Early life risk factors of motor, cognitive and language development: a pooled analysis of studies from low/middle-income countries.

OBJECTIVE: To determine the magnitude of relationships of early life factors with child development in low/middle-income countries (LMICs). DESIGN: Meta-analyses of standardised mean differences (SMDs) estimated from published and unpublished data. DATA SOURCES: We searched Medline, bibliographies of key articles and reviews, and grey literature to identify studies from LMICs that collected data on early life exposures and child development. The most recent search was done on 4 November 2014. We then invited the first authors of the publications and investigators of unpublished studies to participate in the study. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Studies that assessed at least one domain of child development in at least 100 children under 7 years of age and collected at least one early life factor of interest were included in the study. ANALYSES: Linear regression models were used to assess SMDs in child development by parental and child factors within each study. We then produced pooled estimates across studies using random effects meta-analyses. RESULTS: We retrieved data from 21 studies including 20 882 children across 13 LMICs, to assess the associations of exposure to 14 major risk factors with child development. Children of mothers with secondary schooling had 0.14 SD (95% CI 0.05 to 0.25) higher cognitive scores compared with children whose mothers had primary education. Preterm birth was associated with 0.14 SD (-0.24 to -0.05) and 0.23 SD (-0.42 to -0.03) reductions in cognitive and motor scores, respectively. Maternal short stature, anaemia in infancy and lack of access to clean water and sanitation had significant negative associations with cognitive and motor development with effects ranging from -0.18 to -0.10 SDs. CONCLUSIONS: Differential parental, environmental and nutritional factors contribute to disparities in child development across LMICs. Targeting these factors from prepregnancy through childhood may improve health and development of children.

Profiles of gene expression in maternal blood predict offspring birth weight in normal pregnancy.

The association between lower birth weight and increased disease risk in adulthood has drawn attention to the physiological processes that shape the gestational environment. We implement genome-wide transcriptional profiling of maternal blood samples to identify subsets of genes and associated transcription control pathways that predict offspring birth weight. Female participants (N = 178, mean = 27.0 years) in a prospective observational birth cohort study were contacted between 2009 and 2014 to identify new pregnancies. An in-home interview was scheduled for early in the third trimester (mean = 30.3 weeks) to collect pregnancy-related information and a blood sample, and birth weight was measured shortly after delivery. Transcriptional activity in white blood cells was determined with a whole-genome gene expression direct hybridization assay. Fifty transcripts were differentially expressed in association with offspring birth weight, with 18 up-regulated in relation to lower birth weight, and 32 down-regulated. Examination of transcription control pathways identified increased activity of NF-κB, AP-1, EGR1, EGR4, and Gfi families, and reduced the activity of CEBP, in association with lower birth weight. Transcript origin analyses identified non-classical CD16+ monocytes, CD1c+ myeloid dendritic cells, and neutrophils as the primary cellular mediators of differential gene expression. These results point toward a systematic regulatory shift in maternal white blood cell activity in association with lower offspring birth weight, and they suggest that analyses of gene expression during gestation may provide insight into regulatory and cellular mechanisms that influence birth outcomes.

Clustering and determinants of cardiometabolic risk factors among Filipino young adults.

BACKGROUND: With modernization, cardiometabolic disease risk has increased in low and middle-income countries. To better understand cardiometabolic disease etiology, we evaluated the patterning risk factors in a susceptible young adult population. METHODS AND RESULTS: Participants included 1,621 individuals from the 2005 Cebu Longitudinal Health and Nutrition Survey. Using cluster analysis, we grouped individuals by the following biomarkers: triglycerides, HDL and LDL cholesterol, C-reactive protein, blood pressure, homeostasis model assessment of insulin resistance, and fasting glucose. Using multinomial logistic regression models we assessed how diet, adiposity, and environment predicted cardiometabolic clusters. We identified 5 distinct sexspecific clusters: 1) Healthy/High HDL cholesterol (with the addition of high LDL cholesterol in women); 2) Healthy/Low blood pressure; 3) High blood pressure; 4) Insulin resistant/High triglycerides; and 5) High Creactive protein. Low HDL cholesterol was the most prevalent risk factor (63%). In men and women, a higher intake of saturated fat increased the likelihood of being in the healthy clusters. In men, poorer environmental hygiene increased the likelihood of being in the High C-reactive protein cluster, compared to the healthy clusters (OR 0.74 [95% CI 0.60-0.90] and 0.83 [0.70-0.99]). Adiposity most strongly associated with membership to the Insulin resistant/high triglyceride cluster. CONCLUSIONS: Despite the population's youth and leanness, cluster analysis found patterns of cardiometabolic risk. While adiposity measures predicted clustering, diet and environment also independently predicted clustering, emphasizing the importance of screening lean and overweight individuals for cardiometabolic risk. Finding predictors of risk in early adulthood could help inform prevention efforts for future disease.

Cluster analysis reveals important determinants of cardiometabolic risk patterns in Filipino women.

With modernization, the Philippines has experienced increasing rates of obesity and related cardiometabolic diseases. Studying how risk factors cluster in individuals may offer insight into cardiometabolic disease etiology. We used cluster analysis to group women who share the following cardiometabolic biomarkers: fasting triglycerides, HDL-C and LDL-C, C-reactive protein, systolic and diastolic blood pressure, homeostasis model assessment of insulin resistance, and fasting glucose. Participants included 1,768 women (36-69 years) in the Cebu Longitudinal Health and Nutrition Survey. We identified five distinct clusters characterized by: 1) low levels of all risk factors (except HDL-C and LDL-C) or "healthy"; 2) low HDL-C in the absence of other risk factors; 3) elevated blood pressure; 4) insulin resistance; and 5) high C-reactive protein. We identified predictors of cluster membership using multinomial logistic regression. Clusters differed by age, menopausal status, socioeconomic status, saturated fat intake, and combinations of overweight (BMI >23) and high waist circumference (>80 cm). In comparison to the healthy cluster, overweight women without high waist circumference were more likely to be in the high CRP cluster (OR=2.26, 95% CI=1.24-4.11), while women with high waist circumference and not overweight were more likely to be in the elevated blood pressure (OR=2.56, 95% CI=1.20-5.46) or insulin resistant clusters (OR=4.05, 95% CI=1.39-11.8). In addition, a diet lower in saturated fat uniquely increased the likelihood of membership to the low HDL-C cluster. Cluster analysis identified biologically meaningful groups, predicted by modifiable risk factors; this may have implications for the prevention of cardiometabolic diseases.