Women are at higher risk of cardiometabolic diseases with the increase of visceral fat area to skeletal muscle mass ratio compared with men in a Chinese natural population: a cross-sectional study

doi:10.21203/rs.3.rs-1592071/v1

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Women are at higher risk of cardiometabolic diseases with the increase of visceral fat area to skeletal muscle mass ratio compared with men in a Chinese natural population: a cross-sectional study

https://doi.org/10.21203/rs.3.rs-1592071/v1

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Background

Growing evidence has revealed that using BMI for assessment of obesity and cardiometabolic risk has some limitations. Visceral adiposity and skeletal muscle loss may be both correlated with cardiometabolic outcomes. This study aimed to explore the associations between the visceral fat area to skeletal muscle mass ratio (VSR) and the risk of several cardiometabolic diseases including metabolic associated fatty liver disease (MAFLD), hyperglycemia, hypertension, dyslipidemia and hyperuricemia in a young and middle-aged Chinese natural population and further elucidate the differences of these associations between male and female.

Methods

A total of 5158 participants were included in this study. Body composition, anthropometrical and biochemical measurements were performed. The associations between VSR and cardiometabolic diseases were analyzed.

Results

Both in male and female, VSR was positively associated with the five cardiometabolic diseases and with the increase of VSR by one quartile, the ORs increased significantly for all the five cardiometabolic diseases (P _trend<0.001). With regard to the highest versus the lowest quartile of VSR, the ORs for MAFLD, hyperglycemia, hypertension, dyslipidemia and hyperuricemia were 17.23 (95% CI, 12.52–23.71), 15.47 (95% CI, 7.1-33.72), 5.12 (95% CI, 3.88–6.76), 3.16 (95% CI, 2.33–4.28) and 1.89 (95% CI, 1.42–2.51) in male, respectively. In female, the corresponding ORs were 41.15 (95% CI, 25.80-65.63), 21.62 (95% CI, 7.87–59.36), 9.64 (95% CI, 6.88–13.53), 9.34 (95% CI, 6.63–13.14) and 6.58 (95% CI, 3.45–12.56). The results of restricted cubic splines showed there were significant positive non-linear relationships between VSR and the risk of MAFLD, dyslipidemia, hyperglycemia and hypertension in both sex (P for non-linearity < 0.05). The risk was relatively flat until when VSR reached 3.078 cm²/kg in men and 4.750 cm²/kg in women and started to increase rapidly afterwards. In men, however, the risk slowed down after VSR value got to around 4 cm²/kg and the curve became flat and even tended to decline.

Conclusions

VSR was positively associated with cardiometabolic diseases regardless of sex. As VSR increased, the risk of cardiometabolic diseases was significantly higher in women than in men. Women should be more alert to the risk of cardiometabolic diseases caused by the increase of VSR.

Obesity

Visceral fat area

Skeletal muscle mass

Cardiometabolic diseases

Sex difference

The synergistic effect of visceral fat and skeletal muscle mass on cardiometabolic diseases in natural population is unknown yet.
This study demonstrated that visceral fat area to skeletal muscle mass ratio was was positively associated with cardiometabolic diseases.
The risk of cardiometabolic diseases was significantly higher in women than in men with the increase of visceral fat area to skeletal muscle mass ratio.
Restricted cubic spline curve was used to visualize the relationships between visceral fat area to skeletal muscle mass ratio and cardiometabolic diseases.

The prevalence of obesity is increasing dramatically worldwide in the past few decades. Obesity is well recognized as the most important risk factor for cardiometabolic diseases such as metabolic associated fatty liver disease (MAFLD), type 2 diabetes mellitus (T2DM), hypertension, dyslipidemia and hyperuricemia [1]. Obesity and its closely associated cardiometabolic disorders have placed a heavy burden on the public health system globally [2]. Since body mass index (BMI) is the most easily measured parameter of obesity, it is widely used for assessment of obesity in clinical practice. However, BMI fails to accurately distinguish between body fat mass and lean mass, and moreover, it can not describe the distribution of body fat. Mounting evidence suggests that visceral fat deposition is the main cause for the development of cardiometabolic diseases, whereas subcutaneous fat accumulation may be associated with decreased risk of the aforementioned cardiometabolic conditions [3]. Consequently, studies that do not take abdominal obesity into consideration and use BMI to define obesity have most commonly demonstrated the “obesity paradox” [4]. In addition to visceral adiposity, the impact of reduced skeletal muscle mass in the development of insulin resistance, T2DM and other cardiometabolic outcomes has become the focus of attention in recent years [5].

