Skip to main content
Download PDF
Download ePub

Fat-to-muscle ratio as a predictor for dyslipidaemia in transitional-age youth

Lipids in Health and Disease volume 21, Article number: 88 (2022) Cite this article

Abstract

Background

Although dyslipidaemia may have a crucial impact on cardiovascular health in adults, there is a lack of specific data in transitional-age youth. Therefore, this study attempted to evaluate the association of dyslipidaemia with fat-to-muscle ratio (FMR), and establish FMR thresholds for diagnosing dyslipidaemia in transitional-age youth.

Methods

One thousand six hundred sixty individuals aged 16 to 24 years from the baseline of a subcohort in the Northwest China Natural Population Cohort: Ningxia Project were analysed. Anthropometric characteristics were gauged by a bioelectrical impedance analyser, and dyslipidaemia components were measured using a Beckman AU480 chemistry analyser. Additionally, this study used logistic regression to estimate the risk of dyslipidaemia based on FMR quintiles, and calculate the gender-specific ideal cut-off values of dyslipidaemia and its components by the receiver operating characteristic (ROC) curve.

Results

Of the 1660 participants, aged 19.06 ± 1.14 years, 558 males and 1102 females. The prevalence of dyslipidaemia was 13.4% and was significantly associated with FMR quintiles among all participants (P < 0.05). The ideal values of FMR in diagnosing dyslipidaemia were 0.2224 for males and 0.4809 for females, while males had a higher AUC than females (0.7118 vs. 0.6656). Meanwhile, high FMR values were significantly associated with adverse outcomes of dyslipidaemia, hypercholesterolemia and hypertriglyceridaemia (P < 0.05).

Conclusions

The FMR was positively correlated with the prevalence of dyslipidaemia. The FMR can be used as an effective body composition index for diagnosing dyslipidaemia, especially in males, and preventive strategies should be initiated in transitional-age youth to decrease obesity-related dyslipidaemia.

Background

Obesity, a crucial risk factor for chronic diseases, is progressively becoming a global health issue [1, 2], with its prevalence increasing dramatically worldwide [3]. Obesity is associated not only with cardiovascular diseases in children [4] but also with vascular dysfunction and hormonal changes, leading to hypertension, dyslipidaemia and potential cardiovascular events in transitional-aged youth [5]. The transitional age period during youth is an important stage from adolescence to adulthood, ranging from the ages of 16 to 24 years [6]. Additionally, studies have demonstrated that the vast majority of individuals experience significant weight gain between the ages of 18 and 30 [7]. Thus, the incidence of cardiovascular diseases will increase in the future, which will lead to a global increase in deaths [8].

Previous investigations have proven dyslipidaemia to be associated with adult atherosclerosis [9] and regarded it as an effective indicator for predicting future cardiovascular events [10]. In addition, due to the close correlation between obesity and dyslipidaemia, body mass index (BMI), waist circumference (WC) and other obesity-related indicators have already been used to assess dyslipidaemia, metabolic syndrome and obesity-related cardiovascular disease risks [11,12,13,14]. However, BMI cannot accurately reflect muscle and fat content, and WC cannot be used to reflect visceral fat [15, 16]. Moreover, several studies have also suggested using different body composition measures to assess future cardiovascular disease risks [17,18,19]. Notably, fat and muscle mass may be major contributors to metabolic syndrome and cardiovascular diseases [20, 21], and fat mass (FM) is even regarded as an effective indicator to predict metabolic syndrome [18]. Fat accumulation and skeletal muscle attenuation occur simultaneously and are often expressed as the fat-to-muscle ratio (FMR), a substitutable measure for evaluating the proportion of fat and muscle [22].

Recently, the FMR, as a novel anthropometric indicator, has been used to assess dyslipidaemia [23], metabolic syndrome [24] and coronary artery disease [25] in healthy adults. Although the FMR is also considered an indicator of metabolic syndrome in Chinese Han and Buyi populations aged 20 to 80 years [26], there is no agreement on the definition of dyslipidaemia in the context of FMR. Furthermore, the prediction of adult dyslipidaemia has been improved through a variety of measurement methods, but there is a lack of specific data in transitional-age youth. Moreover, current guidelines recommend screening young people for dyslipidaemia [27, 28]. Accordingly, this study hypothesized that the FMR is a feasible diagnostic index for dyslipidaemia in transitional-age youth, explored the association of dyslipidaemia with the FMR, and established the FMR threshold for the diagnosis of dyslipidaemia.

Methods

Study participants

This study is the baseline of a subcohort in the Northwest China Natural Population Cohort: Ningxia Project (CNC-NX), conducted with 1720 transitional-age youth aged 16 to 24 in September 2018. At enrolment, general questionnaires were administered to all participants; subsequently, a battery of anthropometric measurements was completed, and blood samples were used to collect data on biological indicators.

In this prospective study, participants who had studied in the survey area for 3 years or more were included. Participants with poor health status or diseases potentially affecting their body composition were excluded, such as respiratory diseases (n = 9) and congenital muscular dystrophy (n = 1). Simultaneously, participants who had missing anthropometric measurements and blood tests (n = 50) were also excluded from the final study analysis (Fig. 1). Ultimately, 1660 eligible participants were included.

