Body Composition and Its Correlation with Dyslipidemia in Hospital Medical Staff: A Tertiary Hospital Cross-Sectional Analysis

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Body Composition and Its Correlation with Dyslipidemia in Hospital Medical Staff: A Tertiary Hospital Cross-Sectional Analysis

https://doi.org/10.21203/rs.3.rs-4614165/v1

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Background: Dyslipidemia, characterized by abnormal blood lipid levels, significantly enhances the risk for cardiovascular diseases (CVDs). Body composition plays a critical role in the modulation of lipid metabolism and therefore in the pathogenesis of dyslipidemia. This study explores the prevalence of dyslipidemia among medical staff and analyzes the association between body composition and lipid profiles. The objective is to furnish actionable insights for the prevention and management of dyslipidemia in this demographic.

Methods: A number of 696 medical staff members, whoparticipated in a health examination conducted from July to October 2023 was selected in this study. The Inbody720 body composition analyzer was utilized to evaluate body fat composition. Serum concentrations of triglycerides and total cholesterol were also measured. A comprehensive suite of statistical analyses was performed, including independent t-tests, non-parametric tests, Spearman rank correlations, logistic regression, and receiver operating characteristic (ROC) curve analysis, to explore the associations between body composition indicators and dyslipidemia.

Results: The prevalence of dyslipidemia among the medical staff amounted to 37.55%. Notably, several key body composition parameters, including body mass index (BMI), body fat percentage, protein content, muscle mass, skeletal muscle mass index, waist-to-hip fat ratio, visceral fat area, and body water content, were significantly higher in individuals with dyslipidemia (p < 0.05). Serum total cholesterol showed a positive correlation with BMI, body fat percentage, waist-to-hip fat ratio, and visceral fat area. Similar trend of positive correlation was observed for triglycerides and low-density lipoprotein cholesterol (LDL-C) with these aforementioned body composition indicators, whereas high-density lipoprotein cholesterol (HDL-C) exhibited negative correlations. Logistic regression analysis identified age, waist-to-hip fat ratio as significant predictors of dyslipidemia. The ROC curve analysis provided optimal cut-off values for these indicators in diagnosing dyslipidemia.

Conclusion: The study highlights a robust correlation between body composition and the occurrence of dyslipidemia among medical staff. The risk of dyslipidemia can be predicted by assessing body composition. This paper also lays a groundwork for further research on the development of targeted prevention and intervention strategies based on body composition.

Health sciences/Health care

Health sciences/Health occupations

Health sciences/Medical research

Health sciences/Risk factors

Medical staff

Dyslipidemia

Body composition

Dyslipidemia is an abnormality of lipoprotein metabolism in the human body, including hypertriglyceridemia, hypercholesterolemia, and low HDL cholesterol. In recent years, there has been a notable increase in the prevalence of dyslipidemia among the adult Chinese population [^1][^2][^3]. According to the 2018 nationwide survey conducted in China, the overall prevalence of dyslipidemia among adults aged 18 years and above was 35.6% ^[¹^], indicating a significant rise from the figures reported in the 2015 national survey ^[¹^]. This increase was chiefly driven by the rise in the prevalence of high total cholesterol (TC) levels. Notably, the age-standardized prevalence of high TC surged from 4.9% in 2015 to 8.2% in 2018, representing nearly a twofold increase. In parallel, there was a significant upward trend in the prevalence of elevated low-density lipoprotein cholesterol (LDL-C) levels, with the figure climbing to 8.0% by 2018. This represents a notable increase from the 5.6% and 7.2% prevalence rates documented in 2010 and 2015, respectively^[¹^][²^].Dyslipidemia is widely recognized as an independent risk factor for cardiovascular disease (CVD)[^4], which has emerged as a prominent health threat globally[^5][6][7]. Consequently, research focused on the prevention and treatment of cardiovascular diseases is imperative.

Bioelectrical Impedance Analysis (BIA) is a widely adopted clinical method for assessing body composition. This technique effectively quantifies various physiological parameters, including intracellular and external fluid volumes, total body water content [^8][9], adipose tissue, lean-body mass and visceral fat area[^10][11].Body composition assessment holds significance in comprehensively evaluating water, fat, protein, and mineral composition, utilizing bioelectrical impedance, a method validated for its safety and accuracy by researchers and clinical practitioners[^12]. Advances in multi-frequency bioelectrical impedance and bioelectrical impedance vector analysis technology have notably enhanced the specificity and accuracy of this method. The Inbody720 Body Composition Analyzer, utilizing bioelectrical impedance, offers the advantage of ease of operation and non-invasiveness in determining body composition[^13][14].

