Lower Geriatric Nutritional Risk Index Are Associated with A Higher Incidence Of Osteoporosis In Northern China Type 2 Diabetes

Lower Geriatric Nutritional Risk Index Are Associated with A Higher Incidence Of Osteoporosis In Northern China Type 2 Diabetes | 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 Lower Geriatric Nutritional Risk Index Are Associated with A Higher Incidence Of Osteoporosis In Northern China Type 2 Diabetes Yuan yuan Ji, Nan Geng, ying chun niu, Hang Zhao, wen jie fei, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-858125/v2 This work is licensed under a CC BY 4.0 License Status: Under Review Version 2 posted 5 You are reading this latest preprint version Show more versions Abstract Background Osteoporosis (OP) is very common in the elderly population and can lead to fractures and disability. Malnutrition is associated with osteoporosis. The Geriatric Nutrition Risk Index (GNRI) is used to assess the risk of malnutrition and complications associated with nutritional status in older patients and is an important predictor of many diseases. Therefore, we investigated the relationship between GNRI and osteoporosis in patients with type 2 diabetes to help us prevent and detect osteoporosis in time. Methods Retrospective study of 610 elderly patients with type 2 diabetes. Collection of general and laboratory data on the patient, together with measurement of bone mineral density (BMD). The GNRI was calculated based on ideal body weight and serum albumin (ABL) levels. Correlation analysis was applied to determine the relationship between GNRI and BMD and bone metabolism indexes. The predictive value of GNRI for the development of osteoporosis was analyzed by logistic regression analysis and creating a Receiver Operating Characteristic Curve (ROC), calculating the area under the cure (AUC). Results All patients divided into the no nutritional risk group and the nutritional risk group. Compared with the non-nutritional risk group, the nutritional risk group had longer diabetes course, older age, higher HbA1c and higher prevalence of osteoporosis; lower BMI, ABL, FCP, TG, Ca, 25 (OH) D, PTH and lower femoral neck BMD ( P <0.05). Classified in ascending order of GNRI quartiles (Q1-Q4), lower GNRI among type 2 diabetic patients are associated with a higher Incidence of osteoporosis. All patients were grouped with non-osteoporosis group and osteoporosis group. Patients in the non-osteoporotic group had higher GNRI values compared to the osteoporotic group( P <0.05). Correlation analysis revealed that GNRI was positively correlated with lumbar BMD, femoral neck BMD and total hip BMD ( P <0.05). After adjusting confounding factors, Spearman's correlation analysis was concluded that GNRI was positively correlated with Ca, 25(OH)D, PTH and negatively correlated with ALP and PINP. Regression analysis yields, GNRI was significantly associated with osteoporosis. ROC curve analysis was performed with GNRI as the test variable and the presence of osteoporosis as the status variable. The analysis yielded an area under the curve for GNRI of 0.695 and was statistically significant ( P < 0.05), with an optimal GNRI threshold of 99.56 for predicting osteoporosis. Conclusions: Lower Geriatric Nutritional Risk Index among type 2 diabetic patients in northern China are associated with a higher Incidence of osteoporosis. Geriatric Nutrition Risk Index osteoporosis BMD type 2 diabetes mellitus Figures Figure 1 Figure 2 Figure 3 Figure 4 1.introduction Osteoporosis is a prevalent disease among the older adults. As we age, osteoporosis and the increased risk of falls can lead to fractures, which can severely impact on people's quality of life and significantly increase the risk of hospitalization and death [ 1 ]. The incidence of diabetes is increasing as people's dietary patterns shift towards higher energy levels. Hyperglycemia itself increases the production of advanced glycation end products and negatively affects bone mineralization, bone remodeling, also bone strength [ 2 ]. Diabetic complications can also make the risk of osteoporosis much higher [ 3 ]. The findings show that the prevalence of diabetic osteoporosis accounts for approximately more than 50% of diabetic patients [ 4 ]. Moreover, the adverse outcomes after fracture are more severe in diabetic patients than in normoglycemic patients, so it is important to identify and detect high-risk groups early in elderly T2DM patients [ 5 ]. Risk factors for osteoporosis include age, gender, vitamin D levels, muscle strength and nutritional status. The elderly are prone to malnutrition due to their specific metabolic characteristics and disease, and much evidence that malnutrition is an independent risk factor for elderly patients with osteoporosis; studies have shown that low body weight, reduced albumin, and prealbumin can lead to an increased incidence of osteoporotic fractures [ 6 , 7 ].GNRI is calculated based on the ratio between serum albumin and actual weight to ideal weight, assessing the nutritional condition of older adults; According to the results, there are 4 levels: GNRI < 82 indicates high nutritional risk, GNRI:82 to 98 is no risk [ 8 ]. GNRI allows for early detection and diagnosis of malnutrition, timely and appropriate interventions, as well as the identification of conditions at risk for adverse effects, including cancer prognosis, postoperative complications, and mortality in dialysis patients and cardiovascular disease [ 9 – 13 ], and is highly accurate and easy to use clinically. To our knowledge there are fewer studies on the correlation between GNRI and osteoporosis. Liang Wang et al [ 14 ] showed that GNRI is associated with osteoporosis as well as BMD in T2DM patients. Their study population consisted of patients from the Second Hospital of Wenzhou Medical University and Yu Ying Children's Hospital, who had adequate vitamin D. However, in northern China, Majority of population is vitamin D insufficient or deficient; therefore, the study by Wang Liang et al. is not fully representative of the type 2 diabetic population in China. This paper investigates the relationship between GNRI and the development of osteoporosis in northern T2DM and assesses the predictive properties of GNRI for osteoporosis. 2. Topics And Materials 2.1 Topics This study was a cross-sectional observation of all included patients, 610 patients with T2DM which aged ≥ 60 (317 men and 293 women) treated at the Hebei General Hospital from January 2018 to December 2020.The diagnostic criteria of T2DM was based on the 1999 WHO. Exclusion criteria for participants were as follows: (1) individuals with diseases affecting bone metabolism or affect nutritional status, such as malignancies, severe liver diseases, kidney diseases, pituitary-related diseases, thyroid and parathyroid diseases, adrenal diseases, rheumatoid arthritis, acute inflammatory diseases, etc. (2) individuals who are bedridden for long periods of time; (3) individuals who are also taking drugs that affect bone metabolism, such as vitamin D, calcium, bisphosphonates, glucocorticoids, etc. The study was approved by the Ethics Committee of the Hebei Provincial People's Hospital and complies with the Declaration of Helsinki. 2.2 Clinical Information Collection and Laboratory Measurements Patient demographics and clinical characteristics, including information on gender, age, disease duration, and comorbidities, were collected from medical records. Weight was measured while wearing light clothing and height was measured without shoes. Body weight divided by height squared (kg/m 2 ) was used to calculate the body mass index for each patient. Serum samples were collected after fasting (at least eight hours). Triglycerides, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol and albumin levels, glucose metabolism indicators, fasting blood glucose, glycosylated hemoglobin; bone metabolism indicators, alkaline phosphatase, β-CTX, collagen type I N-peptide, 25-hydroxyvitamin D, PTH were measured. In addition, other biochemical markers, such as uric acid, blood creatinine and calcium were tested. Bone densitometry was performed using a dual-energy X-ray bone densitometer to measure bone density values in the lumbar spine (L1-4), femoral neck, and total hip. According to the criteria for the definition of osteoporosis in the 1994 WHO, T values ≤-2.5 standard deviations were obtained for any part of the lumbar spine, femoral neck or total hip. 2.3 Calculating GNRI The GNRI calculation formula is as follows: GNRI = [1.489 × albumin (g/dL)] + [41.7 × (body weight/WL0)]. WL0 represented the ideal body weight (kg) is calculated as follows : For men: height (cm)- 100- [(height (cm) − 150)/4] For women: height (cm)-100- [(height (cm)-150)/2.5] Actual weight divided by ideal weight was set to 1 when actual weight exceeded the ideal weight [ 15 ]. 3. Statistical Analysis All data were statistically analyzed using SPSS 25. Data distribution was evaluated with the Kolmogorov-Smirnov test. Mean ± standard deviation was used to indicate that the data were subject to a normal distribution, analysis of variance (ANOVA) was used for comparisons between groups. The median (25th percentile, 75th percentile) was used to indicate that the data did not conform to a normal distribution; analysis of Kruskal-Wallis was used for comparisons between groups. Categorical data were expressed as frequencies (%), and differences between groups were determined using the χ2 test, Spearman correlation analysis was applied to determine the relationship between GNRI and BMD and each clinical index. Logistic regression analysis was performed to assess the relationship between GNRI and osteoporosis. ROC curves were applied to assess the predictive properties of GNRI for osteoporosis and calculate the area under the receiver operating characteristic curve (AUC). 