Associations between Monocyte to HDL-C ratio and Lumbar Bone Mineral Density in Alcohol dependent Individuals with Depression

Associations between Monocyte to HDL-C ratio and Lumbar Bone Mineral Density in Alcohol dependent Individuals with Depression | 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 Article Associations between Monocyte to HDL-C ratio and Lumbar Bone Mineral Density in Alcohol dependent Individuals with Depression Lili Zhu, Xiaofeng Yuan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5957443/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 May, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Objectives Osteoporosis, a skeletal disorder that reduces bone density, is a significant health concern. Alcohol dependence, a chronic condition, exacerbates public health problems due to its widespread occurrence and association with various comorbidities, including depression. This study aims to explore the relationship between monocyte to high-density lipoprotein cholesterol ratio (MHR) and lumbar bone mineral density (BMD)in individuals with alcohol dependence and depression. Methods From 2009 to 2018, 49,693 participants were enrolled, and after screening, the study included 2,055 individuals with alcohol-dependency and depression. In this study, multivariate regression analysis was employed to assess the association between monocyte to HDL-C ratio and lumbar bone mineral density. Additionally, we conducted interaction tests and subgroup analysis. Results The result showed a negative correlation between MHR and lumbar BMD, which remained significant even after adjusting for covariates. Individuals with less than a 9th-grade education showed a positive link with MHR, while those with a college degree or higher had a negative link. This relationship remained significant in the fully adjusted model. A U-shaped relationship between MHR and lumbar BMD was observed in individuals with a high school diploma/GED, after adjusting for various factors. Conclusions The intricate correlation between MHR and lumbar BMD may suggest the presence of biological interplays and disparities in socioeconomic or behavioral aspects. This underscores the necessity for tailored public health strategies that cater to various educational demographics. Health sciences/Diseases/Psychiatric disorders/Depression Health sciences/Biomarkers/Predictive markers Health sciences/Risk factors Osteoporosis Alcohol-dependent Depression MHR BMD Figures Figure 1 Figure 2 Figure 3 1 Introduction Osteoporosis, a systemic skeletal disorder marked by diminished bone mineral density (BMD), represents a substantial public health issue owing to its correlation with heightened fracture risk and diminished quality of life( 1 , 2 ). Simultaneously, alcohol dependence, a chronic relapsing condition, exacerbates public health problems due to its widespread occurrence and links to various comorbidities, including mental health disorders like depression( 3 ). Depression, frequently seen alongside alcohol use disorders, may alter bone metabolism through its effects on the neuroendocrine and immune systems. Studies indicate a complex relationship between depression and bone health, with evidence showing a link between depressive symptoms and reduced bone mineral density( 4 – 6 ). Previous research has demonstrated that depression elevates cortisol levels by activating the hypothalamic–pituitary–adrenal (HPA) axis, and that hypercortisolemia is a significant causal factor in the deterioration of bone health( 7 ). In addition, increased levels of inflammatory cytokines like interleukin-1β, interleukin-2, and interleukin-6 are found in depression and are linked to reduced bone mineral density( 8 ). The potential biological plausibility of this association encompasses the influence of depressive symptoms on physical activity, dietary consumption, and hormonal regulation, each of which is crucial for sustaining bone health. Moreover, the psychosocial stress associated with depression could trigger neuroendocrine and immune reactions that negatively affect the bone remodeling processes( 9 ). The detrimental impact of both alcohol dependence and depression on bone health is well-documented( 10 ). The monocyte to high-density lipoprotein cholesterol ratio (MHR) is an emerging biomarker that reflects systemic inflammatory status, which integrates two pathophysiological dimensions including monocytes which are pivotal mediators of innate immunity, promote pro-inflammatory responses, and HDL-C provides anti-inflammatory and antioxidant effects through vascular cholesterol clearance and the mitigation of atherosclerosis( 11 ). An elevated MHR indicates an imbalance skewed towards inflammation, which has recently been associated with dysregulation in bone metabolism. Mechanistically, chronic inflammation enhances osteoclast activation while suppressing osteoblast function, thereby accelerating bone resorption and reducing bone mineral density( 12 ). This phenomenon is particularly exacerbated in populations experiencing the dual challenges of alcohol dependence and depression. Alcohol-induced oxidative stress and depression-related hyperactivity of the HPA axis may synergistically intensify this inflammatory cascade. Consequently, MHR emerges as a promising indicator for elucidating the complex interplay between inflammation, addictive-metabolic disorders, and skeletal health, offering valuable clinical insights into the preservation of BMD. Despite a growing body of evidence linking alcohol consumption, depression, and bone health, the specific function of the MHR within the context of lumbar BMD among individuals suffering from alcohol dependence and depression remains inadequately investigated. The principal objective of this cross-sectional study is to scrutinize the correlation between MHR and lumbar BMD in a nationally representative cohort of individuals diagnosed with alcohol dependency and depression. The hypothesis under investigation suggests that an elevated MHR, serving as a marker of systemic inflammation, may exhibit an inverse relationship with lumbar BMD, thereby potentially pinpointing a modifiable risk factor for osteoporosis within this susceptible demographic. By illuminating the role of systemic inflammation within this framework, this study endeavors to augment the comprehension of the multifactorial determinants that influence bone health. 2 Materials and Methods 2.1 Study Design and Participants The National Health and Nutrition Examination Survey (NHANES) is a comprehensive, ongoing, cross-sectional survey in the United States. It is designed to provide objective statistics on a range of health issues and to address emerging public health concerns. All data required for the research was collated from the five continuous NHANES cycles from 2009 to 2018 ( https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2009 ). The protocol of NHANES ( https://www.cdc.gov/nchs/nhanes/about/erb.html ) has been approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board (NHANES 2005–2010: Protocol #2005-06; NHANES 2011–2018: Protocol #2011-17(Continuation of Protocol #2011-17 Effective through October 26, 2017), Protocol #2018-01 (Effective beginning October 26, 2017). Each participant has provided informed consent prior to the commencement of the survey. A total of 49693 participants were enrolled in the study, which aimed to explore the relationship between inflammation indicators and lumbar bone mineral density in depressed individuals who habitually drink alcohol. Dual-energy X-ray absorptiometry (DXA) scans of the lumbar spine were conducted at the NHANES Mobile Examination Center (MEC). The spine scans provide bone measurements for the total spine and vertebrae L1 – L4. Accordingly, participants without depressive symptoms (34056 individuals) and drinking inhibits (1806 individuals) were excluded. Additionally, individuals lacking data about bone mineral density data (2918 participants), monocyte (8805 participants), or high-density lipoprotein cholesterol (53 participants) were also excluded. Following the completion of this screening process, the research encompassed a total of 2,055 alcohol-dependent individuals with depression. The entire process of data selection is shown in Fig. 1 . Depression samples were obtained from participants in the NHANES through the analysis of questionnaire data. The presence of depressive symptoms was determined using the Patient Health Questionnaire-9 (PHQ-9)( 13 ). The PHQ-9 comprises nine items, as follows: 1) anhedonia, 2) depressed mood, 3) sleep disturbance, 4) fatigue, 5) appetite changes, 6) low self-esteem, 7) concentration problems, 8) psychomotor disturbances, and 9) suicidal ideation. A score of 0–4 is indicative of the absence of depressive symptoms. Conversely, a score of ≥ 5 indicates the presence of depressive symptoms, with varying degrees of severity, and was therefore incorporated into the study cohort( 13 , 14 ). Alcohol use questionnaire (ALQ) 130 of NHANES participants was employed to ascertain whether or not an individual engages in the consumption of alcohol. The ALQ130 mainly consisted of the following question: during the past 12 months, on those days that you/SP drank alcoholic beverages, on the average, how many drinks did you/SP have? Based on ALQ130, NHANES respondents were classified into two categories: those who rarely or never drink (i.e., "hardly drinkers") and those who do drink (i.e., " alcohol-dependent individuals"). Only alcohol-dependent individuals were therefore incorporated into this study. 2.2 Systemic inflammation indicators A complete blood count (CBC) was conducted via a Beckman Coulter analyzer, comprising the measurement of white blood cells (WBCs) and platelets. Concurrently, the WBCs were classified into three respective subtypes, including neutrophils, lymphocytes, and monocytes. Certain composite indicators associated with white blood cells, including MHR, neutrophil to lymphocyte ratio (NLR), and platelet to lymphocyte ratio (PLR), serve as reflective measures of the patient's inflammatory status. 2.3 Covariates Demographic information, including age, gender, race/ethnicity, marital status, educational attainment, and family poverty income ratio (PIR) was collected via self-reported questionnaires. Anthropometric measurements such as lumbar bone mineral density, body mass index (BMI) and waist circumference were obtained using standardized protocols. Blood samples were collected to measure levels of high-density lipoprotein cholesterol (HDL-C), total calcium (Ca), serum iron (Fe), and vitamin D (VitD). Diabetes status, hypertension status, smoking status, and drinking habit were also recorded. 2.4 Statistical analysis Descriptive statistics were utilized to encapsulate the demographic and clinical attributes of the participants. Multivariate regression models were applied to evaluate the association between lumbar BMD and MHR, with adjustments made for potential confounding variables. Subgroup analyses were performed to investigate the potential effect modification by gender, race, and educational attainment. The nonlinear association between lumbar BMD and the MHR was examined through the application of smooth curve fitting and generalized additive models. Upon identifying nonlinearity, a recursive method was employed to ascertain the inflection point in the relationship between lumbar BMD and MHR. Subsequently, a two-piecewise linear regression model was applied to each segment delineated by the inflection point. Statistical analysis was conducted using R (version 4.2.0) in combination with EmpowerStats software, with a P value of 0.05 considered statistically significant. 