Medical professionals face an elevated risk of cardiovascular disease due to long-term overload, mental stress, extended periods of sedentary behavior, lack of exercise, and poor work-rest habits. Recent studies have demonstrated a significant increase in the prevalence of chronic metabolic diseases among medical personnel[^15][16]. Dyslipidemia, a significant etiological factor in the development of cardiovascular diseases, has been identified as a critical concern[^17][18]. Understanding the prevalence of dyslipidemia among medical personnel and enhancing the effectiveness of its primary prevention in China is currently an important task.

Increasing evidence showed that body mass is closely associated with metabolic-related diseases^[19]. Body composition parameters such as waist-to-hip ratio, visceral fat area, and body mass index are closely related to the development of certain diseases, including an increased incidence of complications related to Type 2 diabetes mellitus(T2DM)[^20][21][22], arterioslerosis[^23],hypertension[^24] and other chronic diseases[^25][26].It is considered the primary condition for the diagnosis of metabolic syndrome[^27].The aim of this study was to provide a scientific basis for predicting dyslipidemia using body composition-related indicators by analyzing the correlation between dyslipidemia and body composition among medical workers in a tertiary hospital in Zhengzhou City.

1. Study design and participants

Between July and October 2023, a cohort of 696 medical personnel, ranging in age from 24 to 89 years, voluntarily participated in body composition analyses as part of the employee physical examinations at the Second Affiliated Hospital of Zhengzhou University. The study was approved by the Medical Research Ethics Committee of the Second Affiliated Hospital of Zhengzhou University, with the ethical approval number Wei Medical [2020] No. 136. All study subjects signed an informed consent form. Inclusion Criteria: Voluntary participation in this study. Exclusion Criteria: Pregnant women; individuals wearing pacemakers or other electronic medical devices; individuals with metal implants in their bodies and individuals suffering from severe functional disorders of the liver or kidneys.

All eligible participants underwent body composition analysis, and general data, blood lipid and other biochemical test results were collected. The experimental design flow chart of this study is shown in Figure 1.

2. Methodology

2.1 Body Composition Measurement: The height of the medical examiners was measured by a professional dietitian from the Clinical Nutrition Department. The medical examiners removed their shoes, hats, and headdresses, assumed an upright posture with their chests lifted and stomachs tucked in, and positioned their feet with heels close together and toes separated by approximately 60 degrees. Body composition analysis was conducted using the Inbody720 (Biospace, South Korea) body composition analyzer. All medical examiners were required to undergo testing within three hours before fasting, after emptying urine and feces, and to avoid strenuous exercise and remove heavy clothing, objects, and electronic equipment. The medical examiner stood barefoot on the foot electrodes of the body composition analyzer, held the hand electrodes with both hands, and maintained a natural posture with arms separated from the body. The primary measurements included body fat percentage, BMI, protein, muscle mass, body water, waist-to-hip ratio, and visceral fat area. The skeletal muscle mass index (SMI) was calculated from the limb muscle mass as per the formula: SMI = limb muscle mass (kg)/height squared (m²).

2.2 Biochemical Results: Blood samples were collected after 8-12 hours of fasting. Serum samples were centrifuged for 10 min at 4℃ and 3000 rpm, Serum TC, TG, HDL-C, LDL-C concentrations were detected using Roche infinity automatic biochemical analyzer.

2.3 Criteria for Determining Dyslipidemia: Following the Chinese Guidelines for the Management of Blood Lipids (2023)^[28], individuals with elevated TG, TC, LDL-C, or reduced HDL-C in the lipid test were diagnosed as having dyslipidemia and were classified according to the guidelines.

