Analysis of risk factors of metabolic syndrome using a structural equation model: a cohort study

A new metabolic syndrome prediction model for self-evaluation as a primary screening tool in an apparently MetS-free population

BACKGROUND

Metabolic syndrome (MetS) is a growing global public health concern associated with increased morbidity and mortality. The study aimed to establish a simple self-evaluated prediction model to identify MetS.

METHODS

A cross-sectional study based on the American National Health and Nutrition Examination Survey database was performed. Participants aged ≥20 in the 2009-2018 surveys, with no current pregnancy or major morbidities, were included. The model was built with data from 2009 to 2016 and validated using 2017-2018 data. MetS was defined according to AHA/NHLBI guidelines. Multivariable logistic regression was applied to build a prediction model. The area under the receiver operating characteristic curve (AUC) was used to assess the discrimination ability and the maximal Youden's index was used to identify the optimal cut-off value.

RESULTS

The study included 4245 individuals (median age 37, IQR 28-49, 51.8% females) in the training group and 911 individuals (median age 37, IQR 28-52, 52.5% females) in the validation group. Older age, male gender, non-Black race, no postsecondary education, and higher BMI were significantly associated with increased risk of MetS. The final model included age, gender, race, education, and BMI, and showed good discrimination ability (AUC = 0.810, 95% CI 0.793-0.827, sensitivity 80.4%, specificity 66.2%, positive likelihood ratio 2.379, negative likelihood ratio 0.296, PPV 59.6% and NPV 84.5%).

CONCLUSION

A new model for self-evaluation may serve as a primary, easy-to-use screening tool to identify MetS in an apparently MetS-free population. A simple application may serve for primary and secondary prevention, thus enabling risk reduction in the development of cardiovascular morbidity and health expenditure.

Identifying factors associated with of blood pressure using Structural Equation Modeling: evidence from a large Kurdish cohort study in Iran

BACKGROUND

Identifying the risk factors leading to hypertension can help explain why some populations are at a greater risk for developing hypertension than others. The present study seeks to identify the association between the risk factors of hypertension in 35- to 65-year-old participants in western Iran.

METHODS

This cross-sectional study was conducted on 9705 adults from baseline data of Ravansar Non-Communicable Disease (RaNCD) cohort study, in the west region of Iran. Each of the latent variables were confirmed by confirmatory factor analysis. Using Structural Equation Modeling (SEM), we assessed the direct and indirect effects of factors associated with blood pressure.

RESULTS

Socioeconomic status (SES), physical activity, mean of serum lipids, obesity, diabetes and family history of hypertension had a diverse impact on the blood pressure, directly and (or) indirectly. The standardized total effect of SES, physical activity, mean of serum lipids, and obesity were -0.09 vs. -0.14, -0.04 vs. -0.04, 0.13 vs. 0.13 and 0.24 vs. 0.15 in men and women, respectively. Diabetes had a direct relationship with the blood pressure in women (0.03).

CONCLUSION

With regard to control of high blood pressure, public health interventions must target obesity, lifestyle and other risk related to nutritional status such as hyperlipidemia and hyperglycemia in Iranian population and among those with higher SES.

Structural Equation Modelling for Predicting the Relative Contribution of Each Component in the Metabolic Syndrome Status Change

Understanding the factor weighting in the development of metabolic syndrome (MetS) may help to predict the progression for cardiovascular and metabolic diseases. Thus, the aim of this study was to develop a confirmatory model to describe and explain the direct and indirect effect of each component in MetS status change. A total of 3581 individuals diagnosed with MetS, aged 18−102 years, were selected between January 2019 and December 2020 from a community-representative sample of Portuguese adults in a north-eastern Portuguese region to test the model’s goodness of fit. A structural equation modelling (SEM) approach and a two-way ANOVA (age × body composition) were performed to compare the relative contribution of each MetS component using joint interim statement (JIS). Waist circumference (β = 0.189−0.373, p < 0.001), fasting glucose (β = 0.168−0.199, p < 0.001) and systolic blood pressure (β = 0.140−0.162, p < 0.001) had the highest direct effect on the change in MetS status in the overall population and concerning both sexes. Moreover, diastolic blood pressure (DBP), triglycerides (TG) and high-density lipoprotein cholesterol (HDL-c) had a low or non-significant effect. Additionally, an indirect effect was reported for age and body composition involving the change in MetS status. The findings may suggest that other components with higher specificity and sensitivity should be considered to empirically validate the harmonised definition of MetS. Current research provides the first multivariate model for predicting the relative contribution of each component in the MetS status change, specifically in Portuguese adults.