3 Results 3.1 Baseline characteristics of participants At baseline, the study enrolled a total of 2055 alcohol-dependent individuals with depression, with a mean age of 37.202 years and a standard deviation of 12.696. The clinical characteristics of the participants according to the lumbar BMD quartiles are shown in Table 1 , revealing significant variations in age, gender, race/ethnicity, education, BMI, VitD, and monocyte across the quartiles (P < 0.05). Alcohol-dependent individuals with depression in the top lumbar BMD quartile were much likely to be female, non-Hispanic white, some college or AA degree, with higher BMI, VitD, and lower monocyte and MHR when compared to the other categories. No statistically significant associations were found between smoking history, family poverty income ratio, marital status, hypertension, diabetes, waist circumference, total calcium, serum iron, HDL-C, neutrophils, lymphocytes, platelets, PLR and NLR (P > 0.05). Table 1 Baseline cohort characteristics. Lumbar BMD (g/cm2) Total Q 1 0.554–0.930 Q 2 0.930–1.022 Q 3 1.022–1.125 Q 4 1.125–1.754 P-value N (%) 2055 (100.00) 508(24.72) 517(25.16) 514(25.01) 516(25.11) Age (years) 2055 (37.202 ± 12.696) 38.875 ± 12.901 36.947 ± 12.892 35.487 ± 11.333 38.193 ± 12.160 < 0.001 Gender (%) 0.005 Male 1013 (49.294%) 55.951 50.165 47.184 45.716 Female 1042 (50.706%) 44.049 49.835 52.816 54.284 Race/ethnicity (%) < 0.001 Mexican American 342 (16.642%) 13.715 12.007 10.883 6.305 Other Hispanic 231 (11.241%) 10.584 7.039 7.264 5.850 Non-Hispanic White 816 (39.708%) 64.043 59.894 61.137 60.669 Non-Hispanic Black 429 (20.876%) 5.526 11.044 12.386 18.576 Other Race 237 (11.533%) 6.132 10.015 8.330 8.600 Education (%) 0.011 Less than 9th grade 121 (6.234%) 3.974 4.335 2.197 3.818 9-11th grade 329 (16.950%) 15.603 13.460 11.181 11.422 High school graduate/GED 491 (25.296%) 27.601 23.541 29.003 21.914 Some college or AA degree 695 (35.806%) 34.476 37.162 35.576 44.337 College graduate or above 305 (15.714%) 18.347 21.502 22.044 18.508 Smoking History (%) 0.192 Never 817 (40.506%) 36.917 39.052 40.761 39.862 Previous 333 (16.510%) 18.312 15.664 20.497 20.001 Now 867 (42.985%) 44.770 45.284 38.742 40.137 Family poverty income ratio 1895 (2.028 ± 1.539) 2.413 ± 1.563 2.390 ± 1.678 2.480 ± 1.602 2.510 ± 1.660 0.642 Marital status (%) 0.708 Yes 650 (33.488%) 34.563 33.710 36.615 36.627 No 1291 (66.512%) 65.437 66.290 63.385 63.373 Hypertension (%) 0.504 Yes 439 (79.385%) 84.299 80.229 78.022 83.951 No 114 (20.615%) 15.701 19.771 21.978 16.049 Diabetes (%) 0.075 Yes 149 (7.409%) 3.990 4.958 7.079 7.112 No 1862 (92.591%) 96.010 95.042 92.921 92.888 BMI (kg/m2) 2051 (27.631 ± 6.854) 28.635 ± 6.892 28.852 ± 6.591 29.667 ± 7.816 29.630 ± 7.472 0.039 Waist circumference (cm) 2031 (98.717 ± 17.389) 98.879 ± 16.815 98.079 ± 16.432 98.717 ± 18.739 98.902 ± 17.047 0.863 Total calcium (Ca, mg/dL) 2045 (9.383 ± 0.353) 9.388 ± 0.382 9.402 ± 0.323 9.382 ± 0.339 9.368 ± 0.348 0.494 Serum iron (Fe, ug/dL) 2044 (15.576 ± 7.292) 16.237 ± 7.173 16.414 ± 7.322 15.447 ± 6.533 16.154 ± 8.019 0.163 VitD (nmol/L) 2055 (59.187 ± 24.913) 64.217 ± 25.596 64.553 ± 25.651 61.716 ± 21.114 67.285 ± 28.601 0.006 HDL-C (mg/dL) 2055 (52.258 ± 16.596) 53.429 ± 18.409 52.095 ± 16.126 51.614 ± 16.161 54.031 ± 17.334 0.081 Monocytes (1000 cells/uL) 2055 (0.571 ± 0.201) 0.594 ± 0.214 0.598 ± 0.205 0.573 ± 0.190 0.560 ± 0.184 0.007 Lymphocytes (1000 cells/uL) 2055 (2.297 ± 0.731) 2.230 ± 0.707 2.272 ± 0.794 2.310 ± 0.744 2.247 ± 0.700 0.325 Neutrophils (1000 cells/uL) 2055 (4.478 ± 2.238) 4.438 ± 1.871 4.449 ± 1.726 4.606 ± 2.289 4.478 ± 1.824 0.482 Platelets (1000 cells/uL) 2055 (248.007 ± 62.550) 246.445 ± 60.930 240.797 ± 59.641 247.950 ± 61.553 245.724 ± 59.524 0.270 MHR 2055 (0.012 ± 0.006) 0.012 ± 0.006 0.013 ± 0.006 0.012 ± 0.006 0.012 ± 0.006 0.024 PLR 2055 (117.254 ± 44.251) 120.136 ± 44.534 116.102 ± 44.047 115.493 ± 38.778 118.516 ± 43.683 0.267 NLR 2055 (2.082 ± 1.040) 2.144 ± 1.128 2.134 ± 1.019 2.083 ± 0.868 2.139 ± 0.995 0.754 Mean ± SD for continuous variables: the P-value was calculated by the weighted linear regression model. (%) for categorical variables: the P- value was calculated by the weighted chi-square test. BMD, bone mineral density; BMI, body mass index; VitD, vitamin D; HDL-C, high-density lipoprotein cholesterol; MHR, monocyte to HDL-C ratio; PLR, platelet to lymphocyte ratio; NLR, neutrophil to lymphocyte ratio. 3.2 The association between monocyte to HDL-C ratio and lumbar bone mineral density The results of the multivariate regression analysis are presented in Table 2 and Fig. 2. In the unadjusted model, MHR exhibited a negative association with lumbar BMD (β=-1.099, 95% CI: -2.109 to -0.089, P = 0.033). This significant relationship persisted after adjusting for covariates in model 2 (β=-1.283, 95% CI: -2.408 to -0.158, P = 0.026) and model 3 (β=-1.681, 95% CI: -3.218 to -0.144, P = 0.032). Furthermore, when MHR was categorized into quartiles, individuals in the lowest MHR quartile exhibited a 0.02 g/cm² higher lumbar BMD compared to those in the highest MHR quartile (P < 0.001 for trend). Table 2 The association between monocyte to HDL-C ratio and lumbar bone mineral density. MODEL 1 β (95% CI) MODEL 2 β (95% CI) MODEL 3β (95% CI) P-value P-value P-value MHR -1.099 (-2.109, -0.089) -1.283 (-2.408, -0.158) -1.681 (-3.218, -0.144) 0.033 0.026 0.032 Quintiles of MHR Q1 Reference Reference Reference Q2 0.001 (-0.017, 0.018) -0.003 (-0.022, 0.015) -0.010 (-0.031, 0.011) 0.938 0.712 0.331 Q3 -0.026 (-0.044, -0.008) -0.030 (-0.049, -0.012) -0.039 (-0.062, -0.015) 0.004 0.002 0.001 Q4 -0.020 (-0.038, -0.003) -0.025 (-0.045, -0.006) -0.035 (-0.062, -0.008) 0.024 0.011 0.011 P for trend 0.005 0.003 0.011 Model 1: no covariates were adjusted. Model 2: age, gender, educational level and BMI were adjusted. Model 3: age, gender, educational level, BMI, family income-to-poverty ratio, Total calcium, Serum iron, VitD, HDL-C, lymphocytes, neutrophils, platelets, PLR and NLR were adjusted. HDL-C, high-density lipoprotein cholesterol; MHR, monocyte to HDL-C ratio. 3.3 Subgroup analyses of the association of the monocyte to HDL-C ratio with lumbar bone mineral density after adjustment for confounding factors. Subgroup analyses stratified by gender and educational attainment revealed significant interactions, particularly within the educational subgroups (Table 3 ). Specifically, when stratified by gender, the negative correlation between MHR and lumbar BMD remained significant in men (β=-1.625, 95% CI: -2.937, -0.313, P 0.05). Notably, individuals with less than a 9th-grade education exhibited a positive association with MHR, whereas those with a college degree or higher demonstrated a negative association (P < 0.001 for interaction). In the fully adjusted model, this correlation was still significant (P = 0.001 for interaction). Table 3 Subgroup analyses of the association of the monocyte to HDL-C ratio with lumbar bone mineral density. Subgroup Crude β (95% CI) P for interaction MODEL 1 β (95% CI) P for interaction Stratified by gender 0.192 0.325 Men -1.625 (-2.937, -0.313) -0.942 (-2.513, 0.629) Women -0.190 (-1.905, 1.525) 0.159 (-1.943, 2.262) Stratified by race 0.767 0.735 Mexican American 0.934 (-1.283, 3.150) 0.549 (-1.863, 2.961) Other Hispanic -0.085 (-3.107, 2.938) -0.784 (-4.015, 2.447) Non-Hispanic White -0.473 (-1.956, 1.011) -0.835 (-2.697, 1.027) Non-Hispanic Black -0.350 (-3.065, 2.366) -0.830 (-3.920, 2.261) Other Race -1.460 (-4.461, 1.541) -2.190 (-5.540, 1.159) Stratified by education < 0.001 0.001 Less than 9th grade 8.140 (4.006, 12.274) 7.556 (3.432, 11.679) 9-11th grade -1.420 (-3.701, 0.860) -0.889 (-3.267, 1.490) High school graduate/GED -1.624 (-3.526, 0.277) -1.695 (-3.784, 0.394) Some college or AA degree -0.966 (-2.755, 0.823) -0.972 (-3.038, 1.094) College graduate or above -1.912 (-5.322, 1.498) -2.015 (-5.680, 1.651) Crude: no covariates were adjusted. Model 1: age, gender, race, educational level, BMI, Total calcium, Serum iron, VitD, HDL-C, lymphocytes, neutrophils, platelets, PLR and NLR were adjusted. In the subgroup analysis stratified by gender, race, and educational level, the model is not adjusted for gender, race, and educational level respectively. HDL-C, high-density lipoprotein cholesterol; GED, General Educational Development. 3.4 U-shaped association between MHR and lumbar BMD in individuals possessing a high school diploma or GED equivalent The stratified analysis examining the association between the monocyte-to-high-density lipoprotein ratio and lumbar bone mineral density, while adjusting for multiple covariates across varying levels of educational attainment, reveals a significant U-shaped relationship in the subgroup of individuals possessing a high school diploma or GED equivalent, as detailed in Table 4 and Fig. 3 . Notably, within this educational stratum, MHR levels below the threshold of 0.024 are associated with an adjusted β-value of 1.676, with a 95% Confidence Interval (CI) extending from − 11.795 to 15.147. This interval includes zero, suggesting the absence of a statistically significant association at this MHR level (P = 0.808). In contrast, at MHR levels greater than 0.024, the adjusted β-value increases significantly to 30.307, with a 95% confidence interval ranging from 8.152 to 52.462, indicating a strong positive correlation that is statistically significant (P = 0.009). The log-likelihood ratio test supports the model's goodness-of-fit with a P-value of 0.031, thereby confirming the statistical reliability of the observed U-shaped association. The U-shaped relationship indicates a nuanced impact of MHR on health outcomes within this educational stratum, wherein the influence of MHR is minimal at lower levels but becomes significantly positive beyond a certain threshold. This nonlinear association may reflect intricate biological interactions between MHR and lumbar BMD, as well as potential disparities in socioeconomic and behavioral factors. It underscores the necessity for tailored public health interventions that address the specific needs of distinct educational groups. Table 4 Adjusted associations between monocyte to HDL-C ratio with lumbar bone mineral density by education level. Adjusted β (95% CI) P-value High school graduate/GED Inflection point 0.024 MHR 0.024 30.307 (8.152, 52.462) 0.009 Log likelihood ratio 0.031 Age, gender, race, BMI, Total calcium, Serum iron, VitD, HDL-C, monocytes, lymphocytes, neutrophils, platelets, hypertension, diabetes, PLR and NLR were adjusted. HDL-C, high-density lipoprotein cholesterol; MHR, monocyte to HDL-C ratio. 4 Discussion 4.1 Alcohol dependence and Depression Alcohol misuse is the primary contributor to disease and death in both developed and developing nations. Quantitative data on alcohol misuse underscores its substantial medical significance. When evaluating its contribution to physical ailments and self-inflicted fatalities, alcoholism ranks as the fourth leading cause of death within the United States( 15 , 16 ). Depression is frequently identified as the predominant comorbid psychiatric disorder in patients exhibiting alcohol dependence( 17 , 18 ). Given the prevalent manifestation of depressive symptoms in individuals with alcoholism, clinicians and researchers hypothesize a potential correlation between these two disorders. It is postulated that individuals may resort to alcohol consumption as a coping mechanism for underlying depressive conditions. Conversely, depression may predominantly arise as a detrimental consequence of alcohol dependence( 15 ). The primary criterion for evaluation is predicated on the precedence or the fundamental cause. It is evident that the concurrent prevalence of depression and alcohol dependence is not only common but also clinically significant. Focusing solely on either alcoholism or depression presents a skewed perspective. In general, the elevated incidence of alcohol abuse among individuals suffering from depression indicates that research into this demographic and enhancement of their physical and mental well-being could potentially have a substantial influence on public health. 