Relationship of monocyte-to-high density lipoprotein ratio (MHR) and other inflammatory biomarkers (SII, NLR and NHHR) with sarcopenia: a population-based study

Relationship of monocyte-to-high density lipoprotein ratio (MHR) and other inflammatory biomarkers (SII, NLR and NHHR) with sarcopenia: a population-based study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationship of monocyte-to-high density lipoprotein ratio (MHR) and other inflammatory biomarkers (SII, NLR and NHHR) with sarcopenia: a population-based study Zhiwei Xue, Jian Cao, Jianhui Mou, Rui Wang, Peng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5161975/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Feb, 2025 Read the published version in Lipids in Health and Disease → Version 1 posted 14 You are reading this latest preprint version Abstract Objectives In previous studies, several inflammatory biomarkers derived from complete blood cell counts (CBC), including systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and non‑high‑density lipoprotein cholesterol to high‑density lipoprotein cholesterol ratio (NHHR) have been shown to act as predictors of sarcopenia. Whether Monocyte–to–High-Density Lipoprotein Cholesterol Ratio (MHR) can forecast the development of sarcopenia has never been demonstrated. Our study attempts to investigate the correlation between MHR and low muscle mass. Methods The study comprised 10,321 participants aged 20 years and above from the US. Survey-weighted logistic regression was performed to explore the association between ln-transformed MHR, SII, NLR, NHHR and low muscle mass. Furthermore, AUC values and ROC curves were used to assess the predictive effectiveness of ln-transformed MHR and other inflammatory markers (SII, NLR, and NHHR). The bootstrap estimated 95% Cl was shown with the AUC. Results In the fully adjusted model, ln SII, ln NLR, ln NHHR, and ln MHR were positively correlated with low muscle mass. (ln SII: OR = 1.59; 95% CI, 1.37–1.84; ln NLR: OR = 1.35; 95% CI, 1.13–1.60; ln NHHR: OR = 1.49; 95% CI, 1.27–1.75; ln MHR: OR = 1.98; 95% CI, 1.68–2.33) Compared to the lowest quartile of ln MHR, higher quartiles were significantly associated with increased OR for low muscle mass. (p for trend < 0.0001). In ROC analysis, ln MHR has higher AUC values than ln SII, ln NLR, and ln NHHR. (AUC = 0.7545, 95%CI = 0.7385–0.7705) Conclusion ln-transformed MHR, SII, NLR, and NHHR were positively associated with low muscle mass. MHR performs better in predicting sarcopenia compared to SII, NLR, and NHHR. Monocyte-to-high-density lipoprotein-cholesterol ratio Sarcopenia Low muscle mass MHR NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Sarcopenia is a systemic condition linked to the aging process, clinically characterized by a reduction in skeletal muscle mass and impaired functional capacity(1–3). Sarcopenia can cause weakness, falls, and physical disability(4), with a noteworthy effect on well-being and standard of living (5). The prevalence is increasing with the development of an aging society(6). Sarcopenia is predominantly induced by natural aging, but it can also be influenced by additional factors(7). It is reported to occur not solely among the elderly(8). Oxidative stress is the primary pathogenesis of sarcopenia(9), but the degree of obesity(10), sex hormone levels(11), amount of exercise(12), and protein intake(13) may also influence the development of the disease. Prevalence estimates fluctuate based on the definition of sarcopenia; however, even with conservative methodologies, the prevalence in the general population ranges from 5–10%(14). Sarcopenia requires greater consideration on our part. In previous studies, several inflammatory biomarkers derived from CBC, including SII(15), and NHHR(16) have been shown to act as predictors of sarcopenia. Increased NLR levels are significantly correlated with a higher incidence of sarcopenia(17). Monocytes can maintain homeostasis through recruitment but can also promote inflammation(18), whereas HDL-c has strong antioxidant and anti-inflammatory capacity(19). T-cell immunomodulation mitigates chronic inflammation, hence alleviating sarcopenia(20). Reduced muscle mass may be prevalent in an aged Chinese demographic with HDL-C values exceeding 70 mg/dl(21). Monocytes and HDL-c have derived MHR, a new biomarker associated with inflammation and oxidative stress(22). Whether MHR can predict the development of sarcopenia has never been demonstrated. Our study attempts to investigate the correlation between MHR and low muscle mass. 2. Methods Study participants The NHANES database is a nationally representative survey that assesses the nutritional and health status of the U.S. population. It is updated every two years and is organized by the National Center for Health Statistics (NCHS) in the US. Comprehensive information is available at http://www.cdc.gov/nchs/nhanes/ . The study initially comprised 39,156 participants from the United States. Following the exclusion of adults aged under 20 years, as well as cases with unclear information regarding ALM, MHR, SII, NLR, and NHHR. The remaining 10,321 individuals were recruited for this research (Fig. 1 ). Definition of MHR and \(AL{M_{BMI}}\) \(MHR=\frac{{{\text{monocyte(}}{{10}^3}cells/\mu L{\text{)}}}}{{HDL - c(mg/dL)}}\) \(AL{M_{BMI}}=\frac{{appendicular{\text{ }}lean{\text{ }}mass(kg)}}{{BMI(kg/{m^2})}}\) MHR was used as the exposure variable in this study and the formula was given above. The monocyte count was obtained using the Beckman Coulter MAXM automated analytical instrument. More than 9 hours of fasting is required before morning measurements. HDL-c measurement is performed using the Roche Cobas 6000. We do the ln logarithmic function treatment for MHR in the research. Appendicular lean mass (ALM) refers to the total mass of lean soft tissue in the extremities, measured by DXA. Following the FNIH's previously published criteria(23), \(AL{M_{BMI}}\) levels below 0.789 in males and 0.512 in females were classified as low muscle mass. Additionally, the following formulas were used to calculate the NLR, SII, and NHHR: NLR = neutrophil counts/lymphocyte counts, SII = platelet counts × neutrophil counts/lymphocyte counts, NHHR = non-HDL-c/HDL-c. Selection of covariates The multivariable-adjusted models were constructed by using the variables age, sex, race, educational level, smoking, congestive heart failure, coronary artery, stroke, arthritis, diabetes, and cancer. Reducing confounding effects between MHR and \(AL{M_{BMI}}\) by adjusting for these variables. The educational level is divided into three segments based on upper secondary education. Smoking is categorized as yes or no according to whether you have smoked at least 100 cigarettes in your lifetime. The