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

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.

Structural determinants and cardiometabolic typologies related to frailty in community-dwelling older adults

Frailty is a geriatric syndrome linked to adverse outcomes. Co-occurring cardiometabolic factors increase frailty risk; however, their distinct combinations (typologies) associated with frailty are unclear. We aimed to identify subgroups of older adults with distinct cardiometabolic typologies and characterize their relationship with structural determinants and frailty to inform tailored approaches to prevent and delay frailty. This study was cross-sectional design and included 7984 community-dwelling older adults (65+ years) enrolled in the Health and Retirement Study (2006 and 2008). Latent class analysis was performed using seven cardiometabolic indicators (abdominal obesity, obesity, low high-density lipoprotein; and elevated blood pressure, blood sugar, total cholesterol, C-reactive protein). Frailty was indicated by ≥3 features (weakness, slowness, fatigue, low physical activity, unintentional weight loss). Logistic regression was used to examine the relationship between structural determinants (gender, race/ethnicity, and education), cardiometabolic typologies, and frailty. Three cardiometabolic subgroups were identified: insulin-resistant (n = 3547), hypertensive dyslipidemia (n = 1246), and hypertensive (n = 3191). Insulin-resistant subgroup members were more likely to be female, non-Hispanic Black, and college non-graduates; hypertensive dyslipidemia subgroup members were more likely to be non-Hispanic Others and report high school education; and hypertensive subgroup members were more likely to be male and college educated (p≤.05). Frailty risk was higher for females, Hispanic or Non-Hispanic Black older adults, and those with lower education (p≤.001). Frailty risk was greater in the insulin-resistant compared to the other subgroups (both aOR=2.0, both p<.001). Findings highlight a need to design tailored interventions targeting cardiometabolic typologies to prevent and delay frailty.

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.

Body composition affects blood lipids and vascular function in metabolically unhealthy or metabolically healthy older adult women with obesity: A multi-group path analysis/impact verification following latent profile analysis

OBJECTIVES

This study aimed to perform latent profile analysis (LPA) in older adult women with metabolic syndrome to extract a group according to their characteristics while controlling for body composition, blood lipid levels, and vascular function. The study also examined the relationship between different variables.

METHODS

The participants were 35 women aged ≥65 years, who met the Korean female standards for metabolic syndrome. Blood collection, blood pressure measurements, and vascular function measurements were performed. LPA and multigroup path analysis (MGPA) were performed to statistically analyze the effects of body composition on blood lipid levels and vascular function in older adult women with metabolic syndrome. Mplus 8.3 and jamovi 2.0.0 were used for the analyses, and the control significance level was set at .05.

RESULTS

The participants were divided into the following three groups: (G1) metabolically unhealthy participants with normal weight, (G2) normal-weight participants with arteriosclerosis, and (G3) obese but metabolically healthy participants (G3). The effect of body composition on blood lipid levels showed a significant difference only in G1. The effect of body composition on vascular function was influenced by various variables in G1 and G3, while G1 showed a higher explanatory power.

CONCLUSIONS

The MGPA results showed that the relationship between variables differs depending on the group. Therefore, the metabolic syndrome should be studied by subdividing it and confirming the characteristics of the classified groups, such as normal-weight but metabolically unhealthy individuals and obese but metabolically healthy individuals.

Clustering of Cardiometabolic Risk Factors and Dementia Incidence in Older Adults: A Cross-Country Comparison in England, the United States, and China

BACKGROUND

There is mixed evidence for an association between cardiometabolic risk factors and dementia incidence. This study aimed to determine whether different latent classes of cardiometabolic conditions were associated with dementia risk in older adults across England, the United States, and China.

METHODS

A total of 4 511 participants aged 50 and older were drawn from the English Longitudinal Study of Ageing (ELSA), 5 112 from Health and Retirement Study (HRS), and 9 022 from China Health and Retirement Longitudinal Study (CHARLS). Latent class analyses were performed across each data set utilizing 7 baseline cardiometabolic conditions: obesity, low high-density lipoprotein cholesterol, systolic and diastolic blood pressure, hyperglycemia, diabetes, and inflammation. Confounder-adjusted Cox proportional hazards regressions were conducted to estimate dementia incidence by cardiometabolic latent classes.

RESULTS

Three similar cardiometabolic classes were identified across all countries: (i) "relatively healthy/healthy obesity," (ii) "obesity-hypertension," and (iii) "complex cardiometabolic." Across the 3 samples, a total of 1 230 individuals developed dementia over a median of 6.8-12.2 years. Among ELSA and HRS participants, the "complex cardiometabolic" group had a higher dementia risk when compared to the "healthy obesity" groups (England: adjusted hazard ratio [AdjHR] = 1.62 [95% confidence interval {CI} = 1.11-2.37]; United States: AdjHR = 1.31 [95% CI = 1.02-1.68]). However, in CHARLS participants, the "obesity-hypertension" group had a greater risk of dementia when compared to the "relatively healthy" group (AdjHR = 1.28 [95% CI = 1.04-1.57]).

CONCLUSION

This study provides evidence that in western populations, complex cardiometabolic clusters are associated with higher rates of dementia incidence, whereas in a Chinese sample, a different cardiometabolic profile seems to be linked to an increased risk of dementia.

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.

Metabolic syndrome in primary care: A latent variable analysis of electronic medical records

BACKGROUND

The best definition of the metabolic syndrome (MetSyn) remains controversial. The aim was to develop a latent variable measurement model for the MetSyn using primary care data.

METHODS

This retrospective case-control study was based on the data from the IQVIA Disease Analyzer (DA) database including a representative panel of general and internal medicine practices in Germany. 23,020 adult patients, who were newly diagnosed with ≥1 component of the MetSyn (cases; median age [IQR]: 60 [50-72] years; 54 % males) between January 2016 to December 2019, and 2,144 patients without any MetSyn component (controls; 48 [38-61] years; 45 % males) were included. MetSyn was modeled by ICD-10 codes (E66: obesity, I10: hypertension, E11: type 2 diabetes, E78: lipid disorders) and physiological measures (BMI, HbA1c, blood pressure, HDL-cholesterol, LDL-cholesterol, triglycerides).

RESULTS

The covariance structure of the ICD codes and physiological measures could not be accounted for by a single MetSyn factor (root mean square error of approximation, RMSEA=0.085). Enrichment by another factor "Likelihood to Diagnose" significantly increased model fit with the observed data (RMSEA=0.054). "Likelihood to Diagnose" hardly correlated with MetSyn (r = 0.06), and was differentially associated with male sex, older age, and corresponding cardio- and cerebrovascular diseases. MetSyn showed the highest loading on four subordinate factors "hyperlipidemia" (0.70), "obesity" (0.66), "type 2 diabetes" (0.60), followed by "hypertension" (0.42). The ICD codes E66 and E78 were less indicative of these factors relative to BMI and triglycerides/HDL-cholesterol, respectively, whereas E11 and I10 were similarly indicative as their corresponding physiological measures.

CONCLUSIONS

MetSyn was best indicated by BMI, triglycerides (an indirect marker of insulin sensitivity), HbA1c, and ICD-10 diagnosis E11 (type 2 diabetes). The factor "Likelihood to diagnose" probably constitutes a method factor of differential diagnosis systematically contributing to the common consistency of the MetSyn diagnosis in primary care.

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.

Clustering of obesity-related characteristics: A latent class analysis from the Canadian Longitudinal Study on Aging

Measures of obesity, including body mass index (BMI) and waist circumference (WC), do not fully capture the complexity of obesity-related health risks. This study identified distinct classes of obesity-related characteristics and evaluated their associations with BMI, WC, and percent body fat (%BF) using cross-sectional data from 30,096 participants aged 45-85 in the Canadian Longitudinal Study on Aging (2011-2015). Sixteen obesity-related variables, including behavioural, metabolic, physical health, and mental health/social factors, were included in a latent class analysis to identify distinct classes of participants. Adjusted odds ratios (OR) were estimated from logistic regression for associations between each class and obesity defined by BMI, WC and %BF. Six latent classes were identified: "low-risk" (39.8%), "cardiovascular risk" (19.4%), "metabolic risk" (16.9%), "sleep and mental health risk" (12.1%), "multiple and complex risk" (6.7%), and "cardiometabolic risk" (5.1%). Compared to "low-risk", all classes had increased odds of BMI-, WC- and %BF-defined obesity. For example, the "complex and multiple risk" class was associated with obesity by BMI (OR: 10.70, 95% confidence interval (CI): 9.51, 12.04), WC (OR: 9.21, 95% CI: 8,15, 10,41) and %BF (OR: 7.54, 95% CI: 6.21, 9.16). Distinct classes of obesity-related characteristics were identified and were strongly associated with obesity defined by multiple measures.

Diagnosing subclinical facial eczema in cattle: does combining liver enzyme tests increase the accuracy of diagnosis?

AIMS

To assess whether adding glutamate dehydrogenase (GDH) activity measurements to measurements of gamma-glutamyl transferase (GGT) activity appreciably increases the accuracy of diagnosis of subclinical facial eczema (FE) in cattle.

METHODS

As part of a larger study on the impact of FE on productivity, GGT and GDH activities were measured in serum samples collected from 426 cattle from one dairy farm in the Taranaki region in April 2018. Bayesian latent class analysis was then used to estimate herd prevalence of subclinical FE as well as the specificity and sensitivity of the activity in serum of GGT or GDH alone, and of GGT and GDH activities combined, as diagnostic tests for subclinical FE.

RESULTS

The latent class analysis estimated the true prevalence of subclinical FE in the study population as 47.5 (95% probability interval (PI) = 38.3-55.3)%. There was no evidence of any clinically relevant difference between GGT and GDH activities as predictors of subclinical FE; the difference between the areas under the receiver operating characteristic curves for the two measures was 0.005 (95% PI = -0.02 to -0.03). Using the two tests in parallel, with a threshold of 50 IU/L for GGT and 225 IU/L for GDH resulted in specificity and sensitivity of >95%, markedly increasing the accuracy of diagnosis of subclinical FE compared to using GGT or GDH alone at any threshold.

CONCLUSIONS AND CLINICAL RELEVANCE

In this herd, combining the two tests resulted in a clinically relevant improvement in the accuracy of diagnosis of subclinical FE compared to using either test alone, which if used at the individual level will result in fewer cattle being assigned the wrong FE status. This will also apply at the herd level, with combined testing producing fewer false-positive herd test results than using one enzyme alone. This is particularly important for monitoring the efficacy of FE control measures when the expectation should be that the proportion of cattle with FE is very low.

Distinct Microbial Signatures between Periodontal Profile Classes

Precise classification of periodontal disease has been the objective of concerted efforts and has led to the introduction of new consensus-based and data-driven classifications. The purpose of this study was to characterize the microbiological signatures of a latent class analysis (LCA)-derived periodontal stratification system, the Periodontal Profile Class (PPC) taxonomy. We used demographic, microbial (subgingival biofilm composition), and immunological data (serum IgG antibody levels, obtained with checkerboard immunoblotting technique) for 1,450 adult participants of the Dental Atherosclerosis Risk in Communities (ARIC) study, with already generated PPC classifications. Analyses relied on t tests and generalized linear models with Bonferroni correction. Men and African Americans had higher systemic antibody levels against most microorganisms compared to women and Caucasians (P < 0.05). Healthy individuals (PPC-I) had low levels of biofilm bacteria and serum IgG levels against most periodontal pathogens (P < 0.05). Subjects with mild to moderate disease (PPC-II to PPC-III) showed mild/moderate colonization of multiple biofilm pathogens. Individuals with severe disease (PPC-IV) had moderate/high levels of biofilm pathogens and antibody levels for orange/red complexes. High gingival index individuals (PPC-V) showed moderate/high levels of biofilm Campylobacter rectus and Aggregatibacter actinomycetemcomitans. Biofilm composition in individuals with reduced periodontium (PPC-VI) was similar to health but showed moderate to high antibody responses. Those with severe tooth loss (PPC-VII) had significantly high levels of multiple biofilm pathogens, while the systemic antibody response to these microorganisms was comparable to health. The results support a biologic basis for elevated risk for periodontal disease in men and African Americans. Periodontally healthy individuals showed a low biofilm pathogen and low systemic antibody burden. In the presence of PPC disease, a microbial-host imbalance characterized by higher microbial biofilm colonization and/or systemic IgG responses was identified. These results support the notion that subgroups identified by the PPC system present distinct microbial profiles and may be useful in designing future precise biological treatment interventions.

Improved Disease Course Associated With Early Initiation of Biologics in Polyarticular Juvenile Idiopathic Arthritis: Trajectory Analysis of a Childhood Arthritis and Rheumatology Research Alliance Consensus Treatment Plans Study

OBJECTIVE

To investigate the effects of early introduction of biologic disease-modifying antirheumatic drugs (bDMARDs) on the disease course in untreated polyarticular juvenile idiopathic arthritis (JIA).

METHODS

We analyzed data on patients with polyarticular JIA participating in the Start Time Optimization of Biologics in Polyarticular JIA (STOP-JIA) study (n = 400) and a comparator cohort (n = 248) from the Childhood Arthritis and Rheumatology Research Alliance Registry. Latent class trajectory modeling (LCTM) was applied to identify subgroups of patients with distinct disease courses based on disease activity (clinical Juvenile Arthritis Disease Activity Score in 10 joints) over 12 months from baseline.

RESULTS

In the STOP-JIA study, 198 subjects (49.5%) received bDMARDs within 3 months of baseline assessment. LCTM analyses generated 3 latent classes representing 3 distinct disease trajectories, characterized by slow, moderate, or rapid disease activity improvement over time. Subjects in the rapid improvement trajectory attained inactive disease within 6 months from baseline. Odds of being in the rapid improvement trajectory versus the slow improvement trajectory were 3.6 times as high (95% confidence interval 1.32-10.0; P = 0.013) for those treated with bDMARDs ≤3 months from baseline compared with subjects who started bDMARDs >3 months after baseline, after adjusting for demographic characteristics, clinical attributes, and baseline disease activity. Shorter disease duration at first rheumatology visit approached statistical significance as a predictor of favorable trajectory without bDMARD treatment.

CONCLUSION

Starting bDMARDs within 3 months of baseline assessment is associated with more rapid achievement of inactive disease in subjects with untreated polyarticular JIA. These results demonstrate the utility of trajectory analysis of disease course as a method for determining treatment efficacy.

Profiles of US Law Enforcement Officers' Diagnosed Health Conditions: Results From a Probability-Based Sample of Officers

OBJECTIVE

To assess comorbidity across five common health conditions.

METHODS

Surveys were collected from a probability-based sample of US law enforcement officers, and latent class models estimated for hypertension (HTN), high blood cholesterol (LDL), diabetes (DM), a gastrointestinal disorder (GI), and sleep apnea (SA).

RESULTS

The majority of officers (69.4%) were classified in a Healthy profile. One in four officers (23.7%) were classified in a LDL-HTN-DM profile. About 7% of officers were classified in a GI-SA-HTN profile. Age, sex, body mass index (BMI), exercise, and working a rotating shift assignment distinguished class membership.

CONCLUSION

Most officers reported good health. Law enforcement administrative or clinical assessments using readily accessible measures might be informative in identifying risk categories of need for more targeted prevention and treatment support.

Key Relevance of Epigenetic Programming of Adiponectin Gene in Pathogenesis of Metabolic Disorders

Background & Objective::

Significant health and social burdens have been created by the growth of metabolic disorders like type 2 diabetes mellitus (T2DM), atherosclerosis, and non-alcoholic steatohepatitis, worldwide. The number of the affected population is as yet rising, and it is assessed that until 2030, 4−5 million individuals will acquire diabetes. A blend of environmental, genetic, epigenetic, and other factors, such as diet, are accountable for the initiation and progression of metabolic disorders. Several researches have shown strong relevance of adiponectin gene and metabolic disorders. In this review, the potential influence of epigenetic mechanisms of adiponectin gene “ADIPOQ” on increasing the risk of developing metabolic disorders and their potential in treating this major disorder are discussed.

Results & Conclusion::

Various studies have postulated that a series of factors such as maternal High fat diet (HFD), oxidative stress, pro-inflammatory mediators, sleep fragmentation throughout lifetime, from gestation to old age, could accumulate epigenetic marks, including histone remodeling, DNA methylation, and microRNAs (miRNAs) that, in turn, alter the expression of ADIPOQ gene and result in hypoadiponectinemia which precipitates insulin resistance (IR) that in turn might induce or accelerate the onset and development of metabolic disorder. A better understanding of global patterns of epigenetic modifications and further their alterations in metabolic disorders will bestow better treatment strategies design.