Dietary Inflammatory Index is intensity-dependently associated with the Bone Mineral Density at the Lumbar Spine: A Cross- Sectional NHANES Study from 2007 to 2020

Dietary Inflammatory Index is intensity-dependently associated with the Bone Mineral Density at the Lumbar Spine: A Cross- Sectional NHANES Study from 2007 to 2020 | 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 Dietary Inflammatory Index is intensity-dependently associated with the Bone Mineral Density at the Lumbar Spine: A Cross- Sectional NHANES Study from 2007 to 2020 Ying Jiang, Xinyue Chou, Siyu Du, Yimiao Zeng, Zihao Zhang, Tianchang Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4964246/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Bone mineral density (BMD) is a tool to assess bone health. Dietary inflammation index (DII) is an index to reflect the inflammatory effect of a diet. Even though the association between DII and BMD have been extensively studied, the outcome remains controversial. This study aims to reveal the relationship between DII and lumbar spine BMD in both the whole population and in postmenopausal women. Methods We utilized data from the National Health and Nutrition Examination Survey (NHANES) 2007–2020 and included 27027 participants after exclusion procedure. Smooth curve fitting and multivariate regression analysis were used both in the entire population and later in the subgroup analysis. Results In the entire population, the smooth curve fitting revealed a non-linear association with both declining and increasing trends between DII and lumbar spine BMD. Multivariate regression analysis indicated that, after adjustment, the median DII and BMD was negatively associated and the high DII and BMD was positively associated. In postmenopausal women, smooth curve fitting suggested an inverted U-shaped relationship between DII and BMD. We also observed a positive correlation between low DII and lumbar spine BMD. Conclusions DII has both positive and negative relationship with lumbar spine BMD in both the entire population and menopausal women, depending on the intensity of DII. The different trends of the smooth curve fitting may be explained by the low-inflammation state in postmenopausal women mediated by estrogen but more evidence is needed to explain the reason. NHANES dietary inflammatory index lumbar spine bone mineral density postmenopausal women Figures Figure 1 Figure 2 Figure 3 Background Bone mineral density (BMD) at the femoral neck or spine assessed via dual-energy X-ray absorptiometry (DXA) serves as a quantitative measure for calculating the T-score which is critical for diagnosing osteoporosis ( 1 ). Bone loss is a prevalent senile condition in the United States and data from the 2005–2010 National Health and Nutrition Examination Survey (NHANES) indicate that approximately 8.2 million females and 2.0 million males aged 50 and older suffer from osteoporosis, with an additional 27.3 million women and 16.1 million men exhibiting low bone mass ( 2 ). The dietary inflammatory index (DII) is an innovative tool designed to evaluate the impact of a diet on inflammation, utilizing 45 food parameters. Each component to calculate DII can positively or negatively influence specific inflammatory markers, including interlukin-1𝛽 (IL-1𝛽), interlukin-4 (IL-4), interlukin-6 (IL-6), interlukin-10 (IL-10), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP). Diets are classified as pro-inflammatory with a positive DII score, which correlates with elevated levels of these biomarkers, or as anti-inflammatory with a negative DII score, associated with reduced levels of inflammatory cytokines ( 3 ). Even though the impact of inflammatory biomarkers on bone resorption and osteogenesis have been extensively studied ( 4 – 6 ), the association between DII and lumbar spine BMD remains controversial. Despite a research has established a negative relationship between a pro-inflammatory diet and the lumbar spine BMD ( 7 ), some studies have reported there was no significant association between them ( 8 , 9 ). The different results in these studies may be attributed to the methodological limitations of the research. Some studies did not exclude postmenopausal women who are younger than the age limitation that was set at the beginning of the research, and some studies failed to include sufficient cofounders in their multivariate regression models. We made several methodological improvements in this study. First of all, we treated DII as a continuous variable, and analyzed the relationship between it and BMD across different DII tertiles. Second, our analysis incorporated a broad population by utilizing NHANES data from 2007 to 2020. Third, we excluded participants in conditions that were likely to cause bias the outcomes. Finally, we included a comprehensive range of confounding factors and employed smooth curve fitting techniques to visualize the relationship between DII and BMD. Methods 2.1 Study design and participants In this cross-sectional study, we utilized data from NHANES, a nationwide survey conducted by the National Center for Health Statistics at the U.S. Centers for Disease Control and Prevention. NHANES aims to assess the nutritional and health status of the U.S. population and is conducted in two-year cycles with representative sample weights. The study protocol of NHANES has been approved by the institutional review board, and all participants provided written informed consent in accordance with the principles of the Declaration of Helsinki. The data used in the study are available on the CDC (Center for Disease Control and Prevention) website: https://www.cdc.gov/nchs/nhanes/. Clinical trial number: not applicable. We obtained data from NHANES from 2007 to 2020, which included five complete 2-year cycles and one combined cycle (2017-2020). Our study focused on participants who completed 24-hour dietary recall and underwent DXA testing. The detailed selection and exclusion procedure was as follows: (1) Exclude participants less than 20 years. (2) Exclude pregnant participants. (3) Exclude participants with incomplete dietary data. (4) Exclude participants with unreliable calories intake (men 8000 kcal, women 5000 kcal) (5) Exclude participants with self-reported congestive heart failure, coronary heart disease, pulmonary emphysema, chronic bronchitis, chronic obstructive pulmonary disease, cancer or malignancy. (6) Excluded participants with unreliable eGFR (99%). A total of 27027 participants (including 6062 postmenopausal women) were finally included in this study and the detailed selecting procedure was presented in Figure 1. Figure 1 The participants enrollment. A total of 27027 people were included from NHANES (from 2007 to 2020) 2.2 Dietary Data and Dietary inflammatory Index (DII) We calculated the DII following the methods described by Shivappa et al (3) and the dietary data for calculation were obtained from NAHANES. Our study included 28 out of 45 food parameters including energy, carbohydrate, protein, total fat, dietary fiber, cholesterol, total saturated fatty acids, total monounsaturated fatty acids, total polyunsaturated fatty acids, ω-3 polyunsaturated fatty acids, ω-6 polyunsaturated fatty acids, vitamin A, vitamin B1, vitamin B2, vitamin B3, vitamin B6, vitamin B12, vitamin C, vitamin D, vitamin E, folic acid, alcohol, β-carotene, caffeine, iron, magnesium, zinc and selenium. We treated DII as a continuous variable and stratified it into three tertiles for further statistical analysis. 2.3 Lumbar Bone Mineral Density BMD was assessed using DXA. For the periods 2007-2010, 2013-2014, and 2017-2020, we calculated the average BMD from lumbar vertebrae L1 to L4 to represent lumbar spine BMD. However, for the years 2011-2012 and 2015-2016, we used total spine BMD measurements as proxies for lumbar spine BMD. Further details on the DXA measurement protocols can be found at www.cdc.gov/nchs/nhanes/. 2.4 Covariates Covariates in our study included age (in years), systolic blood pressure (SBD, in mmHg), diastolic blood pressure (DSB, in mmHg), estimated glomerular filtration rate (eGFR, in ml/min/1.73 m 2 ), glycohemoglobin (%), total cholesterol (TC, in mg/dL), low density lipoprotein (LDL, in mg/dL), calcium intake (in mg), caffein intake (in mg), alcohol intake (in mg), gender (categorized as male and female), race/ethnicity (categorized as Mexican American, Other Hispanic, non-Hispanic white, non-Hispanic black, or other race – including multi-racial), education level (categorized as more than high school and no more than high school), marital status (categorized as married/living with a partner, widowed/divorced/separated and never married), income, smoking status, body mass index (BMI), arthritis. We deleted patients whose DSB was 0 and took the averages of remained measured SBP and DBP as the statistical data. By using serum creatinine (Scr, in mg/dL) and age, eGFR is calculated through the following formula: eGFR = 186*Scr-1.154*age-0.203*(0.742, female) (ml/min/1.73 m 2 ). Calcium intake, caffein intake and alcohol intake data were obtained in 24-hour food recall. Income was classified according to ratio of family income to poverty (PIR) as poor (<1), near poor (1-3), not poor (≥3) (10). Smoking status was classified according to the NCHS classifications, where individuals who had smoked fewer than 100 cigarettes in their lifetime were considered never smokers, those who had smoked more than 100 cigarettes but were not currently smoking at the time of the survey were classified as ever smokers, and those who had smoked more than 100 cigarettes in their lifetime and were currently smoking at the time of survey were categorized as current smokers. BMI was categorized as underweight or healthy weight (<25 kg/m 2 ), overweight (25-29.9 kg/m 2 ), obese (30-34.9 kg/m 2 ) and severely or extremely obese (≥35 kg/m 2 ). Arthritis was defined as “a doctor told you had arthritis”. Postmenopausal women were selected by “do you still have regular periods”. 2.5 Statistical Analysis We performed statistical analyses using R (version 3.5.3) and EmpowerStats (www.empowerstats.com; X&Y Solution Inc.). We investigated the association between DII and lumbar BMD in the overall population and specifically in menopausal women. Our analysis included baseline characteristic analysis, smooth curve fitting and multivariate logistic regression. In the baseline characteristics section, categorical variables were presented as percentages, and continuous variables were expressed as means ± standard deviation (SD). Differences among DII tertiles were assessed using weighted linear regression for continuous variables and weighted chi-square tests for categorical variables To visually depict the relationship between DII and lumbar BMD, we employed smooth curve fitting with covariates in the entire population, specially in menopausal women. Furthermore, to accurately assess the impact of DII on BMD across different DII tertiles, we utilized weighted multivariate regression analysis in the entire population and among menopausal women, employing three different adjusting models. Results 3.1 Participant Characteristics A total of 27,072 participants were included in the study, comprising 13,629 males and 13,443 females. Baseline characteristics of the entire population, stratified by DII tertiles (weighted), are presented in Table 1. Participants in the highest DII tertile (DII tertile 3) tended to be younger (44.74 years vs. 45.92 years) and more likely to be female (61.59% vs. 38.22%). They also exhibited lower levels of lumbar BMD (1.029 g/cm 2 vs. 1.040 g/cm 2 ), eGFR (105.824 ml/min/1.73 m 2 vs. 111.070 ml/min/1.73 m 2 ), calcium intake (652.63 mg vs. 1310.65 mg), caffeine intake (156.61 mg vs. 184.54 mg), and alcohol intake (7.32 mg vs. 15.56 mg), along with higher levels of total cholesterol (TC) (192.19 mg/dL vs. 190.77 mg/dL), low-density lipoprotein (LDL) (114.19 mg/dL vs. 111.57 mg/dL), and glycohemoglobin (5.63 % vs. 5.56 %) compared to those in the lowest DII tertile (DII tertile 1). Additionally, higher proportions of non-Hispanic Black individuals (15.49 % vs. 8.33 %), other Hispanic individuals (6.95 % vs. 6.13 %), individuals with a maximum education level of high school (46.21% vs. 30.24%), individuals who were widowed, divorced, separated, or never married (19.40% vs. 13.25%, 22.90% vs. 19.28%), economically disadvantaged individuals (18.83% vs. 11.50%, 39.18% vs. 30.47%), current smokers (24.56% vs. 14.31%), individuals with a BMI <30 (18.13% vs. 12.98%, 22.17% vs. 19.26%), and those with arthritis (22.20% vs. 19.83%) were associated with adherence to a high DII diet. Table 1. Baseline characteristics of the entire population by tertiles of dietary inflammatory index (weighted). Dietary inflammatory index Tertile 1 (-5.731-0.300) Tertile 2 (0.300-2.229) Tertile 3 (2.229-5.405) P value DII a -1.23 ± 1.11 1.30 ± 0.55 3.19 ± 0.64 <0.0001 Age (years) a 45.29 ± 15.45 45.25 ± 16.06 44.74 ± 16.90 0.0445 Systolic blood pressure (mmHg) a 120.65 ± 15.89 121.37 ± 16.05 121.33 ± 17.50 0.0044 Diastolic blood pressure (mmHg) a 71.97 ± 10.62 72.21 ± 10.77 71.55 ± 11.32 0.0004 Lumbar BMD (g/cm 2 ) a 1.04 ± 0.16 1.04 ± 0.15 1.03 ± 0.15 <0.0001 eGFR (ml/min/1.73 m 2 ) a 111.07 ± 37.75 108.09 ± 37.91 105.82 ± 38.19 <0.0001 Glycohemoglobin (%) a 5.56 ± 0.85 5.60 ± 0.90 5.63 ± 0.95 <0.0001 TC (mg/dL) a 190.77 ± 39.72 193.54 ± 40.61 192.19 ± 40.41 <0.0001 LDL (mg/dL) a 111.57 ± 34.36 114.34 ± 35.31 114.19 ± 34.61 0.0002 Calcium intake (mg) a 1310.65 ± 680.23 927.23 ± 461.28 652.63 ± 362.31 <0.0001 Caffein intake (mg) a 184.54 ± 223.90 169.78 ± 207.44 156.61 ± 197.53 <0.0001 Alcohol intake (mg) a 15.56 ± 32.07 13.11 ± 31.79 7.32 ± 22.73 <0.0001 Gender (%) <0.0001 Male 61.78 49.17 38.41 Female 38.22 50.83 61.59 Race/ethnicity (%) <0.0001 Mexican American 10.20 9.51 8.28 Other Hispanic 6.13 6.45 6.95 Non-Hispanic White 66.43 63.77 61.99 Non-Hispanic Black 8.33 11.89 15.49 Other races including multi-racial 8.92 8.39 7.29 Education level (%) <0.0001 More than high school 69.76 61.57 53.79 No more than high school 30.24 38.43 46.21 Marital status (%) <0.0001 Married/living with a partner 67.47 64.08 57.70 Widowed/divorced/separated 13.25 16.05 19.40 Never married 19.28 19.87 22.90 Income (%) <0.0001 Poor 11.50 12.42 18.83 Near poor 30.47 36.05 39.18 Not poor 58.03 51.53 41.98 Smoking status (%) <0.0001 Current 14.31 17.45 24.56 Ever 25.36 23.34 19.26 Never 60.33 59.22 56.19 BMI group (%) <0.0001 Underweight or healthy weight 12.98 17.31 18.13 Overweight 19.26 21.59 22.17 obese 34.83 32.28 30.85 Severely or extremely obese 32.93 28.81 28.85 Arthritis (%) 0.0005 Yes 19.83 21.07 22.20 No 80.17 78.93 77.80 3.2 Association of DII with Lumbar Spine BMD The smoothed curve fitting result was presented in Figure 2. In the entire population, there was a nonlinear relationship between DII and lumbar spine BMD, exhibiting varying trends depending on the DII values, but generally showing a declining overall trend. The analysis adjusted for variables including age, gender, race/ethnicity, smoking status, BMI categories, arthritis, SBP, DBP, eGFR, TC, LDL, calcium intake, caffein intake, and alcohol intake. Figure 2 Smooth curve fitting results after adjustment revealed a non-linear relationship between DII and lumbar BMD in the entire population. The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% confidence interval from the fit. We conducted further analysis to explore the associations between Dietary Inflammatory Index (DII) and lumbar spine Bone Mineral Density (BMD) across different DII tertiles within the entire population (Table 2). The results indicated a significant inverse correlation between median DII (Tertile 2) and lumbar spine BMD, which persisted after adjustment (model 1: β = -0.0076; 95% CI: -0.0146, -0.0006, p=0.0325; model 2: β = -0.0078; 95%CI: -0.0147, -0.0010, p=0.0251; model 3: β=-0.0126; 95% CI: -0.0227, -0.0025, p=0.0147). Conversely, a positive correlation was observed between high DII (Tertile 3) and lumbar spine BMD, reaching significance after adjustment in model 3 (model 3: β = 0.0102; 95% CI: 0.0013, 0.0190, p = 0.0241). However, no significant correlation was found between low DII (Tertile 1) and lumbar spine BMD in either unadjusted or adjusted models. Table 2. The association between DII and the lumbar BMD in the entire population (weighted). Tertile 1 (-5.731-0.300) Tertile 2 (0.300-2.229) Tertile 3 (2.229-5.405) Model β (95%CI) P value β (95%CI) P value β (95%CI) P value Model 1 -0.0014 (-0.0049, 0.0021) 0.4361 -0.0076 (-0.0146, -0.0006) 0.0325* 0.0008 (-0.0052, 0.0067) 0.8024 Model 2 -0.0004 (-0.0039, 0.0030) 0.8045 -0.0078 (-0.0147, -0.0010) 0.0251* 0.0012 (-0.0046, 0.0069) 0.6895 Model 3 -0.0010 (-0.0061, 0.0042) 0.7118 -0.0126 (-0.0227, -0.0025) 0.0147* 0.0102 (0.0013, 0.0190) 0.0241* Model 1: unadjusted. Model 2: adjusted for age, gender, race/ethnicity. Model 3: adjusted for age, gender, race/ethnicity, smoker, SBP, DBP, eGFR, BMI group, glycohemoglobin, calcium intake, caffein intake, alcohol intake, TC, LDL, arthritis. * P <0.05 3.3 Subgroup Analysis In the subgroup analysis, a total of 6062 postmenopausal women were included, and baseline characteristics stratified by DII tertiles (weighted) were presented in Table 3. Postmenopausal women adhering to a high DII diet (DII tertile 3) exhibited predominantly higher levels of eGFR (83.69 ml/min/1.73 m 2 vs. 77.97 ml/min/1.73 m 2 ), glycohemoglobin (5.87 % vs. 5.73 %), along with lower levels of calcium intake (585.42 mg vs. 1065.30 mg), caffeine intake (151.67 mg vs. 165.61 mg), and alcohol intake (3.93 mg vs. 7.75 mg) compared to those adhering to a low DII diet (DII tertile 1). Additionally, postmenopausal women who were Mexican American (6.21% vs. 5.02%), other Hispanic (6.14 % vs. 5.33 %), non-Hispanic black (15.34% vs. 9.33%), widowed/divorced/separated (37.47 % vs. 28.41 %) or never married (8.98 % vs. 6.95 %), economically disadvantaged (17.19 % vs. 7.73 %, 41.33% vs. 30.47%), currently smoking (19.54 % vs. 9.12%), and with an education level no higher than high school (49.90 % vs. 31.34 %), a BMI≥30 (24.78 % vs. 17.59 %), and arthritis (46.29 % vs. 41.32 %) were more likely to adhere to a high DII diet. Table 3. Baseline characteristics of the postmenopausal women by tertiles of dietary inflammatory index (weighted). Dietary inflammatory index Tertile 1 (-5.512-0.894) Tertile 2 (0.896-2.622) Tertile 3 (2.623-4.982) P value DII a -0.72 ± 1.22 1.79 ± 0.49 3.49 ± 0.55 <0.0001* Age (years) a 59.96 ± 11.73 59.52 ± 12.80 59.64 ± 13.04 0.4926 Systolic blood pressure (mmHg) a 125.37 ± 18.77 126.76 ± 18.65 127.92 ± 20.64 0.0002* Diastolic blood pressure (mmHg) a 71.35 ± 10.45 71.59 ± 11.02 71.15 ± 12.04 0.4885 Lumbar BMD (g/cm 2 ) a 0.97 ± 0.16 0.98 ± 0.17 0.97 ± 0.16 0.1508 eGFR (ml/min/1.73 m 2 ) a 77.96 ± 30.65 81.11 ± 33.29 83.69 ± 37.32 <0.0001* Glycohemoglobin (%) a 5.73 ± 0.78 5.80 ± 0.87 5.87 ± 1.00 <0.0001* TC (mg/dL) a 206.75 ± 39.46 204.81 ± 40.59 204.45 ± 42.79 0.1666 LDL (mg/dL) a 120.10 ± 36.40 119.40 ± 35.48 120.07 ± 38.17 0.8984 Calcium intake (mg) a 1065.30 ± 511.66 797.89 ± 407.28 585.42 ± 314.01 <0.0001* Caffein intake (mg) a 165.61 ± 167.68 169.02 ± 217.09 151.67 ± 154.67 0.0091* Alcohol intake (mg) a 7.75 ± 17.11 6.17 ± 15.62 3.93 ± 14.14 <0.0001* Race/ethnicity (%) <0.0001* Mexican American 5.02 5.53 6.21 Other Hispanic 5.33 4.68 6.13 Non-Hispanic White 73.06 73.23 66.55 Non-Hispanic Black 9.33 10.88 15.34 Other races including multi-racial 7.26 5.67 5.76 Education level (%) <0.0001* More than high school 68.66 57.00 50.10 No more than high school 31.34 43.00 49.90 Marital status (%) <0.0001* Married/living with a partner 64.63 61.73 53.54 Widowed/divorced/separated 28.41 31.45 37.47 Never married 6.95 6.83 8.98 Income (%) <0.0001* Poor 7.74 10.13 17.19 Near poor 30.47 37.99 41.33 Not poor 61.79 51.88 41.47 Smoking status (%) <0.0001* Current 9.12 12.97 19.54 Ever 27.49 26.01 20.70 Never 63.39 61.02 59.76 BMI group (%) <0.0001* Underweight or healthy weight 33.49 25.24 23.19 Overweight 31.68 30.78 31.09 obese 17.59 22.36 24.78 Severely or extremely obese 17.24 21.62 20.94 Arthritis (%) 0.0064* Yes 41.31 44.31 46.29 No 58.69 55.69 53.71 The smoothed curve fitting result in postmenopausal women was presented in Figure 3. DII and lumbar BMD exhibited an inverse U-shaped association (Figure 3). The analysis adjusted for variables including age, race/ethnicity, smoking status, BMI groups, arthritis, SBP, DBP, eGFR, LDL, calcium intake, caffein intake and alcohol intake. Figure 3 Smooth curve fitting results after adjustment revealed an inverse U-shaped relationship between DII and lumbar BMD in postmenopausal women. The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% confidence interval from the fit. We conducted further analysis to examine the associations between DII and lumbar spine BMD across different DII tertiles among postmenopausal women (Table 4). When DII was in the median tertile (Tertile 2), we initially found a significant negative correlation, but this significance was not maintained in the fully adjusted model (model 1: β=-0.0289; 95%CI: -0.0475, -0.0103, p=0.0023; model 2: β=-0.0253; 95%CI: -0.0435, -0.0071, p=0.0065; model 3: β = -0.0043; 95% CI: -0.0303, 0.0217, p=0.7458). Conversely, when DII was in the low tertile (Tertile 1), we observed a positive correlation between DII and lumbar spine BMD in unadjusted and adjusted models (model 1: β=0.0111; 95%CI: 0.0042, 0.0179, p=0.0015; model 2: β=0.0114; 95%CI: 0.0048, 0.0181, p=0.0008; model 3: β=0.0098; 95%CI: 0.0001, 0.0196, p=0.0.492). However, we did not find a significant correlation between DII and lumbar spine BMD in either unadjusted or adjusted models when DII was high (Tertile 3). Table 4. The association between DII and the lumbar BMD in postmenopausal women (weighted). Tertile 1 (-5.512-0.894) Tertile 2 (0.896-2.622) Tertile 3 (2.623-4.982) Model β (95%CI) P value β (95%CI) P value β (95%CI) P value Model 1 0.0111 (0.0042, 0.0179) 0.0015* -0.0289 (-0.0475, -0.0103) 0.0023* 0.0017 (-0.0136, 0.0170) 0.8247 Model 2 0.0114 (0.0048, 0.0181) 0.0008* -0.0253 (-0.0435, -0.0071) 0.0065* -0.0014 (-0.0160, 0.0131) 0.8458 Model 3 0.0098 (0.0001, 0.0196) 0.0492* -0.0043 (-0.0303, 0.0217) 0.7458 0.0113 (-0.0121, 0.0348) 0.3438 Model 1: unadjusted. Model 2: adjusted for age, race/ethnicity. Model 3: adjusted for age, race/ethnicity, smoker, SBP, DBP, eGFR, BMI group, LDL, glycohemoglobin, calcium intake, caffein intake, alcohol intake, arthritis. * P <0.05 Discussion This study represents one of the largest cross-sectional investigations into the association of DII and the lumbar spine BMD in both the general population and postmenopausal women. Utilizing multivariate regression analysis and smooth curve fitting, we explored these relationships across different DII tertiles in these two groups. In the analysis of the whole population, the smooth curve fitting revealed a non-linear association between DII and lumbar spine BMD, demonstrating both declining and rising trends. Contrary to expectations, the multivariate analysis indicated that diets with the highest pro-inflammatory potential were positively associated with the lumbar spine BMD, whereas diets with the median pro-inflammatory potential were negatively associated with BMD. Similarly surprising, in the subgroup analysis of postmenopausal women, smooth curve fitting suggested an inverted U-shaped relationship between DII and the lumbar spine BMD. The multivariate analysis further indicated that the most anti-inflammatory diets were