The institutional ethics committees at Ningxia Medical University gave their approval for this study (Ethics ID 2018–012, 2020–689), and at the start of the survey, each participant signed a consent form after receiving full information.

Data collection

Trained investigators collected information and baseline data in September 2018, and all the following measures were recorded for each participant.

Demographic data

Following the signing of the informed consent form, participants were invited to fill in a face-to-face questionnaire that included demographic characteristics, including age, sex, marital status, education level, and health conditions, such as lifestyle and behavioural factors, medical history and menstrual history [29]. The information on smoking and alcohol drinking status was defined as smoking ≥ 1 cigarette daily sustained for ≥ 6 months and drinking ≥ 1 time per week sustained for ≥ 6 months, respectively [30]. Education level was divided into two categories: junior college education level (more than or equal to a senior high school) and undergraduate education level. Physical activity (PA) was assessed using the International Physical Activity Questionnaire [31], and graded as low, moderate, or high by the World Health Organization (WHO) guidelines [32].

Anthropometric measurements

The participants fasted for at least 12 h, avoided alcohol, wore light clothing with no shoes, and were measured while standing. Weight and height were measured twice, with averages to the nearest 0.1 kg and 0.1 cm, respectively. Body composition was measured by trained personnel using a single frequency, eight-electrode bioelectrical impedance analyser (BIA) (InBody 370, Seoul, Korea) in accordance with the recommended procedures. Several anthropometric measurements were recorded for the participants, including their FM, total body soft lean mass, skeletal muscle mass (SMM), and other anthropometric factors.

Experimental measurements

Participants fasted the night before their venous blood was drawn. Using the Beckman AU480 chemistry analyser, fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured.

Definition of covariates

Whole-body skeletal muscle mass can be replaced with appendicular skeletal muscle mass (ASM), determined by adding the limb muscle mass together [33]. Calculating the percentage of skeletal muscle mass (ASM %) requires dividing ASM by body weight [34]. BMI was calculated as weight/height2 (kg/m2) [35]. FM was divided by the total body soft lean mass to determine the FMR, which was then divided into quintiles (Q1-Q5) from lowest to highest values. The ranges of FMR across quintiles were < 0.3173, 0.3173- < 0.3772, 0.3772- < 0.4324, 0.4324- < 0.5094, ≥ 0.5094 for female participants; and < 0.1314, 0.1314- < 0.1712, 0.1712- < 0.2352, 0.2352- < 0.3262, ≥ 0.3262 for male participants.

Dyslipidaemia

Dyslipidaemia was defined based on any one of the following characteristics: TC ≥ 6.20 \(\mathrm{mmol}/\mathrm{L}\) (240 mg/dl), TGs ≥ 2.30 \(\mathrm{mmol}/\mathrm{L}\) (200 mg/dl), LDL-C ≥ 4.10 \(\mathrm{mmol}/\mathrm{L}\) (160 mg/dl), HDL-C < 1.00 \(\mathrm{mmol}/\mathrm{L}\) (40 mg/dl) or receiving drug treatment to improve blood lipid levels [36]. In addition, hypercholesterolemia was defined as TC ≥ 6.20 \(\mathrm{mmol}/\mathrm{L}\) (240 mg/dl) and hypertriglyceridaemia as TGs ≥ 2.30 \(\mathrm{mmol}/\mathrm{L}\) (200 mg/dl).

Statistical methods

R 4.0.0 software was used to statistically analyse the research datasets. For continuous variables, the mean and standard deviation (SD) were used as representations. The number of cases and the rate were used to express categorical variables. After determining normality and variance homogeneity with the Kolmogorov–Smirnov test and Levene's test, Student’s T and χ2 tests were used to compare general characteristics by sex, and the T-test was utilized to compare anthropometric parameters according to dyslipidaemia and nondyslipidaemia. ANOVA and χ2 tests were used to compare dyslipidaemia among the FMR quintiles based on sex. Additionally, this study used logistic regression to estimate the risk of dyslipidaemia based on FMR quintiles, and the statistically significant variables from the univariate analysis results were considered in the multivariable model. While the variables with a variance inflation factor (VIF) < 5 were chosen and included in the final adjustment model, multicollinearity diagnosis was also performed on the included variables. Finally, the odds ratio (OR) and 95% confidence interval (CI) were computed after taking age, smoking, drinking, physical activity, education level, and ethnicity into account.

To establish the cut-off values for the FMR, the receiver operating characteristic (ROC) curve was used, with a standard for identifying dyslipidaemia as the ROC curve that is most closely related to (0, 1). Moreover, the optimal cut-off FMR value was obtained based on a maximized Youden’s index, and the sensitivity, specificity and area under the ROC curve (AUC) were also examined. Following participant division was founded on the cut-off FMR value, the Student’s T test and the χ2 test were utilized to compare the dyslipidaemia risk levels among the groups. Every statistical test used two sides, and P < 0.05 indicates statistically significant.