4. Results 4.1 Clinical characteristics of the 610 patients. （Table 1）All patients were grouped according to the GNRI score and divided into the no nutritional risk group and the nutritional risk group. Compared with the non-nutritional risk group, the nutritional risk group had longer diabetes course, older age, lower BMI, lower ABL, higher HbA1c, lower FCP, lower TG, lower Ca, lower 25 (OH) D, lower PTH, lower femoral neck BMD, and higher prevalence of osteoporosis. The differences between the two groups were statistically significant ( P <0.05). Grouping of patients according to whether they have osteoporosis. （Figure 1）Classified in ascending order of GNRI quartiles (Q1-Q4), lower GNRI among type 2 diabetic patients are associated with a higher Incidence of osteoporosis. Table 1 Clinical characteristics of patients stratified by GNRI Variable GNRI＞98 GNRI≤98 P value (n=491) (n=119) male/female 259/232 54/65 0.149 Age (years) 66.00(63.00,69.00) 68.00(64.00,76.00) 0.000 Diabetes duration(years) 10.00(5.00,16.00) 15.00(6.00,20.00) 0.001 BMI (kg/m 2 ) 26.33士3.43 24.99士3.83 0.000 ALB (g/L) 42.36±2.57 35.90±2.35 0.000 HbA1c(mmol/L) 8.12(6.95,9.50) 9.25(7.48,10.80) 0.000 FPG (mmol/L) 6.45(5.48,7.85) 6.62(5.77,7.89) 0.738 FINS (mmol/L) 7.07(4.13,11.54) 5.38(2.89,16.55) 0.460 FCP (mmol/L) 1.85(1.23,2.64) 1.47(0.78,1.96) 0.020 TC (mmol/L) 4.28(3.25,5.35) 4.15(3.45,4.94) 0.331 TG (mmol/L) 1.25 (1.00,1.76) 1.11 (0.80,1.51) 0.001 HDL (mmol/L) 1.19(0.96,1.48) 1.14(0.91,1.40) 0.083 LDL (mmol/L) 2.77(1.78,3.53) 2.59(1.93,3.25) 0.261 Uric(mmol/L) 280.73(204.78,344.55) 253.10(197.50,330.45) 0.114 Ca(mmol/L) 2.30 (2.22,2.36) 2.19(2.10,2.28) 0.000 25(OH)D (ng/mL) 18.19(14.39,23.38) 14.83(11.71,19.59) 0.000 ALP(IU/L) 71.70(50.60,88.70) 73.70(55.53,93.63) 0.337 BGP (ng/mL) 12.71(9.89,16.40) 13.00(9.94,16.51) 0.951 β-CTX (ng/mL) 0.35(0.25,0.53) 0.36(0.25,0.51) 0.691 P1NP (ng/mL) 39.75(30.00,51.87) 42.10(31.68,57.39) 0.159 PTH (ng/mL) 37.82(28.46,48.07) 33.42(23.72,45.08) 0.004 BMD Total lumbar(g/cm 2 ) 0.89±0.16 0.85±0.16 0.140 Femur neck (g/cm 2 ) 0.83±0.16 0.78±0.13 0.023 Total hip (g/cm 2 ) 0.70±0.15 0.65±0.11 0.079 Osteoporosis% 19.8% 51.3% 0.000 （Table 2）All patients were grouped with non-osteoporosis group and osteoporosis group. Compared with the non-osteoporosis group and osteoporosis group had longer diabetes course, older age, lower BMI, lower ABL, lower FCP, lower UA, lower 25 (OH) D, higher BPG, higher P1NP, lower Total lumbar BMD, lower Femur neck BMD, lower Total hip BMD and lower GNRI. The differences between the two groups were statistically significant ( P <0.05). （Figure2）Patients in the non-osteoporotic group had higher GNRI values compared to the osteoporotic group( P <0.05). Table 2: Comparison of indicators between the non-osteoporosis group and osteoporosis group Variable non-osteoporosis osteoporosis P value (n=452) (n=158) male/female 263/189 50/108 0.000 Age (years) 66.00(63.00,69.00) 69(65.75,76.00) 0.000 Diabetes duration(years) 10.00(5.00,16.00) 14.00(7.00,20.00) 0.001 BMI (kg/m 2 ) 26.33士3.43 24.99士3.83 0.002 ALB (g/L) 41.68±3.41 39.44±3.63 0.000 HbA1c(mmol/L) 8.30(7.20,9.80) 8.20(6.90,9.80) 0.536 FPG (mmol/L) 6.47(5.48,7.96) 6.54(5.73,7.56) 0.677 FINS (mmol/L) 7.14(4.27,12.02) 5.81(2.89,10.84) 0.146 FCP (mmol/L) 1.88(1.28,2.63) 1.51(0.82,2.19) 0.026 TC (mmol/L) 4.28(3.36,5.35) 4.14(2.98,4.90) 0.066 TG (mmol/L) 1.23(0.98,1.71) 1.23(0.97,1.55) 0.272 HDL (mmol/L) 1.17(0.95,1.42) 1.24(0.94,1.66) 0.127 LDL (mmol/L) 2.77(1.93,3.52) 2.61(1.69,3.32) 0.135 Uric(mmol/L) 285.10(210.40,345.93) 268.00(174.30,320.00) 0.030 Ca(mmol/L) 2.29 (2.22,2.35) 2.28(2.21,2.37) 0.829 25(OH)D (ng/mL) 18.12(14.20,23.02) 15.31(11.77,21.68) 0.000 ALP(IU/L) 72.75(52.88,92.30) 70.80(44.60,88.40) 0.299 BGP (ng/mL) 12.44(9.81,15.54) 14.57(10.27,19.17) 0.001 β-CTX (ng/mL) 0.35(0.24,0.50) 0.36(0.23,0.61) 0.459 P1NP (ng/mL) 38.83(29.91,51.12) 45.52(31.94,60.51) 0.000 PTH (ng/mL) 36.96(27.84,46.73) 37.94 (23.89,50.02) 0.364 BMD Total lumbar(g/cm 2 ) 0.96±0.15 0.76±0.12 0.000 Femur neck (g/cm 2 ) 0.76±0.12 0.60±0.12 0.000 Total hip (g/cm 2 ) 0.89±0.14 0.74±0.13 0.000 GNRI 103.79(100.78,107.36) 99.45(96.63,103.92) 0.000 4.2 Spearman's correlations between the Geriatric Nutritional Risk Index and bone metabolism indicators. (Figure 3) Correlation analysis revealed that GNRI was positively correlated with lumbar BMD, femoral neck BMD and total hip BMD ( p <0.05). (Table 3) Spearman's correlation analysis yielded a positive correlation between GNRI and Ca, 25(OH)D, PTH. A negative correlation between GNRI and PINP. After adjusting for age, duration of diabetes, HbA1c, TC, TG, HDL, LDL, UA and other confounding factors, it was concluded that GNRI was positively correlated with Ca, 25(OH)D, PTH and negatively correlated with ALP and PINP. Table 3 Correlation between the Geriatric Nutrition Risk Index and indicators of bone metabolism Variables before adjusting after adjusting r P r P Ca(mmol/L) 0.501 0.000 0.386 0.000 25(OH)D (ng/mL) 0.275 0.000 0.210 0.000 ALP (IU/L) 0.036 0.383 -0.119 0.004 BPG (ng/mL) 0.011 0.786 -0.073 0.075 β-CTX (ng/mL) -0.043 0.291 -0.062 0.130 PINP (ng/mL) -0.087 0.033 -0.134 0.001 PTH (ng/mL) 0.127 0.002 0.115 0.005 Annotation：After adjusting for confounding factors, including age, duration of diabetes, HbA1c, TC, TG and UA 4.3 Logistic regression analysis of participants with osteoporosis （Table 4、Table 5）Analysis of the association between GNRI and osteoporosis using logistic regression, and the results are shown in Table 4. After adjusting for sex, age, duration of diabetes, and 25(OH)D, a significant association between GNRI and osteoporosis（Model I）.The risk of osteoporosis in the nutritional risk group was 3.35 times higher than the non-nutritional risk group（Model II）. Table 4: Univariate Logistic regression analysis of osteoporosis Variables SE Odds ratio (95% CI) P Gender（male） 0.196 3.006（2.048，4.412） 0.000 Age (years) 0.015 1.110（1.069，1.132） 0.000 Diabetes duration 0.012 1.037（1.014，1.061） 0.002 UA (mmol/L) 0.001 0.999（0.997，1.000） 0.089 TC (mmol/L) 0.052 0.928（0.838，1.029） 0.157 TG (mmol/L) 0.071 0.959（0.834，1.102） 0.553 Ca(mmol/L) 1.331 0.345（0.025，4.689） 0.424 25（OH）D (ng/mL) 0.013 0.970（0.946，0.996） 0.022 ALP (IU/L) 0.002 0.998（0.994，1.003） 0.515 P1NP (ng/mL) 0.004 1.014（1.006，1.022） 0.000 PTH (ng/mL) 0.005 1.010（1.000，1.020） 0.054 HbA1c (mmol/L) 0.050 0.993（0.900，1.094） 0.880 FPG (mmol/L) 0.111 0.942（0.758，1.171） 0.588 FINS (mmol/L) 0.019 1.004（0.976，1.043） 0.829 FCP (mmol/L) 0.186 0.758（0.572，1.091） 0.136 GNRI 0.018 0.885（0.854，0.917） 0.000 Table 5: Multivariate Logistic regression analysis of osteoporosis SE OR (95% CI) P Model I 0.020 0.907 (0.872,0.943) 0.000 Model II 0.238 3.352 (2.102,5.348) 0.000 Annotation：Gender，Age，Diabetes duration，25（OH）D，P1NP and GNRI are involve in the logistic multivariate regression analysis. SE, standard error. 4.4 Predictive properties of GNRI for osteoporosis （In Figure 4） ROC curve analysis was performed with GNRI as the test variable and the presence of osteoporosis as the status variable. The analysis yielded an area under the curve for GNRI of 0.695 and was statistically significant ( P < 0.05), with an optimal GNRI threshold of 99.56 for predicting osteoporosis, a sensitivity of 81.19%, and a specificity of 52.53%. 5. Discussion Originally used as an indicator to assess nutritional status in the elderly, GNRI is calculated from albumin, weight and height and is a dual assessment of serum albumin and BMI that complements and improves the accuracy of diagnosis. Good nutritional status plays a good role in bone metabolism, Similarly, malnutrition increases the incidence of osteoporosis and fragility fractures [ 16 ]. In the present study we looked at the correlation between GNRI and osteoporosis in northern T2DM patients and showed that there is a positive correlation between GNRI and osteoporosis, the group with lower GNRI values had a higher prevalence of osteoporosis. Divided the participants into osteoporotic and non-osteoporotic groups, and low GNRI values in the osteoporotic group compared to those in the non-osteoporotic group, and the difference was statistically significant. Linear correlation analysis revealed that GNRI was positively correlated with lumbar BMD, femoral neck BMD and total hip BMD ( P < 0.05). In the logistic regression analysis, Significant association of GNRI with osteoporosis. We evaluated the predictive effect of GNRI on osteoporosis using Roc curves. The analysis yielded a GNRI cut-off value of 99.56, similar to the findings of Liang Wang et al, they yielded cut-off value of 98.2 for predicting osteoporosis in men and 99.5 for predicting osteoporosis in women. Our results suggest a significant positive relationship between GNRI and osteoporosis and provide a theoretical basis for screening for osteoporosis in clinical practice. The current mechanism of association between GNRI and osteoporosis is considered to be possibly related to the following. First, malnutrition affects calcium and vitamin D intake, which may increase bone mineral loss in patients, making it difficult to mineralize bone and leading to the development of osteoporosis. Secondly, hypoalbuminemia is a marker of both nutritional status and chronic inflammatory response; hypoalbuminemia activates osteoclasts and inhibits osteoblasts through NF-κB factors, other inflammatory cytokines [ 17 ]; hypoalbuminemia causes a decrease in insulin-like growth factor-1 synthesis, which also leads to a decrease in the number of osteoblasts and a decrease in cellular activity, increased osteoclast lifespan, increased bone resorption, and bone decreased remodeling [ 18 ]. Finally, hypoproteinemia leads to inadequate muscle synthesis and decreased skeletal muscle mass, resulting in decreased balance and gait capacity, which can cause falls as well as the occurrence of fractures [ 19 , 20 ]. Our results yield that a high levels of GNRI have a protective effect on bone metabolism in patients. In contrast, no correlation between GNRI and 25(OH)D in the study by Liang Wang et al. Possible reasons for our different results are that in the south even though nutritional status leads to reduced vitamin intake, vitamin D levels can still be ensured with adequate sun exposure, while in the north insufficient sun exposure combined with nutritional barriers makes it more likely to cause vitamin D deficiency and deficiency. Studies show a close relationship between latitude sunlight deficiency, skin coverage and vitamin D. In China, there is a clear geographical division of vitamin D deficiency, with populations in northern, northeastern and northwestern China north of 35 degrees north latitude being more severely undernourished, while vitamin D levels are adequate in areas south of 25 degrees north latitude and in the middle of the country [ 21 , 22 ]. Our study also found that GNRI was negative correlated with P1NP, are contrary to the findings of Liang Wang et al. Possible reasons for the negative correlation between GNRI and PINP are that PINP secreted by osteoblasts is known to be a marker of bone formation reflecting collagen formation and osteoblast activation, and that during bone turnover, bone formation and resorption are tightly coupled, with accelerated bone turnover predisposing to bone loss; therefore, high GNRI values are associated with low bone turnover and reduced bone loss. For the relationship between GNRI and PTH, the study found a positive correlation between serum PTH and BMI, fat mass, and Mehrotra indicated that reduced PTH is a risk factor for malnutrition [ 23 , 24 ]. PTH can promote the inward flow of calcium ions into adipocytes and stimulate adipose synthesis, and accordingly, low levels of PTH inhibit adipose synthesis and cause protein depletion. In this study we yielded a higher GNRI value corresponding to a higher PTH. Among alkaline phosphatases, bone-specific alkaline phosphatase is closely related to normal bone growth and development, and is a marker of maturation and activity of osteoblasts. However, the specificity of our current assay is not good, and there is some crossover with liver-derived ALP, and we measure total ALP and not BALP; therefore, the relationship of ALP does not accurately reflect the level of bone metabolism. In a logistic regression analysis, advanced age was an independent risk factor for osteoporosis, with bone density decreasing year by year with increasing age, and oxidative stress, which increases osteoclast activity and bone resorption, is a cause of age-related bone loss. The present study similarly concluded that the duration of diabetes is an influential factor in osteoporosis in T2DM. It has been shown that the relative risk (RR) of diabetes duration increases from 1. 40 (1. 08 ~ 1. 82) at less than 5 years to 2. 66 (2. 04 ~ 3. 47) at greater than 15 years [ 25 ]. The non-enzymatic glycosylation response of type 2 diabetes is known to contribute to the decline in bone mass. Hyperglycaemia leads to the accumulation of glycosylation end products (AGE) in the organic bone matrix, resulting in stiffening of type I collagen in the bone matrix, decreased bone strength, increased bone fragility and promotion of osteoblast apoptosis [ 26 ]. Our results suggest that suboptimal glycemic control is not an independent risk factor for the development of osteoporosis, possibly because measured glycemic parameters only reflect recent levels of glycemic control and that measured BMD in type 2 diabetic patients does not fully reflect the state of impaired bone mass in type 2 diabetic patients. Furthermore, in our regression analysis of GNRI and osteoporosis, we concluded that uric acid was not associated with osteoporosis and was neither a protective nor a risk factor for osteoporosis. Several studies have concluded that higher UA levels are protective for osteoporosis [ 25 – 27 ]. Our difference with the results of these studies may be due to gender, region, ethnicity, study methodology and sample size. Finally, the association between serum UA and osteoporosis may be directly or indirectly confounded by the fact that many older adults suffer from two or more chronic diseases, such as obesity, diabetes mellitus, etc. This study has some limitations, firstly, the retrospective nature of this study, it does not provide a mechanism-related explanation for the observed association; and the study is a cross-sectional study and doesn't indicate a causal relationship between GNRI and bone mineral density. Second, the serum data of the patients with the disease, of which only one was collected, and the BMD of each location was also collected once, thus leading to bias. Third, some relevant parameters affecting the study results may have been overlooked in this study, such as history of smoking, alcohol consumption, hormone levels, dietary habits, exercise situation, and history of previous fractures. In summary, study results demonstrated that a lower GNRI is associated with increased osteoporosis and that GNRI is a convenient way to assess nutritional status and osteoporosis in patients with T2DM. Nutritional supplementation therapy may Reducing the incidence of osteoporosis in patients with T2DM. Declarations Ethics approval and consent to participate The ethics committee approved the study.(Hebei General Hospital Ethics Committee No.202137) All individuals participating in the study gave written informed consent. Consent for publication Not applicable Availability of data and materials The data that support the findings of this study are available from [third party name] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [third party name]. Competing interests No conflict of interest between the authors, includingAuthor Contributions Funding No Funding Acknowledgements We thank the staff of the Department of Endocrinology and Metabolism of the Hebei Provincial People's Hospital and all the patients who participated in the study. This work was supported by a grant from the Hebei Provincial Natural Science Foundation (project number H2019307114). The funder had no role in the design of this study. Collection, analysis and interpretation of data; or preparation of the manuscript. Author Contributions Yuanyuan JI:Writing articles and results analysis Nan Geng :Collecting and organizing data Yingchun Niu:Collecting and organizing data Hang Zhao:Technical and material support Wenjie Fei:Technical and material support Shuchun Chen:Technical and material support Luping Ren:Propose research idea, design research proposal References Cianferotti L, Bertoldo F, Bischoff-Ferrari H A, et al. 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Am J Clin Nutr, 2005,82(4):777-783. Durosier-Izart C, Biver E, Merminod F, et al. Peripheral skeleton bone strength is positively correlated with total and dairy protein intakes in healthy postmenopausal women[J]. Am J Clin Nutr, 2017,105(2):513-525. Zheng C M, Wu C C, Lu C L, et al. Hypoalbuminemia differently affects the serum bone turnover markers in hemodialysis patients[J]. Int J Med Sci, 2019,16(12):1583-1592. Afshinnia F, Wong K K, Sundaram B, et al. Hypoalbuminemia and Osteoporosis: Reappraisal of a Controversy[J]. J Clin Endocrinol Metab, 2016,101(1):167-175. Al-Jebawi A F, YoussefAgha A H, Al S H, et al. Attenuated PTH responsiveness to vitamin D deficiency among patients with type 2 diabetes and chronic hyperglycemia[J]. Diabetes Res Clin Pract, 2017,128:119-126. Sotunde O F, Kruger H S, Wright H H, et al. Lean Mass Appears to Be More Strongly Associated with Bone Health than Fat Mass in Urban Black South African Women[J]. J Nutr Health Aging, 2015,19(6):628-636. Gennari C. Calcium and vitamin D nutrition and bone disease of the elderly[J]. Public Health Nutr, 2001,4(2B):547-559. Man P W, van der Meer I M, Lips P, et al. Vitamin D status and bone mineral density in the Chinese population: a review[J]. Arch Osteoporos, 2016,11:14. Kamycheva E, Sundsfjord J, Jorde R. Serum parathyroid hormone level is associated with body mass index. The 5th Tromso study[J]. Eur J Endocrinol, 2004,151(2):167-172. Mehrotra R, Supasyndh O, Berman N, et al. Age-related decline in serum parathyroid hormone in maintenance hemodialysis patients is independent of inflammation and dietary nutrient intake[J]. J Ren Nutr, 2004,14(3):134-142. Koh W P, Wang R, Ang L W, et al. Diabetes and risk of hip fracture in the Singapore Chinese Health Study[J]. Diabetes Care, 2010,33(8):1766-1770. Alikhani M, Alikhani Z, Boyd C, et al. Advanced glycation end products stimulate osteoblast apoptosis via the MAP kinase and cytosolic apoptotic pathways[J]. Bone, 2007,40(2):345-353. Ahn S H, Lee S H, Kim B J, et al. Higher serum uric acid is associated with higher bone mass, lower bone turnover, and lower prevalence of vertebral fracture in healthy postmenopausal women[J]. Osteoporos Int, 2013,24(12):2961-2970. De Pergola G, Giagulli V A, Bartolomeo N, et al. Independent Relationship between Serum Osteocalcin and Uric Acid in a Cohort of Apparently Healthy Obese Subjects[J]. Endocr Metab Immune Disord Drug Targets, 2017,17(3):207-212. Makovey J, Macara M, Chen J S, et al. Serum uric acid plays a protective role for bone loss in peri- and postmenopausal women: a longitudinal study[J]. Bone, 2013,52(1):400-406. Cite Share Download PDF Status: Under Review Version 2 posted Reviewers agreed at journal 29 Jul, 2022 Reviewers invited by journal 25 Jul, 2022 Editor invited by journal 20 Jul, 2022 Editor assigned by journal 14 Jul, 2022 First submitted to journal 03 Jul, 2022 You are reading this latest preprint version Show more versions 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-858125","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":1,"date":"2021-09-13 21:43:15","editorialEvents":[{"type":"communityComments","content":1}],"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}}],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":127750127,"identity":"ba5c6043-181d-4598-b443-8fa0c8261573","order_by":0,"name":"Yuan yuan Ji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACxmYEs+FA4h8bHn72BqK1MB888LAhTUay5wDRFrIlH3zYcNjG4IYDfnXM7TxmEj93HE7sn91jcCBxx3kehhsMjB8+5uBzGI+ZZO+Zw4kz7pwBajlzm4dxdgOz5Mxt+LVI8LYdTmy4kWNwIIHtNg+zzAE2Zl4CWiT/ArXMh2g5x8MmkUBYizTIlg030hIOJLYd4OEhrIWt2Fq2Ld14443kAwcSziTzSPAcbMbrF8P+wxtvvm2zlp13I7H5448KO3v7480HP3zEp6WBwwBINSOLMTbgVg8E8gzsD4BUHV5Fo2AUjIJRMMIBAJJyWz7vOUfMAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1544-2234","institution":"Hebei General Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"yuan","lastName":"Ji","suffix":""},{"id":127750128,"identity":"c220a668-cdfb-44bf-bd9c-61d11b954083","order_by":1,"name":"Nan Geng","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Geng","suffix":""},{"id":127750129,"identity":"7ac3f06e-e151-4f3a-81bd-c3a70c016ab7","order_by":2,"name":"ying chun niu","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"ying","middleName":"chun","lastName":"niu","suffix":""},{"id":127750130,"identity":"96592297-1259-4931-ace7-a8fed8011782","order_by":3,"name":"Hang Zhao","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Zhao","suffix":""},{"id":127750131,"identity":"1ca8a958-a846-49ee-be8a-8d8ff08eec8f","order_by":4,"name":"wen jie fei","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"wen","middleName":"jie","lastName":"fei","suffix":""},{"id":127750132,"identity":"e4681210-a4b8-47a3-8110-c657fcd76185","order_by":5,"name":"Shu chun Chen","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shu","middleName":"chun","lastName":"Chen","suffix":""},{"id":127750133,"identity":"e82217bf-b3f0-40ed-968c-6a4582bcf6b5","order_by":6,"name":"Lu ping Ren","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lu","middleName":"ping","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2021-08-30 10:19:49","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-858125/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-858125/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25039158,"identity":"338d8ef8-77e1-4b30-8a7c-6153b1ef8107","added_by":"auto","created_at":"2022-08-10 14:47:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of osteoporosis prevalence at different GNRI levels\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-858125/v2/5879a2a9ca9f460ff1dcb67e.png"},{"id":25037950,"identity":"4ea57515-51ba-4923-8fba-7451b4e34d33","added_by":"auto","created_at":"2022-08-10 14:42:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15116,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe chart of GNRI score about the non-osteoporotic group and the osteoporotic group.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-858125/v2/ed7edd5bb5dbbede82fe515d.png"},{"id":25037952,"identity":"4ba378d2-75bd-4ce7-86f2-5f521c06e8f1","added_by":"auto","created_at":"2022-08-10 14:42:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79482,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3-1：Correlation between GNRI and Total lumbar spine BMD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e3-2：Correlation between GNRI and hip BMD\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e3-3：Correlation between GNRI and femoral neck BMD\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-858125/v2/21daa114631c388553b5c867.png"},{"id":25039636,"identity":"bee29c03-00d0-410c-901a-35b7da7d47f6","added_by":"auto","created_at":"2022-08-10 14:52:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":27433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Receiver Operating Characteristic Curve of osteoporosis\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-858125/v2/5e9517a2aebac4c99276b21c.png"},{"id":25039643,"identity":"e3e8401c-5fd6-450c-a659-9b010d9250dd","added_by":"auto","created_at":"2022-08-10 14:52:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":614219,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-858125/v2/833697ae-8207-4085-87fd-50548a702020.pdf"}],"financialInterests":"","formattedTitle":"Lower Geriatric Nutritional Risk Index Are Associated with A Higher Incidence Of Osteoporosis In Northern China Type 2 Diabetes","fulltext":[{"header":"1.introduction","content":"\u003cp\u003eOsteoporosis is a prevalent disease among the older adults. As we age, osteoporosis and the increased risk of falls can lead to fractures, which can severely impact on people's quality of life and significantly increase the risk of hospitalization and death [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The incidence of diabetes is increasing as people's dietary patterns shift towards higher energy levels. Hyperglycemia itself increases the production of advanced glycation end products and negatively affects bone mineralization, bone remodeling, also bone strength [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Diabetic complications can also make the risk of osteoporosis much higher [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The findings show that the prevalence of diabetic osteoporosis accounts for approximately more than 50% of diabetic patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, the adverse outcomes after fracture are more severe in diabetic patients than in normoglycemic patients, so it is important to identify and detect high-risk groups early in elderly T2DM patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRisk factors for osteoporosis include age, gender, vitamin D levels, muscle strength and nutritional status. The elderly are prone to malnutrition due to their specific metabolic characteristics and disease, and much evidence that malnutrition is an independent risk factor for elderly patients with osteoporosis; studies have shown that low body weight, reduced albumin, and prealbumin can lead to an increased incidence of osteoporotic fractures [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].GNRI is calculated based on the ratio between serum albumin and actual weight to ideal weight, assessing the nutritional condition of older adults; According to the results, there are 4 levels: GNRI\u0026thinsp;\u0026lt;\u0026thinsp;82 indicates high nutritional risk, GNRI:82 to \u0026lt;\u0026thinsp;92 is moderate nutritional risk, GNRI:92 to \u0026le;\u0026thinsp;98 is low risk, and GNRI\u0026thinsp;\u0026gt;\u0026thinsp;98 is no risk [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. GNRI allows for early detection and diagnosis of malnutrition, timely and appropriate interventions, as well as the identification of conditions at risk for adverse effects, including cancer prognosis, postoperative complications, and mortality in dialysis patients and cardiovascular disease [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and is highly accurate and easy to use clinically.\u003c/p\u003e \u003cp\u003eTo our knowledge there are fewer studies on the correlation between GNRI and osteoporosis. Liang Wang et al [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] showed that GNRI is associated with osteoporosis as well as BMD in T2DM patients. Their study population consisted of patients from the Second Hospital of Wenzhou Medical University and Yu Ying Children's Hospital, who had adequate vitamin D. However, in northern China, Majority of population is vitamin D insufficient or deficient; therefore, the study by Wang Liang et al. is not fully representative of the type 2 diabetic population in China. This paper investigates the relationship between GNRI and the development of osteoporosis in northern T2DM and assesses the predictive properties of GNRI for osteoporosis.\u003c/p\u003e"},{"header":"2. Topics And Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Topics\u003c/h2\u003e \u003cp\u003eThis study was a cross-sectional observation of all included patients, 610 patients with T2DM which aged\u0026thinsp;\u0026ge;\u0026thinsp;60 (317 men and 293 women) treated at the Hebei General Hospital from January 2018 to December 2020.The diagnostic criteria of T2DM was based on the 1999 WHO. Exclusion criteria for participants were as follows: (1) individuals with diseases affecting bone metabolism or affect nutritional status, such as malignancies, severe liver diseases, kidney diseases, pituitary-related diseases, thyroid and parathyroid diseases, adrenal diseases, rheumatoid arthritis, acute inflammatory diseases, etc. (2) individuals who are bedridden for long periods of time; (3) individuals who are also taking drugs that affect bone metabolism, such as vitamin D, calcium, bisphosphonates, glucocorticoids, etc. The study was approved by the Ethics Committee of the Hebei Provincial People's Hospital and complies with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical Information Collection and Laboratory Measurements\u003c/h2\u003e \u003cp\u003ePatient demographics and clinical characteristics, including information on gender, age, disease duration, and comorbidities, were collected from medical records. Weight was measured while wearing light clothing and height was measured without shoes. Body weight divided by height squared (kg/m\u003csup\u003e2\u003c/sup\u003e) was used to calculate the body mass index for each patient.\u003c/p\u003e \u003cp\u003eSerum samples were collected after fasting (at least eight hours). Triglycerides, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol and albumin levels, glucose metabolism indicators, fasting blood glucose, glycosylated hemoglobin; bone metabolism indicators, alkaline phosphatase, β-CTX, collagen type I N-peptide, 25-hydroxyvitamin D, PTH were measured. In addition, other biochemical markers, such as uric acid, blood creatinine and calcium were tested.\u003c/p\u003e \u003cp\u003eBone densitometry was performed using a dual-energy X-ray bone densitometer to measure bone density values in the lumbar spine (L1-4), femoral neck, and total hip. According to the criteria for the definition of osteoporosis in the 1994 WHO, T values \u0026le;-2.5 standard deviations were obtained for any part of the lumbar spine, femoral neck or total hip.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Calculating GNRI\u003c/h2\u003e \u003cp\u003eThe GNRI calculation formula is as follows:\u003c/p\u003e \u003cp\u003eGNRI = [1.489 \u0026times; albumin (g/dL)] + [41.7 \u0026times; (body weight/WL0)].\u003c/p\u003e \u003cp\u003eWL0 represented the ideal body weight (kg) is calculated as follows :\u003c/p\u003e \u003cp\u003eFor men: height (cm)- 100- [(height (cm) \u0026minus;\u0026thinsp;150)/4]\u003c/p\u003e \u003cp\u003eFor women: height (cm)-100- [(height (cm)-150)/2.5]\u003c/p\u003e \u003cp\u003eActual weight divided by ideal weight was set to 1 when actual weight exceeded the ideal weight [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Statistical Analysis","content":"\u003cp\u003eAll data were statistically analyzed using SPSS 25. Data distribution was evaluated with the Kolmogorov-Smirnov test. Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation was used to indicate that the data were subject to a normal distribution, analysis of variance (ANOVA) was used for comparisons between groups. The median (25th percentile, 75th percentile) was used to indicate that the data did not conform to a normal distribution; analysis of Kruskal-Wallis was used for comparisons between groups. Categorical data were expressed as frequencies (%), and differences between groups were determined using the χ2 test, Spearman correlation analysis was applied to determine the relationship between GNRI and BMD and each clinical index. Logistic regression analysis was performed to assess the relationship between GNRI and osteoporosis. ROC curves were applied to assess the predictive properties of GNRI for osteoporosis and calculate the area under the receiver operating characteristic curve (AUC).\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003e\u003cstrong\u003e4.1 Clinical characteristics of the 610 patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e（Table 1）All patients were grouped according to the GNRI score and divided into the no nutritional risk group and the nutritional risk group. Compared with the non-nutritional risk group, the nutritional risk group had longer diabetes course, older age, lower BMI, lower ABL, higher HbA1c, lower FCP, lower TG, lower Ca, lower 25 (OH) D, lower PTH, lower femoral neck BMD, and higher prevalence of osteoporosis. The differences between the two groups were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Grouping of patients according to whether they have osteoporosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e（Figure 1）Classified in ascending order of GNRI quartiles (Q1-Q4), lower GNRI among type 2 diabetic patients are associated with a higher Incidence of osteoporosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Clinical characteristics of patients stratified by GNRI\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003eGNRI＞98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003eGNRI\u0026le;98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"41.76904176904177%\"\u003e\n \u003cp\u003e(n=491)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"42.01474201474201%\"\u003e\n \u003cp\u003e(n=119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.216216216216218%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003emale/female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e259/232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e54/65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e66.00(63.00,69.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e68.00(64.00,76.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eDiabetes duration(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e10.00(5.00,16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e15.00(6.00,20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e26.33士3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e24.99士3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e42.36\u0026plusmn;2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e35.90\u0026plusmn;2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eHbA1c(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e8.12(6.95,9.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e9.25(7.48,10.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e6.45(5.48,7.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e6.62(5.77,7.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eFINS (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e7.07(4.13,11.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e5.38(2.89,16.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eFCP (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e1.85(1.23,2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e1.47(0.78,1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e4.28(3.25,5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e4.15(3.45,4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e1.25 (1.00,1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e1.11 (0.80,1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eHDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e1.19(0.96,1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e1.14(0.91,1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eLDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e2.77(1.78,3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e2.59(1.93,3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eUric(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e280.73(204.78,344.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e253.10(197.50,330.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eCa(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e2.30 (2.22,2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e2.19(2.10,2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003e25(OH)D (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e18.19(14.39,23.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e14.83(11.71,19.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eALP(IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e71.70(50.60,88.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e73.70(55.53,93.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eBGP (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e12.71(9.89,16.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e13.00(9.94,16.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003e\u0026beta;-CTX (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e0.35(0.25,0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e0.36(0.25,0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eP1NP (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e39.75(30.00,51.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e42.10(31.68,57.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003ePTH (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e37.82(28.46,48.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e33.42(23.72,45.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eBMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eTotal lumbar(g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e0.85\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eFemur neck (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e0.83\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e0.78\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eTotal hip (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e0.70\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e0.65\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eOsteoporosis%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e19.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e51.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e（Table 2）All patients were grouped with non-osteoporosis group and osteoporosis group. Compared with the non-osteoporosis group and osteoporosis group had longer diabetes course, older age, lower BMI, lower ABL, lower FCP, lower UA, lower 25 (OH) D, higher BPG, higher P1NP, lower Total lumbar BMD, lower Femur neck BMD, lower Total hip BMD and lower GNRI. The differences between the two groups were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e（Figure2）Patients in the non-osteoporotic group had higher GNRI values compared to the osteoporotic group(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Comparison of indicators between the non-osteoporosis group and osteoporosis group\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003enon-osteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003eosteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"41.76904176904177%\"\u003e\n \u003cp\u003e(n=452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"42.01474201474201%\"\u003e\n \u003cp\u003e(n=158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.216216216216218%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003emale/female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e263/189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e50/108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e66.00(63.00,69.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e69(65.75,76.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eDiabetes duration(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e10.00(5.00,16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e14.00(7.00,20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e26.33士3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e24.99士3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e41.68\u0026plusmn;3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e39.44\u0026plusmn;3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eHbA1c(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e8.30(7.20,9.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e8.20(6.90,9.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e6.47(5.48,7.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e6.54(5.73,7.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eFINS (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e7.14(4.27,12.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e5.81(2.89,10.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eFCP (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e1.88(1.28,2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e1.51(0.82,2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e4.28(3.36,5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e4.14(2.98,4.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e1.23(0.98,1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e1.23(0.97,1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eHDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e1.17(0.95,1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e1.24(0.94,1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eLDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e2.77(1.93,3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e2.61(1.69,3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eUric(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e285.10(210.40,345.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e268.00(174.30,320.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eCa(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e2.29 (2.22,2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e2.28(2.21,2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003e25(OH)D (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e18.12(14.20,23.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e15.31(11.77,21.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eALP(IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e72.75(52.88,92.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e70.80(44.60,88.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eBGP (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e12.44(9.81,15.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e14.57(10.27,19.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003e\u0026beta;-CTX (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e0.35(0.24,0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e0.36(0.23,0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eP1NP (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e38.83(29.91,51.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e45.52(31.94,60.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003ePTH (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e36.96(27.84,46.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e37.94 (23.89,50.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eBMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eTotal lumbar(g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e0.96\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eFemur neck (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e0.60\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eTotal hip (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e0.74\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"30.664395229982965%\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.960817717206133%\"\u003e\n \u003cp\u003e103.79(100.78,107.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.131175468483818%\"\u003e\n \u003cp\u003e99.45(96.63,103.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.243611584327088%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Spearman\u0026apos;s correlations between the Geriatric Nutritional Risk Index and bone metabolism indicators.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(Figure 3) Correlation analysis revealed that GNRI was positively correlated with lumbar BMD, femoral neck BMD and total hip BMD (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e(Table 3) Spearman\u0026apos;s correlation analysis yielded a positive correlation between GNRI and Ca, 25(OH)D, PTH. A negative correlation between GNRI and PINP. After adjusting for age, duration of diabetes, HbA1c, TC, TG, HDL, LDL, UA and other confounding factors, it was concluded that GNRI was positively correlated with Ca, 25(OH)D, PTH and negatively correlated with ALP and PINP.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Correlation between the Geriatric Nutrition Risk Index and indicators of bone metabolism\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"97%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"30.612244897959183%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003ebefore adjusting\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.224489795918368%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eafter adjusting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.895522388059703%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.432835820895523%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.417910447761194%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.402985074626866%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.850746268656717%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eCa(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e25(OH)D (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eALP (IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" width=\"11.34020618556701%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.883720930232556%\"\u003e\n \u003cp\u003eBPG (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.27906976744186%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.465116279069768%\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.116279069767442%\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.25581395348837%\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.883720930232556%\"\u003e\n \u003cp\u003e\u0026beta;-CTX (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.27906976744186%\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.465116279069768%\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.116279069767442%\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.25581395348837%\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.883720930232556%\"\u003e\n \u003cp\u003ePINP (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.27906976744186%\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.465116279069768%\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.116279069767442%\"\u003e\n \u003cp\u003e-0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.25581395348837%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.883720930232556%\"\u003e\n \u003cp\u003ePTH (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.27906976744186%\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.465116279069768%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.116279069767442%\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.25581395348837%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAnnotation：After adjusting for confounding factors, including age, duration of diabetes, HbA1c, TC, TG and UA\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Logistic regression analysis of participants with osteoporosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e（Table 4、Table 5）Analysis of the association between GNRI and osteoporosis using logistic regression, and the results are shown in Table 4. After adjusting for sex, age, duration of diabetes, and 25(OH)D, a significant association between GNRI and osteoporosis（Model I）.The risk of osteoporosis in the nutritional risk group was 3.35 times higher than the non-nutritional risk group（Model II）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Univariate Logistic regression analysis of osteoporosis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.746031746031747%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.052910052910052%\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.09347442680776%\"\u003e\n \u003cp\u003eOdds ratio (95% CI)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6469%;\" width=\"17.10758377425044%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eGender（male）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e3.006（2.048，4.412）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e1.110（1.069，1.132）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eDiabetes duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e1.037（1.014，1.061）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eUA (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e0.999（0.997，1.000）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e0.928（0.838，1.029）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e0.959（0.834，1.102）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eCa(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e1.