4.2 Clinical implications Osteoporosis, alcohol dependence and depression have become major public health concerns in increasingly aging population, both of them are closely associated with severe morbidity and increased mortality. A previous cross-sectional study published in 2022 indicates that people with depression was associated with lower BMD, particularly in the spine, males, Hispanic-White, and not highly educated populations( 19 ). This association between depression and lower BMD is stable and consistent with previous research findings( 7 , 20 – 22 ). A research based on trabecular bone score (TBS) rather than BMD showed that alcohol use disorder, antidepressants, and lithium are associated with poorer bone texture in women( 23 ). Although alcohol dependence and depression often occur simultaneously, previous studies on bone metabolism in alcohol dependent patients with depression were limited and further exploration was needed. As the main risk factor of osteoporosis, chronic low inflammation caused by depression and alcohol can directly influence bone homeostasis( 24 – 26 ). The NLR, PLR and MHR are biomarkers of inflammation and oxidative stress. Especially MHR, is considered as a prognostic biomarker for cardiovascular disease( 27 , 28 ). In our cross-sectional study, using NHANES data, we found a significant U-shaped link between MHR and lumbar BMD in high school-educated individuals with alcohol-dependent depression. When the MHR was below 0.024, there was a corresponding increase in lumbar BMD by 1.676g/cm 2 for each unit increase in MHR. Conversely, when the MHR was precisely 0.024, the BMD was observed to be at its minimal value. However, when the MHR exceeded 0.024, the BMD exhibited a substantial increase of 30.307g/cm 2 for each unit increment in MHR. These results indicated that MHR may have a threshold effect on bone health, shedding light on the intricate relationship between systemic inflammation, metabolic factors, and bone mineral density. To our knowledge, only two prior investigations have been conducted on the correlation between MHR and BMD levels. A cross-sectional study executed in 2024 suggests that, among the non-diabetic elderly population, MHR serves as a defensive mechanism against bone irregularities, exhibiting a direct relationship with lumbar BMD (11) . In the context of Chinese postmenopausal women diagnosed with Type 2 Diabetes Mellitus (T2DM), both MHR and Monocyte to Albumin Ratio (MAR) demonstrated a positive correlation with beta-C-terminal telopeptide ( β-CTX ), and a negative correlation with lumbar and femoral neck BMD( 29 ). The correlation between other inflammatory indices derived from blood cell count and BMD exhibits varying results across different demographic groups. A preceding retrospective analysis involving 893 postmenopausal women revealed a negative correlation between BMD and systemic immune-inflammation index (SII), NLR, and the product of platelet count and neutrophil count (PPN). These factors were also found to be positively associated with the risk of osteoporosis( 30 ). This was further corroborated by a cross-sectional study of 413 postmenopausal women, which also demonstrated a negative relationship between SII and BMD( 31 ). Additionally, research conducted in Turkey indicated an inverse correlation between BMD and NLR, PLR, MLR, and SII in postmenopausal women( 32 ). Research investigating children diagnosed with hypothyroidism has revealed a significant positive correlation between the BMD Z-score and NLR, MLR, PLR, and thyroid stimulating hormone (TSH) levels. However, this correlation was not observed in children who were either healthy or diagnosed with hyperthyroidism( 33 ). A separate retrospective study involving 143 children demonstrated a negative correlation between the BMD Z-score and both MLR and PLR in the obese cohort, while a positive correlation was identified within the control group( 34 ). Chen et al. discovered no significant correlation between MLR and lumbar BMD in adults. However, they noted a positive correlation between NLR and lumbar BMD, and a negative correlation between PLR and lumbar BMD( 35 ). This is further supported by a meta-analysis, which also suggested a negative correlation between both NLR and PLR with BMD( 36 ). To the best of our knowledge, this is the first study monitoring MHR to identify its link to lumbar BMD in alcohol-dependent, depressed patients. The study fills a significant gap in existing literature on the impact of systemic inflammatory markers on BMD patients with alcohol dependence and depression, offering several clinical benefits for doctors. Our research elucidates the interaction between systemic inflammation and bone health within a specific demographic, thereby fostering a comprehensive understanding of the multifactorial determinants of bone density. The implications of these findings could potentially facilitate early identification of individuals at risk and aid in the development of targeted interventions designed to mitigate bone loss and prevent osteoporosis. Moreover, our research has the potential to guide the development of individualized treatment approaches, taking into account the intricate interconnections among alcohol dependence, depressive manifestations, and bone health. This could potentially augment clinical results and ameliorate the life quality of impacted individuals. Additionally, the knowledge derived from this study could provide critical information for public health policymakers, directing the formulation of health management policies specifically designed to cater to the requirements of patients suffering from these coexisting conditions. The study promotes interdisciplinary collaboration in psychiatry, osteology, and immunology, advancing cross-disciplinary research. It provides theoretical and practical contributions to patient care and scientific knowledge in health research. 4.3 Strengths and limitations To date, scholarly investigations concerning BMD have predominantly concentrated on postmenopausal women. The BMD level of patients suffering from alcohol-dependent depression, a subject that has been sparingly addressed in existing literature, remains largely unexplored. This research represents the inaugural study into the association between the systemic inflammatory index and BMD within this particular demographic. This study has a number of strengths, including the use of a combined five cycles of NHANES data based on a nationally representative sample. This benefits from a robust, standardised methodology and a large, nationally representative sample, enhancing the generalisability of the findings. The study's interdisciplinary approach, which brings together psychiatry, bone health and inflammation, makes a significant contribution to our understanding of the complex interactions between mental health and physical well-being. As a cross-sectional study, this research is limited in its ability to establish causality or temporality between systemic inflammation, alcohol dependence, depression, and bone density. The findings are specific to patients suffering from alcohol-dependent depression and may not be generalizable to other populations with different characteristics or healthcare systems. Reliance on self-reported data for certain variables, such as alcohol consumption and depression symptoms, could introduce reporting biases, affecting the accuracy of the results. Current evidence is mostly limited to studies that used BMD, which does not provide more information about the texture of bone tissue and can underestimate fracture risk( 23 , 37 ). The U-shaped link between MHR and BMD in the "High School Graduate/GED" group lacks clear biological explanation. More research is needed to understand why inflammation differently affects the balance between bone formation and resorption. 5 Conclusion In conclusion, this study provides novel insights into the relationship between MHR, a marker of systemic inflammation, and BMD in individuals with alcohol dependency and depression. The observed U-shaped association among participants with a high school diploma or GED equivalent highlights the complex interplay between inflammation, metabolic factors, and bone health. Although constrained by its cross-sectional design, this research identifies MHR as a potential biomarker for monitoring bone health in alcohol-dependent individuals with depression. It is recommended that clinicians consider regular BMD assessments and interventions aimed at modulating inflammation in this population. Future studies should seek to confirm these findings in longitudinal cohorts and investigate the underlying mechanistic pathways. Declarations Author contributions LZ and XY wrote the main manuscript text. LZ prepared tables 1-4. XY prepared figures 1-3. All authors contributed to review and approved the submitted version. Funding This work is supported by grants from the Youth Talent Development Plan of Changzhou Health Commission (grant number CZQM2023006). Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/. Ethics approval and consent to participate All human subjects involved in this study were treated in accordance with the ethical principles outlined in the Declaration of Helsinki, and the study was approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS). The patients/participants provided their written informed consent to participate in this study. Clinical trial number Not applicable. Conflict of interest The authors declare no competing interests. Consent for publication Not applicable. References Walker MD, Shane E. Postmenopausal Osteoporosis. N Engl J Med. 2023;389(21):1979-91. Snyder S. Postmenopausal Osteoporosis. N Engl J Med. 2024;390(7):675-6. McHugh RK, Weiss RD. Alcohol Use Disorder and Depressive Disorders. Alcohol Res. 2019;40(1). Guo X, She Y, Liu Q, Qin J, Wang L, Xu A, et al. Osteoporosis and depression in perimenopausal women: From clinical association to genetic causality. J Affect Disord. 2024;356:371-8. Kashfi SS, Abdollahi G, Hassanzadeh J, Mokarami H, Khani Jeihooni A. The relationship between osteoporosis and depression. Sci Rep. 2022;12(1):11177. Skowronska-Jozwiak E, Galecki P, Glowacka E, Wojtyla C, Bilinski P, Lewinski A. Bone Metabolism in Patients Treated for Depression. Int J Environ Res Public Health. 2020;17(13). Wu Q, Liu B, Tonmoy S. Depression and risk of fracture and bone loss: an updated meta-analysis of prospective studies. Osteoporos Int. 2018;29(6):1303-12. Ganesan K, Teklehaimanot S, Tran TH, Asuncion M, Norris K. Relationship of C-reactive protein and bone mineral density in community-dwelling elderly females. J Natl Med Assoc. 2005;97(3):329-33. Yang F, Liu Y, Chen S, Dai Z, Yang D, Gao D, et al. A GABAergic neural circuit in the ventromedial hypothalamus mediates chronic stress-induced bone loss. J Clin Invest. 2020;130(12):6539-54. Kalinichenko LS, Muhle C, Jia T, Anderheiden F, Datz M, Eberle AL, et al. Neutral sphingomyelinase mediates the co-morbidity trias of alcohol abuse, major depression and bone defects. Mol Psychiatry. 2021;26(12):7403-16. Li X, Yan M, Ji J, Ma Z. Non-diabetic elderly populations: the MHR as a protective factor against bone abnormalities. Frontiers in endocrinology. 2024;15:1408467. Saxena Y, Routh S, Mukhopadhaya A. Immunoporosis: Role of Innate Immune Cells in Osteoporosis. Front Immunol. 