negatively associated with lumbar spine BMD. This study reveals both positive and negative associations between dietary inflammation and the lumbar spine BMD in both the general population and menopausal women, a finding previously overlooked. Prior research failed to recognize the non-linear nature of this relationship, likely due to a lack of continuous DII analysis across different tertiles and the absence of smooth curve fitting in diverse populations. Dietary inflammation may influence BMD via regulating bone remodelling through inflammatory cytokines. Bone remodelling, which involves osteoclasts-mediated bone resorption and osteoblasts-mediated bone synthesis, plays a predominant role in bone structure and function in adults ( 11 ). Osteoclasts initiate remodelling by resorbing bone, which is then partially replenished with osteoblast-produced matrix that later mineralizes ( 5 ). Inflammation, which is often accompanied by increased activated cytokines, can lead to excessive activity of osteoclasts and the inhibition of osteoblasts, which causes bone loss and may lead to a decreased BMD. TNF, IL-6 and IL-1 are key cytokines that significantly upregulate osteoclast activity and suppress osteoblast function. TNF signals through nuclear factor-κB (NF-κB) and mitogen-activated protein kinases (MAPKs), while the IL-6 receptor (IL-6R) activates the JAK–STAT pathway to enhance osteoclast activity. In inhibiting osteoblasts, TNF and IL-6 inhibit MAPK, activate NF-κB, and upregulate SMAD ubiquitylation regulatory factor 1 (SMURF1) and SMURF2. The pro-inflammatory cytokines also upregulate Dickkopf-related protein 1 (DKK1) and sclerostin (SOST), which inhibit the WNT–Frizzled pathway ( 5 ). However, these mechanisms could not explain the positive correlation observed between DII and BMD, as well as the distinct phenomena in differed groups. This discrepancy may be explained by the hypothesis that low-level inflammation promotes bone growth through Wnt proteins ( 12 ). Additionally, estrogen loss, which happens in menopausal women, leads to chronic low-grade production of the proinflammatory cytokines TNF alpha and IL-17 by converting memory T-cells to effector T-cells ( 4 ). Therefore, the smooth curve trend obeserved in menopausal women is similar to the latter two-thirds of the total population. Research found that it is constitutive low-intensity TNF stimulation rather than short-term and high-intensity one that induces strong and persistent expression of Wnt proteins at bone-forming sites, through the NF-jB (p65) and JNK/AP-1 (c-Jun) pathways, followed by subsequent new bone formation ( 6 ) . Though estrogen and androgen both matter in the bone regulation, the differences of hormone constitution in people from different age and sex groups may explain the differed overall levels of lumbar spine BMD. Androgen stimulated the proliferation of pre-osteoblasts and differentiation of osteoblasts. The converted estrogen suppressed osteoclast formation and resorption activity by blocking the receptor activator of nuclear factor k-B ligand pathway ( 13 ). Menopause results from reduced secretion of the ovarian hormones estrogen and progesterone, which takes place as the finite store of ovarian follicles is depleted. In postmenopausal osteoporosis, bone resorption increases by up to 70% due to the increased number of osteoclasts, while the state of bone formation amount cannot make up for the loss. Bone synthesis might increase but to a lesser extent than bone resorption, keep the same as before or decrease by up to 14% ( 15 ). We made improvements by excluding certain variables previously used in similar studies. First, in the subgroup analysis, we didn’t adjust for total cholesterol because we found TC had a mediating effect on the lumbar spine BMD in the menopausal women when in causal mediation analysis. Second, we didn’t take vitamin D intake into consideration because a systematic review has reported that vitamin D intake has no significant effect on the lumbar spine BMD ( 16 ). Our study has some limitations. First, we didn’t include physical activity as a covariate. Different types of exercise might have different effects on the lumbar spine BMD despite similar caloric consumption, which cannot be adequately addressed by the available data in the NHANES including metabolic equivalent (MET), weekly frequency, and duration of each activity. A study using NHANES data found that physical activity, as calculated by MET, weekly frequency and duration of each activity, only showed significant association among menopausal women younger than 65 years ( 17 ). Second, we didn’t adjust for corticosteroid use because it has dual effects of anti-inflammation and causing bone loss, which is hard to adjust via NHANES data. Thirdly, we didn’t adjust for bisphosphonates and calcitonin because the does and frequency of medicine use matter as well, which are unavailable on a large-scale from NHANES database ( 18 ). Fourthly, we did not consider thyroid problems in the logistic regression model because the it is not accurate enough about the actual effect on the BMD by the limited information based on NAHNES. Finally, since this is a cross-sectional study, we are not sure about the causal association between DII and lumbar spine BMD. Conclusions In conclusion, the DII is intensity-dependently associated with the lumbar spine BMD in both the overall population and the menopausal women. In the general population, a median DII (0.300 -2.229) is negatively associated with lumbar spine BMD whereas a high DII (2.229–5.405) shows a positive association. In menopausal women, a low DII (-5.512–0.894) is positively associated with the lumbar spine BMD. The differed effects may be influenced by the dual impact of inflammation intensity-dependent Wnt proteins and chronic low inflammation state induced by estrogen loss in postmenopausal women. Abbreviations BMD bone mineral density DII dietary inflammatory index NHANES National Health and Nutrition Examination Survey DXA dual-energy X-ray absorptiometry IL interleukin TNF tumor necrosis factor CRP C-reactive protein SBP systolic blood pressure DBP diastolic blood pressure eGFR estimated glomerular filtration rate TC total cholesterol LDL low density lipoprotein BMI body mass index SD standard deviation NF-κB nuclear factor-κB MAPKs mitogen-activated protein kinases SMURF SMAD ubiquitylation regulatory factor DKK Dickkopf-related protein SOST sclerostin MET metabolic equivalent Declarations Ethics approval and consent to participate The studies involving humans were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Consent for publication (not available) Availability of data and materials Publicly available datasets were analyzed in this study. This data can be found at: https://www.cdc.gov/nchs/nhanes/index.htm. Clinical trial number: not applicable. Competing interest The author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding The authors declare that no financial support was received for the research, authorship, and/or publication of this article. Authors’ contributions The authors’ responsibilities were as follows -- ZS, YJ and XC designed research; YJ, XC, SD drafted the manuscript; YJ, XC, SD, YZ, ZZ, TY collected and analyzed the data; ZS revised the manuscript. YJ, XC and ZS had primary responsibility for the final content. All authors read and approved the final manuscript. Acknowledgements We are greatly thankful for all participants in the NAHNES for providing data for this study. References the Scientific Advisory Board of the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) and the Committees of Scientific Advisors and National Societies of the International Osteoporosis Foundation (IOF), Kanis JA, Cooper C, Rizzoli R, Reginster J-Y. Executive summary of the European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Calcif Tissue Int. 2019;104:235–8. 10.1007/s00223-018-00512-x . Wright NC, Looker AC, Saag KG, Curtis JR, Delzell ES, Randall S, Dawson-Hughes B. 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;29:2520–6. 10.1002/jbmr.2269 . Shivappa N, Steck SE, Hurley TG, Hussey JR, Hébert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17:1689–96. 10.1017/S1368980013002115 . Wu D, Cline-Smith A, Shashkova E, Perla A, Katyal A, Aurora R. T-Cell Mediated Inflammation in Postmenopausal Osteoporosis. Front Immunol. 2021;12:687551. 10.3389/fimmu.2021.687551 . Redlich K, Smolen JS. Inflammatory bone loss: pathogenesis and therapeutic intervention. Nat Rev Drug Discov. 2012;11:234–50. 10.1038/nrd3669 . Li X, Wang J, Zhan Z, Li S, Zheng Z, Wang T, Zhang K, Pan H, Li Z, Zhang N, et al. Inflammation Intensity–Dependent Expression of Osteoinductive Wnt Proteins Is Critical for Ectopic New Bone Formation in Ankylosing Spondylitis. Arthritis Rheumatol. 2018;70:1056–70. 10.1002/art.40468 . Zhao S, Gao W, Li J, Sun M, Fang J, Tong L, He Y, Wang Y, Zhang Y, Xu Y, et al. Dietary inflammatory index and osteoporosis: the National Health and Nutrition Examination Survey, 2017–2018. Endocrine. 2022;78:587–96. 10.1007/s12020-022-03178-6 . Song D, Kim J, Kang M, Park J, Lee H, Kim D-Y, Park SY, Lim H. Association between the dietary inflammatory index and bone markers in postmenopausal women. PLoS ONE. 2022;17:e0265630. 10.1371/journal.pone.0265630 . Jackson MK, Bilek LD, Waltman NL, Ma J, Hébert JR, Price S, Graeff-Armas L, Poole JA, Mack LR, Hans D et al. Dietary Inflammatory Potential and Bone Outcomes in Midwestern Post-Menopausal Women. Nutrients (2023) 15:4277. 10.3390/nu15194277 Li S, Zeng M. The association between dietary inflammation index and bone mineral density: results from the United States National Health and nutrition examination surveys. Ren Fail. 2023;45:2209200. 10.1080/0886022X.2023.2209200 . Boyle WJ, Simonet WS, Lacey DL. Osteoclast differentiation and activation. Nature. 2003;423:337–42. 10.1038/nature01658 . Collison J. Low-level inflammation promotes bone growth. Nat Rev Rheumatol. 2018;14:249–249. 10.1038/nrrheum.2018.52 . Mohamad NV, Soelaiman I-N, Chin K-Y. A concise review of testosterone and bone health. Clin Interv Aging. 2016;11:1317–24. 10.2147/CIA.S115472 . Takahashi TA, Johnson KM, Menopause. Med Clin North Am. 2015;99:521–34. 10.1016/j.mcna.2015.01.006 . Fischer V, Haffner-Luntzer M. Interaction between bone and immune cells: Implications for postmenopausal osteoporosis. Semin Cell Dev Biol. 2022;123:14–21. 10.1016/j.semcdb.2021.05.014 . Reid IR, Bolland MJ, Grey A. Effects of vitamin D supplements on bone mineral density: a systematic review and meta-analysis. Lancet. 2014;383:146–55. 10.1016/S0140-6736(13)61647-5 . Ji J, Hou Y, Li Z, Zhou Y, Xue H, Wen T, Yang T, Xue L, Tu Y, Ma T. Association between physical activity and bone mineral density in postmenopausal women: a cross-sectional study from the NHANES 2007–2018. J Orthop Surg. 2023;18:501. 10.1186/s13018-023-03976-2 . Khosla S, Hofbauer LC. Osteoporosis treatment: recent developments and ongoing challenges. Lancet Diabetes Endocrinol. 2017;5:898–907. 