3. Statistical Analysis: Data were statistically analyzed using SPSS version 21.0 software. Count data were expressed as the number of cases and percentages, while measurement data conforming to a normal distribution were expressed as mean ± standard deviation (SD). Comparisons between two groups were performed using the t-test. Measures that did not conform to a normal distribution were expressed as median (P25, P75), and comparisons between groups were made using the rank-sum test. Spearman correlation analysis and multifactor logistic regression analysis were employed to analyze the correlation between body composition and blood lipids and to identify potential influencing factors. The sensitivity and specificity of body composition in predicting dyslipidemia were analyzed using ROC curves. p-values < 0.05 was considered statistically significant.

1. Basic Information

A total of 696 medical workers were included in the statistical analysis. Among them, 136 (19.6%) were male, and 559 (80.4%) were female. The age range was 24-89 years, with a mean age of 46.88 ± 15.31 years.

2. Detection Rate of Different Types of Dyslipidemia and Distribution in Different Age Groups

The total detection rate of dyslipidemia among medical workers was 37.5%, with 8.3% detected with hypercholesterolemia, 21% with hypertriglyceridemia, 4% with mixed dyslipidemia, and 8.6% with low HDL-C. The detection rates of total dyslipidemia in males and females were 57.7% and 32.6%, respectively, with statistically significant differences. The detection rates of high TG and low HDL-C in males were significantly higher than those in females, while the detection rates of high TC and mixed dyslipidemia in females were higher than those in males, but the differences were not statistically significant. The detection rates of dyslipidemia stratified by age groups were 5.7%, 37.2%, 11.9%, 16.1%, and 29.1% in the age groups of less than 30, 31-40, 41-50, 51-60, and 61 years and older, respectively. There was a statistical difference in the detection rate of dyslipidemia between age groups (χ² = 15.451, p = 0.004) (Table 1 and Table 2).

3. Comparison of Body Composition Between Normolipidemic and Dyslipidemic Groups

The dyslipidemic group had significantly higher values (p < 0.05) than the normolipidemic group in terms of BMI, body fat percentage, muscle mass, skeletal muscle mass index, waist-to-hip ratio, visceral fat area, and body water, protein(Table 3).

4. Correlation of Lipid Levels with Body Composition Indicators

Serum total cholesterol (TC) was positively correlated with BMI, body fat percentage, waist-to-hip ratio, and visceral fat area (all p < 0.05); Triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) were positively correlated with BMI, body fat percentage, protein, skeletal muscle mass index, waist-to-hip ratio, visceral fat area, and body water (all p < 0.05); High-density lipoprotein cholesterol (HDL-C) was negatively correlated with BMI, protein, skeletal muscle mass index, waist-to-hip ratio, visceral fat area, and body water (p < 0.05). (Table 4).

5. Logistic Regression Analysis of Factors Associated with Dyslipidemia

Multiple logistic regression analysis revealed that age (OR=1.020, 95%CI 1.008~1.032, p=0.001), waist-to-hip ratio (OR=1.131,95%CI1.050~1.219, p=0.001) had statistically significant effects on dyslipidemia.(Table 5).

6. ROC Curve Analysis of Lipids

The Waist-to-Hip Ratio and age have respective ROC AUCs of 0.675 and 0.581 for diagnosing blood lipid abnormalities, with p-values less than 0.05. Refer to Table 6 and Figure 2 for details, all with a p-value of less than 0.05.

Figure 1 Flowchart of the study

Figure 2 The ROC curves for diagnosing blood lipid abnormalities using age and waist-to-hip ratio

Table 1 The detection rate of various types of dyslipidemia

Gender	Hypercholesterolemia	Hypertriglyceridemia	Mixed Hyperlipidemia	Hypoalphalipoproteinemia	Total Dyslipidemiaa
Male	8 (5.8)	49 (35.8)∗	4 (2.9)	28 (20.4)∗	79 (57.7)∗
Females	50 (8.9)	97 (17.4)	24 (4.3)	32 (5.7)	182 (32.6)
Total	58 (8.3)	146 (21.0)	28 (4.0)	60 (8.6)	261 (37.5)

*: p < 0.05 indicates a statistically significant difference in the detection rates of hypertriglyceridemia, hypoalphalipoproteinemia, and total

dyslipidemia between genders.

