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Original Article
Metabolic Risk/Epidemiology Normalized Creatinine-to-Cystatin C Ratio and Risk of Diabetes in Middle-Aged and Older Adults: The China Health and Retirement Longitudinal Study
Shanhu Qiu1,2orcid, Xue Cai1, Bo Xie1, Yang Yuan1,3, Zilin Sun1,3orcidcorresp_icon, Tongzhi Wu4
Diabetes & Metabolism Journal 2022;46(3):476-485.
DOI: https://doi.org/10.4093/dmj.2021.0074
Published online: March 7, 2022
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1Institute of Diabetes, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China

2Department of Endocrinology, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University and The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China

3Department of Endocrinology, Zhongda Hospital, Southeast University, Nanjing, China

4Adelaide Medical School and Centre of Research Excellence (CRE) in Translating Nutritional Science to Good Health, The University of Adelaide, Adelaide, Australia

corresp_icon Corresponding author: Zilin Sun orcid Institute of Diabetes, Zhongda Hospital, School of Medicine, Southeast University, Dingjiaqiao No. 87, Nanjing 210009, China E-mail: sunzilin1963@126.com
• Received: April 22, 2021   • Accepted: June 13, 2021

Copyright © 2022 Korean Diabetes Association

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Background
    Creatinine-to-cystatin C ratio is recently suggested to be a surrogate marker for sarcopenia. However, little is known about its association with diabetes. This study aimed to fill in this gap based on a large-scale prospective cohort.
  • Methods
    A population-based representative sample of 5,055 participants aged ≥45 years from the China Health and Retirement Longitudinal Study was enrolled between 2011 and 2012 and followed at least once during the subsequent surveys at 2013, 2015, or 2018. Creatinine-to-cystatin C ratio was calculated and normalized by body weight. Incident diabetes was ascertained by plasma glucose, glycosylated hemoglobin, self-reported history, or use of anti-diabetic drugs. Logistic regression analysis and mediation analysis were employed.
  • Results
    During follow-up, 634 participants developed diabetes. The risk of diabetes was gradually and significantly decreased with increased normalized creatinine–cystatin C ratio. The multivariable-adjusted odds ratio for diabetes was 0.91 (95% confidence interval, 0.83 to 0.99) per 1 standard deviation higher of normalized creatinine-to-cystatin C ratio, and this relationship remained significant after controlling for muscle strength. The risk reduction in diabetes was significantly larger in participants with normal-weight and high normalized creatinine-to-cystatin C ratio compared with those with overweight/obesity and high normalized creatinine-to-cystatin C ratio (Pinteraction=0.01). Insulin resistance and inflammation appeared to be key mediators accounting for the observed relationship between normalized creatinine-to-cystatin C ratio and risk of diabetes, with their mediating effect being 93.1% and 22.0%, respectively.
  • Conclusion
    High normalized creatinine-to-cystatin C ratio is associated with reduced risk of diabetes in middle-aged and older adults.
1. Higher normalized creatinine–cystatin C ratio was related to lower risk of diabetes.
2. There might exist a “threshold effect” on this relationship.
3. High normalized creatinine-to-cystatin C ratio related to good metabolic control.
4. Insulin resistance and inflammation may primarily mediate the relationship.
Being extensively presented in nucleated cells, cystatin C is recognized as a novel biomarker that outperforms creatinine in reflecting glomerular filtration [1]. In recent years its ratio from creatinine, namely creatinine-to-cystatin C ratio, has been indicated to reflect health wellbeing in a wide range of clinical contexts [2-5]. For example, creatinine-to-cystatin C ratio has been advocated to be a surrogate marker for sarcopenia [2-6]. Moreover, several prospective cohort studies have found that creatinine-to-cystatin C ratio is associated with prognostic outcomes, which include all-cause and cardiovascular mortality [1,7,8], as well as bone fracture [9].
Diabetes has become a worldwide public health burden and its global prevalence is estimated to be projected from 9.3% in 2019 to 10.2% by 2030 and 10.9% by 2045 [10]. Recent evidence points out that low muscle mass assessed by dual X-ray absorptiometry or computed tomography might be implicated in the development of diabetes in middle-aged and older adults [11,12]. However, these gold standard measurement approaches are not feasible for large-scale epidemiological surveys, and the efficacy of the alternative method—bioelectrical impedance analysis in quantifying muscle mass has been questioned [13]. Creatinine-to-cystatin C ratio has recently received substantial interest as a measure for muscle mass, and its accuracy has been well validated against computed tomography in assessing muscle mass among different populations including older adults and cancer patients [2,6,14]. Moreover, the evidence that creatinine-to-cystatin C ratio carries a superior power in detecting myopenia over bioelectrical impedance analysis adds further support to its accuracy [14]. However, to our knowledge, no population-based studies have been conducted to assess whether this ratio is related to the development of diabetes.
To fill in this gap, we investigated the relationship of creatinine-to-cystatin C ratio with risk of diabetes in a cohort of middle-aged and older adults from the China Health and Retirement Longitudinal Study (CHARLS) [15-17]. However, in the present study creatinine-to-cystatin C ratio was normalized by body mass [18], given the evidence that substantial covariance exists between muscle mass or muscle strength and body mass [19,20], and that normalized creatinine-to-cystatin C ratio had a better correlation with insulin sensitivity than the non-normalized ratio [18].
Study population
CHARLS is an ongoing prospective longitudinal study that enrolls middle-aged and older adults (45 years and above), who were randomly and representatively selected from 150 county-level units from 28 provinces in China. The cohort profile of CHARLS has been described previously [15-17] and the detailed information about the sampling approach and study design was also reported on the website at http://charls.pku.edu.cn/index/en.html. In brief, the baseline survey of CHARLS was conducted from June 2011 to March 2012. Participants with computer-assisted personal interview data at baseline were followed consecutively. To date, three waves of follow-up survey had been conducted at 2013, 2015, and 2018. Information relating to demographics, family structure, health status and functioning, and health outcomes including blood biomarkers measurement, were collected. The study design and protocol of CHARLS were approved by the ethical review committee at Peking University (approval No. IRB 00001052-11014), and informed consent was obtained from all participants.
In the present study, we included participants who had information at baseline (at 2011–2012) and were followed at least once during the subsequent three waves (that is, at 2013, 2015, or 2018). Participants were excluded if they: (1) had no data on cystatin C, creatinine, or glycemic measures such as fasting plasma glucose (FPG) or glycosylated hemoglobin (HbA1c); (2) were diagnosed with diabetes at baseline based on the American Diabetes Association 2010 criteria [21]; (3) had missing information on gender, body weight, body mass index (BMI), systolic blood pressure (SBP), or diastolic blood pressure (DBP); (4) were with outlier data (<1 or >99 percentile) on BMI, SBP, or DBP; (5) were with kidney disease or an estimated glomerular filtration rate (eGFR; calculated using the Chinese-based equation [22]) of less than 30 mL/(min×1.73 m2); and (6) aged <45 years. Finally, a total of 5,055 middle-aged and older adults were included in this study (Supplementary Fig. 1).
