Body composition and dyslipidemia in postmenopausal medical workers: a cross-sectional study

Abstract

Objective

Dyslipidemia, characterized by abnormalities in lipid levels, significantly increases the risk of atherosclerotic cardiovascular disease (ASCVD), particularly as its prevalence escalates with age, notably impacting postmenopausal women due to hormonal changes. Understanding the relationship between dyslipidemia and body composition, assessed through techniques like bioelectrical impedance analysis (BIA), is crucial for developing effective prevention and management strategies for cardiovascular disease and related metabolic disorders.This study aimed to evaluate the predictive value of body composition on dyslipidemia in postmenopausal medical workers and inform prevention strategies.

Methods

A cross-sectional study included 207 postmenopausal medical workers undergoing hospital employee physical examinations between July and October 2023. Body composition was assessed using the Inbody720 analyser, and serum triglycerides, total cholesterol, and other physical examination results were recorded. The study analyzed the relationship between body composition and blood lipid levels.

Results

The prevalence of total dyslipidemia in postmenopausal healthcare workers was 46.4%. Participants with dyslipidemia exhibited significantly higher levels of uric acid, fasting blood glucose, glycosylated hemoglobin, BMI, body fat percentage, visceral fat area, body water, and minerals (p < 0.05). Conversely, skeletal muscle mass index was significantly lower in the dyslipidemia group (p = 0.000). Serum triglycerides (TG) showed a positive correlation with BMI(r = 0.377,p = 0.000), body fat percentage(r = 0.271,p = 0.000), skeletal muscle mass index(r = 0.254,p = 0.000), visceral fat area(r = 0.340,p = 0.000), total body water(r = 0.249,p = 0.000), and minerals(r = 0.231,p = 0.001) in postmenopausal medical workers. The binary logistic regression analysis revealedeach unit increase in body fat percentage, the odds of developing dyslipidemia increase by 77.1% (OR, 1.771, 95% CI,1.247–2.516). For each unit increase in total body water (TBW), the odds of developing dyslipidemia increase nearly sixfold (OR = 7.296,95% CI,2.068–25.740). Additionally, for each one-year increase in age, the odds of developing dyslipidemia increase by 5.8% (OR,1.058,95%CI,1.013–1.105).

Conclusion

Skeletal muscle mass index, body fat percentage visceral fat area, minerals, and total body water are strongly correlated with dyslipidemia and can serve as predictors. Our study indicates that body composition is closely related to dyslipidemia. Accordingly, body composition assessment can be introduced in health check-ups for early screening, and interventions involving exercise and diet can be implemented for at-risk populations, providing comprehensive support for the prevention and control of dyslipidemia.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12905-025-04245-6.

