Correlation between serum phosphorus level and sarcopenia in patients with type 2 diabetes mellitus

Correlation between serum phosphorus level and sarcopenia in patients with type 2 diabetes mellitus | 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 Article Correlation between serum phosphorus level and sarcopenia in patients with type 2 diabetes mellitus Xiangyu Gao, Meijian Wang, Wenjie Ma, Rui Wang, Wenchao Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8987825/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Sarcopenia is associated with poor prognosis of type 2 diabetes mellitus (T2DM). Phosphorus (P) deficiency may lead to disturbance of the musculoskeletal system and serve as a potential biomarker of sarcopenia. This study aimed to evaluate the correlation of P and muscle mass in T2DM patients. A total of 1078 T2DM inpatients were recruited. Skeletal muscle index (SMI), along with subtypes of obesity were quantified using a dual energy X-ray absorptiometry. Participants were considered as sarcopenia, when SMI was less than 7.0 kg/m 2 in males and 5.4 kg/m 2 in females. Clinical information and biochemical characteristics were measured and recorded. We found the prevalence of low muscle mass was 20.2% and 13.6% in male and female patients, respectively. Serum P index was associated with a reduced risk of low muscle mass in both genders. This finding was observed before and after adjustment for variables, including for age, A/G ratio, SBP, DBP, duration of diabetes, Ca, FPG, TC, TG, HDL, and LDL. Potentially, there might be nonlinear association between P index and SMI. When the P index was ≤ 0.98, a significant positive correlation with SMI was observed in males. However, when the P index was > 0.98, no significant correlation between P index and SMI. Similarly, in females, P index was positively correlated with SMI when P index was ≤ 1.04, while when P index was > 1.04, a weak and non-significant negative correlation between P and SMI was observed. In conclusion, serum P level is inversely correlated with sarcopenia in T2DM patients. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Serum phosphorus level Sarcopenia Skeletal muscle index T2DM Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Sarcopenia is a clinical syndrome characterized by the progressive loss of skeletal muscle mass and function which often be related to aging[ 1 ]. However, recent researches have revealed a rising prevalence of sarcopenia among younger individuals, especially those with obesity, highlighting the need for greater public awareness of its detrimental effects. Sarcopenia is linked to an increased risk of fractures, malnutrition, and movement disabilities, additionally even reported as a vital chronic complication of diabetes mellitus, with which it associated with increased mortality rates and poor long-term prognosis[ 2 , 3 ]. According to a British meta-analysis, the incidence of sarcopenia was significantly higher in patients diagnosed with type 2 diabetes mellitus (T2DM) (28.4%) than that in the non-diabetic individuals (18.7%)[ 4 ]. Given the adverse consequences of sarcopenia, early identification and prevention are important, especially in patients with diabetes. According to Asian Working Group for Sarcopenia (AWGS), the proposed diagnostic thresholds for assessing low muscle mass include sex-specific cutoffs of 7.0 kg/m² (male) and 5.4 kg/m² (female) when measured through dual-energy X-ray absorptiometry (DXA), while bioelectrical impedance analysis yields comparable values of 7.0 kg/m² for males and 5.7 kg/m² for females. Complementary functional criteria consist of grip strength thresholds below 26 kg for men and 18 kg for women, along with reduced mobility indicators defined by walking speeds slower than 0.8 m/s[ 5 ]. Currently, DXA are most commonly used clinically to measure muscle mass and indirectly predict sarcopenia. Moreover, screening for novel serum biomarkers represents a promising approach to predict muscle mass and achieve prevention of sarcopenia. Phosphorus (P) is a critical macromineral that plays vital roles in various biological functions in human beings. Notably, P plays a vital role in skeletal mineralization and energy metabolism, with its deficiency leading to a significant disruption of the musculoskeletal system[ 6 ]. Phosphorus-containing metabolites are essential components of cellular structures and functions, serving as critical elements in the formation of cell membranes and nucleic acid molecules[ 7 ]. Besides, phosphorus-containing metabolites also play a vital role in metabolic processes and energy metabolism, particularly through the phosphorylation of intermediate metabolites and the storage of energy released in high-energy phosphate bonds, such as those in ATP or phosphocreatine, during oxidative phosphorylation[ 8 , 9 ]. In skeletal muscles, furthermore, P is essential for the provision of high-energy phosphates required for the contractile activity and structural integrity of the muscular membrane and intracellular organelles, including the sarcoplasmic and mitochondrial reticulum[ 10 ]. These roles suggest that the level of P in the serum may correlate to its level in the skeletal muscle, thereby serving a vital biomarker for predicting and evaluating sarcopenia. Currently, the relationship between P and muscle mass in T2DM patients remains underexplored, with the available findings still being largely inconsistent. Therefore, this study was performed to evaluate the correlation of serum P level and low muscle mass in patients with T2DM, in order to explore the potential targets for the prediction and prevention of sarcopenia. Methods Study population In total, 1078 consecutive inpatients (including 579 males and 499 females) from the Department of Endocrinology of Qilu hospital (Qingdao), Shandong University were recruited from September 2017 to September 2019 in this cross-sectional study. All participants were clinically diagnosed with T2DM based on the criteria by the American Diabetic Association, with a fasting plasma glucose (FPG) ≥ 7.0 mmol/L, a 2-hour postprandial plasma glucose ≥ 11.1 mmol/L, or both. Patients were excluded from the study if they were pregnant, undergoing anti-tumor therapy, characterized by severe hepatic and renal failures, experiencing severe bone disease, suffering from acute infectious diseases, and experiencing thyroid and parathyroid dysfunctions. This study was approved by the ethics committees of Qilu Hospital (Qingdao) of Shandong University, additionally, written informed consent were obtained from all participants. Low muscle mass definition Skeletal muscle index (SMI) was measured using the dual-energy X-ray absorptiometry (DXA, Hologic Discovery A, Waltham, MA, USA). Subsequently, SMI was calculated as appendicular skeletal muscle mass in kilograms divided by the square of the body height in meters (kg/m 2 ). Low muscle mass was defined as SMI < 7.0 kg/m2 in males and SMI < 5.4 kg/m2 in females, based on the consensus by the Asian Working Group for Sarcopenia[ 5 ]. Basic characteristics and biochemical measurements Information on basic epidemiological characteristics, including age, sex, height, weight, blood pressures, and the duration of diabetes were collected and recorded. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m2), with patients having BMI ≥ 28 was regarded as obesity. Additionally, DXA, previously mentioned, was used to assist in categorizing patients as either ANDROID or GYNOID, which are different types of obesity. Fasting blood samples were collected from all participant and various biochemical measurements were quantified, including serum albumin to globulin ratio (A/G), FPG, total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), triglyceride (TG), serum calcium (Ca), and P. Statistical analysis Quantitative data following a normal distribution were presented as either mean ± standard deviation (SD) or median (25th percentile, 75th percentile), while categorical data following a normal distribution were summarized using proportions or percentages of patients. Student’s t-test was used for parametric data, whereas the Mann-Whitney U-test or chi-square test was used for non-parametric data to compare the differences in variables between normal muscle mass and less muscle mass groups. The relationships between the two groups were assessed using the Spearman correlation analysis. Moreover, binary logistic regression analysis was used to determine the predictors for low muscle mass after adjusting for potential confounding variables. Additionally, the independent association between the serum P level and SMI was assessed using linear regression, with a smooth curve fitting used to explore this association. A multivariate piecewise linear regression was further used to examine the threshold correlation of the serum P level and SMI based on to the smooth curve fit. All P values < 0.05 were considered to be statistically significant. Given the exploratory nature of this study, we did not apply corrections for multiple comparisons (e.g., Bonferroni or FDR). Future confirmatory studies should incorporate stricter adjustments to mitigate Type I error risks. All analyses were performed using SPSS Version 26.0 (IBM Corp., Armonk, NY, USA). Results The differences of characteristics between patients with low and normal muscle mass A total of 1078 T2DM patients, including 579 males and 499 females were recruited into the study, as indicated in Table 1 . Among these participants, 117 males (20.2%) and 68 females had low muscle mass (13.6%) had low muscle mass. Moreover, among the male subjects, there tended to be higher age, duration of diabetes, HDL as well as lower A/G ratio, ANDROID, SMI, prevalence of obesity, height, weight, BMI, DBP, TG and serum P index in low muscle mass group compared to normal muscle mass group. While in female subjects, age was significantly higher, while A/G ratio, ANDROID, SMI, prevalence of obesity, height, weight, BMI, DBP, TG, and serum P index were significantly lower in the low muscle mass group compared to the normal muscle mass group. A significantly detailed comparison of the two groups in male and female T2DM patients is illustrated more intuitively in Fig. 1 . Table 1 The clinical characteristics differences between T2DM patients with and without low muscle mass Characteristics Male (n = 579) Female (n = 499) Normal muscle mass (n = 462) Low muscle mass (n = 117) P value Normal muscle mass (n = 431) Low muscle mass (n = 68) P value Age (years) 54.68 ± 11.94 60.86 ± 13.47 < 0.001 61.11 ± 10.78 65.85 ± 11.44 0.001 A/G ratio 1.29 ± 0.17 1.15 ± 0.20 < 0.001 1.10 ± 0.15 1.04 ± 0.18 0.003 ANDROID 34.60 ± 6.18 31.93 ± 8.23 0.001 40.52 ± 6.17 37.51 ± 7.72 0.003 GYNOID 26.95 ± 4.55 27.63 ± 5.19 0.197 37.05 ± 4.95 36.17 ± 5.25 0.176 SMI (kg/m 2 ) 