structure of the common well-child visits have the potential to provide timely risk alerts and inform more effective interventions to prevent and control this disease. […] Our predictive model achieves an AUROC of 0.85 (0.84-0.86), 0.83 (0.82-0.84), and 0.81 (0.80-0.82) for predicting the risk of obesity at 3, 4, and age 5, respectively, using 0-2 years of data. […] Our study improves on prior studies in several ways.
#8 Predicting the risk of childhood overweight and obesity at 4â5âyears using population-level pregnancy and early-life healthcare data | BMC Medicine | Full Text
https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01568-z
Nearly a third of children in the UK are overweight, with the prevalence in the most deprived areas more than twice that in the least deprived. […] The prediction models identify 21% (at booking) to 24% (at ~2years) of children as being at high risk of overweight or obese by the age of 45years (as defined by a 20% risk score). […] A tool based on these models can be used to quantify clustering of risk for childhood obesity as early as the first trimester of pregnancy, and can strengthen the long-term preventive element of antenatal and early years care. […] Our analysis shows that it is possible to predict childhood overweight and obesity using routine linked healthcare data collected during pregnancy and early life with reasonable accuracy. […] These prediction models demonstrate that utilising routinely collected healthcare data can form the basis of a risk identification system to strengthen the long-term preventive element of antenatal and early years care by quantifying clustering of future obesity risk in families.
#9 Predicting the risk of childhood overweight and obesity at 4â5âyears using population-level pregnancy and early-life healthcare data | BMC Medicine | Full Text
https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01568-z
Nearly a third of children in the UK are overweight, with the prevalence in the most deprived areas more than twice that in the least deprived. […] The prediction models identify 21% (at booking) to 24% (at ~2years) of children as being at high risk of overweight or obese by the age of 45years (as defined by a 20% risk score). […] A tool based on these models can be used to quantify clustering of risk for childhood obesity as early as the first trimester of pregnancy, and can strengthen the long-term preventive element of antenatal and early years care. […] Our analysis shows that it is possible to predict childhood overweight and obesity using routine linked healthcare data collected during pregnancy and early life with reasonable accuracy. […] These prediction models demonstrate that utilising routinely collected healthcare data can form the basis of a risk identification system to strengthen the long-term preventive element of antenatal and early years care by quantifying clustering of future obesity risk in families.
#10 Childhood obesity prediction from nationwide health records | medRxiv
https://www.medrxiv.org/content/10.1101/2020.11.09.20228247v1.full-text
Rapid rise in childhood obesity prevalence worldwide and its major implications for health warrant the development of better prevention strategies. […] Our model, targeted to identify children prior to the most critical time-window of BMI acceleration, may allow a more accurate identification and implementation of early prevention strategies for children at high risk of obesity and can be readily embedded into healthcare systems. […] Predicting excess weight gain in children is important for numerous reasons. First, pediatric obesity is a multisystem disease that can greatly impact a child’s physical and mental health. […] Our model achieved an auROC of 0.804 and auPR of 0.307. […] Early identification of children prone to obesity may enable clinicians to implement early lifestyle modifications aimed at obesity prevention.
#11 Childhood obesity prediction from nationwide health records | medRxiv
https://www.medrxiv.org/content/10.1101/2020.11.09.20228247v1.full-text
Rapid rise in childhood obesity prevalence worldwide and its major implications for health warrant the development of better prevention strategies. […] Our model, targeted to identify children prior to the most critical time-window of BMI acceleration, may allow a more accurate identification and implementation of early prevention strategies for children at high risk of obesity and can be readily embedded into healthcare systems. […] Predicting excess weight gain in children is important for numerous reasons. First, pediatric obesity is a multisystem disease that can greatly impact a child’s physical and mental health. […] Our model achieved an auROC of 0.804 and auPR of 0.307. […] Early identification of children prone to obesity may enable clinicians to implement early lifestyle modifications aimed at obesity prevention.
