Association between red blood cell distribution width-to-albumin ratio and lumbar spine bone mineral density: a cross-sectional analysis among US adults, 2015–2018

Association between red blood cell distribution width-to-albumin ratio and lumbar spine bone mineral density: a cross-sectional analysis among US adults, 2015–2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between red blood cell distribution width-to-albumin ratio and lumbar spine bone mineral density: a cross-sectional analysis among US adults, 2015–2018 Mengyan Zhao, Chaoyang Liu, Tao Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7092982/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Red blood cell distribution width (RDW) and albumin levels are associated with bone metabolism. However, the relationship between the ratio of the two (RAR) and lumbar spine bone mineral density (BMD) remains unclear. This study aims to explore the association between RAR and lumbar spine BMD and the potential nonlinear relationship. Methods Multivariate logistic regression, restricted cubic spline (RCS) regression, receiver operating characteristic (ROC) analysis, and sensitivity analyses were used to examine the relationship between RAR and lumbar spine BMD based on NHANES data from 2015–2018. The study also used subgroup analyses and interaction tests to explore whether the relationship was stable across populations. Results Elevated RAR is significantly associated with reduced lumbar spine BMD (fully adjusted model β = -0.309, 95% CI: -0.327 to -0.291, P < 0.001). RCS analysis revealed an L-shaped nonlinear association between the two (P for nonlinearity < 0.001), with an inflection point at RAR = 4.25. Below the inflection point, RAR was negatively correlated with BMD (β = -0.410, P < 0.001), while above the inflection point, it was positively correlated (β = 0.438, P < 0.001). Trend analysis showed that increasing RAR quartiles were associated with decreasing BMD (Q4 vs. Q1: β = -0.239, P 0.05), but there were modifying effects in subgroups of education level, BMI, sleep duration, PIR, and hypertension (interaction P < 0.05). Conclusions Elevated RAR is an independent risk factor for reduced lumbar spine BMD, with a threshold effect of 4.25. RAR may serve as a potential biomarker for assessing bone health. Clinical trial number: Not applicable. RAR lumbar spine bone density osteoporosis cross-sectional study NHANES Figures Figure 1 Figure 2 Introduction Osteoporosis is a systemic skeletal disease characterized by reduced bone mass and microstructural deterioration, significantly increasing the risk of fractures and becoming a global public health burden 1 . An estimated 200 million people worldwide are thought to be affected by this illness, which results in 8.9 million fractures annually, of which up to 46% are vertebral fractures. This is directly linked to rising rates of disability and death as well as a reduction in patients' quality of life 2,3 . It is estimated that by 2025, the number of osteoporosis patients worldwide will increase to approximately 300 million, which will result in a huge socioeconomic burden 4,5 . Dual-energy X-ray absorptiometry (DXA) is now the gold standard for clinical evaluation of bone density; nevertheless, its utility in large-scale population screening is limited by its high cost, complicated operation, and radiation exposure concerns 6,7 . For the early detection and risk assessment of osteoporosis, it is crucial to investigate straightforward, non-invasive, and reasonably priced biomarkers. In recent years, persistent inflammation and the formation of senescent cells have been revealed to hinder bone recuperation mechanisms 8 . However, nutritional status is involved in bone metabolism regulation 9,10 . Under inflammatory conditions, TEPP-46 treatment reduced nuclear dimer PKM2 levels, decreased phospho-signal transduction and transcription activator 3 (p-STAT3) expression, and inhibited osteoclast generation and osteoclast-related gene expression 11 . Malnutrition also hinders the mineralization and production of bone matrix 12 . Red blood cell distribution width (RDW) is a measure of red blood cell volume heterogeneity. It is not only a standard criterion for anemia, but also a sensitive marker of systemic inflammation and oxidative stress 13 . Serum albumin, as a key biomarker of nutritional evaluation, is substantially related to sarcopenia and decreased bone density when its levels are lowered 14 . It is worth noting that RDW and albumin concentration convey information about inflammation, oxidative stress, and nutrition from different perspectives 13,15,16 ; All of them may be readily obtained through laboratory testing. As a result, the RDW-to-albumin ratio (RAR) produced by combining these two indicators could be a more useful indicator of bone density than each one by itself. Thus, we postulated that lumbar bone density may be linked to higher RAR. We next investigated the possible relationship between RAR and lumbar bone density using data from NHANES from 2015 to 2018. Methods The Strengthening Reporting of Observational Studies in Epidemiology (STROBE) reporting requirements were adhered to in this study. It made use of a national cross-sectional design and secondary analysis of de-identified and publicly available NHANES data. As a result, neither informed consent nor additional institutional review board permission was needed for the study. Study population A thorough nationwide survey that has been carried out since 1999 is the NHANES database. Using intricate, multistage, and stratified sample methods, it evaluates the socioeconomic standing, health, and nutrition of the American people. For more information about the data, visit ( https://www.cdc.gov/nchs/nhanes/index.htm ). Comprehensive information on the design, methods, and weighting of NHANES has been published previously 17 . Prior to data collection, the National Center for Health Statistics Ethics Review Board approved all study protocols, and each participant signed an informed consent form. Data for surveys was gathered by telephone interviews, household questionnaires, and examinations performed by qualified staff and medical professionals. This cohort study employed data from NHANES from 2015 to 2018, which comprised questionnaire items, laboratory measures, demographic data, and physical examination results. Participants without lumbar spine BMD measurements (N = 10,368), those with missing red blood cell distribution width data (N = 605), those with missing albumin data (N = 1,303), and those under the age of 20 (N = 1,885) were excluded from the analysis. As illustrated in Fig. 1 , 5,065 eligible participants were included in the final analysis. RAR Blood samples had RAR extracted from them. Strict laboratory testing was carried out in accordance with established sample procedures to guarantee the validity and comparability of the data. A Coulter analyzer in the mobile testing facilities used peripheral blood to calculate the RDW (%). A Roche Cobas 6000 chemical analyzer was used to measure the concentration of serum albumin using the bromocresol violet technique. RAR was calculated based on the above metric: RAR = RDW (%)/Albumin (g/dL). Measurement of lumbar spine BMD The key outcome variable, lumbar spine BMD, was measured by Dual-energy x-ray absorptiometry (DXA) scans conducted by certified and trained radiologic technologists. Lumbar spine bone density was expressed in g/cm2, and all scans were performed using Apex 3.2 software on a Hologic Discovery A densitometer (Hologic, Inc., Bedford, Massachusetts). The NHANES website's "Body Composition Procedures Manual" contains comprehensive information about the lumbar spine bone density test. Covariables The research model in this study included the following potential regulatory covariates: age, gender, race/ethnicity, education level, family income, BMI, sleep duration, glycohemoglobin, diabetes, hypertension, and biochemical indicators (blood urea nitrogen, total calcium, total protein, phosphorus, uric acid, and direct HDL-cholesterol). Mexican Americans, other Hispanics, non-Hispanic Whites, non-Hispanic Blacks, and other races make up the race categories. The three categories of education level were college or higher, high school graduate, and below high school. Based on participant self-reported data, diabetes and hypertension were identified. The poverty-income ratio (PIR), which accounts for family size, was used to calculate household income. The participant's height and weight were used to calculate their BMI. Furthermore, NHANES laboratory data provided detailed information about a number of biochemical indicators, including blood urea nitrogen, total calcium, total protein, phosphorus, uric acid, and direct HDL-cholesterol. Statistical analysis According to the NHANES complex sampling architecture, two cycles of data from the National Health and Nutrition Examination Survey (NHANES) were assessed for correctness using the proper weights. R Studio (version 4.2.0) and EmpowerStats (version 4.2) were used for statistical analysis. While categorical variables are represented by counts (n) and percentages (%), continuous variables are displayed as mean ± standard deviation (SD). Weighted chi-square tests were used for categorical variables, and Student's t-tests were used for continuous variables. The association between lumbar spine BMD and the red blood cell distribution width-to-albumin ratio (RAR) was investigated using multiple linear regression models. No covariate adjustments were made in Model 1; age, sex, and race were corrected for in Model 2, and education level, marital status, body mass index (BMI), sleep duration, PIR, direct HDL-cholesterol, total cholesterol, glycohemoglobin, hypertension, and diabetes were further adjusted for in Model 3. RAR was divided into four groups in order to investigate how varying RAR levels affected the BMD of the lumbar spine. Furthermore, the nonlinear association between RAR and lumbar spine BMD was examined using generalized additive models (GAM) and smooth curve fitting approaches. Subgroup analyses were finally carried out. Results Baseline characteristics of participants The study population's baseline characteristics, broken down by RAR quartile, are shown in Table 1 . With mean ages ranging substantially from 43.096 years in the top quartile to 36.962 years in the lowest quartile (p < 0.0001), the analysis included 5,065 participants. The lower lumbar spine BMD groups had a higher percentage of males (69.151% in quartile 1) than the higher quartiles (32.529% in quartile 4, p < 0.001). There was a significant negative