Patterns of clustering of the metabolic syndrome components and its association with coronary heart disease in the Multi-Ethnic Study of Atherosclerosis (MESA): A latent class analysis

Introduction. Fasting glucose (FG) is part of the Adult Treatment Panel III (ATP III) criteria for defining the metabolic syndrome (MetS). Glycated hemoglobin (HbA1c) is a measure of 2-3 month endogenous glucose exposure and is now recommended for diabetes diagnosis and screening for high-risk individuals. The aim of this study was to evaluate if replacing FG with HbA1c to define MetS improves prediction of incident coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) cohort. Methods. We included 11,194 ARIC participants without diabetes (based on diagnosis, medication use, FG ≥126 mg/dL, or HbA1c ≥6.5%) or prevalent CHD at baseline (1990-92). Cox proportional hazards models (adjusted for age, race, and study center) were used to compare the association between MetS defined using HbA1c (5.7-6.4%) or FG (100-125 mg/dL, based on ATP III guidelines) and risk of CHD (defined by myocardial infarction or fatal CHD, event data available through 2009). Results. Study participants had a mean age at baseline of 57 years, 43% were male, and 79% were white; median follow-up time was 16 years. Thirty-four percent of the study population had both normal FG (<100 mg/dL) and HbA1c (<5.7%), 37% had elevated FG and normal HbA1c, 4% had normal FG and elevated HbA1c, and 25% had both elevated FG (100-125 mg/dL) and HbA1c (5.7-6.4%). The association of combined FG and HbA1c categories with incident CHD are shown in the Figure. The adjusted hazard ratio predicting for incident CHD from MetS status was 1.43 (95% CI: 1.25-1.63, c-statistic: 0.61) using FG in the definition of MetS and 1.69 (95% CI: 1.48-1.93, c-statistic: 0.62) in the model replacing FG with HbA1c. Conclusions. Incorporating HbA1c into the definition of the MetS may help in identifying individuals who should be targeted for aggressive CHD risk factor reduction. Additionally, HbA1c may be useful clinically and in research settings for identifying individuals with MetS in cases where FG measures are not available.

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Mental disorders pattern in staff of a military unit in Iran: the role of metabolic syndrome on latent class membership

BMC Psychiatry ◽

10.1186/s12888-021-03537-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Abbas Abbasi-Ghahramanloo ◽

Mohammadkarim Bahadori ◽

Esfandiar Azad ◽

Nooredin Dopeykar ◽

Parisa Mahdizadeh ◽

...

Keyword(s):

Metabolic Syndrome ◽

Mental Disorders ◽

Latent Class Analysis ◽

Latent Class ◽

Adult Population ◽

Class Analysis ◽

Class Membership ◽

Anthropometric Measures ◽

Military Unit

Abstract Introduction Mental disorders are among the most prevalent health problems of the adult population in the world. This study aimed to identify the subgroups of staff based on mental disorders and assess the independent role of metabolic syndrome (MetS) on the membership of participants in each latent class. Methods This cross-sectional study was conducted among 694 staff of a military unit in Tehran in 2017. All staff of this military unit was invited to participate in this study. The collected data included demographic characteristics, anthropometric measures, blood pressure, biochemical parameters, and mental disorders. We performed latent class analysis using a procedure for latent class analysis (PROC LCA) in SAS to identify class membership of mental disorders using Symptom Checklist-90. Results Three latent classes were identified as healthy (92.7%), mild (4.9%), and severe (2.4%) mental disorders. Having higher age significantly decreased the odds of belonging to the mild class (adjusted OR (aOR = 0.21; 95% confidence interval (CI): 0.05–0.83) compared to the healthy class. Also, obesity decreased the odds of membership in mild class (aOR = 0.10, 95% CI: 0.01–0.92) compared to healthy class. On the other hand, being female increased the odds of being in severe class (aOR = 9.76; 95% CI: 1.35–70.65) class in comparison to healthy class. Conclusion This study revealed that 7.3% of staff fell under mild and severe classes. Considering educational workshops in the workplace about mental disorders could be effective in enhancing staff’s knowledge of these disorders. Also, treatment of comorbid mental disorders may help reduce their prevalence and comorbidity.

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Clusters of the Risk Markers and the Pattern of Premature Coronary Heart Disease: An Application of the Latent Class Analysis

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.707070 ◽

2021 ◽

Vol 8 ◽

Author(s):

Leila Jahangiry ◽

Mahdieh Abbasalizad Farhangi ◽

Mahdi Najafi ◽

Parvin Sarbakhsh

Keyword(s):

Risk Factors ◽

Coronary Heart Disease ◽

Latent Class Analysis ◽

High Probability ◽

Latent Class ◽

Class Analysis ◽

Risk Markers ◽

History Of ◽

Opium Addiction ◽

The Mean

Background: Coronary heart disease (CHD) is the major cause of mortality in the world with a significant impact on the younger population. The aim of this study was to identify prematurity among patients with coronary artery bypass graft surgery (CABG) based on the clustering of CHD risk factors.Methods: Patients were recruited from an existing cohort of candidates for CABG surgery named Tehran Heart Center Coronary Outcome Measurement (THC-COM). A latent class analysis (LCA) model was formed using 11 potential risk factors as binary variables: cigarette smoking, obesity, diabetes, family history of CHD, alcohol use, opium addiction, hypertension, history of stroke, history of myocardial infarction (MI), peripheral vascular disease (PVD), and hyperlipidemia (HLP). We analyzed our data to figure out how the patients are going to be clustered based on their risk factors.Results: For 566 patients who were studied, the mean age (SD) and BMI of patients were 59.1 (8.9) and 27.3 (4.1), respectively. The LCA model fit with two latent classes was statistically significant (G2 = 824.87, df = 21, p < 0.0001). The mean (SD) age of patients for Class I and Class II was 55.66 (8.55) and 60.87 (8.66), respectively. Class I (premature) was characterized by a high probability of smoking, alcohol consumption, opium addiction, and a history of MI (P < 0.05), and class II by a high probability of obesity, diabetes, and hypertension.Conclusion: Latent class analysis calculated two groups of severe CHD with distinct risk markers. The younger group, which is characterized by smoking, addiction, and the history of MI, can be regarded as representative of premature CHD.

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