Background

CVD cases increased from 271 million in 1990 (95% UI: 257 to 285 million) to 523 million in 2019 (95% UI: 497 to 550 million), nearly doubling [1]. Dyslipidemia is an important risk factor for atherosclerotic cardiovascular disease (ASCVD) [2], including coronary atherosclerotic heart disease (CAD) and ischaemic stroke. Clinically, it is mainly divided into four categories: hypercholesterolaemia, hypertriglyceridemia, mixed hyperlipidaemia, and low HDL cholesterolemia [3]. Dyslipidemia is common in all populations, and its prevalence increases significantly with age [4]. According to the report of the U.S. Cholesterol Education Program, the overall prevalence of low density lipoprotein cholesterol (LDL-C) in adults over 20 years old was about 33.5%, and the prevalence of high LDL-C gradually increases with age, from 11.7% in 20 ~ 39 years old to 41.2% in 40 ~ 64 years old and 58.2% in people over 65 years old [5]. The results of a cross-sectional survey of serum lipids and lipoproteins in more than 40,000 adults aged ≥ 20 years [6] showed that the incidence of total cholesterol (TC) and LDL-C abnormalities in women gradually increased with the increase of age, especially in the age group of 50 ~ 59 years; TC and LDL- C abnormalities are more common in males than females before age 50, and more frequently in females than males after age 50. Studies show that the prevalence of dyslipidemia among individuals aged 35 and older in China is 34.7% [7], after menopause is 38.8% [8]. According to a health survey report on doctors in China published by the Chinese Medical Association in 2016, 63.6% of doctors had an unsatisfactory health index. The health status of young doctors under 35 years old is particularly concerning. Factors such as high work stress, irregular schedules, and poor diet have led to a high prevalence of chronic diseases among healthcare workers, including obesity, cardiovascular diseases, and diabetes [9]. Research indicates thatthe rate of subhealth among medical workers is as high as 89% to 94%, indicating their health status warrants attention [10]. The rate of dyslipidemia among Chinese healthcare workers ranges from 20.65% to 58.5% [11, 12]. After menopause, the level of oestrogen in the body of women drops sharply, and due to the weakening of the protective effect of oestrogen, it is easy to lead to blood lipid metabolism disorders, which increases the risk of cardiovascular disease (CVD). A prospective cohort study [13] of more than 2,000 postmenopausal women in the United States who participated in a multiracial study of atherosclerosis analysed the relationship between sex hormone levels and CVD, and found that postmenopausal oestradiol levels were inversely correlated with the risk of coronary heart disease. As a result, postmenopausal women have an increased prevalence of dyslipidemia and a significant increase in the risk of ASCVD due to oestrogen deficiency and dysregulation of lipid metabolism. Guidelines for the management of dyslipidemia at home and abroad have listed postmenopausal women as a key management group.

Bioelectrical impedance analysis (BIA) is a method recognized by both Asian and European guidelines for the objective assessment of body composition [14, 15], which can reflect the patient’s intracellular and extracellular fluid, total water content, body fat percentage, and the area of adipose tissue, fat-free tissue, and visceral fat [16–19] . Accurate assessment of changes in a patient’s body composition can help him or her in making medical diagnoses, managing chronic diseases, and formulating nutritional management programs [20]. In addition, body composition is strongly associated with the incidence of certain diseases. Factors such as the waist-to-hip fat ratio, visceral fat area, and body mass index are linked to a higher incidence of complications related to type 2 diabetes mellitus (T2DM) [21]. At present, body composition is also widely used to predict the diagnosis and treatment of diabetes, obesity, osteoporosis, and other diseases [22]. Compared with routine blood tests, body composition analysis is simple, non-invasive, painless, and inexpensive, and has been popularised in clinical practice [23, 24]. There has also been a weak correlation between body fat and blood lipids in older women [25], but the indicators studied are not comprehensive. Therefore, whether there is a correlation between body composition and dyslipidemia in postmenopausal medical workers has not been reported. The purpose of this study was to analyse the correlation between dyslipidemia and human body composition in postmenopausal medical workers in a tertiary hospital in Zhengzhou, so as to provide a scientific basis for the prediction of dyslipidemia by using the relevant indexes of body composition.

Participants and methods

Study design and population

A total of 207 postmenopausal middle-aged and elderly female employees who participated in the physical examination at the Second Affiliated Hospital of Zhengzhou University and voluntarily underwent body composition analysis from July to October 2023 were enrolled in this study. Ethical approval was obtained from the Medical Research Ethics Committee of the Second Affiliated Hospital of Zhengzhou University (approval number: Wei Yi [2020] No. 136). All participants provided written informed consent.

Inclusion criteria: (1) absence of natural menstruation for more than 12 months; (2) provision of informed consent.

Exclusion criteria: (1) unnatural menopause; (2) presence of pacemakers or other electronic medical devices; (3) metal implants; (4) inability to stand independently for the examination; (5) use of special medications such as lipid-lowering drugs; (6) severe liver or kidney dysfunction; (7) malignant tumors; (8) deemed ineligible by the investigator.Objects and methods.

Sociodemographic and lifestyle characteristics

Participants were interviewed using a questionnaire that collected data on their age, gender, marital status, education, and lifestyle factors, including smoking and drinking habits.

Body composition assessment

Body composition measurements were conducted by a professional nutritionist in the Clinical Nutrition Department. Participants were instructed to remove their shoes, hats, and headwear. They were positioned with their chest and abdomen upright, torso straight, head upright, eyes looking forward, feet together, and toes approximately 60 degrees apart. The heels, sacrum, and scapulae were aligned to maintain a “three-point and one-line” posture, after which their height was recorded.