10.1016/S2213-8587(17)30188- . Additional Declarations No competing interests reported. Supplementary Files supplementmediatingeffectanalysisthepostmenopausalwomen.png Supplement: mediating effect analysis the postmenopausal women.png Additional file 1: The mediating effect of total cholesterol between DII and BMD in postmenopausal women Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4964246","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":360827659,"identity":"2b6e90a0-7afc-4193-8692-24d37a5bd240","order_by":0,"name":"Ying Jiang","email":"","orcid":"","institution":"Dingxi People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Jiang","suffix":""},{"id":360827660,"identity":"20ccc51b-cc46-4bd0-af73-b9a99b8e40c6","order_by":1,"name":"Xinyue Chou","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinyue","middleName":"","lastName":"Chou","suffix":""},{"id":360827661,"identity":"311bf0ef-d2c5-4f72-aaba-c005948bf9c7","order_by":2,"name":"Siyu Du","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Du","suffix":""},{"id":360827662,"identity":"a9d25e15-b095-422f-8a8d-799d8c6236f2","order_by":3,"name":"Yimiao Zeng","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yimiao","middleName":"","lastName":"Zeng","suffix":""},{"id":360827663,"identity":"e073170c-e825-4edc-b4c4-8f1edac920fb","order_by":4,"name":"Zihao Zhang","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zihao","middleName":"","lastName":"Zhang","suffix":""},{"id":360827666,"identity":"80f7f7a5-24bf-4c9e-ad86-541009ced55a","order_by":5,"name":"Tianchang Yang","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tianchang","middleName":"","lastName":"Yang","suffix":""},{"id":360827667,"identity":"a837187b-f2dc-44be-a6db-7f76fff3e231","order_by":6,"name":"Zhipeng Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYFCCAyDChoefv4E0LWkykjMOkGbVYRuDhgQi1co3njGT/FFznseA4QDjh485RGhhbDhjJs1z7DaPOXMDs+TMbURoYWY4Y3abge02j2XDATZmXmK0sAG13Pzx7xyPwYEEIrXwALXc4G07QIIWCYZj5b95+5J5JGccbCbOL/IzDm82/PHNzp6fv/ngh4/EaGGQOGEAZTE2EKMeCPjbHxCpchSMglEwCkYsAABq5jbtsxJ6dQAAAABJRU5ErkJggg==","orcid":"","institution":"Dingxi People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhipeng","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-08-23 12:31:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4964246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4964246/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66941653,"identity":"27cc7696-6020-45b2-b1f6-fe02b25910a1","added_by":"auto","created_at":"2024-10-18 08:57:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":297822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe participants enrollment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 27027 people were included from NHANES (from 2007 to 2020)\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4964246/v1/07ec6e69349126cbb0f9887c.jpg"},{"id":66940453,"identity":"12e6db34-fecb-487c-a69e-e006bdd8f0ae","added_by":"auto","created_at":"2024-10-18 08:49:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSmooth curve fitting results after adjustment revealed a non-linear relationship between DII and lumbar BMD in the entire population.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe solid red line represents the smooth curve fit between variables. Blue bands represent the 95% confidence interval from the fit.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4964246/v1/2114d5f047d0327389efd333.jpg"},{"id":66941892,"identity":"d8acf2ab-d54d-47c5-861d-3242a04da167","added_by":"auto","created_at":"2024-10-18 09:05:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":199380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSmooth curve fitting results after adjustment revealed an inverse U-shaped relationship between DII and lumbar BMD in postmenopausal women.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe solid red line represents the smooth curve fit between variables. Blue bands represent the 95% confidence interval from the fit.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4964246/v1/7d6e49a98f596ffa2baf99b7.jpg"},{"id":78972985,"identity":"35c04432-d061-484b-81e0-cc21ce8defcb","added_by":"auto","created_at":"2025-03-21 14:32:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1920163,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4964246/v1/33538273-d009-4039-8450-d822284499f7.pdf"},{"id":66940450,"identity":"950a3e43-3f8f-42ed-96d4-dd03fdd60124","added_by":"auto","created_at":"2024-10-18 08:49:18","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":241732,"visible":true,"origin":"","legend":"\u003cp\u003eSupplement: mediating effect analysis the postmenopausal women.png\u003c/p\u003e\n\u003cp\u003eAdditional file 1: The mediating effect of total cholesterol between DII and BMD in postmenopausal women\u003c/p\u003e","description":"","filename":"supplementmediatingeffectanalysisthepostmenopausalwomen.png","url":"https://assets-eu.researchsquare.com/files/rs-4964246/v1/940cff7b6365d9f3935ae3b8.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dietary Inflammatory Index is intensity-dependently associated with the Bone Mineral Density at the Lumbar Spine: A Cross- Sectional NHANES Study from 2007 to 2020","fulltext":[{"header":"Background","content":"\u003cp\u003eBone mineral density (BMD) at the femoral neck or spine assessed via dual-energy X-ray absorptiometry (DXA) serves as a quantitative measure for calculating the T-score which is critical for diagnosing osteoporosis (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Bone loss is a prevalent senile condition in the United States and data from the 2005\u0026ndash;2010 National Health and Nutrition Examination Survey (NHANES) indicate that approximately 8.2\u0026nbsp;million females and 2.0\u0026nbsp;million males aged 50 and older suffer from osteoporosis, with an additional 27.3\u0026nbsp;million women and 16.1\u0026nbsp;million men exhibiting low bone mass (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dietary inflammatory index (DII) is an innovative tool designed to evaluate the impact of a diet on inflammation, utilizing 45 food parameters. Each component to calculate DII can positively or negatively influence specific inflammatory markers, including interlukin-1\u0026#120573; (IL-1\u0026#120573;), interlukin-4 (IL-4), interlukin-6 (IL-6), interlukin-10 (IL-10), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP). Diets are classified as pro-inflammatory with a positive DII score, which correlates with elevated levels of these biomarkers, or as anti-inflammatory with a negative DII score, associated with reduced levels of inflammatory cytokines (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEven though the impact of inflammatory biomarkers on bone resorption and osteogenesis have been extensively studied (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), the association between DII and lumbar spine BMD remains controversial. Despite a research has established a negative relationship between a pro-inflammatory diet and the lumbar spine BMD (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), some studies have reported there was no significant association between them (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The different results in these studies may be attributed to the methodological limitations of the research. Some studies did not exclude postmenopausal women who are younger than the age limitation that was set at the beginning of the research, and some studies failed to include sufficient cofounders in their multivariate regression models.\u003c/p\u003e \u003cp\u003eWe made several methodological improvements in this study. First of all, we treated DII as a continuous variable, and analyzed the relationship between it and BMD across different DII tertiles. Second, our analysis incorporated a broad population by utilizing NHANES data from 2007 to 2020. Third, we excluded participants in conditions that were likely to cause bias the outcomes. Finally, we included a comprehensive range of confounding factors and employed smooth curve fitting techniques to visualize the relationship between DII and BMD.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e2.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Study design and participants\u003c/h2\u003e\n\u003cp\u003eIn this cross-sectional study, we utilized data from NHANES, a nationwide survey conducted by the National Center for Health Statistics at the U.S. Centers for Disease Control and Prevention. NHANES aims to assess the nutritional and health status of the U.S. population and is conducted in two-year cycles with representative sample weights. The study protocol of NHANES has been approved by the institutional review board, and all participants provided written informed consent in accordance with the principles of the Declaration of Helsinki. The data used in the study are available on the CDC (Center for Disease Control and Prevention) website: https://www.cdc.gov/nchs/nhanes/. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eWe obtained data from NHANES from 2007 to 2020, which included five complete 2-year cycles and one combined cycle (2017-2020). Our study focused on participants who completed 24-hour dietary recall and underwent DXA testing. The detailed selection and exclusion procedure was as follows: (1) Exclude participants less than 20 years. (2) Exclude pregnant participants. (3) Exclude participants with incomplete dietary data. (4) Exclude participants with unreliable calories intake (men \u0026lt; 500 kcal or \u0026gt; 8000 kcal, women \u0026lt;500 kcal or \u0026gt; 5000 kcal) (5) Exclude participants with self-reported congestive heart failure, coronary heart disease, pulmonary emphysema, chronic bronchitis, chronic obstructive pulmonary disease, cancer or malignancy. \u0026nbsp;(6) Excluded participants with unreliable eGFR (\u0026lt;1% or \u0026gt;99%). A total of 27027 participants (including 6062 postmenopausal women) were finally included in this study and the detailed selecting procedure was presented in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1 The participants enrollment.\u0026nbsp;\u003c/strong\u003eA total of 27027 people were included from NHANES (from 2007 to 2020)\u003c/p\u003e\n\u003ch2\u003e2.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Dietary Data and Dietary inflammatory Index (DII)\u003c/h2\u003e\n\u003cp\u003eWe calculated the DII following the methods described by Shivappa et al (3) and the dietary data for calculation were obtained from NAHANES. Our study included 28 out of 45 food parameters including energy, carbohydrate, protein, total fat, dietary fiber, cholesterol, total saturated fatty acids, total monounsaturated fatty acids, total polyunsaturated fatty acids, \u0026omega;-3 polyunsaturated fatty acids, \u0026omega;-6 polyunsaturated fatty acids, vitamin A, vitamin B1, vitamin B2, vitamin B3, vitamin B6, vitamin B12, vitamin C, vitamin D, vitamin E, folic acid, alcohol, \u0026beta;-carotene, caffeine, iron, magnesium, zinc and selenium. We treated DII as a continuous variable and stratified it into three tertiles for further statistical analysis.\u003c/p\u003e\n\u003ch2\u003e2.3\u0026nbsp; \u0026nbsp; \u0026nbsp;Lumbar Bone Mineral Density\u003c/h2\u003e\n\u003cp\u003eBMD was assessed using DXA. For the periods 2007-2010, 2013-2014, and 2017-2020, we calculated the average BMD from lumbar vertebrae L1 to L4 to represent lumbar spine BMD. However, for the years 2011-2012 and 2015-2016, we used total spine BMD measurements as proxies for lumbar spine BMD. Further details on the DXA measurement protocols can be found at www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003ch2\u003e2.4\u0026nbsp; \u0026nbsp; \u0026nbsp;Covariates\u003c/h2\u003e\n\u003cp\u003eCovariates in our study included age (in years), systolic blood pressure (SBD, in mmHg), diastolic blood pressure (DSB, in mmHg), estimated glomerular filtration rate (eGFR, in ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e), glycohemoglobin (%), total cholesterol (TC, in mg/dL), low density lipoprotein (LDL, in mg/dL), calcium intake (in mg), caffein intake (in mg), alcohol intake (in mg), gender (categorized as male and female), race/ethnicity (categorized as Mexican American, Other Hispanic, non-Hispanic white, non-Hispanic black, or other race \u0026ndash; including multi-racial), education level (categorized as more than high school and no more than high school), marital status (categorized as married/living with a partner, widowed/divorced/separated and never married), income, smoking status, body mass index (BMI), arthritis.