Weight loss in elderly orthopaedic patients leads to bone loss

Weight loss in elderly orthopaedic patients leads to bone loss | 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 Weight loss in elderly orthopaedic patients leads to bone loss Xiangxu Chen, Liyong Bai, Chuwei Tian, Yakuan Zhao, Cheng Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1531410/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The number of patients with osteoporosis or low bone mass is increasing annually. Weight loss is reportedly associated with bone loss and is a strong predictor of osteoporosis. It was still not clear the relationship between weight loss and bone loss in the elder. Methods The study included 520 patients aged ≥65 years (178 men and 342 women). Age, gender, weight, and height were recorded. Femoral neck bone mineral density and T-score were investigated using a dual-energy X-ray absorptiometry scanner. Blood calcium (Ca), phosphorus (P), albumin (ALB), alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) levels were measured. Patients were classified by gender (male and female), age (65-79 years and ≥80 years), and T-score: normal, osteopenia and osteoporosis. Results Age, gender, body mass index (BMI), ALP and TG were independent factors for osteoporosis. For the 65-79 and ≥80 year age groups, female patients presented lower T-scores than males. Ca, P, ALB, ALP, TC, HDL and LDL levels were significantly different between men and women in the 65-79 year age group. In addition, BMI and TG levels were significantly decreased in osteoporotic patients compared with normal bone mass patients. TC levels declined in 65-79 year-old male and female patients with osteoporosis. In the group of women aged ≥80 years, osteoporotic patients showed significantly increased ALP levels. Furthermore, we found positive correlations between BMI and TG levels in the groups of male and female patients. However, we found no significant differences in ALB, Ca, P, HDL and LDL levels in osteoporotic compared to normal bone mass patients. Conclusion Osteoporotic patients showed significantly decreased BMI and TG levels compared with those with normal bone mass. BMI was showed positive correlations with TG levels in male and female patients. These results indicate a correlation between weight loss and bone loss and a correlation between lipid profiles and bone mass. Osteoporosis Weight loss Elder patients Body Mass Index Lipid profiles Figures Figure 1 Figure 2 Figure 3 Introduction The number of osteoporotic or low bone mass patients is increasing annually. More than 33% of patients aged 50 years or older are affected by osteoporosis in China [ 1 ]. Osteoporosis is characterized by decreased bone mineral density (BMD) and damaged bone micro-architecture, leading to a low bone mass and increased bone fragility, which increases the risk of fracture [ 2 ]. Many factors contribute to osteoporosis, including age, gender, lifestyle, and diseases [ 3 ]. Dual-energy X-ray absorptiometry (DXA) together with T-score is considered a very important and widely used method for diagnosing osteoporosis [ 4 ]. Normally, osteoporosis is defined by the T-score (normal [ ≥ − 1.0], osteopenia [− 1.0 to − 2.5], and osteoporosis [ ≤ − 2.5] ) according to the recommendations of the World Health Organization (WHO) [ 5 , 6 ]. Recently, many studies have investigated risk factors for osteoporosis, including body mass index (BMI), serum lipid profiles, serum calcium (Ca) and phosphorus (P), serum albumin (ALB), alkaline phosphatase (ALP), serum alanine transaminase (ALT) and aspartate aminotransferase (AST) [ 7 – 11 ]. Previous studies have pointed out the association between obesity and osteoporosis, and that obesity may be a protective factor for osteoporosis [ 12 , 13 ]. Obese patients have higher mechanical loading on the bones which is beneficial for bone mass [ 14 , 15 ]. Moreover, BMI (kg/m 2 )is correlated with the percentage of body fat [ 16 ]. Weight loss is harmful to musculoskeletal health [ 17 ] and has been reported to be associated with bone loss, as well as being a strong predictor of osteoporosis [ 18 ]. Older women who lost weight showed increased bone loss in the hip [ 19 ]. Another study reported that weight loss was related to hip-bone loss in older men and women [ 18 ]. Plasma lipid profiles have also been shown to change in response to weight loss [ 20 ]. Weight loss may thus influence bone and lipid metabolism. However, the relationship between weight loss, BMD, and lipid profiles remains unclear. The aim of our study was to investigate the relationship between weight loss and bone loss and the correlation between lipid profiles and bone mass, and was to explore the association between lipid metabolism and bone metabolism. In our study, we collected and analysed the clinical data from 520 patients aged ≥ 65 years. Methods Study participants In this retrospective observational study, all patients were fractured inpatients at the Trauma Centre of Zhongda Hospital from 2017 to 2020 and aged ≥ 65 years. We excluded the patients with cancer, thyroid disease, hypopituitarism, rheumatoid arthritis and chronic renal failure or renal dysfunction as well as patients accepted a lipid-lowering therapy, synthyroid or hormone-replacement therapy. All clinical data for analysis in our study could be obtained. Patients were classified by gender (male and female), age (65–79 years and ≥ 80 years), and T-score: normal ( ≥ − 1.0), osteopenia (− 1.0 to − 2.5), and osteoporosis ( ≤ − 2.5). Data collection Age, gender, height, and weight were recorded from patient documents. BMI was calculated using the weight and height (kg/m 2 ). Femoral neck BMD and T-scores were investigated using a DXA scanner. According to the WHO classification, the T-score was used to define the categories of BMD. Serum samples were collected immediately after the patients were admitted and serum levels of Ca, P, ALB, AST, ALT, ALP, triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) were analysed. Statistical analysis Data are presented as mean ± standard deviation (SD). IBM SPSS statistics version 25 software and GraphPad Prism version 8.4.0 software were used to analyse the data. After analysing the normality using the Shapiro-Wilk test, data were evaluated by the t-test and non-parametric test between two groups. For three groups, data were evaluated by one way ANOVA. Multivariate analysis was performed by multiple linear regression. Person correlation test was used to analyze the association between BMI and TG. *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001 were defined as different significance levels. Results In total, 520 patients aged ≥ 65 years were included in our study. A total of 178 male patients were investigated, including 48 normal, 81 osteopenic and 49 osteoporotic patients. A total of 342 female patients were included in our study, including 