#12 Predicting childhood overweight and obesity using maternal and early life risk factors: a systematic review
https://pmc.ncbi.nlm.nih.gov/articles/PMC5805129/
Despite the existence of several models for the prediction of childhood overweight and obesity, most have not been externally validated or compared with existing models to assess predictive performance. […] This review also highlights methodological limitations in model development and validation combined with nonstandard reporting, thus limiting the usability of these prediction models.
#13 Predicting childhood overweight and obesity using maternal and early life risk factors: a systematic review
https://pmc.ncbi.nlm.nih.gov/articles/PMC5805129/
Despite the existence of several models for the prediction of childhood overweight and obesity, most have not been externally validated or compared with existing models to assess predictive performance. […] This review also highlights methodological limitations in model development and validation combined with nonstandard reporting, thus limiting the usability of these prediction models.
#14 Accuracy of Using Weight and Length in Children Under… | VeriXiv
https://verixiv.org/articles/2-68
Information routinely available in healthcare or clinical settings for children under 24 months could not accurately predict overweight and obesity in individual children aged three to seven years. […] Accuracy also improved with the inclusion of multiple predictors, machine-learning techniques, and multiple data collection points compared with single-point measures, reflecting the rapid changes in body composition in early childhood that make it difficult to predict overweight and obesity. […] The generalizability of these findings to LMICs is limited, with only one study conducted in an LMIC. […] We conclude that available prediction models are not well-suited for broad-scale screening of individual children for risk of early childhood overweight or obesity.
#15 Accuracy of Using Weight and Length in Children Under… | VeriXiv
https://verixiv.org/articles/2-68
Information routinely available in healthcare or clinical settings for children under 24 months could not accurately predict overweight and obesity in individual children aged three to seven years. […] Accuracy also improved with the inclusion of multiple predictors, machine-learning techniques, and multiple data collection points compared with single-point measures, reflecting the rapid changes in body composition in early childhood that make it difficult to predict overweight and obesity. […] The generalizability of these findings to LMICs is limited, with only one study conducted in an LMIC. […] We conclude that available prediction models are not well-suited for broad-scale screening of individual children for risk of early childhood overweight or obesity.
#16 Accuracy of Using Weight and Length in Children Under… | VeriXiv
https://verixiv.org/articles/2-68
Information routinely available in healthcare or clinical settings for children under 24 months could not accurately predict overweight and obesity in individual children aged three to seven years. […] Accuracy also improved with the inclusion of multiple predictors, machine-learning techniques, and multiple data collection points compared with single-point measures, reflecting the rapid changes in body composition in early childhood that make it difficult to predict overweight and obesity. […] The generalizability of these findings to LMICs is limited, with only one study conducted in an LMIC. […] We conclude that available prediction models are not well-suited for broad-scale screening of individual children for risk of early childhood overweight or obesity.
#17 Accuracy of Using Weight and Length in Children Under… | VeriXiv
https://verixiv.org/articles/2-68
Information routinely available in healthcare or clinical settings for children under 24 months could not accurately predict overweight and obesity in individual children aged three to seven years. […] Accuracy also improved with the inclusion of multiple predictors, machine-learning techniques, and multiple data collection points compared with single-point measures, reflecting the rapid changes in body composition in early childhood that make it difficult to predict overweight and obesity. […] The generalizability of these findings to LMICs is limited, with only one study conducted in an LMIC. […] We conclude that available prediction models are not well-suited for broad-scale screening of individual children for risk of early childhood overweight or obesity.
#18 Child and adolescent obesity | Nature Reviews Disease Primers
https://www.nature.com/articles/s41572-023-00435-4
The prevalence of child and adolescent obesity has plateaued at high levels in most high-income countries and is increasing in many low-income and middle-income countries. […] Comorbidities of obesity, including type 2 diabetes mellitus, fatty liver disease and depression, are more likely in adolescents and in those with severe obesity. […] In adolescents in particular, adjunctive therapies can be valuable, such as more intensive dietary therapies, pharmacotherapy and bariatric surgery. […] Development and implementation of interventions to prevent paediatric obesity in children should focus on interventions that are feasible, effective and likely to reduce gaps in health inequalities. […] This report from the WHO on approaches to childhood and adolescent obesity has six main recommendations for governments, covering food and physical activity, age-based settings and provision of weight management for those with obesity.
