The NHANES 2011-2018 study found a negative correlation between bone mineral density and the non- high density to high density lipoprotein cholesterol ratio (NHHR) in U.S. adults

The NHANES 2011-2018 study found a negative correlation between bone mineral density and the non- high density to high density lipoprotein cholesterol ratio (NHHR) in U.S. adults | 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 The NHANES 2011-2018 study found a negative correlation between bone mineral density and the non- high density to high density lipoprotein cholesterol ratio (NHHR) in U.S. adults Hanwen Zhang, jian Mei, wei Deng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4516124/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 Introduction Many research have shown a negative link between lipids and bone metabolism, and the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) may be a new marker of lipid metabolism. The relationship between NHHR and lumbar bone mineral mass (BMD) is unknown. NHHR and lumbar BMD were the study's main focus. Method NHHR and lumbar BMD were examined using 2011-2018 National Health and Nutrition Examination Survey (NHANES) data and multivariate logistic regression models. Also employed were interaction tests and smoothed curve fitting. Result Our investigation found a connection between increased NHHR levels and decreasing lumbar BMD after adjusting for covariates. All four measurement points showed this association, and lumbar BMD decreased by 0.037 g/cm2 relative to the lowest quartile. Conclusion We discovered an interestingly negative correlation in US citizens between NHHR and lumbar BMD. This emphasizes the need of NHHR in lipid target monitoring. NHHR lumbar BMD NHANES Introduction Age-related osteoporosis (OP) is an inescapable chronic metabolic bone disease ( 1 , 2 ). After the age of 50 or older, OP has been found to affect 20% of males and 30% of women ( 3 , 4 ). Approximately 8.9 million persons worldwide have osteoporotic fractures each year. They have potential to significantly affect quality of life, an issue with global health that will only get worse as we fet older ( 5 ). Since the primary symptom of OP is loss of bone mineral mass (BMD), measuring BMD is the gold standard for diagnosising OP ( 1 , 6 , 7 ). Nowdays the majority of researchers employ BMD as a key predictor of OP severity and to direct clinical care and survival healing analyses ( 8 ). Atherosclerotic cardiovascular disease (ASCVD) is mostly caused by dyslipidemia, and one fo the main cause of ASCVD-related deaths is low-density lipoprotein (LDL-C) ( 9 , 10 ). High-density lipoprotein (HDL-C) has been demonstrated to offer health advantages, despite recycling excess cholesterol in the periphery ( 11 ). Numerous investigations into the connection between lipids and bone density have shown how crucial cholesterol is to bone metabolism ( 12 – 14 ). Postmenopausal women with OP had higher levels of Total cholesterol (TC) and HDL-C, as shown in a meta-analysis ( 15 ). Additionally, statistical analysis revealed a favorable connection between lumbar BMD and HDL-C ( 8 ). A cohort research including 712 females and 450 males did not, however, discover a correlation between TC and BMD ( 16 ). There is reason to question the validity of the correlation between lipid markers and BMD ( 17 , 18 ) since the link between osteoporosis and ASCVD cannot be accounted for by HDL-C levels. An improved ASCVD risk predictor than LDL-C alone is non-high density lipoprotein cholesterol (NHDL-C), a cholesterol measurement carried by atherogenic lipoproteins ( 19 ). In addition, NHDL-C is crucial for the clinical treatment of lipoproteins, according to the National Lipid Association (NLA) ( 20 ). The NHDL-C to HDL-C ratio (NHHR) is a novel way to assess systemic lipids ( 21 ). It has been connected in recent studies to lipid metabolism and several diseases ( 22 , 23 ). Determining whether there was a correlation between NHHR and lumbar BMD was the aim of the current study. It was also hoped that new information about the relationship between lipid markers and OP may be discovered. Accordingly, this research used a composite profile from the NHANES for people ranging in age from 20 to 59 to evaluate the association between NHHR and lumbar BMD. Materials and Methods Sources of data and Study participants NHANES employs nationally representative cross-sectional surveys to provide a comprehensive overview of the health and nutritional status of the American population. Data was collected every two years utilizing a stratified strategy as part of the data gathering methodology. The Ethics Review Board of the National Center for Health Statistics and Research granted authorization for NHANES, and every participant provided written informed permission. This cross-sectional study analyzed individual data from four consecutive NHANES cycles spanning from 2011 to 2018, encompassing a total of 39,156 participants. To establish the final study population, we implemented the subsequent exclusion criteria: The study had four primary exclusion criteria: ( 1 ) individuals under the age of 20; ( 2 ) participants with incomplete data on TC and HDL; ( 3 ) people with incomplete data on lumbar BMD; and ( 4 ) participants with missing responses indicating uncertainty, critical illness, or refusal for the variables of smoking, hypertension, and diabetes mellitus.Finally, this study included a total of 10,793 individuals. ( Fig. 1 ) Figure 1 Flowchart of NHANES sample selection 2011–2018 NHHR NHHR is a variable that is not influenced by other factors and is used to evaluate the level of exposure. NHDL-C was derived by subtracting HDL-C from TC. The NHHR is the ratio of NHDL-C to HDL-C. TC was measured using an enzymatic test and the Trinder reaction in the subject's fasting state. HDL-C was obtained using an enzymatic assay and a specific end-point reaction following the modification of the cholesterol-measuring enzyme with PEG. Lumbar BMD Consistent with previous studies, a Hologic Discovery machine was used to do dual-energy X-ray absorptiometry (DXA) scanning. A densitometer was employed to quantify the BMD values at several anatomical sites, such as the pelvis, right and left ribs, and the thoracic and lumbar vertebrae. The lumbar BMD scan yielded the mean BMD values for the lumbar vertebrae L1–L4. The NHANES Quality Control Center assessed the scan quality. An expert assessment was undertaken on all 100 scans of the individuals that were investigated to verify the accuracy and reliability of the findings. Covariates Covariates included age, gender, race, smoking status, ethnicity, hypertension, diabetes mellitus, poverty-to-income ratio (PIR), education level, total serum calcium, serum phosphorus, vitamin D, and body mass index (BMI). Statistical Analyzes We looked at the survey population that was covered by using NHHR quartiles because NHANES uses a sampling strategy that combines multiple phases and probability. The t-test and the chi-square test were both used. Weighted multiple logistic regression analyses were used to further investigate the linear connection between NHHR and lumbar BMD. There were no variable adjustments in Model 1. Model 2, however, has racial, age, and gender changes. Model 3 includes adjustments for age, sex, race, diabetes mellitus, hypertension, BMI, degree of education, blood calcium, serum phosphorus, vitamin D, and smoking status (defined as having smoked at least 100 cigarettes in one's lifetime). Trend tests were used to analyze linear trend connections in order to investigate the link between NHHR and lumbar BMD. To investigate the relationship between NHHR and lumbar BMD based on gender, race, and the prevalence of smoking, hypertension, and diabetes, subgroup analyses and interaction tests were carried out. Fitting a smooth curve is the solution we implemented to examine non-linear relationships. When a non-linear association between NHHR and lumbar BMD was found, a regression approach was used to find the correlation's inflection point. On both sides of the inflection point, a two-segment linear regression model was then used. At a significance threshold of P < 0.05, the analyses were carried out with PackageR (4.1.3) and EmpowerStats (2.0). The findings were statistically significant. Results Baseline Characteristics of Participants Based on the calculation of NHHR, we categorized NHHR into quartiles according to weighted characteristics (Q1: 0.36-1.91 mmol/L, Q2: 1.92-2.67 mmol/L, Q3: 2.68-3.66 mmol/L, Q4: 3.67-26.85 mmol/L) as shown in Table1 . We conducted a study on a total of 10,793 individuals between the ages of 20 and 59. There were notable disparities in the basic demographic traits throughout the quartiles of NHHR.Participants in the highest quartile of NHHR were likely to be male, Non-Hispanic White, and 41 years or older. Individuals with elevated NHHR had greater educational achievement, higher BMI, decreased levels of vitamin D, serum phosphorus, and lumbar BMD, as well as lower earnings. It is primarily observed in individuals who do not have diabetes or hypertension. Table 1 : Basic characteristics of participants (grouped according to NHHR quartiles) Characteristics Q1 (≤1.91) Q2 (1.92-2.67) Q3 (2.68-3.66) Q4 (≥3.67) P-value Age (years) 36.938±11.836 38.566±11.738 40.35±11.239 41.216±10.495 <0.001 Sex, (%) <0.001 Male 918 (34.241%) 1193 (44.152%) 1498 (55.833%) 1888 (69.234%) Female 1763 (65.759%) 1509 (55.848% 1185 (44.167%) 839 (30.766%) Race/ethnicity, (%) <0.001 Mexican American 282 (10.518%) 360 (13.323%) 427 (15.915%) 521 (19.105%) Other Hispanic 221 (8.243%) 259 (9.585%) 302 (11.256%) 333 (12.211%) Non-Hispanic White 918 (34.241%) 904 (33.457%) 959 (35.744%) 966 (35.424%) Non-Hispanic Black 767 (28.609%) 658 (24.352%) 515 (19.195%) 398 (14.595%) Other Race 493 (18.389%) 521 (19.282%) 480 (17.890%) 509 (18.665%) Education level, n (%) <0.001 Less than 9th grade 107 (3.991%) 145 (5.366%) 173 (6.448%) 242 (8.874%) 9-11th grade (Includes 12th grade with no diploma) 270 (10.071%) 301 (11.140%) 328 (12.225%) 416 (15.255%) High school graduate/GED or equivalent 522 (19.470%) 577 (21.355%) 638 (23.779%) 619 (22.699%) Some college or AA degree 914 (34.092%) 918 (33.975%) 860 (32.054%) 835 (30.620%) College graduate or above 867 (32.339%) 761 (28.164%) 683 (25.457%) 615 (22.552%) Refused 1 (0.037%) 0 (0.000%) 1 (0.037%) 0 (0.000%) Smoking, (%) <0.001 Yes 910 (33.943%) 1004 (37.158%) 1034 (38.539%) 1291 (47.341%) No 1771 (66.057%) 1698 (62.842%) 1649 (61.461%) 1436 (52.659%) Diabetes, (%) <0.001 Yes 139 (5.185%) 167 (6.181%) 242 (9.020%) 277 (10.158%) No 2542 (94.815%) 2535 (93.819%) 2441 (90.980%) 2450 (89.842%) Hypertension, (%) <0.001 Yes 462 (17.232%) 595 (22.021%) 701 (26.127%) 784 (28.750%) No 2219 (82.768%) 2107 (77.979%) 1982 (73.873%) 1943 (71.250%) Vitamin D 62.519 ± 27.988 60.878 ±26.079 60.097 ±24.246 57.836 ±22.275 <0.001 BMI (kg/m 2 ) 25.887 ± 6.352 28.696 ± 7.080 30.247 ± 6.810 31.339 ± 6.326 <0.001 PIR 2.598 ± 1.618 2.541 ± 1.601 2.544 ± 1.570 2.388 ± 1.553 <0.001 Total calcium (mg/dL, mean ± SD) 9.345 ± 0.333 9.350 ± 0.354 9.357 ± 0.339 9.397 ± 0.339 <0.001 Serum phosphorus (mg/dL, mean ± SD) 3.753 ± 0.557 3.719 ± 0.570 3.687 ± 0.565 3.695 ± 0.565 <0.001 Lumbar BMD (g/cm 2 ) 1.066 ± 0.155 1.043 ± 0.153 1.030 ± 0.156 1.012 ± 0.149 <0.001 Mean ± SD for continuous variables: the P value was calculated by the weighted linear regression model (%) for categorical variables: the P value was calculated by the weighted chi-square test Regression analysis between NHHR and lumbar BMD The multiple regression analysis, as presented in Table 2 , demonstrates the association between NHHR and lumbar BMD. Both the original and 2 modified models showed a negative connection between NHHR and lumbar BMD. In the unadjusted model (Model1), there was a drop of 0.009 g/cm 2 in lumbar BMD for every 1-unit rise in NHHR. In the fully adjusted model (Model3), there was a decrease of 0.006 g/cm 2 in lumbar BMD for every 1-unit increase in NHHR. In the fully adjusted model, we conducted a comparison between the quartiles of the NHHR, specifically the highest and lowest quartiles. Empirical data revealed a negative correlation between lumbar BMD and the increase in NHHR, with a decrease of 0.037 g/cm2 in lumbar BMD for every 1-unit rise in NHHR. Table 2 : Relationship between NHHR and lumbar BMD Crude Model (Model1) Partially Adjusted Model (Model 2) Fully Adjusted Model (Model 3) β (95% CI)P-value β (95% CI)P-value β (95% CI)P-value NHHR -0.009 (-0.011, -0.007) <0.00001 -0.006 (-0.008, -0.004) <0.00001 -0.006 (-0.008, -0.004) <0.00001 NHHR Quartile Quartile1 Reference Reference Reference Quartile2 -0.015 (-0.023, -0.007) 0.00029 -0.010 (-0.018, -0.003) 0.00853 -0.013 (-0.021, -0.005) 0.00085 Quartile3 -0.030 (-0.038, -0.022) <0.00001 -0.021 (-0.029, -0.013) <0.00001 -0.025 (-0.033, -0.017) <0.00001 Quartile4 -0.046 (-0.053, -0.038) <0.00001 -0.034 (-0.042, -0.025) <0.00001 -0.037 (-0.045, -0.028) <0.00001 The NHHR has a negative correlation with lumbar BMD We identified a U-shaped curve between NHHR and lumbar BMD using findings of smoothed curve fitting. The linear and segmented linear regression models were shown to vary statistically significantly ( Fig. 2 ) and with a p-value of less than 0.001 by the log-likelihood ratio test. Table 3 shows that an NHHR of 4.15 is the point at which the relationship between NHHR and lumbar BMD inflections. Significantly negative connection between NHHR and lumbar BMD was found when the inflection point (K) of NHHR <4.15 (β, -0.015 [95% CI, -0.018, -0.011]), P4.15 (β, -0.001 [95%CI, -0.003, 0.004], P=0.6025). Table 3 : Presents the results of a threshold effect analysis, using a bipartite linear regression model, to examine the impact of NHHR on lumbar BMD. NHHR Adjust β (95% CI) P value Fitting by linear regression model -0.007 (-0.009, -0.005) <0.0001 Fitting by two-piecewise linear regression model Inflection point (K) 4.15 <4.15 -0.015 (-0.018, -0.011) 4.15 0.001 (-0.003, 0.004) 0.6025 Log likelihood ratio test <0.001 Fig. 2 : Subgroup analyzes By interaction test, we confirmed the association between demographic factors on NHHR and lumbar BMD. Gender and smoking status did not show any significant interaction (p-value > 0.05 for interaction). The association between NHHR and lumbar BMD (interaction P value < 0.05) may change when one looks through the Table 4 structure in terms of race and prevalence of diabetes and hypertension. Within the race categories Non-Hispanic Black, Non-Hispanic White, Other Race, elevated NHHR was adversely correlated with lumbar BMD. In the hypertensive subgroup of patients without hypertension (β, -0.0075 [95% CI, -0.0099, -0.0052]), elevated NHHR was negatively correlated with lumbar BMD. In the diabetic subgroup of patients without diabetes, elevated NHHR was likewise adversely correlated with lumbar BMD (β, -0.0073 [95% CI, -0.0094, -0.0052]). Table 4 : Age, gender, race, smoking status, ethnicity, hypertension, diabetes mellitus, PIR, education level, total serum calcium, serum phosphorus, vitamin D, BMI were adjusted. Subgroup β (95%CI) P-value P for interaction Sex 0.6812 Male -0.0061 (-0.0085, -0.0037) <0.0001 Female -0.0070 (-0.0105, -0.0035) <0.0001 Race 0.0147 Mexican American -0.0008 (-0.0065, 0.0050) 0.7914 Other Hispanic -0.0046 (-0.0098, 0.0007) 0.0889 Non-Hispanic White -0.0052 (-0.0078, -0.0025) 0.0002 Non-Hispanic Black -0.0128 (-0.0196, -0.0059) 0.0002 Other Race 0.0132 (-0.0198, -0.0067) <0.0001 Smoking status 0.5737 Yes -0.0067 (-0.0095, -0.0040) <0.0001 No -0.0057 (-0.0083, -0.0030) <0.0001 Hypertension 0.0036 Yes -0.0008 (-0.0047, 0.0031) 0.6909 No -0.0075 (-0.0099, -0.0052) <0.0001 Diabetes 0.0002 Yes 0.0054 (-0.0009, 0.0117) 0.0951 No -0.0073 (-0.0094, -0.0052) <0.0001 Discussion The relationship between bone metabolism and lipids has been ambiguous for a significant period of time ( 21 , 24 ). The cross-sectional study had 10,793 eligible participants in our analysis. In this study, we examined the relationship between the NHHR index and lumber BMD for the first time. A fascinating finding revealed a correlation between increasing NHHR and decreasing lumbar BMD. Our research indicates a potential association between dyslipidemia and BMD, highlighting the importance of managing lipid levels to optimize BMD. Our analysis revealed variations in the association between NHHR and lumbar BMD among different gender, racial, and age subgroups in our study population. This supports this discovery with prior research that has identified a correlation between BMD and additional characteristics such as ethnicity and gender ( 25 , 26 ). In addition, our findings indicate that individuals with advanced education, BMI, and elevated levels of serum calcium in the body may have a correlation with increased NHHR values. Another cross-sectional study ( 27 , 28 ) likewise discovered a negative correlation between educational achievement and BMD. Our subgroup analysis also revealed that increased NHHR was inversely correlated with lumbar BMD in the patient population without hypertension and diabetes. Previous investigations have found a correlation between elevated blood pressure, diabetes mellitus, and osteoporosis ( 23 , 29 , 30 ). Our study found a U-shaped curve connection between NHHR and lumbar BMD when smoothed curve fitting was used. However, the connection between NHHR and lumbar BMD did not reach statistical significance after the inflection point of 4.15. This suggests that NHHR can predict the pattern of lumbar BMD. Numerous studies have discovered a positive correlation between low levels of BMD and high levels of lipids, including TC and LDL-C, in conjunction with this ongoing improvement in research methodologies ( 22 , 31 – 33 ). This correlation has been seen alongside the ongoing progress in research methods. Research has also discovered that the presence of serum cholesterol or cellular cholesterol has an impact on the growth of osteoblasts and the density of bones ( 34 ). Injections of lipid-lowering drugs such as simvastatin and lovastatin were found to cause cancellous bone formation in an experimental mouse model ( 35 ). Additional research has demonstrated that simvastatin increases osteoblast differentiation by increasing the activities of Akt and Erk1/2 ( 24 , 36 ). Furthermore, animal models that were generated with high levels of fat have also been observed to display OP ( 37 , 38 ). Contrary to this belief, certain studies have disproven it. Brownbill et al. discovered a direct relationship between serum triglycerides and femoral stem BMD in 136 postmenopausal women, as measured by multi-site BMD assessment. Additionally, they observed that serum cholesterol levels of ≥ 240 mg/dL were notably elevated in all regions of the femur, except the femoral neck ( 39 ). A cross-sectional study conducted by Chinese scholars involving 790 Chinese postmenopausal women revealed that the femoral neck and total hip BMD were considerably lower in the group with high levels of HDL-C compared to the group with low levels of HDL-C ( 40 ). However, a recent study that included 12,395 individuals reported no notable change in TC, LDL-C, and other factors in the group with OP ( 41 ). Although there have been many research on various lipid indices and bone metabolism, there is a lack of evidence about the relationship between NHHR and lumbar BMD ( 22 ). The precise cause of the negative relationship between NHHR and lumbar BMD remains uncertain. However, it is worth noting that lipid metabolism abnormalities play a significant role in bone metabolic illnesses and other inflammatory conditions ( 30 , 42 ). Future research should focus on investigating the underlying reasons and clinical strategies for lipid metabolism and osteoporosis. This could involve targeting daily lifestyle and dietary habits to effectively control lipid indices