Clinical and Vitamin Response to a Short-Term Multi-Micronutrient Intervention in Brazilian Children and Teens: From Population Data to Interindividual Responses

Abstract

Scope: Micronutrients are in small amounts in foods, act in concert, and require variable amounts of time to see changes in health and risk for disease. These first principles are incorporated into an intervention study designed to develop new experimental strategies for setting target recommendations for food bioactives for populations and individuals.

Methods and results: A 6-week multivitamin/mineral intervention is conducted in 9-13 year olds. Participants (136) are (i) their own control (n-of-1); (ii) monitored for compliance; (iii) measured for 36 circulating vitamin forms, 30 clinical, anthropometric, and food intake parameters at baseline, post intervention, and following a 6-week washout; and (iv) had their ancestry accounted for as modifier of vitamin baseline or response. The same intervention is repeated the following year (135 participants). Most vitamins respond positively and many clinical parameters change in directions consistent with improved metabolic health to the intervention. Baseline levels of any metabolite predict its own response to the intervention. Elastic net penalized regression models are identified, and significantly predict response to intervention on the basis of multiple vitamin/clinical baseline measures.

Conclusions: The study design, computational methods, and results are a step toward developing recommendations for optimizing vitamin levels and health parameters for individuals.

Keywords: community-based participatory research; metabolic health; micronutrients; targeted and systems nutrition.

Figure 1

Intervention design and overview of statistical analysis.

Figure 2

Admixture analysis. Influence of genetic ancestry on baseline vitamin levels. Ancestry markers from the Human Genome Diversity Project (HGDP) reference populations were used A) to identify admixture in data from unrelated participants from both years as per methods. To test whether linear regression between the ancestral components and baseline vitamin levels existed, a k = 5 model was used to the following covariates: trial year, sex, age, fat mass, and tanner score. Adjusted p‐value of 0.05 was used as significance threshold. B) Baseline TMP and Q1 (Europe) with estimate of regression coefficient (ERC) 4.57, C) baseline vitamin B12 and Q5 (Native American) ECR = 186.53, D) baseline folate and Q5 (Native American) ECR = 2.13, and E) folate response as ratio of V2/V1 and k5 (Native American) with ECR = 0.77.

Figure 3

Interindividual variability in response to intervention for LDL. We have identified individuals whose response exceeded normal within individual day‐to‐day variation (reported to be 10%62). Green: opposite‐responders, gray: non‐responders, and orange: responders to intervention.

Figure 4

Comparison of elastic net and simple model performance across bootstrapped analyses. A) Vitamin and B) clinical variable response. Model performance is shown for each response variable and modeling approach. Performance is measured as a correlation between predicted and observed 2014 response to intervention.

Figure 5

Modeling for nicotinamide response. A) Fitted coefficients and frequency (in parentheses) of predictor variables across 1000 bootstrapped analyses. B) Observed versus predicted response to intervention across bootstrapped analyses. Each boxplot corresponds to an individual participant, and thus shows variation in predicted response for each individual across the bootstrapped analyses.