36 normal, 103 osteopenic subjects, and 203 osteoporotic patients (Table 1). Age, gender, BMI and concentrations of ALP and TG were significantly differences among the groups of normal, osteopenic and osteoporotic patients. Moreover, age, gender, BMI and concentrations of ALP and TG were the independent factors for osteoporosis (Table 1). Table 1: Univariate and multivariate analysis of related factors for osteoporosis CND: Coronary Heart Diseases. Patients were divided into two groups according to age (65–79 years and ≥ 80 years). In order to analyse the differences in gender, a total of 106 men and 188 female were investigated in the 65–79 year age group (Table 2 ). As shown in Table 2 , there were significant differences in the T-score and the concentrations of Ca, P, ALB, ALP, TC, HDL and LDL between male and female patients. However, male patients showed no difference in age, BMI, and the concentrations of AST, ALT and TG in comparison with female patients. Table 2: Male and female patients aged between 65 to 79 years old in related factors for osteoporosis Male (n=106) Female (n=188) P-value Age (Y) 71.25±4.39 72.18±4.52 0.095 T-value -1.51±0.94 -2.5±1.03 p＜0.0001 BMI (Kg/m 2 ) 24.33±3.52 23.63±3.37 0.134 Ca (mmol/L) 2.21±0.13 2.25±0.14 0.027 P (mmol/L) 1.00±0.19 1.06±0.14 0.010 ALB (g/L) 38.40±4.11 39.67±4.21 0.013 ALP (U/L) 69.29±39.15 77.79±35.53 0.032 AST (U/L) 26.86±18.85 27.30±19.34 0.258 ALT (U/L) 40.06±27.97 38.94±28.56 0.953 TG (mmol/L) 1.39±0.73 1.45±0.91 0.727 TC (mmol/L) 4.15±0.95 4.52±1.11 0.005 HDL (mmol/L) 1.13±0.26 1.33±0.45 P＜0.001 LDL (mmol/L) 2.42±0.83 2.68±0.86 0.048 A total of 72 men and 154 women aged ≥ 80 years were examined in our study. The T-score was significantly different in male patients compared to female patients. There were no significant differences in BMI and concentrations of Ca, P, ALB, ALP, AST, ALT, TG, TC, HDL and LDL between male and female patients (Table 3 ). Table 3: Male and female patients aged ≥80 years old in related factors for osteoporosis Male (n=172) Female (n=154) P-value Age (Y) 85.60±4.08 85.65±33.89 0.839 T-value -2.06±1.21 -2.94±1.29 P＜0.0001 BMI (Kg/m 2 ) 22.34±3.26 21.68±3.12 0.150 Ca (mmol/L) 2.20±0.17 2.21±0.19 0.234 P (mmol/L) 1.01±0.28 1.09±0.58 0.092 ALB (g/L) 38.19±4.34 38.04±4.68 0.823 ALP (U/L) 56.21±38.95 52.95±40.44 0.455 AST (U/L) 23.61±9.93 25.08±10.77 0.238 ALT (U/L) 45.61±28.78 54.45±36.12 0.060 TG (mmol/L) 1.07±0.44 1.08±0.49 0.857 TC (mmol/L) 4.03±0.99 4.20±1.03 0.171 HDL (mmol/L) 1.17±0.30 1.21±0.32 0.425 LDL (mmol/L) 2.31±0.78 2.29±0.78 0.926 In the group of 65–79 year-old male patients, it was shown that BMI was decreased in osteoporotic compared to normal bone mass patients. Moreover, TG and TC levels also declined in patients with osteoporosis compared to those with normal bone mass. Osteoporotic patients showed no significant differences in Ca, P, ALB, ALP, AST, ALT, HDL and LDL levels compared to normal bone mass patients. In men aged ≥ 80 years, BMI was significantly lower in osteoporotic than in normal bone mass patients. We also observed decreased TG levels in osteoporotic patients compared to those in normal bone mass patients. Ca, P, ALB, ALP, AST, ALT, TC, HDL and LDL levels were not significantly different between patients with and without osteoporosis (Fig. 1 ). In the group of 65–79 year-old females, BMI, TG and TC were significantly decreased in osteoporotic patients in comparison with normal bone mass patients. Osteoporotic patients also showed no significant differences in Ca, P, ALB, ALP, AST, ALT, HDL and LDL levels compared to normal bone mass patients. In the group of women aged ≥ 80 years, osteoporotic patients showed significantly decreased BMI and TG levels as well as increased ALP levels. We also did not find significant differences in the levels of Ca, P, ALB, AST, ALT, TC, HDL and LDL in osteoporotic patients compared with normal bone mass patients (Fig. 2 ). Furthermore, we found positive correlation between BMI and TG levels in the groups of male patients aged 65–79 and ≥ 80 years. Female patients also showed positive correlation between BMI and TG levels in the groups of 65–79 and ≥ 80 year-old patients (Fig. 3 ). Discussion In our study, we analysed the clinical data of 520 patients aged ≥ 65 years. We found that osteoporotic patients showed significantly decreased BMI and decreased TG levels in comparison with normal bone mass patients. The number of patients diagnosed with osteoporosis is increasing annually and previous studies have shown that many factors contribute to osteoporosis, including age, gender, BMI, and levels of Ca, P, ALB, ALP, AST, ALT, TG, TC, HDL and LDL. Previous studies reported that many factors contribute to osteoporosis. In our studies, we found that age, gender, ALP and TG were independent factor for osteoporosis. Bone mass declined with age. In line with our study, many studies showed increased age contributing to bone mass loss. Based on this, we classified the groups according to the age. In our study, female patients in the 65–79 and ≥ 80 year age groups presented lower T-scores than male patients. In accordance with our results, men have higher peak BMD and higher BMD at a later age than women[ 21 , 22 ]. Significant differences were observed in the levels of Ca, P, ALB, ALP, TC, HDL and LDL between men and women in the 65–79 year age group. These results indicate that men and women present differences in many factors for osteoporosis. Cui et al. supported our results in their analysis of 1035 men and 3953 women where they also showed significant differences in BMI and T-score, as well as in levels of TG, TC, high-densitylipoprotein cholesterol (HDL-C), and low-densitylipoprotein cholesterol (LDL-C) between men and women [ 7 ]. Previous studies have described the correlation between lipid profiles, BMI, and BMD[ 7 , 23 ]. In our study, we found that male and female osteoporotic patients showed significantly decreased BMI in the groups aged 65–79 and ≥ 80 years. Similarly, many studies have reported an association between BMI and BMD. Alay et al. investigated 452 postmenopausal women inan outpatient clinic between 2012 and 2015, and observed decreased BMI related to lower BMD[ 24 ]. In their study, Fawzy et al. analysed 101 men and womenand found that a declinein BMI was associated with lower BMD[ 25 ]. Moreover, BMI is a weight-change profile[ 26 ]. These results suggest that weight loss is associated with bone loss. Moreover, our study demonstrated that male and female osteoporotic patients presented obviously decreased levels of TG in the groups aged 65–79 years and ≥ 80 years. In addition, male and female osteoporotic patients also showed obviously decreased levels of TC in the groups aged 65–79 years. Our study findings were in agreement with those of Cui et al. who showed that BMI and TG levels were significantly lower in patients with osteoporosis than in those with normal BMD. However, there was no significant difference in the TC levels [ 7 ]. They also described an association between osteoporosis and levels of HDL, LDL, TG, and TC[ 7 ]; however, we did not find any significant differences in the levels of HDL and LDL between the two groups in our study. In a meta-analysis, Chen et al. selected ten publications and investigated the relationship between lipid profiles (HLD, LDL, TG, and TC) and osteoporosis in postmenopausal women, and found significantly higher levels of HDL and TC in postmenopausal osteoporosis patients than in the normal BMD group. Although they did not show any significant difference in TG levels, postmenopausal osteoporosis patients presented a lower level of TG[ 27 ]. Alay et al. did not observe significant differences in the levels of HDL, LDL, TG, and TC[ 24 ]. However, Bijelic et al. evaluated lipid profiles and BMI in 100 postmenopausal osteoporotic and normal BMD patients. They proved that osteoporosis in postmenopausal patients was associated with the levels of LDL, TC, and TG. Contrary to our results, they showed an increased level of TG in osteoporotic patients than in those with a normal BMD[ 28 ]. The reason might be that, in their study, there were 72 overweight patients in the osteoporosis group and 54 overweight patients in the normal BMD group. BMI and TG were showed independent factors for osteoporosisin in our study. Osteoporotic male and female patients aged 65–79 and ≥ 80 years had lower BMI and decreased TG levels compared with patients of normal bone mass. Moreover, we also observed a positive correlation between BMI and TG levels in the groups of male and female patients aged 65–79 and ≥ 80 years. These results suggest that weight loss is related to the decreased TG levels, which might contribute to bone loss. It has been reported that lipids may be a predisposing factor for osteoporosis and are associated with bone fragility[ 11 ], and TG metabolism is considered to be correlated with bone metabolism. Ando et al. investigated serum TG levels and N-telopeptide of type I collagen (uNTx) – a bone resorption marker – levels in 113 Japanese middle-aged and elderly women, and found that patients with lower serum TG levels had higher uNTx levels, indicating that low serum TG levels might affect osteoclast activity, leading to bone loss[ 29 ]. Moreover, Dragojevic et al. performed a gene expression study by analysing bone tissue in 50 patients with osteoporosis and 62 controls. They reported that osteoporosis patients had lower osteoblastogenesis, higher osteoclastogenesis, and lower TG metabolism than controls[ 30 ]. These results indicate an association between TG metabolism and bone metabolism. However, the underlying mechanism between TG metabolism and bone metabolism remains unclear, and more studies should be performed in the future. In addition, we did not observe significant differences in other factors for osteoporosis, such as levels of Ca, P, AST, ALT and ALB between the osteoporosis and control groups in our study. Serum levels of Ca and P were normal in postmenopausal women with or without osteoporosis. This may be because osteoporosis leads to lower bone mineralisation, rather than to changes in the ratio of bone mineral to organic matrix. In previous studies, nutrition was reported to be related to osteoporosis, and serum levels of ALB were lower in patients with osteoporosis [ 31 , 32 ]. By analysing the serum ALB of 15539 individuals, hypoalbuminemia (serum ALB less than 3.5 g/dL) was found to be associated with osteoporosis[ 32 ]. Similarly, our study showed that serum levels of ALB were more than 35g/L in normal bone mass patients and osteoporotic patients. This might be the reason that serum ALB is not a factor for diagnosing osteoporosis in our study. AST and ALT were liver enzymes, and they were reported the association with osteoporosis. In agreement with our study, Do et al. selected 7160 subjects to analyze the association between liver enzymes and BMD in Koreans. They did not show the differences in AST and ALT with femur BMD[ 9 ]. The reason might be that we did not select patients with liver diseases. In our study, ALP was an independent factor for osteoporosis, and osteoporotic female patients aged ≥ 80 years showed significantly increased ALP levels. Serum levels of ALP are considered an important method for the diagnosis of some bone diseases, including osteomalacia and Paget’s disease. Increased serum ALP levels in postmenopausal women was reported by high bone turnover[ 33 ]. Serum levels of ALP > 129 U/L are helpful for diagnosing osteoporosis in men[ 34 , 35 ]. This is a single-center retrospective study with a small sample size. Further multicenter and prospective studies, with larger samples, are needed to prove the association between TG metabolism and bone metabolism. In conclusion, osteoporotic patients showed significantly decreased BMI and TG levels in comparison with normal bone mass patients in our study. These results indicate an association between TG metabolism and bone metabolism and provide a new method for the treatment of osteoporosis. Abbreviations BMI: body mass index; BMD: bone mineral density; DXA: dual-energy X-ray absorptiometry; Ca: calcium; P: phosphorus; ALB: albumin; TG: triglyceride; TC: cholesterol; ALP: alkaline phosphatase; AST: aspartate aminotransferase; ALT: alanine transaminase; WHO: World Health Organization; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; CHD：Coronary Heart Diseases; uNTx: N-telopeptide of type I collagen Declarations Acknowledgements Not applicable Authors’ contributions XXC and YFR designed the study. LYB contributed to data collection. XXC and CWT performed statistical analyses. XXC, LYB, YKZ and WJX drafted the manuscript. YFR, CZ, LS, YWZ and HC revised the manuscript. All authors read and approved the final manuscript. Funding There was no funding or support for this study. Availability of data and materials All data used and analyzed during the current study are included in this published article. Ethics approval and consent to participate This study was approved by the IEC for Clinical Research of Zhongda Hospital, Affiliated to Southeast University, and the requirement for informed consent was waived due to the retrospective design of the study. All methods were performed in accordance with the relevant guidelines and regulations. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Chen P, Li Z, Hu Y: Prevalence of osteoporosis in China: a meta-analysis and systematic review . 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Mukaiyama K, Kamimura M, Uchiyama S, Ikegami S, Nakamura Y, Kato H: Elevation of serum alkaline phosphatase (ALP) level in postmenopausal women is caused by high bone turnover . Aging Clin Exp Res 2015, 27 (4):413–418. Kuo TR, Chen CH: Bone biomarker for the clinical assessment of osteoporosis: recent developments and future perspectives . Biomark Res 2017, 5 :18. Fink HA, Litwack-Harrison S, Taylor BC, Bauer DC, Orwoll ES, Lee CG, Barrett-Connor E, Schousboe JT, Kado DM, Garimella PS et al : Clinical utility of routine laboratory testing to identify possible secondary causes in older men with osteoporosis: the Osteoporotic Fractures in Men (MrOS) Study . Osteoporos Int 2016, 27 (1):331–338. Additional Declarations No competing interests reported. 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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-1531410","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":97327290,"identity":"def094eb-4f76-4809-9b98-5ac3d96e3e3f","order_by":0,"name":"Xiangxu Chen","email":"","orcid":"","institution":"Zhongda Hospital, Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Xiangxu","middleName":"","lastName":"Chen","suffix":""},{"id":97327291,"identity":"12aee6e2-5e2c-43c6-94f1-d7ecb2ec97e2","order_by":1,"name":"Liyong Bai","email":"","orcid":"","institution":"Zhongda Hospital, Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Liyong","middleName":"","lastName":"Bai","suffix":""},{"id":97327292,"identity":"d61eaa8c-92b9-4121-bea3-95afdde14a72","order_by":2,"name":"Chuwei Tian","email":"","orcid":"","institution":"Zhongda Hospital, Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Chuwei","middleName":"","lastName":"Tian","suffix":""},{"id":97327293,"identity":"b9d54344-0b84-459c-9a0a-d77971638ea3","order_by":3,"name":"Yakuan Zhao","email":"","orcid":"","institution":"Zhongda Hospital, Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Yakuan","middleName":"","lastName":"Zhao","suffix":""},{"id":97327294,"identity":"0acb75d5-64fe-4819-81c2-f8c6caba20ce","order_by":4,"name":"Cheng Zhang","email":"","orcid":"","institution":"Zhongda Hospital, Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Zhang","suffix":""},{"id":97327295,"identity":"79b7f0cc-3d22-4397-8721-6fce75d2c21a","order_by":5,"name":"Liu Shi","email":"","orcid":"","institution":"Zhongda Hospital, Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Shi","suffix":""},{"id":97327296,"identity":"dcd0d70d-b8c2-49a5-8e13-0756bcbf0401","order_by":6,"name":"Yuanwei Zhang","email":"","orcid":"","institution":"Zhongda Hospital, Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Yuanwei","middleName":"","lastName":"Zhang","suffix":""},{"id":97327297,"identity":"269da5c1-69ab-4f78-9fd6-9798e88f00c6","order_by":7,"name":"Wenjun Xie","email":"","orcid":"","institution":"Zhongda Hospital, Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Wenjun","middleName":"","lastName":"Xie","suffix":""},{"id":97327298,"identity":"e1ba8762-5388-4326-a117-36479285b2cc","order_by":8,"name":"Hui Chen","email":"","orcid":"","institution":"Zhongda Hospital, Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Chen","suffix":""},{"id":97327299,"identity":"59175aae-47b9-480e-813e-1010ee9ecafc","order_by":9,"name":"Yunfeng Rui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFACxgYgIQfEzAcOfKggXosxELMlHpxxhnirQFp4jA/zthCh1nxGcps07w4DOXP+NR8O8DYwyPOLHcCvReZGIlDLGQNjyxlvNxyQ3MFgOHN2An4tEhIgLW1/EjfcOLvhgOEZhgSD28RpMajfcOPMgwOJbSRoSTA438Nw4CBRWngeNlvObTMw3HCDzeBgwxkJIvzCnv7wxts2A3mD84cff/5TYSPPL01ACxCwSEA0g1VKEFQOAswfwBT/AaJUj4JRMApGwQgEAAVZSGjS+uUcAAAAAElFTkSuQmCC","orcid":"","institution":"Zhongda Hospital, Southeast University","correspondingAuthor":true,"prefix":"","firstName":"Yunfeng","middleName":"","lastName":"Rui","suffix":""}],"badges":[],"createdAt":"2022-04-07 01:14:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1531410/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1531410/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":20199288,"identity":"1800776c-7743-4262-9619-7eaf1a52aa0e","added_by":"auto","created_at":"2022-04-11 15:13:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":161398,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences of age, BMI, Ca, P, ALB, ALP, AST, ALT, TG, TC, HDL and LDL in male patients. (A) presented the group of 65-79 years old man; N(N)=35, N(OPA)=50, N(OP)=21; (B) presented the group of ≥80 years old man; N(N)=13, N(OPA)=31, N(OP)=28. N: normal bone mass; OPA: osteopenia; OP: osteoporosis. *P\u0026lt;0.05, **P\u0026lt;0.01 and ***P\u0026lt;0.001 as different significance levels.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1531410/v1/8df1e937e01f65dfdaeff0dd.png"},{"id":20199137,"identity":"4432efc0-dfca-40f1-b1b9-fb930074c220","added_by":"auto","created_at":"2022-04-11 15:08:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160561,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences of age, BMI, Ca, P, ALB, ALP, AST, ALT, TG, TC, HDL and LDL in female patients. (A) presented the group of 65-79 years old female; N(N)=19, N(OPA)=65, N(OP)=104; (B) presented the group of ≥80 years old female; N(N)=17, N(OPA)=38, N(OP)=99. N: normal bone mass; OPA: osteopenia; OP: osteoporosis. *P\u0026lt;0.05, **P\u0026lt;0.01 and ****P\u0026lt;0.0001 as different significance levels.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1531410/v1/a828b1e64b42908d814c8c23.png"},{"id":20199139,"identity":"1667fe42-8aaa-45d0-9dac-eccfcc363e7f","added_by":"auto","created_at":"2022-04-11 15:08:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56754,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between BMI and TG in male and female patients. (A) presented the group of 65-79 years old male patients; N=106; (B) presented the group of ≥80 years old male patients; N=72; (C) presented the group of 65-79 years old female patients; N=188; (D) presented the group of ≥80 years old female patients; N=154. *P\u0026lt;0.05 as a different significance level.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1531410/v1/83583a16fcdd28043982a856.png"},{"id":30366589,"identity":"9e076d82-029d-4800-91f4-ae0a5d94aaa6","added_by":"auto","created_at":"2022-12-15 11:29:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1404794,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1531410/v1/88ff6848-cfd7-4791-8516-78a6b2398854.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Weight loss in elderly orthopaedic patients leads to bone loss","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe number of osteoporotic or low bone mass patients is increasing annually. More than 33% of patients aged 50 years or older are affected by osteoporosis in China [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Osteoporosis is characterized by decreased bone mineral density (BMD) and damaged bone micro-architecture, leading to a low bone mass and increased bone fragility, which increases the risk of fracture [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Many factors contribute to osteoporosis, including age, gender, lifestyle, and diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Dual-energy X-ray absorptiometry (DXA) together with T-score is considered a very important and widely used method for diagnosing osteoporosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Normally, osteoporosis is defined by the T-score (normal [\u0026thinsp;\u0026ge;\u0026thinsp;\u0026minus;\u0026thinsp;1.0], osteopenia [\u0026minus;\u0026thinsp;1.0 to \u0026minus;\u0026thinsp;2.5], and osteoporosis [\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;2.5] ) according to the recommendations of the World Health Organization (WHO) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, many studies have investigated risk factors for osteoporosis, including body mass index (BMI), serum lipid profiles, serum calcium (Ca) and phosphorus (P), serum albumin (ALB), alkaline phosphatase (ALP), serum alanine transaminase (ALT) and aspartate aminotransferase (AST) [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have pointed out the association between obesity and osteoporosis, and that obesity may be a protective factor for osteoporosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Obese patients have higher mechanical loading on the bones which is beneficial for bone mass [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)is correlated with the percentage of body fat [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Weight loss is harmful to musculoskeletal health [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and has been reported to be associated with bone loss, as well as being a strong predictor of osteoporosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Older women who lost weight showed increased bone loss in the hip [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Another study reported that weight loss was related to hip-bone loss in older men and women [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Plasma lipid profiles have also been shown to change in response to weight loss [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Weight loss may thus influence bone and lipid metabolism. However, the relationship between weight loss, BMD, and lipid profiles remains unclear. The aim of our study was to investigate the relationship between weight loss and bone loss and the correlation between lipid profiles and bone mass, and was to explore the association between lipid metabolism and bone metabolism. In our study, we collected and analysed the clinical data from 520 patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eIn this retrospective observational study, all patients were fractured inpatients at the Trauma Centre of Zhongda Hospital from 2017 to 2020 and aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. We excluded the patients with cancer, thyroid disease, hypopituitarism, rheumatoid arthritis and chronic renal failure or renal dysfunction as well as patients accepted a lipid-lowering therapy, synthyroid or hormone-replacement therapy. All clinical data for analysis in our study could be obtained. Patients were classified by gender (male and female), age (65\u0026ndash;79 years and \u0026ge;\u0026thinsp;80 years), and T-score: normal (\u0026thinsp;\u0026ge;\u0026thinsp;\u0026minus;\u0026thinsp;1.0), osteopenia (\u0026minus;\u0026thinsp;1.0 to \u0026minus;\u0026thinsp;2.5), and osteoporosis (\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;2.5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eAge, gender, height, and weight were recorded from patient documents. BMI was calculated using the weight and height (kg/m\u003csup\u003e2\u003c/sup\u003e). Femoral neck BMD and T-scores were investigated using a DXA scanner. According to the WHO classification, the T-score was used to define the categories of BMD. Serum samples were collected immediately after the patients were admitted and serum levels of Ca, P, ALB, AST, ALT, ALP, triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) were analysed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). IBM SPSS statistics version 25 software and GraphPad Prism version 8.4.0 software were used to analyse the data. After analysing the normality using the Shapiro-Wilk test, data were evaluated by the t-test and non-parametric test between two groups. For three groups, data were evaluated by one way ANOVA. Multivariate analysis was performed by multiple linear regression. Person correlation test was used to analyze the association between BMI and TG. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and ****P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 were defined as different significance levels.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, 520 patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years were included in our study. A total of 178 male patients were investigated, including 48 normal, 81 osteopenic and 49 osteoporotic patients. A total of 342 female patients were included in our study, including 36 normal, 103 osteopenic subjects, and 203 osteoporotic patients (Table\u0026nbsp;1). Age, gender, BMI and concentrations of ALP and TG were significantly differences among the groups of normal, osteopenic and osteoporotic patients. Moreover, age, gender, BMI and concentrations of ALP and TG were the independent factors for osteoporosis (Table\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1: Univariate and multivariate analysis of related factors for osteoporosis\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eCND: Coronary Heart Diseases.\u003c/p\u003e\n\u003cp\u003ePatients were divided into two groups according to age (65\u0026ndash;79 years and \u0026ge;\u0026thinsp;80 years). In order to analyse the differences in gender, a total of 106 men and 188 female were investigated in the 65\u0026ndash;79 year age group (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). As shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, there were significant differences in the T-score and the concentrations of Ca, P, ALB, ALP, TC, HDL and LDL between male and female patients. However, male patients showed no difference in age, BMI, and the concentrations of AST, ALT and TG in comparison with female patients.\u003c/p\u003e\n\u003cp\u003eTable 2: Male and female patients aged between 65 to 79 years old in related factors for osteoporosis\u003c/p\u003e\n\u003ctable align=\"left\" border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003e(n=106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e(n=188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eAge (Y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e71.25\u0026plusmn;4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e72.18\u0026plusmn;4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eT-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e-1.51\u0026plusmn;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e-2.5\u0026plusmn;1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003ep＜0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e24.33\u0026plusmn;3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e23.63\u0026plusmn;3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eCa (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e2.21\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e2.25\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eP (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e1.00\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e1.06\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e38.40\u0026plusmn;4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e39.67\u0026plusmn;4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e69.29\u0026plusmn;39.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e77.79\u0026plusmn;35.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e26.86\u0026plusmn;18.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e27.30\u0026plusmn;19.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e40.06\u0026plusmn;27.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e38.94\u0026plusmn;28.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e1.39\u0026plusmn;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e1.45\u0026plusmn;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e4.15\u0026plusmn;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e4.52\u0026plusmn;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eHDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e1.13\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e1.33\u0026plusmn;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003eP＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eLDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.966101694915253%\"\u003e\n \u003cp\u003e2.42\u0026plusmn;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.129943502824858%\"\u003e\n \u003cp\u003e2.68\u0026plusmn;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.903954802259886%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cbr\u003eA total of 72 men and 154 women aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years were examined in our study. The T-score was significantly different in male patients compared to female patients. There were no significant differences in BMI and concentrations of Ca, P, ALB, ALP, AST, ALT, TG, TC, HDL and LDL between male and female patients (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003eTable 3: Male and female patients aged \u0026ge;80 years old in related factors for osteoporosis\u003c/div\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003e(n=172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e(n=154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eAge (Y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e85.60\u0026plusmn;4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e85.65\u0026plusmn;33.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eT-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e-2.06\u0026plusmn;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e-2.94\u0026plusmn;1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eP＜0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e22.34\u0026plusmn;3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e21.68\u0026plusmn;3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eCa (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e2.20\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e2.21\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eP (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e1.01\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e1.09\u0026plusmn;0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e38.19\u0026plusmn;4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e38.04\u0026plusmn;4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e56.21\u0026plusmn;38.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e52.95\u0026plusmn;40.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e23.61\u0026plusmn;9.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e25.08\u0026plusmn;10.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e45.61\u0026plusmn;28.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e54.45\u0026plusmn;36.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e1.07\u0026plusmn;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e1.08\u0026plusmn;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e4.03\u0026plusmn;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e4.20\u0026plusmn;1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eHDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e1.17\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e1.21\u0026plusmn;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003eLDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.43988684582744%\"\u003e\n \u003cp\u003e2.31\u0026plusmn;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.712871287128714%\"\u003e\n \u003cp\u003e2.29\u0026plusmn;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.923620933521924%\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the group of 65\u0026ndash;79 year-old male patients, it was shown that BMI was decreased in osteoporotic compared to normal bone mass patients. Moreover, TG and TC levels also declined in patients with osteoporosis compared to those with normal bone mass. Osteoporotic patients showed no significant differences in Ca, P, ALB, ALP, AST, ALT, HDL and LDL levels compared to normal bone mass patients. In men aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years, BMI was significantly lower in osteoporotic than in normal bone mass patients. We also observed decreased TG levels in osteoporotic patients compared to those in normal bone mass patients. Ca, P, ALB, ALP, AST, ALT, TC, HDL and LDL levels were not significantly different between patients with and without osteoporosis (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn the group of 65\u0026ndash;79 year-old females, BMI, TG and TC were significantly decreased in osteoporotic patients in comparison with normal bone mass patients. Osteoporotic patients also showed no significant differences in Ca, P, ALB, ALP, AST, ALT, HDL and LDL levels compared to normal bone mass patients. In the group of women aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years, osteoporotic patients showed significantly decreased BMI and TG levels as well as increased ALP levels. We also did not find significant differences in the levels of Ca, P, ALB, AST, ALT, TC, HDL and LDL in osteoporotic patients compared with normal bone mass patients (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFurthermore, we found positive correlation between BMI and TG levels in the groups of male patients aged 65\u0026ndash;79 and \u0026ge;\u0026thinsp;80 years. Female patients also showed positive correlation between BMI and TG levels in the groups of 65\u0026ndash;79 and \u0026ge;\u0026thinsp;80 year-old patients (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we analysed the clinical data of 520 patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. We found that osteoporotic patients showed significantly decreased BMI and decreased TG levels in comparison with normal bone mass patients. The number of patients diagnosed with osteoporosis is increasing annually and previous studies have shown that many factors contribute to osteoporosis, including age, gender, BMI, and levels of Ca, P, ALB, ALP, AST, ALT, TG, TC, HDL and LDL.\u003c/p\u003e \u003cp\u003ePrevious studies reported that many factors contribute to osteoporosis. In our studies, we found that age, gender, ALP and TG were independent factor for osteoporosis. Bone mass declined with age. In line with our study, many studies showed increased age contributing to bone mass loss. Based on this, we classified the groups according to the age. In our study, female patients in the 65\u0026ndash;79 and \u0026ge;\u0026thinsp;80 year age groups presented lower T-scores than male patients. In accordance with our results, men have higher peak BMD and higher BMD at a later age than women[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Significant differences were observed in the levels of Ca, P, ALB, ALP, TC, HDL and LDL between men and women in the 65\u0026ndash;79 year age group. These results indicate that men and women present differences in many factors for osteoporosis. Cui et al. supported our results in their analysis of 1035 men and 3953 women where they also showed significant differences in BMI and T-score, as well as in levels of TG, TC, high-densitylipoprotein cholesterol (HDL-C), and low-densitylipoprotein cholesterol (LDL-C) between men and women [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have described the correlation between lipid profiles, BMI, and BMD[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In our study, we found that male and female osteoporotic patients showed significantly decreased BMI in the groups aged 65\u0026ndash;79 and \u0026ge;\u0026thinsp;80 years. Similarly, many studies have reported an association between BMI and BMD. Alay et al. investigated 452 postmenopausal women inan outpatient clinic between 2012 and 2015, and observed decreased BMI related to lower BMD[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In their study, Fawzy et al. analysed 101 men and womenand found that a declinein BMI was associated with lower BMD[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, BMI is a weight-change profile[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These results suggest that weight loss is associated with bone loss. Moreover, our study demonstrated that male and female osteoporotic patients presented obviously decreased levels of TG in the groups aged 65\u0026ndash;79 years and \u0026ge;\u0026thinsp;80 years. In addition, male and female osteoporotic patients also showed obviously decreased levels of TC in the groups aged 65\u0026ndash;79 years. Our study findings were in agreement with those of Cui et al. who showed that BMI and TG levels were significantly lower in patients with osteoporosis than in those with normal BMD. However, there was no significant difference in the TC levels [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. They also described an association between osteoporosis and levels of HDL, LDL, TG, and TC[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; however, we did not find any significant differences in the levels of HDL and LDL between the two groups in our study. In a meta-analysis, Chen et al. selected ten publications and investigated the relationship between lipid profiles (HLD, LDL, TG, and TC) and osteoporosis in postmenopausal women, and found significantly higher levels of HDL and TC in postmenopausal osteoporosis patients than in the normal BMD group. Although they did not show any significant difference in TG levels, postmenopausal osteoporosis patients presented a lower level of TG[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Alay et al. did not observe significant differences in the levels of HDL, LDL, TG, and TC[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, Bijelic et al. evaluated lipid profiles and BMI in 100 postmenopausal osteoporotic and normal BMD patients. They proved that osteoporosis in postmenopausal patients was associated with the levels of LDL, TC, and TG. Contrary to our results, they showed an increased level of TG in osteoporotic patients than in those with a normal BMD[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The reason might be that, in their study, there were 72 overweight patients in the osteoporosis group and 54 overweight patients in the normal BMD group. BMI and TG were showed independent factors for osteoporosisin in our study. Osteoporotic male and female patients aged 65\u0026ndash;79 and \u0026ge;\u0026thinsp;80 years had lower BMI and decreased TG levels compared with patients of normal bone mass. Moreover, we also observed a positive correlation between BMI and TG levels in the groups of male and female patients aged 65\u0026ndash;79 and \u0026ge;\u0026thinsp;80 years. These results suggest that weight loss is related to the decreased TG levels, which might contribute to bone loss.\u003c/p\u003e \u003cp\u003eIt has been reported that lipids may be a predisposing factor for osteoporosis and are associated with bone fragility[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and TG metabolism is considered to be correlated with bone metabolism. Ando et al. investigated serum TG levels and N-telopeptide of type I collagen (uNTx) \u0026ndash; a bone resorption marker \u0026ndash; levels in 113 Japanese middle-aged and elderly women, and found that patients with lower serum TG levels had higher uNTx levels, indicating that low serum TG levels might affect osteoclast activity, leading to bone loss[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, Dragojevic et al. performed a gene expression study by analysing bone tissue in 50 patients with osteoporosis and 62 controls. They reported that osteoporosis patients had lower osteoblastogenesis, higher osteoclastogenesis, and lower TG metabolism than controls[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These results indicate an association between TG metabolism and bone metabolism. However, the underlying mechanism between TG metabolism and bone metabolism remains unclear, and more studies should be performed in the future.