Quantifying Cardiometabolic Risk Using Modifiable Non–Self-Reported Risk Factors
Published Date:Jun 17 2014
Source:Am J Prev Med. 2014; 47(2):131-140.
Hemoglobin A, Glycosylated
Predictive Value Of Tests
Proportional Hazards Models
Sensitivity And Specificity
Pubmed Central ID:PMC4107093
Funding:N01 HC025195/HC/NHLBI NIH HHS/United States
N01-HC-25195/HC/NHLBI NIH HHS/United States
R01 AG040248/AG/NIA NIH HHS/United States
R01 HL107240/HL/NHLBI NIH HHS/United States
R01HL107240/HL/NHLBI NIH HHS/United States
U01 AG027669/AG/NIA NIH HHS/United States
U01 HD051217/HD/NICHD NIH HHS/United States
U01 HD051218/HD/NICHD NIH HHS/United States
U01 HD051256/HD/NICHD NIH HHS/United States
U01 HD051276/HD/NICHD NIH HHS/United States
U01 HD059773/HD/NICHD NIH HHS/United States
U01AG027669/AG/NIA NIH HHS/United States
U01HD051217/HD/NICHD NIH HHS/United States
U01HD051218/HD/NICHD NIH HHS/United States
U01HD051256/HD/NICHD NIH HHS/United States
U01HD051276/HD/NICHD NIH HHS/United States
U01HD059773/HD/NICHD NIH HHS/United States
U01OH008788/OH/NIOSH CDC HHS/United States
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.
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.
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.
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 χ2=9.05 [p=0.338] and 12.54 [p=0.128] for men and women, respectively).
This study presents a risk factor algorithm that provides a convenient and informative way to quantify cardiometabolic risk based on modifiable risk factors that can motivate an individual’s commitment to prevention and intervention.
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