and promote optimal bone metabolism. In this study, we used a new lipid metabolism index. By incorporating a bigger sample size into the study and making modifications for several factors, as well as conducting subgroup analyses on diverse populations, the reliability and authenticity of the structure were enhanced. Nevertheless, due to the cross-sectional nature of this investigation, it was not feasible to determine a cause-and-effect link between NHHR and lumbar BMD. During the first analysis of various populations in the subgroup analysis, it is possible that not all potential influencing factors were taken into account. In order to address this issue, we aim to mitigate it by incorporating a sample size that is adequately large. Conclusion Our work presents novel data about the intricate relationship between lipids and BMD, as we have discovered a noteworthy inverse association between NHHR and lumbar BMD in people residing in the United States. This emphasizes the role of the NHHR in overseeing lipid objectives and underscores the significance of regulating lipid levels for BMD. Exploration of further large-scale prospective studies is still needed. Further extensive prospective studies need to be investigated. Abbreviations Osteoporosis: OP Bone mineral mass: BMD Atherosclerotic cardiovascular disease: ASCVD Low-density lipoprotein: LDL-C High-density lipoprotein: HDL-C Total cholesterol: TC Non-high density lipoprotein cholesterol: NHDL-C National Lipid Association: NLA NHDL-C to HDL-C ratio: NHHR Dual-energy X-ray absorptiometry: DXA Poverty-to-income ratio: PIR Body mass index : BMI Declarations Ethics approval and consent to participate The studies involving human participants were reviewed and approved by NCHS Research Ethics Review Board (ERB). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Consent for publication Relevant data from participants were collected from the publicly accessible NHANES database, eliminating the need for obtaining additional consent. Availability of data materials The data described in this Data note can be freely and openly accessed via https://www.cdc.gov/nchs/nhanes/index.htm. Competing interests The authors declare no competing interest. Funding This work was supported by Sichuan Science and Technology Program (2023NSFSC0659). Authors’ contributions HWZ designed the research and collected, analyzed the data, and drafted the manuscript. JM and WD revised the manuscript. All authors contributed to the article and approved the submitted version. Acknowledgements Gratitude is extended to the NHANES databases for providing access to this valuable data. References Ensrud KE, Crandall CJ, Osteoporosis. Ann Intern Med. 2017;167(3):Itc17–32. Osteoporosis prevention. diagnosis, and therapy. JAMA. 2001;285(6):785–95. Wright NC, Looker AC, Saag KG, Curtis JR, Delzell ES, Randall S, et al. The recent prevalence of osteoporosis and low bone mass in the United States based on bone mineral density at the femoral neck or lumbar spine. J Bone Min Res. 2014;29(11):2520–6. Zhang J, Hu W, Zou Z, Li Y, Kang F, Li J, et al. The role of lipid metabolism in osteoporosis: Clinical implication and cellular mechanism. Genes Dis. 2024;11(4):101122. Compston J, Cooper A, Cooper C, Gittoes N, Gregson C, Harvey N, et al. UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos. 2017;12(1):43. Kanis JA, Harvey NC, Johansson H, Liu E, Vandenput L, Lorentzon M, et al. A decade of FRAX: how has it changed the management of osteoporosis? Aging Clin Exp Res. 2020;32(2):187–96. Anish RJ, Nair A. Osteoporosis management-current and future perspectives - A systemic review. J Orthop. 2024;53:101–13. Xie R, Huang X, Liu Q, Liu M. Positive association between high-density lipoprotein cholesterol and bone mineral density in U.S. adults: the NHANES 2011–2018. J Orthop Surg Res. 2022;17(1):92. Benn M, Tybjærg-Hansen A, Stender S, Frikke-Schmidt R, Nordestgaard BG. Low-density lipoprotein cholesterol and the risk of cancer: a mendelian randomization study. J Natl Cancer Inst. 2011;103(6):508–19. Hajar R. Risk Factors for Coronary Artery Disease: Historical Perspectives. Heart Views. 2017;18(3):109–14. Chiesa ST, Charakida M. High-Density Lipoprotein Function and Dysfunction in Health and Disease. Cardiovasc Drugs Ther. 2019;33(2):207–19. Zeng X, Zhan K, Zhang L, Zeng D, Yu W, Zhang X, et al. The impact of high total cholesterol and high low-density lipoprotein on avascular necrosis of the femoral head in low-energy femoral neck fractures. J Orthop Surg Res. 2017;12(1):30. Adami S, Braga V, Zamboni M, Gatti D, Rossini M, Bakri J, et al. Relationship between lipids and bone mass in 2 cohorts of healthy women and men. Calcif Tissue Int. 2004;74(2):136–42. Solomon DH, Avorn J, Canning CF, Wang PS. Lipid levels and bone mineral density. Am J Med. 2005;118(12):1414. Chen YY, Wang WW, Yang L, Chen WW, Zhang HX. Association between lipid profiles and osteoporosis in postmenopausal women: a meta-analysis. Eur Rev Med Pharmacol Sci. 2018;22(1):1–9. Samelson EJ, Cupples LA, Hannan MT, Wilson PW, Williams SA, Vaccarino V, et al. Long-term effects of serum cholesterol on bone mineral density in women and men: the Framingham Osteoporosis Study. Bone. 2004;34(3):557–61. Buizert PJ, van Schoor NM, Lips P, Deeg DJ, Eekhoff EM. Lipid levels: a link between cardiovascular disease and osteoporosis? J Bone Min Res. 2009;24(6):1103–9. Ackert-Bicknell CL. HDL cholesterol and bone mineral density: is there a genetic link? Bone. 2012;50(2):525–33. Bilgic S, Sniderman AD. Low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol and apolipoprotein B for cardiovascular care. Curr Opin Cardiol. 2024;39(1):49–53. Bays HE, Jones PH, Orringer CE, Brown WV, Jacobson TA. National Lipid Association Annual Summary of Clinical Lipidology 2016. J Clin Lipidol. 2016;10(1 Suppl):S1–43. Zhu L, Lu Z, Zhu L, Ouyang X, Yang Y, He W, et al. Lipoprotein ratios are better than conventional lipid parameters in predicting coronary heart disease in Chinese Han people. Kardiol Pol. 2015;73(10):931–8. Du YZ, Dong QX, Hu HJ, Guo B, Li YH, Zhang J, et al. A cross-sectional analysis of the relationship between the non-high density to high density lipoprotein cholesterol ratio (NHHR) and kidney stone risk in American adults. Lipids Health Dis. 2024;23(1):158. Kim KC, Shin DH, Lee SY, Im JA, Lee DC. Relation between obesity and bone mineral density and vertebral fractures in Korean postmenopausal women. Yonsei Med J. 2010;51(6):857–63. Muruganandan S, Ionescu AM, Sinal CJ. At the Crossroads of the Adipocyte and Osteoclast Differentiation Programs: Future Therapeutic Perspectives. Int J Mol Sci. 2020;21(7). Ning HT, Du Y, Zhao LJ, Tian Q, Feng H, Deng HW. Racial and gender differences in the relationship between sarcopenia and bone mineral density among older adults. Osteoporos Int. 2021;32(5):841–51. Aggarwal R, Bhatt DL, Rodriguez F, Yeh RW, Wadhera RK. Trends in Lipid Concentrations and Lipid Control Among US Adults, 2007–2018. JAMA. 2022;328(8):737–45. Du Y, Zhao LJ, Xu Q, Wu KH, Deng HW. Socioeconomic status and bone mineral density in adults by race/ethnicity and gender: the Louisiana osteoporosis study. Osteoporos Int. 2017;28(5):1699–709. Brennan SL, Pasco JA, Urquhart DM, Oldenburg B, Wang Y, Wluka AE. Association between socioeconomic status and bone mineral density in adults: a systematic review. Osteoporos Int. 2011;22(2):517–27. Chen H, Li J, Wang Q. Associations between bone-alkaline phosphatase and bone mineral density in adults with and without diabetes. Med (Baltim). 2018;97(17):e0432. Chen T, Wang Z, Xie J, Xiao S, Liu N. Trends in lipid profiles and control of LDL-C among adults with diabetes in the United States: An analysis of NHANES 2007–2018. Nutr Metab Cardiovasc Dis. 2023;33(7):1367–76. Gower BA, Casazza K. Divergent effects of obesity on bone health. J Clin Densitom. 2013;16(4):450–4. Gruneisen E, Kremer R, Duque G. Fat as a Friend or Foe of the Bone. Curr Osteoporos Rep. 2024;22(2):245–56. Hussain SM, Ebeling PR, Barker AL, Beilin LJ, Tonkin AM, McNeil JJ. Association of Plasma High-Density Lipoprotein Cholesterol Level With Risk of Fractures in Healthy Older Adults. JAMA Cardiol. 2023;8(3):268–72. Mundy G, Garrett R, Harris S, Chan J, Chen D, Rossini G, et al. Stimulation of bone formation in vitro and in rodents by statins. Science. 1999;286(5446):1946–9. Ghosh-Choudhury N, Mandal CC, Choudhury GG. Statin-induced Ras activation integrates the phosphatidylinositol 3-kinase signal to Akt and MAPK for bone morphogenetic protein-2 expression in osteoblast differentiation. J Biol Chem. 2007;282(7):4983–93. You L, Sheng ZY, Tang CL, Chen L, Pan L, Chen JY. High cholesterol diet increases osteoporosis risk via inhibiting bone formation in rats. Acta Pharmacol Sin. 2011;32(12):1498–504. Yang J, Park OJ, Kim J, Han S, Yang Y, Yun CH, et al. Adiponectin Deficiency Triggers Bone Loss by Up-Regulation of Osteoclastogenesis and Down-Regulation of Osteoblastogenesis. Front Endocrinol (Lausanne). 2019;10:815. Kim JY, Min JY, Baek JM, Ahn SJ, Jun HY, Yoon KH, et al. CTRP3 acts as a negative regulator of osteoclastogenesis through AMPK-c-Fos-NFATc1 signaling in vitro and RANKL-induced calvarial bone destruction in vivo. Bone. 2015;79:242–51. Brownbill RA, Ilich JZ. Lipid profile and bone paradox: higher serum lipids are associated with higher bone mineral density in postmenopausal women. J Womens Health (Larchmt). 2006;15(3):261–70. Li S, Guo H, Liu Y, Wu F, Zhang H, Zhang Z, et al. Relationships of serum lipid profiles and bone mineral density in postmenopausal Chinese women. Clin Endocrinol (Oxf). 2015;82(1):53–8. Zhao H, Li Y, Zhang M, Qi L, Tang Y. Blood lipid levels in patients with osteopenia and osteoporosis:a systematic review and meta-analysis. J Bone Min Metab. 2021;39(3):510–20. Chao HW, Chao SW, Lin H, Ku HC, Cheng CF. Homeostasis of Glucose and Lipid in Non-Alcoholic Fatty Liver Disease. Int J Mol Sci. 2019;20(2). 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. 