7.96(7.54,8.51) 6.51(6.27,6.80) < 0.001 6.35(5.90,6.84) 5.18(4.95,5.34) < 0.001 Obesity (n, %) 367(79.4%) 82(70.1%) 0.03 319(74%) 39(57.4%) 0.005 Height (cm) 173.26 ± 5.83 171.04 ± 8.74 0.001 160.67 ± 5.12 158.99 ± 5.43 0.013 Weight (Kg) 82.95 ± 14.24 68.90 ± 11.44 < 0.001 70.51 ± 12.41 56.38 ± 7.65 < 0.001 BMI (kg/m2) 27.57 ± 4.09 23.69 ± 4.86 < 0.001 27.29 ± 4.50 22.30 ± 2.86 < 0.001 SBP (mmHg) 140.40 ± 19.80 137.68 ± 21.46 0.192 143.38 ± 20.64 142.44 ± 22.13 0.731 DBP (mmHg) 82.49 ± 12.93 76.20 ± 12.30 < 0.001 76.53 ± 12.44 74.18 ± 10.96 0.142 Duration (years) 7 (4, 10) 8 (6, 11) 0.008 8 (5, 10) 8 (5, 10) 0.858 FPG (mmol/L) 8.15 ± 2.77 7.96 ± 3.20 0.564 7.72 ± 2.89 7.19 ± 2.74 0.154 TC (mmol/L) 4.54 ± 1.15 4.48 ± 1.09 0.606 4.70 ± 1.35 4.60 ± 1.17 0.583 HDL (mmol/L) 1.14 ± 0.27 1.24 ± 0.35 0.004 1.29 ± 0.37 1.35 ± 0.35 0.192 LDL (mmol/L) 3.0 ± 0.90 2.97 ± 0.95 0.819 3.0 ± 0.93 2.90 ± 1.0 0.408 TG (mmol/L) 1.67(1.07,2.52) 1.10(0.85,1.77) < 0.001 1.47(1.04,2.15) 1.32(0.89,1.67) 0.004 Ca (mmol/L) 2.30 ± 0.12 2.28 ± 0.13 0.147 2.30 ± 0.11 2.30 ± 0.13 0.958 P (mmol/L) 1.21 ± 0.18 1.14 ± 0.18 < 0.001 1.26 ± 0.17 1.20 ± 0.21 0.017 Abbreviations: A/G, Serum albumin to globulin ratio; SMI, skeletal muscle index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TC, total cholesterol; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; TG, triglyceride; Ca, serum calcium and P, serum phosphorus. The correlation between low muscle mass and the clinical parameters Spearman correlation analysis was applied to investigate the whole clinical parameters that might be related to low muscle mass. As indicated in Table 2 , age, duration of diabetes, and HDL were positively correlated with low muscle mass ( r > 0, P < 0.05), while A/G ratio, ANDROID, SMI, prevalence of obesity, height, weight, BMI, DBP, TG and serum P index were negatively correlated with low muscle mass ( r < 0, P 0, P < 0.05). While A/G ratio, ANDROID, SMI, prevalence of obesity, height, weight, BMI, and TG were negatively correlated with low muscle mass ( r < 0, P < 0.05). Notably, it should be noted that the serum P index showed a weak, non-significant negative correlation with low muscle mass in female subjects (r=-0.074, p = 0.099), suggesting minimal clinical relevance. We identified SMI as a quantitative indicator of low muscle mass, subsequently, we conducted linear regression analysis to further explore the correlation between SMI and clinical characteristics. As Table 3 shown, SMI was correlated with age, A/G ratio, ANDROID, prevalence of obesity, height, weight, BMI, DBP, duration of diabetes, HDL, TG, serum Ca and P indexes ( P < 0.05) in male subjects. In female counterparts, SMI was correlated with age, A/G ratio, ANDROID, GYNOID, prevalence of obesity, height, weight, BMI, DBP, HDL, and P index ( P < 0.05). Among them, we are most concerned about the consistent positive linear correlation between SMI and P index, with (β = 1.248, 95% CI: 0.568–1.929) in male patients and (β = 0.862, 95% CI: 0.374–1.349) in female patients, respectively. Furthermore, both male and female patients were categorized into four groups based on the quartiles of the serum P levels: Q1 (< 1.06 mmol/L), Q2 (1.06–1.19 mmol/L), Q3 (1.19–1.33 mmol/L), and Q4 (≥ 1.33 mmol/L) in males and Q1 (< 1.13 mmol/L), Q2 (1.13–1.26 mmol/L), Q3 (1.26–1.36 mmol/L), and Q4 (≥ 1.36 mmol/L) in females. Additionally, the results revealed that elevated P levels reduced the risk of low muscle mass in males ( P for trend = 0.001) and in females ( P for trend = 0.020). Table 2 Correlation of low muscle mass with clinical parameters by Spearman analysis Variables Male Female r P value r P value Age (years) 0.199 < 0.001 0.140 0.002 A/G ratio -0.275 < 0.001 -0.108 0.016 ANDROID -0.144 0.001 -0.125 0.005 GYNOID 0.050 0.234 -0.063 0.163 SMI (kg/m 2 ) -0.695 < 0.001 -0.594 < 0.001 Obesity (%) -0.090 0.030 -0.127 0.005 Height (cm) -0.119 0.004 -0.123 0.006 Weight (Kg) -0.478 < 0.001 -0.436 < 0.001 BMI (kg/m2) -0.474 < 0.001 -0.429 < 0.001 SBP (mmHg) -0.051 0.217 -0.023 0.607 DBP (mmHg) -0.199 < 0.001 -0.059 0.188 Duration (years) 0.111 0.008 0.008 0.858 FPG (mmol/L) -0.047 0.255 -0.068 0.130 TC (mmol/L) -0.008 0.844 -0.018 0.696 HDL (mmol/L) 0.120 0.004 0.075 0.095 LDL (mmol/L) -0.014 0.728 -0.037 0.406 TG (mmol/L) -0.220 < 0.001 -0.127 0.004 Ca (mmol/L) -0.053 0.201 -0.017 0.707 P (mmol/L) -0.131 0.002 -0.074 0.099 Abbreviations: A/G, Serum albumin to globulin ratio; SMI, skeletal muscle index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TC, total cholesterol; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; TG, triglyceride; Ca, serum calcium and P, serum phosphorus. Table 3 The correlation between SMI and clinical characteristics by Linear regression analysis Male Female β (95% CI) P value β (95% CI) P value Age (years) -0.028 (-0.038, -0.018) < 0.001 -0.021 (-0.028, -0.013) < 0.001 A/G ratio 1.583 (0.916, 2.250) < 0.001 1.004 (0.449, 1.559) < 0.001 ANDROID 0.044 (0.026, 0.062) < 0.001 0.033 (0.020, 0.046) < 0.001 GYNOID 0.025 (-0.002, 0.051) 0.068 0.019 (0.002, 0.036) 0.027 Obesity (n, %) 0.365 (0.067, 0.662) 0.016 0.342 (0.155, 0.530) < 0.001 Height (cm) 0.035 (0.016, 0.053) < 0.001 0.019 (0.003, 0.036) 0.021 Weight (Kg) 0.050 (0.043, 0.057) < 0.001 0.047 (0.042, 0.052) < 0.001 BMI (kg/m2) 0.151 (0.126, 0.176) < 0.001 0.131 (0.117, 0.146) < 0.001 SBP (mmHg) 0.005 (-0.001, 0.011) 0.136 0.003 (-0.001, 0.007) 0.170 DBP (mmHg) 0.020 (0.011, 0.030) < 0.001 0.013 (0.006, 0.020) < 0.001 Duration (years) -0.020 (-0.039, -0.001) 0.036 -0.003 (-0.017, 0.011) 0.714 FPG (mmol/L) 0.017 (-0.027, 0.060) 0.452 -0.005 (-0.035, 0.025) 0.726 TC (mmol/L) 0.031 (-0.079, 0.141) 0.585 -0.009 (-0.073, 0.056) 0.793 HDL (mmol/L) -0.576 (-1.006, -0.145) 0.009 -0.270 (-0.504, -0.035) 0.024 LDL (mmol/L) 0.040 (-0.098, 0.177) 0.571 0.003 (-0.088, 0.094) 0.943 TG (mmol/L) 0.086 (0.026, 0.145) 0.005 0.055 (-0.009, 0.118) 0.092 Ca (mmol/L) 1.216 (0.167, 2.265) 0.023 0.175 (-0.558, 0.908) 0.693 P (mmol/L) 1.248 (0.568, 1.929) < 0.001 0.862 (0.374, 1.349) 0.001 P for trend 0.001 0.020 Abbreviations: A/G, Serum albumin to globulin ratio; SMI, skeletal muscle index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TC, total cholesterol; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; TG, triglyceride; Ca, serum calcium and P, serum phosphorus. The association between the serum P level and low muscle mass The logistic regression analysis further confirmed the association between the serum P level and low muscle mass, as shown in Table 4 and Fig. 2 . Notably, the serum P index was associated with a decreased risk of low muscle mass in both male (OR = 0.117, 95% CI: 0.036–0.382, P < 0.001) and female (OR = 0.166, 95% CI: 0.037–0.735, P = 0.018) T2DM participants with no adjustment (model 1). After adjusting for age, P index was found to be negatively associated with low muscle mass in males (OR = 0.237, 95% CI: 0.069–0.819, P = 0.023) and females (OR = 0.170, 95% CI: 0.036–0.794, P = 0.024) (model 2). And after adjusting for basic clinical parameters that lacked laboratory data such as age, SBP, DBP and duration of diabetes, P index was found to be correlated with a reduced risk of low muscle mass in males (OR = 0.214, 95% CI: 0.060–0.755, P = 0.017) and females (OR = 0.172, 95% CI: 0.037-0.800, P = 0.025) (model 3). Furthermore, P index was still inversely correlated with low muscle mass after adjusting for age, A/G ratio, SBP, DBP, duration of diabetes, Ca, FPG, TC, TG, HDL, and LDL both in male patients (OR = 0.187, 95% CI: 0.045–0.785, P = 0.022) and female patients (OR = 0.161, 95% CI: 0.032–0.816, P = 0.027) (model 4). Table 4 The association between P index and low muscle mass Male Female Groups N SMI (kg/m 2 ) H P N SMI (kg/m 2 ) H P Q1 132 7.49 (6.77–8.11) 19.05 < 0.001 115 6.06 (5.61–6.63) 9.76 0.021 Q2 154 7.78 (7.09–8.47) 127 6.06 (5.64–6.67) Q3 145 7.75 (7.17–8.21) 126 6.35 (5.73–6.79) Q4 148 7.89 (7.30–8.64) 131 6.37 (5.82–6.89) Model 1: not adjusted Model 2: adjusted for age Model 3: adjusted for age, SBP, DBP, duration of diabetes Model 4: adjusted for age, A/G ratio, SBP, DBP, duration of diabetes, Ca, FPG, TC, TG, HDL, LDL The association between serum P index and SMI Participants were categorized into four groups based on the quartiles of the serum P level as previously stated and displayed in Table 5 . We found that SMI were significantly increased with ascending serum P level, suggesting a positive correlation between P index and SMI in both genders. Furthermore, a potential nonlinear association between P index and SMI was indentified in both male and female subjects, which shown in Fig. 3 . To further analyze this relationship, a two-piecewise linear regression model was applied (Fig. 4 ). When P index was ≤ 0.98, it was significantly positive correlated with SMI in male patients (β = 3.299, 95% CI: 0.635–5.962, P = 0.015). However, when P index was > 0.98, there was no significant correlation between P index and SMI ( P > 0.05), as shown in Table 6 . Similarly, in female participants, a positive correlation between P index and SMI was observed when P index was ≤ 1.04 (β = 3.217, 95% CI: 1.105–5.329, P = 0.003). Conversely, when P index was > 1.04, there was no statistical significance, as shown in Table 7 . Table 5 The comparison of SMI by quartiles of serum phosphorus level Male Female OR (95%CI) P value OR (95%CI) P value Model 1 0.117(0.036–0.382) < 0.001 0.166(0.037–0.735) 0.018 Model 2 0.237(0.069–0.819) 0.023 0.170(0.036–0.794) 0.024 Model 3 0.214(0.060–0.755) 0.017 0.172(0.037-0.800) 0.025 Model 4 0.187(0.045–0.785) 0.022 0.161(0.032–0.816) 0.027 Abbreviations: SMI, skeletal muscle index. Serum phosphorus level in males was classified as Q1 (< 1.06 mmol/L), Q2 (1.06–1.19 mmol/L), Q3 (1.19–1.33 mmol/L), and Q4 (≥ 1.33 mmol/L). Serum phosphorus level in females was classified as Q1 (< 1.13 mmol/L), Q2 (1.13–1.26 mmol/L), Q3 (1.26–1.36 mmol/L), and Q4 (≥ 1.36 mmol/L). Table 6 The correlation between P and SMI in male subjects Models β (95% CI) P value Model I One line slope 0.427 (-0.072, 0.926) 0.094 Model II Turning point 0.98 ≤ 0.98 slope 1 3.299 (0.635, 5.962) 0.015 > 0.98 slope 2 0.143 (-0.418, 0.704) 0.618 Abbreviations: P, serum phosphorus; SMI, skeletal muscle index. Table 7 The correlation between P and SMI in female subjects Models β (95% CI) P value Model I One line slope 0.862 (0.375, 1.348) 1.04 slope 2 0.524 (-0.043, 1.091) 0.071 Abbreviations: P, serum phosphorus; SMI, skeletal muscle index. Discussion In recent years, sarcopenia, which often be characterized by loss of skeletal muscle mass, reduced muscle strength and/or impaired physical performance, has been increasingly recognized as one of vital chronic complications of diabetes mellitus. Low muscle mass is closely associated with poor prognosis of diabetes. Consequently, an increasing number of studies