2021;12:687037. Negeri ZF, Levis B, Sun Y, He C, Krishnan A, Wu Y, et al. Accuracy of the Patient Health Questionnaire-9 for screening to detect major depression: updated systematic review and individual participant data meta-analysis. BMJ. 2021;375:n2183. Terlizzi EP, Zablotsky B. Symptoms of Anxiety and Depression Among Adults: United States, 2019 and 2022. Natl Health Stat Report. 2024(213). McGrath PJ, Nunes EV, Quitkin FM. Current concepts in the treatment of depression in alcohol-dependent patients. Psychiatr Clin North Am. 2000;23(4):695-711, V. Carvalho AF, Heilig M, Perez A, Probst C, Rehm J. Alcohol use disorders. Lancet. 2019;394(10200):781-92. Burns L, Teesson M. Alcohol use disorders comorbid with anxiety, depression and drug use disorders. Findings from the Australian National Survey of Mental Health and Well Being. Drug Alcohol Depend. 2002;68(3):299-307. Boschloo L, Vogelzangs N, van den Brink W, Smit JH, Veltman DJ, Beekman AT, et al. Alcohol use disorders and the course of depressive and anxiety disorders. Br J Psychiatry. 2012;200(6):476-84. Ma M, Liu X, Jia G, Liu Z, Zhang K, He L, et al. The association between depression and bone metabolism: a US nationally representative cross-sectional study. Arch Osteoporos. 2022;17(1):113. Robbins J, Hirsch C, Whitmer R, Cauley J, Harris T. The association of bone mineral density and depression in an older population. J Am Geriatr Soc. 2001;49(6):732-6. Wu Q, Liu J, Gallegos-Orozco JF, Hentz JG. Depression, fracture risk, and bone loss: a meta-analysis of cohort studies. Osteoporos Int. 2010;21(10):1627-35. Lyles KW. Osteoporosis and depression: shedding more light upon a complex relationship. J Am Geriatr Soc. 2001;49(6):827-8. Hafizi S, Lix LM, Hans D, Bolton JM, Leslie WD. Association of mental disorders and psychotropic medications with bone texture as measured with trabecular bone score. Bone. 2022;165:116565. Mandelli L, Milaneschi Y, Hiles S, Serretti A, Penninx BW. Unhealthy lifestyle impacts on biological systems involved in stress response: hypothalamic-pituitary-adrenal axis, inflammation and autonomous nervous system. Int Clin Psychopharmacol. 2023;38(3):127-35. Kushioka J, Chow SK, Toya M, Tsubosaka M, Shen H, Gao Q, et al. Bone regeneration in inflammation with aging and cell-based immunomodulatory therapy. Inflamm Regen. 2023;43(1):29. Livshits G, Kalinkovich A. Targeting chronic inflammation as a potential adjuvant therapy for osteoporosis. Life Sci. 2022;306:120847. Acikgoz SK, Acikgoz E, Sensoy B, Topal S, Aydogdu S. Monocyte to high-density lipoprotein cholesterol ratio is predictive of in-hospital and five-year mortality in ST-segment elevation myocardial infarction. Cardiol J. 2016;23(5):505-12. Chen SA, Zhang MM, Zheng M, Liu F, Sun L, Bao ZY, et al. The preablation monocyte/ high density lipoprotein ratio predicts the late recurrence of paroxysmal atrial fibrillation after radiofrequency ablation. BMC Cardiovasc Disord. 2020;20(1):401. Huang R, Chen Y, Tu M, Wang W. Monocyte to high-density lipoprotein and apolipoprotein A1 ratios are associated with bone homeostasis imbalance caused by chronic inflammation in postmenopausal women with type 2 diabetes mellitus. Front Pharmacol. 2022;13:1062999. Tang Y, Peng B, Liu J, Liu Z, Xia Y, Geng B. Systemic immune-inflammation index and bone mineral density in postmenopausal women: A cross-sectional study of the national health and nutrition examination survey (NHANES) 2007-2018. Front Immunol. 2022;13:975400. Du YN, Chen YJ, Zhang HY, Wang X, Zhang ZF. Inverse association between systemic immune-inflammation index and bone mineral density in postmenopausal women. Gynecol Endocrinol. 2021;37(7):650-4. Yolacan H, Guler S. Inverse Correlation Between Bone Mineral Density and Systemic Immune Inflammation Index in Postmenopausal Turkish Women. Cureus. 2023;15(4):e37463. Bala MM, Bala KA. Bone mineral density (BMD) and neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), and platelet-lymphocyte ratio (PLR) in childhood thyroid diseases. Eur Rev Med Pharmacol Sci. 2022;26(6):1945-51. Bala MM, Bala KA. Bone mineral density and complete blood count ratios in children and adolescents with obesity. Eur Rev Med Pharmacol Sci. 2022;26(1):249-56. Chen S, Sun X, Jin J, Zhou G, Li Z. Association between inflammatory markers and bone mineral density: a cross-sectional study from NHANES 2007-2010. J Orthop Surg Res. 2023;18(1):305. Liu YC, Yang TI, Huang SW, Kuo YJ, Chen YP. Associations of the Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio with Osteoporosis: A Meta-Analysis. Diagnostics (Basel). 2022;12(12). Sale JE, Bogoch E, Meadows L, Gignac M, Frankel L, Inrig T, et al. Bone Mineral Density Reporting Underestimates Fracture Risk in Ontario. Health (Irvine Calif). 2015;7(5):566-71. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 02 May, 2025 Reviews received at journal 19 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviews received at journal 10 Apr, 2025 Reviewers agreed at journal 10 Apr, 2025 Reviewers invited by journal 09 Apr, 2025 Submission checks completed at journal 01 Apr, 2025 First submitted to journal 22 Mar, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5957443","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440674588,"identity":"00209f9b-ab8f-426b-a00d-65de26cf6e13","order_by":0,"name":"Lili Zhu","email":"","orcid":"","institution":"The Third Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Zhu","suffix":""},{"id":440674589,"identity":"1676bd3e-e6fd-4e50-b437-f92d81b822cd","order_by":1,"name":"Xiaofeng Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIie3RMQrCMBTG8dSAXR7W8YVK9QLCg4IoiF7F0sHVsWNFiEvBuXgP50qHLj1AxcXcoBcQraNTMgrmP3+/4SWM2Ww/maMeSC/w3P1etWaEhzRP+Ehk5SFEQzLDpOZLarZyCCaAmriPN9kHkSvJkK2CaaojdckXuQTw/Eg+diwOZ4WOVKfrXUgEcY6OhKyILlpScuY/JQHdrh00ItWhh1hvgBrHkIjuFsKkAJFF3SOTwS2DJna6ryzWnlsp1SarQEsm3wPSzD+NU4ORzWaz/XlvwSVDS2MkIz4AAAAASUVORK5CYII=","orcid":"","institution":"The Third Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2025-02-04 10:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5957443/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5957443/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-00885-8","type":"published","date":"2025-05-06T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80295544,"identity":"52831d74-32a5-4c85-96f9-ff1eb12d9143","added_by":"auto","created_at":"2025-04-10 08:35:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":285356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of participants included in the analyses\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5957443/v1/e6ebbba621fe9a7d96ec8c82.png"},{"id":80295610,"identity":"3480f692-4c5b-4dee-8ebf-14c3c925d8f8","added_by":"auto","created_at":"2025-04-10 08:35:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121742,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe association between monocyte to HDL-C ratio and lumbar bone mineral density in alcohol-dependent individuals with depression. (A) Each black point represents a sample. (B) Solid red line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. Age, gender, educational level, BMI, family income-to-poverty ratio, Total calcium, Serum iron, VitD, HDL-C, lymphocytes, neutrophils, platelets, PLR and NLR were adjusted. HDL-C, high-density lipoprotein cholesterol; MHR, monocyte to HDL-C ratio.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5957443/v1/6ba1c4483068a39e26b2eea4.png"},{"id":80295548,"identity":"772d1fb7-3174-4303-802f-6674981048e7","added_by":"auto","created_at":"2025-04-10 08:35:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":218509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariable-adjusted associations between monocyte to HDL-C ratio and lumbar bone mineral density in individuals with alcohol-dependency and depression stratified by educational level. (A) the whole population; (B) less than 9th grade; (C) high school graduate/GED; (D) college graduate or above. Age, gender, race, BMI, Total calcium, Serum iron, VitD, HDL-C, monocytes, lymphocytes, neutrophils, platelets, hypertension, diabetes, PLR and NLR were adjusted. BMD, bone mineral density; GED, General Educational Development; MHR, monocyte to HDL-C ratio.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5957443/v1/c63103ecf575fd0d80e05656.png"},{"id":82537599,"identity":"ca8ebe50-dab8-4d54-a88b-1cbf42c03cf6","added_by":"auto","created_at":"2025-05-12 16:09:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2174306,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5957443/v1/00e00bc5-2014-476a-aa04-dbb7c29a6d30.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between Monocyte to HDL-C ratio and Lumbar Bone Mineral Density in Alcohol dependent Individuals with Depression","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eOsteoporosis, a systemic skeletal disorder marked by diminished bone mineral density (BMD), represents a substantial public health issue owing to its correlation with heightened fracture risk and diminished quality of life(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Simultaneously, alcohol dependence, a chronic relapsing condition, exacerbates public health problems due to its widespread occurrence and links to various comorbidities, including mental health disorders like depression(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Depression, frequently seen alongside alcohol use disorders, may alter bone metabolism through its effects on the neuroendocrine and immune systems. Studies indicate a complex relationship between depression and bone health, with evidence showing a link between depressive symptoms and reduced bone mineral density(\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Previous research has demonstrated that depression elevates cortisol levels by activating the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis, and that hypercortisolemia is a significant causal factor in the deterioration of bone health(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In addition, increased levels of inflammatory cytokines like interleukin-1β, interleukin-2, and interleukin-6 are found in depression and are linked to reduced bone mineral density(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The potential biological plausibility of this association encompasses the influence of depressive symptoms on physical activity, dietary consumption, and hormonal regulation, each of which is crucial for sustaining bone health. Moreover, the psychosocial stress associated with depression could trigger neuroendocrine and immune reactions that negatively affect the bone remodeling processes(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe detrimental impact of both alcohol dependence and depression on bone health is well-documented(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The monocyte to high-density lipoprotein cholesterol ratio (MHR) is an emerging biomarker that reflects systemic inflammatory status, which integrates two pathophysiological dimensions including monocytes which are pivotal mediators of innate immunity, promote pro-inflammatory responses, and HDL-C provides anti-inflammatory and antioxidant effects through vascular cholesterol clearance and the mitigation of atherosclerosis(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). An elevated MHR indicates an imbalance skewed towards inflammation, which has recently been associated with dysregulation in bone metabolism. Mechanistically, chronic inflammation enhances osteoclast activation while suppressing osteoblast function, thereby accelerating bone resorption and reducing bone mineral density(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This phenomenon is particularly exacerbated in populations experiencing the dual challenges of alcohol dependence and depression. Alcohol-induced oxidative stress and depression-related hyperactivity of the HPA axis may synergistically intensify this inflammatory cascade. Consequently, MHR emerges as a promising indicator for elucidating the complex interplay between inflammation, addictive-metabolic disorders, and skeletal health, offering valuable clinical insights into the preservation of BMD.\u003c/p\u003e \u003cp\u003eDespite a growing body of evidence linking alcohol consumption, depression, and bone health, the specific function of the MHR within the context of lumbar BMD among individuals suffering from alcohol dependence and depression remains inadequately investigated.\u003c/p\u003e \u003cp\u003eThe principal objective of this cross-sectional study is to scrutinize the correlation between MHR and lumbar BMD in a nationally representative cohort of individuals diagnosed with alcohol dependency and depression. The hypothesis under investigation suggests that an elevated MHR, serving as a marker of systemic inflammation, may exhibit an inverse relationship with lumbar BMD, thereby potentially pinpointing a modifiable risk factor for osteoporosis within this susceptible demographic. By illuminating the role of systemic inflammation within this framework, this study endeavors to augment the comprehension of the multifactorial determinants that influence bone health.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Participants\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a comprehensive, ongoing, cross-sectional survey in the United States. It is designed to provide objective statistics on a range of health issues and to address emerging public health concerns. All data required for the research was collated from the five continuous NHANES cycles from 2009 to 2018 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2009\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2009\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The protocol of NHANES (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/about/erb.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/about/erb.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) has been approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board (NHANES 2005\u0026ndash;2010: Protocol #2005-06; NHANES 2011\u0026ndash;2018: Protocol #2011-17(Continuation of Protocol #2011-17 Effective through October 26, 2017), Protocol #2018-01 (Effective beginning October 26, 2017). Each participant has provided informed consent prior to the commencement of the survey.\u003c/p\u003e \u003cp\u003eA total of 49693 participants were enrolled in the study, which aimed to explore the relationship between inflammation indicators and lumbar bone mineral density in depressed individuals who habitually drink alcohol. Dual-energy X-ray absorptiometry (DXA) scans of the lumbar spine were conducted at the NHANES Mobile Examination Center (MEC). The spine scans provide bone measurements for the total spine and vertebrae L1 \u0026ndash; L4. Accordingly, participants without depressive symptoms (34056 individuals) and drinking inhibits (1806 individuals) were excluded. Additionally, individuals lacking data about bone mineral density data (2918 participants), monocyte (8805 participants), or high-density lipoprotein cholesterol (53 participants) were also excluded. Following the completion of this screening process, the research encompassed a total of 2,055 alcohol-dependent individuals with depression. The entire process of data selection is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDepression samples were obtained from participants in the NHANES through the analysis of questionnaire data. The presence of depressive symptoms was determined using the Patient Health Questionnaire-9 (PHQ-9)(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The PHQ-9 comprises nine items, as follows: 1) anhedonia, 2) depressed mood, 3) sleep disturbance, 4) fatigue, 5) appetite changes, 6) low self-esteem, 7) concentration problems, 8) psychomotor disturbances, and 9) suicidal ideation. A score of 0\u0026ndash;4 is indicative of the absence of depressive symptoms. Conversely, a score of \u0026ge;\u0026thinsp;5 indicates the presence of depressive symptoms, with varying degrees of severity, and was therefore incorporated into the study cohort(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlcohol use questionnaire (ALQ) 130 of NHANES participants was employed to ascertain whether or not an individual engages in the consumption of alcohol. The ALQ130 mainly consisted of the following question: during the past 12 months, on those days that you/SP drank alcoholic beverages, on the average, how many drinks did you/SP have? Based on ALQ130, NHANES respondents were classified into two categories: those who rarely or never drink (i.e., \"hardly drinkers\") and those who do drink (i.e., \" alcohol-dependent individuals\"). Only alcohol-dependent individuals were therefore incorporated into this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Systemic inflammation indicators\u003c/h2\u003e \u003cp\u003eA complete blood count (CBC) was conducted via a Beckman Coulter analyzer, comprising the measurement of white blood cells (WBCs) and platelets. Concurrently, the WBCs were classified into three respective subtypes, including neutrophils, lymphocytes, and monocytes. Certain composite indicators associated with white blood cells, including MHR, neutrophil to lymphocyte ratio (NLR), and platelet to lymphocyte ratio (PLR), serve as reflective measures of the patient's inflammatory status.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Covariates\u003c/h2\u003e \u003cp\u003eDemographic information, including age, gender, race/ethnicity, marital status, educational attainment, and family poverty income ratio (PIR) was collected via self-reported questionnaires. Anthropometric measurements such as lumbar bone mineral density, body mass index (BMI) and waist circumference were obtained using standardized protocols. Blood samples were collected to measure levels of high-density lipoprotein cholesterol (HDL-C), total calcium (Ca), serum iron (Fe), and vitamin D (VitD). Diabetes status, hypertension status, smoking status, and drinking habit were also recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were utilized to encapsulate the demographic and clinical attributes of the participants. Multivariate regression models were applied to evaluate the association between lumbar BMD and MHR, with adjustments made for potential confounding variables. Subgroup analyses were performed to investigate the potential effect modification by gender, race, and educational attainment. The nonlinear association between lumbar BMD and the MHR was examined through the application of smooth curve fitting and generalized additive models. Upon identifying nonlinearity, a recursive method was employed to ascertain the inflection point in the relationship between lumbar BMD and MHR. Subsequently, a two-piecewise linear regression model was applied to each segment delineated by the inflection point. Statistical analysis was conducted using R (version 4.2.0) in combination with EmpowerStats software, with a P value of 0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of participants\u003c/h2\u003e \u003cp\u003eAt baseline, the study enrolled a total of 2055 alcohol-dependent individuals with depression, with a mean age of 37.202 years and a standard deviation of 12.696. The clinical characteristics of the participants according to the lumbar BMD quartiles are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, revealing significant variations in age, gender, race/ethnicity, education, BMI, VitD, and monocyte across the quartiles (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Alcohol-dependent individuals with depression in the top lumbar BMD quartile were much likely to be female, non-Hispanic white, some college or AA degree, with higher BMI, VitD, and lower monocyte and MHR when compared to the other categories. No statistically significant associations were found between smoking history, family poverty income ratio, marital status, hypertension, diabetes, waist circumference, total calcium, serum iron, HDL-C, neutrophils, lymphocytes, platelets, PLR and NLR (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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 cohort characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumbar BMD (g/cm2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ 1\u003c/p\u003e \u003cp\u003e0.554\u0026ndash;0.930\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ 2\u003c/p\u003e \u003cp\u003e0.930\u0026ndash;1.022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ 3\u003c/p\u003e \u003cp\u003e1.022\u0026ndash;1.125\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ 4\u003c/p\u003e \u003cp\u003e1.125\u0026ndash;1.754\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\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\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2055 (100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e508(24.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e517(25.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e514(25.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e516(25.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2055 (37.202\u0026thinsp;\u0026plusmn;\u0026thinsp;12.696)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.875\u0026thinsp;\u0026plusmn;\u0026thinsp;12.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.947\u0026thinsp;\u0026plusmn;\u0026thinsp;12.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.487\u0026thinsp;\u0026plusmn;\u0026thinsp;11.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.193\u0026thinsp;\u0026plusmn;\u0026thinsp;12.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eGender (%)\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\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\u003e1013 (49.294%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e1042 (50.706%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity (%)\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u0026nbsp;American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e342 (16.642%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u0026nbsp;Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231 (11.241%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic\u0026nbsp;White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e816 (39.708%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic\u0026nbsp;Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e429 (20.876%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u0026nbsp;Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e237 (11.533%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (%)\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121 (6.234%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e329 (16.950%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e491 (25.296%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e695 (35.806%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e305 (15.714%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking History (%)\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817 (40.506%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e333 (16.510%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e867 (42.985%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily poverty income ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1895 (2.028\u0026thinsp;\u0026plusmn;\u0026thinsp;1.539)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.413\u0026thinsp;\u0026plusmn;\u0026thinsp;1.