Visceral fat and skeletal muscle mass both play important roles in cardiometabolic risk, therefore they may have a synergistic effect on cardiometabolic disorders [6]. However, there are limited large-scale studies regarding their combined effect on cardiometabolic outcomes. What’s more, it is generally recognized that there exists sex difference in visceral fat distribution [7]. Whether the joint effect of these two distinct compartments of body composition differs between different sex is unknown yet. Therefore, this study aimed to explore the association between the visceral fat area to skeletal muscle mass ratio (VSR) and the risk of several cardiometabolic diseases including MAFLD, hyperglycemia, hypertension, dyslipidemia and hyperuricemia in a Chinese population undergoing regular health checkup and further elucidate the difference of this association between men and women.

Study population

The group who took an annual health checkup in the Health Management Center of Tianjin Union Medical Center from September to December in 2020 was our target population. The total number of people during this period was 5319. Individuals were excluded if they were after the implantation of pacemaker, accompanied by unstable neurological or psychiatric conditions, unwilling to sign the informed consent or their information was incomplete. Finally, a total of 5158 participants were included in this study after 161 were excluded. The sample collection process was shown in Figure 1. The study was approved by the Medical Ethics Committee of Tianjin Union Medical Center (No. 2021C06) and written informed consents were provided by all the participants.

Anthropometrical measurement and body composition evaluation

After an overnight fasting, all participants were instructed to wear in light clothing without shoes and perform anthropometrical evaluations including height and weight. BMI was calculated by weight in kilograms divided by square of height in meters. Body composition including body fat mass (BFM), fat free mass (FFM), skeletal muscle mass (SMM), visceral fat area (VFA) and skeletal muscle index (SMI) was measured by the direct segmental multi-frequency bioelectrical impedance analysis (BIA) method (Inbody 770, Biospace Co., BR-Chinese-C7-B-140218). VSR was the ratio of VFA to SMM. The participants were required to stand upright bare feet with arms straight and hold the handles of the analyzer, so that the palms, thumbs, heels and soles were all in full contact with an 8-pole tactile electrode. Then the measurement began and the data were automatically saved in the computer after the measurement completed.

Clinical and biochemical measurement

Past medical histories and demographic characteristics including age, sex were recorded in detail in all of the participants. After a 10-minute rest, blood pressure was measured for three times with an automatic electronic blood pressure monitor (AC-05C, Ling Qian, China) and the mean value was taken. Uniformly trained ultrasound physicians with ultrasound diagnosis system (Philips, Phoenix and Neusoft Medical Systems Co., Ltd., China) performed abdominal ultrasound examination. All blood samples were collected in the morning after an overnight fasting, centrifuged as soon as possible and stored at -80℃ for subsequent detection assays. Serum uric acid (SUA), fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) levels were determined using an automatic biochemical analyzer (TBA120FR, Toshiba, Japan).

Definitions of cardiometabolic diseases

Hepatic steatosis was established when two or more of the following requirements given in parentheses were met according to the abdominal ultrasonography (diffuse enhanced echo of liver with liver echogenicity greater than kidney or spleen, deep attenuation of ultrasound signal, vascular blurring). MAFLD was diagnosed as hepatic steatosis with overweight/obesity, T2DM or evidence of metabolic dysregulation [8]. Hyperglycemia was defined as FPG ≥7.0 mmol/L or previously diagnosed as diabetes. Hypertension was defined as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg or previously diagnosed as hypertension. Dyslipidemia was defined as TC ≥6.2 mmol/L or LDL-C ≥4.1 mmol/L or HDL-C <1.0 mmol/L or TG ≥2.3 mmol/L based on the Chinese guidelines for the management of dyslipidemia in adults (2016) [9] or previously diagnosed as dyslipidemia. Hyperuricemia was defined as SUA ≥420 μmol/L according to the guideline for the diagnosis and treatment of hyperuricemia and gout in China (2019) [10] or previously diagnosed as hyperuricemia or gout.