Assessment of demographic and anthropometric information
Information on age, gender, history of smoking (smoker and non-smoker) or drinking (drinker or non-drinker), history of disease (e.g., hypertension, heart disease, kidney disease) was collected using standardized questionnaires. Height (cm) and body weight (kg) were measured. Blood pressure was measured trice in the morning using an automatic monitor (OmronTM HEM-7200 Monitor, Omron [Dalian] Co. LTD., Dalian, China) in a relaxed state. Hypertension was defined as a mean of SBP ≥140 mm Hg, a mean of DBP ≥90 mm Hg, a history of hypertension, or the use of anti-hypertensive agents. History of heart disease or kidney disease was ascertained by questionnaires.
Measurement of blood biomarkers
All participants were asked to fast overnight, and blood samples were taken in the next morning. However, for a small proportion (about 8%) of participants who did not fast, blood samples were also taken, while their plasma glucose data were treated as random plasma glucose. The whole blood and plasma samples were stored at –80°C and were later used to measure FPG, HbA1c, total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglyceride (TG), uric acid (UA), creatinine, high-sensitivity C-reactive protein (hs-CRP), and cystatin C. Details of the methods for these blood-based bioassays were described in the “2011–2012 National Baseline Blood Data Users’ Guide” (http://charls.pku.edu.cn/index/en.html). Specifically, creatinine was measured by rate-blanked and compensated Jaffe creatinine method (within-assay and between-assay coefficient of variation was <1.60% and <2.10%, respectively), and cystatin C was by particle-enhanced turbimetric assay (within-assay and between-assay coefficient of variation was <5.00% and <5.00%, respectively). Moreover, in this study dyslipidemia was defined as TC ≥240 mg/dL, LDL ≥160 mg/dL, HDL <40 mg/dL, TG ≥200 mg/dL, a history of dyslipidemia, and/or the use of lipid-lowering medications.
Calculation of normalized creatinine-to-cystatin C ratio and other parameters
In this study normalized creatinine-to-cystatin C ratio was determined as: creatinine (mg/dL)/cystatin C (mg/L)×10/body mass (kg). Insulin resistance was assessed by the metabolic score for insulin resistance (METS-IR), which was calculated as: ln (2×FPG [mg/dL]+TG [mg/dL])/ln (HDL [mg/dL])×BMI (kg/m2) [23]. BMI was obtained as: body weight (kg)/height (m)2, and overweight or obesity was defined as BMI ≥24 kg/m2 [24]. Mean arterial pressure (MAP) was calculated as: (SBP [mm Hg]+2×DBP [mm Hg])/3, and eGFR was as: 2,374.78×([creatinine×88.4]^[–0.54753])×(age^[–0.25011]) for male and 2,374.78×([creatinine×88.4]^[–0.54753])×(age^[–0.25011])×0.8526126 for female based on the Xiangya equation [22].
Assessment of muscle strength
Muscle strength was evaluated by handgrip strength (kg) and chair-rising time (second). For handgrip strength, it was determined using a handgrip dynamometer (YuejianTM WL-1000 dynamometer, Nantong Yuejian Physical Measurement Instrument Co. Ltd., Nantong, China) twice, and the average data for the dominant hand were chosen for the analysis. And for chair-rising time, it was assessed using a stopwatch by guiding participants to stand-up and sit-down on a chair at their fastest pace for five times [15].
Ascertainment of diabetes
Incident diabetes was ascertained by: FPG ≥7.0 mmol/L (126 mg/dL), random plasma glucose ≥11.1 mmol/L (200 mg/dL), HbA1c ≥6.5% (48 mmol/mol), a self-reported history of diabetes, or the use of anti-diabetic drugs [21,25].
Statistical analysis
Continuous variables were presented as mean±standard deviation (SD), and categorical variables were as numbers (%). One-way analysis of variance, unpaired t-test, and chi-square test were employed to compare differences when appropriate. Normalized creatinine-to-cystatin C ratio was managed by two approaches: (1) being categorized into three groups (lowest, middle, and highest) defined by tertiles, and (2) being treated as a continuous variable expressed as per 1 SD higher. Odds ratio (OR) and 95% confidence interval (CI) for the association of normalized creatinine-to-cystatin C ratio with risk of diabetes were examined by logistic regression analysis. For this, three different models were introduced: model 1 without adjustment, model 2 adjusted for age and gender, and model 3 additionally adjusted for history of smoking and drinking, disease status (hypertension, dyslipidemia, and heart disease), MAP, HbA1c, TC/HDL cholesterol, TG, LDL, UA, and hs-CRP. For participants with available data on muscle strength, separate analyses based on model 3 were conducted by further controlling for handgrip strength and chair-rising time, respectively.
Restricted cubic spline curve, adjusted for covariables in model 3, was used to depict the association of normalized creatinine-to-cystatin C ratio with risk of diabetes, with the nonlinearity being assessed by Wald test. Subgroup analysis of age (≥60 years vs. <60 years), gender (male vs. female), history of smoking (yes vs. no), or history of drinking (yes vs. no), and sensitivity analysis by excluding participants without fasting samples, were also performed. Mediation analysis based on generalized structural equation model [26,27] was employed to evaluate the role of cardiometabolic factors (such as blood pressure, glycemic markers, lipid profiles, and inflammatory marker) in mediating the risk of diabetes associated with normalized creatinine-to-cystatin C ratio. For this, all the cardiometabolic factors and normalized creatinine-to-cystatin C ratio were expressed in continuous scales, and the risk of diabetes was as a dichotomous variable. The mediation effect was calculated as the indirect effect (mediated by cardiometabolic factors)/total effect×100%.
All analyses were performed using Stata version 14.0 (StataCorp LP, College Station, TX, USA), and a P<0.05 was considered statistically significant.
Baseline characteristics of enrolled participants
A total of 5,055 participants (mean age 60.0±9.7 years; 46.9% males) were included in this study. The baseline characteristics of enrolled participants according to the tertiles of normalized creatinine-to-cystatin C ratio are summarized in Table 1. The proportions of participants with hypertension, dyslipidemia, or heart disease in different tertiles decreased gradually with increases in normalized creatinine-to-cystatin C ratio (all P≤0.003), while the percentages of smoker or drinker increased. BMI and blood pressure including SBP, DBP, and MAP were significantly lower in groups with higher normalized creatinine-to-cystatin C ratio (all P<0.001). For laboratory biomarkers, HbA1c, METS-IR, and hs-CRP were also lower in the higher normalized creatinine-to-cystatin C ratio tertiles, while TC, HDL, UA, and creatinine were progressively greater (all P<0.001).
Normalized creatinine-to-cystatin C ratio and risk of diabetes
During the 7.0-year follow-up, 634 (12.5%) participants developed diabetes. Participants with incident diabetes had a worse metabolic profile than those without in general (Supplementary Table 1), and their normalized creatinine-to-cystatin C ratio was lower (0.13±0.04 vs. 0.14±0.04, P=0.003). The association between normalized creatinine-to-cystatin C ratio and risk of diabetes is shown in Table 2, which suggested that the ORs for diabetes were gradually lower with larger normalized creatinine-to-cystatin C ratios. In the unadjusted model, the OR for diabetes was 0.72 (95% CI, 0.59 to 0.88) and 0.64 (95% CI, 0.52 to 0.79) for the middle and highest tertiles, respectively, compared with the reference (the lowest tertile). These associations were slightly attenuated but maintained significant after controlling for different covariables (model 2 and model 3, Table 2). The multivariable-adjusted OR for diabetes was 0.91 (95% CI, 0.83 to 0.99) per 1 SD higher of normalized creatinine-to-cystatin C ratio (Table 2). Notably, this OR was still significant even after controlling for grip strength (0.91; 95% CI, 0.84 to 0.99) or chair-rising time (0.91; 95% CI, 0.83 to 0.99).