Body composition analysis was conducted using an InBody720 body composition analyzer (InBody Co., Ltd., Seoul, South Korea), a machine frequently employed in clinical settings that utilizes bioelectrical impedance to measure body composition [26–28].All participants are required to fast for 3 h before the test, avoid strenuous exercise, and remove outerwear, heavy objects, and electronic devices. Measurements were conducted in strict adherence to the operating procedures. Participants stood barefoot on the foot electrodes of the analyzer, grasped the hand electrodes firmly with both hands, allowing the arms to hang naturally and remain separate from the body.

The primary measurement parameters included body fat percentage (BFP), body mass index (BMI), total body water (TBW), body mineral content (BMC), and visceral fat area (VFA). Additionally, the skeletal muscle mass index (SMI) was calculated using the formula:

Biochemical results assessment

Serum samples were collected after 8–12 h of fasting. The following biochemical indicators were analyzed using the Roche Infinity automatic biochemical analyzer: total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), urea (UR), creatinine (CR), uric acid (UA), and blood glucose (GLU). Glycosylated hemoglobin (HbA1c) was measured using the Premier Hb9210 analyzer.

Dyslipidemia assessment

Dyslipidemia was diagnosed based on the Chinese Guidelines for the Management of Blood Lipids (2023) [3]. Patients with elevated triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), or reduced high-density lipoprotein cholesterol (HDL-C) in their blood lipid profile were classified as having dyslipidemia, following the guidelines.

Sample size calculation

This study primarily explores the correlation between body composition indicators in postmenopausal women and dyslipidemia. The sample size estimation for correlation studies uses the Fisher’s Z transformation formula:

We set the two-sided significance level at α = 0.05 and the statistical power at 1 - β = 80%. Based on previous literature, we estimate the correlation coefficient between body composition and dyslipidemia to be between 0.1 and 0.4 [29–31], and we set it at 0.2 for our calculations. According to the formula, the calculated sample size is approximately 191 cases. To ensure the stability and reliability of the study, we increased the sample size by 10% to account for measurement error and individual differences, resulting in a planned sample size of 210 cases. However, due to factors such as the willingness of postmenopausal women to participate, we ultimately included 207 cases in the study. A post-hoc analysis showed that the statistical power of the existing sample size reached 82.5%, which is still sufficient to effectively reveal the correlation between body composition and dyslipidemia.

Statistical analysis

Data analysis was performed using SPSS statistical software, version 21.0. A p-value of < 0.05 was considered statistically significant.The normality of continuous variables was assessed using the Shapiro–Wilk test and the Kolmogorov–Smirnov test. For numerical data, the number of cases and composition ratios (%) were calculated, and chi-squared tests were employed to evaluate significant differences between groups. Continuous variables following a normal distribution were expressed as mean ± standard deviation (± s), comparisons between two groups, an independent samples t-test is used.while those not conforming to a normal distribution were represented as median (P25, P75), and statistical comparisons between the two groups are performed using the Mann-Whitney U test. Depending on the data distribution, parametric or nonparametric tests were applied for statistical comparisons.

To compare the correlation between body composition and blood lipid levels, Pearson correlation analysis is used if both groups of data conform to a normal distribution. If at least one group does not conform to a normal distribution, Spearman correlation analysis is used. A binary logistic regression analysis is conducted to examine the factors affecting blood lipid levels. Odds ratios (ORs) with 95% confidence intervals (95%CI) were calculated to identify potential influencing factors. Receiver operating characteristic (ROC) curves were employed to assess the sensitivity and specificity of body composition analysis in predicting dyslipidemia.

Results

Basic information

A total of 207 postmenopausal medical workers were included in the statistical analysis. The participants’ ages ranged from 47 to 89 years, with an average age of 62.94 ± 9.88 years.

