Population-based risk factors for elevated alanine aminotransferase in a South Texas Mexican-American population.

TitlePopulation-based risk factors for elevated alanine aminotransferase in a South Texas Mexican-American population.
Publication TypeJournal Article
Year of Publication2012
AuthorsQu, H-Q, Li, Q, Grove, ML, Lu, Y, Pan, J-J, Rentfro, AR, Bickel, PE, Fallon, MB, Hanis, CL, Boerwinkle, E, McCormick, JB, Fisher-Hoch, SP
JournalArch Med Res
Date Published2012 Aug
KeywordsAlanine Transaminase, Female, Humans, Male, Mexican Americans, Risk Factors, Support Vector Machine, Texas

BACKGROUND AND AIMS: Elevated alanine aminotransferase (ALT >40 IU/mL) is a marker of liver injury but provides little insight into etiology. We aimed to identify and stratify risk factors associated with elevated ALT in a randomly selected population with a high prevalence of elevated ALT (39%), obesity (49%) and diabetes (30%).METHODS: Two machine learning methods, the support vector machine (SVM) and Bayesian logistic regression (BLR), were used to capture risk factors in a community cohort of 1532 adults from the Cameron County Hispanic Cohort (CCHC). A total of 28 predictor variables were used in the prediction models. The recently identified genetic marker rs738409 on the PNPLA3 gene was genotyped using the Sequenom iPLEX assay.RESULTS: The four major risk factors for elevated ALT were fasting plasma insulin level and insulin resistance, increased BMI and total body weight, plasma triglycerides and non-HDL cholesterol, and diastolic hypertension. In spite of the highly significant association of rs738409 in females, the role of rs738409 in the prediction model is minimal, compared to other epidemiological risk factors. Age and drug and alcohol consumption were not independent determinants of elevated ALT in this analysis.CONCLUSIONS: The risk factors most strongly associated with elevated ALT in this population are components of the metabolic syndrome and point to nonalcoholic fatty liver disease (NAFLD). This population-based model identifies the likely cause of liver disease without the requirement of individual pathological diagnosis of liver diseases. Use of such a model can greatly contribute to a population-based approach to prevention of liver disease.

Alternate JournalArch Med Res
PubMed ID22959976
PubMed Central IDPMC3590902
Grant List1U54RR023417-01 / RR / NCRR NIH HHS / United States
P20 MD000170 / MD / NIMHD NIH HHS / United States
MD000170 P20 / MD / NIMHD NIH HHS / United States
UL1 RR024148 / RR / NCRR NIH HHS / United States
R01 DK056804 / DK / NIDDK NIH HHS / United States

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