Table 2 Detection of Dyslipidemia in Different Age Groups

Age (years)	n	Normal Blood Lipids	Dyslipidemia	Detection Rate of Dyslipidemia (%)	Chi-Square (χ²)	p
≤30 years	44	29	15	5.7	15.451	0.004
31-40 years	311	214	97	37.2
41-50 years	87	56	31	11.9
51-60 years	99	57	42	16.1
≥ 61 years	155	79	76	29.1
Total	696	435	261	37.5

Table 3 Comparison of Body Composition Between Normolipidemic and Dyslipidemic Groups [Median (P25, P75), Mean ± SD]

Normal	Normal Lipid Group (n = 435)	Dyslipidemia Group (n = 261)	Z/T Value	p-Value
BMI	22.40 (20.70,24.10)	24.50 (22.60,26.50)	-8.89	0.000
Body Fat Percentage	29.88±6.00	31.20±5.53	2.88	0.004
Muscle Mass	38.20 (35.30,41.70)	41.20 (37.15,48.50)	-6.70	0.000
Skeletal Muscle Mass Index	6.33 (5.94, 6.84)	6.79 (6.21, 7.67)	-7.13	0.000
Waist-to-Hip Ratio	0.87 (0.83,0.90)	0.90 (0.86,0.93)	-7.76	0.000
Visceral Fat Area	75.10 (60.80, 88.30)	88.10 (73.80, 100.05)	-7.29	0.000
Body Water	29.70 (27.50, 32.50)	32.10 (29.00, 37.75)	-6.67	0.000
Protein	7.90（7.30,8.60）	8.50（7.70,10.00）	-6.55	0.000

Table 4 Correlation Analysis Between Body Composition and Lipid Levels

Variable	TC		TG		HDL		LDL
Variable	r	p-Value	r	p-Value	r	p-Value	r	p-Value
BMI	0.115	0.002	0.379	0.000	-0.294	0.000	0.209	0.000
Body Fat Percentage	0.131	0.001	0.192	0.000	-0.020	0.591	0.163	0.000
Protein	0.011	0.656	0.143	0.000	-0.184	0.000	0.0067	0.009
Skeletal Muscle Mass Index	0.008	0.843	0.238	0.000	-0.310	0.000	0.104	0.006
Waist-to-Hip Ratio	0.107	0.005	0.394	0.000	-0.270	0.000	0.188	0.000
Visceral Fat Area	0.128	0.001	0.353	0.000	-0.204	0.000	0.197	0.000
Body Water	0.011	0.769	0.217	0.000	-0.270	0.000	0.090	0.000

Table 5 Logistic Regression Analysis of Factors Associated with Dyslipidemia

Factor	B	S.E,	Wals	Sig.	Exp (B)	95% CI for EXP(B)
Factor	B	S.E,	Wals	Sig.	Exp (B)	Lower Limit	Upper Limit
Age	0.020	0.006	10.509	0.001	1.020	1.008	1.032
Skeletal Muscle Mass Index	0.587	0.603	0.948	0.330	1.799	0.552	5.867
BMI	0.141	0.122	1.339	0.247	1.152	0.907	1.463
Body Fat Percentage	0.083	0.089	0.874	0.350	1.086	0.913	1.293
Waist-to-Hip Ratio	0.124	0.038	10.485	0.001	1.131	1.050	1.219
Visceral Fat Area	-0.042	0.029	2.144	0.143	0.959	0.906	1.014
Body Water	-0.071	0.267	0.070	0.791	0.932	0.553	1.571
Protein	0.311	0.665	0.219	0.640	1.365	0.371	5.024

Table 6 ROC Curve Analysis of Waist-to-Hip Ratio, Age for Diagnosis of Dyslipidemia

Factor	optimal cut-off value	AUC	95 per cent CI	P-value	Sensitivity (%)	Specificity (%)
Waist-to-Hip Ratio	0.89	0.675	0.634~0.716	0.000	54.4	73.3
Age (Years)	46.5	0.581	0.537~0.625	0.000	49.0	64.0

In this study, we observed that the total detection rate of dyslipidemia among the staff of a tertiary hospital was 37.4%, which is higher than the rate of 35.6% reported for adults aged 18 years and older in the 2020 report on the nutritional and chronic disease status of the Chinese population^[¹^]. The high prevalence of dyslipidemia among hospital staff indicates a potential gap in self-care practices, even among those with medical expertise. It is particularly concerning that the age group of 31 to 40 years old exhibited the highest incidence of dyslipidemia. This upward trend within this demographic may be linked to the intense job stress and suboptimal dietary and lifestyle choices often observed among young professionals.

Hypertriglyceridemia was identified as the main type of dyslipidemia in this population, which is consistent with previous findings^[¹^]. This condition is associated with various factors including age, gender, and lifestyle habits such as diet and exercise[^29][30]. The significantly higher detection rate of dyslipidemia in men (57.7%) compared to women (32.6%) aligns with the findings of Li Jingjing et al^[31]and may be related to factors such as men's higher work pressure, smoking, alcohol consumption, and frequent eating out. In contrast, women's generally healthier lifestyle habits, coupled with the protective effect of estrogen, may aid in promoting fat metabolism [^32].

Body composition analysis, utilizing bioelectrical impedance, provides insights into the main chemical components of the human body, including fat, muscle mass, total water volume, and visceral fat area[^{33][34][35][36]}. Body composition, defined as the ratio of adipose tissue to lean body mass (including bone, muscle, and other vital tissues), is an essential parameter for evaluating an individual's health status, nutritional adequacy, functional capacity, and overall physical fitness [^37].This analysis is instrumental in assessing health status as the composition of these components varies according to gender, age, race, and occupation, and is influenced by dietary structure and exercise habits [^38][39]. Research has established a correlation between body composition and carbohydrate and lipid metabolism, highlighting the role of body composition analysis in the early detection of metabolic disease abnormalities [^40][41][42].

The predictive model for dyslipidemia constructed using body composition analysis results demonstrates potential efficacy for dyslipidemia prediction. The bioelectrical impedance method is simple, non-invasive, and suitable for clinical applications or large-scale epidemiological studies [^43][44][45]. Furthermore, body composition analysis is increasingly utilized to predict the diagnosis and treatment of conditions such as diabetes, obesity, and osteoporosis [^46].

In this study, we found that BMI, body fat percentage, muscle mass, skeletal muscle mass index, waist-to-hip ratio, visceral fat area, and body water content, protein were significantly higher in the dyslipidemic group compared to the normolipidemic group. Triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) showed positive correlations with these body composition indicators, while high-density lipoprotein cholesterol (HDL-C) exhibited negative correlations, which is in line with the findings of Feng Hang et al [^47]. Multiple logistic regression analysis identified age, waist-to-hip ratio as independent risk factors for dyslipidemia. Our ROC analysis suggests that early dyslipidemia should be considered when waist-to-hip ratio and age exceed certain thresholds.

There are several limitations in this study. Firstly, the study's descriptive design confirmed a correlation between body composition and dyslipidemia but could not establish causality. Consequently, we plan further longitudinal studies to explore this causal relationship. Secondly, the study's cohort was limited to medical staff from a single hospital, including those who are retired yet rehired. Given China's vast geographic diversity, significant differences in dietary habits and body composition across ethnic groups, and the variety of hospital types, future research should expand to include diverse regions, ethnicities, and hospital settings to enhance the representativeness and generalizability of the findings. Therefore, a multi-center study is planned as the next phase. Finally, the sample size in this study was relatively small compared to other cross-sectional studies. However, through meticulous adjustment for potential con-founders and the application of various statistical analysis techniques, the reliability and comprehensiveness of the findings were maintained.

In conclusion, body composition analysis is linked to the prevalence of dyslipidemia. Medical staffs, facing demanding schedules, can greatly benefit from the accessible, expedient, and non-invasiveness of body composition assessments. By assessing body composition, the risk of dyslipidemia can be predicted, which is conducive to the treatment and monitoring of the disease. This study provides a scientific basis for medical personnel to formulate preventive and control measures for dyslipidemia.

CVD Cardiovascular disease

BIA Bioelectrical Impedance Analysis

TC Total cholesterol

TG Triglycerides

LDL-C Low-density lipoprotein cholesterol

HDL-C High-density lipoprotein cholesterol

95% CI 95% confidence intervals

Author contributions

YFF and NJW designed the research.YFF,NJW,SY completed data collection.YFF and NJW analyzed and interpreted the data.ZJY,JRP and 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 datasets used and/or analyzed during the current study are available from the corresponding author on 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.

Competing interests

The authors declare no conflict of interest.

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Body Composition and Its Correlation with Dyslipidemia in Hospital Medical Staff: A Tertiary Hospital Cross-Sectional Analysis

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