Detection rate of dyslipidemia

The overall detection rate of dyslipidemia among postmenopausal medical workers was 46.4%, comprising 16.4% with hypercholesterolemia (hyper-TC), 28.5% with hypertriglyceridemia (hyper-TG), 7.7% with mixed hyperlipidemia, and 4.3% with low high-density lipoprotein cholesterol (hypo-HDL-C).

Comparison of biochemical results and body composition

There were no statistically significant differences in age, marital status, education level, and smoking and drinking history between the normal lipid group and the abnormal lipid group. The demographic characteristics such as age, education level, marital status, smoking history, and drinking history between the normal lipid group and the abnormal lipid group showed no significant differences (p > 0.05).In the dyslipidemia group, levels of uric acid, fasting blood glucose, and glycosylated hemoglobin were significantly higher than those in the normal blood lipid group (p < 0.05). Regarding body composition, BMI, body fat percentage, visceral fat area, body water content, and mineral content were significantly elevated in the dyslipidemia group compared to the normal group (p < 0.05). Conversely, the skeletal muscle mass index was significantly lower in the dyslipidemia group (p < 0.05). Detailed results are presented in Table 1.

Table 1.

Comparison of sociodemographic Characteristics, body Composition, and biochemical profile between the normal lipid group and the abnormal lipid group [M (P25, P75), ()]

Indicator	Normal Lipid Group	Dyslipidemia Group	X2/Z/t Value	p-Value
Age (years)b	61(54,69)	63(55,70)	-0.791	0.429
Educationa a			2.705	0.259
High school or less	17(47.2%)	19(52.8%)
College or university	87(53.7%)	75(46.3%)
Postgraduate and above	7(77.8%)	2(22.2%)
Marital Status a			0.000	0.995
Married	96(53.6%)	83(46.6%)
Single/Widowed/Divorced	15(53.6%)	13(46.4%)
Smoking history a			0.000	1.000
Yes	3(50%)	3(50%)
No	108(53.75%)	93(46.3%)
Drinking historya			2.618	0.106
Yes	12(40%)	18(60%)
No	99(55.9%)	78(44.1%)
ALT(U/L)b	16.00 (13.00,22.00)	15.00 (12.00,22.00)	-0.492	0.623
AST(U/L)b	21.00 (18.00,24.00)	18.00 (16.00,22.00)	-2.522	0.012*
ALP(U/L)b	73.00 (62.00,90.00)	83.00 (68.00,100.00)	-2.755	0.006*
UR(mmol/L)b	5.03 (4.34,5.98)	4.92 (4.28,5.72)	-0.720	0.471
CR(µmol/L)b	59.00 (53.00,66.00)	59.00 (53.00,67.00)	-0.095	0.924
UA(µmol/L)c	268.09 ± 66.57	295.60 ± 63.93	-3.020	0.003*
GLU(mmol/L)b	5.07 (4.82,5.59)	5.27 (4.96,5.66)	-2.104	0.035*
HBA1c(%)b	5.69 (5.49,5.95)	5.80 (5.59,6.15)	-2.465	0.014*
BMI(kg/m²)c	22.38 ± 2.50	23.93 ± 2.84	-4.184	0.000*
BFP(%)c	31.32 ± 6.08	33.08 ± 4.83	-2.242	0.025*
SMI(kg/m²)c	6.44 ± 0.67	6.07 ± 0.54	-4.443	0.000*
VFA(cm2)c	78.34 ± 20.22	87.42 ± 20.24	-3.220	0.001*
TBW(L)c	28.32 ± 2.73	30.15 ± 3.27	-4.345	0.000*
BMC(kg)c	2.72 ± 0.27	2.88 ± 0.30	-3.969	0.000*

1.The abbreviated names of the variables: ALT alanine aminotransferase, AST aspartate aminotransferase, ALP alkaline phosphatase, UR urea, CR creatinine, UA uric acid, GLU blood glucose, HbA1c Glycosylated hemoglobin, BMI body mass index, BFP body fat percentage, SMI skeletal muscle mass index, VFA visceral fat area, TBW total body water, BMC body mineral content

2.^aChi-Square Test, ^bMann-Whitney U test, ^cIndependent samples t-test

3. *Significant at p < 0.05

Correlation between lipid levels and body composition indices

In postmenopausal medical workers, serum triglyceride (TG) levels were positively correlated with body mass index (BMI), body fat percentage (BFP), skeletal muscle mass index (SMI), visceral fat area (VFA), total body water (TBW), and bone mineral content (BMC) (all p < 0.05). However, no significant correlations were observed between serum low-density lipoprotein cholesterol (LDL-C), the ratio of total cholesterol to high-density lipoprotein cholesterol (TC/HDL-C), and body composition indices (all p > 0.05). Detailed results are presented in Table 2.