\u003c/p\u003e \u003cp\u003eIn addition, we did not observe significant differences in other factors for osteoporosis, such as levels of Ca, P, AST, ALT and ALB between the osteoporosis and control groups in our study. Serum levels of Ca and P were normal in postmenopausal women with or without osteoporosis. This may be because osteoporosis leads to lower bone mineralisation, rather than to changes in the ratio of bone mineral to organic matrix. In previous studies, nutrition was reported to be related to osteoporosis, and serum levels of ALB were lower in patients with osteoporosis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. By analysing the serum ALB of 15539 individuals, hypoalbuminemia (serum ALB less than 3.5 g/dL) was found to be associated with osteoporosis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, our study showed that serum levels of ALB were more than 35g/L in normal bone mass patients and osteoporotic patients. This might be the reason that serum ALB is not a factor for diagnosing osteoporosis in our study. AST and ALT were liver enzymes, and they were reported the association with osteoporosis. In agreement with our study, Do et al. selected 7160 subjects to analyze the association between liver enzymes and BMD in Koreans. They did not show the differences in AST and ALT with femur BMD[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The reason might be that we did not select patients with liver diseases. In our study, ALP was an independent factor for osteoporosis, and osteoporotic female patients aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years showed significantly increased ALP levels. Serum levels of ALP are considered an important method for the diagnosis of some bone diseases, including osteomalacia and Paget\u0026rsquo;s disease. Increased serum ALP levels in postmenopausal women was reported by high bone turnover[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Serum levels of ALP\u0026thinsp;\u0026gt;\u0026thinsp;129 U/L are helpful for diagnosing osteoporosis in men[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis is a single-center retrospective study with a small sample size. Further multicenter and prospective studies, with larger samples, are needed to prove the association between TG metabolism and bone metabolism.\u003c/p\u003e \u003cp\u003eIn conclusion, osteoporotic patients showed significantly decreased BMI and TG levels in comparison with normal bone mass patients in our study. These results indicate an association between TG metabolism and bone metabolism and provide a new method for the treatment of osteoporosis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI: body mass index; BMD: bone mineral density; DXA: dual-energy X-ray absorptiometry; Ca: calcium; P: phosphorus; ALB: albumin; TG: triglyceride; TC: cholesterol; ALP: alkaline phosphatase; AST: aspartate aminotransferase; ALT: alanine transaminase; WHO: World Health Organization; HDL-C: high-density lipoprotein \u0026nbsp;cholesterol; LDL-C: low-density lipoprotein \u0026nbsp;cholesterol; CHD：Coronary Heart Diseases; uNTx: N-telopeptide of type I collagen\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXXC and YFR designed the study. LYB contributed to data collection. XXC and CWT performed statistical analyses. XXC, LYB, YKZ and WJX drafted the manuscript. YFR, CZ, LS, YWZ and HC revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no funding or support for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used and analyzed during the current study are included in this published article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the IEC for Clinical Research of Zhongda Hospital, Affiliated to Southeast University, and the requirement for informed consent was waived due to the retrospective design of the study.\u0026nbsp;All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eChen P, Li Z, Hu Y: \u003cstrong\u003ePrevalence of osteoporosis in China: a meta-analysis and systematic review\u003c/strong\u003e. 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Osteoporos Int 2016, \u003cstrong\u003e27\u003c/strong\u003e(1):331\u0026ndash;338.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Osteoporosis, Weight loss, Elder patients, Body Mass Index, Lipid profiles","lastPublishedDoi":"10.21203/rs.3.rs-1531410/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1531410/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe number of patients with osteoporosis or low bone mass is increasing annually. Weight loss is reportedly associated with bone loss and is a strong predictor of osteoporosis. It was still not clear the relationship between weight loss and bone loss in the elder.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe study included 520 patients aged ≥65 years (178 men and 342 women). Age, gender, weight, and height were recorded. Femoral neck bone mineral density and T-score were investigated using a dual-energy X-ray absorptiometry scanner. Blood calcium (Ca), phosphorus (P), albumin (ALB), alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), triglyceride (TG), total cholesterol (TC), high-density lipoprotein\u0026nbsp;(HDL) and low-density lipoprotein\u0026nbsp;(LDL) levels were measured. Patients were classified by gender (male and female), age (65-79 years and ≥80 years), and T-score: normal, osteopenia and osteoporosis.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAge, gender, body mass index (BMI), ALP and TG were independent factors for osteoporosis. For the 65-79 and ≥80 year age groups, female patients presented lower T-scores than males. Ca, P, ALB, ALP, TC, HDL and LDL levels were significantly different between men and women in the 65-79 year age group. In addition, BMI and TG levels were significantly decreased in osteoporotic patients compared with normal bone mass patients. TC levels declined in 65-79 year-old male and female patients with osteoporosis. In the group of women aged ≥80 years, osteoporotic patients showed significantly increased ALP levels. Furthermore, we found positive correlations between BMI and TG levels in the groups of male and female patients. However, we found no significant differences in ALB, Ca, P, HDL and LDL levels in osteoporotic compared to normal bone mass patients.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOsteoporotic patients showed significantly decreased BMI and TG levels compared with those with normal bone mass. BMI was showed positive correlations with TG levels in male and female patients. These results indicate a correlation between weight loss and bone loss and a correlation between lipid profiles and bone mass.\u003c/p\u003e","manuscriptTitle":"Weight loss in elderly orthopaedic patients leads to bone loss","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-04-11 15:08:45","doi":"10.21203/rs.3.rs-1531410/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":"0575991a-9f89-4b06-862d-123f3345c06b","owner":[],"postedDate":"April 11th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-12-15T11:29:32+00:00","versionOfRecord":[],"versionCreatedAt":"2022-04-11 15:08:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1531410","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1531410","identity":"rs-1531410","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}