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Purpose. The current study sought to expand implications of physiological weathering through the application of latent profile analysis to stress biomarkers to address limitations of traditional allostatic load calculations. Methods. Latent profile analysis was applied biomarkers used in traditional allostatic load metrics to identify physiological risk profiles in the 2007-20010 National Health and Nutritional and Examination Survey. Multinomial logistic regression was used to determine the probability of risk profiles by race/ethnicity, age, gender, and poverty income ratio (PIR). Mean allostatic load score was assessed across each risk profile. Results. Latent profile analysis identified four distinct profiles labeled low risk, inflammatory risk, cardiovascular risk, and hypertension risk. Race, age, and gender significantly increased odds of exhibiting a risk profile. Compared to Whites, Hispanics had significant higher odds of inflammatory (OR=1.43, 95% CI [1.06-1.92]) and cardiovascular risk profiles (OR=1.63, 95% CI [1.09-2.43]) while Blacks had higher odds of inflammatory (OR=1.76 95% CI [1.25-2.47]), cardiovascular (OR=2.12, 95% CI, [1.39-3.27]) and hypertension risk profiles (OR= 1.78, 95% CI [1.21-2.59]). Females held significant greater odds of all risk categories except hypertension in which they held the lowest odds (OR= .19, 95% CI [.14-.25]). Mean allostatic load scores were highest in the inflammatory (M=3.99, SD=1.66) and cardiovascular risk profiles (M=4.4, SD=1.84). Conclusions. Employing latent profile analysis may expand traditional allostatic load methodology by identifying physiological risk patterns among those who experience allostatic load early in life. This may be useful for examining how cultural specific interventions may reduce cardiovascular risk among those exhibiting physiological risk profiles.

Physiological Risk Profiles and Allostatic Load: Using Latent Profile Analysis to Examine Socioeconomic Differences in Physiological Patterns of Risk