Next Article in Journal
Influence of Pneumonia on the Survival of Patients with COPD
Previous Article in Journal
Cochlear Implantation Outcome in Children with DFNB1 locus Pathogenic Variants
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 9
Issue 1
10.3390/jcm9010227
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Creatinine to Body Weight Ratio Is Associated with Incident Diabetes: Population-Based Cohort Study

by
Yoshitaka Hashimoto
1,
Takuro Okamura
1,
Masahide Hamaguchi
1,*,
Akihiro Obora
2,
Takao Kojima
2 and
Michiaki Fukui
1
1
Department of Endocrinology and Metabolism, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
2
Department of Gastroenterology, Asahi University Hospital, Gifu 501-0223, Japan
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(1), 227; https://doi.org/10.3390/jcm9010227
Submission received: 13 December 2019 / Revised: 29 December 2019 / Accepted: 8 January 2020 / Published: 15 January 2020
(This article belongs to the Section Endocrinology & Metabolism)
Browse Figures
Review Reports Versions Notes

Abstract

:
We investigated the association between creatinine to body weight (Cre/BW) ratio and incident diabetes. In this cohort study, 9659 men and 7417 women were follow up mean (SD) 5.6 (3.5) years and 5.4 (3.4) years, respectively. For men, tertile 1 (T1; n = 3176), Cre/BW < 0.01275; tertile 2 (T2; n = 3258), 0.01275 ≤ Cre/BW < 0.0148; and tertile 3 (T3; n = 3225), Cre/BW ≥ 0.0148; and for women, T1 (n = 2437), Cre/BMI < 0.0118; T2 (n = 2516), 0.0118 ≤ Cre/BMI < 0.014; and T3 (n = 2477), Cre/BMI ≥ 0.014. Among them, 362 men and 102 women developed diabetes. The hazard ratios (HRs) of incident diabetes in the T2 group was 0.56 (95% CI 0.44–0.71, p < 0.001) in men and 0.61 (0.38–0.99, p = 0.045) in women and in the T3 group was 0.42 (0.32–0.54, p < 0.001) in men and 0.55 (0.34–0.89, p = 0.014) in women after adjusting for covariates, compared with the T1 group. Moreover, Δ0.001 incremental of Cre/BW is negatively associated with incident diabetes (adjusted HR 0.84, 95% CI 0.80–0.88, p < 0.001 for men and 0.88, 0.81–0.96, p = 0.003 for women). In conclusion, Cre/BW ratio is inversely related to incident diabetes. Checking Cre/BW ratios may predict future diabetes risks.

1. Introduction

People with type 2 diabetes are increasing and reached 10 million in 2016 in Japan [1]. Hence, it is an important task for us to prevent and treat type 2 diabetes. Previous studies revealed that obesity is a one of the reasons why type 2 diabetes is rapidly increasing [2,3,4].
Diabetes accelerates the reduction of muscle mass by hyperglycemia, insulin resistance, and inflammatory cytokines [5]. In addition, a recent study showed that the degree of glucose tolerance impairment has a close association with decreased lean body mass in non-diabetic women [6]. Therefore, muscle mass is also an important prevention and treatment target for type 2 diabetes.
A pervious study revealed that weight-adjusted appendicular skeletal muscle mass, defined as skeletal muscle mass/body weight, is a risk of type 2 diabetes [7]. In addition, non alcoholic fatty liver disease (NAFLD) [8,9,10,11] and nonalcoholic steatohepatitis [12,13,14], which are closely associated with type 2 diabetes, have an association with weight-adjusted appendicular skeletal muscle mass. In contrast, serum creatinine (Cre), which is known as a marker of kidney function, is influenced by muscle size since muscle mass creates Cre. Serum Cre is reported to be associated with total skeletal muscle mass [15,16,17]. In addition, we recently revealed that creatinine to body weight (BW) (Cre/BW) ratio is associated with a risk of incident NAFLD [18]. Therefore, we hypothesized that Cre/BW also would be associated with incident type 2 diabetes. Here, we investigated the association between Cre/BW ratio and incident type 2 diabetes in this population-based historical cohort study.

2. Materials and Methods

2.1. Study Patients

The NAGALA (NAfld in Gifu Area, Longitudinal Analysis) study is a historical cohort study of individuals who underwent a medical health check-up program at Asahi University Hospital (Gifu, Japan) starting from 1994 [19]. This medical program is very famous in Japan and is intended to not only find chronic illness and their risks, but also contribute to the health promotion. This center annually received more than 8000 medical examiners. Among the examiners, about 60% of them repeat the program once or twice a year. After obtaining written informed consent, masking personal identifiable information was performed and the medical data of the individuals was stored in a database. For this present analysis, we extracted the data of the individuals who underwent the medical check-up during the years from 2004 to 2014 and who did not have diabetes at the first examination. We set the primary endpoint of this study as the incident of diabetes. The Asahi University Hospital ethics committee permitted this study (approval number 2018-09-01) and this study was performed according to the Declaration of Helsinki. Exclusion criteria were as follows: no follow-up examination, missing data of covariates (including high-density lipoprotein (HDL) cholesterol, BW, and lifestyle factors), and serum creatinine levels over 1.2 mg/dL and over 1.0 mg/dL for men and women, indicating renal dysfunction, were also excluded [20]. In addition, fasting plasma glucose was ≥6.1 mmol/L was also excluded [2] because these participants had a high risk of incident diabetes.

2.2. Data Collection and Measurements

Body mass index (BMI) was calculated; BMI (kg/m2) = BW (kg)/height2 (m2). A standardized self-administered questionnaire was used to obtain the data of lifestyle factors, including alcohol, smoking habits, physical activity, and medical history. Alcohol consumption was measured by estimating mean ethanol intake according to the type and quantity of alcohol consumed (/week) over the past one month. The participants divided into four groups according to the alcohol consumption: none to minimal, <40 g/week; light, 40–140 g/week; moderate, 140–280 g/week; or heavy alcohol consumption, >280 g/week [21]. The participants were also divided into three groups by smoking status: never smoker, ex-smoker, or current smoker. To categorize participants into a non- or regular exerciser, we investigated the participants’ recreational and sports activities. We defined regular exercisers as participants who played any type of sports >1×/week regularly [22]. HbA1c ≥6.5%, fasting plasma glucose ≥7 mmol/L [23], or self-report, including usage of medication for diabetes, were defined as type 2 diabetes. We defined the onset as the time when diabetes was first identified. Standard enzymatic methods were used for serum Cre. We calculated Cre/BW as Cre divided by BW. Insulin resistance was evaluated by triglycerides to high-density lipoprotein (HDL) cholesterol ratio (TG/HDL) [24] and estimated glucose infusion rate (EGIR) [25].