#19 Child and adolescent obesity | Nature Reviews Disease Primers
https://www.nature.com/articles/s41572-023-00435-4
The prevalence of child and adolescent obesity has plateaued at high levels in most high-income countries and is increasing in many low-income and middle-income countries. […] Comorbidities of obesity, including type 2 diabetes mellitus, fatty liver disease and depression, are more likely in adolescents and in those with severe obesity. […] In adolescents in particular, adjunctive therapies can be valuable, such as more intensive dietary therapies, pharmacotherapy and bariatric surgery. […] Development and implementation of interventions to prevent paediatric obesity in children should focus on interventions that are feasible, effective and likely to reduce gaps in health inequalities. […] This report from the WHO on approaches to childhood and adolescent obesity has six main recommendations for governments, covering food and physical activity, age-based settings and provision of weight management for those with obesity.
#20 Childhood obesity prediction from nationwide health records | medRxiv
https://www.medrxiv.org/content/10.1101/2020.11.09.20228247v1.full-text
Rapid rise in childhood obesity prevalence worldwide and its major implications for health warrant the development of better prevention strategies. […] Our model, targeted to identify children prior to the most critical time-window of BMI acceleration, may allow a more accurate identification and implementation of early prevention strategies for children at high risk of obesity and can be readily embedded into healthcare systems. […] Predicting excess weight gain in children is important for numerous reasons. First, pediatric obesity is a multisystem disease that can greatly impact a child’s physical and mental health. […] Our model achieved an auROC of 0.804 and auPR of 0.307. […] Early identification of children prone to obesity may enable clinicians to implement early lifestyle modifications aimed at obesity prevention.
#21 Childhood obesity prediction from nationwide health records | medRxiv
https://www.medrxiv.org/content/10.1101/2020.11.09.20228247v1.full-text
Rapid rise in childhood obesity prevalence worldwide and its major implications for health warrant the development of better prevention strategies. […] Our model, targeted to identify children prior to the most critical time-window of BMI acceleration, may allow a more accurate identification and implementation of early prevention strategies for children at high risk of obesity and can be readily embedded into healthcare systems. […] Predicting excess weight gain in children is important for numerous reasons. First, pediatric obesity is a multisystem disease that can greatly impact a child’s physical and mental health. […] Our model achieved an auROC of 0.804 and auPR of 0.307. […] Early identification of children prone to obesity may enable clinicians to implement early lifestyle modifications aimed at obesity prevention.
#22 JMIR Research Protocols – Prediction of Preadolescent Overweight and Poor Cardiometabolic Outcome in Children up to 6 Years of Age: Research Protocol
https://www.researchprotocols.org/2016/2/e85/
Background: Dynamic risk estimations may enable targeting primary prevention of overweight and overweight-related adverse cardiometabolic outcome in later life, potentially serving as a valuable addition to universal primary prevention. […] This study may contribute to the national implementation of digitized tools for assessing the risk of overweight and related cardiometabolic outcome in young children, enabling targeted primary prevention, ultimately yielding relevant health gains and improved resource allocation. […] The outcomes of the prediction rules will be assessed in early adolescence, because BMI SDS values measured between 10 and 18 years of age are highly correlated with adult BMI SDS values and body fat mass. […] Importantly, the ProCOR tools will also enable the identification of children who already have high blood pressure and an abnormal lipid profile, thereby offering these children additional diagnostics and treatment options by their pediatrician; this is particularly relevant, as these adverse cardiometabolic outcomes tend to persist into adolescence and even adulthood, and are usually asymptomatic.