have focused on exploring the epidemiology and the underlying mechanisms of low skeletal muscle mass in diabetic patients. Therefore, the early detection and diagnosis of sarcopenia is particularly important to facilitate timely follow-up intervention, which helps in mitigating the deterioration of syndrome. In our previous researches, we have found that ALT/AST was inversely correlated with muscle mass in patients with T2DM[ 11 ]. Besides, creatine kinase (CK) is negatively correlated with low muscle mass among patients with T2DM[ 12 ]. TyG index, a novel marker of insulin resistance, was inversely correlated with the presence of sarcopenia in T2DM patients[ 13 ]. Moreover, higher TG/HDL-C ratio was also demonstrated to be correlated with skeletal muscle mass in T2DM patients[ 14 ]. However, there remains a significant gap in literature given that only a limited number of studies have reported the relationship between serum electrolyte level and muscle mass. Our present study indicated that higher serum P level was correlated with a reduced risk of low muscle mass in patients with T2DM, that is, P index was positively associated with SMI. The adjusted odds ratio (OR) for serum P index in males (OR = 0.187, 95% CI: 0.045–0.785, P = 0.022) indicates an 81.3% reduction in low muscle mass risk per unit increase in serum P index. And there may be an 83.9% reduction in low muscle mass risk per unit increase in serum P index among female patients (OR = 0.161, 95% CI: 0.032–0.816, P = 0.027). Besides, the β-coefficient for P in T2DM patients reflects a clinically meaningful increase in P with higher SMI, with β = 1.248 in males ( P < 0.001) and β = 0.862 in females ( P = 0.001), respectively. Similarly, another study based on hemodialysis patients reported that serum P level was lower in the low muscle mass group compared to the normal muscle mass group ( P = 0.015), this observation is consistent with our findings to some extent[ 15 ]. However, one Japanese research reported that serum P concentration in weakened grip strength group was significantly higher than that of patients without weakened grip strength (3.8 vs. 3.3 mg/dL, P < 0.01), demonstrated that serum P concentration was negatively correlated with grip strength. Additionally, this negative association was also confirmed after adjusting for the confounding factors[ 16 ], which was contrary to our conclusion. In our study, Spearman's rank correlation coefficient analysis revealed some different results between the sexes: while the relationship was statistically significant in males, and no significant association was observed in females. These discrepancies underscore the potential influence of sex-specific factors on phosphorus metabolism and muscle homeostasis, which warrant further investigation. The lack of statistical significance in female participants contrasts with the male cohort, suggesting that biological or hormonal differences may modulate phosphorus’s role in muscle metabolism. For instance, estrogen’s influence on mineral homeostasis or sex-specific fat distribution patterns could partially explain these disparities. Future mechanistic studies are needed to explore these hypotheses. The differences among these results may be related to the variables in population, region and measurement methods, etc.. Furthermore, findings from a systematic review of Netherlands – specifically in studies exploring the potential role of minerals in the prevention and treatment of sarcopenia in the elderly–failed to establish a relationship between P with muscle-reducing outcomes, given that analysis incorporated insufficient number of studies, resulting in a low quality of evidence[ 17 ]. The results of the current limited studies on sarcopenia remain inconsistent, underscoring the need for further randomized controlled trials to determine the influence of serum P on muscle mass in patients with T2DM. As mentioned above, P plays a vital role in skeletal mineralization and energy metabolism, and P-containing metabolites are key elements in the formation of cell membranes and nucleic acid molecules, serving as essential components of cellular structures and functions[ 7 ]. P-containing metabolites also participates in oxidative phosphorylation through the phosphorylation of intermediate metabolites and the storage of energy released in high-energy phosphate bonds, such as those in ATP or phosphocreatine[ 8 , 9 ]. Besides, P is essential for the provision of high-energy phosphates required for the contractile activity and structural integrity of the muscular membrane and intracellular organelles in skeletal muscles[ 10 ]. Thus P is essential for skeletal muscle bioenergetics and structural integrity, while its role in T2DM-specific metabolic derangements need further exploration. Insulin resistance, a typical feature of T2DM, may impair phosphorus-mediated pathways critical for muscle maintenance. In addition to the association between phosphorus and muscle mass, our study also showed that patients with T2DM in the low muscle mass group, both males and females, exhibited a higher age, as well as lower A/G ratio, ANDROID, prevalence of obesity, height, weight, BMI, and TG compared to the normal muscle mass group. It is well-established that muscle mass decreases with advancing age, a phenomenon that is supported by numerous studies previously mentioned. Additionally, obesity is recognized as a typical risk factor for sarcopenia. A multicenter study in Japan conducted among community-dwelling older adults showed that obesity and hypertension are independent predictors of sarcopenia among this cohort[ 18 ]. Additionally, previous studies have demonstrated that the metabolic risk factors, including BMI, are significantly related to loss of skeletal muscle or occurrence of sarcopenia[ 19 ]. Contrastingly, findings from a cross-sectional study which assessed older people residing in 11 long-term nursing homes in Australia indicated that low BMI is a predictive risk factor for sarcopenia[ 20 ]. A four-year follow-up longitudinal study in China showed that a higher BMI serves a protective factor against muscle mass loss, but increases the risk factor of reduced gait speed[ 21 ]. As for patients with T2DM, several studies have indicated that BMI is positively correlated with muscle mass, in other words, the prevalence of sarcopenia significantly increases as the level of BMI decreases in patients with T2DM[ 22 – 25 ]. Consistently, this study demonstrates that T2DM patients with low muscle mass exhibit lower BMI compared to those with normal muscle mass. These findings may initially appear contradictory, as obesity is typically linked to adverse metabolic outcomes. However, our results align with emerging evidence on sarcopenic obesity, where higher BMI may mask underlying muscle loss. Furthermore, BMI inherently reflects total body mass, which includes both fat and muscle components. Thus, while BMI and SMI are positively correlated, the preservation of muscle mass in T2DM patients with higher BMI may mitigate sarcopenia risk, even in the presence of excess adiposity. While obesity is associated with an elevated risk of diabetic complications, it is also inversely correlated with the risk of developing sarcopenia in people with T2DM. This study provides exploratory evidence of an inverse correlation between serum P levels and low muscle mass in patients with T2DM. However, given the cross-sectional design and exploratory nature of this findings, it has several potential limitations in terms of the sample size and the duration of follow-up. Such cross-sectional design and lack of alpha-risk adjustment limit causal inference and increase the risk of false positives. Future work should prioritize longitudinal cohorts, stratified analyses, and corrections for multiple comparisons to strengthen conclusions. To address these challenges, future research should employ prospective and longitudinal studies involving a larger sample size comprising participants from various race and age groups. And some confounders such as parathyroid hormone (PTH), dietary phosphate intake and chronic kidney disease stages should also be assessed, which may influence serum P and muscle metabolism. Future prospective studies incorporating these variables are warranted to validate our findings and refine mechanistic insights. Besides, future studies should explore underlying mechanisms such as tissue-specific phosphorus dynamics, insulin signaling interactions, and sex dependence in T2DM. Additionally, randomized controlled trials are necessary to validate the findings of this study, as well as to promote a comprehensive understanding of the correlation between serum P level and low muscle mass in patients with T2DM. Conclusion In conclusion, serum phosphorus level is inversely correlated with low muscle mass in patients with T2DM. Abbreviations A/G Serum albumin to globulin ratio SBP systolic blood pressure DBP, Declarations Competing interests: All authors declare no conflicts of interest for this work. Informed consent statement : All participants, or their legal guardian, provided written informed consent in accordance with the Declaration of Helsinki prior to study enrollment. Funding: This work was supported by Qingdao Outstanding Health Professional Development Fund; Qingdao Key Health Discipline Development Fund; and Natural Science Foundation of Qingdao (23-2-1-190-zyyd-jch). Author Contribution Xiangyu Gao: Formal analysis, Writing – original draft. Meijian Wang: Formal analysis, Writing – review and editing. Wenjie Ma: Investigation. Wenchao Hu: Conceptualization, Funding acquisition. Rui Wang: Data curation, Supervision, Project administration. Acknowledgement All authors would like to thank Dr. Xiaotian Ma, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University for her statistical guidance. Besides, we also acknowledge assistance from medical writers, proof-readers and editors. Data Availability All datasets generated for this study were all included in the manuscript files and the raw data may be obtained from the corresponding author for appropriate justification. References Umegaki, H. Sarcopenia and frailty in older patients with diabetes mellitus. Geriatr. Gerontol. Int. 16 , 293–299 (2016). Hanna, J. S. Sarcopenia and critical illness: a deadly combination in the elderly. JPEN J. Parenter. Enter. Nutr. 39 , 273–281 (2015). Park, S. W. et al. Excessive loss of skeletal muscle mass in older adults with type 2 diabetes. Diabetes Care . 32 , 1993–1997 (2009). Veronese, N. et al. Effect of nutritional supplementations on physical performance and muscle strength parameters in older people: A systematic review and meta-analysis. Ageing Res. Rev. 51 , 48–54 (2019). Chen, L. K. et al. Arai. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. J. Am. Med. Dir. Assoc. 