Factors Associated With Small Size at Birth in Nepal: Further Analysis of Nepal Demographic and Health Survey 2011

Background: The global Low Birth Weight (LBW) rate is reported to be 15.5% with more than 95% of these LBW infants being from developing countries. LBW is a major factor associated with neonatal deaths in developing countries. The determinants of low birth weight in Nepal have rarely been studied. This study aimed to identify the factors associated with small size at birth among under-five children. Methods: Data from the 2011 Nepal Demographic and Health Survey (NDHS) were used. The association between small size at birth and explanatory variables were analysed using Chi-square tests (χ2) followed by logistic regression. Complex Sample Analysis was used to adjust for study design and sampling.Results: A total of 5240 mother- singleton under five child pairs were included in the analysis, of which 936 (16.0%) children were reported as small size at birth. Of 1922 infants whose birth weight was recorded, 235 (11.5%) infants had low birth weight (<2500 grams). The mean birth weight was 3030 grams (standard deviation: 648.249 grams). The mothers who had no antenatal visits were more likely (odds ratio (OR) 1.315; 95% confidence interval (CI) (1.042-1.661)) to have small size infants than those who had attended four or more antenatal visits. Mothers who lived in the Far-western development region were more likely to have (OR 1.698; 95% CI (1.228-2.349)) small size infants as compared to mothers from the Eastern development region. Female infants were more likely (OR 1.530; 95% CI (1.245-1.880)) to be at risk of being small than males. Conclusion: One in every six infants was reported to be small at birth. Attendance of antenatal care programs appeared to have a significant impact on birth size. Adequate antenatal care visits combined with counselling and nutritional supplementation should be a focus to reduce adverse birth outcomes such as small size at birth, especially in the geographically and economically disadvantaged areas such as Far-western region of Nepal

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.

OBJECTIVE: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. METHODS: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. RESULTS: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. CONCLUSION: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI