Sex- specific clustering of metabolic syndrome components and incidence of cardiovascular disease: A latent class analysis in a population-based cohort study

Ahanchi, Noushin Sadat; Hadaegh, Farzad; Azizi, Fereidoun; Khalili, Davood

doi:10.1016/j.jdiacomp.2021.107942

Cited by 5 publications

(4 citation statements)

References 47 publications

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“…An important issue is related to the lack of knowledge about possible significant subtypes of metabolic syndrome, which may explain satisfactorily well the variability among individuals diagnosed with the syndrome 9 , 41 . The findings of this study add evidence to the findings of Riahi et al 40 , Ahanchi et al 23 , and Ahanchi et al 25 on the presence of an underlying, non-dichotomous factor that explains the interdependence of metabolic abnormalities related to metabolic syndrome.…”

Section: Discussionsupporting

confidence: 87%

“…While some authors have conducted separate analyses of the components of metabolic syndrome 14 , 21 , other researchers have preferred to identify and describe its heterogeneity 20 , 22 , 23 . A methodological approach recently incorporated into this latter type of analysis includes latent variable modeling, particularly latent class analysis (LCA) 23 , 24 , 25 . The use of LCA can improve knowledge about the occurrence of metabolic syndrome by identifying and analyzing subtypes/patterns of the syndrome with relevant expressions in a specific population of interest.…”

Section: Introductionmentioning

confidence: 99%

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

Galvão,

Matos,

Almeida

et al. 2023

Cad. Saúde Pública

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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.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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

Galvão,

Matos,

Almeida

et al. 2023

Cad. Saúde Pública

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“…For example, LPA/LCA has been used to identify distinct phenotypes based on several risk factors for prognostic and early prevention (Ayton et al, 2020; Bime et al, 2019; Ganesalingam et al, 2009). It has also been used to explore distinct patient symptom profiles (Ahanchi et al, 2021; Conley, 2017; Schmiege et al, 2012) so that those with the most significant symptom burden who need more attention can be identified (Chen et al, 2021; Crane et al, 2020; Lockwood et al, 2020; Ryan et al, 2007). This article emphasizes the innovative use of LPA/LCA to create a moderator based on several correlated variables.…”

Section: Discussionmentioning

confidence: 99%

Latent Profile/Class Analysis Identifying Differentiated Intervention Effects

et al. 2022

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Background:The randomized clinical trial is generally considered the most rigorous study design for evaluating overall intervention effects. Because of patient heterogeneity, subgroup analysis is often used to identify differential intervention effects. In research of behavioral interventions, such subgroups often depend on a latent construct measured by multiple correlated observed variables.Objectives: The purpose of this article was to illustrate latent class analysis/latent profile analysis as a helpful tool to characterize latent subgroups, conduct exploratory subgroup analysis, and identify potential differential intervention effects using clinical trial data.Methods: After reviewing different approaches for subgroup analysis, latent class analysis/latent profile analysis was chosen to identify heterogeneous patient groups based on multiple correlated variables. This approach is superior in this specific scenario because of its ability to control Type I error, assess intersection of multiple moderators, and improve interpretability. We used a case study example to illustrate the process of identifying latent classes as potential moderators based on both clinical and perceived risk scores and then tested the differential effects of health coaching in improving health behavior for patients with elevated risk of developing coronary heart disease.Results: We identified three classes based on one clinical risk score and four perceived risk measures for individuals with high risk of developing coronary heart disease. Compared to other classes we assessed, individuals in the class with low clinical risk and low perceived risk benefit most from health coaching to improve their physical activity levels.Discussion: Latent class analysis/latent profile analysis offers a person-centered approach to identifying distinct patient profiles that can be used as moderators for subgroup analysis. This offers tremendous opportunity to identify differential intervention effects in behavioral research.

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“…Latent class analysis is a probabilistic clustering methodology that assumes that within a population there are latent or unmeasured classes and that the relationship between any two variables is explainable by the latent variables (26). This model-based clustering approach has been used to identify clusters of individuals with cardiovascular disease and assign an individual to a class (or cluster) where that individual has the greatest probability of membership (27)(28)(29)(30). Latent class analysis uses item-response probability, which is the probability that a variable is conditional for class membership, to identify the distinctness of each class (28)(29)(30).…”

Section: Latent Class Analysismentioning

confidence: 99%

Ideal Cardiovascular Health in Young Adults With Established Cardiovascular Diseases

Leopold

Antman

2022

Front. Cardiovasc. Med.

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There has been an increase in the prevalence of cardiovascular diseases among young adults in the United States that has been attributed, in part, to a rise in overweight and obesity, use of combustible tobacco and unhealthy diet and exercise patterns. These factors are influenced further by socioeconomic status and other social determinants of health. In the My Research Legacy study, we examined ideal cardiovascular health in young adults aged 18– <50 years with cardiovascular disease using the Life's Simple 7 survey and data from digital health devices. Young adults with cardiovascular disease (n = 349) were older, had a lower socioeconomic status, a higher prevalence of risk factors, and lower Life's Simple 7 Health Scores (6.4 ± 1.5 vs. 7.1 ± 1.5, p < 0.01) compared to young adults without cardiovascular disease (n = 696). Analysis of digital health device data revealed that young adults with cardiovascular disease performed a similar number of weekly minutes of moderate and vigorous exercise as those without disease leading to similar ideal activity scores. Young adults with cardiovascular disease also shared similarities in modifiable risk factors with adults aged ≥50 years with cardiovascular disease (n = 217), including weight, dietary habits, and weekly minutes of exercise. Latent class analysis identified two phenogroups of young adults with cardiovascular disease: phenogroup 1 was characterized by more advantageous cardiovascular health factors and behaviors resulting in higher Life's Simple 7 Health Scores than phenogroup 2 (7.4 ± 1.2 vs. 5.5 ± 1.1, p < 0.01). These findings in young adults with cardiovascular disease may inform the design of behavioral and therapeutic interventions in the future to decrease cardiovascular morbidity and mortality.

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Sex- specific clustering of metabolic syndrome components and incidence of cardiovascular disease: A latent class analysis in a population-based cohort study

Cited by 5 publications

References 47 publications

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

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

Latent Profile/Class Analysis Identifying Differentiated Intervention Effects

Ideal Cardiovascular Health in Young Adults With Established Cardiovascular Diseases

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