Longitudinal association between sleep and 5-year incident metabolic syndrome in older Chinese adults: a community-based cohort study

PURPOSE

Our aim was to investigate the association between sleep and the development of metabolic syndrome (MetS) in Chinese older adults and to accumulate evidence for the prevention of MetS through sleep management.

METHODS

This prospective study followed 3005 participants aged over 60 derived from the Weitang Geriatric Diseases Study who were without MetS at baseline. MetS was defined according to the Adult Treatment Panel III (ATP III) criteria. Logistic regression models were fit to assess the association between sleep and MetS incident and a linear regression model was used to examine the impact of sleep duration on every component of MetS. Data on sleep-related parameters were obtained based on a self-reported questionnaire.

RESULTS

After five-year follow-up, 13.51% participants developed MetS, of which 46.86% were women. The incidence of MetS was highest among adults who slept 6 h or less and lowest among those who slept 7 h after adjusted for multiple variables. Subgroup analyses showed no gender specificity. The variation of fasting plasma glucose (FBG) for ≥9 h per night was significantly lower than that for 7.01-7.99 h per night (β = -0.18, P < 0.05). Sleeping for 8-8.99 h also decreased the variation of diastolic blood pressure (DBP) compared to 7.01-7.99 h (β = -0.84, P < 0.05).

CONCLUSION

We conclude that both short and long sleep duration are risk factors for MetS incident in older adults.

Feasibility of identifying and describing the burden of early-onset metabolic syndrome in primary care electronic medical record data: a cross-sectional analysis

BACKGROUND

The prevalence of metabolic syndrome is growing worldwide, yet remains underinvestigated in Canadian young adults. We sought to explore the use of a harmonized case definition specific to early-onset metabolic syndrome and determine its feasibility in assessing the prevalence of metabolic syndrome among electronic medical record (EMR) data of young adults in Northern Alberta.

METHODS

We conducted a cross-sectional study using a sample of EMR data from young adult patients aged 18-40 years and residing in Northern Alberta, who had an encounter with a participating primary care clinic between June 29, 2015, and June 29, 2018. Physical examination, laboratory investigation and disease diagnosis data were collected. A case definition and algorithm were developed to assess the feasibility of identifying metabolic syndrome, including measures for body mass index (BMI), blood pressure (BP), dysglycemia, hypertriglyceridemia, high-density lipoprotein cholesterol, diabetes and hypertension.

RESULTS

Among 15 766 young adults, the case definition suggested the prevalence of metabolic syndrome was 4.4%, 95% confidence interval (CI) 4.1%-4.7%. The most frequent 3-factor combination (41.6%, 95% CI 37.9%-45.3%) of metabolic syndrome criteria consisted of being overweight or obese, having elevated BP and hypertriglyceridemia. Half of metabolic syndrome cases (51.3%, 95% CI 47.6%-55.0%) were missing measures for fasting blood glucose, and one-fifth were missing a hemoglobin A_1c (HbA_1c) level. Notably, most young adults with a BMI of 25 or greater were missing HbA_1c (68.7%, 95% CI 67.6%-69.8%), fasting blood glucose (84.0%, 95% CI 83.2%-84.8%) and triglyceride testing (79.0%, 95% CI 78.1%-79.9%).

INTERPRETATION

We have shown that our case definition is feasible in identifying early-onset metabolic syndrome using EMR data; however, the degree of missing data limits the feasibility in assessing prevalence. Further investigation is required to validate this case definition for metabolic syndrome in the EMR data, which may involve comparing this definition to other validated metabolic syndrome case definitions.