information of congestive heart failure, coronary artery, stroke, arthritis, diabetes, and cancer can be found in the questionnaire module of the NHANES database. Statistical analysis Since MHR showed a skewed distribution in the analyses, we used the natural logarithmic function for transformation and divided the ln transformed MHR into quartiles. Continuous variables were expressed as mean ± standard deviation in the descriptive analyses, and the Student's t-test was employed to evaluate the differences among groups. However, categorical variables were indicated as percentages in the descriptive analyses, and the chi-squared test was used in assessing differences among groups. Multiple regression analysis was employed to analyze the association between inflammatory indicators and low muscle mass. We built three models to calculate odds ratios (OR) and 95% confidence intervals (CI), Model 1 with no adjusted variables; Model 2 adjusted for four variables: gender, age [years], race, and educational level; and Model 3 adjusted for the four variables in Model 2 and congestive heart failure [No or Yes], coronary artery [No or Yes], stroke [No or Yes], arthritis [No or Yes], diabetes [No or Yes], cancer [No or Yes]. Furthermore, area under the curve (AUC) values and receiver operating characteristic (ROC) curves were used to assess the predictive effectiveness of ln-transformed MHR and other inflammatory markers (SII, NLR, and NHHR). The bootstrap estimated 95% Cl was shown with the AUC. Additionally, subgroup analyses and interaction tests were performed to validate the findings further. Data for this study were analyzed using R software and EmpowerStats. P < 0.05 was considered statistically significant. 3. Results Baseline characteristics of participants In our study, the participants were 50.77% female and 49.23% male, with an average age of 39.32 ± 11.49 years and an average ln transformed MHR of -2.72 ± 0.99. The prevalence of cancer, stroke, coronary heart disease, congestive heart failure, arthritis, diabetes mellitus, and low muscle mass was 3.68%, 1.46%, 0.97%, 1.02%, 13.93%, 7.36%, and 8.79%. All other variable subgroup associations were significant except for age, stroke, and cancer subgroups. Compared with the lowest MHR quartile, the highest MHR quartile was often Men, Non-Hispanic White, Other Hispanic, Mexican American, and Smoker (Table 1 ). Table 1 Baseline characteristics of the study population. Ln MHR Q1 (n = 2547) Q2 (n = 2596) Q3 (n = 2576) Q4 (n = 2602) P value ln NHHR -2.94 ± 0.94 -2.77 ± 0.96 -2.63 ± 0.93 -2.44 ± 0.90 < 0.001 ln SII -2.88 ± 0.96 -2.76 ± 1.00 -2.65 ± 0.96 -2.52 ± 0.93 < 0.001 ln NLR -2.83 ± 0.93 -2.73 ± 0.97 -2.64 ± 0.93 -2.53 ± 0.90 < 0.001 ln MHR -3.22 ± 0.93 -2.85 ± 0.95 -2.59 ± 0.92 -2.23 ± 0.90 < 0.001 Age (years) 40.28 ± 11.65 38.79 ± 11.58 39.10 ± 11.53 39.12 ± 11.16 < 0.001 Sex (%) < 0.001 Men 30.90% 43.62% 54.62% 67.45% Women 69.10% 56.38% 45.38% 32.55% Race (%) < 0.001 Mexican American 11.39% 15.68% 15.99% 17.45% Other Hispanic 9.03% 9.56% 11.76% 11.64% Non-Hispanic White 29.25% 34.37% 36.10% 39.78% Non-Hispanic Black 26.50% 21.70% 19.18% 14.95% Other Races 23.83% 18.69% 16.96% 16.18% Educational level (%) < 0.001 Less than high school 13.98% 17.96% 18.87% 21.64% High school or GED 17.94% 20.27% 23.37% 25.29% Above high school 68.08% 61.77% 57.76% 53.07% Smoking (%) < 0.001 No 68.55% 64.51% 58.70% 52.04% Yes 31.45% 35.49% 41.30% 47.96% Diabetes (%) < 0.001 No 95.52% 93.72% 92.00% 89.35% Yes 4.48% 6.28% 8.00% 10.65% Coronary artery disease (%) < 0.001 No 99.49% 99.34% 98.95% 98.35% Yes 0.51% 0.66% 1.05% 1.65% Congestive heart failure (%) 0.030 No 99.37% 99.04% 98.99% 98.54% Yes 0.63% 0.96% 1.01% 1.46% Cancer (%) 0.919 No 96.23% 96.22% 96.27% 96.54% Yes 3.77% 3.78% 3.73% 3.46% Arthritis (%) 0.025 No 87.39% 86.71% 85.48% 84.70% Yes 12.61% 13.29% 14.52% 15.30% Stroke (%) 0.113 No 98.51% 98.77% 98.80% 98.08% Yes 1.49% 1.23% 1.20% 1.92% These inflammatory markers were transformed by the ln log function to give the data a positive distribution for more accurate results. Furthermore, we constructed three regression models to explore the association of ln SII (continuous), ln NLR (continuous), ln NHHR (continuous), ln MHR (continuous), ln MHR (quartile) and low muscle mass (Table 2 ). In model 1 and model 2, the results revealed that each of the ln-transformed inflammatory biomarkers exhibited a significant positive correlation with low muscle mass. (p < 0.05) In model 3, ln SII, ln NLR, ln NHHR, and ln MHR were positively correlated with low muscle mass. (ln SII: OR = 1.59; 95% CI, 1.37–1.84; ln NLR: OR = 1.35; 95% CI, 1.13–1.60; ln NHHR: OR = 1.49; 95% CI, 1.27–1.75; ln MHR: OR = 1.98; 95% CI, 1.68–2.33) This means that the odds of the disease increase by 59%, 35%, 49%, and 98% for each unit increase in ln SII, ln NLR, ln NHHR, or ln MHR. Compared to the lowest quartile of ln MHR, higher quartiles were significantly associated with increased OR for low muscle mass. (p for trend < 0.0001). Table 2 indicates the correlation between ln MHR, other inflammatory biomarkers and low muscle mass. Model 1 Model 2 Model 3 OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value ln SII (continuous) 1.68 (1.47, 1.92) < 0.0001 1.61 (1.39, 1.86) < 0.0001 1.59 (1.37, 1.84) < 0.0001 ln NLR (continuous) 1.57 (1.34, 1.84) < 0.0001 1.39 (1.17, 1.65) 0.0001 1.35 (1.13, 1.60) 0.0008 ln NHHR (continuous) 1.98 (1.70, 2.29) < 0.0001 1.53 (1.30, 1.80) < 0.0001 1.49 (1.27, 1.75) < 0.0001 ln MHR (continuous) 2.05 (1.77, 2.37) < 0.0001 2.04 (1.73, 2.39) < 0.0001 1.98 (1.68, 2.33) < 0.0001 ln MHR (quartile) Quartile 1 Reference Reference Reference Quartile 2 1.54 (1.23, 1.92) 0.0001 1.50 (1.19, 1.89) 0.0006 1.47 (1.17, 1.85) 0.0011 Quartile 3 1.85 (1.49, 2.30) < 0.0001 1.79 (1.42, 2.25) < 0.0001 1.73 (1.38, 2.17) < 0.0001 Quartile 4 2.43 (1.97, 3.00) < 0.0001 2.33 (1.86, 2.92) < 0.0001 2.22 (1.77, 2.78) < 0.0001 P for trend 0.0011 < 0.0001 < 0.0001 Subgroup analysis The results of the interaction test demonstrated that sex, race, educational level, smoking, congestive heart failure, arthritis, stroke, and cancer subgroups did not have a significant effect on the association between MHR and low muscle mass (p for interaction > 0.05). However, in the subgroups of diabetes and coronary heart disease, Nonetheless, in the subgroups of coronary heart disease and diabetes, significant differences were observed in the findings related to the presence or absence of the disease. Significant differences were also found in the age subgroups. Stronger correlation between MHR and the occurrence of low muscle mass in age < 50, diabetic, and coronary heart disease participants compared with those aged ≥ 50, non-diabetic, and coronary heart disease participants (Table 3 ). Table 3 Subgroup analysis for the correlation between MHR and low muscle mass. Odds Ratio(95%Cl) P for interaction Sex 0.2788 Male 2.17 (1.71, 2.74) < 0.0001 Female 1.81 (1.44, 2.27) < 0.0001 Age 0.0410 < 50y 1.61 (1.24, 2.09) 0.0004 ≥ 50y 2.27 (1.84, 2.80) < 0.0001 Race 0.1548 Mexican American 1.44 (1.06, 1.95) 0.0194 Other Hispanic 2.73 (1.77, 4.23) < 0.0001 Non-Hispanic White 2.14 (1.57, 2.90) < 0.0001 Non-Hispanic Black 2.09 (1.17, 3.75) 0.0133 Other Races 2.15 (1.49, 3.12) < 0.0001 Educational level 0.3053 Less than high school 1.75 (1.30, 2.36) 0.0002 High school or GED 1.97 (1.44, 2.70) < 0.0001 Above high school 2.13 (1.68, 2.69) < 0.0001 Smoking 0.1220 No 1.80 (1.46, 2.20) < 0.0001 Yes 2.32 (1.78, 3.01) < 0.0001 Diabetes 0.0006 No 2.20 (1.84, 2.63) < 0.0001 Yes 0.97 (0.63, 1.49) 0.8806 Coronary artery disease 0.0229 No 1.94 (1.64, 2.28) < 0.0001 Yes 10.77 (2.21, 52.37) 0.0032 Congestive heart failure 0.3278 No 1.96 (1.66, 2.31) < 0.0001 Yes 3.93 (0.96, 16.13) 0.0577 Cancer 0.8114 No 1.98 (1.68, 2.35) < 0.0001 Yes 1.81 (0.87, 3.76) 0.1118 Stroke 0.3570 No 1.96 (1.66, 2.31) < 0.0001 Yes 3.44 (1.02, 11.64) 0.0466 Arthritis 0.9150 No 1.99 (1.66, 2.38) < 0.0001 Yes 1.94 (1.37, 2.77) 0.0002 ROC analysis ROC analyses were conducted to explore the predictive capacity of ln MHR and other ln-transformed inflammatory markers for low muscle mass. As shown in Fig. 2 , ln MHR has higher AUC values than other inflammatory markers (AUC = 0.7545, 95%CI = 0.7385–0.7705). The result suggests that the predictive value of MHR for low muscle mass was higher compared to NLR, SII, NHHR in this study. Additionally, red shading shows the bootstrap estimated 95% Cl with the AUC (Fig. 3 ). 4. Discussion This is the first cross-sectional study to demonstrate a positive correlation between MHR and low muscle mass. And we did ln transformation of MHR and other systemic immune indices derived from CBC. The ln-transformed MHR, SII, NLR, and NHHR all showed a significantly positive correlation with low muscle mass. In ROC analysis, ln MHR has higher AUC values than other inflammatory markers. Moreover, in subgroup analysis, age, coronary heart disease, and diabetes mellitus modified the association between MHR and low muscle mass. Kanat et al. conducted a cross-sectional retrospective study in Turkey that included 262 sarcopenia patients and found that sarcopenic patients had a higher MHR than non-sarcopenic participants. The results are consistent with our findings in the U.S. population(24). Guo et al. investigated the U.S. NHANES database and found an association between higher NLR, SII, and a higher prevalence of low muscle mass(17). Hao et al. found that NHHR can serve as a novel predictor of low muscle mass and has a negative correlation with muscle mass(16). These conclusions do not differ from the results of our data analyses. In addition, we performed a ROC analysis of the correlation between these systemic inflammatory indicators and low muscle mass. ln MHR has the largest AUC and could be a better predictor of low muscle mass. The mechanisms of sarcopenia are associated with a number of factors, including oxidative stress, inflammation, and insulin resistance(25). Furthermore, these factors can interact with one another, resulting in a vicious cycle(26). Oxidative stress caused by cellular senescence after aging promotes inflammation(27). Some studies have indicated a correlation between chronic inflammation and low muscle mass(28). Moreover, patients with sarcopenia have elevated levels of the cytokines IL-6 and TNF-α(29). Cytokines regulate inflammation; however, prolonged elevations are deleterious to muscle mass(30). Inflammation-induced lipolysis and redistribution can lead to ectopic fat infiltration in multiple organs(31), especially in the vicinity of skeletal muscle(32), resulting in loss of muscle mass(33). Lipids enter skeletal muscle and are metabolized to generate substantial energy, with lipid oxidation accounting for nearly two-thirds of the energy in resting skeletal muscle. Lipid accumulation and metabolism in muscle cells is a method for acquiring energy sources(34). MHR is an indicator of lipid metabolism and systemic inflammation(35) that has been shown to be associated with a variety of diseases. The NHANES database revealed that MHR was associated with hypertension, chronic kidney disease (CKD)(36), abdominal aortic calcification (AAC)(37), and coronary heart disease (CHD)(38). Monocytes and HDL-C are involved in the processes of oxidative stress, inflammation, and lipid metabolism(39). Both are strongly correlated with the development of sarcopenia. Moreover, subgroup analyses and interaction test results demonstrated that age, diabetes, and coronary heart disease significantly influenced the correlation between MHR and sarcopenia. Individuals with coronary artery disease exhibited a significantly increased odds ratio compared to those without coronary artery disease. The underlying mechanism may be linked to an increased predisposition to dyslipidemia in individuals with a low skeletal muscle mass index(40). In general, diabetes accelerates sarcopenia via processes including hyperglycemia, chronic inflammation, and oxidative stress(41). However, our study revealed that the correlation between MHR and the incidence of low muscle mass was reduced in individuals with diabetes. This may be attributed to the fact that this population has adopted a healthier lifestyle of exercise and diet after the disease, leading to biased results. In addition, MHR was more strongly correlated with low muscle mass in the < 50 years group compared to the older age group. It is possible that the presence of other potential confounders in the upper age groups influenced the results. These findings require further research. Our research has several strengths. First, the sample size is huge and nationally representative. Second, numerous confounders were adjusted to make the results more reliable. Third, the predictive value of multiple inflammatory indicators in correlation with outcome variables was evaluated through the use of ROC analysis. Additionally, there are some limitations to our research. Firstly, it was not possible to take into account the effects of all potential confounders on this study, which could have introduced bias into the results. Secondly, multiple testing in multiple logistic regression analyses presents several limitations, including inflated type I error rates, decreased statistical power, interpretation complexity, computational burdens, and practical constraints. Thirdly, the study was unable to fully elucidate certain mechanisms and causal relationships, thus indicating the necessity for further in-depth research. 