15 , 95–101 (2014). Carpenter, T. O. Primary Disorders of Phosphate Metabolism, in: K.R. Feingold, B. Anawalt, M.R. Blackman, A. Boyce, G. Chrousos, E. Corpas, W.W. de Herder, K. Dhatariya, K. Dungan, J. Hofland, S. Kalra, G. Kaltsas, N. Kapoor, C. Koch, P. Kopp, M. Korbonits, C.S. Kovacs, W. Kuohung, B. Laferrère, M. Levy, E.A. McGee, R. McLachlan, M. New, J. Purnell, R. Sahay, A.S. Shah, F. Singer, M.A. Sperling, C.A. Stratakis, D.L. Trence, D.P. Wilson (Eds.) Endotext, MDText.com, Inc. Copyright © 2000–2024, MDText.com, Inc., South Dartmouth (MA), (2000). Hinkley, J. M. & Coen, P. M. Muscle phosphorus metabolites in sarcopenia. Aging (Albany NY) . 12 , 15880–15881 (2020). Peacock, M. Phosphate Metabolism in Health and Disease. Calcif Tissue Int. 108 , 3–15 (2021). Gasmi, A. et al. Severin. Phosphocalcic metabolism and the role of vitamin D, vitamin K2, and nattokinase supplementation. Crit. Rev. Food Sci. Nutr. 62 , 7062–7071 (2022). Hinkley, J. M. et al. Coen. Older adults with sarcopenia have distinct skeletal muscle phosphodiester, phosphocreatine, and phospholipid profiles. Aging Cell. 19 , e13135 (2020). Ma, W., Hu, W., Liu, Y. & He, L. Association between ALT/AST and Muscle Mass in Patients with Type 2 Diabetes Mellitus. Mediators Inflamm . 9480228 (2022). (2022). Hu, W., Ma, Y., He, L. & Xing, D. The correlation between serum creatine kinase with low muscle mass in type 2 diabetes patients. J. Investig Med. 71 , 279–285 (2023). Hu, W., Ma, Y. & Xing, D. Association of triglyceride-glucose index and the presence of low muscle mass in type 2 diabetes patients. Clin. Exp. Med. 23 , 943–949 (2023). Fu, Q., Zhang, Z., Hu, W. & Yang, Y. The correlation of triglyceride/high-density lipoprotein cholesterol ratio with muscle mass in type 2 diabetes patients. BMC Endocr. Disord . 23 , 93 (2023). Lin, Y. L., Wang, C. H., Lai, Y. H., Kuo, C. H. & Syu, R. J. Hsu. Negative correlation between leptin serum levels and sarcopenia in hemodialysis patients. Int. J. Clin. Exp. Pathol. 11 , 1715–1723 (2018). Tanaka, S. et al. M. Fujishiro. Inverse Correlation Between Grip Strength and Serum Phosphorus: A Retrospective Observational Study in Japanese Elderly with Poorly Controlled Type 2 Diabetes. Geriatrics (Basel) 5 , (2020). van Dronkelaar, C. et al. Minerals and Sarcopenia. The Role of Calcium, Iron, Magnesium, Phosphorus, Potassium, Selenium, Sodium, and Zinc on Muscle Mass, Muscle Strength, and Physical Performance in Older Adults: A Systematic Review. J. Am. Med. Dir. Assoc. 19 , 6–11e13 (2018). Kurose, S. et al. Prevalence and risk factors of sarcopenia in community-dwelling older adults visiting regional medical institutions from the Kadoma Sarcopenia Study. Sci. Rep. 10 , 19129 (2020). Du, Y. & Oh, C. No. Associations between Sarcopenia and Metabolic Risk Factors: A Systematic Review and Meta-Analysis. J. Obes. Metab. Syndr. 27 , 175–185 (2018). Senior, H. E., Henwood, T. R., Beller, E. M., Mitchell, G. K. & Keogh, J. W. Prevalence and risk factors of sarcopenia among adults living in nursing homes. Maturitas 82 , 418–423 (2015). Zhang, Y. et al. Dong. Prevalence and Risk Factors Governing the Loss of Muscle Function in Elderly Sarcopenia Patients: A longitudinal Study in China with 4 Years of Follow-Up. J. Nutr. Health Aging . 24 , 518–524 (2020). Sazlina, S. G., Lee, P. Y., Chan, Y. M., MS, A. H. & Tan, N. C. The prevalence and factors associated with sarcopenia among community living elderly with type 2 diabetes mellitus in primary care clinics in Malaysia. PLoS One . 15 , e0233299 (2020). Chen, F. et al. Risk Factors for Sarcopenia in the Elderly with Type 2 Diabetes Mellitus and the Effect of Metformin. J Diabetes Res . 3950404 (2020). (2020). Hashimoto, Y. et al. Fukui. Sarcopenia is associated with blood pressure variability in older patients with type 2 diabetes: A cross-sectional study of the KAMOGAWA-DM cohort study. Geriatr. Gerontol. Int. 18 , 1345–1349 (2018). Bouchi, R. et al. Sarcopenia is associated with incident albuminuria in patients with type 2 diabetes: A retrospective observational study. J. Diabetes Investig . 8 , 783–787 (2017). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Apr, 2026 Reviews received at journal 24 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers invited by journal 04 Mar, 2026 Editor invited by journal 03 Mar, 2026 Editor assigned by journal 28 Feb, 2026 Submission checks completed at journal 28 Feb, 2026 First submitted to journal 27 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8987825","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":602190125,"identity":"96ef8f2c-4fa4-48d4-a81c-fc0e651e810e","order_by":0,"name":"Xiangyu Gao","email":"","orcid":"","institution":"Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xiangyu","middleName":"","lastName":"Gao","suffix":""},{"id":602190126,"identity":"e5b01fee-65a1-4818-b6ce-ebd65c63197e","order_by":1,"name":"Meijian Wang","email":"","orcid":"","institution":"Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Meijian","middleName":"","lastName":"Wang","suffix":""},{"id":602190127,"identity":"445db7eb-b9ac-4838-a6a2-d166c66fca75","order_by":2,"name":"Wenjie Ma","email":"","orcid":"","institution":"Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Ma","suffix":""},{"id":602190128,"identity":"623bbcad-20ef-4ce6-9af1-7bd31653c213","order_by":3,"name":"Rui Wang","email":"","orcid":"","institution":"Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Wang","suffix":""},{"id":602190129,"identity":"a78f7e36-1f70-4c2e-9fc7-c06d36bf142d","order_by":4,"name":"Wenchao Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYFCCAwzMQFKOjb35AHEaeKBajPl4jiUQq4UBrCVxnkSOAnFa7BlPp0kXttmltzHkMDD8qNhGjC1nt0nPbEvObWM4e4Cx58xtIrXwbjuQ28bYl8DM2EaClnQ2Zh4D0rQksLERreXA2c3WvP+SDdt42BIOEuUX9hlnN97mOWMnLz//8cEHPyqI0MIgcQDBPoBLESrgbyBO3SgYBaNgFIxgAAAm2TklFJzARwAAAABJRU5ErkJggg==","orcid":"","institution":"Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Wenchao","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2026-02-27 12:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8987825/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8987825/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104321266,"identity":"56f1017d-c9e0-4f2d-8477-c96e3652e97a","added_by":"auto","created_at":"2026-03-10 13:19:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignificant differences between subgroups divided by muscle mass in male and female T2DM patients.\u003c/strong\u003e \u003csup\u003ea\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003eb\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01 and \u003csup\u003ec\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. Abbreviations: A/G, Serum albumin to globulin ratio; SMI, skeletal muscle index; BMI, body mass index; DBP, diastolic blood pressure; HDL, high-density lipoprotein cholesterol; TG, triglyceride; P, serum phosphorus.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8987825/v1/682902fc5c4d431933580c78.jpg"},{"id":104321267,"identity":"5ae5d64a-c77b-4c52-a83e-7f3bd73e30ea","added_by":"auto","created_at":"2026-03-10 13:19:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172076,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe correlation between clinical characteristics and low muscle mass in male and female T2DM patients.\u003c/strong\u003e Abbreviations: A/G, Serum albumin to globulin ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TC, total cholesterol; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; TG, triglyceride; P, serum phosphorus.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8987825/v1/a93f2671e512cc878f1bbd72.jpg"},{"id":104405649,"identity":"4978d7d7-06fb-4b43-aa5a-0ec6c27611c9","added_by":"auto","created_at":"2026-03-11 12:23:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":90844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between serum P index and SMI in male and female T2DM patients. \u003c/strong\u003eAbbreviations: SMI, skeletal muscle index; P, serum phosphorus.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8987825/v1/51d8e73f85d32e98238ba8bf.jpg"},{"id":104321268,"identity":"fc225507-c285-4a10-be92-a504e161a283","added_by":"auto","created_at":"2026-03-10 13:19:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75719,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between P index and SMI by smooth curve fitting in male and female T2DM patients.\u003c/strong\u003eThreshold values (P ≤ 0.98 mmol/L in males, P ≤ 1.04 in females). Abbreviations: SMI, skeletal muscle index; P, serum phosphorus.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8987825/v1/9cef287205f84a9fa549ec2f.jpg"},{"id":104779555,"identity":"bb6f019f-3591-4e4f-beea-4b4b89f75222","added_by":"auto","created_at":"2026-03-17 07:42:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1942033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8987825/v1/9a2c2e49-b8b7-477b-9e21-d28527fecae4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation between serum phosphorus level and sarcopenia in patients with type 2 diabetes mellitus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia is a clinical syndrome characterized by the progressive loss of skeletal muscle mass and function which often be related to aging[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, recent researches have revealed a rising prevalence of sarcopenia among younger individuals, especially those with obesity, highlighting the need for greater public awareness of its detrimental effects. Sarcopenia is linked to an increased risk of fractures, malnutrition, and movement disabilities, additionally even reported as a vital chronic complication of diabetes mellitus, with which it associated with increased mortality rates and poor long-term prognosis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to a British meta-analysis, the incidence of sarcopenia was significantly higher in patients diagnosed with type 2 diabetes mellitus (T2DM) (28.4%) than that in the non-diabetic individuals (18.7%)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Given the adverse consequences of sarcopenia, early identification and prevention are important, especially in patients with diabetes. According to Asian Working Group for Sarcopenia (AWGS), the proposed diagnostic thresholds for assessing low muscle mass include sex-specific cutoffs of 7.0 kg/m\u0026sup2; (male) and 5.4 kg/m\u0026sup2; (female) when measured through dual-energy X-ray absorptiometry (DXA), while bioelectrical impedance analysis yields comparable values of 7.0 kg/m\u0026sup2; for males and 5.7 kg/m\u0026sup2; for females. Complementary functional criteria consist of grip strength thresholds below 26 kg for men and 18 kg for women, along with reduced mobility indicators defined by walking speeds slower than 0.8 m/s[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Currently, DXA are most commonly used clinically to measure muscle mass and indirectly predict sarcopenia. Moreover, screening for novel serum biomarkers represents a promising approach to predict muscle mass and achieve prevention of sarcopenia.