2.3. Statistical Analysis

We used EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria) [26] and JMP version 13.0 software (SAS Institute Inc., Cary, NC, USA) for the statistical analyses. Mean (standard deviation (SD)) was used for continuous variables and number was used for categorical variables. Because the case of diabetes differed between sex and the distribution of Cre/BW differed between sex, we investigated the following statistical analyses in men and women separately. A p value < 0.05 was set for statistical significance. We classified the participants into three groups according to tertile of Cre: For men, tertile 1 (T1), <0.85 mg/dL; tertile 2 (T2), 0.86 ≤ Cre ≤ 1 mg/dL; and tertile 3 (T3), Cre ≥ 1 mg/dL; and for women, T1, Cre ≤ 0.6 mg/dL; tertile 2 (T2), 0.61 ≤ Cre ≤ 0.7 mg/dL; and tertile 3 (T3), Cre ≥ 0.71 mg/dL. Moreover, we classified the participants into three groups according to tertile of Cre/BW ratio: For men, tertile 1 (T1), Cre/BW < 0.01275; tertile 2 (T2), 0.01275 ≤ Cre/BW < 0.0148; and tertile 3 (T3), Cre/BW ≥ 0.0148; and for women, T1, Cre/BW < 0.0118; T2, 0.0118 ≤ Cre/BW < 0.014; and T3, Cre/BW ≥ 0.014. One-way ANOVA and Tukey’s Honest Significant Difference test were used for continuous variables and Pearson’s chi-squared test were used for categorical variables. Kaplan–Meier analysis and log-rank tests were performed to evaluate the difference among the groups according to Cre or Cre/BW ratio. In the log-rank test analyses, a Bonferroni correction was used and set a p value <0.0167 to be statistically significant. Further, to examine the effect of the Cre/BW ratio, analysis using a Cox hazard model adjusting for age, fasting plasma glucose, alcohol consumption, exercise, and smoking at baseline examination was conducted for both categorized and continuous variables on the incident diabetes. The association between TG/HDL and EGIR and Cre/BW ratio were evaluated by Pearson’s correlation coefficient. Because there is a close association between Cre/BW and BMI, waist circumference or eGFR (BMI, r = −0.58, p < 0.001 for men and r = −0.54, p < 0.001 for women; waist circumference, r = −0.58, p < 0.001 for men and r = −0.56, p < 0.001 for women; and eGFR, r = −0.58, p < 0.001 for men and r = −0.61, p < 0.001 for women), we did not use these factors as covariates. Proportional hazards assumption was examined by EZR.

3. Results

In this study, we included 25,890 participants (14,947 men and 10,943 women; Figure 1). Among them, 7517 participants (4166 men and 3351 women) did not receive follow-up examination, 16 participants (15 men and one woman) had missing data of covariates, and 181 participants (163 men and 18 women) had renal dysfunction. In addition, to keep proportional hazards, we excluded 1100 participants whose fasting plasma glucose was ≥6.1 mmol/L (944 men and 156 women). Thus, this study used 17,076 participants (9659 men and 7417 women).
Table 1 represented the baseline characteristics of the study participants according to the Cre/BW ratio. Age of T1 was older than the other groups in both men and women. BMI of T1 was bigger than the other groups in both men and women, and Cre of T1 was lower than the other groups in both men and women. Moreover, the baseline metabolic parameters (glucose, blood pressure, and cholesterol) of the T1 group were worse than in the other groups in both men and women. In addition, TG/HDL and EGIR were associated with Cre/BW ratio (TG/HDL, r = −0.13, p < 0.001 for men and r = −0.12, p < 0.001 for women and EGIR, r = 0.33, p < 0.001 for men and r = 0.32, p < 0.001 for women).
During mean (SD) 5.6 (3.5) years follow-up in men and 5.4 (3.4) years follow-up in women, 362 men and 102 women were newly diagnosed with diabetes. The cumulative incidence rate of incident diabetes was 5.2% (incident n/total n = 171/3275) for men and 1.9% (47/2437) for women in T1, 3.3% (incident n/total n = 105/3159) for men and 1.1% (27/2515) for women in T2, and 2.7% (incident n/total n = 86/3325) for men and 1.1% (27/2465) for women in T3.
Figure 2 represents the results of Kaplan–Meier analysis according to the Cre tertiles; there was no statistically significant difference among Cre tertiles in both men and women.
Figure 3 shows the results of Kaplan–Meier analysis according to the Cre/BW ratio tertiles. The proportion of incident diabetes in the T1 group was higher than those in the other groups (all p < 0.001).
Table 2 shows the results of Cox hazard model of Cre/BW ratio on incident diabetes. Compared with the T1 group, the hazard ratio (HR) of incident diabetes in the T2 group was 0.56 (95% CI 0.44–0.71, p < 0.001) in men and 0.61 (95% CI 0.37–0.98, p = 0.042) in women and that in the T3 groups was 0.42 (95% CI 0.32–0.54, p < 0.001) in men and 0.53 (95% CI 0.32–0.85, p = 0.008) in women in model 1. Furthermore, Cre/BW ratio was negatively associated with incident diabetes (HR of Δ0.001 incremental of Cre/BW, 0.84 (95% CI 0.80–0.88, p < 0.001 in men and HR of Δ0.001 incremental of Cre/BW, 0.88 95% CI 0.81–0.96, p = 0.003) in women) in model 2.

