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

Metabolic syndrome clustering patterns and the association with cardiovascular disease among post-menopausal Korean women

The metabolic syndrome (MetS) is a group of diseases conceptualized as a clustering of risk factors, with the risks of developing MetS in women varying significantly before and after menopause. This study investigated MetS clustering patterns and their association with cardiovascular disease (CVD) risk among post-menopausal women (n = 2479) using data from the Korean Genome Epidemiology Study. Using latent class analysis, three groups emerged: diabetic (5.6%), hypertensive (35.2%), and low-risk (59.2%). Relative to the low-risk group, the diabetic group demonstrated associations with older age, a family history of chronic disease, an increased Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), an elevated white blood cell (WBC) count, experience with hysterectomy, being a non-drinker, more physical activity, and excessive sleep. The hypertensive group was associated with older age, lower monthly income, a family history of chronic disease, increased HOMA-IR, a higher WBC count, more physical activity, and excessive sleep. The diabetic and hypertensive groups had a significantly higher CVD risk than the low-risk group (diabetic: odds ratio [OR] = 2.41 [1.11, 5.27]; hypertensive: OR = 2.46 [1.33, 4.55]). This study identified potential markers for MetS screening in post-menopausal women, highlighting the need for early intervention and personalized healthcare for middle-aged women to reduce CVD risk following menopause.

The association of the apolipoprotein B/A1 ratio and the metabolic syndrome in children and adolescents: a systematic review and meta-analysis

Objectives

Metabolic syndrome (MetS) is a constellation of coexisting cardiovascular risk factors. This study aimed to assess the evidence for the association between the apolipoprotein B/A1 ratio, apolipoprotein B, and apolipoprotein A1, and the MetS in children and adolescents.

Methods

The English electronic databases including PubMed, Embase, Web of Science, and Scopus were searched up to February 28, 2022. To ascertain the validity of eligible studies, modified JBI scale was used. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were pooled using the random-effects model to evaluate the association between the apolipoprotein B/A1 ratio, apolipoprotein B, and apolipoprotein A1 and the MetS. Heterogeneity amongst the studies was determined by the use of the Galbraith diagram, Cochran's Q-test, and I2 test. Publication bias was assessed using Egger's and Begg's tests.

Results

From 7356 records, 5 studies were included in the meta-analysis, representing a total number of 232 participants with MetS and 1320 participants as control group. The results indicated that increased levels of apolipoprotein B/A1 ratio (SMD 1.26; 95% CI: 1.04, 1.47) and apolipoprotein B (SMD 0.75; 95% CI: 0.36, 1.14) and decreased levels of apolipoprotein A1 (SMD -0.53; 95% CI: -0.69, -0.37) are linked to the presence of MetS. The notable findings were, children and adolescents with MetS had elevated levels of the apolipoprotein B/A1 ratio, apolipoprotein B, and decreased levels of apolipoprotein A1.

Conclusions

Our results suggest the need to evaluate the levels of apolipoproteins for detecting the risk of MetS in children and adolescents.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01235-z.

Patterns of metabolic syndrome and associated factors in women from the ELSA-Brasil: a latent class analysis approach

This study aimed to identify patterns of metabolic syndrome among women and estimate their prevalence and relationship with sociodemographic and biological characteristics. In total, 5,836 women were evaluated using baseline data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Patterns of metabolic syndrome were defined via latent class analysis, using the following metabolic abnormalities as indicators: abdominal obesity, hyperglycemia, hypertension, hypertriglyceridemia, and reduced HDL cholesterol. The relationship between these patterns and individual characteristics was assessed using latent class analysis with covariates. Three patterns of metabolic syndrome were identified: high metabolic expression, moderate metabolic expression, and low metabolic expression. The first two patterns represented most women (53.8%) in the study. Women with complete primary or secondary education and belonging to lower social classes were more likely to have higher metabolic expression. Black and mixed-race women were more likely to have moderate metabolic expression. Menopausal women aged 50 years and older were more often classified into patterns of greater health risk. This study addressed the heterogeneous nature of metabolic syndrome, identifying three distinct profiles for the syndrome among women. The combination of abdominal obesity, hyperglycemia, and hypertension represents the main metabolic profile found among ELSA-Brasil participants. Sociodemographic and biological factors were important predictors of patterns of metabolic syndrome.