Cubic spline analysis suggested that there is no clear evidence of a nonlinear association between normalized creatinine-to-cystatin C ratio and risk of diabetes (Pnonlinearity=0.13). The risk of diabetes decreased progressively with increases in normalized creatinine-to-cystatin C ratio up to 0.15 but plateaued thereafter (Fig. 1).
Subgroup and joint analyses
The ORs for diabetes per 1 SD higher of normalized creatinine-to-cystatin C ratio did not vary substantially by subgroups based on gender, age, history of smoking, or history of drinking (all Pinteraction≥0.34) (Supplementary Table 2). Moreover, the results remained comparable in analyses that excluded participants without fasting blood samples (OR, 0.89; 95% CI, 0.81 to 0.98).
To assess the joint effect of BMI and normalized creatinine-to-cystatin C ratio on risk of diabetes, participants were classed as with or without overweight/obesity, or with low (the lowest tertile) or high (the middle and highest tertiles) normalized creatinine-to-cystatin C ratio. Compared with participants with overweight/obesity and low normalized creatinine-to-cystatin C ratio, only those with normal-weight and high normalized creatinine-to-cystatin C ratio showed significantly reduced risk of diabetes (OR, 0.62; 95% CI, 0.49 to 0.78; model 3) (Table 3). Moreover, their magnitude of risk reduction was significantly larger than participants with overweight/obesity and high normalized creatinine-to-cystatin C ratio (Pinteraction=0.01) but not significantly than participants with normal-weight and low normalized creatinine-to-cystatin C ratio (Pinteraction=0.28).
Mediation analysis
Both simple and multivariable linear regression analyses suggested that normalized creatinine-to-cystatin C ratio was negatively and significantly correlated with blood pressure, METSIR, eGFR, and hs-CRP, positively and significantly with TC, TG, HDL, and UA (all P<0.001) (Table 4), but non-significantly with FPG or HbA1c. Mediation analysis showed further that METS-IR may fully mediate the relationship between normalized creatinine-to-cystatin C ratio and risk of diabetes, with its mediating effect being 93.1% (Fig. 2). Moreover, hs-CRP, HDL, SBP, and DBP may also mediate this relationship, with their mediating effect at 22.0%, 17.4%, 12.5%, and 8.3%, respectively (Fig. 2). However, FPG, HbA1c, TC, TG, and LDL did not have any significant moderating effects.
Summary of main findings
Our study, which is based on a large-scale population-based prospective cohort, showed for the first time in middle-aged and older adults that: (1) higher normalized creatinine-to-cystatin C ratio was associated with lower risk of diabetes independent of traditional cardiometabolic factors and muscle strength; (2) reducing body weight could lower the risk of diabetes in participants with a high normalized creatinine-to-cystatin C ratio; and (3) the association of normalized creatinine-to-cystatin C ratio with risk of diabetes appeared to be mediated primarily by insulin resistance (assessed by METS-IR) and inflammation (evaluated by hs-CRP).
Interpretations and implications
Our study showed that high normalized creatinine-to-cystatin C ratio reduced risk of diabetes in middle-aged and older adults. One might assume that this relationship is attributable to variations in muscle mass and/or muscle strength [5,6,18, 28], both of which are identified determinants for risk of diabetes [20,29,30]. However, in the present study, the association between high normalized creatinine-to-cystatin C ratio and low risk of diabetes was independent of muscle strength. Moreover, the correlation between creatinine-to-cystatin C ratio and muscle mass or muscle strength was reported to be weak to moderate (the correlation coefficient ranged from 0.27 to 0.57) [3,18,28,31]. It is therefore plausible that there exist factors complementing muscle mass or muscle strength. Indeed, we observed that normalized creatinine-to-cystatin C ratio was also inversely associated with blood pressure, insulin resistance (assessed by METS-IR), and inflammation (presented by hs-CRP), all of which were closely related to risk of diabetes [32-34]. Moreover, normalized creatinine-to-cystatin C ratio was also found to be significantly and negatively correlated with BMI (data not shown), suggesting that this ratio may additionally help to reflect the extent of fat mass accumulation, at least partly. Notably, our mediation analysis revealed that the amelioration of insulin resistance or inflammation, as a result of high normalized creatinine-to-cystatin C ratio, might be the key drivers in reducing risk of diabetes, with their mediating effect at around 93% and 22%, respectively.
Although our restricted cubic spline analysis showed that there was no significant evidence of a nonlinear association between normalized creatinine-to-cystatin C ratio and risk of diabetes, improvements in normalized creatinine-to-cystatin C ratio beyond 0.15 do not appear to yield additional benefit in reducing risk of diabetes, indicative of a “threshold effect.” Moreover, our study found that participants with normal-weight and high normalized creatinine-to-cystatin C ratio showed significantly larger risk reduction in diabetes than those with overweight/obesity and high normalized creatinine-to-cystatin C ratio. This indicates that reducing body weight could help to lower the risk of diabetes, even in participants who had a high normalized creatinine-to-cystatin C ratio. However, neither sex nor age affected the association of normalized creatinine-to-cystatin C ratio with risk of diabetes in our study.
Strengths and limitations
The strengths of this study include a nationally representative sample of middle-aged and older Chinese adults, a relatively long follow-up period, a standardized and validated measurement of creatinine and cystatin C, as well as the use of subgroup analysis, cubic spline analysis, and mediation analysis, which facilitates an enriched interpretation of the association of normalized creatinine-to-cystatin C ratio with risk of diabetes. However, our study has several limitations. First, despite of the effort to adjust for a series of covariables, residual confounding could not be completely ruled out. For example, cardiorespiratory fitness may affect the onset of diabetes [35], while the lack of this information precluded an opportunity to undertake this analysis. Second, creatinine and cystatin C were measured only once, while this may introduce the risk of measurement error or overestimation. However, the protocols for their measurements were well-validated (http://charls.pku.edu.cn/index/en.html). Third, oral glucose tolerance test was not conducted to ascertain incident diabetes, which may underestimate the incidence of diabetes. Finally, our study focused mainly on Chinese Han adults, and it remains unclear whether our conclusions could be generalized for other ethnic populations.
In conclusion, high normalized creatinine-to-cystatin C ratio is associated with reduced risk of diabetes in middle-aged and older adults, and this association might be primarily attributable to the amelioration of insulin resistance and inflammation. Interventions designed to increase normalized creatinine-to-cystatin C ratio is therefore worth being recommended to lower risk of diabetes.
Supplementary materials related to this article can be found online at https://doi.org/10.4093/dmj.2021.0074.
Supplementary Table 1.
Baseline characteristics of participants stratified by incident diabetes
dmj-2021-0074-suppl1.pdf
Supplementary Table 2.
Subgroup analysis on the association of normalized creatinine-to-cystatin C ratio with risk of diabetes
dmj-2021-0074-suppl2.pdf
Supplementary Fig. 1.
Flowchart. BMI, body mass index; eGFR, estimated glomerular filtration rate.
dmj-2021-0074-suppl3.pdf

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conception or design: S.Q., X.C., Z.S., T.W.

Acquisition, analysis, or interpretation of data: S.Q., X.C., B.X., Y.Y., Z.S.

Drafting the work or revising: S.Q., B.X., Y.Y., T.W.

Final approval of the manuscript: S.Q., X.C., B.X., Y.Y., Z.S., T.W.

FUNDING

This work was partly supported by the National Key R&D Program of China (grant No. 2016YFC1305700) and the Nanjing Special Fund for Health Science and Technology Development (No. YKK18261). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgements
None
Fig. 1.
Cubic spline curve for the relationship between normalized creatinine-to-cystatin C ratio and risk of diabetes. The cubic spline curve analysis was adjusted for age, gender, history of smoking and drinking, presence of hypertension, dyslipidemia, and heart disease, mean arterial pressure, glycosylated hemoglobin, total cholesterol/high-density lipoprotein, triglyceride, low-density lipoprotein, uric acid, and high-sensitivity C-reactive protein. OR, odds ratio.
dmj-2021-0074f1.jpg
Fig. 2.
Mediation analysis for the relationship between normalized creatinine-to-cystatin C ratio and risk of diabetes. Mediation analysis (which included the tests for coefficients of a, b, and c) was conducted based on the generalized structural equation model using “GSEM” command in Stata. SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin; METS-IR, the metabolic score for insulin resistance; TC, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; hs-CRP, high-sensitivity C-reactive protein; eGFR, estimated glomerular filtration rate.
dmj-2021-0074f2.jpg
dmj-2021-0074f3.jpg
Table 1.
Baseline characteristics of enrolled participants
Variable Total Tertiles of normalized creatinine-to-cystatin C ratio
P valuea
Lowest (0.10±0.01) Middle (0.13±0.008) High (0.18±0.03)
No. of participants 5,055 1,685 1,685 1,685
Age, yr 60.0±9.7 60.3±10.0 60.0±9.5 59.6±9.7 0.119
Male sex 2,372 (46.9) 696 (41.3) 814 (48.3) 862 (51.2) <0.001
Smokingb 1,987 (39.3) 585 (34.7) 686 (40.7) 716 (42.5) <0.001
Drinking 1,643 (32.5) 478 (28.4) 572 (33.9) 593 (35.2) <0.001
Presence of
 Hypertension 1,987 (39.3) 788 (46.8) 689 (40.9) 510 (30.3) <0.001
 Dyslipidemia 1,958 (38.7) 708 (42.0) 626 (37.2) 624 (37.0) 0.003
 Heart diseaseb 531 (10.5) 234 (13.9) 160 (9.5) 137 (8.1) <0.001
BMI, kg/m2 23.2±3.4 25.2±3.4 23.0±2.8 21.3±2.7 <0.001
SBP, mm Hg 130.1±20.1 133.1±20.3 130.3±20.1 126.8±19.4 <0.001
DBP, mm Hg 75.0±11.1 76.5±11.2 75.2±11.0 73.4±10.8 <0.001
MAP, mm Hg 93.4±13.0 95.3±13.1 93.6±12.9 91.2±12.6 <0.001
FPG, mg/dLc 100.5±10.9 100.8±10.5 100.4±10.7 100.2±11.4 0.227
HbA1c, % 5.1±0.4 5.1±0.4 5.1±0.4 5.1±0.4 0.023
HbA1c, mmol/mol 32±4.4 32±4.4 32±4.4 32±4.4 0.023
METS-IRc 34.4±7.1 37.8±7.1 33.9±6.1 31.4±6.4 <0.001
TC, mg/dL 192.6±37.5 189.2±37.6 192.3±36.5 196.3±38.3 <0.001
TG, mg/dL 121.7±74.1 121.6±67.4 119.6±69.6 124.1±84.0 0.210
HDL, mg/dL 52.0±14.7 49.6±13.3 52.4±14.6 54.1±15.9 <0.001
LDL, mg/dL 117.1±34.1 117.2±34.7 117.5±33.2 116.7±34.5 0.812
UA, mg/dL 4.4±1.2 4.3±1.2 4.4±1.2 4.5±1.3 <0.001
Cr, mg/dL 0.77±0.18 0.71±0.17 0.77±0.16 0.84±0.18 <0.001
eGFR, mL/min/1.73 m2 79.5±9.5 82.7±10.5 79.2±8.4 76.4±8.4 <0.001
hs-CRP, mg/Ld 2.4±6.0 3.0±6.9 2.3±6.3 2.0±4.4 <0.001