4. Discussion

In this large-scale cohort study, we examined the association between Cre or Cre/BW ratio and incident diabetes and showed that decreased Cre/BW, but not Cre, is associated with incident diabetes in both sexes.
The possible explanations for the relationship between Cre/BW ratio and incident diabetes are as follows. It is well known that during hyperinsulinemia-euglycemia status, muscle mass takes up 80%–90% of glucose in the blood [27]. Lower muscle mass can have reduced capacity of glucose uptake from the blood [6]. Moreover, low muscle mass and inciden diabetes can be mediated by insulin resistance, a key pathogenic mechanism of diabetes [28]. It is well known that height-adjusted SMI, which defined as appendicular skeletal muscle mass/height2, is an important marker for sarcopenia [29]. However, heavier weight leads to muscle mass increase, regardless of fat mass [8]. Thus, the proportion of muscle mass per body weight is important. In fact, not height-adjusted SMI, but weight-adjusted appendicular skeletal muscle mass, is associated with cardiometabolic risk factors and insulin resistance [30,31,32,33,34]. Moreover, weight-adjusted appendicular skeletal muscle mass is associated with incident diabetes [7], metabolic syndrome [35], and NAFLD [36,37,38]. This is because the low weight-adjusted appendicular skeletal muscle mass is associated with increased visceral fat. Increasing visceral fat is associated with incident diabetes though the increasing of inflammatory cytokines [39]. In fact, Cre/BW ratio was associated with TG/HDL and EGIR, which are known markers of insulin resistance, in this study. Taking these findings together, Cre/BW ratio is associated with incident diabetes.
Although serum Cre was not associated with incident diabetes in this study population, previous studies have shown that low Cre is associated with the presence of [40,41,42,43] or incident diabetes [44,45,46,47]. These studies showed that the risk of incident diabetes in the lowest serum Cre group was higher than that in highest serum Cre group [44,45,46,47]. However, a previous study also showed that there is a nonlinear relationship between serum Cre and incident diabetes [45]. On the other hand, there is a linear relationship between serum Cre/BW and incident diabetes in this study. This might be because that although there is a close association between Cre and Cre/BW, the data is dissociated in some cases. These dissociated cases were an important target in incident diabetes. Thus, the reason why the result about serum Cre level and incident diabetes in this study was different from the previous studies might be that the ratio of dissociated cases was different.
Several strengths of this study exist, including the relatively large number of participants included. However, this study has limitations as well. First, there is a possibility of underestimating cases of diabetes because we did not have the data of an oral glucose tolerance test. However, the rate of incident diabetes was almost same as the previous meta-analysis of the Japanese population [48]. In addition, a familiarity for type 2 diabetes mellitus might be a source of bias. Second, this study included Japanese individuals only, thus, whether our study results generalize to non-Japanese populations, especially non-Asian populations, is uncertain and further studies might be needed. Third, we did not have data on insulin levels, thus, the association between insulin resistance, such as homeostasis model assessment insulin resistance and Cre/BW ratio, was unclear. However, we showed the association between Cre/BW ratio and TG/HDL and EGIR, which are known as markers of insulin resistance [24,25]. Furthermore, we did not have data of skeletal muscle mass and body fat mass. Fourth, the design of this study was retrospective. Thus, further studies are needed to confirm our results. Moreover, because of nature of the health check program, nearly 40% of participants did not receive a follow-up examination. Thus, this might be a selection bias. However, the clinical backgrounds between the participants with/without follow-up examination was not different (Table S1). Lastly, the data of some other elements such as diet and insulin levels, which may confound the association between Cre/BW and diabetes, were lacking.

5. Conclusions

In conclusion, this study is the first to demonstrate that Cre/BW ratio is negatively associated with incident diabetes in both men and women. These results proposed that a low Cre/BW ratio predicts an increased risk of diabetes.

Supplementary Materials

The following are available online at https://www.mdpi.com/2077-0383/9/1/227/s1, Table S1: Clinical characteristics difference between the participants with and without follow-up.

Author Contributions

Y.H. designed the study, interpreted data, and wrote the manuscript; T.O. researched data and contributed to discussion; M.H. researched and interpreted data, and reviewed/edited the manuscript; A.O. and T.K. researched data and contributed to discussion; M.F. researched data and reviewed/edited the manuscript. All authors have approved the submitted version and agree to be personally accountable for the author’s own contributions. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

We thank all staff members of Asahi University Hospital and we also would like to thank Editage (www.editage.jp) for English language editing.

Conflicts of Interest

Potential conflicts of interest are as follows: Y.H. has received grant support from Asahi Kasei Pharma. M.F. has received grants, honoraria, and research supports from AstraZeneca plc., Nippon Boehringer Ingelheim Co., Ltd., Sanofi K.K., Daiichi Sankyo Co., Kyowa Hakko Kirin Company Ltd., Kissei Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Kowa Company, Ltd., Novo Nordisk Pharma Ltd., Sanwa Kagaku Kenkyusho Co., Ltd., Astellas Pharma Inc., MSD K.K., Ltd., Ono Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Corporation, and Takeda Pharmaceutical Co., Ltd. These sponsors were not concerned with this study. The authors and their immediate families have not received any financial payments or other benefits from any commercial entity related to the subject of this article. The authors received no current funding for this study and this does not alter their adherence to all the journal policies on sharing data and materials. The other authors have nothing to disclose.

