Nutrient intakes from complementary foods are associated with cardiometabolic biomarkers among undernourished Peruvian children

Abstract

Relatively little is known about how the diet of chronically undernourished children may impact cardiometabolic biomarkers. The objective of this exploratory study was to characterise relationships between dietary patterns and the cardiometabolic profile of 153 3-5-year-old Peruvian children with a high prevalence of chronic undernutrition. We collected monthly dietary recalls from children when they were 9-24 months old. At 3-5 years, additional dietary recalls were collected, and blood pressure, height, weight, subscapular skinfolds and fasting plasma glucose, insulin and lipid profiles were assessed. Nutrient intakes were expressed as average density per 100 kcals (i) from 9 to 24 months and (ii) at follow-up. The treelet transform and sparse reduced rank regress'ion (RRR) were used to summarize nutrient intake data. Linear regression models were then used to compare these factors to cardiometabolic outcomes and anthropometry. Linear regression models adjusting for subscapular skinfold-for-age Z-scores (SSFZ) were then used to test whether observed relationships were mediated by body composition. 26 % of children were stunted at 3-5 years old. Both treelet transform and sparse RRR-derived child dietary factors are related to protein intake and associated with total cholesterol and SSFZ. Associations between dietary factors and insulin were attenuated after adjusting for SSFZ, suggesting that body composition mediated these relationships. Dietary factors in early childhood, influenced by protein intake, are associated with cholesterol profiles, fasting glucose and body fat in a chronically undernourished population.

Keywords: Cardiometabolic biomarkers; Complementary foods; HAZ, height-for-age Z-score, based on the WHO reference standard; HDL-c, high-density lipoprotein cholesterol; HDLZ, sd of mean HDL-c, based on the distribution of the sample; HOMA-IR, homeostatic model assessment-insulin resistance; INZ, sd of insulin, based on the distribution of the sample; LDC-c, low-density lipoprotein cholesterol; MAPZ, sd of mean arterial blood pressure, based on the distribution of the sample; MFP, meat, fish or poultry; Nutrient intake; PCA, principal components analysis; Peru; RRR, reduced rank regression; Stunting; TC, total cholesterol; TG, triglycerides; TGZ, sd of triglycerides, based on the distribution of the sample; WAZ, weight-for-age Z-score, based on the WHO reference standard; WHZ, weight-for-height Z-score, based on the WHO reference standard; vLDL-c, very low-density lipoprotein cholesterol.