Values are presented as mean±standard deviation or number (%).

BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin; METS-IR, the metabolic score for insulin resistance; TC, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; UA, uric acid; Cr, creatinine; eGFR, estimated glomerular filtration rate; hs-CRP, high-sensitivity C-reactive protein.

a Data were compared using one-way analysis of variance or chi-square test when appropriate,

b There were 4 and 35 participants without information on history of smoking and heart disease, respectively,

c There were 475 participants without fasting blood samples,

d It was log-transformed before analysis.

Table 2.
Normalized creatinine-to-cystatin C ratio and risk of diabetes
Variable Normalized creatinine-to-cystatin C ratio
Lowest (tertile 1) Middle (tertile 2) Highest (tertile 3) Per 1 SD higher
Total no. 1,685 1,685 1,685 5,055
No. of cases 261 196 177 634
 Model 1a 1 (ref) 0.72 (0.59–0.88) 0.64 (0.52–0.79) 0.85 (0.78–0.94)
 Model 2b 1 (ref) 0.74 (0.60–0.90) 0.66 (0.54–0.81) 0.87 (0.79–0.95)
 Model 3c 1 (ref) 0.81 (0.65–0.99) 0.76 (0.61–0.94) 0.91 (0.83–0.99)

Values are presented as odds ratio (95% confidence interval).

SD, standard deviation.

a Unadjusted,

b Adjusted for age and gender,

c Adjusted for age, gender, history of smoking and drinking, presence of hypertension, dyslipidemia, and heart disease, mean arterial pressure, glycosylated hemoglobin, total cholesterol/high-density lipoprotein, triglyceride, low-density lipoprotein, uric acid, and high-sensitivity C-reactive protein.

Table 3.
Joint effect of body mass index and normalized creatinine-to-cystatin C ratio on risk of diabetes
Variable Overweight/obesity (BMI ≥24 kg/m2)
Normal-weight (BMI <24 kg/m2)
Low NCCR High NCCR Low NCCR High NCCR
Total no. 1,048 857 637 2,513
No. of cases 188 149 73 224
 Model 1a 1 (Ref) 0.96 (0.76–1.22) 0.59 (0.44–0.79) 0.45 (0.36–0.55)
 Model 2b 1 (Ref) 1.01 (0.79–1.28) 0.56 (0.42–0.75) 0.44 (0.36–0.55)
 Model 3c 1 (Ref) 0.95 (0.74–1.22) 0.77 (0.56–1.05) 0.62 (0.49–0.78)

Values are presented as odds ratio (95% confidence interval). Low NCCR referred to the lowest tertile of NCCR, and high NCCR was defined as the middle and highest tertiles.

BMI, body mass index; NCCR, normalized creatinine-to-cystatin C ratio.

a Unadjusted,

b Adjusted for age and gender,

c Adjusted for age, gender, history of smoking and drinking, presence of hypertension, dyslipidemia, and heart disease, mean arterial pressure, glycosylated hemoglobin, total cholesterol/high-density lipoprotein, triglyceride, low-density lipoprotein, uric acid, and high-sensitivity C-reactive protein.

Table 4.
Association between normalized creatinine-to-cystatin C ratio and cardiometabolic markers
Independent variable Simple linear regression analysis Independent variable
Multivariable linear regression analysisa
P value Sβ’ P value
SBP –0.11 <0.001 –0.11 <0.001
DBP –0.09 <0.001 –0.10 <0.001
FPG –0.01 0.553 –0.006 0.679
HbA1c –0.03 0.086 –0.02 0.120
METS-IR –0.34 <0.001 –0.35 <0.001
TC 0.11 <0.001 0.12 <0.001
TG 0.07 <0.001 0.07 <0.001
HDL 0.10 <0.001 0.10 <0.001
LDL –0.0001 >0.999 0.009 0.518
UA 0.07 <0.001 0.06 <0.001
eGFR –0.30 <0.001 –0.41 <0.001
hs-CRPb –0.14 <0.001 –0.15 <0.001

Sβ, standardized regression coefficient; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin; METS-IR, the metabolic score for insulin resistance; TC, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; UA, uric acid; eGFR, estimated glomerular filtration rate; hs-CRP, high-sensitivity C-reactive protein.

a Adjusted for age and gender,

b It was log-transformed before analysis.

