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Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child's body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child's current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child's growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child's obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to examine modifiable mediators for socioeconomic disparities in childhood obesity in the United States.Methods: This study used the data of 1,211 mother-child dyads from a US national birth cohort from pregnancy to 6 years post partum. Socioeconomic status was indicated by maternal education (college graduate vs. less) and family income (>185% vs. ≤185% of the poverty line). Single- and multiple-factor mediation analyses were conducted for socioeconomic disparities in childhood obesity at 6 years, adjusting for demographics.Results: The confounder-adjusted relative risk of childhood obesity was 1.79 for low maternal education and 1.42 for low family income. Low-maternal-education-related obesity was individually mediated by maternal preconception BMI (percentage of indirect effect, 8.8%), smoking during pregnancy (7.0%), infant weight gain (14.4%), child sleep duration (11.4%), and TV viewing during weekdays at 6 years (4.9%). Low-family-income-related obesity was mediated by maternal preconception BMI (18.5%), smoking during pregnancy (6.3%), child sleep duration (12.8%), and the home learning environment at 6 years (26.2%). In multiple-mediator analysis, significant mediators together mediated 54.0% of maternal-education-related or 39.4% of family-income-related disparities.Conclusions: Maternal preconception BMI, smoking during pregnancy, infant weight gain, child sleep, TV viewing, and the home learning environment substantially mediated socioeconomic disparities in childhood obesity in the United States. [ABSTRACT FROM AUTHOR]

Background Comparing body fat and the effect of body fat on metabolic abnormalities in Chinese and USA teenagers may provide clues for the early prevention and control of cardiovascular disease (CVD). We aimed to compare the prevalence of glucose and lipid metabolism abnormalities, body fat amount and distribution, and the effect of body fat on glucose and lipid metabolism in Chinese and US teenagers. Methods We included 5424 Chinese teenagers (48.5% male) from the China Child and Adolescent Cardiovascular Health (CCACH) study and 8704 USA teenagers (55.6% male) from the USA National Health and Nutrition Examination Survey (NHANES). Blood lipid, blood glucose, and body fat indicators were obtained using the same standardised measurements. Results The prevalence of dyslipidaemia in Chinese teenagers was of those in the USA (hypercholesterolaemia = 3.5% vs 7.4%; high low-density lipoprotein cholesterol (LDL-C) = 3.6% vs 5.0%; low high-density lipoprotein cholesterol (HDL-C)=9.9% vs 14.3%; hypertriglyceridaemia=3.7% vs 10.1%) (P<0.05). However, with the increase in body mass index (BMI), the prevalence of high LDL-C increased more in Chinese than in US teenagers, even exceeding them in the obese group (2.7% in non-overweight to 9.7% in overweight group in China, P<0.05; 3.5% in non-overweight to 6.5% in the obese group in the USA, P<0.05). The prevalence of impaired fasting glucose was higher in China than in the USA (28.0% vs 17.5%, P<0.05). Besides, Chinese adolescents are more likely to accumulate fat in the abdomen, and the per-unit fat increase would bring a higher risk of dyslipidaemia in Chinese boys than in USA boys. Conclusions Dyslipidaemia was more prevalent in US teenagers than in Chinese teenagers, but with the increase in BMI, the prevalence of high LDL-C increased more in Chinese than in US teenagers. Impaired fasting glucose (IFG) was significantly more prevalent in China than in the USA. The unfavoured body fat and higher risk of body fat on metabolic disorders in Chinese teenagers suggest that Chinese teenagers should pay more attention to the adverse effect of body fat on metabolic abnormalities. [ABSTRACT FROM AUTHOR]