5. Conclusion ln-transformed MHR, SII, NLR, and NHHR were positively associated with low muscle mass. MHR performs better in predicting sarcopenia compared to SII, NLR, and NHHR. Declarations Data availability statement This study examined datasets that are accessible to the public. The data can be accessed at: https://www.cdc.gov/nchs/nhanes/. Ethical statement The portions of this study involving human participants, human materials, or human data were conducted in accordance with the Declaration of Helsinki and were approved by the NCHS Ethics Review Board. The patients/participants provided their written informed consent to participate in this study. Author contributions ZWX: Conceptualization, Data curation, Methodology, Writing – original draft. JC, JHM, RW: Formal analysis, Validation, Visualization. PL: Supervision, Writing – review & editing. Funding none. Conflict of interest none. References Liu JC, Dong SS, Shen H, Yang DY, Chen BB, Ma XY, et al. Multi-omics research in sarcopenia: Current progress and future prospects. Ageing Res Rev. 2022;76:101576. Kuchay MS, Martínez-Montoro JI, Kaur P, Fernández-García JC, Ramos-Molina B. 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Lipids Health Dis. 2024;23(1):173. 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. Hashimoto Y, Takahashi F, Okamura T, Hamaguchi M, Fukui M. Diet, exercise, and pharmacotherapy for sarcopenia in people with diabetes. Metabolism. 2023;144:155585. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 08 Feb, 2025 Read the published version in Lipids in Health and Disease → Version 1 posted Editorial decision: Revision requested 17 Oct, 2024 Reviews received at journal 15 Oct, 2024 Reviews received at journal 14 Oct, 2024 Reviewers agreed at journal 11 Oct, 2024 Reviews received at journal 10 Oct, 2024 Reviews received at journal 08 Oct, 2024 Reviewers agreed at journal 07 Oct, 2024 Reviewers agreed at journal 05 Oct, 2024 Reviewers agreed at journal 05 Oct, 2024 Reviewers agreed at journal 05 Oct, 2024 Reviewers invited by journal 03 Oct, 2024 Editor assigned by journal 27 Sep, 2024 Submission checks completed at journal 27 Sep, 2024 First submitted to journal 26 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5161975","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367089570,"identity":"31f719b7-8ec4-46cb-8e7f-4ba89ea6ab41","order_by":0,"name":"Zhiwei Xue","email":"","orcid":"","institution":"Department of Orthopaedigs, China-Japan Union Hospital of Jilin University, Changchun, Jilin China","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Xue","suffix":""},{"id":367089571,"identity":"e9f83ecc-6b7c-4de0-a274-4f78e1a1583a","order_by":1,"name":"Jian Cao","email":"","orcid":"","institution":"Department of Orthopaedigs, China-Japan Union Hospital of Jilin University, Changchun, Jilin China","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Cao","suffix":""},{"id":367089573,"identity":"235caaad-0512-4a6a-8cd7-7346bb9896af","order_by":2,"name":"Jianhui Mou","email":"","orcid":"","institution":"Department of Orthopaedigs, China-Japan Union Hospital of Jilin University, Changchun, Jilin China","correspondingAuthor":false,"prefix":"","firstName":"Jianhui","middleName":"","lastName":"Mou","suffix":""},{"id":367089575,"identity":"0dfadcca-4795-4f58-bf42-cda1ea3c8f44","order_by":3,"name":"Rui Wang","email":"","orcid":"","institution":"Department of Orthopaedigs, China-Japan Union Hospital of Jilin University, Changchun, Jilin China","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Wang","suffix":""},{"id":367089577,"identity":"4bbc11c0-0146-474e-8033-d3918dcc64ec","order_by":4,"name":"Peng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYBACPgYGxgcSBSBmApFa2BgYmA0kDEjUwibBQKIW3mMVFgaHGfjZcwwYfu4gSgtf2g0JoBbJnjcGjL1niNLCYwbWYnAjx4CZsY1ILQUgLfYkaWEA2yJBvBa+ZAkJg3QeiTPPCg72EqOFn4H34GeJCms5/vbkjQ9+EqOFQf4NA7MEAwMPiH2AGA0MIMWMH4hUOgpGwSgYBSMUAAApbCfnzO7IHQAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Orthopaedigs, China-Japan Union Hospital of Jilin University, Changchun, Jilin China","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-09-27 03:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5161975/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5161975/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12944-025-02464-2","type":"published","date":"2025-02-08T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71538113,"identity":"90e07d00-6dec-417b-8429-1a3623f009af","added_by":"auto","created_at":"2024-12-16 14:21:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65062,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participants selection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5161975/v1/4b19bb5ac207a06ef084a8c5.png"},{"id":71538110,"identity":"26e2607b-585a-4efc-b741-97727bbaac66","added_by":"auto","created_at":"2024-12-16 14:21:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54765,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves and the AUC values of the four ln-transformed inflammatory markers (MHR, SII, NLR, and NHHR) in diagnosing low muscle mass.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5161975/v1/ae076688afbb1ccb48feeca2.png"},{"id":71539706,"identity":"237078ab-319a-40fb-a291-48977b239806","added_by":"auto","created_at":"2024-12-16 14:29:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67807,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves. Red shading shows the bootstrap estimated 95% Cl with the AUC.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5161975/v1/e828ef847727fd1841da48ff.png"},{"id":71538114,"identity":"469840a3-4f3e-4559-b6b3-b03734b32189","added_by":"auto","created_at":"2024-12-16 14:21:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70957,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Results section.\u003c/p\u003e","description":"","filename":"UF1.png","url":"https://assets-eu.researchsquare.com/files/rs-5161975/v1/afe953edbf877cf0b6d3f4e2.png"},{"id":75929974,"identity":"b2fd78bd-26b5-4128-88f9-c75c48cc0bc0","added_by":"auto","created_at":"2025-02-10 16:08:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1121474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5161975/v1/9f416b43-91ea-4174-b5de-3c29ccd32742.pdf"},{"id":71538111,"identity":"b664eb6f-4212-4d21-9cc2-e0e01851a734","added_by":"auto","created_at":"2024-12-16 14:21:42","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":15071,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5161975/v1/e9ce45e62ea6dad958fc4a61.