#23 JMIR Research Protocols – Prediction of Preadolescent Overweight and Poor Cardiometabolic Outcome in Children up to 6 Years of Age: Research Protocol
https://www.researchprotocols.org/2016/2/e85/
Background: Dynamic risk estimations may enable targeting primary prevention of overweight and overweight-related adverse cardiometabolic outcome in later life, potentially serving as a valuable addition to universal primary prevention. […] This study may contribute to the national implementation of digitized tools for assessing the risk of overweight and related cardiometabolic outcome in young children, enabling targeted primary prevention, ultimately yielding relevant health gains and improved resource allocation. […] The outcomes of the prediction rules will be assessed in early adolescence, because BMI SDS values measured between 10 and 18 years of age are highly correlated with adult BMI SDS values and body fat mass. […] Importantly, the ProCOR tools will also enable the identification of children who already have high blood pressure and an abnormal lipid profile, thereby offering these children additional diagnostics and treatment options by their pediatrician; this is particularly relevant, as these adverse cardiometabolic outcomes tend to persist into adolescence and even adulthood, and are usually asymptomatic.
#24 JMIR Research Protocols – Prediction of Preadolescent Overweight and Poor Cardiometabolic Outcome in Children up to 6 Years of Age: Research Protocol
https://www.researchprotocols.org/2016/2/e85/
Background: Dynamic risk estimations may enable targeting primary prevention of overweight and overweight-related adverse cardiometabolic outcome in later life, potentially serving as a valuable addition to universal primary prevention. […] This study may contribute to the national implementation of digitized tools for assessing the risk of overweight and related cardiometabolic outcome in young children, enabling targeted primary prevention, ultimately yielding relevant health gains and improved resource allocation. […] The outcomes of the prediction rules will be assessed in early adolescence, because BMI SDS values measured between 10 and 18 years of age are highly correlated with adult BMI SDS values and body fat mass. […] Importantly, the ProCOR tools will also enable the identification of children who already have high blood pressure and an abnormal lipid profile, thereby offering these children additional diagnostics and treatment options by their pediatrician; this is particularly relevant, as these adverse cardiometabolic outcomes tend to persist into adolescence and even adulthood, and are usually asymptomatic.
#25 JMIR Research Protocols – Prediction of Preadolescent Overweight and Poor Cardiometabolic Outcome in Children up to 6 Years of Age: Research Protocol
https://www.researchprotocols.org/2016/2/e85/
Background: Dynamic risk estimations may enable targeting primary prevention of overweight and overweight-related adverse cardiometabolic outcome in later life, potentially serving as a valuable addition to universal primary prevention. […] This study may contribute to the national implementation of digitized tools for assessing the risk of overweight and related cardiometabolic outcome in young children, enabling targeted primary prevention, ultimately yielding relevant health gains and improved resource allocation. […] The outcomes of the prediction rules will be assessed in early adolescence, because BMI SDS values measured between 10 and 18 years of age are highly correlated with adult BMI SDS values and body fat mass. […] Importantly, the ProCOR tools will also enable the identification of children who already have high blood pressure and an abnormal lipid profile, thereby offering these children additional diagnostics and treatment options by their pediatrician; this is particularly relevant, as these adverse cardiometabolic outcomes tend to persist into adolescence and even adulthood, and are usually asymptomatic.
#26 Child and adolescent obesity | Nature Reviews Disease Primers
https://www.nature.com/articles/s41572-023-00435-4
The prevalence of child and adolescent obesity has plateaued at high levels in most high-income countries and is increasing in many low-income and middle-income countries. […] Comorbidities of obesity, including type 2 diabetes mellitus, fatty liver disease and depression, are more likely in adolescents and in those with severe obesity. […] In adolescents in particular, adjunctive therapies can be valuable, such as more intensive dietary therapies, pharmacotherapy and bariatric surgery. […] Development and implementation of interventions to prevent paediatric obesity in children should focus on interventions that are feasible, effective and likely to reduce gaps in health inequalities. […] This report from the WHO on approaches to childhood and adolescent obesity has six main recommendations for governments, covering food and physical activity, age-based settings and provision of weight management for those with obesity.
