Identifying Key Determinants of Childhood Obesity: A Narrative Review of Machine Learning Studies

Machine learning is a class of algorithms able to handle a large number of predictors with potentially nonlinear relationships. By applying machine learning to obesity, researchers can examine how risk factors across multiple settings (e.g., school and home) interact to best predict childhood obesity risk. In this narrative review, we provide an overview of studies that have applied machine learning to predict childhood obesity using a combination of sociodemographic and behavioral risk factors. The objective is to summarize the key determinants of obesity identified in existing machine learning studies and highlight opportunities for future machine learning applications in the field. Of 15 peer-reviewed studies, approximately half examined early childhood (0-24 months of age) determinants. These studies identified child's weight history (e.g., history of overweight/obesity or large increases in weight-related measures between birth and 24 months of age) and parental overweight/obesity (current or prior) as key risk factors, whereas the remaining studies indicated that social factors and physical inactivity were important in middle childhood and late childhood/adolescence. Across age groups, findings suggested that race/ethnic-specific models may be needed to accurately predict obesity from middle childhood onward. Future studies should consider using existing large data sets to take advantage of the benefits of machine learning and should collect a wider range of novel risk factors (e.g., psychosocial and sociocultural determinants of health) to better predict childhood obesity. Ultimately, such research can aid in the development of effective obesity prevention interventions, particularly ones that address the disproportionate burden of obesity experienced by racial/ethnic minorities

Snacking characteristics and patterns and their associations with diet quality and BMI in the Childhood Obesity Prevention and Treatment Research Consortium

Objective: To describe snacking characteristics and patterns in children and examine associations with diet quality and BMI. Design: Children's weight and height were measured. Participants/adult proxies completed multiple 24 h dietary recalls. Snack occasions were self-identified. Snack patterns were derived for each sample using exploratory factor analysis. Associations of snacking characteristics and patterns with Healthy Eating Index-2010 (HEI-2010) score and BMI were examined using multivariable linear regression models. Setting: Childhood Obesity Prevention and Treatment Research (COPTR) Consortium, USA: NET-Works, GROW, GOALS and IMPACT studies. Participants: Predominantly low-income, racial/ethnic minorities: NET-Works (n 534, 2-4-year-olds); GROW (n 610, 3-5-year-olds); GOALS (n 241, 7-11-year-olds); IMPACT (n 360, 10-13-year-olds).Results: Two snack patterns were derived for three studies: a meal-like pattern and a beverage pattern. The IMPACT study had a similar meal-like pattern and a dairy/grains pattern. A positive association was observed between meal-like pattern adherence and HEI-2010 score (P for trend < 0-01) and snack occasion frequency and HEI-2010 score (β coefficient (95 % CI): NET-Works, 0-14 (0-04, 0-23); GROW, 0-12 (0-02, 0-21)) among younger children. A preference for snacking while using a screen was inversely associated with HEI-2010 score in all studies except IMPACT (β coefficient (95 % CI): NET-Works, -3-15 (-5-37, -0-92); GROW, -2-44 (-4-27, -0-61); GOALS, -5-80 (-8-74, -2-86)). Associations with BMI were almost all null. Conclusions: Meal-like and beverage patterns described most children's snack intake, although patterns for non-Hispanic Blacks or adolescents may differ. Diets of 2-5-year-olds may benefit from frequent meal-like pattern snack consumption and diets of all children may benefit from decreasing screen use during eating occasions

Association of food parenting practice patterns with obesogenic dietary intake in Hispanic/Latino youth: Results from the Hispanic Community Children's Health Study/Study of Latino Youth (SOL Youth)

