Associations of a metabolic syndrome severity score with coronary heart disease and diabetes in fasting vs. non-fasting individuals

• Published in: Nutrition, metabolism, and cardiovascular diseases metabolism, and cardiovascular diseases, 2020-01, Vol.30 (1), p.92-98
• Main Authors: DeBoer, Mark D., Filipp, Stephanie L., Gurka, Matthew J.
• Format: Article
• Language: English
• Subjects: Biomarkers - blood; Blood Glucose - analysis; Blood Pressure; Cardiovascular disease; Coronary Disease - blood; Coronary Disease - diagnosis; Coronary Disease - epidemiology; Coronary Disease - physiopathology; Diabetes; Diabetes Mellitus, Type 2 - blood; Diabetes Mellitus, Type 2 - diagnosis; Diabetes Mellitus, Type 2 - epidemiology; Diabetes Mellitus, Type 2 - physiopathology; Fasting; Fasting - blood; Female; Humans; Incidence; Lipids - blood; Male; Metabolic syndrome; Metabolic Syndrome - blood; Metabolic Syndrome - diagnosis; Metabolic Syndrome - epidemiology; Metabolic Syndrome - physiopathology; Middle Aged; Predictive Value of Tests; Risk; Risk Assessment; Risk Factors; Severity of Illness Index; Time Factors; United States - epidemiology
• ISSN: 0939-4753; 1590-3729
• DOI: 10.1016/j.numecd.2019.08.010

Many traditional assessments of risk for coronary heart disease (CHD) and diabetes require laboratory studies performed after an 8-h fast. We assessed whether metabolic-syndrome (MetS) severity would remain linked to future CHD and diabetes even when assessed from non-fasting samples. Participants in the Atherosclerosis Risk in Communities study were assessed at 4 visits and followed for 20-years of adjudicated CHD outcomes. We used Cox proportional-hazard models (for 20-year CHD outcomes) and logistic regression (for 9-year diabetes outcomes) to compare incident disease risk associated with a race/ethnicity-specific MetS-severity Z-score (MetS-Z) calculated in participants who were fasting (≥8 h) or non-fasting. All analyses were adjusted for sex, race, education, income and smoking. MetS Z-scores were overall similar between participants who were always fasting vs. those non-fasting at Visits 1–3 (all values −0.1 to 0.4), while MetS-Z for participants who were non-fasting at Visit-4 were higher at each visit. Baseline MetS-Z was linked to future CHD when calculated from both fasting and non-fasting measurements, with hazard ratio (HR) for fasting MetS-Z 1.53 (95% confidence interval [CI] 1.42, 1.66) and for non-fasting 1.28 (CI 1.08, 1.51). MetS-Z at Visit-1 also remained linked to future diabetes when measured from non-fasting samples, with odds ratio for fasting MetS-Z 3.10 (CI 2.88, 3.35) and for non-fasting 1.92 (CI 1.05, 3.51). MetS-Z remained linked to future CHD and diabetes when assessed from non-fasting samples. A score such as this may allow for identification of at-risk individuals and serve as a motivation toward interventions to reduce risk. •A metabolic syndrome severity (MetS) Z-score was similar in fasting & non-fasting.•MetS-Z from fasting & non-fasting samples was similarly associated with future CVD.•MetS-Z was associated with future diabetes, but only when glucose was included.•MetS-Z when calculated from EHR systems may assist in informing clinical care.