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e0.345（0.025，4.689）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003e25（OH）D (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e0.970（0.946，0.996）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eALP (IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e0.998（0.994，1.003）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eP1NP (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e1.014（1.006，1.022）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003ePTH (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e1.010（1.000，1.020）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eHbA1c (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e0.993（0.900，1.094）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e0.942（0.758，1.171）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eFINS (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e1.004（0.976，1.043）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eFCP (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e0.758（0.572，1.091）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.2409%;\" width=\"31.802120141342755%\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0803%;\" width=\"10.070671378091873%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43.1424%;\" width=\"41.1660777385159%\"\u003e\n \u003cp\u003e0.885（0.854，0.917）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.259%;\" width=\"12.014134275618375%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Multivariate Logistic regression analysis of osteoporosis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable align=\"\" border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.591760299625467%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.662921348314608%\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.453183520599254%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.292134831460675%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.591760299625467%\"\u003e\n \u003cp\u003eModel I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.662921348314608%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.453183520599254%\"\u003e\n \u003cp\u003e0.907 (0.872,0.943)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.292134831460675%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.591760299625467%\"\u003e\n \u003cp\u003eModel II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.662921348314608%\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.453183520599254%\"\u003e\n \u003cp\u003e3.352 (2.102,5.348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.292134831460675%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAnnotation：Gender，Age，Diabetes duration，25（OH）D，P1NP and\u0026nbsp;GNRI are involve in the logistic multivariate regression analysis. SE, standard error.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Predictive properties of GNRI for osteoporosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e（In Figure 4）\u0026nbsp;ROC curve analysis was performed with GNRI as the test variable and the presence of osteoporosis as the status variable. The analysis yielded an area under the curve for GNRI of 0.695 and was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05), with an optimal GNRI threshold of 99.56 for predicting osteoporosis, a sensitivity of 81.19%, and a specificity of 52.53%.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eOriginally used as an indicator to assess nutritional status in the elderly, GNRI is calculated from albumin, weight and height and is a dual assessment of serum albumin and BMI that complements and improves the accuracy of diagnosis. Good nutritional status plays a good role in bone metabolism, Similarly, malnutrition increases the incidence of osteoporosis and fragility fractures [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the present study we looked at the correlation between GNRI and osteoporosis in northern T2DM patients and showed that there is a positive correlation between GNRI and osteoporosis, the group with lower GNRI values had a higher prevalence of osteoporosis. Divided the participants into osteoporotic and non-osteoporotic groups, and low GNRI values in the osteoporotic group compared to those in the non-osteoporotic group, and the difference was statistically significant. Linear correlation analysis revealed that GNRI was positively correlated with lumbar BMD, femoral neck BMD and total hip BMD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the logistic regression analysis, Significant association of GNRI with osteoporosis. We evaluated the predictive effect of GNRI on osteoporosis using Roc curves. The analysis yielded a GNRI cut-off value of 99.56, similar to the findings of Liang Wang et al, they yielded cut-off value of 98.2 for predicting osteoporosis in men and 99.5 for predicting osteoporosis in women. Our results suggest a significant positive relationship between GNRI and osteoporosis and provide a theoretical basis for screening for osteoporosis in clinical practice.\u003c/p\u003e \u003cp\u003eThe current mechanism of association between GNRI and osteoporosis is considered to be possibly related to the following. First, malnutrition affects calcium and vitamin D intake, which may increase bone mineral loss in patients, making it difficult to mineralize bone and leading to the development of osteoporosis. Secondly, hypoalbuminemia is a marker of both nutritional status and chronic inflammatory response; hypoalbuminemia activates osteoclasts and inhibits osteoblasts through NF-κB factors, other inflammatory cytokines [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; hypoalbuminemia causes a decrease in insulin-like growth factor-1 synthesis, which also leads to a decrease in the number of osteoblasts and a decrease in cellular activity, increased osteoclast lifespan, increased bone resorption, and bone decreased remodeling [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Finally, hypoproteinemia leads to inadequate muscle synthesis and decreased skeletal muscle mass, resulting in decreased balance and gait capacity, which can cause falls as well as the occurrence of fractures [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur results yield that a high levels of GNRI have a protective effect on bone metabolism in patients. In contrast, no correlation between GNRI and 25(OH)D in the study by Liang Wang et al. Possible reasons for our different results are that in the south even though nutritional status leads to reduced vitamin intake, vitamin D levels can still be ensured with adequate sun exposure, while in the north insufficient sun exposure combined with nutritional barriers makes it more likely to cause vitamin D deficiency and deficiency. Studies show a close relationship between latitude sunlight deficiency, skin coverage and vitamin D. In China, there is a clear geographical division of vitamin D deficiency, with populations in northern, northeastern and northwestern China north of 35 degrees north latitude being more severely undernourished, while vitamin D levels are adequate in areas south of 25 degrees north latitude and in the middle of the country [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study also found that GNRI was negative correlated with P1NP, are contrary to the findings of Liang Wang et al. Possible reasons for the negative correlation between GNRI and PINP are that PINP secreted by osteoblasts is known to be a marker of bone formation reflecting collagen formation and osteoblast activation, and that during bone turnover, bone formation and resorption are tightly coupled, with accelerated bone turnover predisposing to bone loss; therefore, high GNRI values are associated with low bone turnover and reduced bone loss. For the relationship between GNRI and PTH, the study found a positive correlation between serum PTH and BMI, fat mass, and Mehrotra indicated that reduced PTH is a risk factor for malnutrition [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. PTH can promote the inward flow of calcium ions into adipocytes and stimulate adipose synthesis, and accordingly, low levels of PTH inhibit adipose synthesis and cause protein depletion. In this study we yielded a higher GNRI value corresponding to a higher PTH. Among alkaline phosphatases, bone-specific alkaline phosphatase is closely related to normal bone growth and development, and is a marker of maturation and activity of osteoblasts. However, the specificity of our current assay is not good, and there is some crossover with liver-derived ALP, and we measure total ALP and not BALP; therefore, the relationship of ALP does not accurately reflect the level of bone metabolism.\u003c/p\u003e \u003cp\u003eIn a logistic regression analysis, advanced age was an independent risk factor for osteoporosis, with bone density decreasing year by year with increasing age, and oxidative stress, which increases osteoclast activity and bone resorption, is a cause of age-related bone loss. The present study similarly concluded that the duration of diabetes is an influential factor in osteoporosis in T2DM. It has been shown that the relative risk (RR) of diabetes duration increases from 1. 40 (1. 08\u0026thinsp;~\u0026thinsp;1. 82) at less than 5 years to 2. 66 (2. 04\u0026thinsp;~\u0026thinsp;3. 