Calculation of the prevalence of cardiometabolic diseases

The prevalence of MAFLD, hyperglycemia, hypertension, dyslipidemia and hyperuricemia was calculated by the number of patients with MAFLD, hyperglycemia, hypertension, dyslipidemia and hyperuricemia in the study population divided by the total number of people in the study population.

Statistical analysis

For continuous variables, Kolmogorov-Smimov test was first performed to evaluate whether the data were normally distributed or not. Continuous variables of normal and non-normal distribution were expressed as mean ± standard deviation and median (interquartile range), respectively. Categorical variables were presented as percentage or frequency. Continuous data were analyzed using the independent two-sample t test or the Mann-Whitney U test. Homogeneity of variances was tested by Levene’s Test. Chi-squared test was performed for analysis of categorical variables. Binary logistic regression analysis was adopted to estimate the odds ratio (OR) and 95% confidence interval (CI) between VSR and MAFLD, hyperglycemia, hypertension, dyslipidemia and hyperuricemia after it was confirmed that the regression model could fit the data correctly by Hosmer-Lemeshow test. ANOVA was used to verify whether there were non-linear associations between VSR and cardiometabolic diseases and then restricted cubic splines were used to evaluate the non-linear relationships between VSR and the risk of cardiometabolic diseases. Knots were placed at the 33th, 66th and 99th percentiles. All statistical analyses were performed using SAS version 9.4 for Windows (SAS Institute., Cary, NC). P value <0.05 was considered to be statistically significant.

Clinical characteristics of the study population

A total of 5158 participants were recruited in this study, consisting of 2154 men (41.8%) and 3004 women (58.2%). The clinical characteristics of the study population were presented in Table 1. The average age of all the participants was 44.54 ± 14.71 years old, indicating that this was mainly a young and middle-aged population. The overall prevalence of MAFLD, dyslipidemia, hypertension, hyperuricemia and hyperglycemia was 30.9%, 27%, 25.4%, 12.6%, 6.0%, respectively. BMI and some parameters of body composition including BFM, FFM, SMM and SMI were significantly higher in male than in female (P <0.001). VFA was obviously higher in female than in male (P =0.002), displaying an absolutely opposite trend to that of BMI. Women had relatively high VFA and low SMM, so VSR was significantly higher in women than in men (P <0.001). The prevalence of MALFD, hyperglycemia, hypertension, dyslipidemia and hyperuricemia was higher in male than in female (P <0.001).

Prevalence of cardiometabolic diseases according to VSR quartiles in male and female

According to the VSR quartiles, men and women were divided into four subgroups, respectively. The prevalence of MAFLD, hyperglycemia, hypertension, dyslipidemia and hyperuricemia was calculated in the four subgroups of both sex separately. Results showed that with the increase of VSR, the prevalence of the five cardiometabolic conditions rose up both in male and female. Compared with participants with VSR in the other three quartiles, participants with VSR in the fourth quartile had the highest prevalence of the above-mentioned cardiometabolic diseases (P <0.001), regardless of sex. Moreover, in each of the same quartile, the prevalence of cardiometabolic diseases in male was higher than in female (P <0.001). The prevalence of MAFLD was the highest among the five cardiometabolic diseases (P <0.001). The results were exhibited in Figure 2.