References

  1. Health, Labour and Welfare Statistics Association. Journal of Health and Welfare Statistics 2016–2017. (In Japanese) Tokyo Japan. Available online: https://www.mhlw.go.jp/bunya/kenkou/eiyou/dl/h28-houkoku.pdf2019 (accessed on 28 March 2019).
  2. Okamura, T.; Hashimoto, Y.; Hamaguchi, M.; Obora, A.; Kojima, T.; Fukui, M. Ectopic fat obesity presents the greatest risk for incident type 2 diabetes: A population-based longitudinal study. Int. J. Obes. 2019, 43, 139–148. [Google Scholar] [CrossRef] [PubMed]
  3. Mitsuhashi, K.; Hashimoto, Y.; Tanaka, M.; Toda, H.; Matsumoto, S.; Ushigome, E.; Asano, M.; Yamazaki, M.; Oda, Y.; Fukui, M. Combined effect of body mass index and waist-height ratio on incident diabetes; a population based cohort study. J. Clin. Biochem. Nutr. 2017, 61, 118–122. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Hamman, R.F.; Wing, R.R.; Edelstein, S.L.; Lachin, J.M.; Bray, G.A.; Delahanty, L.; Hoskin, M.; Kriska, A.M.; Mayer-Davis, E.J.; Pi-Sunyer, X.; et al. Effect of weight loss with lifestyle intervention on risk of diabetes. Diabetes Care 2006, 29, 2102–2107. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Lien, A.S.; Hwang, J.S.; Jiang, Y.D. Diabetes related fatigue sarcopenia, frailty. J. Diabetes Investig. 2018, 9, 3–4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Someya, Y.; Tamura, Y.; Suzuki, R.; Kaga, H.; Kadowaki, S.; Sugimoto, D.; Kakehi, S.; Funayama, T.; Furukawa, Y.; Takeno, K.; et al. Characteristics of Glucose Metabolism in Underweight Japanese Women. J. Endocr. Soc. 2018, 2, 279–289. [Google Scholar] [CrossRef]
  7. Son, J.W.; Lee, S.S.; Kim, S.R.; Yoo, S.J.; Cha, B.Y.; Son, H.Y.; Cho, N.H. Low muscle mass and risk of type 2 diabetes in middle-aged and older adults: Findings from the KoGES. Diabetologia 2017, 60, 865–872. [Google Scholar] [CrossRef] [Green Version]
  8. Hashimoto, Y.; Osaka, T.; Fukuda, T.; Tanaka, M.; Yamazaki, M.; Fukui, M. The The relationship between hepatic steatosis and skeletal muscle mass index in men with type 2 diabetes. Endocr. J. 2016, 63, 877–884. [Google Scholar] [CrossRef] [Green Version]
  9. Hong, H.C.; Hwang, S.Y.; Choi, H.Y.; Yoo, H.J.; Seo, J.A.; Kim, S.G.; Kim, N.H.; Baik, S.H.; Choi, D.S.; Choi, K.M. Relationship between sarcopenia and nonalcoholic fatty liver disease: The Korean Sarcopenic Obesity Study. Hepatology 2014, 59, 1772–1778. [Google Scholar] [CrossRef]
  10. Lee, Y.H.; Jung, K.S.; Kim, S.U.; Yoon, H.J.; Yun, Y.J.; Lee, B.W.; Kang, E.S.; Han, K.H.; Lee, H.C.; Cha, B.S. Sarcopaenia is associated with NAFLD independently of obesity and insulin resistance: Nationwide surveys (KNHANES 2008–2011). J. Hepatol. 2015, 63, 486–493. [Google Scholar] [CrossRef]
  11. Peng, T.C.; Wu, L.W.; Chen, W.L.; Liaw, F.Y.; Chang, Y.W.; Kao, T.W. Nonalcoholic fatty liver disease and sarcopenia in a Western population (NHANES III): The importance of sarcopenia definition. Clin. Nutr. 2019, 38, 422–428. [Google Scholar] [CrossRef]
  12. Osaka, T.; Hashimoto, Y.; Fukuda, T.; Tanaka, M.; Yamazaki, M.; Fukui, M. Relationship between skeletal muscle mass and hepatic fibrosis in patients with type 2 diabetes. Diabetes Metab. 2017, 43, 184–186. [Google Scholar] [CrossRef] [PubMed]
  13. Petta, S.; Ciminnisi, S.; Di Marco, V.; Cabibi, D.; Cammà, C.; Licata, A.; Marchesini, G.; Craxì, A. Sarcopenia is associated with severe liver fibrosis in patients with non-alcoholic fatty liver disease. Aliment. Pharmacol. Ther. 2017, 45, 510–518. [Google Scholar] [CrossRef] [Green Version]
  14. Koo, B.K.; Kim, D.; Joo, S.K.; Kim, J.H.; Chang, M.S.; Kim, B.G.; Lee, K.L.; Kim, W. Sarcopenia is an independent risk factor for non-alcoholic steatohepatitis and significant fibrosis. J. Hepatol. 2017, 66, 123–131. [Google Scholar] [CrossRef] [PubMed]
  15. Baxmann, A.C.; Ahmed, M.S.; Marques, N.C.; Menon, V.B.; Pereira, A.B.; Kirsztajn, G.M.; Heilberg, I.P. Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. Clin. J. Am. Soc. Nephrol. 2008, 3, 348–354. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Viollet, L.; Gailey, S.; Thornton, D.J.; Friedman, N.R.; Flanigan, K.M.; Mahan, J.D.; Mendell, J.R. Utility of cystatin C to monitor renal function in Duchenne muscular dystrophy. Muscle Nerve 2009, 40, 438–442. [Google Scholar] [CrossRef] [Green Version]
  17. Griggs, R.C.; Forbes, G.; Moxley, R.T.; Herr, B.E. The assessment of muscle mass in progressive neuromuscular disease. Neurology 1983, 33, 158–165. [Google Scholar] [CrossRef]
  18. Okamura, T.; Hashimoto, Y.; Hamaguchi, M.; Obora, A.; Kojima, T.; Fukui, M. Creatinine-to-bodyweight ratio is a predictor of incident non-alcoholic fatty liver disease: A population-based longitudinal study. Hepatol. Res. 2019, in press. [Google Scholar] [CrossRef] [Green Version]
  19. Hamaguchi, M.; Kojima, T.; Takeda, N.; Nakagawa, T.; Taniguchi, H.; Fujii, K.; Omatsu, T.; Nakajima, T.; Sarui, H.; Shimazaki, M.; et al. The metabolic syndrome as a predictor of nonalcoholic fatty liver disease. Ann. Intern. Med. 2005, 143, 722–728. [Google Scholar] [CrossRef]
  20. Evaluation of Elevated Creatinine. Available online: https://bestpractice.bmj.com/topics/en-us/935 (accessed on 4 May 2019).
  21. Hashimoto, Y.; Okamura, T.; Hamaguchi, M.; Obora, A.; Kojima, T.; Fukui, M. Impact of respiratory function on the progression from metabolically healthy non-overweight to metabolically abnormal phenotype. Nutr. Metab. Cardiov. Dis. 2018, 28, 922–928. [Google Scholar] [CrossRef]
  22. Ryu, S.; Chang, Y.; Kim, D.I.; Kim, W.S.; Suh, B.S. gamma-Glutamyltransferase as a predictor of chronic kidney disease in nonhypertensive and nondiabetic Korean men. Clin. Chem. 2007, 53, 71–77. [Google Scholar] [CrossRef] [Green Version]
  23. American Diabetes Association. Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 2012, 35 (Suppl. 1), S64–S71. [Google Scholar]