\u003c/p\u003e \u003cp\u003ePhosphorus (P) is a critical macromineral that plays vital roles in various biological functions in human beings. Notably, P plays a vital role in skeletal mineralization and energy metabolism, with its deficiency leading to a significant disruption of the musculoskeletal system[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Phosphorus-containing metabolites are essential components of cellular structures and functions, serving as critical elements in the formation of cell membranes and nucleic acid molecules[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Besides, phosphorus-containing metabolites also play a vital role in metabolic processes and energy metabolism, particularly through the phosphorylation of intermediate metabolites and the storage of energy released in high-energy phosphate bonds, such as those in ATP or phosphocreatine, during oxidative phosphorylation[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In skeletal muscles, furthermore, P is essential for the provision of high-energy phosphates required for the contractile activity and structural integrity of the muscular membrane and intracellular organelles, including the sarcoplasmic and mitochondrial reticulum[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These roles suggest that the level of P in the serum may correlate to its level in the skeletal muscle, thereby serving a vital biomarker for predicting and evaluating sarcopenia.\u003c/p\u003e \u003cp\u003eCurrently, the relationship between P and muscle mass in T2DM patients remains underexplored, with the available findings still being largely inconsistent. Therefore, this study was performed to evaluate the correlation of serum P level and low muscle mass in patients with T2DM, in order to explore the potential targets for the prediction and prevention of sarcopenia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eIn total, 1078 consecutive inpatients (including 579 males and 499 females) from the Department of Endocrinology of Qilu hospital (Qingdao), Shandong University were recruited from September 2017 to September 2019 in this cross-sectional study. All participants were clinically diagnosed with T2DM based on the criteria by the American Diabetic Association, with a fasting plasma glucose (FPG)\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, a 2-hour postprandial plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L, or both. Patients were excluded from the study if they were pregnant, undergoing anti-tumor therapy, characterized by severe hepatic and renal failures, experiencing severe bone disease, suffering from acute infectious diseases, and experiencing thyroid and parathyroid dysfunctions. This study was approved by the ethics committees of Qilu Hospital (Qingdao) of Shandong University, additionally, written informed consent were obtained from all participants.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLow muscle mass definition\u003c/h3\u003e\n\u003cp\u003eSkeletal muscle index (SMI) was measured using the dual-energy X-ray absorptiometry (DXA, Hologic Discovery A, Waltham, MA, USA). Subsequently, SMI was calculated as appendicular skeletal muscle mass in kilograms divided by the square of the body height in meters (kg/m\u003csup\u003e2\u003c/sup\u003e). Low muscle mass was defined as SMI\u0026thinsp;\u0026lt;\u0026thinsp;7.0 kg/m2 in males and SMI\u0026thinsp;\u0026lt;\u0026thinsp;5.4 kg/m2 in females, based on the consensus by the Asian Working Group for Sarcopenia[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eBasic characteristics and biochemical measurements\u003c/h3\u003e\n\u003cp\u003eInformation on basic epidemiological characteristics, including age, sex, height, weight, blood pressures, and the duration of diabetes were collected and recorded. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m2), with patients having BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 was regarded as obesity. Additionally, DXA, previously mentioned, was used to assist in categorizing patients as either ANDROID or GYNOID, which are different types of obesity. Fasting blood samples were collected from all participant and various biochemical measurements were quantified, including serum albumin to globulin ratio (A/G), FPG, total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), triglyceride (TG), serum calcium (Ca), and P.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eQuantitative data following a normal distribution were presented as either mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (25th percentile, 75th percentile), while categorical data following a normal distribution were summarized using proportions or percentages of patients. Student\u0026rsquo;s t-test was used for parametric data, whereas the Mann-Whitney U-test or chi-square test was used for non-parametric data to compare the differences in variables between normal muscle mass and less muscle mass groups. The relationships between the two groups were assessed using the Spearman correlation analysis. Moreover, binary logistic regression analysis was used to determine the predictors for low muscle mass after adjusting for potential confounding variables. Additionally, the independent association between the serum P level and SMI was assessed using linear regression, with a smooth curve fitting used to explore this association. A multivariate piecewise linear regression was further used to examine the threshold correlation of the serum P level and SMI based on to the smooth curve fit. All \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to be statistically significant. Given the exploratory nature of this study, we did not apply corrections for multiple comparisons (e.g., Bonferroni or FDR). Future confirmatory studies should incorporate stricter adjustments to mitigate Type I error risks. All analyses were performed using SPSS Version 26.0 (IBM Corp., Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe differences of characteristics between patients with low and normal muscle mass\u003c/h2\u003e \u003cp\u003eA total of 1078 T2DM patients, including 579 males and 499 females were recruited into the study, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among these participants, 117 males (20.2%) and 68 females had low muscle mass (13.6%) had low muscle mass. Moreover, among the male subjects, there tended to be higher age, duration of diabetes, HDL as well as lower A/G ratio, ANDROID, SMI, prevalence of obesity, height, weight, BMI, DBP, TG and serum P index in low muscle mass group compared to normal muscle mass group. While in female subjects, age was significantly higher, while A/G ratio, ANDROID, SMI, prevalence of obesity, height, weight, BMI, DBP, TG, and serum P index were significantly lower in the low muscle mass group compared to the normal muscle mass group. A significantly detailed comparison of the two groups in male and female T2DM patients is illustrated more intuitively in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe clinical characteristics differences between T2DM patients with and without low muscle mass\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMale (n\u0026thinsp;=\u0026thinsp;579)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eFemale (n\u0026thinsp;=\u0026thinsp;499)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal muscle mass (n\u0026thinsp;=\u0026thinsp;462)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow muscle mass (n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNormal muscle mass (n\u0026thinsp;=\u0026thinsp;431)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow muscle mass (n\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.68\u0026thinsp;\u0026plusmn;\u0026thinsp;11.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.86\u0026thinsp;\u0026plusmn;\u0026thinsp;13.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.11\u0026thinsp;\u0026plusmn;\u0026thinsp;10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.85\u0026thinsp;\u0026plusmn;\u0026thinsp;11.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA/G ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANDROID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.60\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.93\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.52\u0026thinsp;\u0026plusmn;\u0026thinsp;6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.51\u0026thinsp;\u0026plusmn;\u0026thinsp;7.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGYNOID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.95\u0026thinsp;\u0026plusmn;\u0026thinsp;4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.63\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.96(7.54,8.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.51(6.27,6.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.35(5.90,6.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.18(4.95,5.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e367(79.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82(70.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e319(74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39(57.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173.26\u0026thinsp;\u0026plusmn;\u0026thinsp;5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171.04\u0026thinsp;\u0026plusmn;\u0026thinsp;8.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e160.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e158.99\u0026thinsp;\u0026plusmn;\u0026thinsp;5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.95\u0026thinsp;\u0026plusmn;\u0026thinsp;14.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.90\u0026thinsp;\u0026plusmn;\u0026thinsp;11.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.51\u0026thinsp;\u0026plusmn;\u0026thinsp;12.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.38\u0026thinsp;\u0026plusmn;\u0026thinsp;7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.69\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.40\u0026thinsp;\u0026plusmn;\u0026thinsp;19.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.68\u0026thinsp;\u0026plusmn;\u0026thinsp;21.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143.38\u0026thinsp;\u0026plusmn;\u0026thinsp;20.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e142.44\u0026thinsp;\u0026plusmn;\u0026thinsp;22.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.49\u0026thinsp;\u0026plusmn;\u0026thinsp;12.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.20\u0026thinsp;\u0026plusmn;\u0026thinsp;12.