Associations between VSR and cardiometabolic diseases

The associations of VSR with cardiometabolic diseases were explored by binary logistic regression analysis and the results were shown in Table 2. We could see that in both sex, VSR was positively correlated with the five cardiometabolic diseases and with the increase of VSR by one quartile, the ORs increased significantly for all the five cardiometabolic diseases (P _trend<0.001). With regard to the highest versus the lowest quartile, the ORs for MAFLD, hyperglycemia, hypertension, dyslipidemia and hyperuricemia were 17.23 (95% CI, 12.52-23.71), 15.47 (95% CI, 7.1-33.72), 5.12 (95% CI, 3.88-6.76), 3.16 (95% CI, 2.33-4.28) and 1.89 (95% CI, 1.42-2.51) in male, respectively. In female, the corresponding ORs were as follows, 41.15 (95% CI, 25.80-65.63), 21.62 (95% CI, 7.87-59.36), 9.64 (95% CI, 6.88-13.53), 9.34 (95% CI, 6.63-13.14) and 6.58 (95% CI, 3.45-12.56). The non-linear relationships of VSR with cardiometabolic diseases in both sex were further explored by restricted cubic splines. Results showed there were significant positive non-linear relationships between VSR and the risk of MAFLD, dyslipidemia, hyperglycemia and hypertension in both sex (P for non-linearity <0.05). The risk was relatively flat until when VSR reached 3.078 cm²/kg in men and 4.750 cm²/kg in women and then started to increase rapidly afterwards. In men, however, the risk slowed down after VSR value got to around 4 cm²/kg and the curve became relatively flat and even tended to decline. The non-linear correlations of VSR with hyperuricemia were not statistically significant in both sex, but the previous binary logistic regression analysis had shown significant positive associations between VSR and hyperuricemia (P_trend<0.001). The results were shown in Figure 3.

The prevalence of obesity is dramatically increasing annually around the world. Due to westernized lifestyle and lack of physical activity, the rise of obesity in China is especially alarming. According to the latest epidemiological survey conducted in China, the national prevalence were 34.3% for overweight and 16.4% for obesity in adults [11], which had been soaring far more than double in the past few decades. There is strong evidence that obesity can lead to increased risks of major cardiometabolic diseases including MAFLD, T2DM, hypertension, dyslipidemia and hyperuricemia. Obesity has become a global public health challenge.

BMI might be a simple and easily accessible index in clinical practice, therefore it is still used as the main criteria for assessing obesity. However, numerous studies have found that individuals diagnosed as overweight or obese by BMI might have no or few cardiometabolic risk factors and even the lowest all-cause mortality [12, 13]. People with similar BMI values might have substantially different levels of health risk [14]. These unexpected findings have come to be termed as the “obesity paradox”. One of the main explanations for mechanisms of the “obesity paradox” might be that these studies used BMI to define obesity, which could not perfectly reflect the amount and distribution of body fat, nor could it precisely differentiate between fat mass and lean body mass [15]. A growing body of evidence has suggested that visceral adiposity and skeletal muscle loss may be both associated with cardiometabolic outcomes. Skeletal muscle loss usually coexists with excessive fat mass, which has come to be known as sarcopenic obesity, a new category of obesity [16]. Based on this fact, we investigated the synergistic effect of visceral fat and skeletal muscle mass on cardiometabolic disorders and explored the sex difference of this association between men and women.

This study was conducted in a natural population undergoing regular health checkup. The mean age of all the participants was around 44 years old, indicating that this study population was mainly middle-aged and was the backbone of social production and development. However, the prevalence of the five cardiometabolic diseases of this population was alarming, with MAFLD being the highest (30.9%), followed by dyslipidemia (27%), hypertension (25.4%), hyperuricemia (12.6%) and hyperglycemia (6.0%). These prevalence rates were similar to those found in previous epidemiological studies in Chinese ethnic except for hyperglycemia [17, 18, 19]. According to the latest epidemiological survey, the prevalence of diabetes in China is around 12.8% [20], which is much higher than the result in our study. This discrepancy may be attributed to the fact that some patients with T2DM may have elevated postprandial blood glucose at the early stage of the disease, but their FPG can be still remained in the normal range [21]. The data in this study came from a population receiving their annual health checkup, with only one fasting blood sample taken and FPG measured, which might miss some diabetic patients. In addition, participants recruited in this study were mainly young and middle-aged people, which might also lead to the relatively low prevalence of hyperglycemia. Since there exist considerable differences in both body fat distribution and mass of body fat and skeletal muscle between men and women due to different sex hormone levels, all the participants were divided into two groups by sex. We can see that BMI, BFM, FFM, SMM and SMI were significantly higher in male than in female. VFA as the most critical risk factor for cardiometabolic diseases, however, was obviously higher in female than that in male, displaying an absolutely opposite trend to that of BMI. Thus, VSR calculated as the ratio of VFA to SMM was significantly higher in women than in men.

