Association Between Liver Fibrosis (FIB-4) Index and Vertebral Fracture in Middle-aged and Older Adults in the United States: A Cross-Sectional Study of NHANES 2013-2014

Association Between Liver Fibrosis (FIB-4) Index and Vertebral Fracture in Middle-aged and Older Adults in the United States: A Cross-Sectional Study of NHANES 2013-2014 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Liver Fibrosis (FIB-4) Index and Vertebral Fracture in Middle-aged and Older Adults in the United States: A Cross-Sectional Study of NHANES 2013-2014 Yuwei Gou, Heng Yin, Yongjie Wen, Xiansong Xie, Zhoujing Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5790214/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 Objective This study was to examine the association between FIB-4(fibrosis index based on four factors) and the occurrence of vertebral fractures in Middle-aged and Older Americans. Methods Patients ⩾45years of age from the 2013–2014 NHANES database were selected for this study. Restricted cubic spline models and weighted logistic regression models were used to assess the association between FIB-4 and the occurrence of vertebral fractures in Middle-aged and Older Americans. The predictive value of the FIB-4 on the occurrence of vertebral fractures was assessed using receiver operating characteristic curves (ROC). To examine the robustness of the main findings, a sensitivity analysis was conducted. Results A total of 2188 patients were included in the analysis, of whole 147 suffered vertebral fractures. Fully adjusted logistic regression showed a significant linear relationship between FIB-4 and the occurrence of vertebral fracture in Middle-aged and Older Americans (P < 0.05),with a linear relationship observed between the FIB-4 index and the risk of vertebral fractures. Additionally, the FIB-4 index demonstrated good predictive performance for the incidence of vertebral fractures. Conclusions This study found that the FIB-4 index, as a novel and non-invasive liver fibrosis biomarker, can predict the risk of vertebral fractures in middle-aged and older Americans, which has clinical significance for the prevention and management of vertebral fractures in middle-aged and older Americans. Vertebral fracture FIB-4 NHANES Figures Figure 1 Figure 2 Figure 3 1 Introduction Vertebral fractures are a common type of fracture in middle-aged and older adults and are closely associated with osteoporosis [ 1 ]. Studies have shown that the high incidence of vertebral fractures is strongly linked to a decrease in patient survival rates [ 2 ]. Among individuals over the age of 50, at least one in five have one or more vertebral fractures [ 3 ]. The incidence of thoracolumbar fractures in individuals aged 60 and above, caused by low-energy trauma, is four times higher than in younger individuals, with an incidence rate of approximately 30 per 100,000 people annually, and more than two-thirds of these patients are over the age of 60 [ 4 ]. Vertebral fractures are more common in postmenopausal women and often result in acute and chronic pain, functional impairment, and even disability, significantly reducing quality of life. Long-term, they can lead to spinal kyphosis, further affecting life expectancy and mortality rates [ 5 ]. However, there is no consensus on the optimal treatment for vertebral fractures. Therefore, early implementation of appropriate interventions is crucial for reducing the risk of vertebral fractures, improving patient quality of life, and extending lifespan. Liver fibrosis is a major predictor of liver-related complications in patients with non-alcoholic fatty liver disease (NAFLD) [ 6 ] and an important marker of poor disease prognosis. In recent years, several studies have revealed a potential association between NAFLD and osteoporosis. NAFLD has been recognized as a high-risk factor for osteoporosis [ 7 ] and is closely associated with a decrease in bone mineral density (BMD) [ 8 , 9 ]. Some studies suggest that compared to patients without NAFLD, those with NAFLD have significantly lower BMD, and this is negatively correlated with hip BMD [ 9 ]. Additionally, NAFLD is closely linked to reduced BMD in both adults and children, as well as a history of osteoporotic fractures [ 10 , 11 ]. NAFLD is one of the most common chronic liver diseases and, as a globally prevalent non-communicable disease, it significantly impacts human health [ 12 ]. Studies have found that low BMD is commonly observed in various liver diseases, including NAFLD [ 13 ]. Some scholars suggest that liver fibrosis is the hepatic manifestation of metabolic syndrome and may have a potential link to osteoporosis and fracture risk [ 14 ]. Further research has pointed out that the severity of liver fibrosis is negatively correlated with BMD, revealing a potential association between liver fibrosis and bone health [ 15 ]. The Fibrosis (FIB-4) index is a convenient and non-invasive tool for assessing liver fibrosis, with validated predictive models used to accurately evaluate liver fibrosis [ 16 ]. Studies have shown that a higher FIB-4 index is significantly associated with a decrease in BMD, increasing bone fragility and fracture risk [ 17 ]. However, no studies have specifically investigated the relationship between the FIB-4 index and vertebral fractures. Therefore, this study aims to explore the association between the FIB-4 index and vertebral fractures, and to evaluate the effectiveness of the FIB-4 index in predicting vertebral fracture risk in US middle-aged and older adults using receiver operating characteristic (ROC) curves. 2 Methods 2.1 Samples and data sources Relevant samples and data used in this study were all derived from NHANES (National Health and Nutrition Examination Survey) between 2013 and 2014. NHANES is a continuous cross-sectional research survey managed by the National Center for Health Statistics (NCHS) to evaluate the nutritional and health status of adults and children in the United States. This study was approved by the Research Ethics Review Committee of the National Center for Health Statistics and obtained the informed consent from the participants. 2.2 Study design and population In the National Health and Nutrition Examination Survey (NHANES) 2013–2014, among all 10,175 subjects, 6,845 patients who did not receive dual-energy X-ray, 106 patients who did not diagnosed with vertebral fracture after receiving dual-energy X-ray, 669 patients with missing covariate and inflammatory index data and 367 patients under 45 years old were excluded. Finally, 2,188patients were included in the analysis. The process is presented in Fig. 1 . Inclusion criteria as follows: Patients were included if they were ≥ 45 years old in NHANES from 2013 to 2014. Exclusion criteria were as follows: Patients were excluded if (1) they were < 45 years old; (2) they were not evaluated by dual-energy X-ray absorptiometry; (3) there was a lack of FIB-4 data; (4) the covariate data were missing. In this study, the Mobile Examination Center (MEC) weights were applied. 2.3 Definition and measurement of vertebral fracture The vertebral fracture was measured by the lateral view of the thoracolumbar spine with dual-energy X-ray absorptiometry (DXA). The image resolution of the lateral spine scan obtained by DXA is close to that of a standard radiograph. The accuracy of DXA VFA and standard radiograph in detecting moderate to severe vertebral fractures is similar (defined by Genant's semi-quantitative criteria (1993) [ 18 ]. Lateral DXA scanning of the thoracolumbar spine was performed in the NHANES MEC from 2013 to 2014. According to the Vertebral Fracture Status Summary, the vertebral status was given 1 for normal (no fracture between T4-L4, unevaluable vertebra in T7-L4 ≤ 1), 2 for fracture (mild, moderate, or severe fracture between T4-L4) and 3 for unexplained status (no fractures and unevaluable vertebra between T7-L4 ≤ 1). In this study, any level of mild, moderate, or severe fractures between T4-L4 were defined as vertebral fractures. 2.4 Measurement of FIB-4 The Beckman Coulter DxH 800 in the NHANES MEC was utilized to count blood samples and provide blood cell distribution of all participants. The Beckman Coulter method of counting and sizing was used to derive complete blood count (CBC) parameters, combined with automatic dilution, and mixing equipment for sample processing and a single-beam spectrophotometer for hemoglobin determination. The VCS technique was applied in differential counting of whole blood cell count .The Beckman Coulter DxH 800 instrument at the NHANES Mobile Examination Center (MEC) performs complete blood cell counts on blood samples and provides blood cell distribution for all participants. The methods used to derive CBC parameters are based on Beckman Coulter's counting and quantification techniques. The complete blood count classification utilizes VCS technology. The serum or plasma levels of ALT(Aspartate Aminotransferase) and AST(Alanine Aminotransferase) are measured using the kinetic rate method. Serum specimens are processed, stored, and shipped to the collaborating laboratory service in Ottumwa, Iowa, for analysis. The FIB-4 index is calculated using the following formula:FIB-4 = [Age (years) × AST (U/L)] / [Platelet count (10^9/L) × ALT (U/L)^(1/2)]. 