Background: Breastfeeding is widely recognized for its potential to reduce childhood obesity. However, research investigating these benefits in children breastfed for a short duration (up to 6 months) remains limited despite this being a common practice globally. Methods: This study focused on a population breastfed for 6 months or less to determine the potential benefits of short-term breastfeeding for preventing childhood obesity. Data were collected from five survey cycles of an US-based population study (the National Health and Nutrition Examination Survey (NHANES)), spanning 2009–2020. A sample of 3,211 children aged 2–6 years was selected, including 1,373 never breastfed and 1,838 ever breastfed. Logistic regression analysis examined the direct association between short-term breastfeeding and childhood obesity. Subsequent subgroup analyses were conducted. Additionally, stratified logistic regression explored the relationship between childhood obesity and the introduction of other early nutrition in both ever-breastfed and never-breastfed children. Results: Overall, breastfeeding for 6 months or less did not directly prevent childhood obesity. However, among participants with older mothers (aged 35 or above), short-term breastfeeding was associated with a lower risk of childhood obesity compared to never being breastfed (OR 0.31, 95% CI: 0.17, 0.59). Similarly, children aged 3–4 years who were breastfed for > 3 ~ 6 months exhibited a lower obesity risk (OR 0.56, 95% CI: 0.35, 0.89). In ever-breastfed children, delayed infant formula introduction was linked to a lower risk of obesity (P-trend < 0.05: introduction at age ≤ 1 vs. >1 ~ 3 vs. >3 months). Conversely, for non-breastfed children, introducing milk (other than breast milk or formula) later (≥ 12 versus < 12 months) and introducing alternatives to whole cow's milk were associated with lower obesity risks (OR 0.54, 95% CI: 0.37, 0.78; OR 0.21, 95% CI: 0.08, 0.60, respectively). Notably, these trends were not observed in ever-breastfed children. Conclusions: Short-term breastfeeding may offer some benefits in preventing childhood obesity for specific populations. Additionally, it could potentially mitigate risks associated with the introduction of formula and cow's milk at inappropriate times. [ABSTRACT FROM AUTHOR]

Suicide is the second leading cause of death among individuals aged 5 to 24 in the United States (US). However, the precursors to suicide often do not surface, making suicide prevention challenging. This study aims to develop a machine learning model for predicting suicide ideation (SI), suicide planning (SP), and suicide attempts (SA) among adolescents in the US during the coronavirus pandemic. We used the 2021 Adolescent Behaviors and Experiences Survey Data. Class imbalance was addressed using the proposed data augmentation method tailored for binary variables, Modified Synthetic Minority Over-Sampling Technique. Five different ML models were trained and compared. SHapley Additive exPlanations analysis was conducted for explainability. The Logistic Regression model, identified as the most effective, showed superior performance across all targets, achieving high scores in recall: 0.82, accuracy: 0.80, and area under the Receiver Operating Characteristic curve: 0.88. Variables such as sad feelings, hopelessness, sexual behavior, and being overweight were noted as the most important predictors. Our model holds promise in helping health policymakers design effective public health interventions. By identifying vulnerable sub-groups within regions, our model can guide the implementation of tailored interventions that facilitate early identification and referral to medical treatment. [ABSTRACT FROM AUTHOR]

Importance: The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020. However, despite the recognition of long-term weight gain as an important public health issue, there is a paucity of studies studying the long-term weight gain and building models for long-term projection. Methods: A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017–2020) was conducted in patients who completed the weight questionnaire and had accurate data for both weight at time of survey and weight ten years ago. Multistate gradient boost modeling classifiers were used to generate covariate dependent transition matrices and Markov chains were utilized for multistate modeling. Results: Of the 6146 patients that met the inclusion criteria, 3024 (49%) of patients were male and 3122 (51%) of patients were female. There were 2252 (37%) White patients, 1257 (20%) Hispanic patients, 1636 (37%) Black patients, and 739 (12%) Asian patients. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight (Fig. 1). A total of 2411 (39%) patients lost weight, and 3735 (61%) patients gained weight (Table 1). We observed that 87 (1%) of patients were underweight (BMI < 18.5), 2058 (33%) were normal weight (18.5 ≤ BMI < 25), 1376 (22%) were overweight (25 ≤ BMI < 30) and 2625 (43%) were obese (BMI > 30). From analysis of the transitions between normal/underweight, overweight, and obese, we observed that after 10 years, of the patients who were underweight, 65% stayed underweight, 32% became normal weight, 2% became overweight, and 2% became obese. After 10 years, of the patients who were normal weight, 3% became underweight, 78% stayed normal weight, 17% became overweight, and 2% became obese. Of the patients who were overweight, 71% stayed overweight, 0% became underweight, 14% became normal weight, and 15% became obese. Of the patients who were obese, 84% stayed obese, 0% became underweight, 1% became normal weight, and 14% became overweight. Conclusions: United States adults are at risk of transitioning from normal weight to becoming overweight or obese. Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions. [ABSTRACT FROM AUTHOR]