Results

General characteristics

Of the 1660 participants, aged 19.06 ± 1.14 years, 558 males and 1102 females. Regarding anthropometric measurements, men had higher weight, height, BMI, WC, ASM, ASM%, and soft lean mass but lower FM and FMR values than women (P < 0.001). Regarding the laboratory measurements, men had higher levels of TC, TGs, and FBG and an even higher prevalence of dyslipidaemia than women (P < 0.05). Furthermore, men also had higher levels of drinking and smoking consumption than women (P < 0.001). As shown in Table 1.

Table 1 Characteristics of the subjects
Full size table

Correlations of dyslipidaemia with general characteristics

The entire participant pool was split into two groups based on dyslipidaemia status (dyslipidaemia and nondyslipidaemia); as shown in Table 2, sex, educational background, WC, ASM, ASM%, FM, BMI, and the FMR were significantly correlated with dyslipidaemia (P < 0.05). Conversely, the mean age, smoking and alcohol intake status, and physical activity showed no significant differences between transitional-age youth with and without dyslipidaemia. Moreover, the FMR in transitional-age youth with dyslipidaemia was higher than that in those without dyslipidaemia (P < 0.05).

Table 2 Baseline characteristics among subjects by dyslipidaemia and nondyslipidaemia status
Full size table

Correlations between dyslipidaemia and FMR

Table 3 demonstrates the significant relationship among BMI, dyslipidaemia, and dyslipidaemia components, except HDL-C, and the FMR quintiles. The prevalence of dyslipidaemia increased with the FMR, even after adjustment for possible confounders, for both males and females (P < 0.001; Table 4). In comparison to Q1, the corrected ORs values of dyslipidaemia in FMR Q2, Q3, Q4, and Q5 were 1.57 (95% CI: 0.61–4.03), 2.22 (95% CI: 0.90–5.46), 3.29 (95% CI: 1.39–7.81), and 7.56 (95% CI: 3.29–17.38), respectively, for males and 0.74 (95% CI: 0.34–1.60), 1.26 (95% CI: 0.63–2.52), 1.45 (95% CI: 0.74–2.88) and 3.04 (95% CI: 1.63–5.67), respectively, for females.

Table 3 Obesity-related characteristics of the subjects according to FMR quintiles
Full size table
Table 4 Logistic regression analysis of association between FMR and the risk of dyslipidaemia
Full size table

The FMR cut-off value for dyslipidaemia and its components

Figure 2 displays the gender-specific ROC curves for dyslipidaemia and its components. For detecting dyslipidaemia, the cut-off value of the FMR was 0.2224 for males and 0.4809 for females and specificity was lower in males than in females (0.6430 vs. 0.7680). The AUC and sensitivity were also higher in males than in females (0.7047, 0.7350 vs. 0.6411, 0.4790). Furthermore, additional secondary analyses were performed for the ability of the FMR to predict hypercholesterolemia and hypertriglyceridaemia. For predicting hypercholesterolemia, the cut-off ratio value, sensitivity and specificity were 0.2251, 0.8378, and 0.6065 in males and 0.4826, 0.5152, and 0.7625 in females, while males had a higher AUC than females (0.7118 vs. 0.6656). For predicting hypertriglyceridaemia, the cut-off ratio, AUC, sensitivity and specificity were lower in males than in females (0.3294, 0.7033, 0.5385, and 0.8252 vs. 0.6865, 0.7695, 0.5556, and 0.9716). The sex-specific cut-off point of FMR for identifying higher risks of dyslipidaemia indicates that those with elevated FMR are more likely to experience adverse outcomes from dyslipidaemia (P < 0.05; Table 5). Meanwhile, the multivariable-adjusted ORs of dyslipidaemia, hypercholesterolemia and hypertriglyceridaemia according to the sex-specific FMR cut-off level were significant (Table 6), which were 4.67 (95% CI: 2.85–7.63), 6.85 (95% CI: 2.77–16.96), and 2.41 (95% CI: 1.04–5.60), respectively, in men and 3.01 (95% CI: 2.07–4.49), 3.20 (95% CI: 1.91–5.38), and 4.60 (95% CI: 1.07–19.83), respectively, in women.

Table 5 Fat-to-muscle ratio detection thresholds based on sex
Full size table
Table 6 Odds ratios for dyslipidaemia and its components according to the sex-specific FMR cut-off level
Full size table

Discussion

According to previous studies, in addition to a high BMI, which is often used as an effective indicator of obesity and cardiovascular disease risk across a wide population, some body composition measurements have been used to detect cardiovascular disease risk, which has been well reported in several previous studies [37, 38]. The proportion of visceral adipose to thigh muscle area was thought to be a suitable indicator of glycometabolism and insulin resistance in middle-aged women [39, 40]. The SMM, FM, and body fat percentage were linked to metabolic syndrome [17, 41, 42], and muscle strength was inversely correlated with the risk of cardiovascular diseases [43]. In addition, a loss of muscle mass can account for decreases in physical activity and the basal metabolic rate. Conversely, visceral obesity, sarcopenic obesity and high FMI are favourably correlated with metabolic syndrome and cardiovascular diseases [44, 45]. However, the danger of cardiovascular diseases cannot currently be assessed simultaneously by a comprehensive predictor, although various types of body composition indicators have been used to predict the validity of metabolic dysfunction. Compared to other body composition indices, the FMR is thought of as a new-type predictor for metabolic syndrome [22] and cardiovascular disease risk [40] in recent years.

Furthermore, dyslipidaemia in childhood, adolescence, and even during the transitional period of youth may have a crucial impact on cardiovascular health in adulthood. Additionally, the connection between accumulated fat and dyslipidaemia has been revealed in numerous studies, and the non-high-density lipoprotein cholesterol and obesity indices are related and considered useful screening tools for atherosclerotic cardiovascular disease risk [46, 47]. Similarly, the risk of dyslipidaemia has also been shown to be significantly increased by low skeletal muscle mass [48]. However, the passage from adolescence to adulthood seems to be marked by significant changes in lifestyle that affect the emergence of obesity. Furthermore, the quality of life of patients was positively impacted by conventional lipid-lowering drugs [49, 50], but patients with dyslipidaemia may have side effects (such as muscle symptoms) during treatment [51], which will affect their muscle health and lead to the further deterioration of their physical condition. Therefore, this research is more concerned about the connection between dyslipidaemia and changes in the FMR in transitional-age youth.

Comparisons with other studies and what does the current work add to the existing knowledge

The present study, which is the baseline of a subcohort from the Northwest China Natural Population Cohort: Ningxia Project (CNC-NX), revealed a positive correlation between the prevalence of dyslipidaemia and a high FMR value. Additionally, the FMR served as an effective predictor for diagnosing dyslipidaemia, and the sensitivity of the cut-off FMR value was high in males, while the specificity of the cut-off FMR value was high in females. Moreover, there has never been a study with transitional-age youth to examine the relationships between the FMR and dyslipidaemia and to establish thresholds to facilitate the diagnosis of a high risk of dyslipidaemia that we are aware of.

According to prior research, FMR is a more accurate potential predictor of cardiovascular disease risk assessment than other individual components and has been applied in clinical practice [52, 53]. Several studies measured body composition by BIA and developed the ideal FMR cut-off value for metabolic syndrome detection [22]. Similarly, this study also found the association of the highest FMR value with dyslipidaemia at baseline, and the ideal values of FMR in diagnosing dyslipidaemia were 0.2224 for males and 0.4809 for females, which supported the hypothesis that the FMR is a feasible predictive index of dyslipidaemia in transitional-age youth. The potential mechanism is that adipocytes and macrophages associated with adipose tissue secrete more pro-inflammatory adipokines as body fat accumulation, including tumor necrosis factor-α and serum amyloid A, which may lead to a discrepancy between pro- and anti-inflammatory adipokines and promote dyslipidaemia [54, 55]. Meanwhile, skeletal muscle is regarded as an essential insulin-responsive endocrine organ, and muscle loss worsens glycemic control and insulin sensitivity, which may facilitate the onset of dyslipidaemia [56, 57]. Thus, the simultaneous occurrence of fat accumulation and skeletal muscle reduction can cause muscle inflammation and adversely affect myocyte metabolism, resulting in insulin resistance and promoting dyslipidaemia [58, 59].

Based on an earlier study, which observed that the FMR increased with age from 35 to 74 years [29], while this phenomenon may also have occurred in transitional-age youth in the current study. Therefore, preventive strategies can be initiated in transitional-age youth to decrease cardiovascular risk factors in adulthood, thereby reducing the morbidity and mortality of future heart diseases. Importantly, to clarify the exact mechanism between the FMR and the risks of dyslipidaemia, future longitudinal research and further work are particularly needed.

Study strengths and limitations

Many advantages come from this study: potential confounding elements such as socioeconomic status and lifestyle were taken into account when conducting the analyses for this study. In addition, this study also determined the difference in the FMR in predicting dyslipidaemia according to sex. In addition, the current study has some constraints that should be considered. First, BIA, a trustworthy and practical technique, was used in place of dual-energy X-ray absorptiometry, the industry standard for human body composition detection [60]. However, this research used unified measurement methods at baseline and follow-up to avoid errors as much as possible. Second, the analysis data were from the baseline data of a cohort study and a relatively small sample of a transitional-age youth population, so the application range of the cut-off FMR values is limited.

Conclusions

Many guidelines recommend early screening for dyslipidaemia before adulthood. This study demonstrated that the FMR serves as a practical predictor for dyslipidaemia, especially in males. Therefore, keeping a relatively low FMR is beneficial for preventing dyslipidaemia in transitional-age youth. Meanwhile, FMR should be taken into account in lipid management in clinical practice and preventive strategies should be initiated in transitional-age youth to decrease obesity-related dyslipidaemia.