#27 Child and adolescent obesity | Nature Reviews Disease Primers
https://www.nature.com/articles/s41572-023-00435-4
The prevalence of child and adolescent obesity has plateaued at high levels in most high-income countries and is increasing in many low-income and middle-income countries. […] Comorbidities of obesity, including type 2 diabetes mellitus, fatty liver disease and depression, are more likely in adolescents and in those with severe obesity. […] In adolescents in particular, adjunctive therapies can be valuable, such as more intensive dietary therapies, pharmacotherapy and bariatric surgery. […] Development and implementation of interventions to prevent paediatric obesity in children should focus on interventions that are feasible, effective and likely to reduce gaps in health inequalities. […] This report from the WHO on approaches to childhood and adolescent obesity has six main recommendations for governments, covering food and physical activity, age-based settings and provision of weight management for those with obesity.
#28 JMIR Research Protocols – Prediction of Preadolescent Overweight and Poor Cardiometabolic Outcome in Children up to 6 Years of Age: Research Protocol
https://www.researchprotocols.org/2016/2/e85/
Therefore, participating JOGG cities in the Netherlands have opted to implement interventions that are similar to Mini-MEND, a program aimed at children and families with increased risk at the age of 2-4 (or 5) years; this program is currently being evaluated in Australia and the United States. […] This individualized, targeted approach will bridge an existing gap in the primary prevention of overweight and related adverse cardiometabolic outcomes. […] One potential limitation of our study is that the outcomes of the prediction models were examined in early adolescence; moreover, the cohort data do not currently offer the opportunity to study these outcomes at adulthood. […] By providing longitudinal guidance to children, the Dutch CHC is highly specialized with respect to the growth and development of children, suggesting that performing dynamic risk assessments is a very feasible approach in this setting. […] A potential disadvantage associated with applying the ProCOR tools is that it may cause harm due to stigmatization and parental and/or individual concerns.
#29 JMIR Research Protocols – Prediction of Preadolescent Overweight and Poor Cardiometabolic Outcome in Children up to 6 Years of Age: Research Protocol
https://www.researchprotocols.org/2016/2/e85/
Therefore, participating JOGG cities in the Netherlands have opted to implement interventions that are similar to Mini-MEND, a program aimed at children and families with increased risk at the age of 2-4 (or 5) years; this program is currently being evaluated in Australia and the United States. […] This individualized, targeted approach will bridge an existing gap in the primary prevention of overweight and related adverse cardiometabolic outcomes. […] One potential limitation of our study is that the outcomes of the prediction models were examined in early adolescence; moreover, the cohort data do not currently offer the opportunity to study these outcomes at adulthood. […] By providing longitudinal guidance to children, the Dutch CHC is highly specialized with respect to the growth and development of children, suggesting that performing dynamic risk assessments is a very feasible approach in this setting. […] A potential disadvantage associated with applying the ProCOR tools is that it may cause harm due to stigmatization and parental and/or individual concerns.
#30 Child and adolescent obesity | Nature Reviews Disease Primers
https://www.nature.com/articles/s41572-023-00435-4
To our knowledge, the first RCT of semaglutide 2.4 mg, administered weekly by subcutaneous injection, in adolescents with obesity. […] To our knowledge, the first RCT of liraglutide, administered daily via subcutaneous injection, in adolescents with obesity. […] A new, comprehensive clinical practice guideline outlining current recommendations on assessment and treatment of children and adolescents with obesity. […] To our knowledge, the first systematic review to show that structured and professionally led weight management interventions in children and adolescents with obesity are associated with reductions in eating disorder risk and symptoms.
#31 Child and adolescent obesity | Nature Reviews Disease Primers
https://www.nature.com/articles/s41572-023-00435-4
To our knowledge, the first RCT of semaglutide 2.4 mg, administered weekly by subcutaneous injection, in adolescents with obesity. […] To our knowledge, the first RCT of liraglutide, administered daily via subcutaneous injection, in adolescents with obesity. […] A new, comprehensive clinical practice guideline outlining current recommendations on assessment and treatment of children and adolescents with obesity. […] To our knowledge, the first systematic review to show that structured and professionally led weight management interventions in children and adolescents with obesity are associated with reductions in eating disorder risk and symptoms.