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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-4516124","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314110716,"identity":"148c43ba-c1e0-4cf5-bd2b-5bf21b494770","order_by":0,"name":"Hanwen Zhang","email":"","orcid":"","institution":"Department of Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hanwen","middleName":"","lastName":"Zhang","suffix":""},{"id":314110717,"identity":"91bd22e8-3eca-47d5-9e8f-11ab65bea91d","order_by":1,"name":"jian Mei","email":"","orcid":"","institution":"Department of Orthopedic Surgery, Experimental Orthopedics, Centre for Medical Biotechnology (ZMB), University of Regensburg","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"jian","middleName":"","lastName":"Mei","suffix":""},{"id":314110718,"identity":"3d679a08-741c-43ea-8f2b-c5564cbe3b5f","order_by":2,"name":"wei Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYBACfv7mg49//LCRI16L5IxjycaMPWnGxGsxOJBjJs3AdjixgQQtQFsKeJjT+44nMH74mEOMww4D/TLDgi135pkHzJIztxGhhQ9oiwEPD0/uhhsJbMy8xGhhAPpFgodNIt2AaC0CIO/zsBkkEK8FFMiGM3sSDGeeedhMnF9AUfngw4//8nzHkw9++EiUX+DgAAlRA9OSQKqOUTAKRsEoGCkAAEy+PN6p3PekAAAAAElFTkSuQmCC","orcid":"","institution":"Pidu District People's Hospital, the Third Affiliated Hospital of Chengdu Medical College","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"wei","middleName":"","lastName":"Deng","suffix":""}],"badges":[],"createdAt":"2024-06-02 07:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4516124/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4516124/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62375523,"identity":"8dd40e40-9f8c-48c3-80dd-d43f0330f2d2","added_by":"auto","created_at":"2024-08-13 13:07:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":684811,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4516124/v1/bdb20066-ae24-4be8-9de3-8ca2b5ae96ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The NHANES 2011-2018 study found a negative correlation between bone mineral density and the non- high density to high density lipoprotein cholesterol ratio (NHHR) in U.S. adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAge-related osteoporosis (OP) is an inescapable chronic metabolic bone disease (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). After the age of 50 or older, OP has been found to affect 20% of males and 30% of women (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Approximately 8.9\u0026nbsp;million persons worldwide have osteoporotic fractures each year. They have potential to significantly affect quality of life, an issue with global health that will only get worse as we fet older (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Since the primary symptom of OP is loss of bone mineral mass (BMD), measuring BMD is the gold standard for diagnosising OP (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Nowdays the majority of researchers employ BMD as a key predictor of OP severity and to direct clinical care and survival healing analyses (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAtherosclerotic cardiovascular disease (ASCVD) is mostly caused by dyslipidemia, and one fo the main cause of ASCVD-related deaths is low-density lipoprotein (LDL-C) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). High-density lipoprotein (HDL-C) has been demonstrated to offer health advantages, despite recycling excess cholesterol in the periphery (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Numerous investigations into the connection between lipids and bone density have shown how crucial cholesterol is to bone metabolism (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePostmenopausal women with OP had higher levels of Total cholesterol (TC) and HDL-C, as shown in a meta-analysis (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Additionally, statistical analysis revealed a favorable connection between lumbar BMD and HDL-C (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). A cohort research including 712 females and 450 males did not, however, discover a correlation between TC and BMD (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). There is reason to question the validity of the correlation between lipid markers and BMD (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) since the link between osteoporosis and ASCVD cannot be accounted for by HDL-C levels. An improved ASCVD risk predictor than LDL-C alone is non-high density lipoprotein cholesterol (NHDL-C), a cholesterol measurement carried by atherogenic lipoproteins (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In addition, NHDL-C is crucial for the clinical treatment of lipoproteins, according to the National Lipid Association (NLA) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The NHDL-C to HDL-C ratio (NHHR) is a novel way to assess systemic lipids (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). It has been connected in recent studies to lipid metabolism and several diseases (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Determining whether there was a correlation between NHHR and lumbar BMD was the aim of the current study. It was also hoped that new information about the relationship between lipid markers and OP may be discovered. Accordingly, this research used a composite profile from the NHANES for people ranging in age from 20 to 59 to evaluate the association between NHHR and lumbar BMD.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSources of data and Study participants\u003c/h2\u003e \u003cp\u003eNHANES employs nationally representative cross-sectional surveys to provide a comprehensive overview of the health and nutritional status of the American population. Data was collected every two years utilizing a stratified strategy as part of the data gathering methodology. The Ethics Review Board of the National Center for Health Statistics and Research granted authorization for NHANES, and every participant provided written informed permission. This cross-sectional study analyzed individual data from four consecutive NHANES cycles spanning from 2011 to 2018, encompassing a total of 39,156 participants. To establish the final study population, we implemented the subsequent exclusion criteria: The study had four primary exclusion criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) individuals under the age of 20; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) participants with incomplete data on TC and HDL; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) people with incomplete data on lumbar BMD; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) participants with missing responses indicating uncertainty, critical illness, or refusal for the variables of smoking, hypertension, and diabetes mellitus.Finally, this study included a total of 10,793 individuals. (\u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure\u0026nbsp;1\u003c/strong\u003e \u003cp\u003eFlowchart of NHANES sample selection 2011\u0026ndash;2018\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eNHHR\u003c/h2\u003e \u003cp\u003eNHHR is a variable that is not influenced by other factors and is used to evaluate the level of exposure. NHDL-C was derived by subtracting HDL-C from TC. The NHHR is the ratio of NHDL-C to HDL-C. TC was measured using an enzymatic test and the Trinder reaction in the subject's fasting state. HDL-C was obtained using an enzymatic assay and a specific end-point reaction following the modification of the cholesterol-measuring enzyme with PEG.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLumbar BMD\u003c/h2\u003e \u003cp\u003eConsistent with previous studies, a Hologic Discovery machine was used to do dual-energy X-ray absorptiometry (DXA) scanning. A densitometer was employed to quantify the BMD values at several anatomical sites, such as the pelvis, right and left ribs, and the thoracic and lumbar vertebrae. The lumbar BMD scan yielded the mean BMD values for the lumbar vertebrae L1\u0026ndash;L4. The NHANES Quality Control Center assessed the scan quality. An expert assessment was undertaken on all 100 scans of the individuals that were investigated to verify the accuracy and reliability of the findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eCovariates included age, gender, race, smoking status, ethnicity, hypertension, diabetes mellitus, poverty-to-income ratio (PIR), education level, total serum calcium, serum phosphorus, vitamin D, and body mass index (BMI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyzes\u003c/h2\u003e \u003cp\u003eWe looked at the survey population that was covered by using NHHR quartiles because NHANES uses a sampling strategy that combines multiple phases and probability. The t-test and the chi-square test were both used. Weighted multiple logistic regression analyses were used to further investigate the linear connection between NHHR and lumbar BMD. There were no variable adjustments in Model 1. Model 2, however, has racial, age, and gender changes. Model 3 includes adjustments for age, sex, race, diabetes mellitus, hypertension, BMI, degree of education, blood calcium, serum phosphorus, vitamin D, and smoking status (defined as having smoked at least 100 cigarettes in one's lifetime). Trend tests were used to analyze linear trend connections in order to investigate the link between NHHR and lumbar BMD. To investigate the relationship between NHHR and lumbar BMD based on gender, race, and the prevalence of smoking, hypertension, and diabetes, subgroup analyses and interaction tests were carried out. Fitting a smooth curve is the solution we implemented to examine non-linear relationships. When a non-linear association between NHHR and lumbar BMD was found, a regression approach was used to find the correlation's inflection point. On both sides of the inflection point, a two-segment linear regression model was then used. At a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the analyses were carried out with PackageR (4.1.3) and EmpowerStats (2.0). The findings were statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Characteristics of Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the calculation of NHHR, we categorized NHHR into quartiles according to weighted characteristics (Q1: 0.36-1.91 mmol/L, Q2: 1.92-2.67 mmol/L, Q3: 2.68-3.66 mmol/L, Q4: 3.67-26.85 mmol/L) as shown in \u003cstrong\u003eTable1\u003c/strong\u003e. We conducted a study on a total of 10,793 individuals between the ages of 20 and 59. There were notable disparities in the basic demographic traits throughout the quartiles of NHHR.Participants in the highest quartile of NHHR were likely to be male, Non-Hispanic White, and 41 years or older. Individuals with elevated NHHR had greater educational achievement, higher BMI, decreased levels of vitamin D, serum phosphorus, and lumbar BMD, as well as lower earnings. It is primarily observed in individuals who do not have diabetes or hypertension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Basic characteristics of participants (grouped according to NHHR quartiles)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003cp\u003e(\u0026le;1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003cp\u003e(1.92-2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003cp\u003e(2.68-3.