Table 2.

Correlation analysis of body composition and blood lipid levels

Variable	TG		LDL		TC/HDL
	r-value	p-value	r-value	p-value	r-value	p-value
BMIa	0.377	0.000*	0.097	0.165	0.088	0.209
BFPa	0.271	0.000*	0.129	0.063	0.115	0.100
SMIa	0.254	0.000*	0.038	0.591	0.001	0.993
VFAa	0.340	0.000*	0.085	0.222	0.083	0.232
TBWa	0.249	0.000*	0.025	0.726	-0.038	0.589
BMCa	0.231	0.001*	0.046	0.513	0.008	0.914

1.^aPearson correlation analysis

2.*Significant at p < 0.05

Logistic regression analysis of factors associated with dyslipidemia

Binary logistic regression analysis revealed that body mass index (BMI) (OR = 1.449, 95% CI: 0.904–2.324, p = 0.124), body fat percentage (BFP) (OR = 1.771, 95% CI: 1.247–2.516, p = 0.001), skeletal muscle mass index (SMI) (OR = 0.011, 95% CI: 0.000–0.473, p = 0.019), visceral fat area (VFA) (OR = 0.838, 95% CI: 0.757–0.928, p = 0.001), bone mineral content (BMC) (OR = 0.001, 95% CI: 0.000–0.443, p = 0.027), and total body water (TBW) (OR = 7.296, 95% CI: 2.068–25.740, p = 0.002) ,age(OR = 1.058,95%CI:1.013,1.105,p = 0.011)were significantly associated with dyslipidemia. The results are presented in Table 3.

Table 3.

Logistic regression analysis of factors related to dyslipidemia

Influencing Factors	B	S.E,	Wals	Sig.	Exp (B)	95% CI for EXP(B)
						Lower	Upper
BMI	0.371	0.241	2.370	0.124	1.449	0.904	2.324
BFP	0.572	0.179	10.192	0.001*	1.771	1.247	2.516
SMI	-4.466	1.897	5.545	0.019*	0.000	0.000	0.473
VFA	-0.177	0.052	11.588	0.001*	0.838	0.757	0.928
BMC	-6.996	3.154	4.920	0.027*	0.001	0.000	0.443
TBW	1.987	0.643	9.544	0.002*	7.296	2.068	25.740
AGE	0.056	0.022	6.449	0.011*	1.058	1.013	1.105

Note: *Significant at p < 0.05

ROC curve analysis

The receiver operating characteristic (ROC) curve analysis demonstrated that body fat percentage (BFP), skeletal muscle mass index (SMI), visceral fat area (VFA), bone mineral content (BMC), total body water (TBW) and age had areas under the ROC curve (AUC) for diagnosing dyslipidemia of0.590, 0.646, 0.628, 0.647, and 0.660, respectively. All values were statistically significant with p < 0.05. However, the area under the age curve is 0.532, and the difference is not statistically significant (p = 0.430). The results are presented in Table 4; Fig. 1.

Table 4.

ROC curve analysis of BFP, SMI, VFA, BMC, and TBW for the diagnosis of dyslipidemia

Influence factors	optimal cut-off value	AUC	95%CI	p value	Sensitivity (%)	Specificity (%)
BFP	29.25	0.590	0.513 ~ 0.668	0.025*	82.3	36.0
SMI	6.10	0.646	0.571 ~ 0.720	0.000*	68.8	53.2
VFA	70.95	0.628	0.553 ~ 0.704	0.001*	82.3	39.6
BMC	2.74	0.647	0.573 ~ 0.721	0.000*	70.8	52.3
TBW	27.95	0.660	0.587 ~ 0.734	0.000*	77.1	49.5

Note: *Significant at p < 0.05

Fig. 1.