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship of monocyte-to-high density lipoprotein ratio (MHR) and other inflammatory biomarkers (SII, NLR and NHHR) with sarcopenia: a population-based study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSarcopenia is a systemic condition linked to the aging process, clinically characterized by a reduction in skeletal muscle mass and impaired functional capacity(1\u0026ndash;3). Sarcopenia can cause weakness, falls, and physical disability(4), with a noteworthy effect on well-being and standard of living (5). The prevalence is increasing with the development of an aging society(6). Sarcopenia is predominantly induced by natural aging, but it can also be influenced by additional factors(7). It is reported to occur not solely among the elderly(8). Oxidative stress is the primary pathogenesis of sarcopenia(9), but the degree of obesity(10), sex hormone levels(11), amount of exercise(12), and protein intake(13) may also influence the development of the disease. Prevalence estimates fluctuate based on the definition of sarcopenia; however, even with conservative methodologies, the prevalence in the general population ranges from 5\u0026ndash;10%(14). Sarcopenia requires greater consideration on our part.\u003c/p\u003e \u003cp\u003eIn previous studies, several inflammatory biomarkers derived from CBC, including SII(15), and NHHR(16) have been shown to act as predictors of sarcopenia. Increased NLR levels are significantly correlated with a higher incidence of sarcopenia(17). Monocytes can maintain homeostasis through recruitment but can also promote inflammation(18), whereas HDL-c has strong antioxidant and anti-inflammatory capacity(19). T-cell immunomodulation mitigates chronic inflammation, hence alleviating sarcopenia(20). Reduced muscle mass may be prevalent in an aged Chinese demographic with HDL-C values exceeding 70 mg/dl(21). Monocytes and HDL-c have derived MHR, a new biomarker associated with inflammation and oxidative stress(22). Whether MHR can predict the development of sarcopenia has never been demonstrated. Our study attempts to investigate the correlation between MHR and low muscle mass.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eStudy participants\u003c/p\u003e \u003cp\u003eThe NHANES database is a nationally representative survey that assesses the nutritional and health status of the U.S. population. It is updated every two years and is organized by the National Center for Health Statistics (NCHS) in the US. Comprehensive information is available at\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"http://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The study initially comprised 39,156 participants from the United States. Following the exclusion of adults aged under 20 years, as well as cases with unclear information regarding ALM, MHR, SII, NLR, and NHHR. The remaining 10,321 individuals were recruited for this research (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDefinition of MHR and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(AL{M_{BMI}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(MHR=\\frac{{{\\text{monocyte(}}{{10}^3}cells/\\mu L{\\text{)}}}}{{HDL - c(mg/dL)}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(AL{M_{BMI}}=\\frac{{appendicular{\\text{ }}lean{\\text{ }}mass(kg)}}{{BMI(kg/{m^2})}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eMHR was used as the exposure variable in this study and the formula was given above. The monocyte count was obtained using the Beckman Coulter MAXM automated analytical instrument. More than 9 hours of fasting is required before morning measurements. HDL-c measurement is performed using the Roche Cobas 6000. We do the ln logarithmic function treatment for MHR in the research. Appendicular lean mass (ALM) refers to the total mass of lean soft tissue in the extremities, measured by DXA. Following the FNIH's previously published criteria(23), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(AL{M_{BMI}}\\)\u003c/span\u003e\u003c/span\u003e levels below 0.789 in males and 0.512 in females were classified as low muscle mass. Additionally, the following formulas were used to calculate the NLR, SII, and NHHR: NLR\u0026thinsp;=\u0026thinsp;neutrophil counts/lymphocyte counts, SII\u0026thinsp;=\u0026thinsp;platelet counts \u0026times; neutrophil counts/lymphocyte counts, NHHR\u0026thinsp;=\u0026thinsp;non-HDL-c/HDL-c.\u003c/p\u003e \u003cp\u003eSelection of covariates\u003c/p\u003e \u003cp\u003eThe multivariable-adjusted models were constructed by using the variables age, sex, race, educational level, smoking, congestive heart failure, coronary artery, stroke, arthritis, diabetes, and cancer. Reducing confounding effects between MHR and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(AL{M_{BMI}}\\)\u003c/span\u003e\u003c/span\u003e by adjusting for these variables. The educational level is divided into three segments based on upper secondary education. Smoking is categorized as yes or no according to whether you have smoked at least 100 cigarettes in your lifetime. The information of congestive heart failure, coronary artery, stroke, arthritis, diabetes, and cancer can be found in the questionnaire module of the NHANES database.\u003c/p\u003e \u003cp\u003eStatistical analysis\u003c/p\u003e \u003cp\u003eSince MHR showed a skewed distribution in the analyses, we used the natural logarithmic function for transformation and divided the ln transformed MHR into quartiles. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation in the descriptive analyses, and the Student's t-test was employed to evaluate the differences among groups. However, categorical variables were indicated as percentages in the descriptive analyses, and the chi-squared test was used in assessing differences among groups. Multiple regression analysis was employed to analyze the association between inflammatory indicators and low muscle mass. We built three models to calculate odds ratios (OR) and 95% confidence intervals (CI), Model 1 with no adjusted variables; Model 2 adjusted for four variables: gender, age [years], race, and educational level; and Model 3 adjusted for the four variables in Model 2 and congestive heart failure [No or Yes], coronary artery [No or Yes], stroke [No or Yes], arthritis [No or Yes], diabetes [No or Yes], cancer [No or Yes]. Furthermore, area under the curve (AUC) values and receiver operating characteristic (ROC) curves were used to assess the predictive effectiveness of ln-transformed MHR and other inflammatory markers (SII, NLR, and NHHR). The bootstrap estimated 95% Cl was shown with the AUC. Additionally, subgroup analyses and interaction tests were performed to validate the findings further.\u003c/p\u003e \u003cp\u003eData for this study were analyzed using R software and EmpowerStats. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eBaseline characteristics of participants\u003c/p\u003e \u003cp\u003eIn our study, the participants were 50.77% female and 49.23% male, with an average age of 39.32\u0026thinsp;\u0026plusmn;\u0026thinsp;11.49 years and an average ln transformed MHR of -2.