Fig. 1
figure 1

An outline of the procedure for choosing the study population

Full size image
Fig. 2
figure 2

The sex-specific FMR cut-off points and ROC curves for identifying dyslipidaemia and its components. (A) Males, dyslipidaemia; (B) Females, dyslipidaemia. (C) Males, hypercholesterolemia; (D) Females, hypercholesterolemia. (E) Males, hypertriglyceridaemia; (F) Females, hypertriglyceridaemia

Full size image

Availability of data and materials

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

Abbreviations

FMR:

Fat-to-muscle Ratio

BMI:

Body mass index

WC:

Waist circumference

FM:

Fat mass

ASM:

Appendicular skeletal muscle mass

HDL-C:

High-density lipoprotein cholesterol

LDL-C:

Low-density lipoprotein cholesterol

TC:

Total cholesterol

TGs:

Triglycerides

95% CI:

95% Confidence intervals

AUC:

Area under the curve

ROC:

Receiver operating characteristic

References

  1. Hruby A, Hu FB. The Epidemiology of Obesity: A Big Picture. Pharmacoeconomics. 2015;33:673–89.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism-Clinical and Experimental. 2019;92:6–10.

    Article  CAS  PubMed  Google Scholar 

  3. Kim YJ, Kwon EY, Kim JW, Lee Y, Ryu R, Yun J, Kim M, Choi MS. Intervention Study on the Efficacy and Safety of Platycodon grandiflorus Ethanol Extract in Overweight or Moderately Obese Adults: A Single-Center, Randomized, Double-Blind, Placebo-Controlled Trial. Nutrients. 2019;11(10):2445.

  4. Raghuveer G. Lifetime cardiovascular risk of childhood obesity. Am J Clin Nutr. 2010;91(5):1514S-1519S.

    Article  PubMed  Google Scholar 

  5. Mocnik M, Marcun Varda N. Cardiovascular Risk Factors in Children with Obesity, Preventive Diagnostics and Possible Interventions. Metabolites. 2021;11(8):551.

  6. Kaligis F, Ismail RI, Wiguna T, Prasetyo S, Indriatmi W, Gunardi H, Pandia V, Magdalena CC. Mental Health Problems and Needs among Transitional-Age Youth in Indonesia. Int J Environ Res Public Health. 2021;18(8):4046.

  7. Truesdale KP, Stevens J, Lewis CE, Schreiner PJ, Loria CM, Cai J. Changes in risk factors for cardiovascular disease by baseline weight status in young adults who maintain or gain weight over 15 years: the CARDIA study. Int J Obes (Lond). 2006;30:1397–407.

    Article  CAS  Google Scholar 

  8. World Health Organization. Global Health Estimates: Life Expectancy and Leading Causes of Death and Disability. Available online: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates (accessed on 15 March 2022).

  9. Magnussen CG, Raitakari OT, Thomson R, Juonala M, Patel DA, Viikari JSA, Marniemi J, Srinivasan SR, Berenson GS, Dwyer T, Venn A. Utility of currently recommended pediatric dyslipidemia classifications in predicting dyslipidemia in adulthood - Evidence from the childhood determinants of adult health (CDAH) study, Cardiovascular Risk in Young Finns Study, and Bogalusa Heart Study. Circulation. 2008;117:32–42.

    Article  PubMed  Google Scholar 

  10. Lorenz MW, Markus HS, Bots ML, Rosvall M, Sitzer M. Prediction of clinical cardiovascular events with carotid intima-media thickness - A systematic review and meta-analysis. Circulation. 2007;115:459–67.

    Article  PubMed  Google Scholar 

  11. Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, Allison TG, Batsis JA, Sert-Kuniyoshi FH, Lopez-Jimenez F. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes. 2008;32:959–66.

    Article  CAS  Google Scholar 

  12. Shea JL, King MTC, Yi Y, Gulliver W, Sun G. Body fat percentage is associated with cardiometabolic dysregulation in BMI-defined normal weight subjects. Nutr Metab Cardiovasc Dis. 2012;22:741–7.

    Article  CAS  PubMed  Google Scholar 

  13. Klein S, Allison DB, Heymsfiield SB, Kelley DE, Leibel RL, Nonas C, Kahn R. Waist circumference and cardiometabolic risk: A consensus statement from shaping America’s health: Association for weight management and obesity prevention; NAASO, The Obesity Society; The American Society for Nutrition. And the American Diabetes Association Obesity. 2007;15:1061–7.

    PubMed  Google Scholar 

  14. Shah RV, Murthy VL, Abbasi SA, Blankstein R, Kwong RY, Goldfine AB, Jerosch-Herold M, Lima JAC, Ding J, Allison MA. Visceral adiposity and the risk of metabolic syndrome across body mass index: the MESA Study. JACC Cardiovasc Imaging. 2014;7:1221–35.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Franzosi MG. Should we continue to use BMI as a cardiovascular risk factor? Lancet. 2006;368:624–5.

    Article  PubMed  Google Scholar 

  16. Alberti K, Zimmet P, Shaw J. Metabolic syndrome - a new world-wide definition A consensus statement from the international diabetes federation. Diabetic Medicine. 2006;23:469–80.