  24. Fukuda, Y.; Hashimoto, Y.; Hamaguchi, M.; Fukuda, T.; Nakamura, N.; Ohbora, A.; Kato, T.; Kojima, T.; Fukui, M. Triglycerides to high-density lipoprotein cholesterol ratio is an independent predictor of incident fatty liver; a population-based cohort study. Liver Int. 2016, 36, 713–720. [Google Scholar] [CrossRef] [PubMed]
  25. Tajiri, Y.; Sato, S.; Kato, T.; Nakayama, H.; Yamada, K. Surrogate index for insulin sensitivity composed of factors not using glucose and insulin in Japanese patients with diabetes. J. Diabetes Investig. 2011, 2, 140–147. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Kanda, Y. Investigation of the freely available easy-to-use software “EZR” for medical statistics. Bone Marrow Transpl. 2013, 48, 452–458. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Duncan, B.B.; Schmidt, M.I.; Pankow, J.S.; Ballantyne, C.M.; Couper, D.; Vigo, A.; Hoogeveen, R.; Folsom, A.R.; Heiss, G. Atherosclerosis Risk in Communities Study. Low-grade systemic inflammation and the development of type 2 diabetes: The atherosclerosis risk in communities study. Diabetes 2003, 52, 1799–1805. [Google Scholar] [CrossRef] [Green Version]
  28. Srikanthan, P.; Karlamangla, A.S. Relative muscle mass is inversely associated with insulin resistance and prediabetes. Findings from the third National Health and Nutrition Examination Survey. J. Clin. Endocrinol. Metab. 2011, 96, 2898–2903. [Google Scholar]
  29. Chen, L.K.; Liu, L.K.; Woo, J.; Assantachai, P.; Auyeung, T.W.; Bahyah, K.S.; Chou, M.Y.; Chen, L.Y.; Hsu, P.S.; Krairit, O.; et al. Sarcopenia in Asia: Consensus report of the Asian Working Group for Sarcopenia. J. Am. Med. Dir. Assoc. 2014, 15, 95–101. [Google Scholar] [CrossRef]
  30. Kim, T.N.; Park, M.S.; Lee, E.J.; Chung, H.S.; Yoo, H.J.; Kang, H.J.; Song, W.; Baik, S.H.; Choi, K.M. Comparisons of three different methods for defining sarcopenia: An aspect of cardiometabolic risk. Sci. Rep. 2017, 7, 6491. [Google Scholar] [CrossRef] [Green Version]
  31. Furushima, T.; Miyachi, M.; Iemitsu, M.; Murakami, H.; Kawano, H.; Gando, Y.; Kawakami, R.; Sanada, K. Comparison between clinical significance of height-adjusted and weight-adjusted appendicular skeletal muscle mass. J. Physiol. Anthropol. 2017, 36, 15. [Google Scholar] [CrossRef] [Green Version]
  32. Takamura, T.; Kita, Y.; Nakagen, M.; Sakurai, M.; Isobe, Y.; Takeshita, Y.; Kawai, K.; Urabe, T.; Kaneko, S. Weight-adjusted lean body mass and calf circumference are protective against obesity-associated insulin resistance and metabolic abnormalities. Heliyon 2017, 3, e00347. [Google Scholar] [CrossRef] [Green Version]
  33. Lim, S.; Kim, J.H.; Yoon, J.W.; Kang, S.M.; Choi, S.H.; Park, Y.J.; Kim, K.W.; Lim, J.Y.; Park, K.S.; Jang, H.C. Sarcopenic obesity: Prevalence and association with metabolic syndrome in the Korean Longitudinal Study on Health and Aging (KLoSHA). Diabetes Care 2010, 33, 1652–1654. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Scott, D.; Park, M.S.; Kim, T.N.; Ryu, J.Y.; Hong, H.C.; Yoo, H.J.; Baik, S.H.; Jones, G.; Choi, K.M. Associations of Low Muscle Mass and the Metabolic Syndrome in Caucasian and Asian Middle-aged and Older Adults. J. Nutr. Health Aging 2016, 20, 248–255. [Google Scholar] [CrossRef] [PubMed]
  35. Park, B.S.; Yoon, J.S. Relative skeletal muscle mass is associated with development of metabolic syndrome. Diabetes Metab. J. 2013, 37, 458–464. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Osaka, T.; Hashimoto, Y.; Okamura, T.; Fukuda, T.; Yamazaki, M.; Hamaguchi, M.; Fukui, M. Reduction of Fat to Muscle Mass Ratio Is Associated with Improvement of Liver Stiffness in Diabetic Patients with Non-Alcoholic Fatty Liver Disease. J. Clin. Med. 2019, 8, 2175. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Shida, T.; Oshida, N.; Oh, S.; Okada, K.; Shoda, J. Progressive reduction in skeletal muscle mass to visceral fat area ratio is associated with a worsening of the hepatic conditions of non-alcoholic fatty liver disease. Diabetes Metab. Syndr. Obes. 2019, 12, 495–503. [Google Scholar] [CrossRef] [Green Version]
  38. Mizuno, N.; Seko, Y.; Kataoka, S.; Okuda, K.; Furuta, M.; Takemura, M.; Taketani, H.; Hara, T.; Umemura, A.; Nishikawa, T.; et al. Increase in the skeletal muscle mass to body fat mass ratio predicts the decline in transaminase in patients with nonalcoholic fatty liver disease. J. Gastroenterol. 2019, 54, 160–170. [Google Scholar] [CrossRef]
  39. Fontana, L.; Eagon, J.C.; Trujillo, M.E.; Scherer, P.E.; Klein, S. Visceral fat adipokine secretion is associated with systemic inflammation in obese humans. Diabetes 2007, 56, 1010–1013. [Google Scholar] [CrossRef] [Green Version]
  40. Nayak, B.S.; Butcher, D.M.; Bujhawan, S.; Chang, D.; Chang, S.; Cabral-Samaroo, D.; Cadan, S.; Buchoon, V.; Budhram, L.; Boyce, M.; et al. Association of low serum creatinine, abnormal lipid profile, gender, age and ethnicity with type 2 diabetes mellitus in Trinidad and Tobago. Diabetes Res. Clin. Pract. 2011, 91, 342–347. [Google Scholar] [CrossRef]
  41. Moon, J.S.; Lee, J.E.; Yoon, J.S. Variation in serum creatinine level is correlated to risk of type 2 diabetes. Endocrinol. Metab. 2013, 28, 207–213. [Google Scholar] [CrossRef] [Green Version]
  42. Harita, N.; Hayashi, T.; Sato, K.K.; Nakamura, Y.; Yoneda, T.; Endo, G.; Kambe, H. Lower serum creatinine is a new risk factor of type 2 diabetes: The Kansai healthcare study. Diabetes Care 2009, 32, 424–426. [Google Scholar] [CrossRef] [Green Version]
  43. Hjelmesæth, J.; Røislien, J.; Nordstrand, N.; Hofsø, D.; Hager, H.; Hartmann, A. Low serum creatinine is associated with type 2 diabetes in morbidly obese women and men: A cross-sectional study. BMC Endocr. Disord. 2010, 10, 6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Bao, X.; Gu, Y.; Zhang, Q.; Liu, L.; Meng, G.; Wu, H.; Xia, Y.; Shi, H.; Wang, H.; Sun, S.; et al. Low serum creatinine predicts risk for type 2 diabetes. Diabetes Metab. Res. Rev. 2018, 34, e3011. [Google Scholar] [CrossRef] [PubMed]