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.53\u0026thinsp;\u0026plusmn;\u0026thinsp;12.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.18\u0026thinsp;\u0026plusmn;\u0026thinsp;10.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (4, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (6, 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (5, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (5, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.96\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67(1.07,2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10(0.85,1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.47(1.04,2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.32(0.89,1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: A/G, Serum albumin to globulin ratio; SMI, skeletal muscle index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TC, total cholesterol; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; TG, triglyceride; Ca, serum calcium and P, serum phosphorus.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe correlation between low muscle mass and the clinical parameters\u003c/h3\u003e\n\u003cp\u003eSpearman correlation analysis was applied to investigate the whole clinical parameters that might be related to low muscle mass. As indicated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, age, duration of diabetes, and HDL were positively correlated with low muscle mass (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while A/G ratio, ANDROID, SMI, prevalence of obesity, height, weight, BMI, DBP, TG and serum P index were negatively correlated with low muscle mass (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in male patients. Among the female subjects, there were positive correlation between age and low muscle mass (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). While A/G ratio, ANDROID, SMI, prevalence of obesity, height, weight, BMI, and TG were negatively correlated with low muscle mass (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, it should be noted that the serum P index showed a weak, non-significant negative correlation with low muscle mass in female subjects (r=-0.074, p\u0026thinsp;=\u0026thinsp;0.099), suggesting minimal clinical relevance. We identified SMI as a quantitative indicator of low muscle mass, subsequently, we conducted linear regression analysis to further explore the correlation between SMI and clinical characteristics. As Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shown, SMI was correlated with age, A/G ratio, ANDROID, prevalence of obesity, height, weight, BMI, DBP, duration of diabetes, HDL, TG, serum Ca and P indexes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in male subjects. In female counterparts, SMI was correlated with age, A/G ratio, ANDROID, GYNOID, prevalence of obesity, height, weight, BMI, DBP, HDL, and P index (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among them, we are most concerned about the consistent positive linear correlation between SMI and P index, with (β\u0026thinsp;=\u0026thinsp;1.248, 95% CI: 0.568\u0026ndash;1.929) in male patients and (β\u0026thinsp;=\u0026thinsp;0.862, 95% CI: 0.374\u0026ndash;1.349) in female patients, respectively. Furthermore, both male and female patients were categorized into four groups based on the quartiles of the serum P levels: Q1 (\u0026lt;\u0026thinsp;1.06 mmol/L), Q2 (1.06\u0026ndash;1.19 mmol/L), Q3 (1.19\u0026ndash;1.33 mmol/L), and Q4 (\u0026ge;\u0026thinsp;1.33 mmol/L) in males and Q1 (\u0026lt;\u0026thinsp;1.13 mmol/L), Q2 (1.13\u0026ndash;1.26 mmol/L), Q3 (1.26\u0026ndash;1.36 mmol/L), and Q4 (\u0026ge;\u0026thinsp;1.36 mmol/L) in females. Additionally, the results revealed that elevated P levels reduced the risk of low muscle mass in males (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.001) and in females (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.020).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation of low muscle mass with clinical parameters by Spearman analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA/G ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANDROID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGYNOID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: A/G, Serum albumin to globulin ratio; SMI, skeletal muscle index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TC, total cholesterol; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; TG, triglyceride; Ca, serum calcium and P, serum phosphorus.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe correlation between SMI and clinical characteristics by Linear regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.028 (-0.038, -0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.021 (-0.028, -0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA/G ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.583 (0.916, 2.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.004 (0.449, 1.559)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANDROID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.044 (0.026, 0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033 (0.020, 0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGYNOID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025 (-0.002, 0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019 (0.002, 0.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.365 (0.067, 0.662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.342 (0.155, 0.530)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035 (0.016, 0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019 (0.003, 0.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.050 (0.043, 0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047 (0.042, 0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.151 (0.126, 0.176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.131 (0.117, 0.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005 (-0.001, 0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003 (-0.001, 0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.020 (0.011, 0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013 (0.006, 0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.020 (-0.039, -0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.003 (-0.017, 0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.017 (-0.027, 0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.005 (-0.035, 0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.031 (-0.079, 0.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.009 (-0.073, 0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.576 (-1.006, -0.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.270 (-0.504, -0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.040 (-0.098, 0.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003 (-0.088, 0.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.086 (0.026, 0.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055 (-0.009, 0.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.216 (0.167, 2.265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.175 (-0.558, 0.908)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.248 (0.568, 1.929)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.862 (0.374, 1.349)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: A/G, Serum albumin to globulin ratio; SMI, skeletal muscle index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TC, total cholesterol; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; TG, triglyceride; Ca, serum calcium and P, serum phosphorus.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eThe association between the serum P level and low muscle mass\u003c/h3\u003e\n\u003cp\u003eThe logistic regression analysis further confirmed the association between the serum P level and low muscle mass, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Notably, the serum P index was associated with a decreased risk of low muscle mass in both male (OR\u0026thinsp;=\u0026thinsp;0.117, 95% CI: 0.036\u0026ndash;0.382, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and female (OR\u0026thinsp;=\u0026thinsp;0.166, 95% CI: 0.037\u0026ndash;0.735, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) T2DM participants with no adjustment (model 1). After adjusting for age, P index was found to be negatively associated with low muscle mass in males (OR\u0026thinsp;=\u0026thinsp;0.237, 95% CI: 0.069\u0026ndash;0.819, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) and females (OR\u0026thinsp;=\u0026thinsp;0.170, 95% CI: 0.036\u0026ndash;0.794, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) (model 2). And after adjusting for basic clinical parameters that lacked laboratory data such as age, SBP, DBP and duration of diabetes, P index was found to be correlated with a reduced risk of low muscle mass in males (OR\u0026thinsp;=\u0026thinsp;0.214, 95% CI: 0.060\u0026ndash;0.755, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) and females (OR\u0026thinsp;=\u0026thinsp;0.172, 95% CI: 0.037-0.800, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) (model 3). Furthermore, P index was still inversely correlated with low muscle mass after adjusting for age, A/G ratio, SBP, DBP, duration of diabetes, Ca, FPG, TC, TG, HDL, and LDL both in male patients (OR\u0026thinsp;=\u0026thinsp;0.187, 95% CI: 0.045\u0026ndash;0.785, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) and female patients (OR\u0026thinsp;=\u0026thinsp;0.161, 95% CI: 0.032\u0026ndash;0.816, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) (model 4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe association between P index and low muscle mass\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.49 (6.77\u0026ndash;8.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e19.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.06 (5.61\u0026ndash;6.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e9.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.78 (7.09\u0026ndash;8.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.06 (5.64\u0026ndash;6.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.75 (7.17\u0026ndash;8.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.35 (5.73\u0026ndash;6.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.89 (7.30\u0026ndash;8.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.37 (5.82\u0026ndash;6.