Plant-Based Milk Consumption and Growth in Children 1-10 Years of Age.

BACKGROUND: The effects of plant-based milk consumption on the growth of children are unclear. OBJECTIVES: We aimed to evaluate the relationship between plant-based milk consumption and BMI in childhood. Secondary objectives were to examine the association with height and whether these relationships are mediated by dairy milk intake and modified by age or the type of plant-based milk consumed. METHODS: A prospective cohort study was conducted in healthy children aged 1-10 y through the TARGet Kids! primary care research network in Toronto, Canada. Linear mixed-effect modeling and logistic generalized estimating equations were used to evaluate the association between plant-based milk consumption (number of 250 mL cups/d) and BMI. A mediation analysis was conducted to examine whether dairy milk intake mediated these relationships. Effect modification by age and type of plant-based milk was explored. RESULTS: Among 7195 children (mean age: 3.1 y; 52.3% male), higher plant-based milk consumption was associated with lower BMI (P = 0.0002) and height (P = 0.005). No association was found with BMI categories. Lower dairy milk intake partially mediated these relationships. A child aged 5 y who consumed 3 cups of plant-based milk compared with 3 cups of dairy milk had a lower weight of 0.5 kg and lower height of 0.8 cm. Associations did not change over time and were similar for children who consumed soy milk compared with other plant-based milks. CONCLUSIONS: Plant-based milk consumption was associated with lower BMI and height, but both were within the normal range on average. Future longitudinal studies are needed to determine whether these associations persist over time.

Evaluation of ethnic differences in cardiometabolic risk in children.

PURPOSE: In adults, cardiometabolic conditions manifest differently by ethnicity with South Asians particularly predisposed. Whether these differences arise in childhood remains narrowly explored. To address this evidence gap, we examined whether children of different ethnicities display differences in cardiometabolic risk (CMR). METHODS: A cross-sectional analysis was conducted among 5557 children (3-11 years). Multivariable linear regression models adjusted for age, sex, z-body mass index, and demographic factors were used to estimate differences in CMR outcomes between children with parents that self-reported European ancestry (reference group) and one of 13 other ethnicities (African, Arab, East Asian, Latin American, South Asian, Southeast Asian, Mixed Ethnicities, and Other). The primary outcome was a CMR score, calculated as the sum of age- and sex-standardized waist circumference, systolic blood pressure (SBP), glucose, log-triglycerides, and inverse high-density lipoprotein cholesterol (HDL-C), divided by √5. RESULTS: Lower mean CMR scores were observed among children with African (ß = -0.62, 95% CI: -0.92; -0.32) and East Asian (ß = -0.41, 95% CI: -0.68, -0.15) ancestry compared to children with European ancestry. Children with South Asian ancestry had higher SBP (ß = 2.25, 95% CI: 1.27, 3.22) and non-HDL-C (ß = 0.17, 95% CI: 0.07, 0.26) than children with European ancestry. CONCLUSIONS: Ethnic differences in CMR were observed in early and middle childhood.

Maternal Preconception Body Mass Index and Early Childhood Nutritional Risk.

BACKGROUND: Risk factors for problematic child eating behaviors and food preferences are thought to begin during the preconception period. It is unknown if maternal preconception body mass index (BMI) is associated with child nutritional risk factors (eg, poor dietary intake and eating behaviors). OBJECTIVES: We aimed to determine whether maternal preconception BMI was associated with child nutritional risk. METHODS: In this longitudinal cohort study, a secondary data analysis of children ages 18 mo to 5 y were recruited from The Applied Research Group for Kids (TARGet Kids!), a primary care practice-based research network in Canada. The primary exposure was maternal preconception BMI. The primary outcome was parent-reported child nutritional risk score, measured using the Nutrition Screening for Every Preschooler/Toddler (NutriSTEP), an age-appropriate validated questionnaire. Fitted linear mixed effects models analyzed associations between maternal preconception BMI and child nutritional risk after adjusting for covariates. RESULTS: This study included 4733 children with 8611 repeated NutriSTEP observations obtained between ages 18 mo to 5 y. The mean (standard deviation [SD]) maternal preconception BMI was 23.6 (4.4), where 73.1% of mothers had a BMI ≤24.9 kg/m2, and 26.9% had a BMI ≥25 kg/m2. The mean (SD) NutriSTEP total score was 13.5 (6.2), with 86.6% at low risk (score <21) and 13.4% at high risk (score ≥21). Each 1 unit increase in maternal preconception BMI was associated with a 0.09 increase in NutriSTEP total score (95% confidence interval [CI]: 0.05, 0.12; P ≤ 0.001). After stratification, each 1 unit increase in maternal BMI was associated with a 0.06 increase in mean NutriSTEP total score (95% CI: 0.007, 0.11; P = 0.025) in toddlers and 0.11 increase in mean NutriSTEP total score (95% CI: 0.07, 0.15; P < 0.001) in preschoolers. CONCLUSION: Higher maternal preconception BMI is associated with slightly higher NutriSTEP total scores. This provides evidence that the preconception period may be an important time to focus on for improving childhood nutrition. This study was registered at clinicaltrials.gov as NCT01869530.