  45. Kashima, S.; Inoue, K.; Matsumoto, M.; Akimoto, K. Low serum creatinine is a type 2 diabetes risk factor in men and women: The Yuport Health Checkup Center cohort study. Diabetes Metab. 2017, 43, 460–464. [Google Scholar] [CrossRef] [PubMed]
  46. Takeuchi, M.; Imano, H.; Muraki, I.; Shimizu, Y.; Hayama-Terada, M.; Kitamura, A.; Okada, T.; Kiyama, M.; Iso, H. Serum creatinine levels and risk of incident type 2 diabetes mellitus or dysglycemia in middle-aged Japanese men: A retrospective cohort study. BMJ Open Diabetes Res. Care 2018, 6, e000492. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Hu, H.; Nakagawa, T.; Honda, T.; Yamamoto, S.; Okazaki, H.; Yamamoto, M.; Miyamoto, T.; Eguchi, M.; Kochi, T.; Shimizu, M.; et al. Japan Epidemiology Collaboration on Occupational Health Study Group. Low serum creatinine and risk of diabetes: The Japan Epidemiology Collaboration on Occupational Health Study. J. Diabetes Investig. 2019, 10, 1209–1214. [Google Scholar] [CrossRef]
  48. Goto, A.; Goto, M.; Noda, M.; Tsugane, S. Incidence of Type 2 Diabetes in Japan: A Systematic Review and Meta-Analysis. PLoS ONE 2013, 8, e74699. [Google Scholar] [CrossRef]
Jcm 09 00227 g001 550
Figure 1. Inclusion and exclusion flow chart.
Figure 1. Inclusion and exclusion flow chart.
Jcm 09 00227 g001
Jcm 09 00227 g002 550
Figure 2. Kaplan–Meier analysis of incident diabetes according to the Cre tertiles. The vertical axis is the diabetes free rate and the horizontal axis is time as months. (a) Kaplan–Meier analysis for incident diabetes in men. Red line indicates tertile 1 (Cre < 0.85 mg/dL). Green line indicates tertile 2 (Cre 0.86 ≤ Cre ≤ 1 mg/dL). Blue line indicates tertile 3 (Cre ≥ 1 mg/dL). Log rank test was used to investigate the association among the tertiles of Cre and p value was 0.991. (b) Kaplan–Meier analysis for incident diabetes in women. Red line indicates tertile 1 (Cre < 0.6 mg/dL). Green line indicates tertile 2 (0.61 ≤ Cre ≤ 0.7 mg/dL). Blue line indicates tertile 3 (Cre ≥ 0.71 mg/dL). Log rank test used to investigate the association among the groups of Cre tertiles. To correct familiar error, Bonferroni correction was used and a p value < 0.0167 was considered statistically significant. Tertile 1 vs. Tertile 2, p value = 0.45; Tertile 1 vs. Tertile 3, p value = 0.087; and Tertile 2 vs. Tertile 3, p value = 0.014.
Figure 2. Kaplan–Meier analysis of incident diabetes according to the Cre tertiles. The vertical axis is the diabetes free rate and the horizontal axis is time as months. (a) Kaplan–Meier analysis for incident diabetes in men. Red line indicates tertile 1 (Cre < 0.85 mg/dL). Green line indicates tertile 2 (Cre 0.86 ≤ Cre ≤ 1 mg/dL). Blue line indicates tertile 3 (Cre ≥ 1 mg/dL). Log rank test was used to investigate the association among the tertiles of Cre and p value was 0.991. (b) Kaplan–Meier analysis for incident diabetes in women. Red line indicates tertile 1 (Cre < 0.6 mg/dL). Green line indicates tertile 2 (0.61 ≤ Cre ≤ 0.7 mg/dL). Blue line indicates tertile 3 (Cre ≥ 0.71 mg/dL). Log rank test used to investigate the association among the groups of Cre tertiles. To correct familiar error, Bonferroni correction was used and a p value < 0.0167 was considered statistically significant. Tertile 1 vs. Tertile 2, p value = 0.45; Tertile 1 vs. Tertile 3, p value = 0.087; and Tertile 2 vs. Tertile 3, p value = 0.014.
Jcm 09 00227 g002
Jcm 09 00227 g003 550
Figure 3. Kaplan–Meier analysis of incident diabetes according to the Cre/BW ratio tertiles. The vertical axis is diabetes free rate and the horizontal axis is time as months. (a) Kaplan–Meier analysis for incident diabetes in men. Red line indicates tertile 1 (Cre/BW ratio < 0.01275). Green line indicates tertile 2 (0.01275 ≤ Cre/BW ratio < 0.0148). Blue line indicates tertile 3 (Cre/BW ratio ≥ 0.0148). Log rank test was performed to investigate the association among the groups of Cre/BW ratio. Bonferroni correction was performed to correct familiar error and a p value < 0.0167 was considered statistically significant and all p values < 0.001. (b) Kaplan–Meier analysis for incident diabetes in women. Red line indicates tertile 1 (Cre/BW ratio < 0.0118). Green line indicates tertile 2 (0.0118 ≤ Cre/BW ratio < 0.014). Blue line indicates tertile 3 (Cre/BW ratio ≥ 0.014). Log rank test was performed to investigate the association among the groups of Cre/BW ratio. To correct familiar error, Bonferroni correction was used and a p value < 0.0167 was considered statistically significant. Tertile 1 vs. Tertile 2, p value = 0.003 and Tertile 1 vs. Tertile 3, p value = 0.006 and Tertile 2 vs. Tertile 3, p value = 0.854.
Figure 3. Kaplan–Meier analysis of incident diabetes according to the Cre/BW ratio tertiles. The vertical axis is diabetes free rate and the horizontal axis is time as months. (a) Kaplan–Meier analysis for incident diabetes in men. Red line indicates tertile 1 (Cre/BW ratio < 0.01275). Green line indicates tertile 2 (0.01275 ≤ Cre/BW ratio < 0.0148). Blue line indicates tertile 3 (Cre/BW ratio ≥ 0.0148). Log rank test was performed to investigate the association among the groups of Cre/BW ratio. Bonferroni correction was performed to correct familiar error and a p value < 0.0167 was considered statistically significant and all p values < 0.001. (b) Kaplan–Meier analysis for incident diabetes in women. Red line indicates tertile 1 (Cre/BW ratio < 0.0118). Green line indicates tertile 2 (0.0118 ≤ Cre/BW ratio < 0.014). Blue line indicates tertile 3 (Cre/BW ratio ≥ 0.014). Log rank test was performed to investigate the association among the groups of Cre/BW ratio. To correct familiar error, Bonferroni correction was used and a p value < 0.0167 was considered statistically significant. Tertile 1 vs. Tertile 2, p value = 0.003 and Tertile 1 vs. Tertile 3, p value = 0.006 and Tertile 2 vs. Tertile 3, p value = 0.854.