    Article  CAS  PubMed  Google Scholar 

  17. Moon JH, Choo SR, Kim JS. Relationship between Low Muscle Mass and Metabolic Syndrome in Elderly People with Normal Body Mass Index. Journal of bone metabolism. 2015;22:99–106.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ramirez-Velez R, Correa-Bautista JE, Sanders-Tordecilla A, Ojeda-Pardo ML, Cobo-Mejia EA, Castellanos-Vega RD, Garcia-Hermoso A, Gonzalez-Jimenez E, Schmidt-RioValle J, Gonzalez-Ruiz K. Percentage of Body Fat and Fat Mass Index as a Screening Tool for Metabolic Syndrome Prediction in Colombian University Students. Nutrients. 2017;9(9):1009.

  19. Liu PJ, Ma F, Lou HP, Liu YP. The utility of fat mass index vs. body mass index and percentage of body fat in the screening of metabolic syndrome. Bmc Public Health. 2013;13:629.

  20. Lim KI, Yang SJ, Kim TN, Yoo HJ, Kang HJ, Song W, Baik SH, Choi DS, Choi KM. The association between the ratio of visceral fat to thigh muscle area and metabolic syndrome: the Korean Sarcopenic Obesity Study (KSOS). Clin Endocrinol. 2010;73:588–94.

    Article  CAS  Google Scholar 

  21. Kim TN, Park MS, Lim KI, Yang SJ, Yoo HJ, Kang HJ, Song W, Seo JA, Kim SG, Kim NH, et al. Skeletal muscle mass to visceral fat area ratio is associated with metabolic syndrome and arterial stiffness: The Korean Sarcopenic Obesity Study (KSOS). Diabetes Res Clin Pract. 2011;93:285–91.

    Article  PubMed  Google Scholar 

  22. Seo YG, Song HJ, Song YR. Fat-to-muscle ratio as a predictor of insulin resistance and metabolic syndrome in Korean adults. J Cachexia Sarcopenia Muscle. 2020;11:710–25.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Cho AR, Lee JH, Kwon YJ. Fat-to-Muscle Ratios and the Non-Achievement of LDL Cholesterol Targets: Analysis of the Korean Genome and Epidemiology Study. J Cardiovasc Dev Dis. 2021;8(8):96.

  24. Ramirez-Velez R, Carrillo HA, Correa-Bautista JE, Schmidt-RioValle J, Gonzalez-Jimenez E, Correa-Rodriguez M, Gonzalez-Ruiz K, Garcia-Hermoso A. Fat-to-Muscle Ratio: A New Anthropometric Indicator as a Screening Tool for Metabolic Syndrome in Young Colombian People. Nutrients. 2018;10(8):1027.

  25. Eun Y, Lee SN, Song SW, Kim HN, Kim SH, Lee YA, Kang SG, Rho JS, Yoo KD. Fat-to-muscle Ratio: A New Indicator for Coronary Artery Disease in Healthy Adults. Int J Med Sci. 2021;18:3738–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Xu K, Zhu HJ, Chen S, Chen L, Wang X, Zhang LY, Pan L, Wang L, Feng K, Wang K, et al. Fat-to-muscle Ratio: A New Anthropometric Indicator for Predicting Metabolic Syndrome in the Han and Bouyei Populations from Guizhou Province. China Biomedical and Environmental Sciences. 2018;31:261–71.

    CAS  PubMed  Google Scholar 

  27. Chou R, Dana T, Blazina I, Daeges M, Bougatsos C, Jeanne TL. Screening for Dyslipidemia in Younger Adults: A Systematic Review for the U.S. Preventive Services Task Force. Annals of Internal Medicine. 2016;165:560.

    Article  PubMed  Google Scholar 

  28. Nantsupawat N, Booncharoen A, Wisetborisut A, Jiraporncharoen W, Pinyopornpanish K, Chutarattanakul L, Angkurawaranon C. Appropriate Total cholesterol cut-offs for detection of abnormal LDL cholesterol and non-HDL cholesterol among low cardiovascular risk population. Lipids in Health & Disease. 2019;18(1):28.

  29. Zhang JX, Li J, Chen C, Yin T, Wang QA, Li XX, Wang FX, Zhao JH, Zhao Y, Zhang YH. Reference values of skeletal muscle mass, fat mass and fat-to-muscle ratio for rural middle age and older adults in western China. Arch Gerontol Geriatr. 2021;95: 104389.

    Article  PubMed  Google Scholar 

  30. Li Y, Xiaoqing J, Xinhua T, Xiaoling S, Xiaoling X, Wei Y, Zengwu W, Xin W, Pinpin Z, Jing Y. Effects of a comprehensive intervention on hypertension control in Chinese employees working in universities based on mixed models. Sci Rep. 2019;9:19187.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Sjostram M, Ainsworth B, Bauman A, Bull F, Craig C, Sallis J. Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire (IPAQ)-Short and Long Forms. 2005; Available online: http://www.ipaq.ki.se/scoring. Accessed 10 June 2018.