Nutritional risk in early childhood and parent-reported school concerns.

OBJECTIVE: To determine if nutritional risk in early childhood is associated with parent-reported school concerns. DESIGN: A prospective cohort study conducted through the TARGet Kids! primary care research network (2011-2018). Nutritional risk was measured between 18 months and 5 years of age using validated parent-completed NutriSTEP® questionnaires with eating behaviour and dietary intake subscores (0 = lowest and 68 = highest total nutritional risk score). Parent-reported school concerns were measured at school age (4-10 years of age) and included: speech and language; learning; attention; behaviour; social relationships; physical coordination; fine motor coordination and self-help skills and independence. The primary outcome was any parent-reported school concerns, and individual school concerns were used as secondary outcomes. Multiple logistic regression models were conducted adjusting for clinically relevant confounders to assess the relationship between nutritional risk and school concerns. SETTING: Toronto, Canada. PARTICIPANTS: Children aged 18 months to 10 years. RESULTS: The study included 3655 children, 52 % were male, mean NutriSTEP® score was 14·4 (sd 6·4). Each 1 sd increase in NutriSTEP® total score was associated with a 1·18 times increased odds of school concerns (adj OR: 1·18, 95 % CI 1·07, 1·28, P = 0·0004), and high nutritional risk was associated with a 1·42 times increased odds of school concerns (adj OR: 1·42, 95 % CI 1·13, 1·78, P = 0·002). CONCLUSIONS: Nutritional risk in early childhood was associated with school concerns. Nutritional interventions in early childhood may reveal opportunities to enhance school outcomes.

Identifying longitudinal-growth patterns from infancy to childhood: a study comparing multiple clustering techniques.

BACKGROUND: Most studies on children evaluate longitudinal growth as an important health indicator. Different methods have been used to detect growth patterns across childhood, but with no comparison between them to evaluate result consistency. We explored the variation in growth patterns as detected by different clustering and latent class modelling techniques. Moreover, we investigated how the characteristics/features (e.g. slope, tempo, velocity) of longitudinal growth influence pattern detection. METHODS: We studied 1134 children from The Applied Research Group for Kids cohort with longitudinal-growth measurements [height, weight, body mass index (BMI)] available from birth until 12 years of age. Growth patterns were identified by latent class mixed models (LCMM) and time-series clustering (TSC) using various algorithms and distance measures. Time-invariant features were extracted from all growth measures. A random forest classifier was used to predict the identified growth patterns for each growth measure using the extracted features. RESULTS: Overall, 72 TSC configurations were tested. For BMI, we identified three growth patterns by both TSC and LCMM. The clustering agreement was 58% between LCMM and TS clusters, whereas it varied between 30.8% and 93.3% within the TSC configurations. The extracted features (n = 67) predicted the identified patterns for each growth measure with accuracy of 82%-89%. Specific feature categories were identified as the most important predictors for patterns of all tested growth measures. CONCLUSION: Growth-pattern detection is affected by the method employed. This can impact on comparisons across different populations or associations between growth patterns and health outcomes. Growth features can be reliably used as predictors of growth patterns.

U-Flourish university students well-being and academic success longitudinal study: a study protocol.