correlation between lumbar spine BMD and education level above high school, with 62.791% of participants in quartile 1 having higher education than high school or equivalent, compared to only 55.097% in quartile 4 (p < 0.001). Health indicators such as hypertension and diabetes were significantly more prevalent in higher lumbar spine BMD groups (p < 0.0001). These findings underscore the intricate interplay between lumbar spine BMD, demographic factors, and health status within this population. Table 1 Basic characteristics of participants by RAR quartile Covariates RAR P -value Q1(2.320–2.930) N = 1,248 Q2(2.932–3.114) N = 1,279 Q3(3.116–3.324) N = 1,253 Q4(3.325–5.040) N = 1,285 Age (years, mean ± SD) 36.962 ± 10.992 39.427 ± 11.254 40.632 ± 11.012 43.096 ± 10.729 < 0.001 Sex (%) < 0.001 Male 863 (69.151%) 705 (55.121%) 559 (44.613%) 418 (32.529%) Female 385 (30.849%) 574 (44.879%) 694 (55.387%) 867 (67.471%) Race/ethnicity (%) < 0.001 Mexican American 155 (12.420%) 216 (16.888%) 226 (18.037%) 242 (18.833%) Other Hispanic 109 (8.734%) 148 (11.572%) 157 (12.530%) 173 (13.463%) Non-Hispanic white 474 (37.981%) 379 (29.633%) 361 (28.811%) 297 (23.113%) Non-Hispanic black 261 (20.913%) 255 (19.937%) 256 (20.431%) 321 (24.981%) Other race/multiracial 249 (19.952%) 281 (21.970%) 253 (20.192%) 252 (19.611%) Education level (%) < 0.001 Below high school 195 (15.638%) 213 (16.654%) 268 (21.389%) 274 (21.323%) High school or equivalent 269 (21.572%) 277 (21.658%) 261 (20.830%) 303 (23.580%) Above high school 783 (62.791%) 789 (61.689%) 724 (57.781%) 708 (55.097%) Marital status (%) < 0.001 Married/living with partner 762 (61.107%) 811 (63.409%) 802 (64.006%) 777 (60.467%) Separated/divorced/widowed 130 (10.425%) 147 (11.493%) 174 (13.887%) 243 (18.911%) Never married 355 (28.468%) 321 (25.098%) 277 (22.107%) 265 (20.623%) BMI (kg/m2, Mean ± SD) 27.491 ± 5.716 28.776 ± 6.820 29.610 ± 6.859 31.870 ± 7.981 < 0.001 Sleep duration (hours, Mean ± SD) 129.601 ± 7.650 131.882 ± 8.155 133.662 ± 8.525 135.682 ± 8.340 < 0.001 PIR(Mean ± SD) 2.703 ± 1.630 2.663 ± 1.628 2.414 ± 1.584 2.402 ± 1.618 < 0.001 Direct HDL-Cholesterol(Mean ± SD) 54.450 ± 16.822 52.477 ± 16.124 51.280 ± 15.188 52.341 ± 15.816 < 0.001 Total Cholesterol(Mean ± SD) 189.797 ± 39.571 191.175 ± 39.826 191.657 ± 41.448 190.291 ± 38.298 0.641 Glycohemoglobin(Mean ± SD) 5.502 ± 0.889 5.616 ± 1.007 5.686 ± 1.049 5.889 ± 1.312 < 0.001 Blood Urea Nitrogen(Mean ± SD) 4.929 ± 1.465 4.920 ± 1.686 4.741 ± 1.398 4.662 ± 1.856 < 0.001 Total Calcium(Mean ± SD) 2.370 ± 0.074 2.339 ± 0.077 2.315 ± 0.076 2.277 ± 0.079 < 0.001 Total Protein(Mean ± SD) 74.154 ± 3.714 72.635 ± 3.784 71.702 ± 4.064 70.592 ± 4.515 < 0.001 Uric acid(Mean ± SD) 334.665 ± 82.790 320.877 ± 86.161 312.816 ± 85.398 306.574 ± 83.183 < 0.001 Phosphorus(Mean ± SD) 1.198 ± 0.173 1.189 ± 0.168 1.160 ± 0.182 1.152 ± 0.177 < 0.001 Hypertension (%) < 0.001 Yes 272 (21.795%) 297 (23.221%) 301 (24.022%) 356 (27.704%) No 976 (78.205%) 982 (76.779%) 947 (75.579%) 928 (72.218%) Diabetes (%) < 0.001 Yes 76 (6.090%) 93 (7.271%) 94 (7.502%) 143 (11.128%) No 1155 (92.548%) 1154 (90.227%) 1138 (90.822%) 1105 (85.992%) Lumbar spine BMD (g/cm2, (Mean ± SD)) 1.141 ± 0.149 1.054 ± 0.138 1.016 ± 0.127 0.951 ± 0.134 < 0.001 Mean (SD) for continuous variables, % for categorical variables. NHANES, National Health and Nutrition Examination Survey; PIR, poverty income ratio; BMI, body mass index; Lumbar spine BMD, lumbar spine bone mineral density; RAR, red blood cell distribution width-to-albumin ratio. Association between RAR and lumbar spine BMD The relationship between RAR and lumbar spine BMD is shown in Table 2 . According to our findings, a lower lumbar spine BMD is associated with an elevated RAR. RAR was linked to significantly lower odds of lumbar spine BMD in Model 1, which did not account for any covariates. This resulted in an odds ratio (β) of -0.231 (95% CI: -0.247, -0.215). This finding suggests a clear correlation between lower lumbar spine BMD and higher RAR. Age, sex, and ethnicity were among the demographic factors that were adjusted for in Model 2. With a β of -0.253 (95% CI: -0.272, -0.235), the correlation in this model remained significant, indicating that higher RAR was still linked to lower lumbar spine BMD even after controlling for these demographic factors. Model 3 further refined the analysis by adjusting for additional health-related factors, including Education level, Marital status, body mass index (BMI), Sleep duration, PIR, Direct HDL-Cholesterol, Total Cholesterol, Glycohemoglobin, hypertension, and diabetes. The results retained statistical significance, with a β of -0.309 (95% CI: -0.327, -0.291). This finding underscores the robustness of the association between RAR and lumbar spine BMD. Additionally, when total RAR was stratified into quartiles, this association remained statistically significant. In comparison to the reference group (Q1), participants in the second quartile (Q2) showed a significant decrease in lumbar spine BMD, with a β of -0.093 (95% CI: -0.106, -0.080) in Model 1 and − 0.110 (95% CI: -0.124, -0.095) in Model 3. With βs of -0.184 (95% CI, -0.199, -0.169) in Model 1 and − 0.239 (95% CI, -0.255, -0.222) in Model 3, individuals in the four quartiles (Q4) also showed a greater correlation. All models showed significant trend analysis across RAR quartiles, with p for trend being < 0.0001, < 0.0001, and 0.0240. Table 2 Association between RAR and lumbar spine BMD N Model 1 Model 2 Model 3 β (95% CI) β (95% CI) β (95% CI) RAR 5065 -0.231 (-0.247, -0.215) -0.253 (-0.272, -0.235) -0.309 (-0.327, -0.291) RAR quartile Q1 1248 Ref. Ref. Ref. Q2 1279 -0.093 (-0.106, -0.080) -0.097 (-0.111, -0.084) -0.110 (-0.124, -0.095) Q3 1253 -0.123 (-0.138, -0.108) -0.132 (-0.148, -0.116) -0.154 (-0.170, -0.139) Q4 1285 -0.184 (-0.199, -0.169) -0.200 (-0.217, -0.184) -0.239 (-0.255, -0.222) P for trend 5065 < 0.0001 < 0.0001 0.0240 Model 1 : no covariates were adjusted Model 2 : age, sex, and race/ethnicity were adjusted Model 3 : age, sex, race/ethnicity, education level, marital status, PIR, BMI, sleep duration, hypertension, diabetes, Direct HDL-Cholesterol, Total Cholesterol, Glycohemoglobin. 95% CI, 95% confidence interval Dose-relationship between RAR and lumbar spine BMD Restricted cubic spline analyses (RCS) demonstrate a nonlinear relationship between RAR and lumbar spine BMD(P for nonlinear < 0.001), characterized by an L-shaped curve, as illustrated in Fig. 2 . As detailed in Table 3 , threshold effect analysis identified a critical turning point at 4.25. Before this critical point, a robust negative relationship exists between RAR and lumbar spine BMD (β = -0.410; 95% CI: -0.424, -0.396; P < 0.0001); However, beyond this point, the correlation shifts to a pronounced positive correlation (β = 0.438; 95% CI: 0.273, 0.604; P < 0.0001). Table 3 Threshold effect analysis of the relationship between RAR and lumbar spine BMD Outcome lumbar spine BMD OR (95% CI) P-value Model I One line effect -0.390 (-0.403, -0.376) < 0.0001 Model II Inflection point (K) 4.21 <K -0.410 (-0.424, -0.396) K 0.438 (0.273, 0.604) < 0.0001 P for log-likelihood ratio test < 0.001 Adjusted for age, sex, race/ethnicity, education level, marital status, PIR, BMI, sleep duration, hypertension, diabetes, Direct HDL-Cholesterol, Total Cholesterol, and Glycohemoglobin. Stratification analysis Subgroup analysis was performed based on age, sex, race/ethnicity, education level, marital status, PIR, BMI, sleep duration, hypertension, diabetes, Direct HDL-Cholesterol, Total Cholesterol, and Glycohemoglobin (Table 4 ). RAR was significantly associated with lumbar spine BMD in all subgroups. No interaction effects were observed between plasma RAR concentration and age (P for interaction = 0.8721), Race/ethnicity (P for interaction = 0.1156), Marital status (P for interaction = 0.4909), Direct HDL-Cholesterol (P for interaction = 0.6568), Total Cholesterol (P for interaction = 0.3550), Glycohemoglobin (P for interaction = 0.7043), Total Protein (P for interaction = 0.0546), Phosphorus (P for interaction = 0.6729), and Diabetes (P for interaction = 0.1294); therefore, these variables did not significantly alter the association between RAR and lumbar spine BMD. Table 4 Stratified analyses of the association between RAR and lumbar spine BMD. Subgroup N β (95% CI) P -interaction Sex < 0.0001 Male 2545 -0.342 (-0.360, -0.324) Female 2520 -0.277 (-0.295, -0.260) Age 0.8721 < 40 2456 -0.310 (-0.329, -0.292) ≥ 40 2609 -0.308 (-0.326, -0.291) Race/ethnicity 0.1156 Mexican American 839 -0.279 (-0.316, -0.241) Other Hispanic 587 -0.285 (-0.328, -0.243) Non-Hispanic white 1511 -0.309 (-0.326, -0.293) Non-Hispanic black 1093 -0.338 (-0.370, -0.305) Other race/multiracial 1035 -0.321 (-0.357, -0.284) Education level 0.0035 Below high school 950 -0.260 (-0.294, -0.226) High school or equivalent 1110 -0.301 (-0.326, -0.277) Above high school 3004 -0.321 (-0.337, -0.306) Marital status 0.4909 Married/living with partner 3152 -0.309 (-0.324, -0.293) Separated/divorced/widowed 694 -0.326 (-0.359, -0.293) Never married 1218 -0.302 (-0.327, -0.277) BMI 0.0355 15.5–24.3 1258 -0.294 (-0.317, -0.271) 24.4–28.3 1266 -0.314 (-0.340, -0.289) 28.4–33.1 1256 -0.340 (-0.366, -0.314) 33.2–65.8 1271 -0.298 (-0.321, -0.275) Sleep duration < 0.0001 119–131 2455 -0.337 (-0.356, -0.318) 132–148 2610 -0.289 (-0.306, -0.272) PIR 0.0032 0–1.45 1527 -0.276 (-0.300, -0.252) 1.46–3.28 1531 -0.313 (-0.335, -0.290) 3.29–5.00 1532 -0.325 (-0.344, -0.307) Direct HDL-Cholesterol 0.6568 6–49 2477 -0.306 (-0.325, -0.287) 50–166 2585 -0.312 (-0.328, -0.295) Total Cholesterol 0.3550 80–186 2500 -0.305 (-0.322, -0.288) 187–545 2562 -0.316 (-0.334, -0.297) Glycohemoglobin 0.7043 4.1–5.4 2520 -0.311 (-0.328, -0.294) 5.5–16.5 2539 -0.306 (-0.325, -0.288) Total Calcium < 0.0001 1.65–2.3 2222 -0.347 (-0.365, -0.330) 2.325–2.875 2841 -0.432 (-0.449, -0.414) Total Protein 0.0546 52–71 2208 -0.379 (-0.396, -0.362) 72–101 2852 -0.401 (-0.419, -0.383) Uric acid < 0.0001 47.6–303.3 2406 -0.363 (-0.380, -0.345) 309.3–1070.6 2657 -0.419 (-0.436, -0.401) Phosphorus 0.6729 0.581–1.13 2246 -0.392 (-0.411, -0.374) 1.162–3.1 2817 -0.388 (-0.404, -0.371) Hypertension 0.0009 Yes 1226 -0.276 (-0.301, -0.252) No 3833 -0.320 (-0.335, -0.305) Diabetes 0.1294 Yes 406 -0.267 (-0.311, -0.223) No 4552 -0.313 (-0.327, -0.300) The models were adjusted for age, sex, race/ethnicity, education level, marital status, PIR, BMI, sleep duration, hypertension, diabetes, Direct HDL-Cholesterol, Total Cholesterol, and Glycohemoglobin, except for the corresponding stratification variable. CI, confidence interval; lumbar spine