ROC Curves for Age, BFP, SMI, VFA, BMC, and TBW to Diagnose Dyslipidemia in Menopausal Women

Discussions

Aging is an unavoidable natural process, and elderly women must face the dual impact of aging and menopause [32]. Postmenopausal women experience an increased prevalence of dyslipidemia due to lipid metabolism disorders and a deficiency in estrogen levels [33, 34]. , thereby increasing the risk of cardiovascular disease [35]. Therefore, early identification of high-risk factors and monitoring of dyslipidaemia are critical measures to prevent and control cardiovascular diseases in postmenopausal women. In this study of postmenopausal medical workers, the total detection rate of dyslipidaemia was 46.4%, which was lower compared to the reported rates of 69.9% and 68.7% in postmenopausal women as reported by Nie Guangning [36]and Ghazizheya Zainati [37].This discrepancy may be attributed to the fact that, as medical workers, these individuals have relevant medical knowledge and a higher level of awareness regarding self-healthcare.

Blood tests are the gold standard for diagnosing dyslipidemia. However, due to issues like vascular hardening, malnutrition, and economic factors, elderly individuals often find it challenging to undergo regular blood tests. Bioelectrical impedance analysis (BIA) is an attractive method for identifying body composition due to its affordability and noninvasive nature. This test can be completed in just a few minutes, does not require highly trained personnel, and provides immediate results, making it undoubtedly more popular [38, 39]. Currently, body composition analysis is widely employed to support the diagnosis and management of diabetes, obesity, osteoporosis, and other diseases [40].This study aims to explore the predictive value of this noninvasive method of body composition analysis for dyslipidemia.

A cohort study of perimenopausal women at Peking Union Medical College Hospital showed that as menopause progresses, the rates of obesity and central obesity gradually increase [41]. In women matched by age and body mass index (BMI), postmenopausal women often have a higher proportion of central fat compared to premenopausal women, with visceral fat potentially increasing from 5% to 8% before menopause to 15%-20% after menopause [42]. Data from the Study of Women Across the Nation (SWAN) indicate that early in the menopausal transition, the rate of fat gain doubles, and lean body mass significantly decreases; these changes in body composition persist for up to two years after the final menstrual period (FMP) [43]. Weight gain accompanied by an increase in central fat distribution is very common among postmenopausal women, and these changes are the result of a combination of aging, decreased estrogen levels, and other unique factors affecting menopausal women [44]. Central obesity can lead to various adverse metabolic consequences, including abnormal glucose levels, dyslipidemia, hypertension, and cardiovascular diseases [45]. In recent years, several studies have found that changes in E2 and FSH secretion levels in postmenopausal women regulate postmenopausal lipid metabolism by modulating the type [46] and amount of energy intake [47], participating in lipid metabolism in multiple tissues [48, 49], and regulating energy expenditure [49, 50]. Exercise, whether aerobic [51]or resistance training [52], has a positive effect on lipid metabolism in postmenopausal women. Regular physical activity can improve insulin sensitivity and glucose control, lower cholesterol levels and blood pressure, and reduce cardiovascular disease risk and all-cause mortality [53]. Dietary composition also has an impact on lipid metabolism; for example, the Mediterranean diet has been shown to reduce the risk of cardiovascular diseases, and adherence to the Mediterranean diet seems to lower the risk of weight gain during menopause and alleviate menopause-related symptoms [54].