2.5 Covariate selection Considering the influence of other factors on vertebral fracture, this study included factors that may affect the risk of vertebral fractures, such as age, gender (male or female), smoking history (smokers or non-smokers; smokers were defined as having smoked at least 100 cigarettes in their lifetime), drinking history (drinker or non-drinker: drinker was defined as drinking at least 12 cups of alcoholic beverages a year, including beer, wine and any other type of alcoholic beverages. “At least 12 cups of any kind of alcoholic beverage” was defined as 12 ounces of beer, 5 ounces of wine or 1.5 ounces of liquor), hypertension (with or without hypertension; hypertension was defined as ever being told of having hypertension by doctors), diabetes (with or without diabetes; diabetes was defined as ever being told of having diabetes by doctors), work activities (intense work activities or non-intense work activities: intense work activities were defined as work involving intense activities that lead to a sharp increase in breathing or heart rate, such as carrying or lifting heavy objects, digging or building for at least 10 minutes), osteoporosis (with or without osteoporosis; osteoporosis was defined as ever being told of having osteoporosis), vitamin D, race (Mexican American, other Hispanics, non-Hispanic whites, non-Hispanic Blacks, and other races-including multi-races),Body mass index, education level (less than elementary school, elementary school, high school, college, above college), and poverty-to-income ratio. 2.6 Statistical analysis NHANES adopted a complex, multistage, probability sampling design. Shapiro-Wilk test was adopted to evaluate data distribution. For data of normal distribution, continuous variables were expressed as mean ± standard deviation, and data of non-normal distribution were expressed as median and interquartile range in brackets. Chi-square test, Mann-Whitney U test or independent t-test were conducted to compare the differences between the two groups. In this study, a weighted univariate logistic regression was used to screen the influencing factors of vertebral fracture risk in middle-aged and older Americans. Weighted multivariate logistic regression (WLMR) models were applied to evaluate the association of FIB-4 with vertebral fracture in middle-aged and older Americans. The rough model was initially fitted without adjustment, and then the influencing factors with p < 0.05 were incorporated into subsequent models for adjustment. The results were expressed by the ratio (OR) and its 95% confidence interval (CI). The area under the curve (AUC) was calculated to evaluate the predictive ability of FIB-4 for the risk of vertebral fracture in middle-aged and older Americans.The restricted cubic spline (RCS) model was used to study the linear or nonlinear relationship of FIB-4 with vertebral fracture in middle-aged and older Americans. To examine the robustness of the main findings, a two-step sensitivity analysis was conducted: in Model 1, an unweighted logistic regression model was adopted; in Model 2, a weighted logistic regression after excluding patients diagnosed with osteoporosis at baseline was conducted. The statistical software Rstudio 4.3.3 was used, and a P -value threshold of 0.05 or less was considered statistically significant (bilateral). 3 Results 3.1 Baseline characteristics of participants A total of 2188 patients were included in this study, including 1059males (48%) and 1129females (52%), with an average age of 59 years. There was a statistical difference between the two groups in age, and participants in the fracture group were older than those of the non-fracture group ( P < 0.001). We found that there was statistical significance between osteoporosis, age,hypertension,poverty-to-income ratio ,FIB-4 and vertebral fracture in middle-aged and older Americans(p < 0.05). Baseline characteristics of participants are provided in Table 1 . Table 1 Baseline information on FIB-4 and fracture. Characteristic N 1 Overall, N = 91,557,517 2 0, N = 85,852,643 2 1, N = 5,704,874 2 p-value 3 Drink 2,188 1,574(78%) 1,465(79%) 109(73%) 0.3 Body Mass Index 2,188 28(25,32) 28(25,32) 27(25,31) 0.2 Hypertension 2,188 1,129(48%) 1,031(47%) 98(64%) 0.005 Gender 2,188 0.6 female 1,129(52%) 1,058(52%) 71(49%) man 1,059(48%) 983(48%) 76(51%) Age 2,188 59(51,67) 58(51,66) 67(59,78) < 0.001 Race 2,188 0.13 Mexican American 256(5.5%) 241(5.6%) 15(4.5%) Non-Hispanic Black 438(9.5%) 423(9.9%) 15(4.7%) Non-Hispanic White 1,063(75%) 961(75%) 102(84%) Other Hispanic 190(3.8%) 184(4.0%) 6(1.8%) Other Race - Including Multi-Racial 241(5.6%) 232(5.7%) 9(4.8%) Level of Education 2,188 > 0.9 High school 504(21%) 464(21%) 40(24%) junior school 286(9.7%) 270(9.8%) 16(8.5%) junior school below 184(4.5%) 173(4.6%) 11(3.9%) University 638(31%) 598(31%) 40(31%) University above 576(33%) 536(33%) 40(33%) Poverty-to-Income Ratio 2,188 3.40(1.71,5.00) 3.48(1.74,5.00) 2.44(1.37,3.97) < 0.001 Diabetes 2,188 427(15%) 399(15%) 28(17%) 0.6 0steoporosis 2,188 197(8.5%) 167(7.9%) 30(18%) 0.007 Physical Activity 2,188 354(18%) 333(18%) 21(14%) 0.4 Smoke 2,188 1,078(48%) 997(47%) 81(58%) 0.14 VitamineD 2,188 73(56,93) 73(55,92) 72(57,100) 0.4 FIB-4 2,188 1.32(1.02,1.75) 1.30(1.01,1.72) 1.59(1.28,1.94) < 0.001 1 N not Missing (unweighted) 2 n (unweighted)(%); Median(25%,75%) 3 chi-squared test with Rao & Scott’s second-order correction; Wilcoxon rank-sum test for complex survey samples 3.2 Association between covariates and vertebral fracture The univariate logistic regression analysis showed that age, osteoporosis,hypertension,poverty-to-income ratio were the risk factors of vertebral fracture in middle-aged and older Americans(p < 0.05). They were included as confounders in the weighted multivariate logistic regression analysis. as shown in Table 2 . Table 2 Association between covariates and vertebral fracture. factor level OR CI P 1 Drink NO Ref Drink YES 0.74 0.45–1.24 0.27 2 Body Mass Index 0.97 0.93-1 0.07 3 Hypertension NO Ref Hypertension YES 2 1.31–3.04 0.01 4 Gender female Ref Gender man 1.13 0.68–1.88 0.64 5 Age 1.07 1.05–1.1 0 6 Race Non-Hispanic Black 0.58 0.26–1.31 0.22 Race Non-Hispanic White 1.38 0.65–2.95 0.42 Race Other Hispanic 0.55 0.13–2.4 0.44 Race Other Race-Including Multi-Racial 1.04 0.28–3.93 0.95 7 Level of Education junior school 0.76 0.3–1.97 0.59 Level of Education junior school below 0.75 0.39–1.47 0.42 Level of Education University 0.88 0.54–1.43 0.61 Level of Education University above 0.88 0.51–1.53 0.66 8 Poverty-to-Income Ratio 0.81 0.72–0.9 0.02 9 Diabetes NO Ref Diabetes YES 1.14 0.66–1.96 0.64 10 0steoporosis NO Ref 0steoporosis YES 2.54 1.39–4.61 0 11 Physical Activity NO Ref Physical Activity YES 0.72 0.33–1.56 0.42 12 Smoke NO Ref Smoke YES 1.51 0.89–2.54 0.15 13 VitamineD 1 1-1.01 0.07 3.3 Association between FIB-4 and vertebral fracture Weighted multivariate logistic regression was used to analyze the association between FIB-4 and vertebral fracture in middle-aged and older Americans(Model 1, in which confounding factors were not adjusted, showed that vertebral fractures were statistically significantly associated with FIB-4 ( P < 0.05) in middle-aged and older Americans. After adjusting the factors of age, osteoporosis,hypertension,poverty-to-income ratio in Model 2, the results of weighted multivariate logistic regression showed that there was a significant positive correlation between FIB-4 and the risk of vertebral fracture in middle-aged and older Americans (OR: 1.294, 95% CI: 1.038–1.613), as illustrated in Table 3 . Table 3 Association of FIB-4 with vertebral fracture. Model1 Model2 OR 95% CI P OR 95% CI P FIB-4 1.366 1.139, 1.638 0.002 1.294 1.038, 1.613 0.03 Model 1: unadjusted;Model 2: adjusted for Hypertension、Poverty-to-Income Ratio、0steoporosis、Age 3.4 Predictive ability of FIB-4 on the risk of vertebral fracture in middle-aged and older Americans The receiver operating characteristic (ROC) curve was used to predict the effectiveness of FIB-4 for predicting the risk of vertebral fracture in middle-aged and older Americans. The results revealed that FIB-4(AUC = 0.654) demonstrated positive predictive performance for the risk of vertebral fractures in middle-aged and older Americans (Fig. 2 ). 3.5 Linear or nonlinear relationship between FIB-4 and vertebral fracture in middle-aged and older Americans using restricted cubic spline (RCS) model The linear and nonlinear relationship was assessed between FIB-4 and vertebral fracture in middle-aged and older Americans.We observed a linear relationship between FIB-4 and the risk of vertebral fractures in middle-aged and elderly Americans under unadjusted confounding factors (p for non-linearity < 0.05). When FIB-4 exceeded approximately 1.5, a significant increase in the risk of vertebral fractures was observed as FIB-4 values continued to rise. After adjusting for factors of hypertension, age, poverty-to-income ratio, and osteoporosis, this relationship remained evident. as depicted in Fig. 3 . 