BMD, lumbar spine bone mineral density. Discussion This study, based on large-scale population data from the US National Health and Nutrition Examination Survey (NHANES) 2015–2018, is the first to explore the association between red cell distribution width-to-albumin ratio (RAR) and lumbar spine bone mineral density (BMD). The results showed that an increased RAR was significantly associated with reduced lumbar BMD, and this association remained robust after multiple adjustments. Additionally, the study found a nonlinear L-shaped dose-response relationship between the two, with consistent effects observed across multiple subgroups. Using multi-model regression analysis, this study discovered that lumbar spine BMD significantly dropped by 0.309 g/cm² (95% CI: -0.327, -0.291) for every unit rise in RAR. These results suggest that RAR may be a potential biomarker for abnormal bone metabolism. Elevated red blood cell distribution width (RDW) is often associated with chronic inflammation and oxidative stress 13,18 . Inflammatory factors can activate osteoclasts 19–21 , inhibit osteoblast differentiation 22,23 , leading to bone loss. Hypoalbuminemia is a sign of malnutrition and protein deficiency 24,25 . Protein is a key raw material for bone matrix synthesis 26,27 . Research has shown a direct association between low albumin levels and reduced bone mineral density, highlighting the key role of albumin in regulating bone metabolism 28 . Systemic inflammation is often associated with malnutrition and plays a key role in bone loss 29 , In this study, we found that RAR exhibits an L-shaped nonlinear association with lumbar spine BMD (inflection point at 4.25): When RAR < 4.25, an increase in RAR is strongly negatively correlated with a decrease in BMD (β = -0.410, P < 0.0001), indicating that inflammatory or nutritional deficiencies during this stage exert a negative impact on bone density, When RAR ≥ 4.25, the direction of the association reverses (β = 0.438, P < 0.0001), possibly reflecting the activation of compensatory mechanisms or the presence of unmeasured confounding factors. This aligns with previous studies indicating that patients with chronic inflammation, malnutrition, and corticosteroid use often exhibit reduced bone density 30–32 . This finding highlights the importance of the RAR threshold in clinical assessment, and future studies are needed to validate its feasibility as a tool for stratifying osteoporosis risk. Subgroup analysis showed that the negative correlation between RAR and lumbar BMD was significant across different ages, races, marital statuses, and metabolic indicator levels (all P-interaction > 0.05). Confirmed the universality of the association. However, differences in effect sizes were observed in the following subgroups: gender differences, with a stronger negative correlation in males (β = -0.342), Studies have shown that there is a negative correlation between androgens and inflammation indices 33 , However, the protective effect of androgens on bone metabolism may be related to increased inflammation. In BMI, overweight/obese individuals (BMI 28.4–33.1 kg/m²) showed the strongest association (β = -0.340), Similar to previous studies, obesity is associated with chronic inflammation 34–36 , Chronic inflammation leads to the production of various cytokines and adipokines, which may promote bone destruction, suggesting that obesity-related chronic inflammation may amplify the bone damage effects of RAR 37 . Educational attainment showed a weaker association among the low-educated population (β = -0.260), which may be due to health inequalities masking the true effect of biomarkers. However, the precise processes by which increased RAR levels are linked to decreased lumbar spine BMD are still unclear. The correlations we discovered between osteoporosis and RAR may be partially explained by the interaction between RDW and inflammation, as well as the findings of RAR and disorders linked to inflammation. Low albumin levels, a crucial sign of nutritional health, may contribute significantly to the development of osteoporosis, confirming RAR's potential as a thorough biomarker for osteoporosis screening. Our study has several strengths. First, it uses a nationally representative sample (N = 5,065) with a complex sampling and weighting design. Second, standardized measurements (DXA, laboratory indicators) reduce measurement bias. Third, it fully adjusts for demographic, metabolic, and behavioral covariates. However, it also has some limitations. First, as a cross-sectional study, it cannot establish a causal relationship between RAR and osteoporosis. Second, key bone metabolism indicators (such as vitamin D and PTH) were not included. However, the biological mechanisms underlying RAR still require experimental validation. Conclusions In US adults, our study showed a negative relationship between RAR and lumbar spine BMD. Routine laboratory tests can rapidly and readily measure RAR, which could be a useful and straightforward metric for osteoporosis early detection, which could be crucial for osteoporosis prophylaxis. To confirm and investigate the underlying mechanisms, more research is required. Declarations Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement The data utilized in this study are publicly available from the National Health and Nutrition Examination Survey (NHANES) database, which can be accessed at https://www.cdc.gov/nchs/nhanes/index.htm. All materials and data supporting the findings of this study are included in the published article and its supplementary files. More detailed data and the code used for analysis can be obtained from the corresponding author upon reasonable request. This study analyzed publicly available datasets. These data can be found at: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. Author contributions Mengyan Zhao conceived and designed the experiments and contributed significantly to this paper as first author; Chaoyang Liu performed the data analysis. Tao Guo is the corresponding author. All authors contributed to the manuscript and approved the submitted version. Funding National Natural Scientific Foundation of China, Grant/Award Number: 82260431. Basic plan project of Guizhou Provincial Science and Technology Department, Grant/Award Number: ZK [2022] Normal 247. Guizhou Province Science and Technology Support Plan, Guizhou Science and Technology Cooperation Support [2023] General 196. Guizhou Provincial People’s Hospital, Grant/Award Number: GZSYBS [2021] 05. Ethics declarations Ethics approval and consent to participate Not applicable. Competing interests The authors declare no competing interests. References Reid, I. R. & Billington, E. O. 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Br J Nutr 119, 143–152 (2018). Salvagno, G. L., Sanchis-Gomar, F., Picanza, A. & Lippi, G. Red blood cell distribution width: A simple parameter with multiple clinical applications. Crit Rev Clin Lab Sci 52, 86–105 (2015). Rondanelli, M. et al. A path model of sarcopenia on bone mass loss in elderly subjects. The Journal of nutrition, health and aging 18, 15–21 (2014). Fanali, G. et al. Human serum albumin: from bench to bedside. Mol Aspects Med 33, 209–290 (2012). Lu, Z. et al. Oxidative Stress and Psychiatric Disorders: Evidence from the Bidirectional Mendelian Randomization Study. Antioxidants (Basel) 11, 1386 (2022). Parsons, V. L. et al. Design and estimation for the national health interview survey, 2006-2015. Vital Health Stat 2 1–53 (2014). Lippi, G. et al. Relation between red blood cell distribution width and inflammatory biomarkers in a large cohort of unselected outpatients. Arch Pathol Lab Med 133, 628–632 (2009). S, Z. et al. Sr-MOF-based hydrogel promotes diabetic tissue regeneration through simultaneous antimicrobial and antiinflammatory properties. Materials today. Bio 32, (2025). Wang, Y.-B. et al. Iron dysregulation, ferroptosis, and oxidative stress in diabetic osteoporosis: Mechanisms, bone metabolism disruption, and therapeutic strategies. World J Diabetes 16, 106720 (2025). Isojima, T. et al. Bone marrow neutrophil progenitors suppress osteoclast formation in murine cortical and trabecular bone. Blood blood.2025028310 (2025) doi:10.1182/blood.2025028310. Dong, R. et al. The surface protein Gbp of Fusobacterium nucleatum inhibits osteogenic differentiation by inactivating the Wnt/β-catenin pathway via binding to Annexin A2. J Transl Med 23, 540 (2025). Upadhyay, P. & Kumar, S. Diabetes and Bone Health: A Comprehensive Review of Impacts and Mechanisms. Diabetes Metab Res Rev 41, e70062 (2025). Soeters, P. B., Wolfe, R. R. & Shenkin, A. Hypoalbuminemia: Pathogenesis and Clinical Significance. JPEN J Parenter Enteral Nutr 43, 181–193 (2019). Gatta, A., Verardo, A. & Bolognesi, M. Hypoalbuminemia. Intern Emerg Med 7 Suppl 3, S193-199 (2012). Raimondi, L. et al. Mesenchymal stem cell secretome discovery in response to a brushite-coated titanium alloy: highlighted a specific signature of factors involved in bone healing. Biomater Adv 177, 214391 (2025). Wang, Y., Cao, Y. & Fan, Z. Mettl7a alleviated bone loss in osteoporosis mice by targeting the O-GlcNAcylation of Bsp via m6A methylation. Stem Cells Transl Med 14, szaf024 (2025). Gao, P., Min, J., Zhong, L. & Shao, M. Association between albumin corrected anion gap and all-cause mortality in critically ill patients with acute kidney injury: a retrospective study based on MIMIC-IV database. Ren Fail 45, 2282708 (2023). Liu, Y., Yang, Y., Li, Y., Ding, W. & Yang, X. Association between nutritional and inflammatory status and mortality outcomes in patients with osteoporosis and osteopenia. J Nutr Biochem 143, 109936 (2025). Ito, K. et al. Skeletal Muscle Mass Index Is Positively Associated With Bone Mineral Density in Hemodialysis Patients. Front Med (Lausanne) 7, 187 (2020). Fan, W. et al. Xanthohumol Alleviates Inflammatory Bowel Disease-Associated Osteoporosis via Regulating Gut Microbial Metabolites. Phytother Res (2025) doi:10.1002/ptr.70001. Yang, Y. J. & Jeon, S. R. Metabolic musculoskeletal disorders in patients with inflammatory bowel disease. Korean J Intern Med 40, 181–195 (2025). Xie, W. et al. Cancer disrupts sex hormone-inflammation relationships: Analysis of ALI in males from NHANES 2007-2018. PLoS One 20, e0325796 (2025). Giordano, C. et al. Complex Interplay Between Obesity and BRCA1/2-Associated Breast Cancer: An Overview. Obes Rev e13969 (2025) doi:10.1111/obr.13969. Wang, P., Hou, C., Wong, F. S. & Wen, L. Toll-like receptors in B cells and obesity. Trends Mol Med S1471-4914(25)00116–9 (2025) doi:10.1016/j.molmed.2025.05.005. Mandal, M., Mamun, M. A. A., Rakib, A. & Singh, U. P. High-fat diet-induced adipose tissue-resident macrophages, T cells, and dendritic cells modulate chronic inflammation and adipogenesis during obesity. Front Immunol 16, 1524544 (2025). Stroe-Ionescu, A.