#32 Child and adolescent obesity | Nature Reviews Disease Primers
https://www.nature.com/articles/s41572-023-00435-4
To our knowledge, the first RCT of semaglutide 2.4 mg, administered weekly by subcutaneous injection, in adolescents with obesity. […] To our knowledge, the first RCT of liraglutide, administered daily via subcutaneous injection, in adolescents with obesity. […] A new, comprehensive clinical practice guideline outlining current recommendations on assessment and treatment of children and adolescents with obesity. […] To our knowledge, the first systematic review to show that structured and professionally led weight management interventions in children and adolescents with obesity are associated with reductions in eating disorder risk and symptoms.
#33 Child and adolescent obesity | Nature Reviews Disease Primers
https://www.nature.com/articles/s41572-023-00435-4
To our knowledge, the first RCT of semaglutide 2.4 mg, administered weekly by subcutaneous injection, in adolescents with obesity. […] To our knowledge, the first RCT of liraglutide, administered daily via subcutaneous injection, in adolescents with obesity. […] A new, comprehensive clinical practice guideline outlining current recommendations on assessment and treatment of children and adolescents with obesity. […] To our knowledge, the first systematic review to show that structured and professionally led weight management interventions in children and adolescents with obesity are associated with reductions in eating disorder risk and symptoms.
#34 Childhood obesity prediction from nationwide health records | medRxiv
https://www.medrxiv.org/content/10.1101/2020.11.09.20228247v1.full-text
Our study paves the way for future trials of focused intervention addressing the real life efficacy of this approach. […] Overall, our large-scale national risk assessment model is an important step in prioritizing prevention strategies for childhood obesity. Childhood obesity can be accurately predicted during infancy or even at birth.
#35 Childhood obesity prediction from nationwide health records | medRxiv
https://www.medrxiv.org/content/10.1101/2020.11.09.20228247v1.full-text
Our study paves the way for future trials of focused intervention addressing the real life efficacy of this approach. […] Overall, our large-scale national risk assessment model is an important step in prioritizing prevention strategies for childhood obesity. Childhood obesity can be accurately predicted during infancy or even at birth.
#36 Accuracy of Using Weight and Length in Children Under… | VeriXiv
https://verixiv.org/articles/2-68
The global increase in early childhood overweight and obesity has prompted interest in early prediction of overweight and obesity to allow timely intervention and prevent lifelong consequences. […] Existing prediction models are not well-suited for broad-scale screening of individual children for risk of early childhood overweight or obesity. […] Despite the growing interest in early detection of obesity in children, much of the research has focused on school-aged children. […] Inaccurate screening may miss children who would benefit from interventions. […] Therefore, high accuracy is needed when screening for early childhood overweight and obesity. […] This systematic review specifically aimed to (1) determine whether information routinely collected in healthcare or community settings for children under 24 months can accurately predict overweight and obesity in individual children aged three to seven years; (2) assess whether adding predictors (e.g., sex, race, ethnicity, health status) improves prediction accuracy; and (3) assess the validity of predictions across study characteristics (i.e., outcome prevalence, sample size, study location).
#37 Accuracy of Using Weight and Length in Children Under… | VeriXiv
https://verixiv.org/articles/2-68
The global increase in early childhood overweight and obesity has prompted interest in early prediction of overweight and obesity to allow timely intervention and prevent lifelong consequences. […] Existing prediction models are not well-suited for broad-scale screening of individual children for risk of early childhood overweight or obesity. […] Despite the growing interest in early detection of obesity in children, much of the research has focused on school-aged children. […] Inaccurate screening may miss children who would benefit from interventions. […] Therefore, high accuracy is needed when screening for early childhood overweight and obesity. […] This systematic review specifically aimed to (1) determine whether information routinely collected in healthcare or community settings for children under 24 months can accurately predict overweight and obesity in individual children aged three to seven years; (2) assess whether adding predictors (e.g., sex, race, ethnicity, health status) improves prediction accuracy; and (3) assess the validity of predictions across study characteristics (i.e., outcome prevalence, sample size, study location).
