The Association Between Hand Grip Strength and Chronic Kidney Disease Progression: Insights from SMP-CKD Studies

The Association Between Hand Grip Strength and Chronic Kidney Disease Progression: Insights from SMP-CKD Studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Association Between Hand Grip Strength and Chronic Kidney Disease Progression: Insights from SMP-CKD Studies Qiong Huang, Linyi Chen, Wenwei Ouyang, Xi-na Jie, Li-zhe Fu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5292199/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 May, 2025 Read the published version in International Urology and Nephrology → Version 1 posted You are reading this latest preprint version Abstract Purpose This study aims to investigate the relationship between handgrip strength (HGS) and the progression of chronic kidney disease (CKD) in non-dialysis patients in China, as part of the Self-Management Program for Patients with CKD Cohort (SMP-CKD). Methods In the SMP-CKD cohort, we utilized Cox regression and Kaplan-Meier survival analysis to explore the association between HGS and CKD progression. Data were stratified by sex-specific HGS quartiles, sarcopenia status, and HGS thresholds. The HGS thresholds were determined through curve analysis of HGS against composite renal outcomes. Group differences were compared to assess the impact of HGS on CKD outcomes. Results A total of 441 participants (mean age 57.0 ± 17 years, 56.0% male) with CKD stages 3–5 from the SMP-CKD cohort who underwent grip strength evaluation between April 2019 and June 2024 were included in the analysis. The findings revealed that participants in the highest bilateral HGS quartile had a significantly lower risk of renal endpoints, with a hazard ratio (HR) of 0.109 (95% CI: 0.044–0.272) compared to those in the lowest quartile. Patients with sarcopenia exhibited more than twice the risk of increased serum creatinine or acute CKD exacerbations (HR 2.429, 95% CI: 1.218–4.846), as well as a markedly higher risk of severe renal endpoints (HR 4.237, 95% CI: 1.595–11.256). Gender-specific cutoffs identified through ROC analysis were 64.35 kg for men and 39.35 kg for women. Participants with bilateral HGS above these thresholds demonstrated better renal outcomes, underscoring the protective effect of higher HGS against CKD progression. Conclusion The study provides strong evidence that HGS is a crucial factor in reducing the risk of CKD progression. Higher levels of HGS are significantly associated with a lower occurrence of renal endpoint events. Handgrip strength sarcopenia chronic kidney disease SMP-CKD Figures Figure 1 Figure 2 Figure 3 Introduction Chronic kidney disease (CKD) is a growing global health burden characterized by a gradual loss of kidney function. As of 2017, the global prevalence of CKD was 9.1%[ 1 ], while in China it reached 10.8%[ 2 ], affecting over 150 million people and imposing a significant economic burden. CKD is marked by high prevalence, high disability rates, high medical costs, and inadequate patient education, making it a major public health issue worldwide[ 1 ]. Early-stage CKD often lacks obvious symptoms, leading to underdiagnosis and poor management. CKD also increases the risk of cardiovascular disease, strokes, and progression to end-stage renal disease (ESRD), requiring dialysis or transplantation[ 3 , 4 ]. Identifying reliable, non-invasive biomarkers to predict CKD progression is crucial for improving patient outcomes and reducing healthcare costs. Hand grip strength (HGS) has emerged as a potential biomarker in various medical conditions due to its ease of measurement and its correlation with overall muscle strength and physical function. The possible link between HGS and CKD progression has garnered increasing attention. Muscle wasting and reduced physical function are common among CKD patients, often resulting from malnutrition, inflammation, and metabolic derangements associated with impaired kidney function[ 5 – 11 ]. Furthermore, CKD complications—such as cardiovascular events, mental health issues, and increased fracture risk—further diminish the quality of life and elevate mortality rates, all of which are associated with low HGS[ 12 – 14 ]. This reinforces the notion of a potential connection between CKD and HGS. As CKD progresses, the risk of sarcopenia—characterized by the loss of skeletal muscle mass and strength—increases, further exacerbating the decline in physical function and overall health status. Despite this plausible link, comprehensive studies investigating the relationship between HGS and CKD progression remain limited. Most prior investigations into HGS and CKD have been cross-sectional, limiting their ability to clarify the causal relationship between HGS and CKD[ 15 , 16 ]. Additionally, previous research has primarily focused on dialysis patients, with fewer studies on the non-dialysis population[ 17 – 21 ]. Approximately a decade ago, a single study identified HGS as an independent predictor of poor composite renal outcomes in 128 non-dialysis patients. However, the limited sample size and short follow-up period of this study constrained the generalizability of its findings[ 22 ]. Moreover, while HGS is used to assess sarcopenia, current research often evaluates HGS alone without considering comprehensive sarcopenia diagnosis and its impact on CKD progression[ 23 , 24 ]. HGS cutoff values also differ across countries and populations, making it challenging to establish a standardized threshold for CKD patients[ 25 ]. Identifying a specific cutoff for CKD patients is crucial for accurate risk stratification and clinical application. To address these gaps, this research investigates the relationship between HGS and CKD progression in non-dialysis CKD patients in China. Using data from the Guangdong Provincial Hospital of Chinese Medicine's Self-Management Program for Patients with Chronic Kidney Disease (SMP-CKD) cohort, this study aims to overcome previous limitations with a larger sample size and extended follow-up. Additionally, it will explore HGS as an indicator for sarcopenia diagnosis and its implications for CKD progression, ultimately determining appropriate HGS cutoff values tailored to CKD patients. Methods Population and study design The SMP-CKD cohort, conducted at the Guangdong Provincial Hospital of Chinese Medicine (GPHCM), is an ongoing multi-center observational study (Ethics Approval No. 2019-153-01; Chinese Clinical Trial Registry No. ChiCTR1900024633) anticipated to last around 10 years[ 26 ]. This ambispective cohort study includes a retrospective phase spanning July 2015 to July 2019 and a prospective phase from August 2019 through August 2024. The longitudinal cohort part of this study leverages the prospective phase of the SMP-CKD database, encompassing data from 550 individuals diagnosed with CKD stages 3 to 5. It collects demographic data, clinical diagnoses, medical histories, laboratory findings, circulating lipid profiles, and distribution assessments. After exclusions of 97 subjects without data of HGS and covariates, 441 selected participants were included in the study. (Fig. 1 ) Exposure and covariates Handgrip strength was assessed using a handgrip dynamometer, adjusted for each participant's hand size, by a trained examiner. Participants performed three trials per hand, alternating between hands with a 60-second rest in between, while standing unless hindered by physical limitations. Additionally, information on past surgeries for arthritis or carpal tunnel syndrome, which could influence handgrip strength, was collected. The related organization ensured quality control through regular examiner retraining, equipment maintenance, and systematic data review to identify inconsistencies or errors, thereby guaranteeing the reliability and accuracy of handgrip strength measurements as a proxy for muscle strength. The above methods for measuring, recording, and checking grip strength. We collected left hand, right hand, and bilateral grip strength in the SMP-CKD cohort, and patients will be excluded when only one hand is measured due to some conditions (such as advanced arteriovenous fistula, stroke, upper limb fracture, etc.) Serum Creatinine and Covariate information Serum creatinine(SCr)measurements play a critical role in diagnosing and managing renal diseases. In addition, the urinary protein/creatinine ratio (UPCR) refers to the protein/creatinine ratio in mg/g. Estimated glomerular filtration rate(eGFR) was determined through the CKD-EPI equation, yielding eGFR values in mL/min/1.73 m^2[ 27 ]. CKD is defined as eGFR 30 mg/g. Outcome 1 is a composite renal endpoint including initiation of chronic dialysis or renal transplantation, a sustained 40% decline in eGFR, doubling of SCr from baseline, eGFR below 5 mL/min/1.73m², or all-cause mortality. Outcome 2 includes initiation of chronic dialysis or renal transplantation, or all-cause mortality. The Asian Working Group for Sarcopenia (AWGS) 2019 consensus defines sarcopenia as "age-related loss of muscle mass, plus low muscle strength, and/or low physical performance"[ 28 ]. According to this updated definition, sarcopenia of CKD in the study is identified by a handgrip strength of less than 28 kg for men and less than 18 kg for women, along with height-adjusted muscle mass cutoffs (skeletal muscle index) of bioimpedance values less than 7.0 kg/m² for men and less than 5.7 kg/m² for women. This study also incorporated a range of both continuous and categorical variables as covariates. These encompassed gender, education, physical activity, and marriage status alongside smoking and alcohol consumption statuses. The continuous variables in this context included age (expressed in years), serum albumin (noted as Alb in g/L), body mass index (BMI, calculated as kg/m²), and the eGFR (ml/min/1.73m²). We considered all these covariates as potential influencing factors in examining the link between the bilateral HGS and creatinine levels. Moreover, the SPM-CKD cohort adds assessments of hemoglobin and lipid metabolism index, alongside follow-up duration, for a more comprehensive evaluation of CKD progression. Protopathic in SMP-CKD was categorized into primary glomerulonephritis (encompassing chronic nephritis, nephropathy syndrome, and IgA nephropathy), hypertensive renal disease, diabetic nephropathy, other secondary nephrosis (such as systemic lupus erythematosus nephritis, Henoch-Schoenlein purpura, hepatitis B virus-associated nephritis, and obstructive nephropathy), or etiologies that remained unidentified. Statistical Analysis In the baseline data, continuous variables that followed a normal distribution were presented as the mean ± standard deviation (SD), whereas variables with a skewed distribution were shown as the median (interquartile range, IQR). Categorical variables were represented as n (%, weighted). To assess the statistical differences between groups, we utilized the Student's t-test or the Mann-Whitney U test for continuous variables and the chi-square test for categorical variables. Baseline characteristics were summarized as mean (SD) or median (IQR) for continuous variables, and as proportions for categorical variables, stratified by sex-specific bilateral HGS quartiles within the SMP-CKD cohort. Gender-specific cutoff values for predicting grip strength outcomes were determined using Receiver Operating Characteristic (ROC) curve analysis. Cox regression and Kaplan-Meier survival curves were subsequently employed to assess the variation in renal composite outcomes among patients with differing HGS levels. Data were stratified by sex-specific HGS quartiles, sarcopenia status, and HGS thresholds. Group differences in the impact of HGS on renal outcomes were analyzed, with p-values calculated using the log-rank test. All data was collected using R (version 4.3.2,www-rproject.com). A p < 0.05 was considered statistically significant. Results General characteristics of SMP-CKD In the cohort study involving 441 participants, the average age of the study population was 57.0 years, with a standard deviation (SD) of 17.0 years. The mean follow-up time was 852.48 ± 443.03 days. Among