-Ş. et al. Adipose Tissue-Derived Mediators in Multiple Myeloma: Linking Obesity to Bone Disease via Inflammatory Pathways. Int J Mol Sci 26, 5618 (2025). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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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-7092982","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494373067,"identity":"76f1c4a2-cb8b-4137-92cf-c96dfc369f1b","order_by":0,"name":"Mengyan Zhao","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mengyan","middleName":"","lastName":"Zhao","suffix":""},{"id":494373068,"identity":"b3d56276-36aa-42be-a4d7-b90ae14aeab3","order_by":1,"name":"Chaoyang Liu","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chaoyang","middleName":"","lastName":"Liu","suffix":""},{"id":494373069,"identity":"15ddd7e2-97fa-43f7-a88f-58c88fe339ce","order_by":2,"name":"Tao Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBAC+/sPGw5+4LGR42dvIFbPgeTGxxIyacaSPQeI1pLebMBjczhxw40EInUwNhxsk5DIYWZsuPl44w2GGptoglqYGRvbJArOsDEzzk4rtmA4lpbbQEgLUG2bhGQPDxuzdI6ZBGPDYcJaeNiAWnj/SfCwSZ4hUosEDyPQ+zwGEjwSPERqMZBgBAYyTwJQD9AvCcT4xUCC/QEwKv/X7z9+eOONDzU2hLWgak8gRTlEC6k6RsEoGAWjYGQAAMREO+sPoaoqAAAAAElFTkSuQmCC","orcid":"","institution":"Guizhou Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Tao","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2025-07-10 12:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7092982/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7092982/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88260001,"identity":"60da22e7-4a34-4096-be04-4a120df45c73","added_by":"auto","created_at":"2025-08-04 15:09:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":289934,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the sample selection from NHANES 2015–2018\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7092982/v1/63b4e211d0169dc217fca255.png"},{"id":88260729,"identity":"a0aa977a-f318-44e5-932e-e09786bf8fa8","added_by":"auto","created_at":"2025-08-04 15:17:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63380,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationship using a restricted cubic spline. Association between RAR and lumbar spine BMD. The solid red line represents the estimated value, and the blue dashed areas indicate their corresponding 95% confidence interval. All models were adjusted for age, sex, race/ethnicity, education level, marital status, PIR, BMI, sleep duration, hypertension, diabetes, Direct HDL-Cholesterol, Total Cholesterol, and Glycohemoglobin.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7092982/v1/6f155221b8f1ae019c60f12b.png"},{"id":91616664,"identity":"ae246d33-155e-47f4-826e-dd286524c90f","added_by":"auto","created_at":"2025-09-18 10:39:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1933718,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7092982/v1/d78c3228-f0a3-471c-bf63-24f953075c6d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssociation between red blood cell distribution width-to-albumin ratio and lumbar spine bone mineral density: a cross-sectional analysis among US adults, 2015–2018\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoporosis is a systemic skeletal disease characterized by reduced bone mass and microstructural deterioration, significantly increasing the risk of fractures and becoming a global public health burden\u003csup\u003e1\u003c/sup\u003e. An estimated 200\u0026nbsp;million people worldwide are thought to be affected by this illness, which results in 8.9\u0026nbsp;million fractures annually, of which up to 46% are vertebral fractures. This is directly linked to rising rates of disability and death as well as a reduction in patients' quality of life\u003csup\u003e2,3\u003c/sup\u003e. It is estimated that by 2025, the number of osteoporosis patients worldwide will increase to approximately 300\u0026nbsp;million, which will result in a huge socioeconomic burden\u003csup\u003e4,5\u003c/sup\u003e. Dual-energy X-ray absorptiometry (DXA) is now the gold standard for clinical evaluation of bone density; nevertheless, its utility in large-scale population screening is limited by its high cost, complicated operation, and radiation exposure concerns\u003csup\u003e6,7\u003c/sup\u003e. For the early detection and risk assessment of osteoporosis, it is crucial to investigate straightforward, non-invasive, and reasonably priced biomarkers.\u003c/p\u003e\u003cp\u003eIn recent years, persistent inflammation and the formation of senescent cells have been revealed to hinder bone recuperation mechanisms\u003csup\u003e8\u003c/sup\u003e. However, nutritional status is involved in bone metabolism regulation\u003csup\u003e9,10\u003c/sup\u003e. Under inflammatory conditions, TEPP-46 treatment reduced nuclear dimer PKM2 levels, decreased phospho-signal transduction and transcription activator 3 (p-STAT3) expression, and inhibited osteoclast generation and osteoclast-related gene expression\u003csup\u003e11\u003c/sup\u003e. Malnutrition also hinders the mineralization and production of bone matrix \u003csup\u003e12\u003c/sup\u003e. Red blood cell distribution width (RDW) is a measure of red blood cell volume heterogeneity. It is not only a standard criterion for anemia, but also a sensitive marker of systemic inflammation and oxidative stress\u003csup\u003e13\u003c/sup\u003e. Serum albumin, as a key biomarker of nutritional evaluation, is substantially related to sarcopenia and decreased bone density when its levels are lowered \u003csup\u003e14\u003c/sup\u003e. It is worth noting that RDW and albumin concentration convey information about inflammation, oxidative stress, and nutrition from different perspectives\u003csup\u003e13,15,16\u003c/sup\u003e; All of them may be readily obtained through laboratory testing. As a result, the RDW-to-albumin ratio (RAR) produced by combining these two indicators could be a more useful indicator of bone density than each one by itself.\u003c/p\u003e\u003cp\u003eThus, we postulated that lumbar bone density may be linked to higher RAR. We next investigated the possible relationship between RAR and lumbar bone density using data from NHANES from 2015 to 2018.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe Strengthening Reporting of Observational Studies in Epidemiology (STROBE) reporting requirements were adhered to in this study. It made use of a national cross-sectional design and secondary analysis of de-identified and publicly available NHANES data. As a result, neither informed consent nor additional institutional review board permission was needed for the study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA thorough nationwide survey that has been carried out since 1999 is the NHANES database. Using intricate, multistage, and stratified sample methods, it evaluates the socioeconomic standing, health, and nutrition of the American people. For more information about the data, visit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Comprehensive information on the design, methods, and weighting of NHANES has been published previously\u003csup\u003e17\u003c/sup\u003e. Prior to data collection, the National Center for Health Statistics Ethics Review Board approved all study protocols, and each participant signed an informed consent form. Data for surveys was gathered by telephone interviews, household questionnaires, and examinations performed by qualified staff and medical professionals.\u003c/p\u003e\u003cp\u003eThis cohort study employed data from NHANES from 2015 to 2018, which comprised questionnaire items, laboratory measures, demographic data, and physical examination results. Participants without lumbar spine BMD measurements (N\u0026thinsp;=\u0026thinsp;10,368), those with missing red blood cell distribution width data (N\u0026thinsp;=\u0026thinsp;605), those with missing albumin data (N\u0026thinsp;=\u0026thinsp;1,303), and those under the age of 20 (N\u0026thinsp;=\u0026thinsp;1,885) were excluded from the analysis. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 5,065 eligible participants were included in the final analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRAR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBlood samples had RAR extracted from them. Strict laboratory testing was carried out in accordance with established sample procedures to guarantee the validity and comparability of the data. A Coulter analyzer in the mobile testing facilities used peripheral blood to calculate the RDW (%). A Roche Cobas 6000 chemical analyzer was used to measure the concentration of serum albumin using the bromocresol violet technique.\u003c/p\u003e\u003cp\u003eRAR was calculated based on the above metric: RAR\u0026thinsp;=\u0026thinsp;RDW (%)/Albumin (g/dL).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurement of lumbar spine BMD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe key outcome variable, lumbar spine BMD, was measured by Dual-energy x-ray absorptiometry (DXA) scans conducted by certified and trained radiologic technologists. Lumbar spine bone density was expressed in g/cm2, and all scans were performed using Apex 3.2 software on a Hologic Discovery A densitometer (Hologic, Inc., Bedford, Massachusetts). The NHANES website's \"Body Composition Procedures Manual\" contains comprehensive information about the lumbar spine bone density test.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe research model in this study included the following potential regulatory covariates: age, gender, race/ethnicity, education level, family income, BMI, sleep duration, glycohemoglobin, diabetes, hypertension, and biochemical indicators (blood urea nitrogen, total calcium, total protein, phosphorus, uric acid, and direct HDL-cholesterol). Mexican Americans, other Hispanics, non-Hispanic Whites, non-Hispanic Blacks, and other races make up the race categories. The three categories of education level were college or higher, high school graduate, and below high school. Based on participant self-reported data, diabetes and hypertension were identified. The poverty-income ratio (PIR), which accounts for family size, was used to calculate household income. The participant's height and weight were used to calculate their BMI. Furthermore, NHANES laboratory data provided detailed information about a number of biochemical indicators, including blood urea nitrogen, total calcium, total protein, phosphorus, uric acid, and direct HDL-cholesterol.