The effect of smoking on latent hazard classes of metabolic syndrome using latent class causal analysis method in the Iranian population

BACKGROUND

The prevalence of metabolic syndrome is increasing worldwide. Clinical guidelines consider metabolic syndrome as an all or none medical condition. One proposed method for classifying metabolic syndrome is latent class analysis (LCA). One approach to causal inference in LCA is using propensity score (PS) methods. The aim of this study was to investigate the causal effect of smoking on latent hazard classes of metabolic syndrome using the method of latent class causal analysis.

METHODS

In this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS). 4857 participants aged over 20 years with complete information on exposure (smoking) and confounders in the third phase (2005-2008) were included. Metabolic syndrome was evaluated as outcome and latent variable in LCA in the data of the fifth phase (2014-2015). The step-by-step procedure for conducting causal inference in LCA included: (1) PS estimation and evaluation of overlap, (2) calculation of inverse probability-of-treatment weighting (IPTW), (3) PS matching, (4) evaluating balance of confounding variables between exposure groups, and (5) conducting LCA using the weighted or matched data set.

RESULTS

Based on the results of IPTW which compared the low, medium and high risk classes of metabolic syndrome (compared to a class without metabolic syndrome), no association was found between smoking and the metabolic syndrome latent classes. PS matching which compared low and moderate risk classes compared to class without metabolic syndrome, showed that smoking increases the probability of being in the low-risk class of metabolic syndrome (OR: 2.19; 95% CI: 1.32, 3.63). In the unadjusted analysis, smoking increased the chances of being in the low-risk (OR: 1.45; 95% CI: 1.01, 2.08) and moderate-risk (OR: 1.68; 95% CI: 1.18, 2.40) classes of metabolic syndrome compared to the class without metabolic syndrome.

CONCLUSIONS

Based on the results, the causal effect of smoking on latent hazard classes of metabolic syndrome can be different based on the type of PS method. In adjusted analysis, no relationship was observed between smoking and moderate-risk and high-risk classes of metabolic syndrome.

Clinical utility of lipid ratios as potential predictors of metabolic syndrome among the elderly population: Birjand Longitudinal Aging Study (BLAS)

BACKGROUND

Elderly adults are at higher risk of developing metabolic syndrome (MetS). The present study aims to investigate the relationship between lipid ratios and MetS in the elderly population.

METHODS

This study was conducted on elderly population of Birjand during 2018-2019. The data of this study was driven from Birjand Longitudinal Aging Study (BLAS). The participants were selected based on multistage stratified cluster sampling. Patients were categorized into quartiles according to the lipid ratios (TG/HDL-C, LDL-C/HDL-C, non-HDL/HDL-C), and the relationship between lipid ratio quartiles and MetS was determined by Logistic Regression using Odds Ratio. Finally, the optimal cut-off for each lipid ratio in MetS diagnosis was calculated according to the Area Under the Curve (AUC).

RESULTS

This study included 1356 individuals, of whom 655 were men and 701 were women. In our study, the crude prevalence of MetS was 792 (58%), including 543 (77.5%) women and 249 (38%) men. Increasing trends were observed in quartiles of all lipid ratios for TC, LDL-C, TG, and DBP. TG/HDL was also the best lipid ratio to diagnose the MetS, based on NCEP ATP III criteria. One unit increased in level of TG/HDL resulted in 3.94 (OR: 3.94; 95%CI: 2.48-6.6) and 11.56 (OR: 11.56; 95%CI: 6.93-19.29) increasing risk of having MetS in quartile 3 and 4 compared to quartile 1, respectively. In men and women, the cutoff for TG/HDL was 3.5 and 3.0, respectively.