3.6 Sensitivity Analyses To examine the robustness of the main findings, a sensitivity analysis was conducted. Firstly, the unweighted multiple logistic regression Model 1 showed that there was still a statistically significant association between FIB-4 and vertebral fractures in middle-aged and older Americans (Model 1: OR: 1.161, 95%CI: 1.046–1.306, p < 0.05).Secondly, after excluding patients diagnosed with osteoporosis at baseline, the weighted logistic regression Model 2 also showed a consistent association between FIB-4 and the risk of vertebral fractures middle-aged and older Americans (Model 2: OR: 1.309, 95%CI: 1.038–1.650, p < 0.05) (Table 4 ). Table 4 Sensitivity analysis Model1 Model2 OR 95% CI P OR 95% CI P FIB-4 1.161 1.046, 1.306 0.006 1.309 1.038, 1.650 0.027 Notes:M odel:Using unweighted logistic regression models;Model2: Weighted logistic regression model for excluding patients diagnosed with osteoporosis at baseline,(adjusted for Hypertension、Poverty-to-Income Ratio、0steoporosis、Age) 4 Discussion This study explored the association between the Fibrosis-4 (FIB-4) index and the risk of vertebral fractures in middle-aged and elderly Americans. The results showed a significant positive correlation between FIB-4 and the risk of vertebral fractures in middle-aged and elderly Americans. After adjusting for confounding factors, the study found a linear relationship between FIB-4 and the risk of vertebral fractures, with an increase in FIB-4 values being accompanied by a significant rise in fracture risk. Moreover, FIB-4 demonstrated positive predictive ability for assessing the risk of vertebral fractures. Existing studies have shown that osteoporosis and reduced bone strength are among the main causes of vertebral fractures [ 19 , 20 ]. This study found that the risk of vertebral fractures in the osteoporosis group was significantly higher than in the non-osteoporosis group. Osteoporosis can lead to reduced bone mass and increased bone fragility, which may result in vertebral fractures even under minor external forces [ 21 ]. Furthermore, studies have also indicated that the risk of vertebral fractures is age-related, with women having a higher incidence of hip and spinal fractures at older ages [ 22 ]. This study further adjusted for confounding factors such as age and osteoporosis to confirm their impact on vertebral fracture risk. Additionally, the study found that the poverty-to-income ratio is associated with vertebral fracture risk, which may be related to lower socioeconomic status, malnutrition, and limited access to healthcare. FIB-4, as a non-invasive biomarker for assessing liver fibrosis, has been widely used for the screening and risk stratification of liver fibrosis. Studies have shown that FIB-4 has high accuracy in predicting advanced liver fibrosis [ 23 , 24 ]. Recent meta-analyses have also demonstrated that the predictive performance of FIB-4 is comparable to that of liver biopsy, and it has become the first-step assessment tool in international NAFLD risk stratification algorithms [ 25 , 26 ]. Furthermore, platelet count, as a component of the FIB-4 index, is closely associated with hepatocyte damage and is considered a reliable marker of chronic liver disease [ 27 ]. There are studies investigating the relationship between liver fibrosis and BMD (bone mineral density), but the results remain inconsistent. Some researchers have found a correlation between the two [ 28 ], while others have reported no significant association [ 29 ]. Research has shown that the relationship between liver fibrosis and BMD is influenced by factors such as age, gender, BMI, smoking, alcohol consumption, and occupational activity [ 30 ]. Osteoporosis is closely linked to reduced BMD and increases the risk of fractures. A study by Li M et al. [ 31 ] revealed an association between liver fibrosis and osteoporotic fractures in patients with NAFLD. Similarly, Barchetta I et al. [ 32 ] demonstrated a strong relationship between liver fibrosis, lower BMD, and osteoporosis. A cross-sectional study conducted in Italy found that higher FIB-4 levels were significantly associated with lower BMD and an increased risk of bone loss/osteoporotic fractures [ 33 ].In this study, we also found a significant positive correlation between FIB-4 and vertebral fractures, consistent with previous findings. Low BMD is closely associated with multiple factors, including metabolic syndrome, which itself is related to liver fibrosis [ 34 ]. A study by Chung GE et al. [ 35 ] indicated that a high fatty liver index is associated with an increased risk of vertebral fractures. Furthermore, in sensitivity analysis, this study observed a significant positive correlation between FIB-4 levels and spinal fractures. Notably, even in patients without osteoporosis, FIB-4 was associated with the risk of vertebral fractures. Therefore, FIB-4 serves as an independent risk factor for vertebral fractures and has certain predictive value. AST and ALT are hematological markers of hepatocyte injury. Studies have found that ALT is associated with BMD in NAFLD patients. Research has shown that the severity of liver fibrosis is related to ALT [ 36 ], as well as systemic inflammation and oxidative stress in NAFLD patients [ 37 , 38 ]. It has also been found that a decrease in BMD is associated with inflammation and oxidative stress [ 39 , 40 ]. Purnak T et al. [ 41 ] demonstrated that elevated ALT is associated with reduced BMD. Xia MF et al. [ 42 ] found that as ALT levels increased in patients with liver fibrosis, the risk of osteoporosis also increased, and BMD tended to deteriorate when both liver fibrosis and elevated ALT levels were present. Therefore, ALT can be used as a clinical biomarker for the risk of osteoporosis in patients with liver fibrosis [ 43 ].Additionally, Do HJ et al. [ 44 ] reported a correlation between AST and femoral neck BMD, and after further adjustment for confounding factors, they found that AST and ALT were also statistically associated with lumbar spine BMD. They observed that higher AST and ALT levels were related to the diagnosis of osteoporosis. In this study, the increase in FIB-4 was found to be associated with a higher risk of vertebral fractures, which is consistent with the findings of the aforementioned studies. Regarding the relationship between liver fibrosis and fractures, although some studies have not clearly established a connection between the two, existing studies have explored related mechanisms, including liver steatosis, fibrosis, obesity, insulin resistance, and alterations in gut microbiota [ 45 , 46 ]. Changes in gut microbiota can activate inflammatory factors, thereby affecting liver and bone health. Adiponectin can increase insulin sensitivity, reduce lipid accumulation, and mitigate liver fibrosis by regulating inflammation [ 47 ]. A decline in adiponectin levels is associated with the progression of liver fibrosis. At the same time, adiponectin receptors play a role in osteocytes, limiting the formation of osteoclasts [ 48 ]. Therefore, bone loss in patients with liver fibrosis may be related to adiponectin levels.Studies have also found that patients with liver fibrosis exhibit lower osteocalcin levels, which are associated with bone formation and pancreas-liver metabolism [ 49 ]. Inflammatory factors such as TNF-α, IL-1, and IL-17 are involved in the pathogenesis of osteoporosis, promoting osteoclast activation and bone resorption [ 50 , 51 ]. Patients with NAFLD often exhibit lower levels of osteoprotegerin (OPG) and higher levels of TNF-α and IL-6 [ 52 ]. Moreover, osteopontin (OPN) is associated with NAFLD and liver fibrosis [ 53 ]. Insulin-like growth factor-1 (IGF-1), a factor produced by both the liver and bones, plays a role in bone remodeling. Studies have shown that NAFLD and osteoporosis patients have reduced IGF-1 levels, indicating that IGF-1 plays an important role in the liver-bone axis [ 54 , 55 ]. In conclusion, this study demonstrates a significant association between the FIB-4 index and the risk of vertebral fractures in middle-aged and elderly Americans. The strength of our study lies in the use of the FIB-4 index to assess liver fibrosis, marking the first time that the relationship between FIB-4 and vertebral fractures has been explored. However, this study also has several limitations. First, due to the constraints of the public database, the included covariates may be insufficient, and other potential factors influencing vertebral fractures in middle-aged and elderly Americans were not considered. Additionally, as this study is a cross-sectional study, it is not possible to determine the temporal sequence between FIB-4 levels and vertebral fractures, making it difficult to establish a causal relationship between liver fibrosis and fractures. Further multicenter and prospective studies are needed to explore other factors related to vertebral fractures to provide more reliable conclusions. 5 Conclusion This study reveals a significant association between the FIB-4 index and the risk of vertebral fractures in middle-aged and elderly Americans, suggesting that FIB-4, as a novel and non-invasive biomarker for assessing liver fibrosis, has the potential to predict the risk of vertebral fractures. Declarations Data availability statement The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Funding statement This work was supported by Bethune Foundation: Special Research Fund for Vertebra Reinforcement Therapy for Spinal Pathologic Fractures [Grant Number BK-JP2020001]. Conflict of interest disclosure The authors have no competing interests to declare that are relevant to the content of this article Ethics statement This study was based on a public database and approved by the Research Ethics Review Committee of the National Center for Health Statistics (NCHS). All participants provided written informed consent. Permission to reproduce material from other sources Not applicable. Author contributions All authors contributed to the study conception and design. Writing - original draft preparation: Yuwei Gou; Writing - review and editing: Yuwei Gou, Heng Yin,Xiansong Xie; Conceptualization: Heng Yin, Yuwei Gou,Zhoujing Wang; Methodology: Yuwei Gou, Yongjie Wen; Formal analysis and investigation: Yuwei Gou, QianChen,Yingbo Zhang; Funding acquisition: Haiyang Xie; Resources: Haiyang Xie; Supervision: Haiyang Xie, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. 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J Hepatol. 2016. 