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.390\u0026thinsp;\u0026plusmn;\u0026thinsp;1.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.480\u0026thinsp;\u0026plusmn;\u0026thinsp;1.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.510\u0026thinsp;\u0026plusmn;\u0026thinsp;1.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (%)\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.708\u003c/p\u003e \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\u003e650 (33.488%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e1291 (66.512%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.504\u003c/p\u003e \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\u003e439 (79.385%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e114 (20.615%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.075\u003c/p\u003e \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\u003e149 (7.409%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e1862 (92.591%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2051 (27.631\u0026thinsp;\u0026plusmn;\u0026thinsp;6.854)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.635\u0026thinsp;\u0026plusmn;\u0026thinsp;6.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.852\u0026thinsp;\u0026plusmn;\u0026thinsp;6.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.667\u0026thinsp;\u0026plusmn;\u0026thinsp;7.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.630\u0026thinsp;\u0026plusmn;\u0026thinsp;7.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2031 (98.717\u0026thinsp;\u0026plusmn;\u0026thinsp;17.389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.879\u0026thinsp;\u0026plusmn;\u0026thinsp;16.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.079\u0026thinsp;\u0026plusmn;\u0026thinsp;16.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.717\u0026thinsp;\u0026plusmn;\u0026thinsp;18.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.902\u0026thinsp;\u0026plusmn;\u0026thinsp;17.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal calcium (Ca, mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2045 (9.383\u0026thinsp;\u0026plusmn;\u0026thinsp;0.353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.388\u0026thinsp;\u0026plusmn;\u0026thinsp;0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.402\u0026thinsp;\u0026plusmn;\u0026thinsp;0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.382\u0026thinsp;\u0026plusmn;\u0026thinsp;0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.368\u0026thinsp;\u0026plusmn;\u0026thinsp;0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum iron (Fe, ug/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2044 (15.576\u0026thinsp;\u0026plusmn;\u0026thinsp;7.292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.237\u0026thinsp;\u0026plusmn;\u0026thinsp;7.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.414\u0026thinsp;\u0026plusmn;\u0026thinsp;7.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.447\u0026thinsp;\u0026plusmn;\u0026thinsp;6.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.154\u0026thinsp;\u0026plusmn;\u0026thinsp;8.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitD (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2055 (59.187\u0026thinsp;\u0026plusmn;\u0026thinsp;24.913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.217\u0026thinsp;\u0026plusmn;\u0026thinsp;25.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.553\u0026thinsp;\u0026plusmn;\u0026thinsp;25.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.716\u0026thinsp;\u0026plusmn;\u0026thinsp;21.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67.285\u0026thinsp;\u0026plusmn;\u0026thinsp;28.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2055 (52.258\u0026thinsp;\u0026plusmn;\u0026thinsp;16.596)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.429\u0026thinsp;\u0026plusmn;\u0026thinsp;18.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.095\u0026thinsp;\u0026plusmn;\u0026thinsp;16.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.614\u0026thinsp;\u0026plusmn;\u0026thinsp;16.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.031\u0026thinsp;\u0026plusmn;\u0026thinsp;17.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes (1000 cells/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2055 (0.571\u0026thinsp;\u0026plusmn;\u0026thinsp;0.201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.594\u0026thinsp;\u0026plusmn;\u0026thinsp;0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.598\u0026thinsp;\u0026plusmn;\u0026thinsp;0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.573\u0026thinsp;\u0026plusmn;\u0026thinsp;0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.560\u0026thinsp;\u0026plusmn;\u0026thinsp;0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (1000 cells/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2055 (2.297\u0026thinsp;\u0026plusmn;\u0026thinsp;0.731)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.230\u0026thinsp;\u0026plusmn;\u0026thinsp;0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.272\u0026thinsp;\u0026plusmn;\u0026thinsp;0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.310\u0026thinsp;\u0026plusmn;\u0026thinsp;0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.247\u0026thinsp;\u0026plusmn;\u0026thinsp;0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (1000 cells/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2055 (4.478\u0026thinsp;\u0026plusmn;\u0026thinsp;2.238)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.438\u0026thinsp;\u0026plusmn;\u0026thinsp;1.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.449\u0026thinsp;\u0026plusmn;\u0026thinsp;1.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.606\u0026thinsp;\u0026plusmn;\u0026thinsp;2.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.478\u0026thinsp;\u0026plusmn;\u0026thinsp;1.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (1000 cells/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2055 (248.007\u0026thinsp;\u0026plusmn;\u0026thinsp;62.550)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e246.445\u0026thinsp;\u0026plusmn;\u0026thinsp;60.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e240.797\u0026thinsp;\u0026plusmn;\u0026thinsp;59.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e247.950\u0026thinsp;\u0026plusmn;\u0026thinsp;61.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e245.724\u0026thinsp;\u0026plusmn;\u0026thinsp;59.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2055 (0.012\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2055 (117.254\u0026thinsp;\u0026plusmn;\u0026thinsp;44.251)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120.136\u0026thinsp;\u0026plusmn;\u0026thinsp;44.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116.102\u0026thinsp;\u0026plusmn;\u0026thinsp;44.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115.493\u0026thinsp;\u0026plusmn;\u0026thinsp;38.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e118.516\u0026thinsp;\u0026plusmn;\u0026thinsp;43.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2055 (2.082\u0026thinsp;\u0026plusmn;\u0026thinsp;1.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.144\u0026thinsp;\u0026plusmn;\u0026thinsp;1.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.134\u0026thinsp;\u0026plusmn;\u0026thinsp;1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.083\u0026thinsp;\u0026plusmn;\u0026thinsp;0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.139\u0026thinsp;\u0026plusmn;\u0026thinsp;0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.754\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\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables: the P-value was calculated by the weighted linear regression model. (%) for categorical variables: the P- value was calculated by the weighted chi-square test. BMD, bone mineral density; BMI, body mass index; VitD, vitamin D; HDL-C, high-density lipoprotein cholesterol; MHR, monocyte to HDL-C ratio; PLR, platelet to lymphocyte ratio; NLR, neutrophil to lymphocyte ratio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The association between monocyte to HDL-C ratio and lumbar bone mineral density\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of the multivariate regression analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;2. In the unadjusted model, MHR exhibited a negative association with lumbar BMD (β=-1.099, 95% CI: -2.109 to -0.089, P\u0026thinsp;=\u0026thinsp;0.033). This significant relationship persisted after adjusting for covariates in model 2 (β=-1.283, 95% CI: -2.408 to -0.158, P\u0026thinsp;=\u0026thinsp;0.026) and model 3 (β=-1.681, 95% CI: -3.218 to -0.144, P\u0026thinsp;=\u0026thinsp;0.032). Furthermore, when MHR was categorized into quartiles, individuals in the lowest MHR quartile exhibited a 0.02 g/cm\u0026sup2; higher lumbar BMD compared to those in the highest MHR quartile (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for trend).\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\u003eThe association between monocyte to HDL-C ratio and lumbar bone mineral density.\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 β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMODEL 2 β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMODEL 3β (95% CI)\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\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003eMHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.099 (-2.109, -0.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.283 (-2.408, -0.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.681 (-3.218, -0.144)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintiles of MHR\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\u003eQ1\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\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001 (-0.017, 0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.003 (-0.022, 0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.010 (-0.031, 0.011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.026 (-0.044, -0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.030 (-0.049, -0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.039 (-0.062, -0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.020 (-0.038, -0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.025 (-0.045, -0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.035 (-0.062, -0.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\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\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\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\u003eModel 1: no covariates were adjusted. Model 2: age, gender, educational level and BMI were adjusted. Model 3: age, gender, educational level, BMI, family income-to-poverty ratio, Total calcium, Serum iron, VitD, HDL-C, lymphocytes, neutrophils, platelets, PLR and NLR were adjusted. HDL-C, high-density lipoprotein cholesterol; MHR, monocyte to HDL-C ratio.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Subgroup analyses of the association of the monocyte to HDL-C ratio with lumbar bone mineral density after adjustment for confounding factors.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSubgroup analyses stratified by gender and educational attainment revealed significant interactions, particularly within the educational subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, when stratified by gender, the negative correlation between MHR and lumbar BMD remained significant in men (β=-1.625, 95% CI: -2.937, -0.313, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, no significant differences were observed in the correlation between MHR and BMD across various ethnic groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Notably, individuals with less than a 9th-grade education exhibited a positive association with MHR, whereas those with a college degree or higher demonstrated a negative association (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for interaction). In the fully adjusted model, this correlation was still significant (P\u0026thinsp;=\u0026thinsp;0.001 for interaction).