Based on the quartiles of VSR, men and women were further divided into four subgroups, respectively. Results showed that with the increase of VSR, the prevalence of the five cardiometabolic diseases rose up both in male and female. Participants with VSR in the fourth quartile had the highest prevalence of the above-mentioned cardiometabolic diseases, regardless of sex. This suggested that VSR was positively associated with the development of cardiometabolic diseases. To our knowledge, this is the first study that evaluates the relationship between VSR and cardiometabolic diseases in relatively large-scale natural population in China. In addition, in each of the same quartile, the prevalence of cardiometabolic diseases in male was higher than in female, which is consistent with the outcomes of previous researches [22]. Of note, MAFLD may be more common in postmenopausal women than in men, for women in this stage have lost the protective effect of estrogen [23]. The prevalence of MAFLD was the highest among the five cardiometabolic diseases, which might owe to the strong correlations between low muscle mass or sarcopenia and MAFLD [24].

To further clarify the associations between VSR and cardiometabolic diseases, we then performed binary logistic regression analysis. Results showed that VSR was positively correlated with the five cardiometabolic diseases and with the increase of VSR by one quartile, the ORs increased significantly for all the five cardiometabolic diseases both in male and female, which further robustly confirmed the associations between VSR and cardiometabolic diseases. With regard to the highest versus the lowest quartile, the ORs for the five diseases were dramatically higher in female than in male, which meant that the risk of these cardiometabolic diseases was obviously higher in female than in male. Furthermore, we used restricted cubic splines to visualize the relations of VSR with the five cardiometabolic diseases in men and women. We found that there were significant positive non-linear relationships between VSR and the risk of MAFLD, dyslipidemia, hyperglycemia and hypertension in both sex. Nevertheless, there were still differences between men and women. In women, the risk started to rise up rapidly after VSR reached a certain threshold. In men, however, the risk increased initially but tended to slow down when VSR arrived at a certain value and the curve became flat and even tended to decline. This also indicated that as VSR increased, the risk of cardiometabolic diseases was higher in women than in men, consistent with the result of binary logistic regression analysis. Similar results are found in numerous previous studies, which have shown that compared with men, women have greater risk of cardiometabolic diseases, such as MAFLD, diabetes, cardiovascular disease and hypertriglyceridemia [25, 26].

There are several strengths in our study. First of all, this is the first analysis to examine the associations of the joint effect of visceral fat and skeletal muscle mass on cardiometabolic disorders in a large natural population of Chinese ethnic. Secondly, this study has clarified sex differences of these associations based on the significant differences of body composition in men and women. Thirdly, we have used restricted cubic spline curves to clearly visualize the non-linear relationships between VSR and cardiometabolic diseases when they are not linearly related. The study also has several limitations. Firstly, this is a cross-sectional study, which can not prove the causality between VSR and cardiometabolic diseases. Secondly, body composition has been measured by BIA method, which is not the most perfect way to measure actual body lean mass and fat mass. However, BIA is a relatively simple and harmless yet reliable and popular tool for assessing body composition. Thirdly, the participants enrolled in this study were those who received their annual health checkup and only their fasting blood samples could be obtained, which might lead to the missed diagnosis of some patients with hyperglycemia.

Perspectives and significance

Our study found that the risk of cardiometabolic diseases rose up in both males and females with the increasing of VSR. Nevertheless, there existed sex difference in the association between VSR and cardiometabolic diseases. Women were at higher risk of cardiometabolic diseases with the increase of VSR compared with men. Further research is needed to elucidate more possible mechanisms for the sex difference of the association between the synergistic effect of visceral fat and skeletal muscle mass and cardiometabolic diseases.

In conclusion, our results suggested that VSR was positively associated with cardiometabolic diseases regardless of sex. As VSR increased, the risk of cardiometabolic diseases in women was significantly higher than in men. These results indicated that women should be more alert to the risk of cardiometabolic diseases caused by the increase of VSR than men. Well-designed, prospective cohort studies including larger sample sizes, other age and ethnic groups are needed to further confirm and replicate our conclusions.