CONCLUSIONS

Our results showed that the TG/HDL-C is superior to the LDL-C/HDL-C and the non-HDL /HDL-C to predict MetS among the elderly adults.

Critical assessment of the metabolic syndrome definitions in the adult general population of the United States - the Multi-Ethnic Study of Atherosclerosis (MESA)

Background

Metabolic syndrome (MetS) consists of a cluster of cardiometabolic risk factors and is an important determining factor for cardiovascular diseases (CVDs). We intended to use latent class analysis to classify the study population into several clusters.

Methods

The baseline information of 6,814 participants of the Multi-Ethnic Study of Atherosclerosis (MESA) aged 45-84 years in 2000-2002 was used. The latent class analysis was conducted to extract different patterns of components. SAS 9.2 and Stata 12 software were used for analysis.

Results

The components of MetS tend to accumulate, hence it would be feasible to categorize the population into three classes: [1] Non-Metabolic Syndrome Latent Class (NonMetS-LC), [2] Low Risk Latent Class (LowR-LC), and [3] Metabolic Syndrome Latent Class (MetS-LC). In women, adding high-density lipoprotein (HDL) component to the two-component combinations of NonMetS-LC will transfer the individual to MetS-LC, and it was found in 100% of combinations of MetS-LC. However, in men, blood pressure (BP) played such a similar role, which was found in 97.36% of combinations of MetS-LC.

Conclusion

Results showed that clinical value of each MetS component is different by gender. The main component in men was elevated BP; while low HDL and elevated fasting blood sugar (FBS) were in next ranks. However, the main component in women was low HDL; while elevated BP and FBS were in next ranks. Special attention should be paid to BP and HDL components, because these can be useful for clinicians and health policy-makers in diagnosis and screening. In conclusion, this study showed that revisions might be needed for the MetS definitions.

Graphical abstract

Cardiometabolic risk profiles in a Sri Lankan twin and singleton sample

Introduction

Prevention of cardiovascular disease and diabetes is a priority in low- and middle-income countries, especially in South Asia where these are leading causes of morbidity and mortality. The metabolic syndrome is a tool to identify cardiometabolic risk, but the validity of the metabolic syndrome as a clinical construct is debated. This study tested the existence of the metabolic syndrome, explored alternative cardiometabolic risk characterisations, and examined genetic and environmental factors in a South Asian population sample.

Methods

Data came from the Colombo Twin and Singleton follow-up Study, which recruited twins and singletons in Colombo, Sri Lanka, in 2012–2015 (n = 3476). Latent class analysis tested the clustering of metabolic syndrome indicators (waist circumference, high-density lipoprotein cholesterol, triglycerides, blood pressure, fasting plasma glucose, medications, and diabetes). Regression analyses tested cross-sectional associations between the identified latent cardiometabolic classes and sociodemographic covariates and health behaviours. Structural equation modelling estimated genetic and environmental contributions to cardiometabolic risk profiles. All analyses were stratified by sex (n = 1509 men, n = 1967 women).

Results

Three classes were identified in men: 1) “Healthy” (52.3%), 2) “Central obesity, high triglycerides, high fasting plasma glucose” (40.2%), and 3) “Central obesity, high triglycerides, diabetes” (7.6%). Four classes were identified in women: 1) “Healthy” (53.2%), 2) “Very high central obesity, low high-density lipoprotein cholesterol, raised fasting plasma glucose” (32.8%), 3) “Very high central obesity, diabetes” (7.2%) and 4) “Central obesity, hypertension, raised fasting plasma glucose” (6.8%). Older age in men and women, and high socioeconomic status in men, was associated with cardiometabolic risk classes, compared to the “Healthy” classes. In men, individual differences in cardiometabolic class membership were due to environmental effects. In women, genetic differences predicted class membership.

