Skip to content. | Skip to navigation

Personal tools
You are here: Home / Publications / Clustering and Determinants of Cardiometabolic Risk Factors among Filipino Young Adults

Clustering and Determinants of Cardiometabolic Risk Factors among Filipino Young Adults

Zubair, Niha; Kuzawa, Chris W.; Lee, Nanette R.; McDade, Thomas W.; & Adair, Linda S. (2014). Clustering and Determinants of Cardiometabolic Risk Factors among Filipino Young Adults. Asia Pacific Journal of Clinical Nutrition, 23(1), 148-58. PMCID: PMC4100915

Zubair, Niha; Kuzawa, Chris W.; Lee, Nanette R.; McDade, Thomas W.; & Adair, Linda S. (2014). Clustering and Determinants of Cardiometabolic Risk Factors among Filipino Young Adults. Asia Pacific Journal of Clinical Nutrition, 23(1), 148-58. PMCID: PMC4100915

Octet Stream icon 218.ris — Octet Stream, 2 kB (2,316 bytes)

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.




JOUR



Zubair, Niha
Kuzawa, Chris W.
Lee, Nanette R.
McDade, Thomas W.
Adair, Linda S.



2014


Asia Pacific Journal of Clinical Nutrition

23

1

148-58







10.6133/apjcn.2014.23.1.06

PMC4100915


218