Ethics approval and consent to participate

The study was approved by the Medical Ethics Committee of Tianjin Union Medical Center (No. 2021C06) and written informed consents were provided by all the participants.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by Grants from Natural Science Foundation of Tianjin (19JCZDJC36100).

Authors' contributions

SZ analyzed and interpreted the patient data and was the major contributor in writing the manuscript. YPH analyzed and interpreted the data. JL, MYZ, XCW, YJZ and MY D assisted with data collection. XHW assisted with data analyses. CJL and JNL contributed to the design of the study. All authors read and approved the final manuscript.

Acknowledgements

We gratefully acknowledge all the participants in the study and the Health Management Center of Tianjin Union Medical Center for the possibility to perform this study.

Author details

¹Department of Endocrinology, Health Management Center, Tianjin Union Medical Center, Nankai University Affiliated Hospital, No. 190 of Jieyuan Road, Hongqiao District, Tianjin, 300121, China. ²Tianjin Centers for Disease Control and Prevention, Tianjin, China. ³School of Medicine, Nankai University, Tianjin, China.

Dorota Drozdz J, Alvarez-Pitti M, Wójcik C, Borghi R, Gabbianelli A, Mazur, et al. Obesity and Cardiometabolic Risk Factors: From Childhood to Adulthood. Nutrients. 2021;3(11):4176.
Muka T, Imo D, Jaspers L, Colpani V, Chaker L, Sven J, van der Lee, et al. The global impact of non-communicable diseases on healthcare spending and national income: a systematic review. Eur J Epidemiol. 2015;30(4):251–77.
Neeland IJ, Ross R, Després JP, Matsuzawa Y, Yamashita S, Shai I, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol. 2019;7(9):715–25.
James A, Greenberg. The obesity paradox in the US population. Am J Clin Nutr. 2013;97(6):1195–200.
Kim H-K, Lee MJ, Kim EH, Bae S-J, Kim KW. Chul-Hee Kim. Comparison of muscle mass and quality between metabolically healthy and unhealthy phenotypes. Obes (Silver Spring). 2021;29(8):1375–86.
Bizri IE, Batsis JA. Linking epidemiology and molecular mechanisms in sarcopenic obesity in populations. Proc Nutr Soc. 2020; 1–9.
Susan K, Fried M-J, Lee K, Karastergiou. Shaping fat distribution: New insights into the molecular determinants of depot- and sex-dependent adipose biology. Obes (Silver Spring). 2015;23(7):1345–52.
Mohammed Eslam PN, Newsome SK, Sarin QM, Anstee G, Targher M, Romero-Gomez, et al. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J Hepatol. 2020;73(1):202–9.
Joint committee for guideline revision. 2016 Chinese guidelines for the management of dyslipidemia in adults. J Geriatr Cardiol. 2018; 15 (1): 1–29.
The Chinese Society of Endocrinology, Chinese Medical Association. Guideline for the diagnosis and management of hyperuricemia and gout in China. (2019). Chinese Journal of Endocrinology and Metabolism. 2020; 36 (1): 1–13.
Pan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021;9(6):373–92.
Katherine M, Flegal BK, Kit H, Orpana, Barry I, Graubard. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309(1):71–82.
Veronese N, Cereda E, Solmi M, Fowler SA, Manzato E, Maggi S, et al. Inverse relationship between body mass index and mortality in older nursing home residents: a meta-analysis of 19,538 elderly subjects. Obes Rev. 2015;16(11):1001–15.
Pedro González-Muniesa M-A, Mártinez-González FB, Hu J-P, Després Y, Matsuzawa, Ruth JF, Loos, et al. Obes Nat Rev Dis Primers. 2017;3:17034.
Lee DH, Keum NaNa, Frank B, Hu EJ, Orav EB, Rimm, Walter C, Willett, et al. Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study. BMJ. 2018;362:k2575.
Gao Q, Mei F, Shang Y, Chen KHu,F, Zhao L, et al. Global prevalence of sarcopenic obesity in older adults: A systematic review and meta-analysis. Clin Nutr. 2021;40(7):4633–41.
Wu Y, Zheng Q, Zou B, Yeo YH, Li X, Li J, et al. The epidemiology of MAFLD in Mainland China with analysis by adjusted gross regional domestic product: a meta-analysis. Hepatol Int. 2020;14(2):259–69.
Wang Z, Chen Z, Zhang L, Wang X, Hao G, Zhang Z, et al. Status of Hypertension in China: Results From the China Hypertension Survey, 2012–2015. Circulation. 2018;137(22):2344–56.
Li Y, Shen Z, Zhu B, Zhang H, Zhang X, Xiaoqiang Ding. Demographic, regional and temporal trends of hyperuricemia epidemics in mainland China from 2000 to 2019: a systematic review and meta-analysis. Glob Health Action. 2021;14(1):1874652.
Li Y, Teng D, Shi X, Qin G, Qin Y, Quan H, et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study. BMJ. 2020;369:m997.
Kumar AA, Satheesh G, Vijayakumar G, Chandran M, Prabhu PR, Simon L, et al. Postprandial Metabolism is Impaired in Overweight Normoglycemic Young Adults without Family History of Diabetes. Sci Rep. 2020;10(1):353.
Lonardo A, Nascimbeni F, Ballestri S, Fairweather D, Win S, Than TA, et al. Sex Differences in Nonalcoholic Fatty Liver Disease: State of the Art and Identification of Research Gaps. Hepatology. 2019;70(4):1457–69.
Patrizia Burra D, Bizzaro A, Gonta S, Shalaby M, Gambato MC, Morelli, et al. Clinical impact of sexual dimorphism in non-alcoholic fatty liver disease (MAFLD) and non-alcoholic steatohepatitis (NASH). Liver Int. 2021;41(8):1713–33.
Da Gan Lu, Wang M, Jia Y, Ru Y, Ma W, Zheng, et al. Low muscle mass and low muscle strength associate with nonalcoholic fatty liver disease. Clin Nutr. 2020;39(4):1124–30.
Yafeng Wang AO'Neil, Wang YJiao,L, Huang J, Lan Y, et al. Sex differences in the association between diabetes and risk of cardiovascular disease, cancer, and all-cause and cause-specific mortality: a systematic review and meta-analysis of 5,162,654 participants. BMC Med. 2019;17(1):136.
Wang X-H, Lin J-N, Liu G-Z, Fan H-M, Huang Y-P, Li C-J, et al. Women Are at a Higher Risk of Chronic Metabolic Diseases Compared to Men With Increasing Body Mass Index in China. Front Endocrinol (Lausanne). 2020;11:127.

