Faculty Bibliography

The objective of this study was to determine how well Hispanic/Latino siblings' diet quality correlate with each other and whether social and environmental factors explained potential differences. Hispanic/Latino 8-16-year-olds from the cross-sectional Hispanic Community Children's Health Study/Study of Latino Youth (SOL Youth) with at least one sibling enrolled in the study were examined (n = 740). Diet quality was assessed with the Healthy Eating Index 2010 (HEI-2010), calculated from two 24-h recalls. Mixed effects models were used with HEI-2010 score as the outcome, and correlations in siblings' diet quality were assessed with intraclass correlation coefficients (ICCs). All models were examined stratified by age and sex. Diet-related social and environmental measures were added as fixed effects in a secondary analysis. Mean (standard deviation) overall HEI-2010 score was 53.8 (13.0). The ICC for siblings' HEI-2010 score was 0.31 (95% CI: 0.25, 0.38). Siblings who were born <3 vs. ≥3 years apart had stronger correlations in overall diet quality (0.47 [95% CI: 0.37, 0.58] vs. 0.21 [95% CI: 0.13, 0.30]), but no differences were observed in overall HEI-2010 score according to sex. Greater peer support for fruit and vegetable intake (β = 1.42 [95% CI: 0.62, 2.21]) and greater away-from-home food consumption (β = -1.24 [95% CI: -2.15, -0.32]) were associated with differences in siblings' diet quality. Overall diet quality scores of Hispanic/Latino siblings in this study were slightly correlated, with stronger correlations among siblings closer in age. Differences in peer support and foods consumed outside the home may explain differences in siblings' diet quality. Future research should investigate additional determinants of differences in siblings' diets.

Exclusion of special populations (older adults; pregnant women, children, and adolescents; individuals of lower socioeconomic status and/or who live in rural communities; people from racial and ethnic minority groups; individuals from sexual or gender minority groups; and individuals with disabilities) in research is a pervasive problem, despite efforts and policy changes by the National Institutes of Health and other organizations. These populations are adversely impacted by social determinants of health (SDOH) that reduce access and ability to participate in biomedical research. In March of 2020, the Northwestern University Clinical and Translational Sciences Institute hosted the "Lifespan and Life Course Research: integrating strategies" "Un-Meeting" to discuss barriers and solutions to underrepresentation of special populations in biomedical research. The COVID-19 pandemic highlighted how exclusion of representative populations in research can increase health inequities. We applied findings of this meeting to perform a literature review of barriers and solutions to recruitment and retention of representative populations in research and to discuss how findings are important to research conducted during the ongoing COVID-19 pandemic. We highlight the role of SDOH, review barriers and solutions to underrepresentation, and discuss the importance of a structural competency framework to improve research participation and retention among special populations.

Objective/UNASSIGNED: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/UNASSIGNED:). Effect measure modification by covariates was examined. Results/UNASSIGNED: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/UNASSIGNED: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.

BACKGROUND:Hispanic/Latino youth are disproportionately burdened by obesity and have a high prevalence of prediabetes and dyslipidemia. Differences in parent and child acculturation related to language use and preference (i.e., language acculturation) are associated with adverse cardiometabolic health behaviors, but no study has examined associations with cardiometabolic markers. PURPOSE:To determine whether discordance in parent-child language acculturation (parent-child acculturation gap) was associated with poor youth cardiometabolic health. METHODS:Hispanic/Latino 8-16-year-olds (n = 1,466) and parents from the Hispanic Community Children's Health Study/Study of Latino Youth (SOL Youth) were examined. Mean scores for the Brief ARSMA-II's Anglo (AOS) and Latino (LOS) Orientation Scales represented language acculturation. Cardiometabolic markers included youth body mass index (BMI) percentile, blood pressure percentiles, and dysglycemia and hyperlipidemia measures. Missing data were imputed. Survey-weighted multivariable linear regression examined the association of youth, parent, and youth × parent (the acculturation gap) AOS and LOS scores separately with each cardiometabolic marker. RESULTS:Youth reported greater English and lower Spanish use than parents. Greater discordance in AOS scores was associated with elevated BMI percentile only (p-for-interaction < .01). The LOS acculturation gap was not associated with any outcome. Adjustment for acculturative stress, family functioning and closeness, parenting style, and youth's diet and physical activity did not alter findings. Removal of nonsignificant acculturation gaps did not indicate an association between individual youth or parent AOS or LOS scores and any cardiometabolic marker. CONCLUSIONS:Discordance in Hispanic/Latino parent-child dyads' English use may relate to increased risk for childhood obesity. Future studies should identify mediators of this association.