The Environmental Influences on Child Health Outcomes (ECHO)-Wide Cohort Study (EWC), a collaborative research design comprising 69 cohorts in 31 consortia, was funded by the National Institutes of Health (NIH) in 2016 to improve children's health in the United States. The EWC harmonizes extant data and collects new data using a standardized protocol, the ECHO-Wide Cohort Data Collection Protocol (EWCP). EWCP visits occur at least once per life stage, but the frequency and timing of the visits vary across cohorts. As of March 4, 2022, the EWC cohorts contributed data from 60,553 children and consented 29,622 children for new EWCP data and biospecimen collection. The median (interquartile range) age of EWCP-enrolled children was 7.5 years (3.7–11.1). Surveys, interviews, standardized examinations, laboratory analyses, and medical record abstraction are used to obtain information in 5 main outcome areas: pre-, peri-, and postnatal outcomes; neurodevelopment; obesity; airways; and positive health. Exposures include factors at the level of place (e.g. air pollution, neighborhood socioeconomic status), family (e.g. parental mental health), and individuals (e.g. diet, genomics). [ABSTRACT FROM AUTHOR]

The article focuses on a decline in the health advantage of U.S. Hispanic Americans over non-Hispanic White populations in the U.S. Topics discussed include the interest of the research community in the phenomenon called the Hispanic paradox, studies on similar health advantages with other immigrants in the U.S., studies on environmental exposures that harm Hispanic populations, and the rise in obesity and its impact on the Hispanic paradox.

Obesity affects 42.4% of adults and 19.3% of children in the United States. Childhood obesity drives many comorbidities including hypertension, fatty liver disease, and type 2 diabetes mellitus. Prior research suggests that aberrant compositional development of the gut microbiome, with low-grade inflammation, precedes being overweight. Therefore, childhood may provide opportunities for interventions that shape the microbiome to mitigate obesity-related diseases. Children with obesity have gut microbiota compositional and functional differences, including increased proinflammatory bacterial taxa, compared to lean controls. Restoration of the gut microbiota to a healthy state may ameliorate conditions associated with obesity and help maintain a healthy weight. Pediatric bariatric (weight-loss) surgery is an effective treatment for childhood obesity; however, there is limited research into the role of the gut microbiome after weight-loss surgery in children. This review will discuss the magnitude of childhood obesity, the importance of the developing microbiome in establishing metabolic pathways, interventions such as bariatric surgery that may modulate the gut microbiome, and future directions for the potential development of microbiome-based therapeutics to treat obesity. [ABSTRACT FROM AUTHOR]

Background: Fatty liver disease is a common metabolic abnormality in adolescents with obesity but remains understudied in early childhood.Objectives: To describe hepatic fat deposition in prepubertal children and examine cross-sectional associations with metabolic markers and body composition.Methods: Data were from 286 children ages 4 to 8 years old in the Healthy Start Study, a longitudinal prebirth cohort in Colorado (USA). Assessments included magnetic resonance imaging to quantify hepatic and abdominal fats, fasting blood draws to measure metabolic markers, and air displacement plethysmography to measure body composition (fat mass and fat-free mass).Results: The median (interquartile range) for hepatic fat was 1.65% (1.24%, 2.11%). Log-transformed hepatic fat was higher in Hispanic [mean (95% CI): 0.63 (0.52, 0.74)] vs non-Hispanic white children [0.46 (0.38, 0.53), P = 0.01] and children with overweight/obesity [0.64 (0.49, 0.79)] vs normal-weight [0.47 (0.40, 0.53), P = 0.02]. Higher log-hepatic fat was associated with higher insulin [β (95% CI): 1.47 (0.61, 2.33) uIU/mL, P = 0.001] and estimated insulin resistance (homeostatic model assessment) [0.40 (0.20, 0.60), P < 0.001] in the full sample and glucose [5.53 (2.84, 8.21) mg/dL, P < 0.001] and triglycerides [10.92 (2.92,18.91) mg/dL, P = 0.008] in boys, in linear regression models adjusted for sociodemographics, maternal/perinatal confounders, and percentage body fat. Log-hepatic fat was also associated with abdominal subcutaneous adipose tissue [SAT; 7.37 (1.12,13.60) mm2, P = 0.02] in unadjusted models, but this was attenuated and insignificant after adjusting for confounders.Conclusions: While hepatic fat was low in children 4 to 8 years old, it was independently associated with estimated insulin resistance and exhibited sex-specific associations with glucose and triglycerides, suggesting hepatic fat may be an early indicator of metabolic dysfunction in youth. [ABSTRACT FROM AUTHOR]