  • 1. Jung CY, Joo YS, Kim HW, Han SH, Yoo TH, Kang SW, et al. Creatinine-cystatin C ratio and mortality in patients receiving intensive care and continuous kidney replacement therapy: a retrospective cohort study. Am J Kidney Dis 2021;77:509-16.ArticlePubMed
  • 2. Barreto EF, Poyant JO, Coville HH, Dierkhising RA, Kennedy CC, Gajic O, et al. Validation of the sarcopenia index to assess muscle mass in the critically ill: a novel application of kidney function markers. Clin Nutr 2019;38:1362-7.ArticlePubMed
  • 3. Fu X, Tian Z, Wen S, Sun H, Thapa S, Xiong H, et al. A new index based on serum creatinine and cystatin C is useful for assessing sarcopenia in patients with advanced cancer. Nutrition 2021;82:111032.ArticlePubMed
  • 4. Osaka T, Hamaguchi M, Hashimoto Y, Ushigome E, Tanaka M, Yamazaki M, et al. Decreased the creatinine to cystatin C ratio is a surrogate marker of sarcopenia in patients with type 2 diabetes. Diabetes Res Clin Pract 2018;139:52-8.ArticlePubMed
  • 5. Tan L, Li R, Hu X, Zhu Y, Bao T, Zuo Y, et al. Serum creatinine/cystatin C ratio as a case-finding tool for low handgrip strength in Chinese middle-aged and older adults. Sci Rep 2020;10:14028.ArticlePubMedPMCPDF
  • 6. Tabara Y, Kohara K, Okada Y, Ohyagi Y, Igase M. Creatinineto-cystatin C ratio as a marker of skeletal muscle mass in older adults: J-SHIPP study. Clin Nutr 2020;39:1857-62.ArticlePubMed
  • 7. Tang T, Zhuo Y, Xie L, Wang H, Yang M. Sarcopenia index based on serum creatinine and cystatin C is associated with 3-year mortality in hospitalized older patients. Sci Rep 2020;10:1260.ArticlePubMedPMCPDF
  • 8. Lee HS, Park KW, Kang J, Ki YJ, Chang M, Han JK, et al. Sarcopenia index as a predictor of clinical outcomes in older patients with coronary artery disease. J Clin Med 2020;9:3121.ArticlePubMedPMC
  • 9. Komorita Y, Iwase M, Fujii H, Ide H, Ohkuma T, Jodai-Kitamura T, et al. The serum creatinine to cystatin C ratio predicts bone fracture in patients with type 2 diabetes: the Fukuoka Diabetes Registry. Diabetes Res Clin Pract 2018;146:202-10.ArticlePubMed
  • 10. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2019;157:107843.ArticlePubMed
  • 11. Kalyani RR, Metter EJ, Xue QL, Egan JM, Chia CW, Studenski S, et al. The relationship of lean body mass with aging to the development of diabetes. J Endocr Soc 2020;4:bvaa043.ArticlePubMedPMCPDF
  • 12. Larsen BA, Wassel CL, Kritchevsky SB, Strotmeyer ES, Criqui MH, Kanaya AM, et al. Association of muscle mass, area, and strength with incident diabetes in older adults: the Health ABC Study. J Clin Endocrinol Metab 2016;101:1847-55.ArticlePubMedPMCPDF
  • 13. Li JJ, Wittert GA, Vincent A, Atlantis E, Shi Z, Appleton SL, et al. Muscle grip strength predicts incident type 2 diabetes: population-based cohort study. Metabolism 2016;65:883-92.ArticlePubMed
  • 14. Ulmann G, Kai J, Durand JP, Neveux N, Jouinot A, De Bandt JP, et al. Creatinine-to-cystatin C ratio and bioelectrical impedance analysis for the assessement of low lean body mass in cancer patients: comparison to L3-computed tomography scan. Nutrition 2021;81:110895.ArticlePubMed
  • 15. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol 2014;43:61-8.ArticlePubMedPMC
  • 16. Chen X, Crimmins E, Hu PP, Kim JK, Meng Q, Strauss J, et al. Venous blood-based biomarkers in the China Health and Retirement Longitudinal Study: rationale, design, and results from the 2015 wave. Am J Epidemiol 2019;188:1871-7.ArticlePubMedPMCPDF
  • 17. Cai X, Qiu S, Liu S, Lu Y, Luo D, Li R, et al. Body-weight fluctuation and risk of diabetes in older adults: the China Health and Retirement Longitudinal Study (CHARLS). Diabetes Res Clin Pract 2020;169:108419.ArticlePubMed
  • 18. Nishida K, Hashimoto Y, Kaji A, Okamura T, Sakai R, Kitagawa N, et al. Creatinine/(cystatin C × body weight) ratio is associated with skeletal muscle mass index. Endocr J 2020;67:733-40.ArticlePubMed
  • 19. Peterson MD, Duchowny K, Meng Q, Wang Y, Chen X, Zhao Y. Low normalized grip strength is a biomarker for cardiometabolic disease and physical disabilities among U.S. and Chinese adults. J Gerontol A Biol Sci Med Sci 2017;72:1525-31.ArticlePubMedPMC
  • 20. Peterson MD, Zhang P, Choksi P, Markides KS, Al Snih S. Muscle weakness thresholds for prediction of diabetes in adults. Sports Med 2016;46:619-28.ArticlePubMedPMCPDF
  • 21. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2010;33(Suppl 1):S62-9.ArticlePubMedPMCPDF
  • 22. Li DY, Yin WJ, Yi YH, Zhang BK, Zhao J, Zhu CN, et al. Development and validation of a more accurate estimating equation for glomerular filtration rate in a Chinese population. Kidney Int 2019;95:636-46.ArticlePubMed
  • 23. Bello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A, et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. Eur J Endocrinol 2018;178:533-44.ArticlePubMed
  • 24. Weng J, Ji L, Jia W, Lu J, Zhou Z, Zou D, et al. Standards of care for type 2 diabetes in China. Diabetes Metab Res Rev 2016;32:442-58.ArticlePubMedPMCPDF
  • 25. Kim BY, Won JC, Lee JH, Kim HS, Park JH, Ha KH, et al. Diabetes fact sheets in Korea, 2018: an appraisal of current status. Diabetes Metab J 2019;43:487-94.ArticlePubMedPMCPDF
  • 26. Gunzler D, Chen T, Wu P, Zhang H. Introduction to mediation analysis with structural equation modeling. Shanghai Arch Psychiatry 2013;25:390-4.PubMedPMC
  • 27. Albert JM, Geng C, Nelson S. Causal mediation analysis with a latent mediator. Biom J 2016;58:535-48.ArticlePubMedPMC
  • 28. Lin YL, Chen SY, Lai YH, Wang CH, Kuo CH, Liou HH, et al. Serum creatinine to cystatin C ratio predicts skeletal muscle mass and strength in patients with non-dialysis chronic kidney disease. Clin Nutr 2020;39:2435-41.ArticlePubMed
  • 29. Hong S, Chang Y, Jung HS, Yun KE, Shin H, Ryu S. Relative muscle mass and the risk of incident type 2 diabetes: a cohort study. PLoS One 2017;12:e0188650.ArticlePubMedPMC
  • 30. Karvonen-Gutierrez CA, Peng Q, Peterson M, Duchowny K, Nan B, Harlow S. Low grip strength predicts incident diabetes among mid-life women: the Michigan Study of Women’s Health Across the Nation. Age Ageing 2018;47:685-91.ArticlePubMedPMC
  • 31. Kashani KB, Frazee EN, Kukralova L, Sarvottam K, Herasevich V, Young PM, et al. Evaluating muscle mass by using markers of kidney function: development of the sarcopenia index. Crit Care Med 2017;45:e23-9.ArticlePubMed
  • 32. Tatsumi Y, Morimoto A, Asayama K, Sonoda N, Miyamatsu N, Ohno Y, et al. Risk of developing type 2 diabetes according to blood pressure levels and presence or absence of hypertensive treatment: the Saku study. Hypertens Res 2019;42:105-13.ArticlePubMedPDF
  • 33. Zhang M, Liu D, Qin P, Liu Y, Sun X, Li H, et al. Association of metabolic score for insulin resistance and its 6-year change with incident type 2 diabetes mellitus. J Diabetes 2021;13:725-34.ArticlePubMedPDF
  • 34. Wang X, Bao W, Liu J, Ouyang YY, Wang D, Rong S, et al. Inflammatory markers and risk of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care 2013;36:166-75.PubMed
  • 35. Qiu S, Cai X, Yang B, Du Z, Cai M, Sun Z, et al. Association between cardiorespiratory fitness and risk of type 2 diabetes: a meta-analysis. Obesity (Silver Spring) 2019;27:315-24.ArticlePubMedPDF