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAccording to the NHANES complex sampling architecture, two cycles of data from the National Health and Nutrition Examination Survey (NHANES) were assessed for correctness using the proper weights. R Studio (version 4.2.0) and EmpowerStats (version 4.2) were used for statistical analysis. While categorical variables are represented by counts (n) and percentages (%), continuous variables are displayed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Weighted chi-square tests were used for categorical variables, and Student's t-tests were used for continuous variables. The association between lumbar spine BMD and the red blood cell distribution width-to-albumin ratio (RAR) was investigated using multiple linear regression models. No covariate adjustments were made in Model 1; age, sex, and race were corrected for in Model 2, and education level, marital status, body mass index (BMI), sleep duration, PIR, direct HDL-cholesterol, total cholesterol, glycohemoglobin, hypertension, and diabetes were further adjusted for in Model 3. RAR was divided into four groups in order to investigate how varying RAR levels affected the BMD of the lumbar spine. Furthermore, the nonlinear association between RAR and lumbar spine BMD was examined using generalized additive models (GAM) and smooth curve fitting approaches. Subgroup analyses were finally carried out.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline characteristics of participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study population's baseline characteristics, broken down by RAR quartile, are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. With mean ages ranging substantially from 43.096 years in the top quartile to 36.962 years in the lowest quartile (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), the analysis included 5,065 participants. The lower lumbar spine BMD groups had a higher percentage of males (69.151% in quartile 1) than the higher quartiles (32.529% in quartile 4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was a significant negative correlation between lumbar spine BMD and education level above high school, with 62.791% of participants in quartile 1 having higher education than high school or equivalent, compared to only 55.097% in quartile 4 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Health indicators such as hypertension and diabetes were significantly more prevalent in higher lumbar spine BMD groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). These findings underscore the intricate interplay between lumbar spine BMD, demographic factors, and health status within this population.\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\u003eBasic characteristics of participants by RAR quartile\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCovariates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eRAR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1(2.320\u0026ndash;2.930)\u003c/p\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,248\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2(2.932\u0026ndash;3.114)\u003c/p\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,279\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ3(3.116\u0026ndash;3.324)\u003c/p\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,253\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ4(3.325\u0026ndash;5.040)\u003c/p\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,285\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.962\u0026thinsp;\u0026plusmn;\u0026thinsp;10.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.427\u0026thinsp;\u0026plusmn;\u0026thinsp;11.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.632\u0026thinsp;\u0026plusmn;\u0026thinsp;11.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.096\u0026thinsp;\u0026plusmn;\u0026thinsp;10.729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e863 (69.151%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e705 (55.121%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e559 (44.613%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e418 (32.529%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e385 (30.849%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e574 (44.879%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e694 (55.387%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e867 (67.471%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace/ethnicity (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMexican American\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155 (12.420%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e216 (16.888%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e226 (18.037%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e242 (18.833%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther Hispanic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109 (8.734%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148 (11.572%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e157 (12.530%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e173 (13.463%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-Hispanic white\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e474 (37.981%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e379 (29.633%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e361 (28.811%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e297 (23.113%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-Hispanic black\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e261 (20.913%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e255 (19.937%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e256 (20.431%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e321 (24.981%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther race/multiracial\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e249 (19.952%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e281 (21.970%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e253 (20.192%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e252 (19.611%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBelow high school\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195 (15.638%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e213 (16.654%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e268 (21.389%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e274 (21.323%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh school or equivalent\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e269 (21.572%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e277 (21.658%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e261 (20.830%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e303 (23.580%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAbove high school\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e783 (62.791%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e789 (61.689%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e724 (57.781%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e708 (55.097%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarried/living with partner\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e762 (61.107%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e811 (63.409%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e802 (64.006%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e777 (60.467%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSeparated/divorced/widowed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (10.425%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147 (11.493%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e174 (13.887%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e243 (18.911%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNever married\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e355 (28.468%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e321 (25.098%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e277 (22.107%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e265 (20.623%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (kg/m2, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.491\u0026thinsp;\u0026plusmn;\u0026thinsp;5.716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.776\u0026thinsp;\u0026plusmn;\u0026thinsp;6.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.610\u0026thinsp;\u0026plusmn;\u0026thinsp;6.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.870\u0026thinsp;\u0026plusmn;\u0026thinsp;7.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSleep duration (hours, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129.601\u0026thinsp;\u0026plusmn;\u0026thinsp;7.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.882\u0026thinsp;\u0026plusmn;\u0026thinsp;8.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e133.662\u0026thinsp;\u0026plusmn;\u0026thinsp;8.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e135.682\u0026thinsp;\u0026plusmn;\u0026thinsp;8.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePIR(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.703\u0026thinsp;\u0026plusmn;\u0026thinsp;1.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.663\u0026thinsp;\u0026plusmn;\u0026thinsp;1.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.414\u0026thinsp;\u0026plusmn;\u0026thinsp;1.584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.402\u0026thinsp;\u0026plusmn;\u0026thinsp;1.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDirect HDL-Cholesterol(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.450\u0026thinsp;\u0026plusmn;\u0026thinsp;16.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.477\u0026thinsp;\u0026plusmn;\u0026thinsp;16.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.280\u0026thinsp;\u0026plusmn;\u0026thinsp;15.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.341\u0026thinsp;\u0026plusmn;\u0026thinsp;15.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Cholesterol(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e189.797\u0026thinsp;\u0026plusmn;\u0026thinsp;39.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e191.175\u0026thinsp;\u0026plusmn;\u0026thinsp;39.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e191.657\u0026thinsp;\u0026plusmn;\u0026thinsp;41.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e190.291\u0026thinsp;\u0026plusmn;\u0026thinsp;38.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.641\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlycohemoglobin(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.502\u0026thinsp;\u0026plusmn;\u0026thinsp;0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.616\u0026thinsp;\u0026plusmn;\u0026thinsp;1.