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003e(\u0026ge;3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e36.938\u0026plusmn;11.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e38.566\u0026plusmn;11.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e40.35\u0026plusmn;11.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003e41.216\u0026plusmn;10.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eSex, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e918 (34.241%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e1193 (44.152%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e1498 (55.833%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003e1888 (69.234%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e1763 (65.759%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e1509 (55.848%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e1185 (44.167%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003e839 (30.766%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eRace/ethnicity, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e282 (10.518%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e360 (13.323%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e427 (15.915%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003e521 (19.105%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e221 (8.243%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e259 (9.585%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e302 (11.256%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003e333 (12.211%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e918 (34.241%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e904 (33.457%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e959 (35.744%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003e966 (35.424%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e767 (28.609%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e658 (24.352%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e515 (19.195%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e398 (14.595%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e493 (18.389%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e521 (19.282%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e480 (17.890%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e509 (18.665%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eEducation level, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eLess than 9th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e107 (3.991%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e145 (5.366%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e173 (6.448%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e242 (8.874%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003e9-11th grade (Includes 12th grade with no diploma)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e270 (10.071%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e301 (11.140%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e328 (12.225%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e416 (15.255%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eHigh school graduate/GED or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e522 (19.470%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e577 (21.355%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e638 (23.779%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e619 (22.699%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eSome college or AA degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e914 (34.092%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e918 (33.975%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e860 (32.054%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e835 (30.620%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eCollege graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e867 (32.339%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e761 (28.164%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e683 (25.457%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e615 (22.552%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eRefused\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e1 (0.037%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e0 (0.000%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e1 (0.037%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e0 (0.000%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e910 (33.943%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e1004 (37.158%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e1034 (38.539%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e1291 (47.341%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e1771 (66.057%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e1698 (62.842%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e1649 (61.461%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e1436 (52.659%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e139 (5.185%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e167 (6.181%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e242 (9.020%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e277 (10.158%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e2542 (94.815%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e2535 (93.819%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e2441 (90.980%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e2450 (89.842%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e462 (17.232%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e595 (22.021%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e701 (26.127%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e784 (28.750%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e2219 (82.768%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e2107 (77.979%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e1982 (73.873%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e1943 (71.250%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eVitamin D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e62.519 \u0026plusmn; 27.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e60.878 \u0026plusmn;26.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e60.097 \u0026plusmn;24.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e57.836 \u0026plusmn;22.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e25.887 \u0026plusmn; 6.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e28.696 \u0026plusmn; 7.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e30.247 \u0026plusmn; 6.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e31.339 \u0026plusmn; 6.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e2.598 \u0026plusmn; 1.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e2.541 \u0026plusmn; 1.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e2.544 \u0026plusmn; 1.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e2.388 \u0026plusmn; 1.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eTotal calcium (mg/dL, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e9.345 \u0026plusmn; 0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e9.350 \u0026plusmn; 0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e9.357 \u0026plusmn; 0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e9.397 \u0026plusmn; 0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eSerum phosphorus (mg/dL, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e3.753 \u0026plusmn; 0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e3.719 \u0026plusmn; 0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e3.687 \u0026plusmn; 0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e3.695 \u0026plusmn; 0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.083743842364534%\" valign=\"top\"\u003e\n \u003cp\u003eLumbar BMD (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e1.066 \u0026plusmn; 0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\"\u003e\n \u003cp\u003e1.043 \u0026plusmn; 0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.916256157635468%\"\u003e\n \u003cp\u003e1.030 \u0026plusmn; 0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.147783251231527%\"\u003e\n \u003cp\u003e1.012 \u0026plusmn; 0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.12807881773399%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables: the P value was calculated by the weighted linear regression model (%) for categorical variables: the P value was calculated by the weighted chi-square