RV Shah et al. found that for every 100 cm² increase in visceral fat, the risk of metabolic syndrome (MetS) increased by 28% in individuals of the same height. Even after adjusting for BMI, visceral fat content remained associated with MetS, and the association was stronger than that of subcutaneous fat [55]. Liu et al. evaluated the body composition of the Chinese population and found that, after adjusting for BMI and body fat percentage, higher fat mass index (body fat mass/height squared) levels were positively correlated with the presence of MetS [56]. We comprehensively analyzed the effects of body composition on blood lipids in postmenopausal healthcare workers. The BMI, body fat percentage, visceral fat area, as well as body water and mineral content in the dyslipidemia group were significantly higher than those in the normal group. BMI represents body mass, while body fat percentage and visceral fat area indicate the amount and distribution of body fat. These indicators were generally higher in the dyslipidemia group, indicating that higher BMI, greater body fat mass, and larger visceral fat area increase the likelihood of dyslipidemia, which aligns with the findings of Shah et al. and Liu et al. [55, 56]. The skeletal muscle mass index of the limbs, calculated after body adjustment, reflects overall skeletal muscle condition and is a necessary factor in the diagnosis of sarcopenia [57]. We observed that the skeletal muscle mass index in the dyslipidemia group was significantly lower than in the normal group, suggesting that lower muscle mass increases the likelihood of dyslipidemia in postmenopausal women, consistent with the findings of He Lili et al. [58] .

Multivariate logistic regression analysis identified skeletal muscle mass index, visceral fat area, mineral content, and total body water, age as independent risk factors for dyslipidemia, demonstrating their effectiveness as predictors of dyslipidemia in postmenopausal women, consistent with the findings of Zou Yifan et al. [59]. It has been suggested that the occurrence of metabolic syndrome is related to changes in body composition, in addition to increases in BMI and waist circumference [60, 61]. To identify high-risk groups earlier and better manage their health, this study aimed to investigate the correlation between body composition indicators and dyslipidemia in postmenopausal medical workers and calculated the cut-off values of each related index. These findings can help identify individuals with dyslipidemia and guide early lifestyle interventions.

Conclusions

This study revealed a significant correlation between body composition and dyslipidemia among healthcare workers through cross-sectional analysis, providing important references for health management in this population. However, it has certain limitations: the study design does not allow for determining causal relationships, different subgroup analyses were not conducted, and it may be affected by selection bias (as we selected a sample from a single hospital) and confounding factors (such as diet, exercise, work stress, etc.). Future research could adopt a longitudinal cohort design to clarify temporal relationships, expand the sample size across multiple centers to improve representativeness, meticulously collect data on confounding variables like exercise and diet, and explore potential mechanisms such as inflammatory markers. Ultimately, this could lead to targeted occupational health intervention measures (such as personalized exercise/nutrition plans) to enhance lipid management among healthcare workers.

Abbreviations

ASCVD

Atherosclerotic cardiovascular disease

CAD

Coronary atherosclerotic heart disease

BIA

Bioelectrical Impedance Analysis

TC

Total cholesterol

TG

Triglycerides

LDL-C

Low-density lipoprotein cholesterol

HDL-C

High-density lipoprotein cholesterol

BFP

Body fat percentage

BMI

Body mass index

TBW

Total body water

BMC

Body mineral content

VFA

Visceral fat area

SMI

Skeletal muscle mass index

ALT

Aminotransferase

AST

Aspartate aminotransferase

ALP

Alkaline phosphatase

UR

Urea

CR

Creatinine

UA

Uric acid

GLU

Blood glucose

HbA1c

Glycosylated hemoglobin

95% CI

95% Confidence intervals

Authors’ contributions

YFF and NJW designed the research. YFF, NJW, LQ completed data collection. LXZ Managed and coordinated data collection efforts, YFF and LXZ analyzed and interpreted the data. ZYB, participated in the discussion. YFF and NJW drafted the manuscript, which was critically revised for important intellectual content by all authors. All authors approved the submitted and final version.

Funding

This study was supported by the Danone Nutrition Centre Dietary Nutrition Research and Mission Fund (DIC2021-10) and the Henan Province Medical Science and Technology Tackling Plan Joint Construction Project (LHGJ20220517).

Data availability

The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted according to the guidelines laid out in the Declaration of Helsinki, and all procedures involving research study participants were approved by the Medical Research Ethics Committee of the Second Affiliated Hospital of Zhengzhou University (2020–136). Written informed consent was obtained from all participants. Verbal consent was witnessed and formally recorded.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.