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.686\u0026thinsp;\u0026plusmn;\u0026thinsp;1.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.889\u0026thinsp;\u0026plusmn;\u0026thinsp;1.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBlood Urea Nitrogen(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.929\u0026thinsp;\u0026plusmn;\u0026thinsp;1.465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.920\u0026thinsp;\u0026plusmn;\u0026thinsp;1.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.741\u0026thinsp;\u0026plusmn;\u0026thinsp;1.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.662\u0026thinsp;\u0026plusmn;\u0026thinsp;1.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Calcium(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.370\u0026thinsp;\u0026plusmn;\u0026thinsp;0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.339\u0026thinsp;\u0026plusmn;\u0026thinsp;0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.315\u0026thinsp;\u0026plusmn;\u0026thinsp;0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.277\u0026thinsp;\u0026plusmn;\u0026thinsp;0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Protein(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.154\u0026thinsp;\u0026plusmn;\u0026thinsp;3.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.635\u0026thinsp;\u0026plusmn;\u0026thinsp;3.784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.702\u0026thinsp;\u0026plusmn;\u0026thinsp;4.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.592\u0026thinsp;\u0026plusmn;\u0026thinsp;4.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUric acid(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334.665\u0026thinsp;\u0026plusmn;\u0026thinsp;82.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e320.877\u0026thinsp;\u0026plusmn;\u0026thinsp;86.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e312.816\u0026thinsp;\u0026plusmn;\u0026thinsp;85.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e306.574\u0026thinsp;\u0026plusmn;\u0026thinsp;83.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhosphorus(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.198\u0026thinsp;\u0026plusmn;\u0026thinsp;0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.189\u0026thinsp;\u0026plusmn;\u0026thinsp;0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.160\u0026thinsp;\u0026plusmn;\u0026thinsp;0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.152\u0026thinsp;\u0026plusmn;\u0026thinsp;0.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e272 (21.795%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e297 (23.221%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e301 (24.022%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e356 (27.704%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e976 (78.205%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e982 (76.779%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e947 (75.579%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e928 (72.218%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (6.090%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 (7.271%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94 (7.502%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e143 (11.128%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1155 (92.548%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1154 (90.227%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1138 (90.822%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1105 (85.992%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLumbar spine BMD (g/cm2, (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.141\u0026thinsp;\u0026plusmn;\u0026thinsp;0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.054\u0026thinsp;\u0026plusmn;\u0026thinsp;0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.016\u0026thinsp;\u0026plusmn;\u0026thinsp;0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.951\u0026thinsp;\u0026plusmn;\u0026thinsp;0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMean (SD) for continuous variables, % for categorical variables. NHANES, National Health and Nutrition Examination Survey; PIR, poverty income ratio; BMI, body mass index; Lumbar spine BMD, lumbar spine bone mineral density; RAR, red blood cell distribution width-to-albumin ratio.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation between RAR and lumbar spine BMD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe relationship between RAR and lumbar spine BMD is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. According to our findings, a lower lumbar spine BMD is associated with an elevated RAR. RAR was linked to significantly lower odds of lumbar spine BMD in Model 1, which did not account for any covariates. This resulted in an odds ratio (β) of -0.231 (95% CI: -0.247, -0.215). This finding suggests a clear correlation between lower lumbar spine BMD and higher RAR. Age, sex, and ethnicity were among the demographic factors that were adjusted for in Model 2. With a β of -0.253 (95% CI: -0.272, -0.235), the correlation in this model remained significant, indicating that higher RAR was still linked to lower lumbar spine BMD even after controlling for these demographic factors. Model 3 further refined the analysis by adjusting for additional health-related factors, including Education level, Marital status, body mass index (BMI), Sleep duration, PIR, Direct HDL-Cholesterol, Total Cholesterol, Glycohemoglobin, hypertension, and diabetes. The results retained statistical significance, with a β of -0.309 (95% CI: -0.327, -0.291). This finding underscores the robustness of the association between RAR and lumbar spine BMD. Additionally, when total RAR was stratified into quartiles, this association remained statistically significant. In comparison to the reference group (Q1), participants in the second quartile (Q2) showed a significant decrease in lumbar spine BMD, with a β of -0.093 (95% CI: -0.106, -0.080) in Model 1 and \u0026minus;\u0026thinsp;0.110 (95% CI: -0.124, -0.095) in Model 3. With βs of -0.184 (95% CI, -0.199, -0.169) in Model 1 and \u0026minus;\u0026thinsp;0.239 (95% CI, -0.255, -0.222) in Model 3, individuals in the four quartiles (Q4) also showed a greater correlation. All models showed significant trend analysis across RAR quartiles, with p for trend being \u0026lt;\u0026thinsp;0.0001, \u0026lt;\u0026thinsp;0.0001, and 0.0240.\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\u003eAssociation between RAR and lumbar spine BMD\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eβ (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRAR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.231 (-0.247, -0.215)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.253 (-0.272, -0.235)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.309 (-0.327, -0.291)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRAR quartile\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQ1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQ2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.093 (-0.106, -0.080)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.097 (-0.111, -0.084)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.110 (-0.124, -0.095)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQ3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.123 (-0.138, -0.108)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.132 (-0.148, -0.116)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.154 (-0.170, -0.139)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQ4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.184 (-0.199, -0.169)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.200 (-0.217, -0.184)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.239 (-0.255, -0.222)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP for trend\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eModel 1\u003c/b\u003e: no covariates were adjusted\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eModel 2\u003c/b\u003e: age, sex, and race/ethnicity were adjusted\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eModel 3\u003c/b\u003e: age, sex, race/ethnicity, education level, marital status, PIR, BMI, sleep duration, hypertension, diabetes, Direct HDL-Cholesterol, Total Cholesterol, Glycohemoglobin. 95% CI, 95% confidence interval\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDose-relationship between RAR and lumbar spine BMD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRestricted cubic spline analyses (RCS) demonstrate a nonlinear relationship between RAR and lumbar spine BMD(P for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001), characterized by an L-shaped curve, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, threshold effect analysis identified a critical turning point at 4.25. Before this critical point, a robust negative relationship exists between RAR and lumbar spine BMD (β = -0.410; 95% CI: -0.424, -0.396; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); However, beyond this point, the correlation shifts to a pronounced positive correlation (β\u0026thinsp;=\u0026thinsp;0.438; 95% CI: 0.273, 0.604; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\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\u003eThreshold effect analysis of the relationship between RAR and lumbar spine BMD\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003elumbar spine BMD\u003c/p\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOne line effect\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.390 (-0.403, -0.376)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel II\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInflection point (K)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;K\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.410 (-0.424, -0.396)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026gt;K\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.438 (0.273, 0.604)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP for log-likelihood ratio test\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eAdjusted for age, sex, race/ethnicity, education level, marital status, PIR, BMI, sleep duration, hypertension, diabetes, Direct HDL-Cholesterol, Total Cholesterol, and Glycohemoglobin.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStratification analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSubgroup analysis was performed based on age, sex, race/ethnicity, education level, marital status, PIR, BMI, sleep duration, hypertension, diabetes, Direct HDL-Cholesterol, Total Cholesterol, and Glycohemoglobin (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e ). RAR was significantly associated with lumbar spine BMD in all subgroups. No interaction effects were observed between plasma RAR concentration and age (P for interaction\u0026thinsp;=\u0026thinsp;0.8721), Race/ethnicity (P for interaction\u0026thinsp;=\u0026thinsp;0.1156), Marital status (P for interaction\u0026thinsp;=\u0026thinsp;0.4909), Direct HDL-Cholesterol (P for interaction\u0026thinsp;=\u0026thinsp;0.6568), Total Cholesterol (P for interaction\u0026thinsp;=\u0026thinsp;0.3550), Glycohemoglobin (P for interaction\u0026thinsp;=\u0026thinsp;0.7043), Total Protein (P for interaction\u0026thinsp;=\u0026thinsp;0.0546), Phosphorus (P for interaction\u0026thinsp;=\u0026thinsp;0.6729), and Diabetes (P for interaction\u0026thinsp;=\u0026thinsp;0.1294); therefore, these variables did not significantly alter the association between RAR and lumbar spine BMD.\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\u003eStratified analyses of the association between RAR and lumbar spine BMD.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.342 (-0.360, -0.324)\u003c/p\u003e \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\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.277 (-0.295, -0.260)\u003c/p\u003e \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\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8721\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;40\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.310 (-0.329, -0.292)\u003c/p\u003e \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\u003e\u003cb\u003e\u0026ge;\u0026thinsp;40\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.308 (-0.326, -0.291)\u003c/p\u003e \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\u003e\u003cb\u003eRace/ethnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMexican American\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.279 (-0.316, -0.241)\u003c/p\u003e \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\u003e\u003cb\u003eOther Hispanic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.285 (-0.328, -0.243)\u003c/p\u003e \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\u003e\u003cb\u003eNon-Hispanic white\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.309 (-0.326, -0.293)\u003c/p\u003e\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\u003e\u003cb\u003eNon-Hispanic black\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.338 (-0.370, -0.305)\u003c/p\u003e \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\u003e\u003cb\u003eOther race/multiracial\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.321 (-0.357, -0.284)\u003c/p\u003e \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\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBelow high school\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.260 (-0.294, -0.226)\u003c/p\u003e \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\u003e\u003cb\u003eHigh school or equivalent\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.301 (-0.326, -0.277)\u003c/p\u003e \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\u003e\u003cb\u003eAbove high school\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.321 (-0.337, -0.306)\u003c/p\u003e \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\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4909\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarried/living with partner\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.309 (-0.324, -0.293)\u003c/p\u003e\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\u003e\u003cb\u003eSeparated/divorced/widowed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.326 (-0.359, -0.293)\u003c/p\u003e \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\u003e\u003cb\u003eNever married\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.302 (-0.327, -0.277)\u003c/p\u003e \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\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0355\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e15.5\u0026ndash;24.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.294 (-0.317, -0.271)\u003c/p\u003e \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\u003e\u003cb\u003e24.4\u0026ndash;28.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.314 (-0.340, -0.289)\u003c/p\u003e \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\u003e\u003cb\u003e28.4\u0026ndash;33.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.340 (-0.366, -0.314)\u003c/p\u003e\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\u003e\u003cb\u003e33.2\u0026ndash;65.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.298 (-0.321, -0.275)\u003c/p\u003e \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\u003e\u003cb\u003eSleep duration\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e119\u0026ndash;131\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.337 (-0.356, -0.318)\u003c/p\u003e\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\u003e\u003cb\u003e132\u0026ndash;148\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.289 (-0.306, -0.272)\u003c/p\u003e\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\u003e\u003cb\u003ePIR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e0\u0026ndash;1.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.276 (-0.300, -0.252)\u003c/p\u003e\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\u003e\u003cb\u003e1.46\u0026ndash;3.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.313 (-0.335, -0.290)\u003c/p\u003e\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\u003e\u003cb\u003e3.29\u0026ndash;5.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.325 (-0.344, -0.307)\u003c/p\u003e\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\u003e\u003cb\u003eDirect HDL-Cholesterol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6568\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u0026ndash;49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.306 (-0.325, -0.287)\u003c/p\u003e\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\u003e\u003cb\u003e50\u0026ndash;166\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.312 (-0.328, -0.295)\u003c/p\u003e\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\u003e\u003cb\u003eTotal Cholesterol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3550\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e80\u0026ndash;186\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.305 (-0.322, -0.288)\u003c/p\u003e\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\u003e\u003cb\u003e187\u0026ndash;545\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.316 (-0.334, -0.297)\u003c/p\u003e\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\u003e\u003cb\u003eGlycohemoglobin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4.1\u0026ndash;5.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.311 (-0.328, -0.294)\u003c/p\u003e\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\u003e\u003cb\u003e5.5\u0026ndash;16.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.306 (-0.325, -0.288)\u003c/p\u003e\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\u003e\u003cb\u003eTotal Calcium\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1.65\u0026ndash;2.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.347 (-0.365, -0.330)\u003c/p\u003e\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\u003e\u003cb\u003e2.325\u0026ndash;2.875\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.432 (-0.449, -0.414)\u003c/p\u003e\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\u003e\u003cb\u003eTotal Protein\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0546\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e52\u0026ndash;71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.379 (-0.396, -0.362)\u003c/p\u003e \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\u003e\u003cb\u003e72\u0026ndash;101\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.401 (-0.419, -0.383)\u003c/p\u003e\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\u003e\u003cb\u003eUric acid\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e47.6\u0026ndash;303.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.363 (-0.380, -0.345)\u003c/p\u003e\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\u003e\u003cb\u003e309.3\u0026ndash;1070.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.419 (-0.436, -0.401)\u003c/p\u003e\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\u003e\u003cb\u003ePhosphorus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6729\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e0.581\u0026ndash;1.13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.392 (-0.411, -0.374)\u003c/p\u003e \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\u003e\u003cb\u003e1.162\u0026ndash;3.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.388 (-0.404, -0.371)\u003c/p\u003e\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\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.276 (-0.301, -0.252)\u003c/p\u003e\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\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.320 (-0.335, -0.305)\u003c/p\u003e \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\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.267 (-0.311, -0.223)\u003c/p\u003e \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\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.313 (-0.327, -0.300)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe models were adjusted for age, sex, race/ethnicity, education level, marital status, PIR, BMI, sleep duration, hypertension, diabetes, Direct HDL-Cholesterol, Total Cholesterol, and Glycohemoglobin, except for the corresponding stratification variable. CI, confidence interval; lumbar spine BMD, lumbar spine bone mineral density.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study, based on large-scale population data from the US National Health and Nutrition Examination Survey (NHANES) 2015\u0026ndash;2018, is the first to explore the association between red cell distribution width-to-albumin ratio (RAR) and lumbar spine bone mineral density (BMD). The results showed that an increased RAR was significantly associated with reduced lumbar BMD, and this association remained robust after multiple adjustments. Additionally, the study found a nonlinear L-shaped dose-response relationship between the two, with consistent effects observed across multiple subgroups.