the participants, 246 (56.0%) were male. A total of 97 participants experienced renal outcome 1 events, while 53 participants experienced renal outcome 2 events. Baseline characteristics of the study participants were analyzed and stratified by sex-specific bilateral HGS quartiles. Participants in higher quartiles of bilateral HGS were observed to be younger. Additionally, they were less likely to have diabetic nephropathy. Furthermore, these participants had lower serum creatinine levels and higher levels of physical activity, Hb, eGFR, and albumin levels (as depicted in Table 1 ). Table 1 Baseline Characteristics of the SMP-CKD Cohort Characteristic Sex-specific handgrip strength, kg p-value b Q1, N = 112 1 Q2, N = 110 Q3, N = 108 a Q4, N = 111 a Women 50 / 112 (45%) 49 / 110 (45%) 47 / 108 (44%) 49 / 111 (44%) > 0.9 Age (years) 65 (57, 70) 59.5 (50.25, 68) 54.5 (43.75, 61) 48 (38, 56.5) < 0.001 BMI (kg/m 2 ) 22.7 (20.4, 24.9) 22.8 (20.8, 25.4) 22.4 (20.7, 24.6) 23.4 (21.2, 25.8) 0.2 SMI (kg/m 2 ) 6.77 ± 1.31 6.84 ± 1.73 7.04 ± 1.37 7.32 ± 1.47 0.01 Smoking behavior (%) 0.9 Never 90 / 112 (80%) 84 / 110 (76%) 86 / 108 (80%) 90 / 111 (81%) Former 14 / 112 (13%) 16 / 110 (15%) 13 / 108 (12%) 11 / 111 (9.9%) Current 8 / 112 (7.1%) 10 / 110 (9.1%) 9 / 108 (8.3%) 10 / 111 (9.0%) Alcohol status (%) < 0.001 Never 83 / 112 (74%) 79 / 110 (72%) 77 / 108 (71%) 73 / 111 (66%) Abstainer 24 / 112 (21%) 25 / 110 (23%) 29 / 108 (27%) 34 / 111 (31%) Current 5 / 112 (4.5%) 6 / 110 (5.5%) 2 / 108 (1.9%) 4 / 111 (3.6%) Activity < 0.001 Vigorous 9 / 112 (8.0%) 11 / 110 (10%) 13 / 108 (12%) 13 / 111 (12%) Moderate 52 / 112 (46%) 51 / 110 (46%) 43 / 108 (40%) 59 / 111 (53%) Low 51 / 112 (46%) 48 / 110 (44%) 52 / 108 (48%) 39 / 111 (35%) Married,n (%) 0.5 Yes 6 / 112 (5.4%) 9 / 110 (8.2%) 12 / 108 (11%) 9 / 111 (8.1%) No 105 / 112 (94%) 101 / 110 (92%) 96 / 108 (89%) 102 / 111 (92%) Unknown 1 / 112 (0.9%) 0 / 110 (0%) 0 / 108 (0%) 0 / 111 (0%) Educational level (%) 0.023 Less than high school 46 / 112 (41%) 40 / 110 (36%) 29 / 108 (27%) 36 / 111 (32%) High school graduate or equivalent 47 / 112 (42%) 40 / 110 (36%) 40 / 108 (37%) 35 / 111 (32%) College degree or more 19 / 112 (17%) 30 / 110 (27%) 39 / 108 (36%) 40 / 111 (36%) Protopathy < 0.001 Primary Glomerulonephritides 23 / 112 (21%) 23 / 110 (21%) 40 / 108 (37%) 38 / 111 (34%) Diabetic nephropathy 15 / 112 (13%) 15 / 110 (14%) 9 / 108 (8.3%) 2 / 111 (1.8%) Hypertensive Renal Disease 3 / 112 (2.7%) 5 / 110 (4.5%) 1 / 108 (0.9%) 3 / 111 (2.7%) Others 26 / 112 (23%) 27 / 110 (25%) 26 / 108 (24%) 28 / 111 (25%) Unknown 23 / 112 (21%) 23 / 110 (21%) 40 / 108 (37%) 38 / 111 (34%) Fellow-up time(day) 1,097 (590, 1,465) 1,288 (732, 1,549) 1,244 (728, 1,494) 1,284 (977, 1,497) 0.11 Use of ACEIs/ARBs 39 (37%) 38 (37%) 41 (39%) 44 (42%) 0.8 Hb(g/L) 126.82 ± 20.82 128.56 ± 18.92 129.65 ± 18.17 138.09 ± 19.31 0.9 TC (mg/dL) 4.94 ± 1.15 4.98 ± 1.22 4.87 ± 1.19 4.96 ± 1.67 0.8 HDL-C (mg/dL) 1.39 ± 0.44 1.35 ± 0.40 1.41 ± 0.41 1.41 ± 0.45 0.8 LDL-C (mg/dL) 3.10 ± 0.96 3.20 ± 1.15 3.07 ± 1.07 3.18 ± 1.48 0.7 SCr (µmol/L) 181.58 ± 118.20 165.75 ± 107.45 157.27 ± 107.23 132.84 ± 104.89 0.003 eGFR (ml/(min・1.73m 2 )) 44.95 ± 27.55 48.62 ± 26.49 56.41 ± 32.36 64.02 ± 29.60 < 0.001 UPCR (mg/g) 0.65 (0.18, 1.49) 0.50 (0.13, 0.87) 0.41 (0.14, 1.09) 0.36 (0.10, 0.77) 0.012 Left HGS(kg) 19 (15, 24) 27 (19, 30) 32 (22, 35) 38 (27, 42) < 0.001 Right HGS(kg) 19 (16, 26) 27 (20, 33) 33 (23, 37) 40 (28, 44) < 0.001 Bilateral HGS(kg) 40 (31, 51) 58 (39, 63) 68 (45, 71) 77 (54, 86) < 0.001 Sarcopenia 47 / 112 (42%) 22 / 110 (20%) 0 / 108 (0%) 0 / 111 (0%) < 0.001 a n (%) b Kruskal-Wallis rank sum test; Pearson’s Chi-squared test; Fisher’s exact test Note: Body Mass Index, BMI; Skeletal Muscle Index, SMI; Primary Glomerulonephritis included chronic nephritis, nephropathy syndrome, and IgA nephropathy. Other secondary nephrosis included systemic lupus erythematosus nephritis, Henoch-Schoenlein purpura, Hepatitis B virus-associated nephritis obstructive nephropathy, etc. Angiotensin-converting enzyme inhibitor, ACEI; Angiotensin receptor blocker, ARB; Hemoglobin, Hb; Triglyceride, TG; total cholesterol, TC; high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; albumin, ALB; Urine protein-to-creatinine, UPCR. Associations of HGS with the risk of composite CKD endpoint Table 2 shows the association of HGS with the composite endpoints of CKD. The results are divided into two parts, Outcome 1 and Outcome 2, and present the hazard ratio (HR) and 95% confidence interval (CI) for three models (Model 1, Model 2, and Model 3), respectively, along with the P-value. Overall, a significant inverse relationship (P < 0.001) was observed between left-hand, right-hand, and bilateral HGS and the risk of the composite renal endpoints of Outcome 1 and Outcome 2. This association remained significant after adjustment for different models. Furthermore, in subsequent analyses using sex-specific HGS quartiles, the adjusted HRs (95% CIs) for Outcome 1 were 0.66 (0.388, 1.124) for the second quartile, 0.44 (0.238, 0.813) for the third quartile, and 0.109 (0.044, 0.272) for the fourth quartile, compared to the lowest quartile. Significant correlations were found for the third and fourth quartiles (P < 0.05). Similarly, for Outcome 2, the adjusted HRs (95% CIs) were 0.366 (0.164, 0.819) for the second quartile, 0.33 (0.141, 0.772) for the third quartile, and 0.053 (0.012, 0.238) for the fourth quartile, all showing significant correlations (P < 0.05). In both Outcome 1 and Outcome 2, trend analysis from Model 1 to Model 3 revealed p-values of less than 0.001, indicating statistical significance. Moreover, the risk of CKD composite endpoints (both Outcome 1 and Outcome 2) is significantly higher in individuals with sarcopenia compared to those without. The HR and 95% CI are 2.429 (1.218, 4.846), p = 0.012, and 4.237 (1.595, 11.256), p = 0.004, respectively. Finally, optimal cut-off points for handgrip strength were identified through ROC curve analysis: 64.35 kg for males and 39.35 kg for females (Fig. 2 ). These points maximized the Youden index, balancing sensitivity and specificity. Subsequently, the population was divided into high and low grip strength groups. After adjusting for covariates, Cox regression analysis revealed that the high grip strength group had significantly fewer renal composite endpoint events for both Outcome 1 and Outcome 2 compared to the low grip strength group (all p-values < 0.001). Table 2 The association between handgrip strength and composite CKD endpoint Table 2 The association between handgrip strength and composite CKD endpoint Term Outcome1(HR (95% CI) P ) Outcome2(HR (95% CI) P ) model1 a model2 b model3 c model1 model2 model3 HGS Left HGS 0.921(0.896,0.946) < 0.001 0.931(0.902,0.961) < 0.001 0.94(0.907,0.974) 0.001 0.905(0.871,0.94) < 0.001 0.927(0.887,0.968)0.001 0.932(0.888,0.978) 0.004 Right HGS 0.927(0.899,0.956) < 0.001 0.925(0.896,0.955) < 0.001 0.925(0.892,0.959) < 0.001 0.921(0.883,0.96) < 0.001 0.93(0.892,0.971) 0.001 0.936(0.892,0.981) 0.006 Bilateral HGS 0.951(0.935,0.968) < 0.001 0.958(0.941,0.974) < 0.001 0.959(0.941,0.979) < 0.001 0.945(0.923,0.967) < 0.001 0.958(0.936,0.981) < 0.001 0.961(0.936,0.986) 0.01 Sex-specific bilateral HGS group1 Ref Ref Ref Ref Ref Ref group2 0.576(0.351,0.945) 0.029 0.663(0.402,1.093)0.107 0.66(0.388,1.124) 0.126 0.368(0.185,0.734) 0.005 0.427(0.203,0.895)0.024 0.366(0.164,0.819 )0.014 group3 0.388(0.225,0.671) 0.001 0.442(0.25,0.781) 0.005 0.44(0.238,0.813) 0.009 0.229(0.103,0.509) < 0.001 0.348(0.155,0.783)0.011 0.330(0.141,0.772) 0.011 group4 0.124(0.058,0.266) < 0.001 0.088(0.036,0.213) < 0.001 0.109(0.044,0.272) < 0.001 0.087(0.029,0.263) < 0.001 0.049(0.011,0.207) < 0.001 0.053(0.012,0.238 ) < 0.001 p for trend < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 Sarcopenia No Ref Ref Ref Ref Ref Ref Yes 2.197(1.33,3.629) 0.002 2.667(1.609,4.421) < 0.001 2.429(1.218,4.846)0.012 2.262(1.137,4.501) 0.02 3.003(1.521,5.925)0.002 4.237(1.595,11.256)0.004 Grouping by threshold Low Ref Ref Ref Ref Ref Ref High 0.264(0.168,0.414) < 0.001 0.283(0.177,0.453) < 0.001 0.32(0.193,0.531) < 0.001 0.208(0.111,0.389) < 0.001 0.244(0.125,0.476) < 0.001 0.279(0.136,0.568) < 0.001 a Model 1 was adjusted for anthropometrics and demographics (age, sex, BMI). b Model 2 was adjusted for Model 1 plus clinical and laboratory values (ALB, Hb, UPCR, eGFR, TG, HDLC and LDLC). c Model 3 was adjusted for Model 2 plus lifestyle factors (physical activity, smoking, drinking, and marital status and Protopathy). Survival analysis of the effect of HGS on renal prognosis in patients with CKD Figure 3 shows the Kaplan-Meier survival curves for composite renal outcomes in CKD patients, grouped by bilateral HGS quartiles, sarcopenia status, and bilateral HGS threshold values within the SPM-CKD cohort. Higher bilateral HGS is consistently associated with better survival rates across all groupings, with significant differences observed (p < 0.001). Specifically, patients in higher bilateral HGS quartiles and those without sarcopenia have significantly better outcomes. For Outcome 1, the survival benefit is more pronounced after three years, whereas for Outcome 2, the benefit remains significant but less marked initially. Notably, the survival analysis indicates that the differences between groups become even more pronounced for both outcomes, emphasizing the importance of handgrip strength in predicting renal outcomes in CKD patients. Kaplan-Meier survival curve analyses demonstrated that patients with bilateral HGS values below these gender-specific cutoffs experienced significantly worse renal outcomes (refer to Fig. 3 , all P values < 0.05). Discussion Handgrip Strength and Renal Outcomes The findings of this study suggest that bilateral HGS may play a crucial role in predicting renal outcomes in patients with CKD. By examining a cohort of 441 participants, this study leveraging a larger sample size and extended follow-up period to potentially offer more nuanced and broadly applicable insights. Another advantage of this study’s design is that it incorporated two distinct renal composite outcome events, referred to as Outcome 1 and Outcome 2. For Outcome 1, participants underwent laboratory tests every three months, allowing for precise tracking of significant increases in serum creatinine levels or acute exacerbations of CKD. This frequent monitoring enabled a more accurate assessment of the short-term fluctuations and acute events in renal function. Outcome 2, on the other hand, distinguished itself by focusing on more severe renal events. This included participants who, despite being in ESRD at enrollment, managed to avoid dialysis for an extended period. This distinction highlights one of the unique aspects of our study: the nuanced categorization of renal outcomes based on severity and progression. The study demonstrates a strong inverse relationship between HGS and poor renal outcomes in CKD patients. Notably, those in the higher HGS quartiles had markedly lower HRs for both Outcome 1 and Outcome 2, compared to the lowest quartile. For instance, the HR for Outcome 1 in the fourth quartile was 0.109, indicating a substantial reduction in risk. Similarly, for Outcome 2, the HR in the fourth quartile was 0.053, underscoring the protective effect of higher HGS against renal deterioration. Impact of Sarcopenia Skeletal muscle is an important protein storage and glucose processing region critical for cardiovascular health[ 29 ]. Patients with sarcopenia have considerably higher levels of atherosclerotic markers such as IVD, FMD, and EAT, according to research. Reduced muscular function may result in a decrease in muscle contraction-inducing substances that have anti-inflammatory properties increasing the risk of developing cardiovascular disease[ 30 ]. Cardiovascular disease contributes significantly to the progression of chronic kidney disease. One of the most serious consequences of CKD is cardiovascular disease, which has a prevalence of up to 63%. In China, the prevalence is 34.5%[ 31 ]. Improving cardiovascular risk factors is becoming an increasingly important focus