\u003c/p\u003e\n\u003cp\u003eWe deleted patients whose DSB was 0 and took the averages of remained measured SBP and DBP as the statistical data. By using serum creatinine (Scr, in mg/dL) and age, eGFR is calculated through the following formula: eGFR = 186*Scr-1.154*age-0.203*(0.742, female) (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e). Calcium intake, caffein intake and alcohol intake data were obtained in 24-hour food recall.\u003c/p\u003e\n\u003cp\u003eIncome was classified according to ratio of family income to poverty (PIR) as poor (\u0026lt;1), near poor (1-3), not poor (\u0026ge;3) (10). Smoking status was classified according to the NCHS classifications, where individuals who had smoked fewer than 100 cigarettes in their lifetime were considered never smokers, those who had smoked more than 100 cigarettes but were not currently smoking at the time of the survey were classified as ever smokers, and those who had smoked more than 100 cigarettes in their lifetime and were currently smoking at the time of survey were categorized as current smokers. BMI was categorized as underweight or healthy weight (\u0026lt;25 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e), obese (30-34.9 kg/m\u003csup\u003e2\u003c/sup\u003e) and severely or extremely obese (\u0026ge;35 kg/m\u003csup\u003e2\u003c/sup\u003e). Arthritis was defined as \u0026ldquo;a doctor told you had arthritis\u0026rdquo;. Postmenopausal women were selected by \u0026ldquo;do you still have regular periods\u0026rdquo;.\u003c/p\u003e\n\u003ch2\u003e2.5\u0026nbsp; \u0026nbsp; \u0026nbsp;Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eWe performed statistical analyses using R (version 3.5.3) and EmpowerStats (www.empowerstats.com; X\u0026amp;Y Solution Inc.). We investigated the association between DII and lumbar BMD in the overall population and specifically in menopausal women. Our analysis included baseline characteristic analysis, smooth curve fitting and multivariate logistic regression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the baseline characteristics section, categorical variables were presented as percentages, and continuous variables were expressed as means\u0026nbsp;\u0026plusmn;\u0026nbsp;standard deviation (SD). Differences among DII tertiles were assessed using weighted linear regression for continuous variables and weighted chi-square tests for categorical variables\u003c/p\u003e\n\u003cp\u003eTo visually depict the relationship between DII and lumbar BMD, we employed smooth curve fitting with covariates in the entire population, specially in menopausal women. Furthermore, to accurately assess the impact of DII on BMD across different DII tertiles, we utilized weighted multivariate regression analysis in the entire population and among menopausal women, employing three different adjusting models.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e3.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Participant Characteristics\u003c/h2\u003e\n\u003cp\u003eA total of 27,072 participants were included in the study, comprising 13,629 males and 13,443 females. Baseline characteristics of the entire population, stratified by DII tertiles (weighted), are presented in Table 1. Participants in the highest DII tertile (DII tertile 3) tended to be younger (44.74 years vs. 45.92 years) and more likely to be female (61.59% vs. 38.22%). They also exhibited lower levels of lumbar BMD (1.029 g/cm\u003csup\u003e2\u003c/sup\u003e vs. 1.040 g/cm\u003csup\u003e2\u003c/sup\u003e), eGFR (105.824 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e vs. 111.070 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e), calcium intake (652.63 mg vs. 1310.65 mg), caffeine intake (156.61 mg vs. 184.54 mg), and alcohol intake (7.32 mg vs. 15.56 mg), along with higher levels of total cholesterol (TC) (192.19 mg/dL vs. 190.77 mg/dL), low-density lipoprotein (LDL) (114.19 mg/dL vs. 111.57 mg/dL), and glycohemoglobin (5.63 % vs. 5.56 %) compared to those in the lowest DII tertile (DII tertile 1). Additionally, higher proportions of non-Hispanic Black individuals (15.49 % vs. 8.33 %), other Hispanic individuals (6.95 % vs. 6.13 %), individuals with a maximum education level of high school (46.21% vs. 30.24%), individuals who were widowed, divorced, separated, or never married (19.40% vs. 13.25%, 22.90% vs. 19.28%), economically disadvantaged individuals (18.83% vs. 11.50%, 39.18% vs. 30.47%), current smokers (24.56% vs. 14.31%), individuals with a BMI \u0026lt;30 (18.13% vs. 12.98%, 22.17% vs. 19.26%), and those with arthritis (22.20% vs. 19.83%) were associated with adherence to a high DII diet.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 620px;\"\u003e\n \u003cp\u003eTable 1. Baseline characteristics of the entire population by tertiles of dietary inflammatory index (weighted).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 392px;\"\u003e\n \u003cp\u003eDietary inflammatory index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eTertile 1 (-5.731-0.300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eTertile 2 (0.300-2.229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eTertile 3 (2.229-5.405)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eDII \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e-1.23 \u0026plusmn; 1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.30 \u0026plusmn; 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e3.19 \u0026plusmn; 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eAge (years) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e45.29 \u0026plusmn; 15.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e45.25 \u0026plusmn; 16.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e44.74 \u0026plusmn; 16.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.0445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e120.65 \u0026plusmn; 15.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e121.37 \u0026plusmn; 16.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e121.33 \u0026plusmn; 17.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.0044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e71.97 \u0026plusmn; 10.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e72.21 \u0026plusmn; 10.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e71.55 \u0026plusmn; 11.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eLumbar BMD (g/cm\u003csup\u003e2\u003c/sup\u003e) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.04 \u0026plusmn; 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.04 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.03 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eeGFR (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e111.07 \u0026plusmn; 37.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e108.09 \u0026plusmn; 37.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e105.82 \u0026plusmn; 38.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eGlycohemoglobin (%) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e5.56 \u0026plusmn; 0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e5.60 \u0026plusmn; 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e5.63 \u0026plusmn; 0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eTC (mg/dL) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e190.77 \u0026plusmn; 39.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e193.54 \u0026plusmn; 40.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e192.19 \u0026plusmn; 40.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eLDL (mg/dL) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e111.57 \u0026plusmn; 34.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e114.34 \u0026plusmn; 35.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e114.19 \u0026plusmn; 34.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eCalcium intake (mg) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1310.65 \u0026plusmn; 680.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e927.23 \u0026plusmn; 461.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e652.63 \u0026plusmn; 362.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eCaffein intake (mg)\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e184.54 \u0026plusmn; 223.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e169.78 \u0026plusmn; 207.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e156.61 \u0026plusmn; 197.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eAlcohol intake (mg)\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e15.56 \u0026plusmn; 32.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e13.11 \u0026plusmn; 31.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e7.32 \u0026plusmn; 22.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eGender (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e61.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e49.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e38.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e38.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e50.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e61.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eRace/ethnicity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e10.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e9.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e8.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e66.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e63.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e61.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e11.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e15.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eOther races including multi-racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e8.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eEducation level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eMore than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e69.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e61.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e53.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eNo more than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e30.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e38.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e46.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eMarital status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eMarried/living with a partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e67.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e64.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e57.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eWidowed/divorced/separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e13.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e16.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e19.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e19.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e19.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e22.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eIncome (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003ePoor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e11.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e18.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eNear poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e30.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e36.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e39.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eNot poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e58.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e51.