\u003c/p\u003e\u003cp\u003eUsing multi-model regression analysis, this study discovered that lumbar spine BMD significantly dropped by 0.309 g/cm\u0026sup2; (95% CI: -0.327, -0.291) for every unit rise in RAR. These results suggest that RAR may be a potential biomarker for abnormal bone metabolism. Elevated red blood cell distribution width (RDW) is often associated with chronic inflammation and oxidative stress\u003csup\u003e13,18\u003c/sup\u003e. Inflammatory factors can activate osteoclasts\u003csup\u003e19\u0026ndash;21\u003c/sup\u003e, inhibit osteoblast differentiation\u003csup\u003e22,23\u003c/sup\u003e, leading to bone loss. Hypoalbuminemia is a sign of malnutrition and protein deficiency\u003csup\u003e24,25\u003c/sup\u003e. Protein is a key raw material for bone matrix synthesis\u003csup\u003e26,27\u003c/sup\u003e. Research has shown a direct association between low albumin levels and reduced bone mineral density, highlighting the key role of albumin in regulating bone metabolism\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSystemic inflammation is often associated with malnutrition and plays a key role in bone loss\u003csup\u003e29\u003c/sup\u003e, In this study, we found that RAR exhibits an L-shaped nonlinear association with lumbar spine BMD (inflection point at 4.25): When RAR\u0026thinsp;\u0026lt;\u0026thinsp;4.25, an increase in RAR is strongly negatively correlated with a decrease in BMD (β = -0.410, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that inflammatory or nutritional deficiencies during this stage exert a negative impact on bone density, When RAR\u0026thinsp;\u0026ge;\u0026thinsp;4.25, the direction of the association reverses (β\u0026thinsp;=\u0026thinsp;0.438, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), possibly reflecting the activation of compensatory mechanisms or the presence of unmeasured confounding factors. This aligns with previous studies indicating that patients with chronic inflammation, malnutrition, and corticosteroid use often exhibit reduced bone density\u003csup\u003e30\u0026ndash;32\u003c/sup\u003e. This finding highlights the importance of the RAR threshold in clinical assessment, and future studies are needed to validate its feasibility as a tool for stratifying osteoporosis risk.\u003c/p\u003e\u003cp\u003eSubgroup analysis showed that the negative correlation between RAR and lumbar BMD was significant across different ages, races, marital statuses, and metabolic indicator levels (all P-interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Confirmed the universality of the association. However, differences in effect sizes were observed in the following subgroups: gender differences, with a stronger negative correlation in males (β = -0.342), Studies have shown that there is a negative correlation between androgens and inflammation indices\u003csup\u003e33\u003c/sup\u003e, However, the protective effect of androgens on bone metabolism may be related to increased inflammation. In BMI, overweight/obese individuals (BMI 28.4\u0026ndash;33.1 kg/m\u0026sup2;) showed the strongest association (β = -0.340), Similar to previous studies, obesity is associated with chronic inflammation\u003csup\u003e34\u0026ndash;36\u003c/sup\u003e, Chronic inflammation leads to the production of various cytokines and adipokines, which may promote bone destruction, suggesting that obesity-related chronic inflammation may amplify the bone damage effects of RAR \u003csup\u003e37\u003c/sup\u003e. Educational attainment showed a weaker association among the low-educated population (β = -0.260), which may be due to health inequalities masking the true effect of biomarkers.\u003c/p\u003e\u003cp\u003eHowever, the precise processes by which increased RAR levels are linked to decreased lumbar spine BMD are still unclear. The correlations we discovered between osteoporosis and RAR may be partially explained by the interaction between RDW and inflammation, as well as the findings of RAR and disorders linked to inflammation. Low albumin levels, a crucial sign of nutritional health, may contribute significantly to the development of osteoporosis, confirming RAR's potential as a thorough biomarker for osteoporosis screening.\u003c/p\u003e\u003cp\u003eOur study has several strengths. First, it uses a nationally representative sample (N\u0026thinsp;=\u0026thinsp;5,065) with a complex sampling and weighting design. Second, standardized measurements (DXA, laboratory indicators) reduce measurement bias. Third, it fully adjusts for demographic, metabolic, and behavioral covariates. However, it also has some limitations. First, as a cross-sectional study, it cannot establish a causal relationship between RAR and osteoporosis. Second, key bone metabolism indicators (such as vitamin D and PTH) were not included. However, the biological mechanisms underlying RAR still require experimental validation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn US adults, our study showed a negative relationship between RAR and lumbar spine BMD. Routine laboratory tests can rapidly and readily measure RAR, which could be a useful and straightforward metric for osteoporosis early detection, which could be crucial for osteoporosis prophylaxis. To confirm and investigate the underlying mechanisms, more research is required.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAssociated Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis section collects any data citations, data availability statements, or supplementary materials included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data utilized in this study are publicly available from the National Health and Nutrition Examination Survey (NHANES) database, which can be accessed at https://www.cdc.gov/nchs/nhanes/index.htm. All materials and data supporting the findings of this study are included in the published article and its supplementary files. More detailed data and the code used for analysis can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eThis study analyzed publicly available datasets. These data can be found at: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMengyan Zhao conceived and designed the experiments and contributed significantly to this paper as first author; Chaoyang Liu performed the data analysis. Tao Guo is the corresponding author. All authors contributed to the manuscript and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Scientific Foundation of China, Grant/Award Number: 82260431. Basic plan project of Guizhou Provincial Science and Technology Department, Grant/Award Number: ZK [2022] Normal 247.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGuizhou Province Science and Technology Support Plan, Guizhou Science and Technology Cooperation Support [2023] General 196. Guizhou Provincial People\u0026rsquo;s Hospital, Grant/Award Number: GZSYBS [2021] 05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eReid, I. R. \u0026amp; Billington, E. O. Drug therapy for osteoporosis in older adults. \u003cem\u003eLancet\u003c/em\u003e 399, 1080\u0026ndash;1092 (2022).\u003c/li\u003e\n\u003cli\u003eMohammadi, S. M., Saniee, N., Mousaviasl, S., Radmanesh, E. \u0026amp; Doustimotlagh, A. H. 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High-fat diet-induced adipose tissue-resident macrophages, T cells, and dendritic cells modulate chronic inflammation and adipogenesis during obesity. \u003cem\u003eFront Immunol\u003c/em\u003e 16, 1524544 (2025).\u003c/li\u003e\n\u003cli\u003eStroe-Ionescu, A.-Ş. \u003cem\u003eet al.\u003c/em\u003e Adipose Tissue-Derived Mediators in Multiple Myeloma: Linking Obesity to Bone Disease via Inflammatory Pathways. \u003cem\u003eInt J Mol Sci\u003c/em\u003e 26, 5618 (2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"RAR, lumbar spine bone density, osteoporosis, cross-sectional study, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-7092982/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7092982/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRed blood cell distribution width (RDW) and albumin levels are associated with bone metabolism. However, the relationship between the ratio of the two (RAR) and lumbar spine bone mineral density (BMD) remains unclear. This study aims to explore the association between RAR and lumbar spine BMD and the potential nonlinear relationship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression, restricted cubic spline (RCS) regression, receiver operating characteristic (ROC) analysis, and sensitivity analyses were used to examine the relationship between RAR and lumbar spine BMD based on NHANES data from 2015–2018. The study also used subgroup analyses and interaction tests to explore whether the relationship was stable across populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElevated RAR is significantly associated with reduced lumbar spine BMD (fully adjusted model β = -0.309, 95% CI: -0.327 to -0.291, P \u0026lt; 0.001). RCS analysis revealed an L-shaped nonlinear association between the two (P for nonlinearity \u0026lt; 0.001), with an inflection point at RAR = 4.25. Below the inflection point, RAR was negatively correlated with BMD (β = -0.410, P \u0026lt; 0.001), while above the inflection point, it was positively correlated (β = 0.438, P \u0026lt; 0.001). Trend analysis showed that increasing RAR quartiles were associated with decreasing BMD (Q4 vs. Q1: β = -0.239, P \u0026lt; 0.001; trend P = 0.024). Subgroup analysis showed consistent results across subgroups of gender, age, and race (interaction P \u0026gt; 0.05), but there were modifying effects in subgroups of education level, BMI, sleep duration, PIR, and hypertension (interaction P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElevated RAR is an independent risk factor for reduced lumbar spine BMD, with a threshold effect of 4.25. RAR may serve as a potential biomarker for assessing bone health.\u003c/p\u003e\n\u003cp\u003eClinical trial number: Not applicable.\u003c/p\u003e","manuscriptTitle":"Association between red blood cell distribution width-to-albumin ratio and lumbar spine bone mineral density: a cross-sectional analysis among US adults, 2015–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 15:08:45","doi":"10.21203/rs.3.rs-7092982/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee9d6b2b-bd3b-4ffb-9c24-d44657ccf201","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T10:38:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-04 15:08:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7092982","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7092982","identity":"rs-7092982","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}