Some food parenting practices (FPPs)are associated with obesogenic dietary intake in non-Hispanic youth, but studies in Hispanics/Latinos are limited. We examined how FPPs relate to obesogenic dietary intake using cross-sectional data from 1214 Hispanic/Latino 8-16-year-olds and their parents/caregivers in the Hispanic Community Children's Health Study/Study of Latino Youth (SOL Youth). Diet was assessed with 2 24-h dietary recalls. Obesogenic items were snack foods, sweets, and high-sugar beverages. Three FPPs (Rules and Limits, Monitoring, and Pressure to Eat)derived from the Parenting strategies for Eating and Activity Scale (PEAS)were assessed. K-means cluster analysis identified 5 groups of parents with similar FPP scores. Survey-weighted multiple logistic regression examined associations of cluster membership with diet. Parents in the controlling (high scores for all FPPs)vs. indulgent (low scores for all FPPs)cluster had a 1.75 (95% CI: 1.02, 3.03)times higher odds of having children with high obesogenic dietary intake. Among parents of 12–16-year-olds, membership in the pressuring (high Pressure to Eat, low Rules and Limits and Monitoring scores)vs. indulgent cluster was associated with a 2.96 (95% CI: 1.51, 5.80)times greater odds of high obesogenic dietary intake. All other associations were null. Future longitudinal examinations of FPPs are needed to determine temporal associations with obesogenic dietary intake in Hispanic/Latino youth

The association of Step-based metrics and adiposity in the Hispanic community Health Study/Study of Latinos

Objective: Examine cross-sectional and longitudinal associations of accelerometer measured step volume (steps/day) and cadence with adiposity and six-year changes in adiposity in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Methods: HCHS/SOL's target population was 60% female with a mean age of 41 years. Cross-sectional (n = 12,353) and longitudinal analyses (n = 9,077) leveraged adjusted complex survey regression models to examine associations between steps/day, and cadence with weight (kg), waist circumference (cm) and body mass index (kg/m2). Effect measure modification by covariates was examined. Results: Lower steps/day and intensity was associated with higher adiposity at baseline. Compared to those in the highest quartile of steps/day those in the lowest quartile have 1.42 95% CI (1.19, 1.70) times the odds of obesity. Compared to those in the highest categories of cadence step-based metrics, those in the lowest categories had a 1.62 95% CI (1.36, 1.93), 2.12 95% CI (1.63, 2.75) and 1.41 95% CI (1.16, 1.70) odds of obesity for peak 30-minute cadence, brisk walking and faster ambulation and bouts of purposeful steps and faster ambulation, respectively. Compared to those with the highest stepping cadences, those with the slowest peak 30-minute cadence and fewest minutes in bouts of purposeful steps and faster ambulation had 0.72 95% CI (0.57, 0.89) and 0.82 95% CI (0.60, 1.14) times the odds of gaining weight, respectively. Conclusion: Inverse cross-sectional relationships were found for steps/day and cadence and adiposity. Over a six-year period, higher step intensity but not volume was associated with higher odds of gaining weight

Associations of changes in fat free mass with risk for type 2 diabetes: Hispanic Community Health Study/Study of Latinos

To determine whether loss of muscle mass (approximated using fat free mass [FFM]) is associated with risk for type 2 diabetes mellitus (T2DM) in Hispanic/Latino adults in the United States. Participants were Hispanic/Latino adults (18–74-year-olds) who completed Visit 2 of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL; multi-site, prospective cohort study; 6.1-year follow-up) and did not have T2DM at baseline (n = 6264). At baseline and Visit 2, FFM was measured using bioelectrical impedance analysis and fasting glucose, HbA1c, and fasting insulin were measured by examiners. Diabetes was defined according to American Diabetes Association criteria. Survey-weighted Poisson regression models examined the association of percent change in relative FFM (%ΔFFM) with incident prediabetes and T2DM. Survey-weighted multivariable regression models examined associations of %ΔFFM with changes in glucose and insulin measures. Relative FFM declined by 2.1% between visits. %ΔFFM was inversely associated with incident prediabetes (p-for-trend = 0.001) and with changes in glucose and insulin measures (p-for-trend <0.0001). Findings were null, except for HOMA-IR, after adjustment for changes in adiposity measures. Associations were generally stronger for individuals with baseline overweight/obesity. Reducing loss of FFM during adulthood may reduce prediabetes risk (primarily insulin resistance), particularly among individuals with overweight/obesity