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99. The prevalence of cancer, stroke, coronary heart disease, congestive heart failure, arthritis, diabetes mellitus, and low muscle mass was 3.68%, 1.46%, 0.97%, 1.02%, 13.93%, 7.36%, and 8.79%. All other variable subgroup associations were significant except for age, stroke, and cancer subgroups. Compared with the lowest MHR quartile, the highest MHR quartile was often Men, Non-Hispanic White, Other Hispanic, Mexican American, and Smoker (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLn MHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2547)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2596)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2576)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2602)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eln NHHR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eln SII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eln NLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eln MHR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.28\u0026thinsp;\u0026plusmn;\u0026thinsp;11.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.79\u0026thinsp;\u0026plusmn;\u0026thinsp;11.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.10\u0026thinsp;\u0026plusmn;\u0026thinsp;11.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.12\u0026thinsp;\u0026plusmn;\u0026thinsp;11.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoronary artery disease (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCongestive heart failure (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArthritis (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStroke (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese inflammatory markers were transformed by the ln log function to give the data a positive distribution for more accurate results. Furthermore, we constructed three regression models to explore the association of ln SII (continuous), ln NLR (continuous), ln NHHR (continuous), ln MHR (continuous), ln MHR (quartile) and low muscle mass (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In model 1 and model 2, the results revealed that each of the ln-transformed inflammatory biomarkers exhibited a significant positive correlation with low muscle mass. (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) In model 3, ln SII, ln NLR, ln NHHR, and ln MHR were positively correlated with low muscle mass. (ln SII: OR\u0026thinsp;=\u0026thinsp;1.59; 95% CI, 1.37\u0026ndash;1.84; ln NLR: OR\u0026thinsp;=\u0026thinsp;1.35; 95% CI, 1.13\u0026ndash;1.60; ln NHHR: OR\u0026thinsp;=\u0026thinsp;1.49; 95% CI, 1.27\u0026ndash;1.75; ln MHR: OR\u0026thinsp;=\u0026thinsp;1.98; 95% CI, 1.68\u0026ndash;2.33) This means that the odds of the disease increase by 59%, 35%, 49%, and 98% for each unit increase in ln SII, ln NLR, ln NHHR, or ln MHR. Compared to the lowest quartile of ln MHR, higher quartiles were significantly associated with increased OR for low muscle mass. (p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eindicates the correlation between ln MHR, other inflammatory biomarkers and low muscle mass.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI) P value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI) P value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI) P value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln SII (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68 (1.47, 1.92)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61 (1.39, 1.86)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59 (1.37, 1.84)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln NLR (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57 (1.34, 1.84)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 (1.17, 1.65) 0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35 (1.13, 1.60) 0.0008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln NHHR (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98 (1.70, 2.29)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53 (1.30, 1.80)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49 (1.27, 1.75)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln MHR (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.05 (1.77, 2.37)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04 (1.73, 2.39)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.98 (1.68, 2.33)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln MHR (quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54 (1.23, 1.92) 0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 (1.19, 1.89) 0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47 (1.17, 1.85) 0.0011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85 (1.49, 2.30)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79 (1.42, 2.25)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73 (1.38, 2.17)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.43 (1.97, 3.00)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.33 (1.86, 2.92)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22 (1.77, 2.78)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSubgroup analysis\u003c/p\u003e \u003cp\u003eThe results of the interaction test demonstrated that sex, race, educational level, smoking, congestive heart failure, arthritis, stroke, and cancer subgroups did not have a significant effect on the association between MHR and low muscle mass (p for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, in the subgroups of diabetes and coronary heart disease, Nonetheless, in the subgroups of coronary heart disease and diabetes, significant differences were observed in the findings related to the presence or absence of the disease. Significant differences were also found in the age subgroups. Stronger correlation between MHR and the occurrence of low muscle mass in age\u0026thinsp;\u0026lt;\u0026thinsp;50, diabetic, and coronary heart disease participants compared with those aged\u0026thinsp;\u0026ge;\u0026thinsp;50, non-diabetic, and coronary heart disease participants (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analysis for the correlation between MHR and low muscle mass.