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      Normalized Creatinine-to-Cystatin C Ratio and Risk of Diabetes in Middle-Aged and Older Adults: The China Health and Retirement Longitudinal Study
      Diabetes Metab J. 2022;46(3):476-485.   Published online March 7, 2022
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    Normalized Creatinine-to-Cystatin C Ratio and Risk of Diabetes in Middle-Aged and Older Adults: The China Health and Retirement Longitudinal Study
    Image Image Image
    Fig. 1. Cubic spline curve for the relationship between normalized creatinine-to-cystatin C ratio and risk of diabetes. The cubic spline curve analysis was adjusted for age, gender, history of smoking and drinking, presence of hypertension, dyslipidemia, and heart disease, mean arterial pressure, glycosylated hemoglobin, total cholesterol/high-density lipoprotein, triglyceride, low-density lipoprotein, uric acid, and high-sensitivity C-reactive protein. OR, odds ratio.
    Fig. 2. Mediation analysis for the relationship between normalized creatinine-to-cystatin C ratio and risk of diabetes. Mediation analysis (which included the tests for coefficients of a, b, and c) was conducted based on the generalized structural equation model using “GSEM” command in Stata. SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin; METS-IR, the metabolic score for insulin resistance; TC, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; hs-CRP, high-sensitivity C-reactive protein; eGFR, estimated glomerular filtration rate.
    Graphical abstract
    Normalized Creatinine-to-Cystatin C Ratio and Risk of Diabetes in Middle-Aged and Older Adults: The China Health and Retirement Longitudinal Study
    Variable Total Tertiles of normalized creatinine-to-cystatin C ratio
    P valuea
    Lowest (0.10±0.01) Middle (0.13±0.008) High (0.18±0.03)
    No. of participants 5,055 1,685 1,685 1,685
    Age, yr 60.0±9.7 60.3±10.0 60.0±9.5 59.6±9.7 0.119
    Male sex 2,372 (46.9) 696 (41.3) 814 (48.3) 862 (51.2) <0.001
    Smokingb 1,987 (39.3) 585 (34.7) 686 (40.7) 716 (42.5) <0.001
    Drinking 1,643 (32.5) 478 (28.4) 572 (33.9) 593 (35.2) <0.001
    Presence of
     Hypertension 1,987 (39.3) 788 (46.8) 689 (40.9) 510 (30.3) <0.001
     Dyslipidemia 1,958 (38.7) 708 (42.0) 626 (37.2) 624 (37.0) 0.003
     Heart diseaseb 531 (10.5) 234 (13.9) 160 (9.5) 137 (8.1) <0.001
    BMI, kg/m2 23.2±3.4 25.2±3.4 23.0±2.8 21.3±2.7 <0.001
    SBP, mm Hg 130.1±20.1 133.1±20.3 130.3±20.1 126.8±19.4 <0.001
    DBP, mm Hg 75.0±11.1 76.5±11.2 75.2±11.0 73.4±10.8 <0.001
    MAP, mm Hg 93.4±13.0 95.3±13.1 93.6±12.9 91.2±12.6 <0.001
    FPG, mg/dLc 100.5±10.9 100.8±10.5 100.4±10.7 100.2±11.4 0.227
    HbA1c, % 5.1±0.4 5.1±0.4 5.1±0.4 5.1±0.4 0.023
    HbA1c, mmol/mol 32±4.4 32±4.4 32±4.4 32±4.4 0.023
    METS-IRc 34.4±7.1 37.8±7.1 33.9±6.1 31.4±6.4 <0.001
    TC, mg/dL 192.6±37.5 189.2±37.6 192.3±36.5 196.3±38.3 <0.001
    TG, mg/dL 121.7±74.1 121.6±67.4 119.6±69.6 124.1±84.0 0.210
    HDL, mg/dL 52.0±14.7 49.6±13.3 52.4±14.6 54.1±15.9 <0.001
    LDL, mg/dL 117.1±34.1 117.2±34.7 117.5±33.2 116.7±34.5 0.812
    UA, mg/dL 4.4±1.2 4.3±1.2 4.4±1.2 4.5±1.3 <0.001
    Cr, mg/dL 0.77±0.18 0.71±0.17 0.77±0.16 0.84±0.18 <0.001
    eGFR, mL/min/1.73 m2 79.5±9.5 82.7±10.5 79.2±8.4 76.4±8.4 <0.001
    hs-CRP, mg/Ld 2.4±6.0 3.0±6.9 2.3±6.3 2.0±4.4 <0.001
    Variable Normalized creatinine-to-cystatin C ratio
    Lowest (tertile 1) Middle (tertile 2) Highest (tertile 3) Per 1 SD higher
    Total no. 1,685 1,685 1,685 5,055
    No. of cases 261 196 177 634
     Model 1a 1 (ref) 0.72 (0.59–0.88) 0.64 (0.52–0.79) 0.85 (0.78–0.94)
     Model 2b 1 (ref) 0.74 (0.60–0.90) 0.66 (0.54–0.81) 0.87 (0.79–0.95)
     Model 3c 1 (ref) 0.81 (0.65–0.99) 0.76 (0.61–0.94) 0.91 (0.83–0.99)
    Variable Overweight/obesity (BMI ≥24 kg/m2)
    Normal-weight (BMI <24 kg/m2)
    Low NCCR High NCCR Low NCCR High NCCR
    Total no. 1,048 857 637 2,513
    No. of cases 188 149 73 224
     Model 1a 1 (Ref) 0.96 (0.76–1.22) 0.59 (0.44–0.79) 0.45 (0.36–0.55)
     Model 2b 1 (Ref) 1.01 (0.79–1.28) 0.56 (0.42–0.75) 0.44 (0.36–0.55)
     Model 3c 1 (Ref) 0.95 (0.74–1.22) 0.77 (0.56–1.05) 0.62 (0.49–0.78)
    Independent variable Simple linear regression analysis Independent variable
    Multivariable linear regression analysisa
    P value Sβ’ P value
    SBP –0.11 <0.001 –0.11 <0.001
    DBP –0.09 <0.001 –0.10 <0.001
    FPG –0.01 0.553 –0.006 0.679
    HbA1c –0.03 0.086 –0.02 0.120
    METS-IR –0.34 <0.001 –0.35 <0.001
    TC 0.11 <0.001 0.12 <0.001
    TG 0.07 <0.001 0.07 <0.001
    HDL 0.10 <0.001 0.10 <0.001
    LDL –0.0001 >0.999 0.009 0.518
    UA 0.07 <0.001 0.06 <0.001
    eGFR –0.30 <0.001 –0.41 <0.001
    hs-CRPb –0.14 <0.001 –0.15 <0.001
    Table 1. Baseline characteristics of enrolled participants

