{'fraction_non_alphanumeric': 0.045041635124905374, 'fraction_numerical': 0.02157456472369417, 'mean_word_length': 4.783369803063457, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
SUN-064 Associations of a Metabolic Syndrome Severity Score with Future Coronary Heart Disease (CHD) and Diabetes in Fasting vs. Non-Fasting Individuals

Abstract BACKGROUND: Many traditional assessments of risk for coronary heart disease (CHD) and diabetes require laboratory studies performed after at least an 8-hour fast. However, obtaining fasting samples is less convenient and may result in fewer opportunities for risk assessment and targeted treatment. We hypothesized that metabolic syndrome (MetS) severity would remain linked to future CHD and diabetes even when assessed from non-fasting samples. METHODS: Participants in the Atherosclerosis Risk in Communities study were assessed at 4 visits and followed for 20-years of adjudicated CHD outcomes. We used a race/ethnicity-specific MetS severity Z-score (MetS-Z) calculated from the five traditional components of MetS: waist circumference, systolic blood pressure, HDL, triglycerides and glucose. In prior studies, this Z-score—as calculated from fasting samples—was associated with future CHD and diabetes independent of the individual MetS components. In the current analysis, we assessed risk of adjudicated CHD outcomes using Cox proportional hazard models for baseline MetS-Z calculated from a) fasting (≥8 hours) samples at Visit 1 or b) non-fasting samples at any of the 4 study visits. We assessed incident diabetes risk using logistic regression for fasting vs. non-fasting samples from Visit 1 for risk of diabetes by Visit 4. All analyses were adjusted for sex, race, education, income and current smoking. RESULTS: MetS Z-scores across visits were overall similar between fasting and non-fasting groups (all values when drawn from both fasting and non-fasting samples -0.1 - 0.4), with the exception of individuals non-fasting at Visit 4, who had higher MetS-Z values already at Visit 1. MetS-Z scores were 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 MetS-Z 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 HR for fasting MetS-Z 3.10 (CI 2.88, 3.35) and for non-fasting MetS-Z 1.92 (CI 1.05, 3.51). CONCLUSIONS: As a marker of long-term risk for chronic disease, MetS-Z remained linked to future CHD and diabetes when assessed from non-fasting samples. A score such as this, calculated through the electronic health record, may allow for identification of at-risk individuals and serve as a motivation toward interventions to improve risk.