\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 analyses of the association of the monocyte to HDL-C ratio with lumbar bone mineral density.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMODEL 1 β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eStratified by gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.325\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\u003e-1.625 (-2.937, -0.313)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.942 (-2.513, 0.629)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e-0.190 (-1.905, 1.525)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159 (-1.943, 2.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStratified by race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican\u0026nbsp;American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.934 (-1.283, 3.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.549 (-1.863, 2.961)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u0026nbsp;Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.085 (-3.107, 2.938)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.784 (-4.015, 2.447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic\u0026nbsp;White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.473 (-1.956, 1.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.835 (-2.697, 1.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic\u0026nbsp;Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.350 (-3.065, 2.366)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.830 (-3.920, 2.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u0026nbsp;Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.460 (-4.461, 1.541)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.190 (-5.540, 1.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStratified by education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.140 (4.006, 12.274)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.556 (3.432, 11.679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.420 (-3.701, 0.860)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.889 (-3.267, 1.490)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.624 (-3.526, 0.277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.695 (-3.784, 0.394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.966 (-2.755, 0.823)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.972 (-3.038, 1.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.912 (-5.322, 1.498)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.015 (-5.680, 1.651)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCrude: no covariates were adjusted. Model 1: age, gender, race, educational level, BMI, Total calcium, Serum iron, VitD, HDL-C, lymphocytes, neutrophils, platelets, PLR and NLR were adjusted. In the subgroup analysis stratified by gender, race, and educational level, the model is not adjusted for gender, race, and educational level respectively. HDL-C, high-density lipoprotein cholesterol; GED, General Educational Development.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.4 U-shaped association between MHR and lumbar BMD in individuals possessing a high school diploma or GED equivalent\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe stratified analysis examining the association between the monocyte-to-high-density lipoprotein ratio and lumbar bone mineral density, while adjusting for multiple covariates across varying levels of educational attainment, reveals a significant U-shaped relationship in the subgroup of individuals possessing a high school diploma or GED equivalent, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Notably, within this educational stratum, MHR levels below the threshold of 0.024 are associated with an adjusted β-value of 1.676, with a 95% Confidence Interval (CI) extending from \u0026minus;\u0026thinsp;11.795 to 15.147. This interval includes zero, suggesting the absence of a statistically significant association at this MHR level (P\u0026thinsp;=\u0026thinsp;0.808). In contrast, at MHR levels greater than 0.024, the adjusted β-value increases significantly to 30.307, with a 95% confidence interval ranging from 8.152 to 52.462, indicating a strong positive correlation that is statistically significant (P\u0026thinsp;=\u0026thinsp;0.009). The log-likelihood ratio test supports the model's goodness-of-fit with a P-value of 0.031, thereby confirming the statistical reliability of the observed U-shaped association.\u003c/p\u003e \u003cp\u003eThe U-shaped relationship indicates a nuanced impact of MHR on health outcomes within this educational stratum, wherein the influence of MHR is minimal at lower levels but becomes significantly positive beyond a certain threshold. This nonlinear association may reflect intricate biological interactions between MHR and lumbar BMD, as well as potential disparities in socioeconomic and behavioral factors. It underscores the necessity for tailored public health interventions that address the specific needs of distinct educational groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted associations between monocyte to HDL-C ratio with lumbar bone mineral density by education level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted β (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\u003eHigh school graduate/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHR \u0026lt;\u0026nbsp;0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.676 (-11.795, 15.147) 0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHR \u0026gt;\u0026nbsp;0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.307 (8.152, 52.462) 0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog likelihood ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.031\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\u003eAge, gender, race, BMI, Total calcium, Serum iron, VitD, HDL-C, monocytes, lymphocytes, neutrophils, platelets, hypertension, diabetes, PLR and NLR were adjusted. HDL-C, high-density lipoprotein cholesterol; MHR, monocyte to HDL-C ratio.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Alcohol dependence and Depression\u003c/h2\u003e \u003cp\u003eAlcohol misuse is the primary contributor to disease and death in both developed and developing nations. Quantitative data on alcohol misuse underscores its substantial medical significance. When evaluating its contribution to physical ailments and self-inflicted fatalities, alcoholism ranks as the fourth leading cause of death within the United States(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Depression is frequently identified as the predominant comorbid psychiatric disorder in patients exhibiting alcohol dependence(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Given the prevalent manifestation of depressive symptoms in individuals with alcoholism, clinicians and researchers hypothesize a potential correlation between these two disorders. It is postulated that individuals may resort to alcohol consumption as a coping mechanism for underlying depressive conditions. Conversely, depression may predominantly arise as a detrimental consequence of alcohol dependence(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The primary criterion for evaluation is predicated on the precedence or the fundamental cause. It is evident that the concurrent prevalence of depression and alcohol dependence is not only common but also clinically significant. Focusing solely on either alcoholism or depression presents a skewed perspective. In general, the elevated incidence of alcohol abuse among individuals suffering from depression indicates that research into this demographic and enhancement of their physical and mental well-being could potentially have a substantial influence on public health.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Clinical implications\u003c/h2\u003e \u003cp\u003eOsteoporosis, alcohol dependence and depression have become major public health concerns in increasingly aging population, both of them are closely associated with severe morbidity and increased mortality. A previous cross-sectional study published in 2022 indicates that people with depression was associated with lower BMD, particularly in the spine, males, Hispanic-White, and not highly educated populations(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This association between depression and lower BMD is stable and consistent with previous research findings(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). A research based on trabecular bone score (TBS) rather than BMD showed that alcohol use disorder, antidepressants, and lithium are associated with poorer bone texture in women(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Although alcohol dependence and depression often occur simultaneously, previous studies on bone metabolism in alcohol dependent patients with depression were limited and further exploration was needed.\u003c/p\u003e \u003cp\u003eAs the main risk factor of osteoporosis, chronic low inflammation caused by depression and alcohol can directly influence bone homeostasis(\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The NLR, PLR and MHR are biomarkers of inflammation and oxidative stress. Especially MHR, is considered as a prognostic biomarker for cardiovascular disease(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In our cross-sectional study, using NHANES data, we found a significant U-shaped link between MHR and lumbar BMD in high school-educated individuals with alcohol-dependent depression. When the MHR was below 0.024, there was a corresponding increase in lumbar BMD by 1.676g/cm\u003csup\u003e2\u003c/sup\u003e for each unit increase in MHR. Conversely, when the MHR was precisely 0.024, the BMD was observed to be at its minimal value. However, when the MHR exceeded 0.024, the BMD exhibited a substantial increase of 30.307g/cm\u003csup\u003e2\u003c/sup\u003e for each unit increment in MHR. These results indicated that MHR may have a threshold effect on bone health, shedding light on the intricate relationship between systemic inflammation, metabolic factors, and bone mineral density.