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Shared patterns of cardiometabolic disease risk factors in Filipino mothers and offspring
Author(s) -
Zubair Niha,
Adair Linda
Publication year - 2011
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.25.1_supplement.982.5
Subject(s) - offspring , overweight , waist , medicine , insulin resistance , cluster (spacecraft) , blood pressure , endocrinology , diabetes mellitus , demography , obesity , physiology , pregnancy , biology , genetics , sociology , computer science , programming language
Previous research identified clusters of Filipino women who share similar cardiometabolic (CM) characteristics. Here we use cluster analysis to study patterns of co‐occurrence of CM risk factors in their young adult (mean age 21.5) offspring. We use data 1271 participants in the 2005 Cebu Longitudinal Health and Nutrition Survey. Offspring clusters were created from sex‐specific Z‐scores of fasting glucose, triglycerides (TAGs), HDL‐C, LDL‐C, C‐reactive protein, systolic and diastolic blood pressure (BP), and HOMA‐IR. Five distinct clusters, similar to those identified in mothers, reflected: low levels of all risk factors ‐“healthy”; low HDL‐C in the absence of other risk factors; elevated BP; low HDL‐C and high fasting glucose; and insulin resistance (IR), elevated TAGs, and high fasting glucose. We used multinomial logistic regression to relate these clusters to offspring characteristics and maternal CM clusters. Compared to the healthy group, dietary saturated fat, waist circumference, and overweight status were strong predictors of clusters representing CM disease risk. Similarly, the maternal low HDL‐C cluster predicted membership in the offspring low HDL‐C (OR 2.27, 95% CI=1.45:3.55) and low HDL‐C/high glucose clusters (2.67, 95%CI=1.73:4.14). Cluster analysis found biologically meaningful groups influenced by maternal clusters and identified modifiable risk factors for CM disease. Grant Funding Source : NIH Grant HL085144‐03