Conclusion

The findings did not support the metabolic syndrome construct. Instead, distinct clinical profiles were identified for men and women, suggesting different aetiological pathways.

The neighbourhood environment and profiles of the metabolic syndrome

BACKGROUND

There is a dearth of studies on how neighbourhood environmental attributes relate to the metabolic syndrome (MetS) and profiles of MetS components. We examined the associations of interrelated aspects of the neighbourhood environment, including air pollution, with MetS status and profiles of MetS components.

METHODS

We used socio-demographic and MetS-related data from 3681 urban adults who participated in the 3rd wave of the Australian Diabetes, Obesity and Lifestyle Study. Neighbourhood environmental attributes included area socio-economic status (SES), population density, street intersection density, non-commercial land use mix, percentages of commercial land, parkland and blue space. Annual average concentrations of NO₂ and PM_2.5 were estimated using satellite-based land-use regression models. Latent class analysis (LCA) identified homogenous groups (latent classes) of participants based on MetS components data. Participants were then classified into five metabolic profiles according to their MetS-components latent class and MetS status. Generalised additive mixed models were used to estimate relationships of environmental attributes with MetS status and metabolic profiles.

RESULTS

LCA yielded three latent classes, one including only participants without MetS ("Lower probability of MetS components" profile). The other two classes/profiles, consisting of participants with and without MetS, were "Medium-to-high probability of high fasting blood glucose, waist circumference and blood pressure" and "Higher probability of MetS components". Area SES was the only significant predictor of MetS status: participants from high SES areas were less likely to have MetS. Area SES, percentage of commercial land and NO₂ were associated with the odds of membership to healthier metabolic profiles without MetS, while annual average concentration of PM_2.5 was associated with unhealthier metabolic profiles with MetS.

CONCLUSIONS

This study supports the utility of operationalising MetS as a combination of latent classes of MetS components and MetS status in studies of environmental correlates. Higher socio-economic advantage, good access to commercial services and low air pollution levels appear to independently contribute to different facets of metabolic health. Future research needs to consider conducting longitudinal studies using fine-grained environmental measures that more accurately characterise the neighbourhood environment in relation to behaviours or other mechanisms related to MetS and its components.

Correlation Analysis Between Required Surgical Indexes and Complications in Patients With Coronary Heart Disease

A total of 215 patients with coronary heart disease (CHD) were analyzed with SPSS. Samples of different genders showed significance in the obtuse marginal branch of the left circumflex branch × 1, the diagonal branch D1 × 1, and the ms PV representation. Patients with left circumflex branch occlusion are more male and tend to be younger. Age displayed a positive correlation with left intima-media thickness (IMT) and right IMT. This indicated that as age increases, the values of left IMT and right IMT increase. Samples of different CHD types showed significance in the obtuse marginal branch of the left circumflex branch × 1, the middle part of RCA × 1, and the middle part of the left anterior descending branch × 1.5. For non-ST-segment elevation angina pectoris with acute total vascular occlusion, the left circumflex artery is the most common, followed by the right coronary artery and anterior descending branch. Ultrasound of carotid IMT in patients with CHD can predict changes in left ventricular function, but no specific correlation between left and right common carotid IMT was found. Samples with or without the medical history of ASCVD showed significance in the branch number of coronary vessel lesions. The value of the branch number of coronary vessel lesions in patients with atherosclerotic cardiovascular disease (ASCVD) was higher than in those without ASCVD. The occurrence of complication is significantly relative with the distance of left circumflex branch × 1, the middle segment of left anterior descending branch × 1.5, and the distance of left anterior descending branch × 1. For patients without complications, the values in the distal left circumflex branch × 1, the middle left anterior descending branch × 1.5, and the distal left anterior descending branch × 1 were higher than those for patients with complications. The VTE scores showed a positive correlation with the proximal part of RCA × 1, the branch number of coronary vessel lesions, the posterior descending branch of left circumflex branch × 1, the distal part of left circumflex branch × 1, and the middle part of left anterior descending branch × 1.5.