  32. Centers for Disease Control and Prevention. CDC global school-based student health survey (GSHS). Available online: http://www.cdc.gov/GSHS/ (Accessed on 12 Aug 2022).

  33. Kim J, Wang ZM, Heymsfield SB, Baumgartner RN, Gallagher D. Total-body skeletal muscle mass: estimation by a new dual-energy X-ray absorptiometry method. Am J Clin Nutr. 2002;76:378–83.

    Article  CAS  PubMed  Google Scholar 

  34. Kim SH, Jeong JB, Kang J, Ahn DW, Kim JW, Kim BG, Lee KL, Oh S, Yoon SH, Park SJ, Lee DH. Association between sarcopenia level and metabolic syndrome. PLoS ONE. 2021;16: e0248856.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Mogren IM. Physical activity and persistent low back pain and pelvic pain post partum. BMC Public Health. 2008;8:417.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Joint committee for guideline revision. 2016 Chinese guidelines for the management of dyslipidemia in adults. J Geriatr Cardiol. 2018;15:1–29.

    PubMed Central  Google Scholar 

  37. Pi-Sunyer X. Changes in body composition and metabolic disease risk. Eur J Clin Nutr. 2019;73:231–5.

    Article  CAS  PubMed  Google Scholar 

  38. Piche M-E, Poirier P, Lemieux I, Despres J-P. Overview of Epidemiology and Contribution of Obesity and Body Fat Distribution to Cardiovascular Disease: An Update. Prog Cardiovasc Dis. 2018;61:103–13.

    Article  PubMed  Google Scholar 

  39. Kim CS, Nam JY, Park JS, Kim DM, Yoon SJ, Ahn CW, Lim SK, Kim KR, Lee HC, Huh KB, Cha BS. The correlation between insulin resistance and the visceral fat to skeletal muscle ratio in middle-aged women. Yonsei Med J. 2004;45:469–78.

    Article  PubMed  Google Scholar 

  40. Chen YY, Fang WH, Wang CC, Kao TW, Yang HF, Wu CJ, Sun YS, Wang YC, Chen WL. Fat-to-muscle ratio is a useful index for cardiometabolic risks: A population-based observational study. PLoS ONE. 2019;14: e0214994.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kim G, Lee SE, Jun JE, Lee YB, Ahn J, Bae JC, Jin SM, Hur KY, Jee JH, Lee MK, Kim JH. Increase in relative skeletal muscle mass over time and its inverse association with metabolic syndrome development: a 7-year retrospective cohort study. Cardiovasc Diabetol. 2018;17(1):23.

  42. Kim K, Park SM. Association of muscle mass and fat mass with insulin resistance and the prevalence of metabolic syndrome in Korean adults: a cross-sectional study. Sci Rep. 2018;8(1):2703.

  43. Atlantis E, Martin SA, Haren MT, Taylor AW, Wittert GA. Florey Adelaide Male Ageing S: Inverse associations between muscle mass, strength, and the metabolic syndrome. Metabolism-Clinical and Experimental. 2009;58:1013–22.

    Article  CAS  PubMed  Google Scholar 

  44. Cho YG, Song HJ, Kim JM, Park KH, Paek YJ, Cho JJ, Caterson I, Kang JG. The estimation of cardiovascular risk factors by body mass index and body fat percentage in Korean male adults. Metab Clin Exp. 2009;58:765–71.

    Article  CAS  PubMed  Google Scholar 

  45. Guaraldi G, Milic J, Sebastiani G, Raggi P. Sarcopenic obesity at the crossroad of pathogenesis of cardiometabolic diseases. Atherosclerosis. 2021;335:84–6.

    Article  CAS  PubMed  Google Scholar 

  46. Sniderman AD, Williams K, Contois JH, Monroe HM, McQueen MJ, de Graaf J, Furberg CD. A meta-analysis of low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol, and apolipoprotein B as markers of cardiovascular risk. Circ Cardiovasc Qual Outcomes. 2011;4:337–45.

    Article  PubMed  Google Scholar 

  47. Dai S, Eissa MA, Steffen LM, Fulton JE, Harrist RB, Labarthe DR. Associations of BMI and its fat-free and fat components with blood lipids in children: Project HeartBeat! Clin Lipido. 2011;6:235–44.

    Article  Google Scholar 

  48. Lee JH, Lee HS, Cho AR, Lee YJ, Kwon YJ. Relationship between muscle mass index and LDL cholesterol target levels: Analysis of two studies of the Korean population. Atherosclerosis. 2021;325:1–7.

    Article  CAS  PubMed  Google Scholar 

  49. De Luca L, Temporelli PL, Riccio C, Gonzini L, Marinacci L, Tartaglione SN, Costa P, Scherillo M, Senni M, Colivicchi F, et al. Clinical outcomes, pharmacological treatment, and quality of life of patients with stable coronary artery diseases managed by cardiologists: 1-year results of the START study. Eur Heart J Qual Care Clin Outcomes. 2019;5:334–42.