47) at greater than 15 years [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The non-enzymatic glycosylation response of type 2 diabetes is known to contribute to the decline in bone mass. Hyperglycaemia leads to the accumulation of glycosylation end products (AGE) in the organic bone matrix, resulting in stiffening of type I collagen in the bone matrix, decreased bone strength, increased bone fragility and promotion of osteoblast apoptosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our results suggest that suboptimal glycemic control is not an independent risk factor for the development of osteoporosis, possibly because measured glycemic parameters only reflect recent levels of glycemic control and that measured BMD in type 2 diabetic patients does not fully reflect the state of impaired bone mass in type 2 diabetic patients.\u003c/p\u003e \u003cp\u003eFurthermore, in our regression analysis of GNRI and osteoporosis, we concluded that uric acid was not associated with osteoporosis and was neither a protective nor a risk factor for osteoporosis. Several studies have concluded that higher UA levels are protective for osteoporosis [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our difference with the results of these studies may be due to gender, region, ethnicity, study methodology and sample size. Finally, the association between serum UA and osteoporosis may be directly or indirectly confounded by the fact that many older adults suffer from two or more chronic diseases, such as obesity, diabetes mellitus, etc.\u003c/p\u003e \u003cp\u003eThis study has some limitations, firstly, the retrospective nature of this study, it does not provide a mechanism-related explanation for the observed association; and the study is a cross-sectional study and doesn't indicate a causal relationship between GNRI and bone mineral density. Second, the serum data of the patients with the disease, of which only one was collected, and the BMD of each location was also collected once, thus leading to bias. Third, some relevant parameters affecting the study results may have been overlooked in this study, such as history of smoking, alcohol consumption, hormone levels, dietary habits, exercise situation, and history of previous fractures.\u003c/p\u003e \u003cp\u003eIn summary, study results demonstrated that a lower GNRI is associated with increased osteoporosis and that GNRI is a convenient way to assess nutritional status and osteoporosis in patients with T2DM. Nutritional supplementation therapy may Reducing the incidence of osteoporosis in patients with T2DM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethics committee approved the study.(Hebei General Hospital Ethics Committee No.202137)\u003c/p\u003e\n\u003cp\u003eAll individuals participating in the study gave written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from [third party name] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [third party name].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest between the authors, includingAuthor Contributions\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the staff of the Department of Endocrinology and Metabolism of the Hebei Provincial People\u0026apos;s Hospital and all the patients who participated in the study. This work was supported by a grant from the Hebei Provincial Natural Science Foundation (project number H2019307114). The funder had no role in the design of this study. Collection, analysis and interpretation of data; or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuanyuan JI:Writing articles and results analysis\u003c/p\u003e\n\u003cp\u003eNan Geng :Collecting and organizing data\u003c/p\u003e\n\u003cp\u003eYingchun Niu:Collecting and organizing data\u003c/p\u003e\n\u003cp\u003eHang Zhao:Technical and material support\u003c/p\u003e\n\u003cp\u003eWenjie Fei:Technical and material support\u003c/p\u003e\n\u003cp\u003eShuchun Chen:Technical and material support\u003c/p\u003e\n\u003cp\u003eLuping Ren:Propose research idea, design research proposal\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCianferotti L, Bertoldo F, Bischoff-Ferrari H A, et al. 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The geriatric nutritional risk index independently predicts adverse outcomes in patients with pyogenic liver abscess[J]. BMC Geriatr, 2019,19(1):14.\u003c/li\u003e\n\u003cli\u003eFunamizu N, Omura K, Takada Y, et al. Geriatric Nutritional Risk Index Less Than 92 Is a Predictor for Late Postpancreatectomy Hemorrhage Following Pancreatoduodenectomy: A Retrospective Cohort Study[J]. Cancers (Basel), 2020,12(10).\u003c/li\u003e\n\u003cli\u003eSasaki M, Miyoshi N, Fujino S, et al. The Geriatric Nutritional Risk Index predicts postoperative complications and prognosis in elderly patients with colorectal cancer after curative surgery[J]. Sci Rep, 2020,10(1):10744.\u003c/li\u003e\n\u003cli\u003eFunamizu N, Omura K, Ozaki T, et al. Geriatric nutritional risk index serves as risk factor of surgical site infection after pancreatoduodenectomy: a validation cohort Ageo study[J]. Gland Surg, 2020,9(6):1982-1988.\u003c/li\u003e\n\u003cli\u003eWang L, Zhang D, Xu J. 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Osteoporos Int, 2013,24(12):2961-2970.\u003c/li\u003e\n\u003cli\u003eDe Pergola G, Giagulli V A, Bartolomeo N, et al. Independent Relationship between Serum Osteocalcin and Uric Acid in a Cohort of Apparently Healthy Obese Subjects[J]. Endocr Metab Immune Disord Drug Targets, 2017,17(3):207-212.\u003c/li\u003e\n\u003cli\u003eMakovey J, Macara M, Chen J S, et al. Serum uric acid plays a protective role for bone loss in peri- and postmenopausal women: a longitudinal study[J]. Bone, 2013,52(1):400-406.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Geriatric Nutrition Risk Index,osteoporosis,BMD,type 2 diabetes mellitus","lastPublishedDoi":"10.21203/rs.3.rs-858125/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-858125/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Osteoporosis (OP) is very common in the elderly population and can lead to fractures and disability. Malnutrition is associated with osteoporosis. The Geriatric Nutrition Risk Index (GNRI) is used to assess the risk of malnutrition and complications associated with nutritional status in older patients and is an important predictor of many diseases. Therefore, we investigated the relationship between GNRI and osteoporosis in patients with type 2 diabetes to help us prevent and detect osteoporosis in time. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e Retrospective study of 610 elderly patients with type 2 diabetes. Collection of general and laboratory data on the patient, together with measurement of bone mineral density (BMD). The GNRI was calculated based on ideal body weight and serum albumin (ABL) levels. Correlation analysis was applied to determine the relationship between GNRI and BMD and bone metabolism indexes. The predictive value of GNRI for the development of osteoporosis was analyzed by logistic regression analysis and creating a Receiver Operating Characteristic Curve (ROC), calculating the area under the cure (AUC).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eAll patients divided into the no nutritional risk group and the nutritional risk group. Compared with the non-nutritional risk group, the nutritional risk group had longer diabetes course, older age, higher HbA1c and higher prevalence of osteoporosis; lower BMI, ABL, FCP, TG, Ca, 25 (OH) D, PTH and lower femoral neck BMD (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Classified in ascending order of GNRI quartiles (Q1-Q4), lower GNRI among type 2 diabetic patients are associated with a higher Incidence of osteoporosis.\u003c/p\u003e\u003cp\u003e\tAll patients were grouped with non-osteoporosis group and osteoporosis group. Patients in the non-osteoporotic group had higher GNRI values compared to the osteoporotic group(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\u003cp\u003e\tCorrelation analysis revealed that GNRI was positively correlated with lumbar BMD, femoral neck BMD and total hip BMD (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). After adjusting confounding factors, Spearman's correlation analysis was concluded that GNRI was positively correlated with Ca, 25(OH)D, PTH and negatively correlated with ALP and PINP. Regression analysis yields, GNRI was significantly associated with osteoporosis. \u003c/p\u003e\u003cp\u003e\tROC curve analysis was performed with GNRI as the test variable and the presence of osteoporosis as the status variable. The analysis yielded an area under the curve for GNRI of 0.695 and was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05), with an optimal GNRI threshold of 99.56 for predicting osteoporosis.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eLower Geriatric Nutritional Risk Index among type 2 diabetic patients in northern China are associated with a higher Incidence of osteoporosis.\u003c/p\u003e","manuscriptTitle":"Lower Geriatric Nutritional Risk Index Are Associated with A Higher Incidence Of Osteoporosis In Northern China Type 2 Diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":"","date":"2022-11-03 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Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":"","date":"2022-10-14 15:23:48","doi":"","editorialEvents":[{"type":"reviewerAgreed","content":"","date":"2022-10-14T15:23:48+00:00","index":0,"fulltext":""},{"type":"editorAssigned","content":"","date":"2022-10-05T01:50:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2022-10-04T11:25:53+00:00","index":"","fulltext":""},{"type":"decision","content":"Minor revision","date":"2022-06-14T10:55:30+00:00","index":"","fulltext":""},{"type":"notPreprinted","content":""}],"status":"timeline","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine 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