Jcm 09 00227 g003
Table
Table 1. Baseline characteristics of study participants according to the tertiles of creatinine/body weight (Cre/BW) ratio.
Table 1. Baseline characteristics of study participants according to the tertiles of creatinine/body weight (Cre/BW) ratio.
MenALL,
n = 9659		Tertile 1 (Cre/BW Ratio < 0.01275),
n = 3176		Tertile 2 (0.01275 ≤ Cre/BW Ratio < 0.0148),
n = 3258		Tertile 3 (Cre/BW Ratio ≥ 0.0148),
n = 3225		p
Age (year)45.3 (9.3)43.5 (8.5)45.4 (9.1) *47.1 (9.8) *,†<0.001
Body weight (kg)67.6 (10.0)75.2 (9.9)67.0 (7.0) *60.6 (6.7) *,†<0.001
Height (cm)170.6 (6.0)172.9 (5.7)170.6 (5.6) *168.1 (5.6) *,†<0.001
Body mass index (kg/m2)23.2 (3.0)25.1 (3.1)23.0 (2.3) *21.5 (2.3) *,†<0.001
Waist circumference (cm)81.1 (8.0)86.0 (7.8)80.7 (6.3) *76.3 (6.5) *,†<0.001
Fasting plasma glucose (mmol/L)5.3 (0.4)5.4 (0.4)5.3 (0.4) *5.3 (0.4) *,†<0.001
Hemoglobin A1c (%)5.2 (0.3)5.2 (0.3)5.2 (0.3) *5.1 (0.3) *,†<0.001
Hemoglobin A1c (mmol/L)32.9 (3.6)33.4 (3.6)32.8 (3.5) *32.5 (3.6) *,†<0.001
Creatinine (mg/dL)0.92 (0.12)0.84 (0.10)0.92 (0.10) *1.01 (0.11) *,†<0.001
Creatinine (µmol/L)81.6 (10.5)74.6 (8.5)81.1 (8.4) *88.9 (9.3) *,†<0.001
Triglycerides (mmol/L)1.2 (0.8)1.3 (0.8)1.2 (0.8) *1.1 (0.7) *,†<0.001
HDL cholesterol (mmol/L)1.3 (0.3)1.2 (0.3)1.3 (0.3) *1.4 (0.4) *,†<0.001
Systolic blood pressure (mmHg)120.2 (14.7)123.4 (14.3)120.0 (14.4) *117.3 (14.8) *,†<0.001
Diastolic blood pressure (mmHg)76.0 (10.2)77.8 (10.1)75.9 (10.0) *74.3 (10.1) *,†<0.001
Exercise (-/+)7822/18372646/5302650/6082526/699<0.001
Smoking (Non/Past/Current)3039/3074/3546995/946/1235984/1064/12101060/1064/1101<0.001
Alcohol intake (Non-min/light/moderate/heavy)5727/1505/1317/11101937/465/398/3761900/515/470/3731890/525/449/3610.111
Cre/BW ratio0.014 (0.002)0.011 (0.001)0.014 (0.001)0.016 (0.002)<0.001
Incident diabetes36216910786<0.001
WomenALL,
n = 7417		Tertile 1 (Cre/BW Ratio < 0.0118),
n = 2437		Tertile 2 (0.0118≤ Cre/BW Ratio < 0.014),
n = 2515		Tertile 3 (Cre/BW Ratio ≥ 0.014),
n = 2465		p
Age (year)44.2 (9.2)43.8 (8.7)44.1 (9.2)44.6 (9.5) *,†0.004
Body weight (kg)52.9 (8.0)58.6 (8.8)52.1 (5.9) *48.0 (5.2) *,†<0.001
Height (cm)158.1 (5.4)159.5 (5.3)158.1 (5.3) *156.7 (5.3) *,†<0.001
Body mass index (kg/m2)21.2 (3.0)23.1 (3.5)20.9 (2.3) *19.6 (2.0) *,†<0.001
Waist circumference (cm)72.1 (8.4)77.5 (8.8)71.4 (6.5) *67.4 (6.3) *,†<0.001
Fasting plasma glucose (mmol/L)5.0 (0.4)5.1 (0.4)5.0 (0.4) *4.9 (0.4) *,†<0.001
Hemoglobin A1c (%)5.2 (0.3)5.2 (0.3)5.2 (0.3) *5.1 (0.3) *<0.001
Hemoglobin A1c (mmol/L)33.2 (3.6)33.7 (3.7)33.0 (3.6) *32.8 (3.7) *<0.001
Creatinine (mg/dL)0.68 (0.10)0.60 (0.08)0.67 (0.08) *0.76 (0.09) *,†<0.001
Creatinine (µmol/L)59.8 (9.0)53.3 (6.7)59.2 (6.8) *66.8 (7.8)*, †<0.001
Triglycerides (mmol/L)0.7 (0.4)0.7 (0.5)0.7 (0.4) *0.7 (0.4) *<0.001
HDL cholesterol (mmol/L)1.6 (0.4)1.6 (0.4)1.6 (0.4) *1.7 (0.4) *,†<0.001
Systolic blood pressure (mmHg)110.6 (15.2)114.4 (15.6)109.9 (14.7) *107.7 (14.5) *,†<0.001
Diastolic blood pressure (mmHg)68.5 (10.2)70.5 (10.6)68.0 (10.0) *67.1 (9.7) *,†<0.001
Exercise (−/+)6188/12292099/3382108/4071981/484<0.001
Smoking (Non/Past/Current)6396/503/5182072/192/1732178/166/1712146/145/1750.084
Alcohol intake (Non-min/light/moderate/heavy)6705/416/211/852235/120/56/262252/156/78/292218/140/77/300.209
Cre/BW ratio0.013 (0.003)0.010 (0.001)0.013 (0.001) *0.016 (0.002) *,†<0.001
Incident diabetes1014727270.013
Cre/BW, Creatinine to body weight; HDL, high density lipoprotein. Continuous variables are expressed as mean (SD), and the differences among tertiles are evaluated by one-way ANOVA and Tukey’s post-hoc tests. Categorized variables are expressed as number, and the differences among tertiles are evaluated by Chi-squared test. * p < 0.05, vs. Tertile 1; † p < 0.05, vs. Tertile 2.
Table
Table 2. Adjusted hazard ratio of incident diabetes.
Table 2. Adjusted hazard ratio of incident diabetes.
MenModel 1Model 2
HR with 95% CIp ValueHR with 95% CIp Value
Age, years1.05 (1.03–1.06)<0.0011.05 (1.04–1.06)<0.001
Fasting plasma glucose, 0.1 mmol/L1.32 (1.28–1.37)<0.0011.32 (1.27–1.36)<0.001
Light drinker0.65 (0.47–0.90)0.0010.66 (0.48–0.91)0.009
Moderate drinker0.54 (0.38–0.77)<0.0010.55 (0.38–0.77)<0.001
Heavy drinker0.92 (0.68–1.24)0.5860.95 (0.70–1.26)0.716
Regular exerciser0.73 (0.53–0.99)0.0460.73 (0.52–0.99)0.039
Ex-smoker0.85 (0.63–1.13)0.2630.84 (0.62–1.12)0.231
Current smoker1.58 (1.22–2.05)<0.0011.55 (1.19–2.02)0.010
Cre/BW ratio tertiles
T1Reference---
T20.56 (0.44–0.71)<0.001--
T30.42 (0.32–0.54)<0.001--
Cre/BW ratio, 0.001 incremental--0.84 (0.80–0.88)<0.001
WomenModel 1Model 2
HR with 95% CIp ValueHR with 95% CIp Value
Age, years1.05 (1.02–1.07)<0.0011.05 (1.02–1.07)<0.001
Fasting plasma glucose, 0.1 mmol/L1.40 (1.33–1.48)<0.0011.40 (1.32–1.48)<0.001
Light drinker0.35 (0.09–0.94)0.0350.34 (0.08–0.91)0.030
Moderate drinker0.81 (0.20–2.24)0.7200.78 (0.19–2.14)0.667
Heavy drinker0.64 (0.10–2.12)0.5120.69 (0.11–2.26)0.587
Regular exerciser0.84 (0.46–1.42)0.5270.86 (0.50–1.50)0.605
Ex-smoker1.50 (0.62–3.08)0.3411.53 (0.63–3.12)0.319
Current smoker3.49 (1.85–6.09)<0.0013.57 (1.91–6.23)<0.001
Cre/BW ratio tertiles
T1Reference---
T20.61 (0.37–0.98)0.042--
T30.53 (0.32–0.85)0.008--
Cre/BW ratio, Δ0.001 incremental--0.88 (0.81–0.96)0.003
Cre/BW, Creatinine to body weight. Hazard ratios of light, moderate, and heavy drinkers evaluated with none-to-minimal drinker as reference. Hazard ratio of regular exerciser evaluated non-exerciser as reference. Hazard ratios of ex- and current smokers evaluated with non-smoker as reference. Hazard ratios of T2 (0.0127 ≤ Cre/BW ratio < 0.0148 in men and 0.0118 ≤ Cre/BW ratio < 0.014 in women) and T3 (Cre/BW ratio ≥ 0.0148 in men and Cre/BW ratio ≥ 0.014 in women) of Cre/BW ratio tertiles evaluated with T1 (Cre/BW ratio < 0.01275 in men and Cre/BW ratio < 0.0118 in women) as reference.

