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

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 proportionalhazard 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 1e3 (all values À0.1 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 nonfasting samples. A score such as this may allow for identification of at-risk individuals and serve as a motivation toward interventions to reduce risk.

Correlation Between Thyroid Nodules and Metabolic Syndrome: A Systematic Review and Meta-Analysis

“…Since the establishment of the definition by the NCEP-ATPIII in May 2001, it has received much attention, with 81,254 relevant publications in PubMed to date and the establishment of various diagnostic criteria, such as the NCEP-ATPIII, IDF, CDS and AHA criteria. One important question in MetS research is whether MetS is associated with other diseases ( 34 ), for example, whether MetS is predictive of coronary heart disease ( 35 ). A large cross-sectional study from China, the Thyroid Disorders, Iodine Status and Diabetes (TIDE): a National Epidemiological Survey Project, investigated the prevalence of metabolic and thyroid diseases in 31 Chinese provinces and cities and found a strong correlation between MetS or its components and thyroid dysfunction, thyroid antibodies and urinary iodine concentration ( 11 – 13 , 16 ).…”

Associations of circulating cell-free DNA, C-reactive protein, and cardiometabolic risk among low-active smokers with elevated depressive symptoms

“…This approach allows for the assessment of the full range of data relevant to cardiometabolic outcomes and avoids the loss of information that occurs with the use of binary threshold variables when metabolic syndrome is clinically defined with thresholds for each component. A previous study demonstrated non-fasting z-score composites of metabolic syndrome risk reliably and accurately predict cardiometabolic outcomes ( DeBoer et al, 2020 ). In order to further assess the metabolic syndrome composite risk score, the relationship of the risk score with a medical history of cardiovascular and metabolic disorders was examined.…”

Accuracy of Different Metabolic Syndrome Definitions for Detecting Increased Carotid Intima-media Thickness in Peruvian Type 2 Diabetes Mellitus Subjects without Cardiovascular Disease.

“…We did not nd studies related to the association between MSSS and subclinical atherosclerosis as a surrogate marker of cardiovascular risk. Gurka et al showed that MSSS has an excellent predictive performance of cardiovascular risk if glucose is not considered (9) or even when fasting values is replaced for non-fasting values into this model (11). The independent association of non-glucose-modi ed MSSS with cardiovascular risk might be explained for a possible weakness of glycemic status for detecting CIMT.…”