Few studies have derived data-driven dietary patterns in youth in the USA. This study examined data-driven dietary patterns and their associations with BMI measures in predominantly low-income, racial/ethnic minority US youth. Data were from baseline assessments of the four Childhood Obesity Prevention and Treatment Research (COPTR) Consortium trials: NET-Works (534 2-4-year-olds), GROW (610 3-5-year-olds), GOALS (241 7-11-year-olds) and IMPACT (360 10-13-year-olds). Weight and height were measured. Children/adult proxies completed three 24-h dietary recalls. Dietary patterns were derived for each site from twenty-four food/beverage groups using k-means cluster analysis. Multivariable linear regression models examined associations of dietary patterns with BMI and percentage of the 95th BMI percentile. Healthy (produce and whole grains) and Unhealthy (fried food, savoury snacks and desserts) patterns were found in NET-Works and GROW. GROW additionally had a dairy- and sugar-sweetened beverage-based pattern. GOALS had a similar Healthy pattern and a pattern resembling a traditional Mexican diet. Associations between dietary patterns and BMI were only observed in IMPACT. In IMPACT, youth in the Sandwich (cold cuts, refined grains, cheese and miscellaneous) compared with Mixed (whole grains and desserts) cluster had significantly higher BMI (β = 0·99 (95 % CI 0·01, 1·97)) and percentage of the 95th BMI percentile (β = 4·17 (95 % CI 0·11, 8·24)). Healthy and Unhealthy patterns were the most common dietary patterns in COPTR youth, but diets may differ according to age, race/ethnicity or geographic location. Public health messages focused on healthy dietary substitutions may help youth mimic a dietary pattern associated with lower BMI.

Individuals of South Asian ethnicity have an increased risk for obesity and related diseases. Foods available in the home during the first 1000 days (conception to 24 months old) are an important determinant of diet, yet no study has examined the association of early-life home food availability (HFA) with later diet and obesity risk in South Asian households. We examined whether obesogenic HFA at 18 months of age is associated with dietary intake and body mass index (BMI) at 36 months of age in low-income Pakistani and White households in the United Kingdom. In this prospective birth cohort study (Born in Bradford 1000), follow-up assessments occurred at 18 (n = 1032) and 36 (n = 986) months of age. Variety and quantity of snack foods and sugar-sweetened beverages (SSBs) in the home and consumed were measured using the HFA Inventory Checklist and food frequency questionnaires, respectively. BMI was calculated using measured length/height and weight. Multinomial logistic regression models examined associations between HFA and tertiles of dietary intake, and multivariable linear regression models assessed associations between HFA and BMI. Pakistani households had a greater variety and quantity of snack foods and SSBs available compared with White households. Variety and quantity of snack foods and SSBs in the home at 18 months were positively associated with children's intake of these items at 36 months, but associations between HFA and BMI were null. Reducing obesogenic HFA during the first 1000 days may promote the development of more healthful diets, though this may not be associated with lower obesity risk during toddlerhood.

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.

AIMS/OBJECTIVE: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. METHODS: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. RESULTS: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. CONCLUSIONS:Reducing loss of FFM during adulthood may reduce prediabetes risk (primarily insulin resistance), particularly among individuals with overweight/obesity.

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.