in managing CKD populations[ 32 ]. The study further underscores the significant impact of sarcopenia on renal outcomes. Patients with sarcopenia faced a markedly higher risk of adverse renal events compared to those without. Specifically, the HR for Outcome 1 was 2.429, indicating a more than twofold increase in risk for significant increases in serum creatinine levels or acute exacerbations of CKD. For Outcome 2, which represents more severe renal endpoints, the HR was 4.237, highlighting an even greater risk. This pronounced disparity, particularly in the more severe outcomes, emphasizes the critical role of muscle mass and strength in the management and prognosis of CKD. Consequently, interventions aimed at preventing or mitigating sarcopenia could potentially improve renal outcomes in this patient population. By addressing sarcopenia, we may be able to not only enhance overall patient health but also specifically target the factors that contribute to worse renal prognosis, thereby improving both the quality of life and clinical outcomes for individuals with CKD. Optimal HGS Cut-off Points Low HGS is linked to an increased risk of CKD, as evidenced by various studies. A cross-sectional survey conducted in China found that individuals with medium to high HGS had a reduced risk of CKD, while those with low HGS were generally older and more prone to CKD[ 15 ]. This suggests that low bilateral HGS is an indicator of increased CKD risk, particularly among the elderly. However, the relationship between bilateral HGS and CKD is evident even among younger populations. A study on children with CKD showed that those in stages 2 to 5 had significantly lower HGS compared to those in stage 1[ 33 ], indicating that lower HGS is associated with more advanced stages of CKD irrespective of age. Meta-analyses and large-scale studies further support the link between HGS and mortality. An analysis of 53,476 participants suggested that the association between HGS and mortality was weaker in individuals aged 60 years or younger[ 34 ]. Conversely, another study involving 6,850 participants found no significant interaction between HGS and age regarding mortality, likely due to the smaller sample size. Nevertheless, a large-scale study involving over 500,000 participants confirmed an association between HGS and health outcomes across all ages, with a stronger association in younger populations[ 35 ]. These results collectively highlight the critical role of HGS as a predictive marker for health outcomes in CKD patients across different age groups[ 18 , 36 ]. However, varying cutoff values across studies hinder consensus and practical application[ 37 ]. Therefore, a standardized HGS cutoff value for CKD patients is needed to guide clinical practice and ensure more conclusive research. Through ROC curve analysis, we identified optimal bilateral HGS cut-off points that maximize the predictive accuracy for renal outcomes. The thresholds were determined to be 64.35 kg for males and 39.35 kg for females. Patients with bilateral HGS below these cut-offs experienced significantly worse renal outcomes, as illustrated by Kaplan-Meier survival curves. This finding provides a practical tool for clinicians to identify CKD patients at higher risk and to tailor interventions accordingly. The clear demarcation of high and low bilateral HGS groups also facilitates more personalized and effective patient management strategies. Implications for Clinical Practice Renal replacement therapy options include kidney transplantation (KT), hemodialysis, and peritoneal dialysis. Among these, KT is the most effective method to reduce the high morbidity and mortality rates in patients with ESRD while providing relatively greater freedom to the patients. However, KT does not completely reverse the damage caused by years of reduced kidney function and dialysis. Additionally, due to the scarcity of donors, patients may face prolonged waiting periods for a transplant. Furthermore, in the post-transplant period, new detrimental factors, particularly those associated with immunosuppression, increase the risk of complications[ 38 ]. Consequently, delaying the progression of kidney failure and postponing the initiation of dialysis can achieve benefits similar to those of a kidney transplant, without the associated drawbacks. Therefore, effective management of CKD, accurate prediction of its progression, and early intervention measures are of paramount importance. HGS accurately predicts overall muscular strength and is a sensitive measure of physical strength. HGS represents overall muscle strength throughout the body and is a sensitive indicator of muscle strength. Skeletal muscle action stimulates the expression of irisin, linked to skeletal muscle hypertrophy and strength[ 39 ]. Cross-sectional and longitudinal studies of more than 40,000 participants at UK Biobank have shown that there is a link between stronger handgrip strength and increased gray matter volume in the brain, and that subcortical, particularly hippocampal and temporal cortical areas, play an important role in muscular fitness, and that the volume of gray matter in these areas is also associated with better mental health, which largely moderates their relationship with handgrip strength. Strong handgrip strength protects against cognitive decline and dementia[ 40 ]. Low handgrip strength may lead to cognitive impairment. According to a 2019 report from the United States Department of Health and Human Services (HHS), around 10 to 40 percent of CKD patients have some cognitive impairment. Cognitive impairment can have a significant influence on patient treatment since patients may be unable to take medication as prescribed, may not be able to undergo peritoneal or hemodialysis, and may require real-time monitoring[ 41 ]. This could be one of the mechanisms by which reduced handgrip strength impacts the prognosis of kidney patients. The clinical implications of these findings are profound. First, incorporating HGS measurement into routine clinical practice offers a simple, cost-effective means to stratify risk among CKD patients. Given the significant association between HGS and renal outcomes, regular monitoring of grip strength could enhance the prognostic accuracy and help in early identification of patients at greater risk of CKD progression. Second, the strong link between sarcopenia and poor renal outcomes highlights the need for comprehensive management approaches that include nutritional and physical interventions to preserve muscle mass and strength[ 42 ]. Such strategies may not only improve overall health but also specifically mitigate the progression of CKD. Third, the identification of sex-specific HGS cut-off points provides a valuable reference for clinicians. By utilizing these thresholds, healthcare providers can better assess the risk profile of CKD patients and implement timely, targeted interventions. Lastly, significant intergroup differences suggesting that long-term follow-up is essential for a comprehensive understanding of outcomes in patients with CKD. The difference was more pronounced in the third year, suggesting a gradual accumulation of long-term effects on muscle mass and strength. Early intervention may have a more significant effect in the long term. Limitation Despite the strengths of this study, certain limitations should be acknowledged. The cohort, while larger than previous studies, still represents a specific population, and the findings may not be universally applicable. Additionally, the observational nature of the study precludes definitive causal inferences. Future research, including randomized controlled trials, is needed to further explore the causal relationship between HGS and renal outcomes and to determine the efficacy of interventions aimed at improving grip strength in CKD patients. Conclusion In conclusion, this study substantiates the role of bilateral HGS as a potent predictor of renal outcomes in CKD patients. The significant inverse relationship between bilateral HGS and adverse renal events underscores the importance of muscle strength in this context. By incorporating HGS measurement into routine clinical practice, and addressing sarcopenia through targeted interventions, healthcare providers can improve the prognosis and quality of life for CKD patients. Further research should continue to explore and validate these findings, paving the way for enhanced patient care and management strategies. Declarations Ethics The research was conducted at the Guangdong Provincial Hospital of Chinese Medicine with Ethics Approval No. 2019-153-01. The Chinese Clinical Trial Registry No. is ChiCTR1900024633. Consent All authors have read and approved the final manuscript and agree with the order of the presentation of authors for publication. Availability The data and material are available from the corresponding authors upon request. Competing interests The authors declare that they have no competing interests. Funding This study was funded by the National Key Research and Development Program of China (2019YFE0196300), the GPHCM Fund for Traditional Chinese Medicine Science and Technology Research (YN2020ZWB05), the Guangdong Province Science and Technology Program (2022A1414020015) and the Foundation of Science, Technology and Innovation Commission of Shenzhen Municipality (JCYJ20180302153701406, KCXFZ20201221173612034). Author contributions Qiong Huang and Linyi Chen planned the study and wrote the original draft. Li-zhe Fu and Fang Tang are responsible for blood collection and filling out participant information, while Xi-na Jie is responsible for collecting data. Wenwei Ouyang analyzed all the statistics. Jing Wang, Yifan Wu, and Xusheng Liu contributed to the concept and design. Acknowledgments We extend our gratitude to all the participants who contributed to this study. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. 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Skeletal muscle insulin resistance is the primary defect in type 2 diabetes. Diabetes Care. 2009;32 Suppl 2:S157-63. Barril G, Nogueira A, Alvarez-García G, Núñez A, Sánchez-González C, Ruperto M. Nutritional Predictors of Mortality after 10 Years of Follow-Up in Patients with Chronic Kidney Disease at a Multidisciplinary Unit of Advanced Chronic Kidney Disease. Nutrients. 2022;14: Amparo FC, Cordeiro AC, Carrero JJ, Cuppari L, Lindholm B, Amodeo C, et al. Malnutrition-inflammation score is associated with handgrip strength in nondialysis-dependent chronic kidney disease patients. J Ren Nutr. 2013;23:283–7. Massini G, Caldiroli L, Molinari P, Carminati FMI, Castellano G, Vettoretti S. Nutritional Strategies to Prevent Muscle Loss and Sarcopenia in Chronic Kidney Disease: What Do We Currently Know? Nutrients. 2023;15: Wang Y, Pu X, Zhu Z, Sun W, Xue L, Ye J. Handgrip strength and the prognosis of patients with heart failure: A meta-analysis. Clin Cardiol. 2023;46:1173–84. Ashdown-Franks G, Stubbs B, Koyanagi A, Schuch F, Firth J, Veronese N, et al. Handgrip strength and depression among 34,129 adults aged 50 years and older in six low- and middle-income countries. J Affect Disord. 2019;243:448–54. Song J, Liu T, Zhao J, Wang S, Dang X, Wang W. Causal associations of hand grip strength with bone mineral density and fracture risk: A mendelian randomization study. Front Endocrinol (Lausanne). 2022;13:1020750. Cheng Y, Liu M, Liu Y, Xu H, Chen X, Zheng H, et al. Chronic kidney disease: prevalence and association with handgrip strength in a cross-sectional study. BMC Nephrol. 2021;22:246. Lee YL, Jin H, Lim JY, Lee SY. Relationship Between Low Handgrip Strength and Chronic Kidney Disease: KNHANES 2014–2017. J Ren Nutr. 2021;31:57–63. Kuki A, Tanaka K, Kushiyama A, Tanaka Y, Motonishi S, Sugano Y, et al. Association of gait speed and grip strength with risk of cardiovascular events in patients on haemodialysis: a prospective study. BMC Nephrol. 