    Values are presented as mean±standard deviation or number (%).

    BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin; METS-IR, the metabolic score for insulin resistance; TC, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; UA, uric acid; Cr, creatinine; eGFR, estimated glomerular filtration rate; hs-CRP, high-sensitivity C-reactive protein.

    Data were compared using one-way analysis of variance or chi-square test when appropriate,

    There were 4 and 35 participants without information on history of smoking and heart disease, respectively,

    There were 475 participants without fasting blood samples,

    It was log-transformed before analysis.

    Table 2. Normalized creatinine-to-cystatin C ratio and risk of diabetes

    Values are presented as odds ratio (95% confidence interval).

    SD, standard deviation.

    Unadjusted,

    Adjusted for age and gender,

    Adjusted for age, gender, history of smoking and drinking, presence of hypertension, dyslipidemia, and heart disease, mean arterial pressure, glycosylated hemoglobin, total cholesterol/high-density lipoprotein, triglyceride, low-density lipoprotein, uric acid, and high-sensitivity C-reactive protein.

    Table 3. Joint effect of body mass index and normalized creatinine-to-cystatin C ratio on risk of diabetes

    Values are presented as odds ratio (95% confidence interval). Low NCCR referred to the lowest tertile of NCCR, and high NCCR was defined as the middle and highest tertiles.

    BMI, body mass index; NCCR, normalized creatinine-to-cystatin C ratio.

    Unadjusted,

    Adjusted for age and gender,

    Adjusted for age, gender, history of smoking and drinking, presence of hypertension, dyslipidemia, and heart disease, mean arterial pressure, glycosylated hemoglobin, total cholesterol/high-density lipoprotein, triglyceride, low-density lipoprotein, uric acid, and high-sensitivity C-reactive protein.

    Table 4. Association between normalized creatinine-to-cystatin C ratio and cardiometabolic markers

    Sβ, standardized regression coefficient; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin; METS-IR, the metabolic score for insulin resistance; TC, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; UA, uric acid; eGFR, estimated glomerular filtration rate; hs-CRP, high-sensitivity C-reactive protein.

    Adjusted for age and gender,

    It was log-transformed before analysis.

    Qiu S, Cai X, Xie B, Yuan Y, Sun Z, Wu T. Normalized Creatinine-to-Cystatin C Ratio and Risk of Diabetes in Middle-Aged and Older Adults: The China Health and Retirement Longitudinal Study. Diabetes Metab J. 2022;46(3):476-485.
    Received: Apr 22, 2021; Accepted: Jun 13, 2021
    DOI: https://doi.org/10.4093/dmj.2021.0074.

    Diabetes Metab J : Diabetes & Metabolism Journal
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