64(6):1388–402. Miele L, Alberelli MA, Martini M, Liguori A, Marrone G, Cocomazzi A, et al. Nonalcoholic fatty liver disease (NAFLD) severity is associated to a nonhemostatic contribution and proinflammatory phenotype of platelets. Transl Res. 2021;231:24–38. Targher G, Lonardo A, Rossini M. Nonalcoholic fatty liver disease and decreased bone mineral density: is there a link? J Endocrinol Invest. 2015;38(8):817–25. Upala S, Jaruvongvanich V, Wijarnpreecha K, Sanguankeo A. Nonalcoholic fatty liver disease and osteoporosis: a systematic review and meta-analysis. J Bone Miner Metab. 2017;35(6):685–93. Lee SH, Yun JM, Kim SH, Seo YG, Min H, Chung E, Bae YS, Ryou IS, Cho B. Association between bone mineral density and nonalcoholic fatty liver disease in Korean adults. J Endocrinol Invest. 2016;39(11):1329–36. Li M, Xu Y, Xu M, Ma L, Wang T, Liu Y, Dai M, Chen Y, Lu J, Liu J, Bi Y, Ning G. Association between nonalcoholic fatty liver disease (NAFLD) and osteoporotic fracture in middle-aged and elderly Chinese. J Clin Endocrinol Metab. 2012;97(6):2033–8. doi: 10.1210/jc.2011-3010 . Epub 2012 Mar 30. PMID: 22466338. Barchetta I, Lubrano C, Cimini FA, Dule S, Passarella G, Dellanno A, Di Biasio A, Leonetti F, Silecchia G, Lenzi A, Cavallo MG. Liver fibrosis is associated with impaired bone mineralization and microstructure in obese individuals with non-alcoholic fatty liver disease. Hepatol Int. 2023;17(2):357–366. doi: 10.1007/s12072-022-10461-1 . Epub 2022 Dec 15. PMID: 36520377. Barchetta I, Lubrano C, Cimini FA, Dule S, Passarella G, Dellanno A, et al. Liver fibrosis is associated with impaired bone mineralization and microstructure in obese individuals with non-alcoholic fatty liver disease. Hepatol Int (2023) 17(2):357–66. doi: 10.1007/s12072-022-10461-1 Qin L, Yang Z, Zhang W, Gu H, Li X, Zhu L, et al.. Metabolic syndrome and osteoporotic fracture: a population-based study in China. BMC Endocr Disord (2016) 16: 27. doi: 10.1186/s12902-016-0106-x Chung GE, Cho EJ, Kim MJ, Yoo JJ, Cho Y, Lee KN, Han K, Kim YJ, Yoon JH, Shin DW, Yu SJ. Association between the fatty liver index and the risk of fracture among individuals over the age of 50 years: a nationwide population-based study. Front Endocrinol (Lausanne). 2023;14:1156996. doi: 10.3389/fendo.2023.1156996 . Wong VW, Wong GL, Tsang SW, Hui AY, Chan AW, Choi PC, et al. Metabolic and histological features of non-alcoholic fatty liver disease patients with different serum alanine aminotransferase levels. Aliment Pharmacol Ther. 2009;29(4):387–396. doi: 10.1111/j.1365-2036.2008.03896.x Kerner A, Avizohar O, Sella R, Bartha P, Zinder O, Markiewicz W, et al. Association between elevated liver enzymes and C-reactive protein: possible hepatic contribution to systemic inflammation in the metabolic syndrome. Arterioscler Thromb Vasc Biol. 2005;25:193–197 Yamada J, Tomiyama H, Yambe M, Koji Y, Motobe K, Shiina K, et al. Elevated serum levels of alanine aminotransferase and gamma glutamyltransferase are markers of inflammation and oxidative stress independent of the metabolic syndrome. Atherosclerosis. 2006;189:198–205. doi: 10.1016/j.atherosclerosis.2005.11.036 Ding C, Parameswaran V, Udayan R, Burgess J, Jones G. Circulating levels of inflammatory markers predict change in bone mineral density and resorption in older adults: a longitudinal study. J Clin Endocrinol Metab. 2008;93:1952–1958. doi: 10.1210/jc.2007-2325 Zhang YB, Zhong ZM, Hou G, Jiang H, Chen JT. Involvement of oxidative stress in age-related bone loss. J Surg Res. 2011;169:e37–e42. doi: 10.1016/j.jss.2011.02.033 Purnak T, Beyazit Y, Ozaslan E, Efe C, Hayretci M. The evaluation of bone mineral density in patients with nonalcoholic fatty liver disease. Wien Klin Wochenschr. 2012;124(15–16):526–31. doi: 10.1007/s00508-012-0211-4 . Epub 2012 Aug 1. Xia MF, Lin HD, Yan HM, Bian H, Chang XX, Zhang LS, He WY, Gao X. The association of liver fat content and serum alanine aminotransferase with bone mineral density in middle-aged and elderly Chinese men and postmenopausal women. J Transl Med. 2016;14:11. doi: 10.1186/s12967-016-0766-3 . Schindhelm RK, Diamant M, Dekker JM, Tushuizen ME, Teerlink T, Heine RJ (2006) Alanine aminotransferase as a marker of non-alcoholic fatty liver disease in relation to type 2 diabetes mellitus and cardiovascular disease. Diabetes Metab Res Rev 22:437–443。 Do HJ, Shin JS, Lee J, Lee YJ, Kim MR, Nam D, Kim EJ, Park Y, Suhr K, Ha IH. Association between liver enzymes and bone mineral density in Koreans: a cross-sectional study. BMC Musculoskelet Disord. 2018;19(1):410. doi: 10.1186/s12891-018-2322-1 . Flessa C-M, Nasiri-Ansari N, Kyrou I, Leca BM, Lianou M, Chatzigeorgiou A, et al.. Genetic and diet-induced animal models for non-alcoholic fatty liver disease (NAFLD) research. Int J Mol Sci. (2022) 23:15791. doi: 10.3390/ijms232415791 Buzzetti E, Pinzani M, Tsochatzis EA. The multiple-hit pathogenesis of non-alcoholic fatty liver disease (NAFLD). Metabolism . (2016) 65:1038–48. doi: 10.1016/j.metabol.2015.12.012 Jung TW, Lee YJ, Lee MW, Kim SM, Jung TW. Full-length adiponectin protects hepatocytes from palmitate-induced apoptosis via inhibition of c-Jun NH 2 terminal kinase. FEBS J. (2009) 276:2278–84. doi: 10.1111/j.1742-4658.2009.06955.x Lewis JW, Edwards JR, Naylor AJ, McGettrick HM. Adiponectin signalling in bone homeostasis, with age and in disease. Bone Res. (2021) 9:1. doi: 10.1038/s41413-020-00122-0 Komori T. Functions of osteocalcin in bone, pancreas, testis, and muscle. Int J Mol Sci. (2020) 21:7513. doi: 10.3390/ijms21207513 Barbour KE, Lui LY, Ensrud KE, Hillier TA, LeBlanc ES, Ing SW, et al.. Inflammatory markers and risk of hip fracture in older white women: the study of osteoporotic fractures. J Bone Miner Res (2014) 29:2057–64. doi: 10.1002/jbmr.2245 Drapkina OM, Elkina AY, Sheptulina AF, Kiselev AR. Non-alcoholic fatty liver disease and bone tissue metabolism: current findings and future perspectives. Int J Mol Sci. (2023) 24:8445. doi: 10.3390/ijms24098445 El Amrousy D, El-Afify D. Osteocalcin and osteoprotegerin levels and their relationship with adipokines and proinflammatory cytokines in children with nonalcoholic fatty liver disease. Cytokine. (2020) 135:155215. doi: 10.1016/j.cyto.2020.155215 Drapkina OM, Elkina AY, Sheptulina AF, Kiselev AR. Non-alcoholic fatty liver disease and bone tissue metabolism: current findings and future perspectives. Int J Mol Sci. (2023) 24:8445. doi: 10.3390/ijms24098445 Wang T-H, Li J-B, Tian Y-G, Zheng J-X, Li X-D, Guo S. Association of TNF-α, IGF-1, and IGFBP-1 levels with the severity of osteopenia in mice with nonalcoholic fatty liver disease. J Orthop Surg Res. (2023) 18:915. doi: 10.1186/s13018-023-04385-1 Zhao J, Lei H, Wang T, Xiong X. Liver-bone crosstalk in non-alcoholic fatty liver disease: Clinical implications and underlying pathophysiology. Front Endocrinol (Lausanne) . (2023) 14:1161402/full. doi: 10.3389/fendo.2023.1161402 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5790214","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":400110190,"identity":"1cf1b2a8-5080-4795-a525-871a81c1c7fd","order_by":0,"name":"Yuwei Gou","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yuwei","middleName":"","lastName":"Gou","suffix":""},{"id":400110191,"identity":"3fa95cbe-f87c-46ff-bb30-05eed32c1817","order_by":1,"name":"Heng Yin","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Yin","suffix":""},{"id":400110192,"identity":"5af0d03e-ccb7-495e-a3c8-d2d578845e0a","order_by":2,"name":"Yongjie Wen","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yongjie","middleName":"","lastName":"Wen","suffix":""},{"id":400110193,"identity":"9a9a541b-e7bc-4473-b51c-ab5308b95e6d","order_by":3,"name":"Xiansong Xie","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiansong","middleName":"","lastName":"Xie","suffix":""},{"id":400110194,"identity":"901e5f90-ef54-47bf-9a78-96b1e2cf5cba","order_by":4,"name":"Zhoujing Wang","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Zhoujing","middleName":"","lastName":"Wang","suffix":""},{"id":400110195,"identity":"d16a8c45-1903-4313-a1dd-9ff1a225c234","order_by":5,"name":"Qian Chen","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Chen","suffix":""},{"id":400110196,"identity":"b5709a14-9a31-4c90-8ef0-c71b9500c79b","order_by":6,"name":"Yingbo Zhang","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yingbo","middleName":"","lastName":"Zhang","suffix":""},{"id":400110197,"identity":"5ea641dc-f15e-41ce-b9e4-0597580d98f7","order_by":7,"name":"Haiyang Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBAC+/nHDz7+Y2BTP5+9gUgtBhI8yQY8BWmMG3sOEK2FwUyC58NhxoYbCURqMZduSDaQMDjMzDjz8cYbDDU20QS1WM45ePCBgUE6G7t0WrEFw7G03AaCeg4kJBskGFjzMM7OMZNgbDhMlBYziQMGzBIMN88QqcXgRoKZZIOBswHDDR4itUj2nEk2ZjBISzDsAfolgRi/8LO3H3zM8McmQZ798MYbH2psiPALsiMlEkhRDtFCqo5RMApGwSgYGQAALy5ABxqeTR8AAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":true,"prefix":"","firstName":"Haiyang","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2025-01-08 15:08:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5790214/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5790214/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73785048,"identity":"5a6ae947-fb4d-411d-84de-2e6cd5d4432b","added_by":"auto","created_at":"2025-01-14 16:07:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74944,"visible":true,"origin":"","legend":"\u003cp\u003eDetailed patient recruitment flowchart.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5790214/v1/b4a44184aea606740fd99dff.jpg"},{"id":73784452,"identity":"9284b641-8250-4407-8b85-27278eb6d930","added_by":"auto","created_at":"2025-01-14 15:59:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19175,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive ability of FIB-4 on the risk of vertebral fracture in middle-aged and elderly Americans.