Share and Cite

MDPI and ACS Style

Hashimoto, Y.; Okamura, T.; Hamaguchi, M.; Obora, A.; Kojima, T.; Fukui, M. Creatinine to Body Weight Ratio Is Associated with Incident Diabetes: Population-Based Cohort Study. J. Clin. Med. 2020, 9, 227. https://doi.org/10.3390/jcm9010227

AMA Style

Hashimoto Y, Okamura T, Hamaguchi M, Obora A, Kojima T, Fukui M. Creatinine to Body Weight Ratio Is Associated with Incident Diabetes: Population-Based Cohort Study. Journal of Clinical Medicine. 2020; 9(1):227. https://doi.org/10.3390/jcm9010227

Chicago/Turabian Style

Hashimoto, Yoshitaka, Takuro Okamura, Masahide Hamaguchi, Akihiro Obora, Takao Kojima, and Michiaki Fukui. 2020. "Creatinine to Body Weight Ratio Is Associated with Incident Diabetes: Population-Based Cohort Study" Journal of Clinical Medicine 9, no. 1: 227. https://doi.org/10.3390/jcm9010227

APA Style

Hashimoto, Y., Okamura, T., Hamaguchi, M., Obora, A., Kojima, T., & Fukui, M. (2020). Creatinine to Body Weight Ratio Is Associated with Incident Diabetes: Population-Based Cohort Study. Journal of Clinical Medicine, 9(1), 227. https://doi.org/10.3390/jcm9010227

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop