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ABSTRACT This study investigated the characteristics of patients with different chronic kidney disease (CKD) stages according to various body mass index (BMI) categories and determined the

influence of BMI in renal function deterioration. We conducted a multicenter, longitudinal cohort study based on the Epidemiology and Risk Factors Surveillance of CKD project (2008–2013) and

National Health Insurance Research Database (2001–2013). A total of 7357 patients with CKD aged 20–85 years from 14 hospitals were included in the study. A higher male sex, diabetes

mellitus (DM) and hypertension were noted among overweight and obese CKD patients, while more cancer prevalence was noted among underweight CKD patients. Charlson comorbidity index was

significantly higher and correlated with BMI among late CKD patients. Patients with BMI < 18.5 kg/m2 exhibited non-significantly higher events of eGFR decline events in both early and

late CKD stages than other BMI groups. BMI alone is not a determinant of CKD progression among our Taiwanese CKD patients. Obesity should be re-defined and body weight manipulation should be

individualized in CKD patients. SIMILAR CONTENT BEING VIEWED BY OTHERS ASSOCIATION OF TRIGLYCERIDE-GLUCOSE-BODY MASS INDEX WITH ALL-CAUSE AND CARDIOVASCULAR MORTALITY AMONG INDIVIDUALS WITH

CHRONIC KIDNEY DISEASE Article Open access 04 September 2024 POPULATION CHARACTERISTICS AND DIAGNOSIS RATE OF CHRONIC KIDNEY DISEASE BY EGFR AND PROTEINURIA IN JAPANESE CLINICAL PRACTICE:

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GLOMERULAR FILTRATION RATE Article Open access 19 April 2024 INTRODUCTION Obesity, a global pandemic problem, is associated with various metabolic disorders and results in a shortened life

span related to adverse health consequences. In Taiwan, the prevalence of overweight and obesity among adults was reported to be 44.1%, of whom 50.8% were men and 36.9% were women, according

to 2005–2008 data1. Moreover, in a survey performed in 2002 and 2007 (Taiwanese Survey on Hypertension, Hyperglycemia, and Hyperlipidemia), the prevalence of obesity increased from 19.2% to

23.3% among men and from 13.4% to 19.0% among women2. The overweight and obesity rates in Taiwan are substantially higher than those in Japan, Singapore, and other neighboring Asian

countries. On the other hand, the prevalence of chronic kidney disease (CKD) in Taiwan is also increasing, with nearly 6 million people undergoing dialysis and approximately 2,000 people

newly diagnosed as having end-stage renal disease (ESRD) annually (http://www.tsn.org.tw/UI/K/K008.aspx). Since the numbers of obese patients with CKD and those undergoing dialysis are also

increasing in parallel, the diagnosis and precise management of obesity have become critical among these patients. Many studies have demonstrated that obesity is an important risk factor for

incident CKD3,4,5,6,7 and increased risk of ESRD8,9,10,11,12. Paradoxically, obesity itself in CKD and ESRD has been found to be associated with more favorable outcomes13,14. A reverse

obesity–mortality association has been consistently observed in patients with ESRD15,16,17; however, conflicting results have been observed among patients with CKD13,18,19,20. Body mass

index (BMI) is a globally accepted anthropometric measure for obesity classification. Recently, many studies have questioned the accuracy of BMI in obesity and excess body fat

assessment21,22,23. Whether BMI can influence the CKD progression among all stages of CKD in the Taiwanese population remains unclear. We conducted a multicenter, longitudinal cohort study

to investigate the characteristics of patients at all CKD stages (CKD stages 1–5 nondialysis [ND]) according to various BMI categories and to determine the influence of BMI in renal function

deterioration by using the data from the Epidemiology and Risk Factors Surveillance of CKD project (2008–2013) and National Health Insurance Research Database (NHIRD) (2001–2013). RESULTS

DEMOGRAPHIC CHARACTERISTICS OF THE PATIENTS A total of 7357 patients with CKD aged 20–85 years from 14 hospitals were included in the study. Patients with ESRD, defined as either receiving

maintenance dialysis during this period or having a kidney transplant, were excluded. After the exclusion of patients with less than 1 year of follow-up (n = 2789) and those with missing or

incomplete data (n = 999), 4022 patients with CKD were finally enrolled in this study (Fig. 1). Among these patients, 2008 had early-stage CKD (CKD stages 1, 2, and 3a) and 2014 had

late-stage CKD (CKD stages 3b, 4, and 5ND). The patients were continually traced from the baseline date to the end of the study period (June 18, 2015). The mean age of the cohort was 62.86 ±

 14.93 years, and 56.9% of the patients were men. The mean baseline eGFR was 51.5 ± 36.15 mL/min per 1.73 m2. The baseline characteristics of patients according to baseline BMI are presented

in Table 1. For further analysis, we also present the baseline characteristics of patients with early-stage CKD (CKD stages 1–3a) in Table 2 and late-stage CKD (CKD stages 3b–5) in Table 3.

The highest prevalence rates of overweight (24 ≤ BMI <27 kg/m2) and obesity (BMI ≥ 27 kg/m2) were observed among patients aged 45–64 years, with the values being 45% and 39.38%

respectively. We also noted a male predominance in BMI (62.7% for overweight and 56.43% for obesity). Patients with higher BMI exhibited significantly higher baseline DM and hypertension

than those with lower BMI (16.15% in the underweight group, compared with 36.05%, 47.29%, and 52.23% in the normal, overweight, and obesity groups, respectively). Furthermore, patients with

BMI < 18.5 kg/m2 exhibited a characteristically higher cancer prevalence rate (18.46%) than the other groups; the prevalence rates were 9.75%, 9.16%, and 8.22% in the normal, overweight,

and obesity groups, respectively. Baseline coronary artery disease (CAD) and stroke did not differ between the groups. Similarly, the baseline Charlson comorbidity index was significantly

lower in the low and normal BMI groups (3.38 ± 2.50 and 3.53 ± 2.39, respectively), compared with the overweight and obesity groups (3.81 ± 2.46 and 3.73 ± 2.41, respectively). Furthermore,

the higher BMI groups had higher medication use and more smoking, alcohol consumption, and betel nut chewing habits. The biochemical analysis revealed significantly higher hemoglobin and

hematocrit levels, AC sugar, triglyceride, and uric acid levels in the higher BMI groups. No significant difference was observed in baseline cholesterol, electrolytes, albumin, UPCR, and

eGFR between the groups (Table 1). We subgrouped our cohort into early- and late-stage CKD (CKD stages 1–3a and 3b–5) and studied their characteristics according to different BMI categories

(Tables 2 and 3). Similar age and sex prevalence rates were observed after subgrouping. Among patients with early-stage CKD, those in the lower BMI group exhibited significantly higher

cancer prevalence (16.67% in with the underweight group) than those in the higher BMI groups (8.64% in the normal, 8.95% in the overweight, and 6.76% in the obesity groups; p = 0.032).

However, among patients with late-stage CKD, those in the lower BMI group had nonsignificantly higher cancer prevalence than those in the higher BMI groups (20% in the underweight group vs.

10.8% in the normal group, 11.39% in the overweight group, and 9.8% in the obesity group; p = 0.08). Higher DM and hypertension rates were observed in overweight and obese patients with

early- and late-stage CKD. The Charlson comorbidity index did not differ significantly with BMI among patients with early-stage CKD (2.57 ± 2.31 in the underweight group vs. 2.69 ± 2.12 in

the normal group, 2.92 ± 2.19 in the overweight group, and 2.86 ± 2.12 in the obesity group; p = 0.1773). However, a significantly higher Charlson comorbidity index was observed among

overweight and obese patients with late-stage CKD (4.07 ± 2.47 in the underweight group vs. 4.32 ± 2.37 in the normal group, 4.71 ± 2.39 in the overweight group, and 4.67 ± 2.35 in the

obesity group; p = 0.003). The initial stages of CKD did not differ significantly among patients with early-stage CKD; by contrast, they differed significantly among patients with late-stage

CKD according to BMI (p = 0.0004). More prevalent CKD stage 3b (18.57% in the underweight group vs. 26.51% in the normal group, 33.3% in the overweight group, and 31.58% in the obesity

group) and stage 4 (28.5% in the underweight group vs. 36.43% in the normal group, 36.35% in the overweight group, and 38.11% in the obesity group) were observed among patients in the higher

BMI group; by contrast, 52.8% of patients in the underweight group had CKD stage 5 compared with 37.06%, 30.32%, 30.31% in the normal, overweight, and obesity groups, respectively. We

analyzed the proportion of eGFR progression events during the follow-up period among patients with CKD stages 1–5 in different BMI groups. We also executed further subgroup analysis of the

proportion of eGFR progression among patients with early-stage CKD (stages 1–3a) and late-stage CKD (stages 3b–5) in different BMI groups. CORRELATION BETWEEN BMI AND CKD PROGRESSION AMONG

PATIENTS WITH CKD STAGES 1–5 Table 4 presents the proportion of eGFR progression events in patients with CKD stages 1–5. The study outcomes are presented as ORs, and the normal group was

used as the reference group to calculate the OR for each group. The underweight group exhibited the highest proportion of events (25%) compared with the normal (19%), overweight (19%), and

obesity (18%) groups. The ORs of eGFR progression events were 1.44 (0.95, 2.18), 0.99 (0.81, 1.2), and 0.95 (0.78, 1.16) in the underweight, overweight, and obesity groups, respectively.

After adjusting for age, sex, previous diabetes, CAD, stroke, cancer, high blood pressure, Charlson score, TB, COPD, ACEI, ARB, fibrate, smoking, alcohol consumption, betel nut chewing,

baseline UPCR, and baseline eGFR, we observed that the OR was 1.35 (0.87, 2.10) in the underweight group compared with 1.02 (0.83, 1.25) and 0.95 (0.77, 1.18) in the overweight and obesity

groups, respectively (Fig. 2). CORRELATION BETWEEN BMI AND CKD PROGRESSION AMONG PATIENTS WITH EARLY-STAGE CKD (STAGES 1–3A) Table 5 presents the proportion of eGFR deterioration events in

patients with CKD stages 1–3a. The study outcomes are presented as ORs, and the normal group was used as the reference group to calculate the OR for each group. The underweight group

exhibited the highest proportion of eGFR deterioration events (20%) compared with the normal (13%), overweight (13%), and obesity (12%) groups. The ORs of eGFR progression events were 1.67

(0.86, 3.25), 0.98 (0.71, 1.34), and 0.91 (0.66, 1.26) in the underweight, overweight, and obesity groups, respectively. After adjusting for age, sex, previous diabetes, CAD, stroke, cancer,

high blood pressure, Charlson score, TB, COPD, ACEI, ARB, fibrate, smoking, alcohol consumption, betel nut chewing, baseline UPCR, and baseline eGFR, we determined that the ORs were 1.42

(0.70, 2.88), 1.06 (0.76, 1.47), and 0.92 (0.66, 1.30) in the underweight, overweight, and obesity groups, respectively (Fig. 3). CORRELATION BETWEEN BMI AND CKD PROGRESSION AMONG PATIENTS

WITH LATE-STAGE CKD (STAGE 3B–5) Table 6 presents the proportion of eGFR deterioration events in patients with CKD stages 3b–5. The study outcomes are presented as ORs, and the normal group

was used as the reference group to calculate the OR for each group. The underweight group exhibited the highest proportion of eGFR deterioration events (30%) compared with the normal (25%),

overweight (25%), and obesity (25%) groups. The ORs of the eGFR progression events were 1.29 (0.76, 2.21), 1.01 (0.79, 1.30), and 1.02 (0.79, 1.31) in the underweight, overweight, and

obesity groups, respectively. After adjusting for age, sex, previous diabetes, CAD, stroke, cancer, high blood pressure, Charlson score, TB, COPD, ACEI, ARB, fibrate, smoking, alcohol

consumption, betel nut chewing, baseline UPCR, and baseline eGFR, we observed that the ORs were 1.33 (0.74, 2.39), 1.04 (0.79, 1.36), and 1.04 (0.79, 1.39) in the underweight, overweight,