    Article  PubMed  Google Scholar 

  50. Cesaro A, Gragnano F, Fimiani F, Moscarella E, Diana V, Pariggiano I, Concilio C, Natale F, Limongelli G, Bossone E, Calabro P. Impact of PCSK9 inhibitors on the quality of life of patients at high cardiovascular risk. Eur J Prev Cardiol. 2020;27:556–8.

    Article  PubMed  Google Scholar 

  51. Gragnano F, Natale F, Concilio C, Fimiani F, Cesaro A, Sperlongano S, Crisci M, Limongelli G, Calabro R, Russo M, et al. Adherence to proprotein convertase subtilisin/kexin 9 inhibitors in high cardiovascular risk patients: an Italian single-center experience. J Cardiovasc Med (Hagerstown). 2018;19:75–7.

    Article  Google Scholar 

  52. Prado CMM, Wells JCK, Smith SR, Stephan BCM, Siervo M. Sarcopenic obesity: A Critical appraisal of the current evidence. Clin Nutr. 2012;31:583–601.

    Article  CAS  PubMed  Google Scholar 

  53. Lee HS, Kim SG, Kim JK, Lee YK, Noh JW, Oh J, Kim HJ, Song YR. Fat-to-Lean Mass Ratio Can Predict Cardiac Events and All-Cause Mortality in Patients Undergoing Hemodialysis. Ann Nutr Metab. 2018;73:241–9.

    Article  CAS  PubMed  Google Scholar 

  54. Gutierrez DA, Puglisi MJ, Hasty AH. Impact of increased adipose tissue mass on inflammation, insulin resistance, and dyslipidemia. Curr DiabRep. 2009;9:26–32.

    CAS  Google Scholar 

  55. Bays HE, Toth PP, Kris-Etherton PM, Abate N, Aronne LJ, Brown WV, Gonzalez-Campoy JM, Jones SR, Kumar R, La Forge R, Samuel VT. Obesity, adiposity, and dyslipidemia: A consensus statement from the National Lipid Association. J Clin Lipidol. 2013;7:304–83.

    Article  PubMed  Google Scholar 

  56. Guillet C, Boirie Y. Insulin resistance: a contributing factor to age-related muscle mass loss? Diabetes Metab. 2005;31:S20–6.

    Article  Google Scholar 

  57. Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuniga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):122.

  58. Jornayvaz FR, Samuel VT, Shulman GI: The Role of Muscle Insulin Resistance in the Pathogenesis of Atherogenic Dyslipidemia and Nonalcoholic Fatty Liver Disease Associated with the Metabolic Syndrome. In Annual Review of Nutrition, Vol 30. Volume 30. Edited by Cousins RJ; 2010: 273–290.

  59. Wu HZ, Ballantyne CM. Skeletal muscle inflammation and insulin resistance in obesity. J Clin Investig. 2017;127:43–54.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Marra M, Sammarco R, De Lorenzo A, Iellamo F, Siervo M, Pietrobelli A, Donini LM, Santarpia L, Cataldi M, Pasanisi F, Contaldo F. Assessment of Body Composition in Health and Disease Using Bioelectrical Impedance Analysis (BIA) and Dual Energy X-Ray Absorptiometry (DXA): A Critical Overview. Contrast Media Mol Imaging. 2019;2019:3548284.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank all participants, administrators and coordinators of the Northwest China Natural Population Cohort: Ningxia Project (CNC-NX) Study Group for their support.

Funding

This research was supported by the National Key Research and Development Program of China (grant number: 2017YFC0907204) and the Key Research and Development Program of Ningxia (grant number: 2021BEG02026).

Author information

Authors and Affiliations

  1. School of Public Health and Management, Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China

    Jia-Xing Zhang, Wen Li, Xiu-Juan Tao, Qing-An Wang, Wan-Lu Liu, Chan Yang, Kai-Rong Wang, Jiang-Wei Qiu, Yi Zhao & Yu-Hong Zhang

  2. Department of Hospital Infection Management, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia, People’s Republic of China

    Chen Chen

  3. Key Laboratory of Environmental Factors and Chronic Disease Control, Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China

    Yi Zhao & Yu-Hong Zhang

Authors
  1. Jia-Xing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiu-Juan Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qing-An Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wan-Lu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kai-Rong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jiang-Wei Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yi Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yu-Hong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.Z. and Y.Z. developed the research concept and design; W.L., X.T., W.L., C.Y., K.W. and J.Q. contributed to the research data collection; J.Z. and Q.W. performed the statistical analysis; J.Z. and C.C. wrote the first draft of the manuscript. The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yi Zhao or Yu-Hong Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Informed consent was obtained from all participants. The study was approved by the Ningxia Medical University Institutional Ethics Committees (Ethics ID 2018–012, 2020–689), according to the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, JX., Li, W., Tao, XJ. et al. Fat-to-muscle ratio as a predictor for dyslipidaemia in transitional-age youth. Lipids Health Dis 21, 88 (2022). https://doi.org/10.1186/s12944-022-01697-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12944-022-01697-9

Keywords

  • Dyslipidaemia
  • Muscle
  • Fat-to-muscle ratio
  • Transitional-age youth

Lipids in Health and Disease

ISSN: 1476-511X