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio(95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.17 (1.71, 2.74)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.81 (1.44, 2.27)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.61 (1.24, 2.09) 0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.27 (1.84, 2.80)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44 (1.06, 1.95) 0.0194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.73 (1.77, 4.23)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.14 (1.57, 2.90)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.09 (1.17, 3.75) 0.0133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.15 (1.49, 3.12)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.75 (1.30, 2.36) 0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.97 (1.44, 2.70)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.13 (1.68, 2.69)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.80 (1.46, 2.20)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.32 (1.78, 3.01)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.20 (1.84, 2.63)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.63, 1.49) 0.8806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoronary artery disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.94 (1.64, 2.28)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.77 (2.21, 52.37) 0.0032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCongestive heart failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.96 (1.66, 2.31)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.93 (0.96, 16.13) 0.0577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.98 (1.68, 2.35)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.81 (0.87, 3.76) 0.1118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStroke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.96 (1.66, 2.31)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.44 (1.02, 11.64) 0.0466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArthritis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.99 (1.66, 2.38)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.94 (1.37, 2.77) 0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eROC analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eROC analyses were conducted to explore the predictive capacity of ln MHR and other ln-transformed inflammatory markers for low muscle mass. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, ln MHR has higher AUC values than other inflammatory markers (AUC\u0026thinsp;=\u0026thinsp;0.7545, 95%CI\u0026thinsp;=\u0026thinsp;0.7385\u0026ndash;0.7705). The result suggests that the predictive value of MHR for low muscle mass was higher compared to NLR, SII, NHHR in this study. Additionally, red shading shows the bootstrap estimated 95% Cl with the AUC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis is the first cross-sectional study to demonstrate a positive correlation between MHR and low muscle mass. And we did ln transformation of MHR and other systemic immune indices derived from CBC. The ln-transformed MHR, SII, NLR, and NHHR all showed a significantly positive correlation with low muscle mass. In ROC analysis, ln MHR has higher AUC values than other inflammatory markers. Moreover, in subgroup analysis, age, coronary heart disease, and diabetes mellitus modified the association between MHR and low muscle mass.\u003c/p\u003e \u003cp\u003eKanat et al. conducted a cross-sectional retrospective study in Turkey that included 262 sarcopenia patients and found that sarcopenic patients had a higher MHR than non-sarcopenic participants. The results are consistent with our findings in the U.S. population(24). Guo et al. investigated the U.S. NHANES database and found an association between higher NLR, SII, and a higher prevalence of low muscle mass(17). Hao et al. found that NHHR can serve as a novel predictor of low muscle mass and has a negative correlation with muscle mass(16). These conclusions do not differ from the results of our data analyses. In addition, we performed a ROC analysis of the correlation between these systemic inflammatory indicators and low muscle mass. ln MHR has the largest AUC and could be a better predictor of low muscle mass.\u003c/p\u003e \u003cp\u003eThe mechanisms of sarcopenia are associated with a number of factors, including oxidative stress, inflammation, and insulin resistance(25). Furthermore, these factors can interact with one another, resulting in a vicious cycle(26). Oxidative stress caused by cellular senescence after aging promotes inflammation(27). Some studies have indicated a correlation between chronic inflammation and low muscle mass(28). Moreover, patients with sarcopenia have elevated levels of the cytokines IL-6 and TNF-α(29). Cytokines regulate inflammation; however, prolonged elevations are deleterious to muscle mass(30). Inflammation-induced lipolysis and redistribution can lead to ectopic fat infiltration in multiple organs(31), especially in the vicinity of skeletal muscle(32), resulting in loss of muscle mass(33). Lipids enter skeletal muscle and are metabolized to generate substantial energy, with lipid oxidation accounting for nearly two-thirds of the energy in resting skeletal muscle. Lipid accumulation and metabolism in muscle cells is a method for acquiring energy sources(34). MHR is an indicator of lipid metabolism and systemic inflammation(35) that has been shown to be associated with a variety of diseases. The NHANES database revealed that MHR was associated with hypertension, chronic kidney disease (CKD)(36), abdominal aortic calcification (AAC)(37), and coronary heart disease (CHD)(38). Monocytes and HDL-C are involved in the processes of oxidative stress, inflammation, and lipid metabolism(39). Both are strongly correlated with the development of sarcopenia. Moreover, subgroup analyses and interaction test results demonstrated that age, diabetes, and coronary heart disease significantly influenced the correlation between MHR and sarcopenia. Individuals with coronary artery disease exhibited a significantly increased odds ratio compared to those without coronary artery disease. The underlying mechanism may be linked to an increased predisposition to dyslipidemia in individuals with a low skeletal muscle mass index(40). In general, diabetes accelerates sarcopenia via processes including hyperglycemia, chronic inflammation, and oxidative stress(41). However, our study revealed that the correlation between MHR and the incidence of low muscle mass was reduced in individuals with diabetes. This may be attributed to the fact that this population has adopted a healthier lifestyle of exercise and diet after the disease, leading to biased results. In addition, MHR was more strongly correlated with low muscle mass in the \u0026lt;\u0026thinsp;50 years group compared to the older age group. It is possible that the presence of other potential confounders in the upper age groups influenced the results. These findings require further research.