Subtypes of Premorbid Metabolic Syndrome and Associated Clinical Outcomes in Older Adults

Background

Metabolic syndrome has been shown to be a risk for new onset of cardiovascular disease (CVD) and type 2 diabetes. The subclasses of metabolic syndrome and any associated adverse health outcomes remain obscure. This study aimed to explore potential subtypes of metabolic syndrome, their associations with incidental diabetes, and any Major Adverse Cardiovascular Events (MACE).

Methods

Data for the retrospective cohort study were extracted from the New Taipei City Elderly Health Examination Database in the years 2014 and 2016. Demographic data, status of metabolic syndrome, its components, and latent class analysis (LCA) were analyzed. All participants were aged 65 years and older, with those having a prior history of CVD, cerebrovascular disease, diabetes mellitus (DM), and currently taking medications for hypertension, diabetes, and dyslipidemia were excluded.

Results

A total of 4,537 senior citizens were enrolled, with 2,207 (48.6%) of them identified as men. The prevalence of both metabolic syndrome and central obesity was increased with age. A 4-latent class model was fitted for participants diagnosed with metabolic syndrome. The central obesity (ABD)+ hyperglycemia (GLU)+ reduced HDL-C (HDL)+ high Blood Pressure (BP) group displayed the highest hazard ratio (HR) for predicting the new onset of diabetes, while the ABD+HDL+BP group showed a high risk for both CVD and MACE when compared after 2 years of follow-up.

Conclusions

This epidemiological analysis demonstrated that the risks of developing new-onset diabetes, CVD, and MACE varied among the different subtypes of metabolic syndrome.

Non-alcoholic fatty liver disease (NAFLD), metabolic syndrome and cardiovascular events in atrial fibrillation. A prospective multicenter cohort study

Whether non-alcoholic fatty liver disease (NAFLD) is associated with an increased risk of cardiovascular events (CVEs) independently from metabolic syndrome (MetS) is still matter of debate. Aim of the study was to investigate the risk of CVEs in a high-risk population of patients with non-valvular atrial fibrillation (AF) according to the presence of MetS and NAFLD. Prospective observational multicenter study including 1,735 patients with non-valvular AF treated with vitamin K antagonists (VKAs) or direct oral anticoagulants (DOACs). NAFLD was defined by a fatty liver index ≥ 60. We categorized patients in 4 groups: 0 = neither MetS or NAFLD (38.6%), 1 = NAFLD alone (12.4%), 2 = MetS alone (19.3%), 3 = both MetS and NAFLD (29.7%). Primary endpoint was a composite of CVEs. Mean age was 75.4 ± 9.4 years, and 41.4% of patients were women. During a mean follow-up of 34.1 ± 22.8 months (4,926.8 patient-years), 155 CVEs were recorded (incidence rate of 3.1%/year): 55 occurred in Group 0 (2.92%/year), 12 in Group 1 (2.17%/year), 45 in Group 2 (4.58%/year) and 43 in Group 3 (2.85%/year). Multivariable Cox regression analysis showed that use of DOACs, and female sex were inversely associated with CVEs, whilst age, heart failure, previous cardiac and cerebrovascular events, and group 2 (Group 2, Hazard Ratio 1.517, 95% Confidence Interval, 1.010-2.280) were directly associated with CVEs. In patients with AF, MetS increases the risk of CVEs. Patients with NAFLD alone have lower cardiovascular risk but may experience higher liver-related complications.