\u003c/p\u003e","description":"","filename":"figure21.png","url":"https://assets-eu.researchsquare.com/files/rs-5790214/v1/7c6d85d0c4dd4ca2a0acea24.png"},{"id":73784453,"identity":"267b30a2-a73b-488d-8ea8-30de4c7157ac","added_by":"auto","created_at":"2025-01-14 15:59:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31568,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Unadjusted restricted cubic spline (RCS) model; (B) RCS model adjusted for confounding factors.\u003c/p\u003e","description":"","filename":"figure22.png","url":"https://assets-eu.researchsquare.com/files/rs-5790214/v1/831b407422cfeb3813ad815f.png"},{"id":73786656,"identity":"10f74114-fb23-47ff-a0c9-6c70d9431cd7","added_by":"auto","created_at":"2025-01-14 16:23:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1218086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5790214/v1/91af43d9-6598-4aac-b5f9-74ed09a7adb7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Liver Fibrosis (FIB-4) Index and Vertebral Fracture in Middle-aged and Older Adults in the United States: A Cross-Sectional Study of NHANES 2013-2014","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eVertebral fractures are a common type of fracture in middle-aged and older adults and are closely associated with osteoporosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Studies have shown that the high incidence of vertebral fractures is strongly linked to a decrease in patient survival rates [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among individuals over the age of 50, at least one in five have one or more vertebral fractures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The incidence of thoracolumbar fractures in individuals aged 60 and above, caused by low-energy trauma, is four times higher than in younger individuals, with an incidence rate of approximately 30 per 100,000 people annually, and more than two-thirds of these patients are over the age of 60 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Vertebral fractures are more common in postmenopausal women and often result in acute and chronic pain, functional impairment, and even disability, significantly reducing quality of life. Long-term, they can lead to spinal kyphosis, further affecting life expectancy and mortality rates [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, there is no consensus on the optimal treatment for vertebral fractures. Therefore, early implementation of appropriate interventions is crucial for reducing the risk of vertebral fractures, improving patient quality of life, and extending lifespan.\u003c/p\u003e \u003cp\u003eLiver fibrosis is a major predictor of liver-related complications in patients with non-alcoholic fatty liver disease (NAFLD) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and an important marker of poor disease prognosis. In recent years, several studies have revealed a potential association between NAFLD and osteoporosis. NAFLD has been recognized as a high-risk factor for osteoporosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and is closely associated with a decrease in bone mineral density (BMD) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Some studies suggest that compared to patients without NAFLD, those with NAFLD have significantly lower BMD, and this is negatively correlated with hip BMD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Additionally, NAFLD is closely linked to reduced BMD in both adults and children, as well as a history of osteoporotic fractures [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNAFLD is one of the most common chronic liver diseases and, as a globally prevalent non-communicable disease, it significantly impacts human health [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies have found that low BMD is commonly observed in various liver diseases, including NAFLD [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Some scholars suggest that liver fibrosis is the hepatic manifestation of metabolic syndrome and may have a potential link to osteoporosis and fracture risk [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Further research has pointed out that the severity of liver fibrosis is negatively correlated with BMD, revealing a potential association between liver fibrosis and bone health [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Fibrosis (FIB-4) index is a convenient and non-invasive tool for assessing liver fibrosis, with validated predictive models used to accurately evaluate liver fibrosis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies have shown that a higher FIB-4 index is significantly associated with a decrease in BMD, increasing bone fragility and fracture risk [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, no studies have specifically investigated the relationship between the FIB-4 index and vertebral fractures. Therefore, this study aims to explore the association between the FIB-4 index and vertebral fractures, and to evaluate the effectiveness of the FIB-4 index in predicting vertebral fracture risk in US middle-aged and older adults using receiver operating characteristic (ROC) curves.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Samples and data sources\u003c/h2\u003e \u003cp\u003eRelevant samples and data used in this study were all derived from NHANES (National Health and Nutrition Examination Survey) between 2013 and 2014. NHANES is a continuous cross-sectional research survey managed by the National Center for Health Statistics (NCHS) to evaluate the nutritional and health status of adults and children in the United States. This study was approved by the Research Ethics Review Committee of the National Center for Health Statistics and obtained the informed consent from the participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study design and population\u003c/h2\u003e \u003cp\u003eIn the National Health and Nutrition Examination Survey (NHANES) 2013\u0026ndash;2014, among all 10,175 subjects, 6,845 patients who did not receive dual-energy X-ray, 106 patients who did not diagnosed with vertebral fracture after receiving dual-energy X-ray, 669 patients with missing covariate and inflammatory index data and 367 patients under 45 years old were excluded. Finally, 2,188patients were included in the analysis. The process is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInclusion criteria as follows: Patients were included if they were \u0026ge;\u0026thinsp;45 years old in NHANES from 2013 to 2014. Exclusion criteria were as follows: Patients were excluded if (1) they were \u0026lt;\u0026thinsp;45 years old; (2) they were not evaluated by dual-energy X-ray absorptiometry; (3) there was a lack of FIB-4 data; (4) the covariate data were missing. In this study, the Mobile Examination Center (MEC) weights were applied.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Definition and measurement of vertebral fracture\u003c/h2\u003e \u003cp\u003eThe vertebral fracture was measured by the lateral view of the thoracolumbar spine with dual-energy X-ray absorptiometry (DXA). The image resolution of the lateral spine scan obtained by DXA is close to that of a standard radiograph. The accuracy of DXA VFA and standard radiograph in detecting moderate to severe vertebral fractures is similar (defined by Genant's semi-quantitative criteria (1993) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Lateral DXA scanning of the thoracolumbar spine was performed in the NHANES MEC from 2013 to 2014. According to the Vertebral Fracture Status Summary, the vertebral status was given 1 for normal (no fracture between T4-L4, unevaluable vertebra in T7-L4\u0026thinsp;\u0026le;\u0026thinsp;1), 2 for fracture (mild, moderate, or severe fracture between T4-L4) and 3 for unexplained status (no fractures and unevaluable vertebra between T7-L4\u0026thinsp;\u0026le;\u0026thinsp;1). In this study, any level of mild, moderate, or severe fractures between T4-L4 were defined as vertebral fractures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Measurement of FIB-4\u003c/h2\u003e \u003cp\u003e The Beckman Coulter DxH 800 in the NHANES MEC was utilized to count blood samples and provide blood cell distribution of all participants. The Beckman Coulter method of counting and sizing was used to derive complete blood count (CBC) parameters, combined with automatic dilution, and mixing equipment for sample processing and a single-beam spectrophotometer for hemoglobin determination. The VCS technique was applied in differential counting of whole blood cell count .The Beckman Coulter DxH 800 instrument at the NHANES Mobile Examination Center (MEC) performs complete blood cell counts on blood samples and provides blood cell distribution for all participants. The methods used to derive CBC parameters are based on Beckman Coulter's counting and quantification techniques. The complete blood count classification utilizes VCS technology. The serum or plasma levels of ALT(Aspartate Aminotransferase) and AST(Alanine Aminotransferase) are measured using the kinetic rate method. Serum specimens are processed, stored, and shipped to the collaborating laboratory service in Ottumwa, Iowa, for analysis. The FIB-4 index is calculated using the following formula:FIB-4 = [Age (years) \u0026times; AST (U/L)] / [Platelet count (10^9/L) \u0026times; ALT (U/L)^(1/2)].