\u003c/p\u003e \u003cp\u003eTo our knowledge, only two prior investigations have been conducted on the correlation between MHR and BMD levels. A cross-sectional study executed in 2024 suggests that, among the non-diabetic elderly population, MHR serves as a defensive mechanism against bone irregularities, exhibiting a direct relationship with lumbar BMD\u003csup\u003e(11)\u003c/sup\u003e. In the context of Chinese postmenopausal women diagnosed with Type 2 Diabetes Mellitus (T2DM), both MHR and Monocyte to Albumin Ratio (MAR) demonstrated a positive correlation with beta-C-terminal telopeptide (\u003cem\u003eβ-CTX\u003c/em\u003e), and a negative correlation with lumbar and femoral neck BMD(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The correlation between other inflammatory indices derived from blood cell count and BMD exhibits varying results across different demographic groups. A preceding retrospective analysis involving 893 postmenopausal women revealed a negative correlation between BMD and systemic immune-inflammation index (SII), NLR, and the product of platelet count and neutrophil count (PPN). These factors were also found to be positively associated with the risk of osteoporosis(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This was further corroborated by a cross-sectional study of 413 postmenopausal women, which also demonstrated a negative relationship between SII and BMD(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Additionally, research conducted in Turkey indicated an inverse correlation between BMD and NLR, PLR, MLR, and SII in postmenopausal women(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Research investigating children diagnosed with hypothyroidism has revealed a significant positive correlation between the BMD Z-score and NLR, MLR, PLR, and thyroid stimulating hormone (TSH) levels. However, this correlation was not observed in children who were either healthy or diagnosed with hyperthyroidism(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). A separate retrospective study involving 143 children demonstrated a negative correlation between the BMD Z-score and both MLR and PLR in the obese cohort, while a positive correlation was identified within the control group(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Chen et al. discovered no significant correlation between MLR and lumbar BMD in adults. However, they noted a positive correlation between NLR and lumbar BMD, and a negative correlation between PLR and lumbar BMD(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). This is further supported by a meta-analysis, which also suggested a negative correlation between both NLR and PLR with BMD(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study monitoring MHR to identify its link to lumbar BMD in alcohol-dependent, depressed patients. The study fills a significant gap in existing literature on the impact of systemic inflammatory markers on BMD patients with alcohol dependence and depression, offering several clinical benefits for doctors. Our research elucidates the interaction between systemic inflammation and bone health within a specific demographic, thereby fostering a comprehensive understanding of the multifactorial determinants of bone density. The implications of these findings could potentially facilitate early identification of individuals at risk and aid in the development of targeted interventions designed to mitigate bone loss and prevent osteoporosis. Moreover, our research has the potential to guide the development of individualized treatment approaches, taking into account the intricate interconnections among alcohol dependence, depressive manifestations, and bone health. This could potentially augment clinical results and ameliorate the life quality of impacted individuals. Additionally, the knowledge derived from this study could provide critical information for public health policymakers, directing the formulation of health management policies specifically designed to cater to the requirements of patients suffering from these coexisting conditions. The study promotes interdisciplinary collaboration in psychiatry, osteology, and immunology, advancing cross-disciplinary research. It provides theoretical and practical contributions to patient care and scientific knowledge in health research.\u003c/p\u003e \u003cp\u003e\u003cb\u003e4.3 Strengths and limitations\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo date, scholarly investigations concerning BMD have predominantly concentrated on postmenopausal women. The BMD level of patients suffering from alcohol-dependent depression, a subject that has been sparingly addressed in existing literature, remains largely unexplored. This research represents the inaugural study into the association between the systemic inflammatory index and BMD within this particular demographic. This study has a number of strengths, including the use of a combined five cycles of NHANES data based on a nationally representative sample. This benefits from a robust, standardised methodology and a large, nationally representative sample, enhancing the generalisability of the findings. The study's interdisciplinary approach, which brings together psychiatry, bone health and inflammation, makes a significant contribution to our understanding of the complex interactions between mental health and physical well-being.\u003c/p\u003e \u003cp\u003eAs a cross-sectional study, this research is limited in its ability to establish causality or temporality between systemic inflammation, alcohol dependence, depression, and bone density. The findings are specific to patients suffering from alcohol-dependent depression and may not be generalizable to other populations with different characteristics or healthcare systems. Reliance on self-reported data for certain variables, such as alcohol consumption and depression symptoms, could introduce reporting biases, affecting the accuracy of the results. Current evidence is mostly limited to studies that used BMD, which does not provide more information about the texture of bone tissue and can underestimate fracture risk(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The U-shaped link between MHR and BMD in the \"High School Graduate/GED\" group lacks clear biological explanation. More research is needed to understand why inflammation differently affects the balance between bone formation and resorption.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, this study provides novel insights into the relationship between MHR, a marker of systemic inflammation, and BMD in individuals with alcohol dependency and depression. The observed U-shaped association among participants with a high school diploma or GED equivalent highlights the complex interplay between inflammation, metabolic factors, and bone health. Although constrained by its cross-sectional design, this research identifies MHR as a potential biomarker for monitoring bone health in alcohol-dependent individuals with depression. It is recommended that clinicians consider regular BMD assessments and interventions aimed at modulating inflammation in this population. Future studies should seek to confirm these findings in longitudinal cohorts and investigate the underlying mechanistic pathways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLZ and XY wrote the main manuscript text. LZ prepared tables 1-4. XY prepared figures 1-3. All authors contributed to review and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by grants from the Youth Talent Development Plan of Changzhou Health Commission (grant number CZQM2023006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll human subjects involved in this study were treated in accordance with the ethical principles outlined in the Declaration of Helsinki, and the study was approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS). The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWalker MD, Shane E. Postmenopausal Osteoporosis. N Engl J Med. 2023;389(21):1979-91.\u003c/li\u003e\n\u003cli\u003eSnyder S. Postmenopausal Osteoporosis. N Engl J Med. 2024;390(7):675-6.\u003c/li\u003e\n\u003cli\u003eMcHugh RK, Weiss RD. Alcohol Use Disorder and Depressive Disorders. Alcohol Res. 2019;40(1).\u003c/li\u003e\n\u003cli\u003eGuo X, She Y, Liu Q, Qin J, Wang L, Xu A, et al. 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Bone mineral density and complete blood count ratios in children and adolescents with obesity. Eur Rev Med Pharmacol Sci. 2022;26(1):249-56.\u003c/li\u003e\n\u003cli\u003eChen S, Sun X, Jin J, Zhou G, Li Z. Association between inflammatory markers and bone mineral density: a cross-sectional study from NHANES 2007-2010. J Orthop Surg Res. 2023;18(1):305.\u003c/li\u003e\n\u003cli\u003eLiu YC, Yang TI, Huang SW, Kuo YJ, Chen YP. Associations of the Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio with Osteoporosis: A Meta-Analysis. Diagnostics (Basel). 2022;12(12).\u003c/li\u003e\n\u003cli\u003eSale JE, Bogoch E, Meadows L, Gignac M, Frankel L, Inrig T, et al. Bone Mineral Density Reporting Underestimates Fracture Risk in Ontario. Health (Irvine Calif). 2015;7(5):566-71.\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Osteoporosis, Alcohol-dependent, Depression, MHR, BMD","lastPublishedDoi":"10.21203/rs.3.rs-5957443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5957443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eOsteoporosis, a skeletal disorder that reduces bone density, is a significant health concern. Alcohol dependence, a chronic condition, exacerbates public health problems due to its widespread occurrence and association with various comorbidities, including depression. This study aims to explore the relationship between monocyte to high-density lipoprotein cholesterol ratio (MHR) and lumbar bone mineral density (BMD)in individuals with alcohol dependence and depression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFrom 2009 to 2018, 49,693 participants were enrolled, and after screening, the study included 2,055 individuals with alcohol-dependency and depression. In this study, multivariate regression analysis was employed to assess the association between monocyte to HDL-C ratio and lumbar bone mineral density. Additionally, we conducted interaction tests and subgroup analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe result showed a negative correlation between MHR and lumbar BMD, which remained significant even after adjusting for covariates. Individuals with less than a 9th-grade education showed a positive link with MHR, while those with a college degree or higher had a negative link. This relationship remained significant in the fully adjusted model. A U-shaped relationship between MHR and lumbar BMD was observed in individuals with a high school diploma/GED, after adjusting for various factors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe intricate correlation between MHR and lumbar BMD may suggest the presence of biological interplays and disparities in socioeconomic or behavioral aspects. This underscores the necessity for tailored public health strategies that cater to various educational demographics.\u003c/p\u003e","manuscriptTitle":"Associations between Monocyte to HDL-C ratio and Lumbar Bone Mineral Density in Alcohol dependent Individuals with Depression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 08:35:03","doi":"10.21203/rs.3.rs-5957443/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-05-02T04:16:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-19T05:59:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284457483043778181924768231062953216372","date":"2025-04-11T20:18:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T17:06:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252153495136198301125731134589624326601","date":"2025-04-10T16:02:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-09T13:43:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T04:47:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-22T11:56:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd021c05-bef5-409d-8c7e-b57b03d1ba6d","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46917813,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"},{"id":46917817,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":46917818,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-05-12T16:05:13+00:00","versionOfRecord":{"articleIdentity":"rs-5957443","link":"https://doi.org/10.1038/s41598-025-00885-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-06 15:57:24","publishedOnDateReadable":"May 6th, 2025"},"versionCreatedAt":"2025-04-10 08:35:03","video":"","vorDoi":"10.1038/s41598-025-00885-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-00885-8","workflowStages":[]},"version":"v1","identity":"rs-5957443","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5957443","identity":"rs-5957443","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}