INTRODUCTION: Over 30% of Canadians between the ages of 16 and 24 years attend university. This period of life coincides with the onset of common mental illnesses. Yet, data to inform university-based mental health prevention and early intervention initiatives are limited. The U-Flourish longitudinal study based out of Queen's University, Canada and involving Oxford University in the UK, is a student informed study funded by the Canadian Institute for Health Research Strategy for Patient Oriented Research (CIHR-SPOR). The primary goal of U-Flourish research is to examine the contribution of risk and resiliency factors to outcomes of well-being and academic success in first year students transitioning to university. METHODS AND ANALYSIS: The study is a longitudinal survey of all first-year undergraduate students entering Queen's University in the fall term of 2018 (and will launch at Oxford University in fall of 2019). In accordance with the CIHR-SPOR definitions, students represent the target population (ie, patient equivalent). Student peer health educators were recruited to inform the design, content and implementation of the study. Baseline surveys of Queen's first year students were completed in the fall of 2018, and follow-up surveys at the end of first year in the spring of 2019. Extensive student-led engagement campaigns were used to maximise participation rates. The baseline survey included measures of personal factors, family factors, environmental factors, psychological and emotional health, and lifestyle factors. Main outcomes include self-reported indicators of mental health at follow-up and mental health service access, as well as objective measures of academic success through linkage to university administrative and academic databases. A combination of mixed effects regression techniques will be employed to determine associations between baseline predictive factors and mental health and academic outcomes. ETHICS AND DISSEMINATION: Ethical approval was obtained by the Health Sciences and Affiliated Teaching Hospitals Research Ethics Board (HSREB) (#6023126) at Queen's University. Findings will be disseminated through international and national peer-reviewed scientific articles and other channels including student-driven support and advocacy groups, newsletters and social media.

Daily and weekly mood ratings using a remote capture method in high-risk offspring of bipolar parents: Compliance and symptom monitoring.

OBJECTIVES: To determine the compliance and clinical utility of weekly and daily electronic mood symptom monitoring in adolescents and young adults at risk for mood disorder. METHODS: Fifty emerging adult offspring of bipolar parents were recruited from the Flourish Canadian high-risk offspring cohort study along with 108 university student controls. Participants were assessed by KSADS/SADS-L semi-structured interviews and used a remote capture method to complete weekly and daily mood symptom ratings using validated scales for 90 consecutive days. Hazard models and generalized estimating equations were used to determine differences in summary scores and regularity of ratings. RESULTS: Seventy-eight and 77% of high-risk offspring and 97% and 93% of controls completed the first 30 days of weekly and daily ratings, respectively. There were no differences in drop-out rates between groups over 90 days (weekly P = 0.2149; daily P = 0.9792). There were no differences in mean summary scores or regularity of weekly anxiety, depressive or hypomanic symptom ratings between high-risk offspring and control groups. However, high-risk offspring compared to controls had daily ratings indicating lower positive affect, higher negative affect and lower self-esteem (P = 0.0317). High-risk offspring with remitted mood disorder compared to those without had more irregularity in weekly anxiety and depressive symptom ratings and daily ratings of lower positive affect, higher negative affect, and higher shame and self-doubt (P = 0.0365). CONCLUSIONS: Findings support that high-resolution electronic mood tracking may be a feasible and clinically useful approach in monitoring emerging psychopathology in young people at high-risk offspring of mood disorder onset or recurrence.

Multi-state models for investigating possible stages leading to bipolar disorder.

BACKGROUND: It has been proposed that bipolar disorder onsets in a predictable progressive sequence of clinical stages. However, there is some debate in regard to a statistical approach to test this hypothesis. The objective of this paper is to investigate two different analysis strategies to determine the best suited model to assess the longitudinal progression of clinical stages in the development of bipolar disorder. METHODS: Data previously collected on 229 subjects at high risk of developing bipolar disorder were used for the statistical analysis. We investigate two statistical approaches for analyzing the relationship between the proposed stages of bipolar disorder: 1) the early stages are considered as time-varying covariates affecting the hazard of bipolar disorder in a Cox proportional hazards model, 2) the early stages are explicitly modelled as states in a non-parametric multi-state model. RESULTS: We found from the Cox model thatthere was evidence that the hazard of bipolar disorder is increased by the onset of major depressive disorder. From the multi-state model, in high-risk offspring the probability of bipolar disorder by age 29 was estimated as 0.2321. Cumulative incidence functions representing the probability of bipolar disorder given major depressive disorder at or before age 18 were estimated using both approaches and found to be similar. CONCLUSIONS: Both the Cox model and multi-state model are useful approaches to the modelling of antecedent risk syndromes. They lead to similar cumulative incidence functions but otherwise each method offers a different advantage.