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Covariate selection\u003c/h2\u003e \u003cp\u003eConsidering the influence of other factors on vertebral fracture, this study included factors that may affect the risk of vertebral fractures, such as age, gender (male or female), smoking history (smokers or non-smokers; smokers were defined as having smoked at least 100 cigarettes in their lifetime), drinking history (drinker or non-drinker: drinker was defined as drinking at least 12 cups of alcoholic beverages a year, including beer, wine and any other type of alcoholic beverages. \u0026ldquo;At least 12 cups of any kind of alcoholic beverage\u0026rdquo; was defined as 12 ounces of beer, 5 ounces of wine or 1.5 ounces of liquor), hypertension (with or without hypertension; hypertension was defined as ever being told of having hypertension by doctors), diabetes (with or without diabetes; diabetes was defined as ever being told of having diabetes by doctors), work activities (intense work activities or non-intense work activities: intense work activities were defined as work involving intense activities that lead to a sharp increase in breathing or heart rate, such as carrying or lifting heavy objects, digging or building for at least 10 minutes), osteoporosis (with or without osteoporosis; osteoporosis was defined as ever being told of having osteoporosis), vitamin D, race (Mexican American, other Hispanics, non-Hispanic whites, non-Hispanic Blacks, and other races-including multi-races),Body mass index, education level (less than elementary school, elementary school, high school, college, above college), and poverty-to-income ratio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eNHANES adopted a complex, multistage, probability sampling design. Shapiro-Wilk test was adopted to evaluate data distribution. For data of normal distribution, continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and data of non-normal distribution were expressed as median and interquartile range in brackets. Chi-square test, Mann-Whitney U test or independent t-test were conducted to compare the differences between the two groups. In this study, a weighted univariate logistic regression was used to screen the influencing factors of vertebral fracture risk in middle-aged and older Americans. Weighted multivariate logistic regression (WLMR) models were applied to evaluate the association of FIB-4 with vertebral fracture in middle-aged and older Americans. The rough model was initially fitted without adjustment, and then the influencing factors with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were incorporated into subsequent models for adjustment. The results were expressed by the ratio (OR) and its 95% confidence interval (CI). The area under the curve (AUC) was calculated to evaluate the predictive ability of FIB-4 for the risk of vertebral fracture in middle-aged and older Americans.The restricted cubic spline (RCS) model was used to study the linear or nonlinear relationship of FIB-4 with vertebral fracture in middle-aged and older Americans. To examine the robustness of the main findings, a two-step sensitivity analysis was conducted: in Model 1, an unweighted logistic regression model was adopted; in Model 2, a weighted logistic regression after excluding patients diagnosed with osteoporosis at baseline was conducted. The statistical software Rstudio 4.3.3 was used, and a \u003cem\u003eP\u003c/em\u003e-value threshold of 0.05 or less was considered statistically significant (bilateral).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of participants\u003c/h2\u003e \u003cp\u003eA total of 2188 patients were included in this study, including 1059males (48%) and 1129females (52%), with an average age of 59 years. There was a statistical difference between the two groups in age, and participants in the fracture group were older than those of the non-fracture group (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001). We found that there was statistical significance between osteoporosis, age,hypertension,poverty-to-income ratio ,FIB-4 and vertebral fracture in middle-aged and older Americans(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Baseline characteristics of participants are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline information on FIB-4 and fracture.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;91,557,517\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0, N\u0026thinsp;=\u0026thinsp;85,852,643\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, N\u0026thinsp;=\u0026thinsp;5,704,874\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,574(78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,465(79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109(73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(25,32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(25,32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(25,31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,129(48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,031(47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98(64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,129(52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,058(52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71(49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,059(48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e983(48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76(51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(51,67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58(51,66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67(59,78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican\u003c/p\u003e \u003cp\u003eAmerican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256(5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e241(5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic\u003c/p\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e438(9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e423(9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15(4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic\u003c/p\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,063(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e961(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102(84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190(3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184(4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241(5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232(5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e504(21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e464(21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ejunior school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286(9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e270(9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16(8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ejunior school below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173(4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e638(31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e598(31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e576(33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e536(33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty-to-Income Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.40(1.71,5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.48(1.74,5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.44(1.37,3.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e427(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e399(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0steoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197(8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167(7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30(18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e354(18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333(18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,078(48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e997(47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81(58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamineD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(56,93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73(55,92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72(57,100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32(1.02,1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30(1.01,1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59(1.28,1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e N not Missing (unweighted)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e n (unweighted)(%); Median(25%,75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e chi-squared test with Rao \u0026amp; Scott\u0026rsquo;s second-order correction; Wilcoxon rank-sum test for complex survey samples\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association between covariates and vertebral fracture\u003c/h2\u003e \u003cp\u003eThe univariate logistic regression analysis showed that age, osteoporosis,hypertension,poverty-to-income ratio were the risk factors of vertebral fracture in middle-aged and older Americans(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). They were included as confounders in the weighted multivariate logistic regression analysis. as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between covariates and vertebral fracture.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003efactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u0026ndash;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31\u0026ndash;3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u0026ndash;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u0026ndash;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u0026ndash;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u0026ndash;2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u0026ndash;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther Race-Including Multi-Racial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u0026ndash;3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ejunior school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u0026ndash;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ejunior school below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u0026ndash;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u0026ndash;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniversity above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u0026ndash;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoverty-to-Income Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u0026ndash;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u0026ndash;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0steoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0steoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39\u0026ndash;4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u0026ndash;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u0026ndash;2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitamineD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association between FIB-4 and vertebral fracture\u003c/h2\u003e \u003cp\u003eWeighted multivariate logistic regression was used to analyze the association between FIB-4 and vertebral fracture in middle-aged and older Americans(Model 1, in which confounding factors were not adjusted, showed that vertebral fractures were statistically significantly associated with FIB-4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in middle-aged and older Americans. After adjusting the factors of age, osteoporosis,hypertension,poverty-to-income ratio in Model 2, the results of weighted multivariate logistic regression showed that there was a significant positive correlation between FIB-4 and the risk of vertebral fracture in middle-aged and older Americans (OR: 1.294, 95% CI: 1.038\u0026ndash;1.613), as illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of FIB-4 with vertebral fracture.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.139, 1.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.038, 1.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eModel 1: unadjusted;Model 2: adjusted for Hypertension、Poverty-to-Income Ratio、0steoporosis、Age\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Predictive ability of FIB-4 on the risk of vertebral fracture in middle-aged and older Americans\u003c/h2\u003e \u003cp\u003eThe receiver operating characteristic (ROC) curve was used to predict the effectiveness of FIB-4 for predicting the risk of vertebral fracture in middle-aged and older Americans. The results revealed that FIB-4(AUC\u0026thinsp;=\u0026thinsp;0.654) demonstrated positive predictive performance for the risk of vertebral fractures in middle-aged and older Americans (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5 Linear or nonlinear relationship between FIB-4 and vertebral fracture in middle-aged and older Americans using restricted cubic spline (RCS) model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe linear and nonlinear relationship was assessed between FIB-4 and vertebral fracture in middle-aged and older Americans.We observed a linear relationship between FIB-4 and the risk of vertebral fractures in middle-aged and elderly Americans under unadjusted confounding factors (p for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When FIB-4 exceeded approximately 1.5, a significant increase in the risk of vertebral fractures was observed as FIB-4 values continued to rise. After adjusting for factors of hypertension, age, poverty-to-income ratio, and osteoporosis, this relationship remained evident. as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eTo examine the robustness of the main findings, a sensitivity analysis was conducted. Firstly, the unweighted multiple logistic regression Model 1 showed that there was still a statistically significant association between FIB-4 and vertebral fractures in middle-aged and older Americans (Model 1: OR: 1.161, 95%CI: 1.046\u0026ndash;1.306, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).Secondly, after excluding patients diagnosed with osteoporosis at baseline, the weighted logistic regression Model 2 also showed a consistent association between FIB-4 and the risk of vertebral fractures middle-aged and older Americans (Model 2: OR: 1.309, 95%CI: 1.038\u0026ndash;1.650, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.046, 1.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.038, 1.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNotes:M\u003c/b\u003eodel:Using unweighted logistic regression models;Model2: Weighted logistic regression model for excluding patients diagnosed with osteoporosis at baseline,(adjusted for Hypertension、Poverty-to-Income Ratio、0steoporosis、Age)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study explored the association between the Fibrosis-4 (FIB-4) index and the risk of vertebral fractures in middle-aged and elderly Americans. The results showed a significant positive correlation between FIB-4 and the risk of vertebral fractures in middle-aged and elderly Americans. After adjusting for confounding factors, the study found a linear relationship between FIB-4 and the risk of vertebral fractures, with an increase in FIB-4 values being accompanied by a significant rise in fracture risk. Moreover, FIB-4 demonstrated positive predictive ability for assessing the risk of vertebral fractures.\u003c/p\u003e \u003cp\u003eExisting studies have shown that osteoporosis and reduced bone strength are among the main causes of vertebral fractures [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This study found that the risk of vertebral fractures in the osteoporosis group was significantly higher than in the non-osteoporosis group. Osteoporosis can lead to reduced bone mass and increased bone fragility, which may result in vertebral fractures even under minor external forces [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, studies have also indicated that the risk of vertebral fractures is age-related, with women having a higher incidence of hip and spinal fractures at older ages [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This study further adjusted for confounding factors such as age and osteoporosis to confirm their impact on vertebral fracture risk. Additionally, the study found that the poverty-to-income ratio is associated with vertebral fracture risk, which may be related to lower socioeconomic status, malnutrition, and limited access to healthcare.\u003c/p\u003e \u003cp\u003eFIB-4, as a non-invasive biomarker for assessing liver fibrosis, has been widely used for the screening and risk stratification of liver fibrosis. Studies have shown that FIB-4 has high accuracy in predicting advanced liver fibrosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Recent meta-analyses have also demonstrated that the predictive performance of FIB-4 is comparable to that of liver biopsy, and it has become the first-step assessment tool in international NAFLD risk stratification algorithms [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, platelet count, as a component of the FIB-4 index, is closely associated with hepatocyte damage and is considered a reliable marker of chronic liver disease [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are studies investigating the relationship between liver fibrosis and BMD (bone mineral density), but the results remain inconsistent. Some researchers have found a correlation between the two [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], while others have reported no significant association [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Research has shown that the relationship between liver fibrosis and BMD is influenced by factors such as age, gender, BMI, smoking, alcohol consumption, and occupational activity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Osteoporosis is closely linked to reduced BMD and increases the risk of fractures. A study by Li M et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] revealed an association between liver fibrosis and osteoporotic fractures in patients with NAFLD. Similarly, Barchetta I et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] demonstrated a strong relationship between liver fibrosis, lower BMD, and osteoporosis. A cross-sectional study conducted in Italy found that higher FIB-4 levels were significantly associated with lower BMD and an increased risk of bone loss/osteoporotic fractures [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].In this study, we also found a significant positive correlation between FIB-4 and vertebral fractures, consistent with previous findings. Low BMD is closely associated with multiple factors, including metabolic syndrome, which itself is related to liver fibrosis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A study by Chung GE et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] indicated that a high fatty liver index is associated with an increased risk of vertebral fractures. Furthermore, in sensitivity analysis, this study observed a significant positive correlation between FIB-4 levels and spinal fractures. Notably, even in patients without osteoporosis, FIB-4 was associated with the risk of vertebral fractures. Therefore, FIB-4 serves as an independent risk factor for vertebral fractures and has certain predictive value.\u003c/p\u003e \u003cp\u003eAST and ALT are hematological markers of hepatocyte injury. Studies have found that ALT is associated with BMD in NAFLD patients. Research has shown that the severity of liver fibrosis is related to ALT [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], as well as systemic inflammation and oxidative stress in NAFLD patients [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. It has also been found that a decrease in BMD is associated with inflammation and oxidative stress [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Purnak T et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] demonstrated that elevated ALT is associated with reduced BMD. Xia MF et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] found that as ALT levels increased in patients with liver fibrosis, the risk of osteoporosis also increased, and BMD tended to deteriorate when both liver fibrosis and elevated ALT levels were present. Therefore, ALT can be used as a clinical biomarker for the risk of osteoporosis in patients with liver fibrosis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].Additionally, Do HJ et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] reported a correlation between AST and femoral neck BMD, and after further adjustment for confounding factors, they found that AST and ALT were also statistically associated with lumbar spine BMD. They observed that higher AST and ALT levels were related to the diagnosis of osteoporosis. In this study, the increase in FIB-4 was found to be associated with a higher risk of vertebral fractures, which is consistent with the findings of the aforementioned studies.\u003c/p\u003e \u003cp\u003eRegarding the relationship between liver fibrosis and fractures, although some studies have not clearly established a connection between the two, existing studies have explored related mechanisms, including liver steatosis, fibrosis, obesity, insulin resistance, and alterations in gut microbiota [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Changes in gut microbiota can activate inflammatory factors, thereby affecting liver and bone health. Adiponectin can increase insulin sensitivity, reduce lipid accumulation, and mitigate liver fibrosis by regulating inflammation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. A decline in adiponectin levels is associated with the progression of liver fibrosis. At the same time, adiponectin receptors play a role in osteocytes, limiting the formation of osteoclasts [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Therefore, bone loss in patients with liver fibrosis may be related to adiponectin levels.Studies have also found that patients with liver fibrosis exhibit lower osteocalcin levels, which are associated with bone formation and pancreas-liver metabolism [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Inflammatory factors such as TNF-α, IL-1, and IL-17 are involved in the pathogenesis of osteoporosis, promoting osteoclast activation and bone resorption [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Patients with NAFLD often exhibit lower levels of osteoprotegerin (OPG) and higher levels of TNF-α and IL-6 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Moreover, osteopontin (OPN) is associated with NAFLD and liver fibrosis [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Insulin-like growth factor-1 (IGF-1), a factor produced by both the liver and bones, plays a role in bone remodeling. Studies have shown that NAFLD and osteoporosis patients have reduced IGF-1 levels, indicating that IGF-1 plays an important role in the liver-bone axis [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, this study demonstrates a significant association between the FIB-4 index and the risk of vertebral fractures in middle-aged and elderly Americans. The strength of our study lies in the use of the FIB-4 index to assess liver fibrosis, marking the first time that the relationship between FIB-4 and vertebral fractures has been explored. However, this study also has several limitations. First, due to the constraints of the public database, the included covariates may be insufficient, and other potential factors influencing vertebral fractures in middle-aged and elderly Americans were not considered. Additionally, as this study is a cross-sectional study, it is not possible to determine the temporal sequence between FIB-4 levels and vertebral fractures, making it difficult to establish a causal relationship between liver fibrosis and fractures. Further multicenter and prospective studies are needed to explore other factors related to vertebral fractures to provide more reliable conclusions.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study reveals a significant association between the FIB-4 index and the risk of vertebral fractures in middle-aged and elderly Americans, suggesting that FIB-4, as a novel and non-invasive biomarker for assessing liver fibrosis, has the potential to predict the risk of vertebral fractures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Bethune Foundation: Special Research Fund for Vertebra Reinforcement Therapy for Spinal Pathologic Fractures [Grant Number BK-JP2020001].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on a public database and approved by the Research Ethics Review Committee of the National Center for Health Statistics (NCHS). All participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to reproduce material from other sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Writing - original draft preparation: Yuwei Gou; Writing - review and editing: Yuwei Gou, Heng Yin,Xiansong Xie; Conceptualization: Heng Yin, Yuwei Gou,Zhoujing Wang; Methodology: Yuwei Gou, Yongjie Wen; Formal analysis and investigation: Yuwei Gou, QianChen,Yingbo Zhang; Funding acquisition: Haiyang Xie; Resources: Haiyang Xie; Supervision: Haiyang Xie, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSomma T, DE Rosa A, Mastantuoni C, Esposito F, Meglio V, Romano F, Ricciardi L, de Divitiis O, DI Somma C. Multidisciplinary management of osteoporotic vertebral fractures. Minerva Endocrinol (Torino). 2022;47(2):189\u0026ndash;202. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.23736/S2724-6507.21.03515-6\u003c/span\u003e\u003cspan address=\"10.23736/S2724-6507.21.03515-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2021 Dec 9. 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Liver-bone crosstalk in non-alcoholic fatty liver disease: Clinical implications and underlying pathophysiology. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e. (2023) 14:1161402/full. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2023.1161402\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2023.1161402\" 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":"Vertebral fracture, FIB-4, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-5790214/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5790214/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study was to examine the association between FIB-4(fibrosis index based on four factors) and the occurrence of vertebral fractures in Middle-aged and Older Americans.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePatients ⩾45years of age from the 2013\u0026ndash;2014 NHANES database were selected for this study. Restricted cubic spline models and weighted logistic regression models were used to assess the association between FIB-4 and the occurrence of vertebral fractures in Middle-aged and Older Americans. The predictive value of the FIB-4 on the occurrence of vertebral fractures was assessed using receiver operating characteristic curves (ROC). To examine the robustness of the main findings, a sensitivity analysis was conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 2188 patients were included in the analysis, of whole 147 suffered vertebral fractures. Fully adjusted logistic regression showed a significant linear relationship between FIB-4 and the occurrence of vertebral fracture in Middle-aged and Older Americans (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05),with a linear relationship observed between the FIB-4 index and the risk of vertebral fractures. Additionally, the FIB-4 index demonstrated good predictive performance for the incidence of vertebral fractures.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study found that the FIB-4 index, as a novel and non-invasive liver fibrosis biomarker, can predict the risk of vertebral fractures in middle-aged and older Americans, which has clinical significance for the prevention and management of vertebral fractures in middle-aged and older Americans.\u003c/p\u003e","manuscriptTitle":"Association Between Liver Fibrosis (FIB-4) Index and Vertebral Fracture in Middle-aged and Older Adults in the United States: A Cross-Sectional Study of NHANES 2013-2014","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-14 15:59:26","doi":"10.21203/rs.3.rs-5790214/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":"0549ce2f-f31d-416d-a5b8-16e904583ef9","owner":[],"postedDate":"January 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T18:08:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-14 15:59:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5790214","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5790214","identity":"rs-5790214","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}