Quantifying cardiometabolic risk using modifiable non-self-reported risk factors

Abstract

Background: Sensitive general cardiometabolic risk assessment tools of modifiable risk factors would be helpful and practical in a range of primary prevention interventions or for preventive health maintenance.

Purpose: To develop and validate a cumulative general cardiometabolic risk score that focuses on non-self-reported modifiable risk factors such as glycosylated hemoglobin (HbA1c) and BMI so as to be sensitive to small changes across a span of major modifiable risk factors, which may not individually cross clinical cut-off points for risk categories.

Methods: We prospectively followed 2,359 cardiovascular disease (CVD)-free subjects from the Framingham offspring cohort over a 14-year follow-up. Baseline (fifth offspring examination cycle) included HbA1c and cholesterol measurements. Gender-specific Cox proportional hazards models were considered to evaluate the effects of non-self-reported modifiable risk factors (blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking, BMI, and HbA1c) on general CVD risk. We constructed 10-year general cardiometabolic risk score functions and evaluated its predictive performance in 2012-2013.

Results: HbA1c was significantly related to general CVD risk. The proposed cardiometabolic general CVD risk model showed good predictive performance as determined by cross-validated discrimination (male C-index=0.703, 95% CI=0.668, 0.734; female C-index=0.762, 95% CI=0.726, 0.801) and calibration (lack-of-fit chi-square=9.05 [p=0.338] and 12.54 [p=0.128] for men and women, respectively).

Conclusions: This study presents a risk factor algorithm that provides a convenient and informative way to quantify cardiometabolic risk on the basis of modifiable risk factors that can motivate an individual's commitment to prevention and intervention.

Trial registration: ClinicalTrials.gov NCT00900159.

Copyright © 2014 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1

Kaplan–Meier survival plots of time (years) to hard CVD outcome for age –gender strata categories. Note: “+” denotes censoring. CVD, cardiovascular disease

Figure 2

Calibration plot comparing agreement between observed, D’Agostino et al. model, proposed model with self-reported diabetes status, and proposed model with HbA1c instead of self–reported diabetes status for 10–year predictions for general CVD risk. Ten groups (equal number of subjects per group) were categorized using proposed risk scores that range from low risk to high risk. Kaplan–Meier estimates were used for observed bars. Men (top panel) and women (bottom panel). CVD, cardiovascular disease; HbA1c, glycosylated hemoglobin

Figure 3

Comparison of cardiometabolic models with self–reported diabetes diagnosis versus Hba1c. From a subset of responders who self-reported diabetes diagnosis (n=94), the average risk scores from the self–reported diabetes model stratified by gender is plotted as a black horizontal line for each group. For those with self–reported diabetes diagnosis (A, men; B, women), there is a significant dose–response relationship between HbA1c levels, with lower HbA1c values conferring less risk than the average, and higher HbA1c values conferring greater risk than the average. Bottom panels (C, D) present mean and SE of risk scores for each category of HbA1c. For those who deny a diabetes diagnosis (C, men; D, women), there is a significant dose-response relationship between HbA1c levels, with lower HbA1c values conferring less risk than the average, and higher HbA1c values conferring greater risk than the average. HbA1c, glycosylated hemoglobin