\u003c/p\u003e \u003cp\u003eOur research has several strengths. First, the sample size is huge and nationally representative. Second, numerous confounders were adjusted to make the results more reliable. Third, the predictive value of multiple inflammatory indicators in correlation with outcome variables was evaluated through the use of ROC analysis. Additionally, there are some limitations to our research. Firstly, it was not possible to take into account the effects of all potential confounders on this study, which could have introduced bias into the results. Secondly, multiple testing in multiple logistic regression analyses presents several limitations, including inflated type I error rates, decreased statistical power, interpretation complexity, computational burdens, and practical constraints. Thirdly, the study was unable to fully elucidate certain mechanisms and causal relationships, thus indicating the necessity for further in-depth research.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eln-transformed MHR, SII, NLR, and NHHR were positively associated with low muscle mass. MHR performs better in predicting sarcopenia compared to SII, NLR, and NHHR.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examined datasets that are accessible to the public. The data can be accessed at: https://www.cdc.gov/nchs/nhanes/. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe portions of this study involving human participants, human materials, or human data were conducted in accordance with the Declaration of Helsinki and were approved by the NCHS Ethics Review Board. The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZWX: Conceptualization, Data curation, Methodology, Writing \u0026ndash; original draft. JC, JHM, RW: Formal analysis, Validation, Visualization. PL: Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu JC, Dong SS, Shen H, Yang DY, Chen BB, Ma XY, et al. Multi-omics research in sarcopenia: Current progress and future prospects. Ageing Res Rev. 2022;76:101576.\u003c/li\u003e\n\u003cli\u003eKuchay MS, Mart\u0026iacute;nez-Montoro JI, Kaur P, Fern\u0026aacute;ndez-Garc\u0026iacute;a JC, Ramos-Molina B. Non-alcoholic fatty liver disease-related fibrosis and sarcopenia: An altered liver-muscle crosstalk leading to increased mortality risk. Ageing Res Rev. 2022;80:101696.\u003c/li\u003e\n\u003cli\u003eKalyani RR, Corriere M, Ferrucci L. 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Metabolism. 2023;144:155585.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Monocyte-to-high-density lipoprotein-cholesterol ratio, Sarcopenia, Low muscle mass, MHR, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-5161975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5161975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eIn previous studies, several inflammatory biomarkers derived from complete blood cell counts (CBC), including systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and non‑high‑density lipoprotein cholesterol to high‑density lipoprotein cholesterol ratio (NHHR) have been shown to act as predictors of sarcopenia. Whether Monocyte\u0026ndash;to\u0026ndash;High-Density Lipoprotein Cholesterol Ratio (MHR) can forecast the development of sarcopenia has never been demonstrated. Our study attempts to investigate the correlation between MHR and low muscle mass.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study comprised 10,321 participants aged 20 years and above from the US. Survey-weighted logistic regression was performed to explore the association between ln-transformed MHR, SII, NLR, NHHR and low muscle mass. Furthermore, AUC values and ROC curves were used to assess the predictive effectiveness of ln-transformed MHR and other inflammatory markers (SII, NLR, and NHHR). The bootstrap estimated 95% Cl was shown with the AUC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the fully adjusted model, ln SII, ln NLR, ln NHHR, and ln MHR were positively correlated with low muscle mass. (ln SII: OR\u0026thinsp;=\u0026thinsp;1.59; 95% CI, 1.37\u0026ndash;1.84; ln NLR: OR\u0026thinsp;=\u0026thinsp;1.35; 95% CI, 1.13\u0026ndash;1.60; ln NHHR: OR\u0026thinsp;=\u0026thinsp;1.49; 95% CI, 1.27\u0026ndash;1.75; ln MHR: OR\u0026thinsp;=\u0026thinsp;1.98; 95% CI, 1.68\u0026ndash;2.33) Compared to the lowest quartile of ln MHR, higher quartiles were significantly associated with increased OR for low muscle mass. (p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In ROC analysis, ln MHR has higher AUC values than ln SII, ln NLR, and ln NHHR. (AUC\u0026thinsp;=\u0026thinsp;0.7545, 95%CI\u0026thinsp;=\u0026thinsp;0.7385\u0026ndash;0.7705)\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eln-transformed MHR, SII, NLR, and NHHR were positively associated with low muscle mass. MHR performs better in predicting sarcopenia compared to SII, NLR, and NHHR.\u003c/p\u003e","manuscriptTitle":"Relationship of monocyte-to-high density lipoprotein ratio (MHR) and other inflammatory biomarkers (SII, NLR and NHHR) with sarcopenia: a population-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 14:21:38","doi":"10.21203/rs.3.rs-5161975/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-17T07:02:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-15T07:04:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-14T16:37:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16031315456231437588831481463480032379","date":"2024-10-11T11:14:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-11T03:29:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-08T05:50:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256112406678720601454622227991383417423","date":"2024-10-07T07:11:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168476887329331787771565781760273516837","date":"2024-10-05T18:45:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66424888606207041929566488993481127408","date":"2024-10-05T14:22:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48832428703374184308677866469435680236","date":"2024-10-05T06:11:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-03T13:53:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-27T13:24:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-27T05:33:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Lipids in Health and Disease","date":"2024-09-27T03:25:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c684dcf7-1b95-4b55-8d73-7897ff3c56c2","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T15:59:31+00:00","versionOfRecord":{"articleIdentity":"rs-5161975","link":"https://doi.org/10.1186/s12944-025-02464-2","journal":{"identity":"lipids-in-health-and-disease","isVorOnly":false,"title":"Lipids in Health and Disease"},"publishedOn":"2025-02-08 15:57:02","publishedOnDateReadable":"February 8th, 2025"},"versionCreatedAt":"2024-12-16 14:21:38","video":"","vorDoi":"10.1186/s12944-025-02464-2","vorDoiUrl":"https://doi.org/10.1186/s12944-025-02464-2","workflowStages":[]},"version":"v1","identity":"rs-5161975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5161975","identity":"rs-5161975","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}