2019;20:196. Hwang SH, Lee DH, Min J, Jeon JY. Handgrip Strength as a Predictor of All-Cause Mortality in Patients With Chronic Kidney Disease Undergoing Dialysis: A Meta-Analysis of Prospective Cohort Studies. J Ren Nutr. 2019;29:471–9. Kittiskulnam P, Chertow GM, Carrero JJ, Delgado C, Kaysen GA, Johansen KL. Sarcopenia and its individual criteria are associated, in part, with mortality among patients on hemodialysis. Kidney Int. 2017;92:238–47. Vogt BP, Borges MCC, Goés CR, Caramori JCT. Handgrip strength is an independent predictor of all-cause mortality in maintenance dialysis patients. Clin Nutr. 2016;35:1429–33. Clarke AL, Zaccardi F, Gould DW, Hull KL, Smith AC, Burton JO, et al. Association of self-reported physical function with survival in patients with chronic kidney disease. Clin Kidney J. 2019;12:122–8. Chang YT, Wu HL, Guo HR, Cheng YY, Tseng CC, Wang MC, et al. Handgrip strength is an independent predictor of renal outcomes in patients with chronic kidney diseases. Nephrol Dial Transplant. 2011;26:3588–95. He P, Ye Z, Liu M, Li H, Zhang Y, Zhou C, et al. Association of handgrip strength and/or walking pace with incident chronic kidney disease: A UK biobank observational study. J Cachexia Sarcopenia Muscle. 2023;14:805–14. Noor H, Reid J, Slee A. Resistance exercise and nutritional interventions for augmenting sarcopenia outcomes in chronic kidney disease: a narrative review. J Cachexia Sarcopenia Muscle. 2021;12:1621–40. Leal VO, Mafra D. Handgrip strength evaluation in CKD: do we have enough evidence? J Bras Nefrol. 2020;42:388–90. Ouyang WW, Chen HF, Xu XY, Zhang XL, Fu LZ, Tang F, et al. Self-management program for patients with chronic kidney disease (SMP-CKD) in Southern China: protocol for an ambispective cohort study. BMC Nephrol. 2022;23:93. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–12. Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020;21:300-7.e2. Norman K, Stobäus N, Gonzalez MC, Schulzke JD, Pirlich M. Hand grip strength: outcome predictor and marker of nutritional status. Clin Nutr. 2011;30:135–42. Bohannon RW. Muscle strength: clinical and prognostic value of hand-grip dynamometry. Curr Opin Clin Nutr Metab Care. 2015;18:465–70. Mok Y, Ballew SH, Matsushita K. Chronic kidney disease measures for cardiovascular risk prediction. Atherosclerosis. 2021;335:110–8. Provenzano M, Coppolino G, Faga T, Garofalo C, Serra R, Andreucci M. Epidemiology of cardiovascular risk in chronic kidney disease patients: the real silent killer. Rev Cardiovasc Med. 2019;20:209–20. Hogan J, Schneider MF, Pai R, Denburg MR, Kogon A, Brooks ER, et al. Grip strength in children with chronic kidney disease. Pediatr Nephrol. 2020;35:891–9. Cooper R, Kuh D, Hardy R. Objectively measured physical capability levels and mortality: systematic review and meta-analysis. Bmj. 2010;341:c4467. Celis-Morales CA, Welsh P, Lyall DM, Steell L, Petermann F, Anderson J, et al. Associations of grip strength with cardiovascular, respiratory, and cancer outcomes and all cause mortality: prospective cohort study of half a million UK Biobank participants. Bmj. 2018;361:k1651. Leal VO, Mafra D, Fouque D, Anjos LA. Use of handgrip strength in the assessment of the muscle function of chronic kidney disease patients on dialysis: a systematic review. Nephrol Dial Transplant. 2011;26:1354–60. Xu X, Yang Z, Ma T, Li Z, Chen Y, Zheng Y, et al. The cut-off values of handgrip strength and lean mass index for sarcopenia among patients on peritoneal dialysis. Nutr Metab (Lond). 2020;17:84. Cohen-Bucay A, Gordon CE, Francis JM. Non-immunological complications following kidney transplantation. F1000Res. 2019;8: Colaianni G, Oranger A, Dicarlo M, Lovero R, Storlino G, Pignataro P, et al. Irisin Serum Levels and Skeletal Muscle Assessment in a Cohort of Charcot-Marie-Tooth Patients. Front Endocrinol (Lausanne). 2022;13:886243. Jiang R, Westwater ML, Noble S, Rosenblatt M, Dai W, Qi S, et al. Associations between grip strength, brain structure, and mental health in > 40,000 participants from the UK Biobank. BMC Med. 2022;20:286. Drew DA, Weiner DE, Sarnak MJ. Cognitive Impairment in CKD: Pathophysiology, Management, and Prevention. Am J Kidney Dis. 2019;74:782–90. Castaneda C, Gordon PL, Uhlin KL, Levey AS, Kehayias JJ, Dwyer JT, et al. Resistance training to counteract the catabolism of a low-protein diet in patients with chronic renal insufficiency. A randomized, controlled trial. Ann Intern Med. 2001;135:965–76. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5292199","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372165423,"identity":"a50e8c09-4022-4273-91a9-1a7ed6d0a271","order_by":0,"name":"Qiong Huang","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qiong","middleName":"","lastName":"Huang","suffix":""},{"id":372165424,"identity":"ab1fcefe-1ad6-4771-9a91-1b87e986e92a","order_by":1,"name":"Linyi Chen","email":"","orcid":"","institution":"Guangzhou University of Chinese 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03:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5292199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5292199/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11255-025-04457-7","type":"published","date":"2025-05-01T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68540608,"identity":"7b1c3fab-a653-4476-94e6-2d73303dce3c","added_by":"auto","created_at":"2024-11-08 10:46:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34311,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the participant selection and analysis process in the SMP-CKD cohort study.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5292199/v1/17082bdbb9477adb724a9637.png"},{"id":68540609,"identity":"c9ef56a8-ded5-4562-8858-07a287ce9274","added_by":"auto","created_at":"2024-11-08 10:46:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15172,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve Analysis of Composite Renal Outcomes of (a) Male vs. (b) Female in CKD Patients\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5292199/v1/b57633e86a46e5a10a198d53.png"},{"id":68540610,"identity":"b6f2e340-dd58-4ae7-af01-706855b891d7","added_by":"auto","created_at":"2024-11-08 10:46:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18573,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analyses of outcome1 and outcome2 among CKD patients across varying cutoffs of bilateral HGS within the SPM-CKD cohort.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5292199/v1/a3e57a828a4c7d4ee22f0e85.png"},{"id":81987818,"identity":"ce2263f7-084a-4ef0-a4af-79e4dacefa1d","added_by":"auto","created_at":"2025-05-05 16:06:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":989208,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5292199/v1/9b00d930-9970-4e93-828e-5a982394e5f6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association Between Hand Grip Strength and Chronic Kidney Disease Progression: Insights from SMP-CKD Studies","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) is a growing global health burden characterized by a gradual loss of kidney function. As of 2017, the global prevalence of CKD was 9.1%[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], while in China it reached 10.8%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], affecting over 150\u0026nbsp;million people and imposing a significant economic burden. CKD is marked by high prevalence, high disability rates, high medical costs, and inadequate patient education, making it a major public health issue worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Early-stage CKD often lacks obvious symptoms, leading to underdiagnosis and poor management. CKD also increases the risk of cardiovascular disease, strokes, and progression to end-stage renal disease (ESRD), requiring dialysis or transplantation[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Identifying reliable, non-invasive biomarkers to predict CKD progression is crucial for improving patient outcomes and reducing healthcare costs.\u003c/p\u003e \u003cp\u003eHand grip strength (HGS) has emerged as a potential biomarker in various medical conditions due to its ease of measurement and its correlation with overall muscle strength and physical function. The possible link between HGS and CKD progression has garnered increasing attention. Muscle wasting and reduced physical function are common among CKD patients, often resulting from malnutrition, inflammation, and metabolic derangements associated with impaired kidney function[\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, CKD complications\u0026mdash;such as cardiovascular events, mental health issues, and increased fracture risk\u0026mdash;further diminish the quality of life and elevate mortality rates, all of which are associated with low HGS[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This reinforces the notion of a potential connection between CKD and HGS. As CKD progresses, the risk of sarcopenia\u0026mdash;characterized by the loss of skeletal muscle mass and strength\u0026mdash;increases, further exacerbating the decline in physical function and overall health status. Despite this plausible link, comprehensive studies investigating the relationship between HGS and CKD progression remain limited.\u003c/p\u003e \u003cp\u003eMost prior investigations into HGS and CKD have been cross-sectional, limiting their ability to clarify the causal relationship between HGS and CKD[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, previous research has primarily focused on dialysis patients, with fewer studies on the non-dialysis population[\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Approximately a decade ago, a single study identified HGS as an independent predictor of poor composite renal outcomes in 128 non-dialysis patients. However, the limited sample size and short follow-up period of this study constrained the generalizability of its findings[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, while HGS is used to assess sarcopenia, current research often evaluates HGS alone without considering comprehensive sarcopenia diagnosis and its impact on CKD progression[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. HGS cutoff values also differ across countries and populations, making it challenging to establish a standardized threshold for CKD patients[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Identifying a specific cutoff for CKD patients is crucial for accurate risk stratification and clinical application.\u003c/p\u003e \u003cp\u003eTo address these gaps, this research investigates the relationship between HGS and CKD progression in non-dialysis CKD patients in China. Using data from the Guangdong Provincial Hospital of Chinese Medicine's Self-Management Program for Patients with Chronic Kidney Disease (SMP-CKD) cohort, this study aims to overcome previous limitations with a larger sample size and extended follow-up. Additionally, it will explore HGS as an indicator for sarcopenia diagnosis and its implications for CKD progression, ultimately determining appropriate HGS cutoff values tailored to CKD patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePopulation and study design\u003c/h2\u003e \u003cp\u003e The SMP-CKD cohort, conducted at the Guangdong Provincial Hospital of Chinese Medicine (GPHCM), is an ongoing multi-center observational study (Ethics Approval No. 2019-153-01; Chinese Clinical Trial Registry No. ChiCTR1900024633) anticipated to last around 10 years[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This ambispective cohort study includes a retrospective phase spanning July 2015 to July 2019 and a prospective phase from August 2019 through August 2024. The longitudinal cohort part of this study leverages the prospective phase of the SMP-CKD database, encompassing data from 550 individuals diagnosed with CKD stages 3 to 5. It collects demographic data, clinical diagnoses, medical histories, laboratory findings, circulating lipid profiles, and distribution assessments. After exclusions of 97 subjects without data of HGS and covariates, 441 selected participants were included in the study. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExposure and covariates\u003c/h3\u003e\n\u003cp\u003eHandgrip strength was assessed using a handgrip dynamometer, adjusted for each participant's hand size, by a trained examiner. Participants performed three trials per hand, alternating between hands with a 60-second rest in between, while standing unless hindered by physical limitations. Additionally, information on past surgeries for arthritis or carpal tunnel syndrome, which could influence handgrip strength, was collected. The related organization ensured quality control through regular examiner retraining, equipment maintenance, and systematic data review to identify inconsistencies or errors, thereby guaranteeing the reliability and accuracy of handgrip strength measurements as a proxy for muscle strength. The above methods for measuring, recording, and checking grip strength. We collected left hand, right hand, and bilateral grip strength in the SMP-CKD cohort, and patients will be excluded when only one hand is measured due to some conditions (such as advanced arteriovenous fistula, stroke, upper limb fracture, etc.)\u003c/p\u003e\n\u003ch3\u003eSerum Creatinine and Covariate information\u003c/h3\u003e\n\u003cp\u003eSerum creatinine(SCr)measurements play a critical role in diagnosing and managing renal diseases. In addition, the urinary protein/creatinine ratio (UPCR) refers to the protein/creatinine ratio in mg/g. Estimated glomerular filtration rate(eGFR) was determined through the CKD-EPI equation, yielding eGFR values in mL/min/1.73 m^2[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. CKD is defined as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min/1.73m2 and/or UPCR\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/g. Outcome 1 is a composite renal endpoint including initiation of chronic dialysis or renal transplantation, a sustained 40% decline in eGFR, doubling of SCr from baseline, eGFR below 5 mL/min/1.73m\u0026sup2;, or all-cause mortality. Outcome 2 includes initiation of chronic dialysis or renal transplantation, or all-cause mortality. The Asian Working Group for Sarcopenia (AWGS) 2019 consensus defines sarcopenia as \"age-related loss of muscle mass, plus low muscle strength, and/or low physical performance\"[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. According to this updated definition, sarcopenia of CKD in the study is identified by a handgrip strength of less than 28 kg for men and less than 18 kg for women, along with height-adjusted muscle mass cutoffs (skeletal muscle index) of bioimpedance values less than 7.0 kg/m\u0026sup2; for men and less than 5.7 kg/m\u0026sup2; for women. This study also incorporated a range of both continuous and categorical variables as covariates. These encompassed gender, education, physical activity, and marriage status alongside smoking and alcohol consumption statuses. The continuous variables in this context included age (expressed in years), serum albumin (noted as Alb in g/L), body mass index (BMI, calculated as kg/m\u0026sup2;), and the eGFR (ml/min/1.73m\u0026sup2;). We considered all these covariates as potential influencing factors in examining the link between the bilateral HGS and creatinine levels. Moreover, the SPM-CKD cohort adds assessments of hemoglobin and lipid metabolism index, alongside follow-up duration, for a more comprehensive evaluation of CKD progression. Protopathic in SMP-CKD was categorized into primary glomerulonephritis (encompassing chronic nephritis, nephropathy syndrome, and IgA nephropathy), hypertensive renal disease, diabetic nephropathy, other secondary nephrosis (such as systemic lupus erythematosus nephritis, Henoch-Schoenlein purpura, hepatitis B virus-associated nephritis, and obstructive nephropathy), or etiologies that remained unidentified.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIn the baseline data, continuous variables that followed a normal distribution were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), whereas variables with a skewed distribution were shown as the median (interquartile range, IQR). Categorical variables were represented as n (%, weighted). To assess the statistical differences between groups, we utilized the Student's t-test or the Mann-Whitney U test for continuous variables and the chi-square test for categorical variables.\u003c/p\u003e \u003cp\u003eBaseline characteristics were summarized as mean (SD) or median (IQR) for continuous variables, and as proportions for categorical variables, stratified by sex-specific bilateral HGS quartiles within the SMP-CKD cohort. Gender-specific cutoff values for predicting grip strength outcomes were determined using Receiver Operating Characteristic (ROC) curve analysis. Cox regression and Kaplan-Meier survival curves were subsequently employed to assess the variation in renal composite outcomes among patients with differing HGS levels. Data were stratified by sex-specific HGS quartiles, sarcopenia status, and HGS thresholds. Group differences in the impact of HGS on renal outcomes were analyzed, with p-values calculated using the log-rank test. All data was collected using R (version 4.3.2,www-rproject.com). A p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGeneral characteristics of SMP-CKD\u003c/h2\u003e \u003cp\u003eIn the cohort study involving 441 participants, the average age of the study population was 57.0 years, with a standard deviation (SD) of 17.0 years. The mean follow-up time was 852.48\u0026thinsp;\u0026plusmn;\u0026thinsp;443.03 days. Among the participants, 246 (56.0%) were male. A total of 97 participants experienced renal outcome 1 events, while 53 participants experienced renal outcome 2 events. Baseline characteristics of the study participants were analyzed and stratified by sex-specific bilateral HGS quartiles. Participants in higher quartiles of bilateral HGS were observed to be younger. Additionally, they were less likely to have diabetic nephropathy. Furthermore, these participants had lower serum creatinine levels and higher levels of physical activity, Hb, eGFR, and albumin levels (as depicted in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of the SMP-CKD Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eSex-specific handgrip strength, kg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1, N\u0026thinsp;=\u0026thinsp;112\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2, N\u0026thinsp;=\u0026thinsp;110\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3, N\u0026thinsp;=\u0026thinsp;108\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4, N\u0026thinsp;=\u0026thinsp;111\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 / 112 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 / 110 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 / 108 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 / 111 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (57, 70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.5 (50.25, 68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.5 (43.75, 61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (38, 56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.7 (20.4, 24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.8 (20.8, 25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.4 (20.7, 24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.4 (21.2, 25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSmoking behavior (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 / 112 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 / 110 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 / 108 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90 / 111 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 / 112 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 / 110 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 / 108 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 / 111 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 / 112 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 / 110 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 / 108 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 / 111 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol status (%)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 / 112 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 / 110 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 / 108 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 / 111 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbstainer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 / 112 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 / 110 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 / 108 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 / 111 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 / 112 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 / 110 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 / 108 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 / 111 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 / 112 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 / 110 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 / 108 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 / 111 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 / 112 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 / 110 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 / 108 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 / 111 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 / 112 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 / 110 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 / 108 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 / 111 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried,n (%)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 / 112 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 / 110 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 / 108 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 / 111 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 / 112 (94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 / 110 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 / 108 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102 / 111 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 / 112 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 / 110 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 / 108 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 / 111 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eEducational level (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 / 112 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 / 110 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 / 108 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 / 111 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 / 112 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 / 110 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 / 108 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 / 111 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege degree or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 / 112 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 / 110 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 / 108 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 / 111 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtopathy\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Glomerulonephritides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 / 112 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 / 110 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 / 108 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 / 111 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 / 112 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 / 110 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 / 108 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 / 111 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertensive Renal Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 / 112 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 / 110 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 / 108 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 / 111 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 / 112 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 / 110 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 / 108 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 / 111 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 / 112 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 / 110 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 / 108 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 / 111 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFellow-up time(day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,097 (590, 1,465)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,288 (732, 1,549)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,244 (728, 1,494)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,284 (977, 1,497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of ACEIs/ARBs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.82\u0026thinsp;\u0026plusmn;\u0026thinsp;20.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.56\u0026thinsp;\u0026plusmn;\u0026thinsp;18.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129.65\u0026thinsp;\u0026plusmn;\u0026thinsp;18.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138.09\u0026thinsp;\u0026plusmn;\u0026thinsp;19.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Albumin (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.13\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.52\u0026thinsp;\u0026plusmn;\u0026thinsp;4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.53\u0026thinsp;\u0026plusmn;\u0026thinsp;4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCr (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181.58\u0026thinsp;\u0026plusmn;\u0026thinsp;118.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165.75\u0026thinsp;\u0026plusmn;\u0026thinsp;107.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157.27\u0026thinsp;\u0026plusmn;\u0026thinsp;107.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132.84\u0026thinsp;\u0026plusmn;\u0026thinsp;104.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (ml/(min・1.73m\u003csup\u003e2\u003c/sup\u003e))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.95\u0026thinsp;\u0026plusmn;\u0026thinsp;27.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.62\u0026thinsp;\u0026plusmn;\u0026thinsp;26.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.41\u0026thinsp;\u0026plusmn;\u0026thinsp;32.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.02\u0026thinsp;\u0026plusmn;\u0026thinsp;29.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPCR (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.18, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50 (0.13, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41 (0.14, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36 (0.10, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft HGS(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (15, 24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (19, 30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (22, 35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (27, 42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight HGS(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (16, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (20, 33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (23, 37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (28, 44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilateral HGS(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (31, 51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (39, 63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (45, 71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77 (54, 86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 / 112 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 / 110 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 / 108 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 / 111 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e n (%) \u003csup\u003eb\u003c/sup\u003e Kruskal-Wallis rank sum test; Pearson\u0026rsquo;s Chi-squared test; Fisher\u0026rsquo;s exact test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNote: Body Mass Index, BMI; Skeletal Muscle Index, SMI; Primary Glomerulonephritis included chronic nephritis, nephropathy syndrome, and IgA nephropathy. Other secondary nephrosis included systemic lupus erythematosus nephritis, Henoch-Schoenlein purpura, Hepatitis B virus-associated nephritis obstructive nephropathy, etc. Angiotensin-converting enzyme inhibitor, ACEI; Angiotensin receptor blocker, ARB; Hemoglobin, Hb; Triglyceride, TG; total cholesterol, TC; high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; albumin, ALB; Urine protein-to-creatinine, UPCR.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociations of HGS with the risk of composite CKD endpoint\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the association of HGS with the composite endpoints of CKD. The results are divided into two parts, Outcome 1 and Outcome 2, and present the hazard ratio (HR) and 95% confidence interval (CI) for three models (Model 1, Model 2, and Model 3), respectively, along with the P-value. Overall, a significant inverse relationship (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was observed between left-hand, right-hand, and bilateral HGS and the risk of the composite renal endpoints of Outcome 1 and Outcome 2. This association remained significant after adjustment for different models.\u003c/p\u003e \u003cp\u003eFurthermore, in subsequent analyses using sex-specific HGS quartiles, the adjusted HRs (95% CIs) for Outcome 1 were 0.66 (0.388, 1.124) for the second quartile, 0.44 (0.238, 0.813) for the third quartile, and 0.109 (0.044, 0.272) for the fourth quartile, compared to the lowest quartile. Significant correlations were found for the third and fourth quartiles (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, for Outcome 2, the adjusted HRs (95% CIs) were 0.366 (0.164, 0.819) for the second quartile, 0.33 (0.141, 0.772) for the third quartile, and 0.053 (0.012, 0.238) for the fourth quartile, all showing significant correlations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In both Outcome 1 and Outcome 2, trend analysis from Model 1 to Model 3 revealed p-values of less than 0.001, indicating statistical significance.\u003c/p\u003e \u003cp\u003eMoreover, the risk of CKD composite endpoints (both Outcome 1 and Outcome 2) is significantly higher in individuals with sarcopenia compared to those without. The HR and 95% CI are 2.429 (1.218, 4.846), p\u0026thinsp;=\u0026thinsp;0.012, and 4.237 (1.595, 11.256), p\u0026thinsp;=\u0026thinsp;0.004, respectively.\u003c/p\u003e \u003cp\u003eFinally, optimal cut-off points for handgrip strength were identified through ROC curve analysis: 64.35 kg for males and 39.35 kg for females (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These points maximized the Youden index, balancing sensitivity and specificity. Subsequently, the population was divided into high and low grip strength groups. After adjusting for covariates, Cox regression analysis revealed that the high grip strength group had significantly fewer renal composite endpoint events for both Outcome 1 and Outcome 2 compared to the low grip strength group (all p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eThe association between handgrip strength and composite CKD endpoint\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e The association between handgrip strength and composite CKD endpoint\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eOutcome1(HR (95% CI) P )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eOutcome2(HR (95% CI) P )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emodel1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emodel2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emodel3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003emodel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003emodel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003emodel3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeft HGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.921(0.896,0.946)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.931(0.902,0.961)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94(0.907,0.974)\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.905(0.871,0.94)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.927(0.887,0.968)0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.932(0.888,0.978)\u003c/p\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight HGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.927(0.899,0.956)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.925(0.896,0.955)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.925(0.892,0.959)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.921(0.883,0.96)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93(0.892,0.971)\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.936(0.892,0.981)\u003c/p\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBilateral HGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.951(0.935,0.968)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.958(0.941,0.974)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.959(0.941,0.979)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.945(0.923,0.967)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.958(0.936,0.981)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.961(0.936,0.986)\u003c/p\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSex-specific bilateral HGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.576(0.351,0.945)\u003c/p\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663(0.402,1.093)0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66(0.388,1.124)\u003c/p\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.368(0.185,0.734)\u003c/p\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.427(0.203,0.895)0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.366(0.164,0.819 )0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.388(0.225,0.671)\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.442(0.25,0.781)\u003c/p\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44(0.238,0.813)\u003c/p\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.229(0.103,0.509)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.348(0.155,0.783)0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.330(0.141,0.772) 0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.124(0.058,0.266)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.088(0.036,0.213)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109(0.044,0.272)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.087(0.029,0.263)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.049(0.011,0.207)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.053(0.012,0.238 )\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.197(1.33,3.629)\u003c/p\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.667(1.609,4.421)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.429(1.218,4.846)0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.262(1.137,4.501)\u003c/p\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.003(1.521,5.925)0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e4.237(1.595,11.256)0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGrouping by threshold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.264(0.168,0.414)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.283(0.177,0.453)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32(0.193,0.531)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.208(0.111,0.389)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.244(0.125,0.476)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.279(0.136,0.568)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Model 1 was adjusted for anthropometrics and demographics (age, sex, BMI).\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eModel 2 was adjusted for Model 1 plus clinical and laboratory values (ALB, Hb, UPCR, eGFR, TG, HDLC and LDLC).\u003c/p\u003e \u003cp\u003e\u003csup\u003ec\u003c/sup\u003eModel 3 was adjusted for Model 2 plus lifestyle factors (physical activity, smoking, drinking, and marital status and Protopathy).