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e41.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eSmoking status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eCurrent\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e14.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e17.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e24.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eEver\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e25.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e23.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e19.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eNever\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e60.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e59.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e56.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eBMI group (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eUnderweight or healthy weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e12.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e17.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e18.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e19.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e21.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e22.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eobese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e34.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e32.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e30.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Severely or extremely obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e32.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e28.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e28.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eArthritis (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e19.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e21.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e22.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e80.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e78.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e77.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.2 \u0026nbsp; \u0026nbsp; Association of DII with Lumbar Spine BMD\u003c/h2\u003e\n\u003cp\u003eThe smoothed curve fitting result was presented in Figure 2. In the entire population, there was a nonlinear relationship between DII and lumbar spine BMD, exhibiting varying trends depending on the DII values, but generally showing a declining overall trend. The analysis adjusted for variables including age, gender, race/ethnicity, smoking status, BMI categories, arthritis, SBP, DBP, eGFR, TC, LDL, calcium intake, caffein intake, and alcohol intake.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2 Smooth curve fitting results after adjustment revealed a non-linear relationship between DII and lumbar BMD in the entire population.\u0026nbsp;\u003c/strong\u003eThe solid red line represents the smooth curve fit between variables. Blue bands represent the 95% confidence interval from the fit.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe conducted further analysis to explore the associations between Dietary Inflammatory Index (DII) and lumbar spine Bone Mineral Density (BMD) across different DII tertiles within the entire population (Table 2). The results indicated a significant inverse correlation between median DII (Tertile 2) and lumbar spine BMD, which persisted after adjustment (model 1:\u0026nbsp;\u0026beta;\u0026nbsp;=\u0026nbsp;-0.0076; 95%\u0026nbsp;CI: -0.0146, -0.0006, p=0.0325; model 2:\u0026nbsp;\u0026beta;\u0026nbsp;=\u0026nbsp;-0.0078; 95%CI: -0.0147, -0.0010, p=0.0251; model 3:\u0026nbsp;\u0026beta;=-0.0126; 95%\u0026nbsp;CI: -0.0227, -0.0025, p=0.0147). Conversely, a positive correlation was observed between high DII (Tertile 3) and lumbar spine BMD, reaching significance after adjustment in model 3 (model 3:\u0026nbsp;\u0026beta;\u0026nbsp;=\u0026nbsp;0.0102; 95%\u0026nbsp;CI: 0.0013, 0.0190, p\u0026nbsp;=\u0026nbsp;0.0241). However, no significant correlation was found between low DII (Tertile 1) and lumbar spine BMD in either unadjusted or adjusted models.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100%;\"\u003e\n \u003cp\u003eTable 2. The association between DII and the lumbar BMD in the entire population (weighted).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.00654%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 32.1895%;\"\u003e\n \u003cp\u003eTertile 1 (-5.731-0.300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 32.3529%;\"\u003e\n \u003cp\u003eTertile 2 (0.300-2.229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 27.451%;\"\u003e\n \u003cp\u003eTertile 3 (2.229-5.405)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.00654%;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8954%;\"\u003e\n \u003cp\u003e\u0026beta; (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2941%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.2222%;\"\u003e\n \u003cp\u003e\u0026beta; (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2941%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4248%;\"\u003e\n \u003cp\u003e\u0026beta; (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86275%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.00654%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8954%;\"\u003e\n \u003cp\u003e-0.0014 (-0.0049, 0.0021)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2941%;\"\u003e\n \u003cp\u003e0.4361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.2222%;\"\u003e\n \u003cp\u003e-0.0076 (-0.0146, -0.0006)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2941%;\"\u003e\n \u003cp\u003e0.0325*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4248%;\"\u003e\n \u003cp\u003e0.0008 (-0.0052, 0.0067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86275%;\"\u003e\n \u003cp\u003e0.8024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.00654%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8954%;\"\u003e\n \u003cp\u003e-0.0004 (-0.0039, 0.0030)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2941%;\"\u003e\n \u003cp\u003e0.8045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.2222%;\"\u003e\n \u003cp\u003e-0.0078 (-0.0147, -0.0010)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2941%;\"\u003e\n \u003cp\u003e0.0251*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4248%;\"\u003e\n \u003cp\u003e0.0012 (-0.0046, 0.0069)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86275%;\"\u003e\n \u003cp\u003e0.6895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.00654%;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8954%;\"\u003e\n \u003cp\u003e-0.0010 (-0.0061, 0.0042)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2941%;\"\u003e\n \u003cp\u003e0.7118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.2222%;\"\u003e\n \u003cp\u003e-0.0126 (-0.0227, -0.0025)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2941%;\"\u003e\n \u003cp\u003e0.0147*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4248%;\"\u003e\n \u003cp\u003e0.0102 (0.0013, 0.0190)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86275%;\"\u003e\n \u003cp\u003e0.0241*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100%;\"\u003e\n \u003cp\u003eModel 1: unadjusted.\u003c/p\u003e\n \u003cp\u003eModel 2: adjusted for age, gender, race/ethnicity.\u003c/p\u003e\n \u003cp\u003eModel 3: adjusted for age, gender, race/ethnicity, smoker, SBP, DBP, eGFR, BMI group, glycohemoglobin, calcium intake, caffein intake, alcohol intake, TC, LDL, arthritis.\u003c/p\u003e\n \u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.3\u0026nbsp; \u0026nbsp; \u0026nbsp;Subgroup Analysis\u003c/h2\u003e\n\u003cp\u003eIn the subgroup analysis, a total of 6062 postmenopausal women were included, and baseline characteristics stratified by DII tertiles (weighted) were presented in Table 3. Postmenopausal women adhering to a high DII diet (DII tertile 3) exhibited predominantly higher levels of eGFR (83.69 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e vs. 77.97 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e), glycohemoglobin (5.87 % vs. 5.73 %), along with lower levels of calcium intake (585.42 mg vs. 1065.30 mg), caffeine intake (151.67 mg vs. 165.61 mg), and alcohol intake (3.93 mg vs. 7.75 mg) compared to those adhering to a low DII diet (DII tertile 1). Additionally, postmenopausal women who were Mexican American (6.21% vs. 5.02%), other Hispanic (6.14 % vs. 5.33 %), non-Hispanic black (15.34% vs. 9.33%), widowed/divorced/separated (37.47 % vs. 28.41 %) or never married (8.98 % vs. 6.95 %), economically disadvantaged (17.19 % vs. 7.73 %, 41.33% vs. 30.47%), currently smoking (19.54 % vs. 9.12%), and with an education level no higher than high school (49.90 % vs. 31.34 %), a BMI\u0026ge;30 (24.78 % vs. 17.59 %), and arthritis (46.29 % vs. 41.32 %) were more likely to adhere to a high DII diet.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"640\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 640px;\"\u003e\n \u003cp\u003eTable 3. Baseline characteristics of the postmenopausal women by tertiles of dietary inflammatory index (weighted).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 404px;\"\u003e\n \u003cp\u003eDietary inflammatory index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eTertile 1 (-5.512-0.894)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eTertile 2 (0.896-2.622)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eTertile 3 (2.623-4.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eDII \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e-0.72 \u0026plusmn; 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e1.79 \u0026plusmn; 0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e3.49 \u0026plusmn; 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eAge (years) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e59.96 \u0026plusmn; 11.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e59.52 \u0026plusmn; 12.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e59.64 \u0026plusmn; 13.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.4926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e125.37 \u0026plusmn; 18.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e126.76 \u0026plusmn; 18.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e127.92 \u0026plusmn; 20.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.0002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e71.35 \u0026plusmn; 10.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e71.59 \u0026plusmn; 11.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e71.15 \u0026plusmn; 12.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.4885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eLumbar BMD (g/cm\u003csup\u003e2\u003c/sup\u003e) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e0.97 \u0026plusmn; 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e0.98 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e0.97 \u0026plusmn; 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.1508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eeGFR (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e77.96 \u0026plusmn; 30.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e81.11 \u0026plusmn; 33.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e83.69 \u0026plusmn; 37.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eGlycohemoglobin (%) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e5.73 \u0026plusmn; 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e5.80 \u0026plusmn; 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e5.87 \u0026plusmn; 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eTC (mg/dL) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e206.75 \u0026plusmn; 39.