#38 Accuracy of Using Weight and Length in Children Under… | VeriXiv
https://verixiv.org/articles/2-68
The global increase in early childhood overweight and obesity has prompted interest in early prediction of overweight and obesity to allow timely intervention and prevent lifelong consequences. […] Existing prediction models are not well-suited for broad-scale screening of individual children for risk of early childhood overweight or obesity. […] Despite the growing interest in early detection of obesity in children, much of the research has focused on school-aged children. […] Inaccurate screening may miss children who would benefit from interventions. […] Therefore, high accuracy is needed when screening for early childhood overweight and obesity. […] This systematic review specifically aimed to (1) determine whether information routinely collected in healthcare or community settings for children under 24 months can accurately predict overweight and obesity in individual children aged three to seven years; (2) assess whether adding predictors (e.g., sex, race, ethnicity, health status) improves prediction accuracy; and (3) assess the validity of predictions across study characteristics (i.e., outcome prevalence, sample size, study location).
#39 JMIR Research Protocols – Prediction of Preadolescent Overweight and Poor Cardiometabolic Outcome in Children up to 6 Years of Age: Research Protocol
https://www.researchprotocols.org/2016/2/e85/
Therefore, participating JOGG cities in the Netherlands have opted to implement interventions that are similar to Mini-MEND, a program aimed at children and families with increased risk at the age of 2-4 (or 5) years; this program is currently being evaluated in Australia and the United States. […] This individualized, targeted approach will bridge an existing gap in the primary prevention of overweight and related adverse cardiometabolic outcomes. […] One potential limitation of our study is that the outcomes of the prediction models were examined in early adolescence; moreover, the cohort data do not currently offer the opportunity to study these outcomes at adulthood. […] By providing longitudinal guidance to children, the Dutch CHC is highly specialized with respect to the growth and development of children, suggesting that performing dynamic risk assessments is a very feasible approach in this setting. […] A potential disadvantage associated with applying the ProCOR tools is that it may cause harm due to stigmatization and parental and/or individual concerns.
#40 JMIR Research Protocols – Prediction of Preadolescent Overweight and Poor Cardiometabolic Outcome in Children up to 6 Years of Age: Research Protocol
https://www.researchprotocols.org/2016/2/e85/
Therefore, participating JOGG cities in the Netherlands have opted to implement interventions that are similar to Mini-MEND, a program aimed at children and families with increased risk at the age of 2-4 (or 5) years; this program is currently being evaluated in Australia and the United States. […] This individualized, targeted approach will bridge an existing gap in the primary prevention of overweight and related adverse cardiometabolic outcomes. […] One potential limitation of our study is that the outcomes of the prediction models were examined in early adolescence; moreover, the cohort data do not currently offer the opportunity to study these outcomes at adulthood. […] By providing longitudinal guidance to children, the Dutch CHC is highly specialized with respect to the growth and development of children, suggesting that performing dynamic risk assessments is a very feasible approach in this setting. […] A potential disadvantage associated with applying the ProCOR tools is that it may cause harm due to stigmatization and parental and/or individual concerns.
#41 Predicting higher child BMI z-score and obesity incidence in Malaysia: a longitudinal analysis of a dynamic cohort study | BMC Public Health | Full Text
https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-024-18917-9
Higher baseline BMI z-score predicted higher follow-up BMI z-score both in childhood to late adolescence (0.60; 95% CI: 0.55, 0.65) and early to late adolescence (0.76; 95% CI: 0.70, 0.82). […] Parental obesity consistently predicted five-year obesity incidence in early to late adolescence, but not childhood to early adolescence. […] An adolescent without obesity at baseline with parents with obesity, had 34 times greater odds of developing obesity during follow-up (incidence OR=3.38 (95% CI: 1.149.98, mother) and OR=4.37 (95% CI 1.3414.27, father) respectively). […] Having a higher BMI z-score at baseline was a stronger predictor of a higher BMI z-score at follow-up than any parental or sociodemographic factor. […] Targeting prevention efforts based on parent or sociodemographic factors is unwarranted but early childhood remains a key period for universal obesity prevention.