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSurvival analysis of the effect of HGS on renal prognosis in patients with CKD\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the Kaplan-Meier survival curves for composite renal outcomes in CKD patients, grouped by bilateral HGS quartiles, sarcopenia status, and bilateral HGS threshold values within the SPM-CKD cohort. Higher bilateral HGS is consistently associated with better survival rates across all groupings, with significant differences observed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, patients in higher bilateral HGS quartiles and those without sarcopenia have significantly better outcomes. For Outcome 1, the survival benefit is more pronounced after three years, whereas for Outcome 2, the benefit remains significant but less marked initially. Notably, the survival analysis indicates that the differences between groups become even more pronounced for both outcomes, emphasizing the importance of handgrip strength in predicting renal outcomes in CKD patients.\u003c/p\u003e \u003cp\u003eKaplan-Meier survival curve analyses demonstrated that patients with bilateral HGS values below these gender-specific cutoffs experienced significantly worse renal outcomes (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHandgrip Strength and Renal Outcomes\u003c/h2\u003e \u003cp\u003eThe findings of this study suggest that bilateral HGS may play a crucial role in predicting renal outcomes in patients with CKD. By examining a cohort of 441 participants, this study leveraging a larger sample size and extended follow-up period to potentially offer more nuanced and broadly applicable insights. Another advantage of this study\u0026rsquo;s design is that it incorporated two distinct renal composite outcome events, referred to as Outcome 1 and Outcome 2. For Outcome 1, participants underwent laboratory tests every three months, allowing for precise tracking of significant increases in serum creatinine levels or acute exacerbations of CKD. This frequent monitoring enabled a more accurate assessment of the short-term fluctuations and acute events in renal function. Outcome 2, on the other hand, distinguished itself by focusing on more severe renal events. This included participants who, despite being in ESRD at enrollment, managed to avoid dialysis for an extended period. This distinction highlights one of the unique aspects of our study: the nuanced categorization of renal outcomes based on severity and progression.\u003c/p\u003e \u003cp\u003eThe study demonstrates a strong inverse relationship between HGS and poor renal outcomes in CKD patients. Notably, those in the higher HGS quartiles had markedly lower HRs for both Outcome 1 and Outcome 2, compared to the lowest quartile. For instance, the HR for Outcome 1 in the fourth quartile was 0.109, indicating a substantial reduction in risk. Similarly, for Outcome 2, the HR in the fourth quartile was 0.053, underscoring the protective effect of higher HGS against renal deterioration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImpact of Sarcopenia\u003c/h2\u003e \u003cp\u003eSkeletal muscle is an important protein storage and glucose processing region critical for cardiovascular health[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Patients with sarcopenia have considerably higher levels of atherosclerotic markers such as IVD, FMD, and EAT, according to research. Reduced muscular function may result in a decrease in muscle contraction-inducing substances that have anti-inflammatory properties increasing the risk of developing cardiovascular disease[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Cardiovascular disease contributes significantly to the progression of chronic kidney disease. One of the most serious consequences of CKD is cardiovascular disease, which has a prevalence of up to 63%. In China, the prevalence is 34.5%[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Improving cardiovascular risk factors is becoming an increasingly important focus in managing CKD populations[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study further underscores the significant impact of sarcopenia on renal outcomes. Patients with sarcopenia faced a markedly higher risk of adverse renal events compared to those without. Specifically, the HR for Outcome 1 was 2.429, indicating a more than twofold increase in risk for significant increases in serum creatinine levels or acute exacerbations of CKD. For Outcome 2, which represents more severe renal endpoints, the HR was 4.237, highlighting an even greater risk. This pronounced disparity, particularly in the more severe outcomes, emphasizes the critical role of muscle mass and strength in the management and prognosis of CKD. Consequently, interventions aimed at preventing or mitigating sarcopenia could potentially improve renal outcomes in this patient population. By addressing sarcopenia, we may be able to not only enhance overall patient health but also specifically target the factors that contribute to worse renal prognosis, thereby improving both the quality of life and clinical outcomes for individuals with CKD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOptimal HGS Cut-off Points\u003c/h2\u003e \u003cp\u003eLow HGS is linked to an increased risk of CKD, as evidenced by various studies. A cross-sectional survey conducted in China found that individuals with medium to high HGS had a reduced risk of CKD, while those with low HGS were generally older and more prone to CKD[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This suggests that low bilateral HGS is an indicator of increased CKD risk, particularly among the elderly. However, the relationship between bilateral HGS and CKD is evident even among younger populations. A study on children with CKD showed that those in stages 2 to 5 had significantly lower HGS compared to those in stage 1[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], indicating that lower HGS is associated with more advanced stages of CKD irrespective of age. Meta-analyses and large-scale studies further support the link between HGS and mortality. An analysis of 53,476 participants suggested that the association between HGS and mortality was weaker in individuals aged 60 years or younger[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Conversely, another study involving 6,850 participants found no significant interaction between HGS and age regarding mortality, likely due to the smaller sample size. Nevertheless, a large-scale study involving over 500,000 participants confirmed an association between HGS and health outcomes across all ages, with a stronger association in younger populations[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese results collectively highlight the critical role of HGS as a predictive marker for health outcomes in CKD patients across different age groups[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, varying cutoff values across studies hinder consensus and practical application[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, a standardized HGS cutoff value for CKD patients is needed to guide clinical practice and ensure more conclusive research. Through ROC curve analysis, we identified optimal bilateral HGS cut-off points that maximize the predictive accuracy for renal outcomes. The thresholds were determined to be 64.35 kg for males and 39.35 kg for females. Patients with bilateral HGS below these cut-offs experienced significantly worse renal outcomes, as illustrated by Kaplan-Meier survival curves. This finding provides a practical tool for clinicians to identify CKD patients at higher risk and to tailor interventions accordingly. The clear demarcation of high and low bilateral HGS groups also facilitates more personalized and effective patient management strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Clinical Practice\u003c/h2\u003e \u003cp\u003eRenal replacement therapy options include kidney transplantation (KT), hemodialysis, and peritoneal dialysis. Among these, KT is the most effective method to reduce the high morbidity and mortality rates in patients with ESRD while providing relatively greater freedom to the patients. However, KT does not completely reverse the damage caused by years of reduced kidney function and dialysis. Additionally, due to the scarcity of donors, patients may face prolonged waiting periods for a transplant. Furthermore, in the post-transplant period, new detrimental factors, particularly those associated with immunosuppression, increase the risk of complications[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Consequently, delaying the progression of kidney failure and postponing the initiation of dialysis can achieve benefits similar to those of a kidney transplant, without the associated drawbacks. Therefore, effective management of CKD, accurate prediction of its progression, and early intervention measures are of paramount importance.\u003c/p\u003e \u003cp\u003eHGS accurately predicts overall muscular strength and is a sensitive measure of physical strength. HGS represents overall muscle strength throughout the body and is a sensitive indicator of muscle strength. Skeletal muscle action stimulates the expression of irisin, linked to skeletal muscle hypertrophy and strength[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Cross-sectional and longitudinal studies of more than 40,000 participants at UK Biobank have shown that there is a link between stronger handgrip strength and increased gray matter volume in the brain, and that subcortical, particularly hippocampal and temporal cortical areas, play an important role in muscular fitness, and that the volume of gray matter in these areas is also associated with better mental health, which largely moderates their relationship with handgrip strength. Strong handgrip strength protects against cognitive decline and dementia[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Low handgrip strength may lead to cognitive impairment. According to a 2019 report from the United States Department of Health and Human Services (HHS), around 10 to 40 percent of CKD patients have some cognitive impairment. Cognitive impairment can have a significant influence on patient treatment since patients may be unable to take medication as prescribed, may not be able to undergo peritoneal or hemodialysis, and may require real-time monitoring[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This could be one of the mechanisms by which reduced handgrip strength impacts the prognosis of kidney patients.\u003c/p\u003e \u003cp\u003eThe clinical implications of these findings are profound. First, incorporating HGS measurement into routine clinical practice offers a simple, cost-effective means to stratify risk among CKD patients. Given the significant association between HGS and renal outcomes, regular monitoring of grip strength could enhance the prognostic accuracy and help in early identification of patients at greater risk of CKD progression. Second, the strong link between sarcopenia and poor renal outcomes highlights the need for comprehensive management approaches that include nutritional and physical interventions to preserve muscle mass and strength[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Such strategies may not only improve overall health but also specifically mitigate the progression of CKD. Third, the identification of sex-specific HGS cut-off points provides a valuable reference for clinicians. By utilizing these thresholds, healthcare providers can better assess the risk profile of CKD patients and implement timely, targeted interventions. Lastly, significant intergroup differences suggesting that long-term follow-up is essential for a comprehensive understanding of outcomes in patients with CKD. The difference was more pronounced in the third year, suggesting a gradual accumulation of long-term effects on muscle mass and strength. Early intervention may have a more significant effect in the long term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eDespite the strengths of this study, certain limitations should be acknowledged. The cohort, while larger than previous studies, still represents a specific population, and the findings may not be universally applicable. Additionally, the observational nature of the study precludes definitive causal inferences. Future research, including randomized controlled trials, is needed to further explore the causal relationship between HGS and renal outcomes and to determine the efficacy of interventions aimed at improving grip strength in CKD patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study substantiates the role of bilateral HGS as a potent predictor of renal outcomes in CKD patients. The significant inverse relationship between bilateral HGS and adverse renal events underscores the importance of muscle strength in this context. By incorporating HGS measurement into routine clinical practice, and addressing sarcopenia through targeted interventions, healthcare providers can improve the prognosis and quality of life for CKD patients. Further research should continue to explore and validate these findings, paving the way for enhanced patient care and management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics\u003c/p\u003e\n\u003cp\u003eThe research was conducted at the Guangdong Provincial Hospital of Chinese Medicine with Ethics Approval No. 2019-153-01. The Chinese Clinical Trial Registry No. is ChiCTR1900024633.