Evidence map›Paper›PMID 24561983›Full record

ArticleAsia Pacific journal of clinical nutrition2014

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

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

Abstract read
In one paragraph

Article in Asia Pacific journal of clinical nutrition, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Niha ZubairCarolina Population Center, University of North Carolina, 123 W Franklin St., Chapel Hill, NC 27516, USA zubair@live.unc.edu.
Chris W Kuzawa
Nanette R Lee
Thomas W McDade
Linda S Adair

Funding

Modeling the Developmental Origins of Adult Disease Risk FactorsR01HD054501 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S · 2008 to 2012
$2.0M
Obesity development and CVD risk factor--Filipino womenR01HL085144 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S · 2006 to 2009
$1.9M
Effect of Health on Education &Work in Filipino YouthR01TW005596 · FIC · UNIVERSITY OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S · 2001 to 2005
$1.5M
FIC NIH HHS R01 TW005596FIC NIH HHS R01-TW05596NHLBI NIH HHS R01 HL085144NHLBI NIH HHS R01-HL085144-03NICHD NIH HHS R01 HD054501NICHD NIH HHS R01-HD054501
6 · The paper itself

Abstract

backgroundWith 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

resultsParticipants 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.

conclusionsDespite 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.

Indexed as

Cardiovascular DiseasesMetabolic DiseasesBlood GlucoseBlood PressureCholesterol, HDLCholesterol, LDLCluster AnalysisC-Reactive ProteinFemaleHealth SurveysHumansInsulin ResistanceMaleNutrition SurveysPhilippinesRisk FactorsBlood GlucoseCholesterol, HDLCholesterol, LDLC-Reactive ProteinTriglycerides

Identifiers

PMID24561983
PMCPMC4100915

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.