Table 1 Characteristics of the study population according to sex
Characteristics	Total (N= 5, 158)	Males (N= 2, 154)	Females (N= 3, 004)	P ^*
Age, y	44.54 ± 14.71	45.48 ± 15.0	43.86 ± 14.47	<0.001
Height, cm	165.46 ± 8.31	172.36 ± 6.31	160.51 ± 5.6	<0.001
Weight, kg	66.78 ± 13.17	76.71 ± 11.58	59.65 ± 8.95	<0.001
BMI, kg/m²	24.26 ± 3.63	25.78 ± 3.36	23.17 ± 3.41	<0.001
SBP, mmHg	122.66 ± 17.88	127.91 ± 16.45	118.89 ± 17.91	<0.001
DBP, mmHg	77.46 ± 10.56	80.66 ± 10.53	75.16 ± 9.97	<0.001
FPG, mmol/L	5.14 (4.83 ~ 5.56)	5.25 (4.92 ~ 5.76)	5.06 (4.77 ~ 5.43)	<0.001
TC, mmol/L	5.08 ± 0.97	5.02 ± 0.91	5.12 ± 1.0	<0.001
TG, mmol/L	1.13 (0.81 ~ 1.63)	1.35 (0.99 ~ 1.91)	1.0 (0.74 ~ 1.42)	<0.001
HDL-C, mmol/L	1.55 ± 0.38	1.39 ± 0.3	1.67 ± 0.38	<0.001
LDL-C, mmol/L	2.71 ± 0.58	2.69 ± 0.55	2.73 ± 0.61	0.004
SUA, umol/L	311.02 ± 82.31	368.2 ± 74.72	270.03 ± 60.06	<0.001
BFM, kg	20.41 ± 6.46	20.99 ± 6.8	19.99 ± 6.18	<0.001
FFM, kg	46.35 ± 9.66	55.68 ± 6.95	39.66 ± 4.29	<0.001
SMM, kg	25.45 ± 5.87	31.15 ± 4.19	21.35 ± 2.56	<0.001
VFA, cm²	95.07 ± 36.01	93.23 ± 34.35	96.38 ± 37.1	0.002
VSR, cm²/kg	3.88 ± 1.62	3.00 ± 1.06	4.51 ± 1.66	<0.001
SMI, kg/m²	6.97 ± 1.08	7.98 ± 0.7	6.25 ± 0.61	<0.001
MAFLD, %	1594 (30.9)	948 (44.0)	646 (21.5)	<0.001
Hyperglycemia, %	312 (6.0)	185 (8.6)	127 (4.2)	<0.001
Hypertension, %	1309 (25.4)	746 (34.6)	563 (18.7)	<0.001
Dyslipidemia, %	1394 (27.0)	669 (31.1)	725 (24.1)	<0.001
Hyperuricemia, %	648 (12.6)	506 (23.5)	142 (4.7)	<0.001
BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose, TC total cholesterol, TG triglycerides, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, SUA serum uric acid, BFM body fat mass, FFM fat free mass, SMM skeletal muscle mass, VFA visceral fat area, VSR visceral fat area to skeletal muscle mass ratio, SMI skeletal muscle index, MAFLD metabolic associated fatty liver diseases * P-value for comparison between males and females