test\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression analysis between NHHR and lumbar BMD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe multiple regression analysis, as presented in \u003cstrong\u003eTable 2\u003c/strong\u003e, demonstrates the association between NHHR and lumbar BMD. Both the original and 2 modified models showed a negative connection between NHHR and lumbar BMD. In the unadjusted model (Model1), there was a drop of 0.009 g/cm\u003csup\u003e2\u003c/sup\u003e in lumbar BMD for every 1-unit rise in NHHR. In the fully adjusted model (Model3), there was a decrease of 0.006 g/cm\u003csup\u003e2\u003c/sup\u003e in lumbar BMD for every 1-unit increase in NHHR. In the fully adjusted model, we conducted a comparison between the quartiles of the NHHR, specifically the highest and lowest quartiles. Empirical data revealed a negative correlation between lumbar BMD and the increase in \u0026nbsp;NHHR, with a decrease of 0.037 g/cm2 in lumbar BMD for every 1-unit rise in NHHR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Relationship between NHHR and lumbar BMD\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.87116564417178%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.533742331288344%\" valign=\"top\"\u003e\n \u003cp\u003eCrude Model (Model1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.987730061349694%\" valign=\"top\"\u003e\n \u003cp\u003ePartially Adjusted Model (Model 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.607361963190183%\" valign=\"top\"\u003e\n \u003cp\u003eFully Adjusted Model (Model 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.918819188191883%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.87084870848709%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.210332103321036%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.87116564417178%\"\u003e\n \u003cp\u003eNHHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.533742331288344%\"\u003e\n \u003cp\u003e-0.009 (-0.011, -0.007) \u0026lt;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.987730061349694%\"\u003e\n \u003cp\u003e-0.006 (-0.008, -0.004) \u0026lt;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.607361963190183%\"\u003e\n \u003cp\u003e-0.006 (-0.008, -0.004) \u0026lt;0.00001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.87116564417178%\" valign=\"top\"\u003e\n \u003cp\u003eNHHR Quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.533742331288344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.987730061349694%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.607361963190183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.87116564417178%\"\u003e\n \u003cp\u003eQuartile1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.533742331288344%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.987730061349694%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.607361963190183%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.87116564417178%\"\u003e\n \u003cp\u003eQuartile2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.533742331288344%\"\u003e\n \u003cp\u003e-0.015 (-0.023, -0.007) 0.00029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.987730061349694%\"\u003e\n \u003cp\u003e-0.010 (-0.018, -0.003) 0.00853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.607361963190183%\"\u003e\n \u003cp\u003e-0.013 (-0.021, -0.005) 0.00085\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.87116564417178%\"\u003e\n \u003cp\u003eQuartile3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.533742331288344%\"\u003e\n \u003cp\u003e-0.030 (-0.038, -0.022) \u0026lt;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.987730061349694%\"\u003e\n \u003cp\u003e-0.021 (-0.029, -0.013) \u0026lt;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.607361963190183%\"\u003e\n \u003cp\u003e-0.025 (-0.033, -0.017) \u0026lt;0.00001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.87116564417178%\"\u003e\n \u003cp\u003eQuartile4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.533742331288344%\"\u003e\n \u003cp\u003e-0.046 (-0.053, -0.038) \u0026lt;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.987730061349694%\"\u003e\n \u003cp\u003e-0.034 (-0.042, -0.025) \u0026lt;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.607361963190183%\"\u003e\n \u003cp\u003e-0.037 (-0.045, -0.028) \u0026lt;0.00001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eThe NHHR has a negative correlation with lumbar BMD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified a U-shaped curve between NHHR and lumbar BMD using findings of smoothed curve fitting. The linear and segmented linear regression models were shown to vary statistically significantly (\u003cstrong\u003eFig. 2\u003c/strong\u003e) and with a p-value of less than 0.001 by the log-likelihood ratio test. \u003cstrong\u003eTable 3\u003c/strong\u003e shows that an NHHR of 4.15 is the point at which the relationship between NHHR and lumbar BMD inflections. Significantly negative connection between NHHR and lumbar BMD was found when\u0026nbsp;the inflection point (K) of NHHR \u0026lt;4.15 (\u0026beta;, -0.015 [95% CI, -0.018, -0.011]), P\u0026lt;0.0001. However, there was no significant correlation between NHHR and lumbar BMD when the infection point (K) \u0026gt;4.15 (\u0026beta;, -0.001 [95%CI, -0.003, 0.004], P=0.6025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Presents the results of a threshold effect analysis, using a bipartite linear regression model, to examine the impact of NHHR on lumbar BMD.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.391465677179966%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNHHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.608534322820034%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cstrong\u003eAdjust \u0026beta; (95% CI) P value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.391465677179966%\" valign=\"top\"\u003e\n \u003cp\u003eFitting by linear regression model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.608534322820034%\"\u003e\n \u003cp\u003e-0.007 (-0.009, -0.005) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.391465677179966%\" valign=\"top\"\u003e\n \u003cp\u003eFitting by two-piecewise linear regression model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.608534322820034%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.391465677179966%\" valign=\"top\"\u003e\n \u003cp\u003eInflection point (K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.608534322820034%\" valign=\"top\"\u003e\n \u003cp\u003e4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.391465677179966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.608534322820034%\" valign=\"top\"\u003e\n \u003cp\u003e-0.015 (-0.018, -0.011) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.391465677179966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.608534322820034%\" valign=\"top\"\u003e\n \u003cp\u003e0.001 (-0.003, 0.004) \u0026nbsp; 0.6025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.391465677179966%\" valign=\"top\"\u003e\n \u003cp\u003eLog likelihood ratio test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.608534322820034%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 2\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analyzes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy interaction test, we confirmed the association between demographic factors on NHHR and lumbar BMD. Gender and smoking status did not show any significant interaction (p-value \u0026gt; 0.05 for interaction). The association between NHHR and lumbar BMD (interaction P value \u0026lt; 0.05) may change when one looks through the \u003cstrong\u003eTable 4\u003c/strong\u003e structure in terms of race and prevalence of diabetes and hypertension. Within the race categories Non-Hispanic Black, Non-Hispanic White, Other Race, elevated NHHR was adversely correlated with lumbar BMD. In the hypertensive subgroup of patients without hypertension (\u0026beta;, -0.0075 [95% CI, -0.0099, -0.0052]), elevated NHHR was negatively correlated with lumbar BMD. In the diabetic subgroup of patients without diabetes, elevated NHHR was likewise adversely correlated with lumbar BMD (\u0026beta;, -0.0073 [95% CI, -0.0094, -0.0052]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e: Age, gender, race, smoking status, ethnicity, hypertension, diabetes mellitus, PIR, education level, total serum calcium, serum phosphorus, vitamin D, BMI were adjusted.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e0.6812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0061 (-0.0085, -0.0037)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0070 (-0.0105, -0.0035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e0.0147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0008 (-0.0065, 0.0050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e0.7914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0046 (-0.0098, 0.0007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e0.0889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0052 (-0.0078, -0.0025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0128 (-0.0196, -0.0059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e0.0132 (-0.0198, -0.0067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e0.5737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0067 (-0.0095, -0.0040)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0057 (-0.0083, -0.0030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0008 (-0.0047, 0.0031)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e0.6909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0075\u0026nbsp;(-0.0099, -0.0052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e0.0054 (-0.0009, 0.0117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e0.0951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.776965265082268%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.02559414990859%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0073\u0026nbsp;(-0.0094, -0.0052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.539305301645339%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.6581352833638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe relationship between bone metabolism and lipids has been ambiguous for a significant period of time (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The cross-sectional study had 10,793 eligible participants in our analysis. In this study, we examined the relationship between the NHHR index and lumber BMD for the first time. A fascinating finding revealed a correlation between increasing NHHR and decreasing lumbar BMD. Our research indicates a potential association between dyslipidemia and BMD, highlighting the importance of managing lipid levels to optimize BMD.