Lots of interesting abstracts and cases were submitted for TCTAP 2023. Below are the accepted ones after a thorough review by our official reviewers. Don’t miss the opportunity to expand your knowledge and interact with authors as well as virtual participants by sharing your opinion in the comment section!


Identification of High-Risk Symptom Cluster Burden Group Among Midlife Menopausal Women With Metabolic Syndrome Using Latent Class Growth Analysis

By Se Hee Min, Eun-Ok Im, Qing Yang


Se Hee Min


Se Hee Min1, Eun-Ok Im2, Qing Yang3


Columbia University School, USA1, Emory University, USA2, Duke University School, USA3
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Women’s Health Issues

Identification of High-Risk Symptom Cluster Burden Group Among Midlife Menopausal Women With Metabolic Syndrome Using Latent Class Growth Analysis

Se Hee Min1, Eun-Ok Im2, Qing Yang3

Columbia University School, USA1, Emory University, USA2, Duke University School, USA3


Metabolic syndrome refers to a constellation of metabolic abnormalities such as central obesity, hypertension, insulin resistance, and dyslipidemia. An individual needs at least three of these metabolic abnormalities to co-occur for a clinical diagnosis to be established. To date, the prevalence of metabolic syndrome is rapidly growing worldwide. When stratified by gender, women are placed at higher risk for developing metabolic syndrome than all other genders. Several factors unique to women include pregnancy-related weight gain, hormonal contraceptive use, and menopause. When further stratified by age group among women, midlife women are the most likely age group to be adversely affected by metabolic syndrome. This may be due to their changes in lipid and hormonal profiles that occur with aging and menopause. Previous studies have established the complex symptoms associated with menopause and metabolic syndrome. When these two conditions co-occur, midlife menopausal women with metabolic syndrome experience multiple co-occurring symptoms or symptom clusters, which often result in significant symptom cluster burden. Yet, there are no studies that have focused on identifying symptom cluster trajectories in midlife menopausal women with metabolic syndrome. Therefore, the aims of this secondary data analysis were to identify meaningful subgroups of midlife menopausal women with metabolic syndrome based on their distinct symptom cluster burden trajectories, and to describe the characteristics of different symptom cluster burden subgroups.


This secondary data analysis used the longitudinal data from baseline to visit 10 from the Study of Women’s Health Across the Nation (SWAN). The eligibility criteria for the current study was: (1) midlife women aged 40-65 years, (2) in peri-menopause or post-menopause, (3) met diagnostic criteria for metabolic syndrome at any point based on the National Cholesterol Education Program Adult Treatment III guidelines. A total of 557 participants met the eligibility criteria. The types of symptom clusters were selected based on our earlier work, which include the psychological/somatic/sexual cluster, sleep/urinary cluster, and vasomotor/genital cluster. The psychological/somatic/sexual cluster included anxiety, depression, frequent mood change, forgetfulness, stiffness, or soreness in joints, neck, or shoulder, and sexual disturbance. The sleep/urinary cluster included sleep disturbance and getting up from sleep to urinate. The vasomotor/genital cluster included cold sweat, night sweat, hot flash, and vaginal dryness. A composite symptom cluster score was derived for each symptom cluster that represents the symptom cluster burden, with scores ranging from 0 to 3 (0=none, 1=mild, 2=moderate, 3=severe).  Little test revealed that the missing data was considered to be missing at random (MAR). Thus, multiple imputation was conducted to create values for sexual disturbance and getting up from sleep, using the complete values from other related symptoms in the same symptom cluster. Multi-trajectory analysis using latent class growth analysis (LCGA) was conducted to join the different developmental trajectories of symptom clusters to identify meaningful subgroups and high-risk subgroup for greater symptom cluster burden over time. Then, descriptive statistics were used to explain the demographic characteristics of each symptom cluster trajectory subgroup, and bivariate analysis to examine the association between each symptom cluster trajectory subgroup and demographic characteristics. 


Our total study sample included 557 participants with a mean age of 45.76 years. The highest percentage of race/ethnicity was White (49.25%), followed by African American (29.89%), Hispanic (10.90%), Japanese (5.83%), and Chinese (4.14%). Based on the empirical summary plot, the severity for psychological/somatic/sexual cluster drops temporarily until year 1, increases until year 7, and then remains constant over time. In contrast, the severity for both sleep/urinary cluster and vasomotor/genital cluster fluctuates over time. A 4-class model with quadratic trend for multi-trajectory model of three symptom clusters was selected based on the statistical fit indices (AIC=-10897.16, BIC=-11055.25), clinical interpretability, and clinical judgement of the authors. A total of four classes were identified: Class 1 (low symptom cluster burden), Class 2 and Class 3 (moderate symptom cluster burden), and Class 4 (high symptom cluster burden). A statistically significant difference was examined in the level of social support (p=0.02) in which Class 4 had the highest percentage of receiving social support none of the time (6.45%) to a little of the time (7.53%). In contrast, Class 1 had the highest combined percentage of receiving social support most of the time (33.15%) to all of the time (51.09%).


The current study identified four symptom cluster trajectory subgroups in midlife menopausal women with metabolic syndrome using latent class growth analysis. In addition, we found a dynamic and interactive nature among the symptom cluster trajectory subgroups. Social support was a significant predictor which needs to be assessed and provided routinely. An understanding and appreciation for the different symptom cluster trajectory subgroups and their dynamic nature will assist clinicians to offer targeted symptom cluster assessment and management in clinical settings.

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