Comorbidity of metabolic syndrome components in a population-based screening program: A latent class analysis

Background: The prevalence of metabolic syndrome (MS) is rapidly increasing in the world. Thus, the aim of the present study was to identify the latent subgroups of Iranian male adults based on MS components and investigate the effect of abnormal alanine aminotransferase (ALT) and aspartate aminotransferase (AST), high total cholesterol (TC), and low-density lipoprotein (LDL) on the odds of membership in each class. Methods: In the present study, we used the data of a population-based screening program conducted on 823 urban adult men aged 25 years and older in city of Qom in 2014. Abdominal obesity, fasting blood sugar (FBS), blood pressure, and serum lipid profile were measured in participants after for at least 8 hours. MS was defined according to the Adults Treatment Panel III criteria. Latent class analysis was used to achieve the aims of study. Analyses were conducted using PROC LCA in SAS 9.2 software. In all analysis, p value < 0.05 was considered statistically significant. Results: There were 3 different latent classes among participants. Latent class 1, non-MS, 55.1%; latent lass 2, at risk, 21.3%; and finally latent class 3, MS, with 23.6% of the participants. Age (OR=0.98, 95% CI: 0.98-0.99, high LDL (OR=0.27, 95% CI: 0.13-0.56), high TC (OR=8.12, 95% CI: 4.40-15.00), and abnormal ALT (OR=2.25, 95% CI 1.49-3.41) were associated with at risk class. Also, only age (OR=1.02, 95% CI: 1.01-1.04) was associated with MS class. The most prevalent components among the participants were having low HDL (34.0%) and high WC (33.9%). Conclusion: Notable percent of samples fell in "at risk" and "MS" classes, which stress the necessity of designing preventive interventions for these specific stratums of population.

Recent advances in managing/understanding the metabolic syndrome

The metabolic syndrome (MetS) concept gathers in a single entity a set of metabolic abnormalities that have in common a close relationship with ectopic deposit of lipids, insulin resistance, and chronic low-grade inflammation. It is a valuable teaching tool to help health professionals to understand and integrate the consequences of lipotoxicity and the adverse metabolic consequences of insulin resistance. Also, it is useful to identify subjects with a high risk for having incident type 2 diabetes. Systems biology studies have gained a prominent role in understanding the interaction between adipose tissue dysfunction, insulin action, and the MetS traits and co-morbidities (that is, non-alcoholic steatohepatitis, or NASH). This approach may allow the identification of new therapeutic targets (that is, de novo lipogenesis inhibitors for NASH). Treatment targets on MetS are the adoption of a healthy lifestyle, weight loss, and the control of the co-morbidities (hyperglycemia, dyslipidemia, arterial hypertension, among others). The long-term goals are the prevention of type 2 diabetes, cardiovascular events, and other MetS-related outcomes. In the last few decades, new drugs derived from the identification of innovative treatment targets have come on the market. These drugs have positive effects on more than one MetS component (that is, hyperglycemia and weight control). New potential treatment targets are under study.

Application of Latent Class Analysis to Identify Metabolic Syndrome Components Patterns in adults: Tehran Lipid and Glucose study

In this study, using latent class analysis (LCA), we investigated whether there are any homogeneous subclasses of individuals exhibiting different profiles of metabolic syndrome (MetS) components. The current study was conducted within the framework of the Tehran Lipid and Glucose Study (TLGS), a population-based cohort including 6448 subjects, aged 20-50 years. We carried out a LCA on MetS components and assessed the association of some demographic and behavioral variables with membership of latent subclasses using multinomial logistic regression. Four latent classes were identified:(1) Low riskclass, with the lowest probabilities for all MetS components (its prevalence rate in men: 29%, women: 64.7%), (2) MetS with diabetes medication (men: 1%, women: 2.3%), (3) Mets without diabetes medication (men: 32%, women: 13.4%), (4) dyslipidemia (men: 38%, women: 19.6%). In men the forth subclass was more significantly associated with being smoker (odds ratio: 4.49; 95% CI: 1.89-9.97). Our study showed that subjects with MetS could be classified in sub-classes with different origins for their metabolic disorders including drug treated diabetes, those with central obesity and dyslipidemia associated with smoking.