\u003c/p\u003e \u003cp\u003eOur analysis revealed variations in the association between NHHR and lumbar BMD among different gender, racial, and age subgroups in our study population. This supports this discovery with prior research that has identified a correlation between BMD and additional characteristics such as ethnicity and gender (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In addition, our findings indicate that individuals with advanced education, BMI, and elevated levels of serum calcium in the body may have a correlation with increased NHHR values. Another cross-sectional study (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) likewise discovered a negative correlation between educational achievement and BMD. Our subgroup analysis also revealed that increased NHHR was inversely correlated with lumbar BMD in the patient population without hypertension and diabetes. Previous investigations have found a correlation between elevated blood pressure, diabetes mellitus, and osteoporosis (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Our study found a U-shaped curve connection between NHHR and lumbar BMD when smoothed curve fitting was used. However, the connection between NHHR and lumbar BMD did not reach statistical significance after the inflection point of 4.15. This suggests that NHHR can predict the pattern of lumbar BMD.\u003c/p\u003e \u003cp\u003eNumerous studies have discovered a positive correlation between low levels of BMD and high levels of lipids, including TC and LDL-C, in conjunction with this ongoing improvement in research methodologies (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). This correlation has been seen alongside the ongoing progress in research methods. Research has also discovered that the presence of serum cholesterol or cellular cholesterol has an impact on the growth of osteoblasts and the density of bones (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Injections of lipid-lowering drugs such as simvastatin and lovastatin were found to cause cancellous bone formation in an experimental mouse model (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Additional research has demonstrated that simvastatin increases osteoblast differentiation by increasing the activities of Akt and Erk1/2 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Furthermore, animal models that were generated with high levels of fat have also been observed to display OP (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Contrary to this belief, certain studies have disproven it. Brownbill et al. discovered a direct relationship between serum triglycerides and femoral stem BMD in 136 postmenopausal women, as measured by multi-site BMD assessment. Additionally, they observed that serum cholesterol levels of \u0026ge;\u0026thinsp;240 mg/dL were notably elevated in all regions of the femur, except the femoral neck (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). A cross-sectional study conducted by Chinese scholars involving 790 Chinese postmenopausal women revealed that the femoral neck and total hip BMD were considerably lower in the group with high levels of HDL-C compared to the group with low levels of HDL-C (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). However, a recent study that included 12,395 individuals reported no notable change in TC, LDL-C, and other factors in the group with OP (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough there have been many research on various lipid indices and bone metabolism, there is a lack of evidence about the relationship between NHHR and lumbar BMD (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The precise cause of the negative relationship between NHHR and lumbar BMD remains uncertain. However, it is worth noting that lipid metabolism abnormalities play a significant role in bone metabolic illnesses and other inflammatory conditions (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Future research should focus on investigating the underlying reasons and clinical strategies for lipid metabolism and osteoporosis. This could involve targeting daily lifestyle and dietary habits to effectively control lipid indices and promote optimal bone metabolism.\u003c/p\u003e \u003cp\u003eIn this study, we used a new lipid metabolism index. By incorporating a bigger sample size into the study and making modifications for several factors, as well as conducting subgroup analyses on diverse populations, the reliability and authenticity of the structure were enhanced. Nevertheless, due to the cross-sectional nature of this investigation, it was not feasible to determine a cause-and-effect link between NHHR and lumbar BMD. During the first analysis of various populations in the subgroup analysis, it is possible that not all potential influencing factors were taken into account. In order to address this issue, we aim to mitigate it by incorporating a sample size that is adequately large.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur work presents novel data about the intricate relationship between lipids and BMD, as we have discovered a noteworthy inverse association between NHHR and lumbar BMD in people residing in the United States. This emphasizes the role of the NHHR in overseeing lipid objectives and underscores the significance of regulating lipid levels for BMD. Exploration of further large-scale prospective studies is still needed. Further extensive prospective studies need to be investigated.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eOsteoporosis:\u003c/strong\u003e OP\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBone mineral mass:\u0026nbsp;\u003c/strong\u003eBMD\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAtherosclerotic cardiovascular disease:\u0026nbsp;\u003c/strong\u003eASCVD\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLow-density lipoprotein:\u0026nbsp;\u003c/strong\u003eLDL-C\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-density lipoprotein:\u0026nbsp;\u003c/strong\u003eHDL-C\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTotal cholesterol:\u003c/strong\u003e TC\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNon-high density lipoprotein cholesterol:\u0026nbsp;\u003c/strong\u003eNHDL-C\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNational Lipid Association:\u0026nbsp;\u003c/strong\u003eNLA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNHDL-C to HDL-C ratio:\u0026nbsp;\u003c/strong\u003eNHHR\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDual-energy X-ray absorptiometry:\u0026nbsp;\u003c/strong\u003eDXA\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePoverty-to-income ratio:\u0026nbsp;\u003c/strong\u003ePIR\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBody mass index\u003c/strong\u003e: BMI\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by NCHS Research Ethics Review Board (ERB). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelevant data from participants were collected from the publicly accessible NHANES database, eliminating the need for obtaining additional consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data described in this Data note can be freely and openly accessed via https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Sichuan Science and Technology Program (2023NSFSC0659).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHWZ designed the research and collected, analyzed the data, and drafted the manuscript. \u0026nbsp;JM and WD revised the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGratitude is extended to the NHANES databases for providing access to this valuable data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEnsrud KE, Crandall CJ, Osteoporosis. Ann Intern Med. 2017;167(3):Itc17\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsteoporosis prevention. diagnosis, and therapy. JAMA. 2001;285(6):785\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright NC, Looker AC, Saag KG, Curtis JR, Delzell ES, Randall S, et al. The recent prevalence of osteoporosis and low bone mass in the United States based on bone mineral density at the femoral neck or lumbar spine. J Bone Min Res. 2014;29(11):2520\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Hu W, Zou Z, Li Y, Kang F, Li J, et al. The role of lipid metabolism in osteoporosis: Clinical implication and cellular mechanism. Genes Dis. 2024;11(4):101122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCompston J, Cooper A, Cooper C, Gittoes N, Gregson C, Harvey N, et al. UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos. 2017;12(1):43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanis JA, Harvey NC, Johansson H, Liu E, Vandenput L, Lorentzon M, et al. A decade of FRAX: how has it changed the management of osteoporosis? Aging Clin Exp Res. 2020;32(2):187\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnish RJ, Nair A. Osteoporosis management-current and future perspectives - A systemic review. J Orthop. 