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e204.81 \u0026plusmn; 40.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e204.45 \u0026plusmn; 42.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.1666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eLDL (mg/dL) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e120.10 \u0026plusmn; 36.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e119.40 \u0026plusmn; 35.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e120.07 \u0026plusmn; 38.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.8984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eCalcium intake (mg) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e1065.30 \u0026plusmn; 511.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e797.89 \u0026plusmn; 407.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e585.42 \u0026plusmn; 314.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eCaffein intake (mg)\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e165.61 \u0026plusmn; 167.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e169.02 \u0026plusmn; 217.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e151.67 \u0026plusmn; 154.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.0091*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eAlcohol intake (mg)\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e7.75 \u0026plusmn; 17.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e6.17 \u0026plusmn; 15.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e3.93 \u0026plusmn; 14.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eRace/ethnicity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e73.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e73.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e66.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e9.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e10.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e15.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eOther races including multi-racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e5.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eEducation level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eMore than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e68.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e57.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e50.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eNo more than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e31.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e43.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e49.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eMarital status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eMarried/living with a partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e64.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e61.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e53.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eWidowed/divorced/separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e28.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e31.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e37.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eIncome (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003ePoor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e7.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e10.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e17.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eNear poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e30.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e37.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e41.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eNot poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e61.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e51.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e41.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eSmoking status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eCurrent\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e9.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e12.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e19.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eEver\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e27.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e26.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e20.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eNever\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e63.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e61.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e59.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eBMI group (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eUnderweight or healthy weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e33.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e25.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e23.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e31.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e30.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e31.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eobese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e17.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e22.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e24.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Severely or extremely obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e17.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e21.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e20.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eArthritis (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.0064*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e41.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e44.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e46.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e58.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e55.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e53.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe smoothed curve fitting result in postmenopausal women was presented in Figure 3. DII and lumbar BMD exhibited an inverse U-shaped association (Figure 3). The analysis adjusted for variables including age, race/ethnicity, smoking status, BMI groups, arthritis, SBP, DBP, eGFR, LDL, calcium intake, caffein intake and alcohol intake.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Smooth curve fitting results after adjustment revealed an inverse U-shaped relationship between DII and lumbar BMD in postmenopausal women.\u003c/strong\u003eThe solid red line represents the smooth curve fit between variables. Blue bands represent the 95% confidence interval from the fit.\u003c/p\u003e\n\u003cp\u003eWe conducted further analysis to examine the associations between DII and lumbar spine BMD across different DII tertiles among postmenopausal women (Table 4). When DII was in the median tertile (Tertile 2), we initially found a significant negative correlation, but this significance was not maintained in the fully adjusted model (model 1: \u0026beta;=-0.0289; 95%CI: -0.0475, -0.0103, p=0.0023; model 2: \u0026beta;=-0.0253; 95%CI: -0.0435, -0.0071, p=0.0065; model 3: \u0026beta;\u0026nbsp;=\u0026nbsp;-0.0043; 95%\u0026nbsp;CI: -0.0303, 0.0217, p=0.7458). Conversely, when DII was in the low tertile (Tertile 1), we observed a positive correlation between DII and lumbar spine BMD in unadjusted and adjusted models (model 1: \u0026beta;=0.0111; 95%CI: 0.0042, 0.0179, p=0.0015; model 2: \u0026beta;=0.0114; 95%CI: 0.0048, 0.0181, p=0.0008; model 3: \u0026beta;=0.0098; 95%CI: 0.0001, 0.0196, p=0.0.492). However, we did not find a significant correlation between DII and lumbar spine BMD in either unadjusted or adjusted models when DII was high (Tertile 3).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 639px;\"\u003e\n \u003cp\u003eTable 4. The association between DII and the lumbar BMD in postmenopausal women (weighted).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 206px;\"\u003e\n \u003cp\u003eTertile 1 (-5.512-0.894)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003eTertile 2 (0.896-2.622)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 175px;\"\u003e\n \u003cp\u003eTertile 3 (2.623-4.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026beta; (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026beta; (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026beta; (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.0111 (0.0042, 0.0179)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.0015*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.0289 (-0.0475, -0.0103)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.0023*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.0017 (-0.0136, 0.0170)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.8247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.0114 (0.0048, 0.0181)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.0008*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.0253 (-0.0435, -0.0071)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.0065*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e-0.0014 (-0.0160, 0.0131)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.8458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.0098 (0.0001, 0.0196)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.0492*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.0043 (-0.0303, 0.0217)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.7458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.0113 (-0.0121, 0.0348)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.3438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 639px;\"\u003e\n \u003cp\u003eModel 1: unadjusted.\u003c/p\u003e\n \u003cp\u003eModel 2: adjusted for age, race/ethnicity.\u003c/p\u003e\n \u003cp\u003eModel 3: adjusted for age, race/ethnicity, smoker, SBP, DBP, eGFR, BMI group, LDL, glycohemoglobin, calcium intake, caffein intake, alcohol intake, arthritis.\u003c/p\u003e\n \u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents one of the largest cross-sectional investigations into the association of DII and the lumbar spine BMD in both the general population and postmenopausal women. Utilizing multivariate regression analysis and smooth curve fitting, we explored these relationships across different DII tertiles in these two groups.\u003c/p\u003e \u003cp\u003eIn the analysis of the whole population, the smooth curve fitting revealed a non-linear association between DII and lumbar spine BMD, demonstrating both declining and rising trends. Contrary to expectations, the multivariate analysis indicated that diets with the highest pro-inflammatory potential were positively associated with the lumbar spine BMD, whereas diets with the median pro-inflammatory potential were negatively associated with BMD. Similarly surprising, in the subgroup analysis of postmenopausal women, smooth curve fitting suggested an inverted U-shaped relationship between DII and the lumbar spine BMD. The multivariate analysis further indicated that the most anti-inflammatory diets were negatively associated with lumbar spine BMD.\u003c/p\u003e \u003cp\u003eThis study reveals both positive and negative associations between dietary inflammation and the lumbar spine BMD in both the general population and menopausal women, a finding previously overlooked. Prior research failed to recognize the non-linear nature of this relationship, likely due to a lack of continuous DII analysis across different tertiles and the absence of smooth curve fitting in diverse populations.