Table 2 OR with 95% CI for associations between VSR and cardiometabolic diseases according to sex
	VSR (cm²/kg)	N	MAFLD	Hyperglycemia	Hypertension	Dyslipidemia	Hyperuricemia
Males
	Q1^*	539	1 (ref.)	1 (ref.)	1 (ref.)	1 (ref.)	1 (ref.)
	Q2	538	4.75 (3.47, 6.50)	5.13 (2.25, 11.67)	1.90 (1.42, 2.54)	1.95 (1.42, 2.68)	1.17 (0.86, 1.58)
	Q3	539	9.18 (6.72, 12.53)	8.29 (3.73, 18.4)	2.95 (2.23, 3.90)	2.65 (1.94, 3.60)	1.41(1.05, 1.89)
	Q4	538	17.23 (12.52, 23.71)	15.47 (7.1, 33.72)	5.12 (3.88, 6.76)	3.16 (2.33, 4.28)	1.89 (1.42, 2.51)
	P _trend		<0.001	<0.001	<0.001	<0.001	<0.001
Females
	Q1	751	1 (ref.)	1 (ref.)	1 (ref.)	1 (ref.)	1 (ref.)
	Q2	751	3.10 (1.85, 5.21)	3.80 (1.25, 11.49)	2.40 (1.66, 3.49)	2.41 (1.65, 3.50)	2.12 (1.03, 4.38)
	Q3	751	10.60 (6.58, 17.06)	7.76 (2.72, 22.14)	3.64 (2.55, 5.20)	4.38 (3.07, 6.24)	3.88 (1.98, 7.61)
	Q4	751	41.15 (25.80, 65.63)	21.62 (7.87, 59.36)	9.64 (6.88, 13.53)	9.34 (6.63, 13.14)	6.58 (3.45, 12.56)
	P _trend		<0.001	<0.001	<0.001	<0.001	<0.001
OR odds ratio, CI confidence interval, VSR visceral fat area to skeletal muscle mass ratio, MAFLD metabolic associated fatty liver disease Quartiles of VSR in males, Q1: 0.35 ~ 2.28, Q2: 2.29 ~ 2.86, Q3: 2.87 ~ 3.56, Q4: 3.57 ~ 8.84; quartiles of VSR in females, Q1: 1.24 ~ 3.16, Q2: 3.17 ~ 4.27, Q3: 4.28 ~ 5.59, Q4: 5.60 ~ 11.32

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Women are at higher risk of cardiometabolic diseases with the increase of visceral fat area to skeletal muscle mass ratio compared with men in a Chinese natural population: a cross-sectional study

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