2024;53:101\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie R, Huang X, Liu Q, Liu M. Positive association between high-density lipoprotein cholesterol and bone mineral density in U.S. adults: the NHANES 2011\u0026ndash;2018. J Orthop Surg Res. 2022;17(1):92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenn M, Tybj\u0026aelig;rg-Hansen A, Stender S, Frikke-Schmidt R, Nordestgaard BG. Low-density lipoprotein cholesterol and the risk of cancer: a mendelian randomization study. J Natl Cancer Inst. 2011;103(6):508\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHajar R. Risk Factors for Coronary Artery Disease: Historical Perspectives. Heart Views. 2017;18(3):109\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiesa ST, Charakida M. High-Density Lipoprotein Function and Dysfunction in Health and Disease. Cardiovasc Drugs Ther. 2019;33(2):207\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng X, Zhan K, Zhang L, Zeng D, Yu W, Zhang X, et al. The impact of high total cholesterol and high low-density lipoprotein on avascular necrosis of the femoral head in low-energy femoral neck fractures. J Orthop Surg Res. 2017;12(1):30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdami S, Braga V, Zamboni M, Gatti D, Rossini M, Bakri J, et al. Relationship between lipids and bone mass in 2 cohorts of healthy women and men. Calcif Tissue Int. 2004;74(2):136\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolomon DH, Avorn J, Canning CF, Wang PS. Lipid levels and bone mineral density. Am J Med. 2005;118(12):1414.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen YY, Wang WW, Yang L, Chen WW, Zhang HX. Association between lipid profiles and osteoporosis in postmenopausal women: a meta-analysis. Eur Rev Med Pharmacol Sci. 2018;22(1):1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamelson EJ, Cupples LA, Hannan MT, Wilson PW, Williams SA, Vaccarino V, et al. Long-term effects of serum cholesterol on bone mineral density in women and men: the Framingham Osteoporosis Study. Bone. 2004;34(3):557\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuizert PJ, van Schoor NM, Lips P, Deeg DJ, Eekhoff EM. Lipid levels: a link between cardiovascular disease and osteoporosis? J Bone Min Res. 2009;24(6):1103\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAckert-Bicknell CL. HDL cholesterol and bone mineral density: is there a genetic link? Bone. 2012;50(2):525\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilgic S, Sniderman AD. Low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol and apolipoprotein B for cardiovascular care. Curr Opin Cardiol. 2024;39(1):49\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBays HE, Jones PH, Orringer CE, Brown WV, Jacobson TA. National Lipid Association Annual Summary of Clinical Lipidology 2016. J Clin Lipidol. 2016;10(1 Suppl):S1\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu L, Lu Z, Zhu L, Ouyang X, Yang Y, He W, et al. Lipoprotein ratios are better than conventional lipid parameters in predicting coronary heart disease in Chinese Han people. Kardiol Pol. 2015;73(10):931\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu YZ, Dong QX, Hu HJ, Guo B, Li YH, Zhang J, et al. A cross-sectional analysis of the relationship between the non-high density to high density lipoprotein cholesterol ratio (NHHR) and kidney stone risk in American adults. Lipids Health Dis. 2024;23(1):158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim KC, Shin DH, Lee SY, Im JA, Lee DC. Relation between obesity and bone mineral density and vertebral fractures in Korean postmenopausal women. Yonsei Med J. 2010;51(6):857\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuruganandan S, Ionescu AM, Sinal CJ. At the Crossroads of the Adipocyte and Osteoclast Differentiation Programs: Future Therapeutic Perspectives. Int J Mol Sci. 2020;21(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNing HT, Du Y, Zhao LJ, Tian Q, Feng H, Deng HW. Racial and gender differences in the relationship between sarcopenia and bone mineral density among older adults. Osteoporos Int. 2021;32(5):841\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAggarwal R, Bhatt DL, Rodriguez F, Yeh RW, Wadhera RK. Trends in Lipid Concentrations and Lipid Control Among US Adults, 2007\u0026ndash;2018. JAMA. 2022;328(8):737\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu Y, Zhao LJ, Xu Q, Wu KH, Deng HW. Socioeconomic status and bone mineral density in adults by race/ethnicity and gender: the Louisiana osteoporosis study. Osteoporos Int. 2017;28(5):1699\u0026ndash;709.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrennan SL, Pasco JA, Urquhart DM, Oldenburg B, Wang Y, Wluka AE. Association between socioeconomic status and bone mineral density in adults: a systematic review. Osteoporos Int. 2011;22(2):517\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, Li J, Wang Q. Associations between bone-alkaline phosphatase and bone mineral density in adults with and without diabetes. Med (Baltim). 2018;97(17):e0432.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen T, Wang Z, Xie J, Xiao S, Liu N. Trends in lipid profiles and control of LDL-C among adults with diabetes in the United States: An analysis of NHANES 2007\u0026ndash;2018. Nutr Metab Cardiovasc Dis. 2023;33(7):1367\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGower BA, Casazza K. Divergent effects of obesity on bone health. J Clin Densitom. 2013;16(4):450\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGruneisen E, Kremer R, Duque G. Fat as a Friend or Foe of the Bone. Curr Osteoporos Rep. 2024;22(2):245\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussain SM, Ebeling PR, Barker AL, Beilin LJ, Tonkin AM, McNeil JJ. Association of Plasma High-Density Lipoprotein Cholesterol Level With Risk of Fractures in Healthy Older Adults. JAMA Cardiol. 2023;8(3):268\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMundy G, Garrett R, Harris S, Chan J, Chen D, Rossini G, et al. Stimulation of bone formation in vitro and in rodents by statins. Science. 1999;286(5446):1946\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhosh-Choudhury N, Mandal CC, Choudhury GG. Statin-induced Ras activation integrates the phosphatidylinositol 3-kinase signal to Akt and MAPK for bone morphogenetic protein-2 expression in osteoblast differentiation. J Biol Chem. 2007;282(7):4983\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou L, Sheng ZY, Tang CL, Chen L, Pan L, Chen JY. High cholesterol diet increases osteoporosis risk via inhibiting bone formation in rats. Acta Pharmacol Sin. 2011;32(12):1498\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Park OJ, Kim J, Han S, Yang Y, Yun CH, et al. Adiponectin Deficiency Triggers Bone Loss by Up-Regulation of Osteoclastogenesis and Down-Regulation of Osteoblastogenesis. Front Endocrinol (Lausanne). 2019;10:815.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JY, Min JY, Baek JM, Ahn SJ, Jun HY, Yoon KH, et al. CTRP3 acts as a negative regulator of osteoclastogenesis through AMPK-c-Fos-NFATc1 signaling in vitro and RANKL-induced calvarial bone destruction in vivo. Bone. 2015;79:242\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrownbill RA, Ilich JZ. Lipid profile and bone paradox: higher serum lipids are associated with higher bone mineral density in postmenopausal women. J Womens Health (Larchmt). 2006;15(3):261\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Guo H, Liu Y, Wu F, Zhang H, Zhang Z, et al. Relationships of serum lipid profiles and bone mineral density in postmenopausal Chinese women. Clin Endocrinol (Oxf). 2015;82(1):53\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao H, Li Y, Zhang M, Qi L, Tang Y. Blood lipid levels in patients with osteopenia and osteoporosis:a systematic review and meta-analysis. J Bone Min Metab. 2021;39(3):510\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao HW, Chao SW, Lin H, Ku HC, Cheng CF. Homeostasis of Glucose and Lipid in Non-Alcoholic Fatty Liver Disease. Int J Mol Sci. 2019;20(2).\u003c/span\u003e\u003c/li\u003e\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":"NHHR, lumbar BMD, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-4516124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4516124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMany research have shown a negative link between lipids and bone metabolism, and the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) may be a new marker of lipid metabolism. The relationship between NHHR and lumbar bone mineral mass (BMD) is unknown. NHHR and lumbar BMD were the study's main focus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNHHR and lumbar BMD were examined using 2011-2018 National Health and Nutrition Examination Survey (NHANES) data and multivariate logistic regression models. Also employed were interaction tests and smoothed curve fitting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur investigation found a connection between increased NHHR levels and decreasing lumbar BMD after adjusting for covariates. All four measurement points showed this association, and lumbar BMD decreased by 0.037 g/cm2 relative to the lowest quartile.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe discovered an interestingly negative correlation in US citizens between NHHR and lumbar BMD. This emphasizes the need of NHHR in lipid target monitoring.\u003c/p\u003e","manuscriptTitle":"The NHANES 2011-2018 study found a negative correlation between bone mineral density and the non- high density to high density lipoprotein cholesterol ratio (NHHR) in U.S. adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-14 13:10:03","doi":"10.21203/rs.3.rs-4516124/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":"69c657c5-01a2-408d-a93e-601efb11c320","owner":[],"postedDate":"June 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-13T12:59:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-14 13:10:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4516124","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4516124","identity":"rs-4516124","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}