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 1: not adjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 2: adjusted for age\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 3: adjusted for age, SBP, DBP, duration of diabetes\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 4: adjusted for age, A/G ratio, SBP, DBP, duration of diabetes, Ca, FPG, TC, TG, HDL, LDL\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe association between serum P index and SMI\u003c/h2\u003e \u003cp\u003eParticipants were categorized into four groups based on the quartiles of the serum P level as previously stated and displayed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. We found that SMI were significantly increased with ascending serum P level, suggesting a positive correlation between P index and SMI in both genders. Furthermore, a potential nonlinear association between P index and SMI was indentified in both male and female subjects, which shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. To further analyze this relationship, a two-piecewise linear regression model was applied (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). When P index was \u0026le;\u0026thinsp;0.98, it was significantly positive correlated with SMI in male patients (β\u0026thinsp;=\u0026thinsp;3.299, 95% CI: 0.635\u0026ndash;5.962, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015). However, when P index was \u0026gt;\u0026thinsp;0.98, there was no significant correlation between P index and SMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Similarly, in female participants, a positive correlation between P index and SMI was observed when P index was \u0026le;\u0026thinsp;1.04 (β\u0026thinsp;=\u0026thinsp;3.217, 95% CI: 1.105\u0026ndash;5.329, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). Conversely, when P index was \u0026gt;\u0026thinsp;1.04, there was no statistical significance, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eThe comparison of SMI by quartiles of serum phosphorus level\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.117(0.036\u0026ndash;0.382)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.166(0.037\u0026ndash;0.735)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.237(0.069\u0026ndash;0.819)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.170(0.036\u0026ndash;0.794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.214(0.060\u0026ndash;0.755)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.172(0.037-0.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.187(0.045\u0026ndash;0.785)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.161(0.032\u0026ndash;0.816)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: SMI, skeletal muscle index.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSerum phosphorus level in males was classified as Q1 (\u0026lt;\u0026thinsp;1.06 mmol/L), Q2 (1.06\u0026ndash;1.19 mmol/L), Q3 (1.19\u0026ndash;1.33 mmol/L), and Q4 (\u0026ge;\u0026thinsp;1.33 mmol/L).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSerum phosphorus level in females was classified as Q1 (\u0026lt;\u0026thinsp;1.13 mmol/L), Q2 (1.13\u0026ndash;1.26 mmol/L), Q3 (1.26\u0026ndash;1.36 mmol/L), and Q4 (\u0026ge;\u0026thinsp;1.36 mmol/L).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe correlation between P and SMI in male subjects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eModel I\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne line slope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.427 (-0.072, 0.926)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eModel II\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurning point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.98 slope 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.299 (0.635, 5.962)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.98 slope 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.143 (-0.418, 0.704)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: P, serum phosphorus; SMI, skeletal muscle index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe correlation between P and SMI in female subjects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eModel I\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne line slope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.862 (0.375, 1.348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eModel II\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurning point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.04 slope 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.217 (1.105, 5.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.04 slope 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.524 (-0.043, 1.091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: P, serum phosphorus; SMI, skeletal muscle index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, sarcopenia, which often be characterized by loss of skeletal muscle mass, reduced muscle strength and/or impaired physical performance, has been increasingly recognized as one of vital chronic complications of diabetes mellitus. Low muscle mass is closely associated with poor prognosis of diabetes. Consequently, an increasing number of studies have focused on exploring the epidemiology and the underlying mechanisms of low skeletal muscle mass in diabetic patients. Therefore, the early detection and diagnosis of sarcopenia is particularly important to facilitate timely follow-up intervention, which helps in mitigating the deterioration of syndrome. In our previous researches, we have found that ALT/AST was inversely correlated with muscle mass in patients with T2DM[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Besides, creatine kinase (CK) is negatively correlated with low muscle mass among patients with T2DM[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. TyG index, a novel marker of insulin resistance, was inversely correlated with the presence of sarcopenia in T2DM patients[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, higher TG/HDL-C ratio was also demonstrated to be correlated with skeletal muscle mass in T2DM patients[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, there remains a significant gap in literature given that only a limited number of studies have reported the relationship between serum electrolyte level and muscle mass.\u003c/p\u003e \u003cp\u003eOur present study indicated that higher serum P level was correlated with a reduced risk of low muscle mass in patients with T2DM, that is, P index was positively associated with SMI. The adjusted odds ratio (OR) for serum P index in males (OR\u0026thinsp;=\u0026thinsp;0.187, 95% CI: 0.045\u0026ndash;0.785, P\u0026thinsp;=\u0026thinsp;0.022) indicates an 81.3% reduction in low muscle mass risk per unit increase in serum P index. And there may be an 83.9% reduction in low muscle mass risk per unit increase in serum P index among female patients (OR\u0026thinsp;=\u0026thinsp;0.161, 95% CI: 0.032\u0026ndash;0.816, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027). Besides, the β-coefficient for P in T2DM patients reflects a clinically meaningful increase in P with higher SMI, with β\u0026thinsp;=\u0026thinsp;1.248 in males (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and β\u0026thinsp;=\u0026thinsp;0.862 in females (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), respectively. Similarly, another study based on hemodialysis patients reported that serum P level was lower in the low muscle mass group compared to the normal muscle mass group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), this observation is consistent with our findings to some extent[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, one Japanese research reported that serum P concentration in weakened grip strength group was significantly higher than that of patients without weakened grip strength (3.8 vs. 3.3 mg/dL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), demonstrated that serum P concentration was negatively correlated with grip strength. Additionally, this negative association was also confirmed after adjusting for the confounding factors[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], which was contrary to our conclusion. In our study, Spearman's rank correlation coefficient analysis revealed some different results between the sexes: while the relationship was statistically significant in males, and no significant association was observed in females. These discrepancies underscore the potential influence of sex-specific factors on phosphorus metabolism and muscle homeostasis, which warrant further investigation. The lack of statistical significance in female participants contrasts with the male cohort, suggesting that biological or hormonal differences may modulate phosphorus\u0026rsquo;s role in muscle metabolism. For instance, estrogen\u0026rsquo;s influence on mineral homeostasis or sex-specific fat distribution patterns could partially explain these disparities. Future mechanistic studies are needed to explore these hypotheses. The differences among these results may be related to the variables in population, region and measurement methods, etc.. Furthermore, findings from a systematic review of Netherlands \u0026ndash; specifically in studies exploring the potential role of minerals in the prevention and treatment of sarcopenia in the elderly\u0026ndash;failed to establish a relationship between P with muscle-reducing outcomes, given that analysis incorporated insufficient number of studies, resulting in a low quality of evidence[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The results of the current limited studies on sarcopenia remain inconsistent, underscoring the need for further randomized controlled trials to determine the influence of serum P on muscle mass in patients with T2DM.\u003c/p\u003e \u003cp\u003eAs mentioned above, P plays a vital role in skeletal mineralization and energy metabolism, and P-containing metabolites are key elements in the formation of cell membranes and nucleic acid molecules, serving as essential components of cellular structures and functions[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. P-containing metabolites also participates in oxidative phosphorylation through the phosphorylation of intermediate metabolites and the storage of energy released in high-energy phosphate bonds, such as those in ATP or phosphocreatine[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Besides, P is essential for the provision of high-energy phosphates required for the contractile activity and structural integrity of the muscular membrane and intracellular organelles in skeletal muscles[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Thus P is essential for skeletal muscle bioenergetics and structural integrity, while its role in T2DM-specific metabolic derangements need further exploration. Insulin resistance, a typical feature of T2DM, may impair phosphorus-mediated pathways critical for muscle maintenance.