\u003c/p\u003e\n\u003cp\u003eConsent\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript and agree with the order of the presentation of authors for publication.\u003c/p\u003e\n\u003cp\u003eAvailability\u003c/p\u003e\n\u003cp\u003eThe data and material\u0026nbsp;are available from the corresponding authors upon request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Key Research and Development Program of China (2019YFE0196300), the GPHCM Fund for Traditional Chinese Medicine Science and Technology Research (YN2020ZWB05), the Guangdong Province Science and Technology Program (2022A1414020015) and the Foundation of Science, Technology and Innovation Commission of Shenzhen Municipality (JCYJ20180302153701406, KCXFZ20201221173612034).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQiong Huang and Linyi Chen planned the study and wrote the original draft. Li-zhe Fu and Fang Tang are responsible for blood collection and filling out participant information, while Xi-na Jie is responsible for collecting data. Wenwei Ouyang analyzed all the statistics. Jing Wang, Yifan Wu, and Xusheng Liu contributed to the concept and design.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to all the participants who contributed to this study.\u003c/p\u003e\n\u003cp\u003ePublisher\u0026rsquo;s note\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal, regional, and national burden of chronic kidney disease, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395:709\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Xu X, Zhang M, Hu C, Zhang X, Li C, et al. Prevalence of Chronic Kidney Disease in China: Results From the Sixth China Chronic Disease and Risk Factor Surveillance. JAMA Intern Med. 2023;183:298\u0026ndash;310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharytan DM. Introduction: Cardiovascular Disease in Chronic Kidney Disease. Semin Nephrol. 2018;38:541.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasson P, Kelly PJ, Craig JC, Lindley RI, Webster AC. 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J Ren Nutr. 2019;29:471\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKittiskulnam P, Chertow GM, Carrero JJ, Delgado C, Kaysen GA, Johansen KL. Sarcopenia and its individual criteria are associated, in part, with mortality among patients on hemodialysis. Kidney Int. 2017;92:238\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVogt BP, Borges MCC, Go\u0026eacute;s CR, Caramori JCT. Handgrip strength is an independent predictor of all-cause mortality in maintenance dialysis patients. Clin Nutr. 2016;35:1429\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClarke AL, Zaccardi F, Gould DW, Hull KL, Smith AC, Burton JO, et al. Association of self-reported physical function with survival in patients with chronic kidney disease. Clin Kidney J. 2019;12:122\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang YT, Wu HL, Guo HR, Cheng YY, Tseng CC, Wang MC, et al. Handgrip strength is an independent predictor of renal outcomes in patients with chronic kidney diseases. Nephrol Dial Transplant. 2011;26:3588\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe P, Ye Z, Liu M, Li H, Zhang Y, Zhou C, et al. Association of handgrip strength and/or walking pace with incident chronic kidney disease: A UK biobank observational study. J Cachexia Sarcopenia Muscle. 2023;14:805\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoor H, Reid J, Slee A. Resistance exercise and nutritional interventions for augmenting sarcopenia outcomes in chronic kidney disease: a narrative review. J Cachexia Sarcopenia Muscle. 2021;12:1621\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeal VO, Mafra D. Handgrip strength evaluation in CKD: do we have enough evidence? J Bras Nefrol. 2020;42:388\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOuyang WW, Chen HF, Xu XY, Zhang XL, Fu LZ, Tang F, et al. Self-management program for patients with chronic kidney disease (SMP-CKD) in Southern China: protocol for an ambispective cohort study. BMC Nephrol. 2022;23:93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020;21:300-7.e2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorman K, Stob\u0026auml;us N, Gonzalez MC, Schulzke JD, Pirlich M. Hand grip strength: outcome predictor and marker of nutritional status. Clin Nutr. 2011;30:135\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBohannon RW. Muscle strength: clinical and prognostic value of hand-grip dynamometry. Curr Opin Clin Nutr Metab Care. 2015;18:465\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMok Y, Ballew SH, Matsushita K. Chronic kidney disease measures for cardiovascular risk prediction. Atherosclerosis. 2021;335:110\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProvenzano M, Coppolino G, Faga T, Garofalo C, Serra R, Andreucci M. Epidemiology of cardiovascular risk in chronic kidney disease patients: the real silent killer. Rev Cardiovasc Med. 2019;20:209\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHogan J, Schneider MF, Pai R, Denburg MR, Kogon A, Brooks ER, et al. Grip strength in children with chronic kidney disease. Pediatr Nephrol. 2020;35:891\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCooper R, Kuh D, Hardy R. Objectively measured physical capability levels and mortality: systematic review and meta-analysis. Bmj. 2010;341:c4467.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCelis-Morales CA, Welsh P, Lyall DM, Steell L, Petermann F, Anderson J, et al. Associations of grip strength with cardiovascular, respiratory, and cancer outcomes and all cause mortality: prospective cohort study of half a million UK Biobank participants. Bmj. 2018;361:k1651.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeal VO, Mafra D, Fouque D, Anjos LA. Use of handgrip strength in the assessment of the muscle function of chronic kidney disease patients on dialysis: a systematic review. Nephrol Dial Transplant. 2011;26:1354\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu X, Yang Z, Ma T, Li Z, Chen Y, Zheng Y, et al. The cut-off values of handgrip strength and lean mass index for sarcopenia among patients on peritoneal dialysis. Nutr Metab (Lond). 2020;17:84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen-Bucay A, Gordon CE, Francis JM. Non-immunological complications following kidney transplantation. F1000Res. 2019;8:\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColaianni G, Oranger A, Dicarlo M, Lovero R, Storlino G, Pignataro P, et al. Irisin Serum Levels and Skeletal Muscle Assessment in a Cohort of Charcot-Marie-Tooth Patients. Front Endocrinol (Lausanne). 2022;13:886243.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang R, Westwater ML, Noble S, Rosenblatt M, Dai W, Qi S, et al. Associations between grip strength, brain structure, and mental health in \u0026gt;\u0026thinsp;40,000 participants from the UK Biobank. BMC Med. 2022;20:286.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrew DA, Weiner DE, Sarnak MJ. Cognitive Impairment in CKD: Pathophysiology, Management, and Prevention. Am J Kidney Dis. 2019;74:782\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastaneda C, Gordon PL, Uhlin KL, Levey AS, Kehayias JJ, Dwyer JT, et al. Resistance training to counteract the catabolism of a low-protein diet in patients with chronic renal insufficiency. A randomized, controlled trial. Ann Intern Med. 2001;135:965\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Handgrip strength, sarcopenia, chronic kidney disease, SMP-CKD","lastPublishedDoi":"10.21203/rs.3.rs-5292199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5292199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aims to investigate the relationship between handgrip strength (HGS) and the progression of chronic kidney disease (CKD) in non-dialysis patients in China, as part of the Self-Management Program for Patients with CKD Cohort (SMP-CKD).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn the SMP-CKD cohort, we utilized Cox regression and Kaplan-Meier survival analysis to explore the association between HGS and CKD progression. Data were stratified by sex-specific HGS quartiles, sarcopenia status, and HGS thresholds. The HGS thresholds were determined through curve analysis of HGS against composite renal outcomes. Group differences were compared to assess the impact of HGS on CKD outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 441 participants (mean age 57.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17 years, 56.0% male) with CKD stages 3\u0026ndash;5 from the SMP-CKD cohort who underwent grip strength evaluation between April 2019 and June 2024 were included in the analysis. The findings revealed that participants in the highest bilateral HGS quartile had a significantly lower risk of renal endpoints, with a hazard ratio (HR) of 0.109 (95% CI: 0.044\u0026ndash;0.272) compared to those in the lowest quartile. Patients with sarcopenia exhibited more than twice the risk of increased serum creatinine or acute CKD exacerbations (HR 2.429, 95% CI: 1.218\u0026ndash;4.846), as well as a markedly higher risk of severe renal endpoints (HR 4.237, 95% CI: 1.595\u0026ndash;11.256). Gender-specific cutoffs identified through ROC analysis were 64.35 kg for men and 39.35 kg for women. Participants with bilateral HGS above these thresholds demonstrated better renal outcomes, underscoring the protective effect of higher HGS against CKD progression.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study provides strong evidence that HGS is a crucial factor in reducing the risk of CKD progression. Higher levels of HGS are significantly associated with a lower occurrence of renal endpoint events.\u003c/p\u003e","manuscriptTitle":"The Association Between Hand Grip Strength and Chronic Kidney Disease Progression: Insights from SMP-CKD Studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-08 10:46:02","doi":"10.21203/rs.3.rs-5292199/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15e881d7-9f9c-48ce-9c50-0bcc4e9835ff","owner":[],"postedDate":"November 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T16:02:15+00:00","versionOfRecord":{"articleIdentity":"rs-5292199","link":"https://doi.org/10.1007/s11255-025-04457-7","journal":{"identity":"international-urology-and-nephrology","isVorOnly":false,"title":"International Urology and Nephrology"},"publishedOn":"2025-05-01 15:57:46","publishedOnDateReadable":"May 1st, 2025"},"versionCreatedAt":"2024-11-08 10:46:02","video":"","vorDoi":"10.1007/s11255-025-04457-7","vorDoiUrl":"https://doi.org/10.1007/s11255-025-04457-7","workflowStages":[]},"version":"v1","identity":"rs-5292199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5292199","identity":"rs-5292199","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}