\u003c/p\u003e \u003cp\u003eDietary inflammation may influence BMD via regulating bone remodelling through inflammatory cytokines. Bone remodelling, which involves osteoclasts-mediated bone resorption and osteoblasts-mediated bone synthesis, plays a predominant role in bone structure and function in adults (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Osteoclasts initiate remodelling by resorbing bone, which is then partially replenished with osteoblast-produced matrix that later mineralizes (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInflammation, which is often accompanied by increased activated cytokines, can lead to excessive activity of osteoclasts and the inhibition of osteoblasts, which causes bone loss and may lead to a decreased BMD. TNF, IL-6 and IL-1 are key cytokines that significantly upregulate osteoclast activity and suppress osteoblast function. TNF signals through nuclear factor-κB (NF-κB) and mitogen-activated protein kinases (MAPKs), while the IL-6 receptor (IL-6R) activates the JAK\u0026ndash;STAT pathway to enhance osteoclast activity. In inhibiting osteoblasts, TNF and IL-6 inhibit MAPK, activate NF-κB, and upregulate SMAD ubiquitylation regulatory factor 1 (SMURF1) and SMURF2. The pro-inflammatory cytokines also upregulate Dickkopf-related protein 1 (DKK1) and sclerostin (SOST), which inhibit the WNT\u0026ndash;Frizzled pathway (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, these mechanisms could not explain the positive correlation observed between DII and BMD, as well as the distinct phenomena in differed groups. This discrepancy may be explained by the hypothesis that low-level inflammation promotes bone growth through Wnt proteins (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Additionally, estrogen loss, which happens in menopausal women, leads to chronic low-grade production of the proinflammatory cytokines TNF alpha and IL-17 by converting memory T-cells to effector T-cells (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Therefore, the smooth curve trend obeserved in menopausal women is similar to the latter two-thirds of the total population.\u003c/p\u003e \u003cp\u003eResearch found that it is constitutive low-intensity TNF stimulation rather than short-term and high-intensity one that induces strong and persistent expression of Wnt proteins at bone-forming sites, through the NF-jB (p65) and JNK/AP-1 (c-Jun) pathways, followed by subsequent new bone formation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eThough estrogen and androgen both matter in the bone regulation, the differences of hormone constitution in people from different age and sex groups may explain the differed overall levels of lumbar spine BMD. Androgen stimulated the proliferation of pre-osteoblasts and differentiation of osteoblasts. The converted estrogen suppressed osteoclast formation and resorption activity by blocking the receptor activator of nuclear factor k-B ligand pathway (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Menopause results from reduced secretion of the ovarian hormones estrogen and progesterone, which takes place as the finite store of ovarian follicles is depleted. In postmenopausal osteoporosis, bone resorption increases by up to 70% due to the increased number of osteoclasts, while the state of bone formation amount cannot make up for the loss. Bone synthesis might increase but to a lesser extent than bone resorption, keep the same as before or decrease by up to 14% (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe made improvements by excluding certain variables previously used in similar studies. First, in the subgroup analysis, we didn\u0026rsquo;t adjust for total cholesterol because we found TC had a mediating effect on the lumbar spine BMD in the menopausal women when in causal mediation analysis. Second, we didn\u0026rsquo;t take vitamin D intake into consideration because a systematic review has reported that vitamin D intake has no significant effect on the lumbar spine BMD (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study has some limitations. First, we didn\u0026rsquo;t include physical activity as a covariate. Different types of exercise might have different effects on the lumbar spine BMD despite similar caloric consumption, which cannot be adequately addressed by the available data in the NHANES including metabolic equivalent (MET), weekly frequency, and duration of each activity. A study using NHANES data found that physical activity, as calculated by MET, weekly frequency and duration of each activity, only showed significant association among menopausal women younger than 65 years (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Second, we didn\u0026rsquo;t adjust for corticosteroid use because it has dual effects of anti-inflammation and causing bone loss, which is hard to adjust via NHANES data. Thirdly, we didn\u0026rsquo;t adjust for bisphosphonates and calcitonin because the does and frequency of medicine use matter as well, which are unavailable on a large-scale from NHANES database (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Fourthly, we did not consider thyroid problems in the logistic regression model because the it is not accurate enough about the actual effect on the BMD by the limited information based on NAHNES. Finally, since this is a cross-sectional study, we are not sure about the causal association between DII and lumbar spine BMD.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, the DII is intensity-dependently associated with the lumbar spine BMD in both the overall population and the menopausal women. In the general population, a median DII (0.300 -2.229) is negatively associated with lumbar spine BMD whereas a high DII (2.229\u0026ndash;5.405) shows a positive association. In menopausal women, a low DII (-5.512\u0026ndash;0.894) is positively associated with the lumbar spine BMD. The differed effects may be influenced by the dual impact of inflammation intensity-dependent Wnt proteins and chronic low inflammation state induced by estrogen loss in postmenopausal women.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebone mineral density\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDII\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edietary inflammatory index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDXA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edual-energy X-ray absorptiometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterleukin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor necrosis factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestimated glomerular filtration rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNF-κB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enuclear factor-κB\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAPKs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emitogen-activated protein kinases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMURF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSMAD ubiquitylation regulatory factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDKK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDickkopf-related protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esclerostin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetabolic equivalent\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe studies involving humans were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003ch2\u003eConsent for publication (not available)\u003c/h2\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found at: https://www.cdc.gov/nchs/nhanes/index.htm. Clinical trial number: not applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interest\u003c/h2\u003e\n\u003cp\u003eThe author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe authors declare that no financial support was received for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eThe authors\u0026rsquo; responsibilities were as follows -- ZS, YJ and XC designed research; YJ, XC, SD drafted the manuscript; YJ, XC, SD, YZ, ZZ, TY collected and analyzed the data; ZS revised the manuscript. YJ, XC and ZS had primary responsibility for the final content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe are greatly thankful for all participants in the NAHNES for providing data for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ethe Scientific Advisory Board of the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) and the Committees of Scientific Advisors and National Societies of the International Osteoporosis Foundation (IOF), Kanis JA, Cooper C, Rizzoli R, Reginster J-Y. Executive summary of the European guidance for the diagnosis and management of osteoporosis in postmenopausal women. 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Lancet Diabetes Endocrinol. 2017;5:898\u0026ndash;907. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2213-8587(17)30188-\u003c/span\u003e\u003cspan address=\"10.1016/S2213-8587(17)30188-\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"NHANES, dietary inflammatory index, lumbar spine, bone mineral density, postmenopausal women","lastPublishedDoi":"10.21203/rs.3.rs-4964246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4964246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBone mineral density (BMD) is a tool to assess bone health. Dietary inflammation index (DII) is an index to reflect the inflammatory effect of a diet. Even though the association between DII and BMD have been extensively studied, the outcome remains controversial. This study aims to reveal the relationship between DII and lumbar spine BMD in both the whole population and in postmenopausal women.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe utilized data from the National Health and Nutrition Examination Survey (NHANES) 2007\u0026ndash;2020 and included 27027 participants after exclusion procedure. Smooth curve fitting and multivariate regression analysis were used both in the entire population and later in the subgroup analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the entire population, the smooth curve fitting revealed a non-linear association with both declining and increasing trends between DII and lumbar spine BMD. Multivariate regression analysis indicated that, after adjustment, the median DII and BMD was negatively associated and the high DII and BMD was positively associated. In postmenopausal women, smooth curve fitting suggested an inverted U-shaped relationship between DII and BMD. We also observed a positive correlation between low DII and lumbar spine BMD.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDII has both positive and negative relationship with lumbar spine BMD in both the entire population and menopausal women, depending on the intensity of DII. The different trends of the smooth curve fitting may be explained by the low-inflammation state in postmenopausal women mediated by estrogen but more evidence is needed to explain the reason.\u003c/p\u003e","manuscriptTitle":"Dietary Inflammatory Index is intensity-dependently associated with the Bone Mineral Density at the Lumbar Spine: A Cross- Sectional NHANES Study from 2007 to 2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 08:49:13","doi":"10.21203/rs.3.rs-4964246/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":"12d06556-e5ce-44c2-885a-4ec3b403db54","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-21T14:23:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 08:49:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4964246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4964246","identity":"rs-4964246","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}