\u003c/p\u003e \u003cp\u003eIn addition to the association between phosphorus and muscle mass, our study also showed that patients with T2DM in the low muscle mass group, both males and females, exhibited a higher age, as well as lower A/G ratio, ANDROID, prevalence of obesity, height, weight, BMI, and TG compared to the normal muscle mass group. It is well-established that muscle mass decreases with advancing age, a phenomenon that is supported by numerous studies previously mentioned. Additionally, obesity is recognized as a typical risk factor for sarcopenia. A multicenter study in Japan conducted among community-dwelling older adults showed that obesity and hypertension are independent predictors of sarcopenia among this cohort[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, previous studies have demonstrated that the metabolic risk factors, including BMI, are significantly related to loss of skeletal muscle or occurrence of sarcopenia[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Contrastingly, findings from a cross-sectional study which assessed older people residing in 11 long-term nursing homes in Australia indicated that low BMI is a predictive risk factor for sarcopenia[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A four-year follow-up longitudinal study in China showed that a higher BMI serves a protective factor against muscle mass loss, but increases the risk factor of reduced gait speed[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As for patients with T2DM, several studies have indicated that BMI is positively correlated with muscle mass, in other words, the prevalence of sarcopenia significantly increases as the level of BMI decreases in patients with T2DM[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Consistently, this study demonstrates that T2DM patients with low muscle mass exhibit lower BMI compared to those with normal muscle mass. These findings may initially appear contradictory, as obesity is typically linked to adverse metabolic outcomes. However, our results align with emerging evidence on sarcopenic obesity, where higher BMI may mask underlying muscle loss. Furthermore, BMI inherently reflects total body mass, which includes both fat and muscle components. Thus, while BMI and SMI are positively correlated, the preservation of muscle mass in T2DM patients with higher BMI may mitigate sarcopenia risk, even in the presence of excess adiposity. While obesity is associated with an elevated risk of diabetic complications, it is also inversely correlated with the risk of developing sarcopenia in people with T2DM.\u003c/p\u003e \u003cp\u003eThis study provides exploratory evidence of an inverse correlation between serum P levels and low muscle mass in patients with T2DM. However, given the cross-sectional design and exploratory nature of this findings, it has several potential limitations in terms of the sample size and the duration of follow-up. Such cross-sectional design and lack of alpha-risk adjustment limit causal inference and increase the risk of false positives. Future work should prioritize longitudinal cohorts, stratified analyses, and corrections for multiple comparisons to strengthen conclusions. To address these challenges, future research should employ prospective and longitudinal studies involving a larger sample size comprising participants from various race and age groups. And some confounders such as parathyroid hormone (PTH), dietary phosphate intake and chronic kidney disease stages should also be assessed, which may influence serum P and muscle metabolism. Future prospective studies incorporating these variables are warranted to validate our findings and refine mechanistic insights. Besides, future studies should explore underlying mechanisms such as tissue-specific phosphorus dynamics, insulin signaling interactions, and sex dependence in T2DM. Additionally, randomized controlled trials are necessary to validate the findings of this study, as well as to promote a comprehensive understanding of the correlation between serum P level and low muscle mass in patients with T2DM.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, serum phosphorus level is inversely correlated with low muscle mass in patients with T2DM.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eA/G\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSerum albumin to globulin ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP,\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eAll authors declare no conflicts of interest for this work.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eInformed consent\u003c/h2\u003e \u003cp\u003e\u003cb\u003estatement\u003c/b\u003e: All participants, or their legal guardian, provided written informed consent in accordance with the Declaration of Helsinki prior to study enrollment.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by Qingdao Outstanding Health Professional Development Fund; Qingdao Key Health Discipline Development Fund; and Natural Science Foundation of Qingdao (23-2-1-190-zyyd-jch).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXiangyu Gao: Formal analysis, Writing \u0026ndash; original draft. Meijian Wang: Formal analysis, Writing \u0026ndash; review and editing. Wenjie Ma: Investigation. Wenchao Hu: Conceptualization, Funding acquisition. Rui Wang: Data curation, Supervision, Project administration.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAll authors would like to thank Dr. Xiaotian Ma, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University for her statistical guidance. Besides, we also acknowledge assistance from medical writers, proof-readers and editors.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll datasets generated for this study were all included in the manuscript files and the raw data may be obtained from the corresponding author for appropriate justification.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUmegaki, H. Sarcopenia and frailty in older patients with diabetes mellitus. \u003cem\u003eGeriatr. Gerontol. 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Health Aging\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e, 518\u0026ndash;524 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSazlina, S. G., Lee, P. Y., Chan, Y. M., MS, A. H. \u0026amp; Tan, N. C. The prevalence and factors associated with sarcopenia among community living elderly with type 2 diabetes mellitus in primary care clinics in Malaysia. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, e0233299 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, F. et al. Risk Factors for Sarcopenia in the Elderly with Type 2 Diabetes Mellitus and the Effect of Metformin. \u003cem\u003eJ Diabetes Res\u003c/em\u003e. 3950404 (2020). (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashimoto, Y. et al. Fukui. Sarcopenia is associated with blood pressure variability in older patients with type 2 diabetes: A cross-sectional study of the KAMOGAWA-DM cohort study. \u003cem\u003eGeriatr. Gerontol. Int.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 1345\u0026ndash;1349 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouchi, R. et al. Sarcopenia is associated with incident albuminuria in patients with type 2 diabetes: A retrospective observational study. \u003cem\u003eJ. Diabetes Investig\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 783\u0026ndash;787 (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Serum phosphorus level, Sarcopenia, Skeletal muscle index, T2DM","lastPublishedDoi":"10.21203/rs.3.rs-8987825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8987825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSarcopenia is associated with poor prognosis of type 2 diabetes mellitus (T2DM). Phosphorus (P) deficiency may lead to disturbance of the musculoskeletal system and serve as a potential biomarker of sarcopenia. This study aimed to evaluate the correlation of P and muscle mass in T2DM patients. A total of 1078 T2DM inpatients were recruited. Skeletal muscle index (SMI), along with subtypes of obesity were quantified using a dual energy X-ray absorptiometry. Participants were considered as sarcopenia, when SMI was less than 7.0 kg/m\u003csup\u003e2\u003c/sup\u003e in males and 5.4 kg/m\u003csup\u003e2\u003c/sup\u003e in females. Clinical information and biochemical characteristics were measured and recorded. We found the prevalence of low muscle mass was 20.2% and 13.6% in male and female patients, respectively. Serum P index was associated with a reduced risk of low muscle mass in both genders. This finding was observed before and after adjustment for variables, including for age, A/G ratio, SBP, DBP, duration of diabetes, Ca, FPG, TC, TG, HDL, and LDL. Potentially, there might be nonlinear association between P index and SMI. When the P index was \u0026le;\u0026thinsp;0.98, a significant positive correlation with SMI was observed in males. However, when the P index was \u0026gt;\u0026thinsp;0.98, no significant correlation between P index and SMI. Similarly, in females, P index was positively correlated with SMI when P index was \u0026le;\u0026thinsp;1.04, while when P index was \u0026gt;\u0026thinsp;1.04, a weak and non-significant negative correlation between P and SMI was observed. In conclusion, serum P level is inversely correlated with sarcopenia in T2DM patients.\u003c/p\u003e","manuscriptTitle":"Correlation between serum phosphorus level and sarcopenia in patients with type 2 diabetes mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 13:19:03","doi":"10.21203/rs.3.rs-8987825/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-08T11:21:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T05:56:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T05:21:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308852583768443188039263416075354203489","date":"2026-03-07T05:06:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268856286700265313491125058753852733215","date":"2026-03-06T08:27:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-04T11:13:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-03T13:16:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-28T12:25:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-28T12:25:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-27T11:58:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b48b193-3485-44f4-aa0f-8956f655e445","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64088205,"name":"Health sciences/Biomarkers"},{"id":64088206,"name":"Health sciences/Diseases"},{"id":64088207,"name":"Health sciences/Endocrinology"},{"id":64088208,"name":"Health sciences/Health care"},{"id":64088209,"name":"Health sciences/Medical research"},{"id":64088210,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-07T11:53:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 13:19:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8987825","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8987825","identity":"rs-8987825","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}