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Comparative Study
. 2020 Jan 3;30(1):92-98.
doi: 10.1016/j.numecd.2019.08.010. Epub 2019 Aug 24.

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

Mark D DeBoer  1 , Stephanie L Filipp  2 , Matthew J Gurka  3
Affiliations
Comparative Study

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

Mark D DeBoer et al. Nutr Metab Cardiovasc Dis. .

Abstract

Background and aims: 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.

Methods and results: 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).

Conclusions: 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.

Keywords: Cardiovascular disease; Diabetes; Fasting; Metabolic syndrome; Risk.

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Conflict of interest statement

Completing Interests: None

Figures

Figure 1.
Figure 1.
Mean MetS-Z Severity Score by Visit and Fasting Status.
Figure 2.
Figure 2.. Hazard Ratios of Incident CVD: Fasting (at Visit 1) vs. First Non-Fasting MetS-Z.
Data shown are from Cox Proportional Hazards Regression, comparing fasting vs. non-fasting “first” values (Visit 1 for those n=8072 who fasted all four visits, and the non-fasting value for those n=483 with a non-fasting MetS-Z). The model included sex, education, income, race, age and smoking status (at time of relevant visit) as covariates. The “Capped” score entered all values of triglycerides ≥400 mg/dL and glucose ≥250 mg/dL at these cut-off values. Interaction p-values between fasting status and MetS-Z: Standard: p = 0.0500, No-Glucose: p = 0.3255, “Capped”: p = 0.2937.
Figure 3.
Figure 3.. Odds Ratios of Incident Diabetes for MetS-Z by Fasting Status at Visit 1.
Data shown are from logistic regression, comparing fasting vs. non-fasting Visit 1 values. The model included sex, education, income, race, age and smoking status (at time of relevant visit) as covariates. The “Capped” score entered all values of triglycerides ≥400 mg/dL at these cut-off values; glucose values did not require capping in this analysis because no participants had diabetes at the time of MetS-Z assessment. There were no significant interactions between fasting status and all three forms of MetS-Z (standard p = 0.1212, no glucose p = 0.0745, trimmed p = 0.1259).
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