and obesity groups, respectively (Fig. 4). DISCUSSION In this prospective cohort study, we evaluated the characteristics of a CKD cohort according to various BMI categories. Subsequently, we

investigated the association between BMI and the risk of eGFR decline among patients with different CKD stages. The highest prevalence of overweight and obesity was observed among men and

the working age group (45–64 years old) in both early- and late-stage CKD. Previous studies conducted on the Japanese24,25 and Malay populations26 have demonstrated a male sex-specific

association between BMI and kidney disease; similarly, from our baseline data, we observed a higher male prevalence among overweight and obese CKD patients. The mechanism underlying the male

sex-specific association between higher BMI and CKD remains unclear; however, several studies have identified that BMI reflects visceral fat more efficiently in men than in women27,28.

Generally, men exhibit a higher risk of kidney disease and develop the disease earlier in life than women because of hormonal and lifestyle influences29,30,31,32. We observed a significantly

higher prevalence of DM and hypertension among overweight and obese patients with CKD (both early- and late-stage CKD). This observational association might not represent cause and effect;

since obesity itself is associated with various adverse sequelae from metabolic syndrome, as well as from comorbidities including DM and hypertension33,34, and all these conditions are

associated with CKD. Patients with early-stage CKD with a lower BMI exhibited significantly higher cancer prevalence; however, non-significantly higher prevalence was observed in patients

with late-stage CKD. This might also not represent a causal relation; nevertheless, many studies have revealed a bidirectional association between CKD and cancer35,36. Cancer patients with

lower BMI exhibited associated nutritional disturbances and tended to have reduced renal function status from nutritional and specified therapies. A Korean study reported a significantly

higher risk of CKD and proteinuria among cancer survivors37. Furthermore, both CKD and ESRD are higher risks from a number of malignancies38,39. We calculated the Charlson scores at

different CKD stages, which did not differ significantly among patients with early-stage CKD in different BMI categories. However, the median Charlson score increased significantly among

overweight and obese patients with late-stage CKD (Fig. 5), which demonstrates the presence of more comorbidity among these patients. The initial CKD stages did not differ significantly with

BMI among patients with early-stage CKD (Table 2). However, in patients with late-stage CKD, the prevalence of the initial stages of CKD differed significantly according to BMI (Table 6),

with a higher prevalence of CKD stages 3b and 4 observed in overweight and obese patients. We observed a significantly higher number of patients with CKD stage 5 to be underweight (BMI < 

18.5). The reason for this finding is unknown, and additional studies are required to confirm whether any nutritional and concurrent comorbidity might play a role in such lower BMI

prevalence among these patients. An obesity paradox was supposed for stage 5 CKD, because low BMI represents more uremia-associated inflammatory cachexia and high BMI represents fewer uremic

consequences and more favorable health40,41,42. More emphasis on improved and adequate nutrition is required for these patients with advanced-stage CKD compared with control obesity in

healthy patients. A follow-up analysis revealed non-significantly increased CKD progression events among underweight patients compared with overweight and obese patients in both early and

late CKD stages. This observed association persisted after adjustment for age, sex, previous diabetes, CAD, stroke, cancer, high blood pressure, Charlson score, TB, COPD, ACEI, ARB, fibrate,

smoking, alcohol consumption, betel nut chewing, baseline UPCR, and baseline eGFR, and it was consistently present in the subgroup analysis among all patients with CKD. Our results are

consistent with the so-called obesity paradox43 among patients with CKD; we found non-significantly higher kidney disease progression events among underweight patients compared with

overweight and obese patients in all stages of CKD. Although several mechanisms have been proposed for patients with late-stage CKD42, we observed the same paradox among patients with

early-stage CKD, which might be explained by the older age and higher cancer prevalence among patients with lower BMI. In patients with late-stage CKD, lower BMI was associated with poor

nutritional status42 or higher prevalence of metabolically obese normal-weight individuals with a higher comorbidity burden44. Our study results are consistent with those of other population

studies. Data from a nationally representative cohort of US veterans with eGFR < 60 mL/min indicated a U-shaped association between BMI and the risk of renal progression20, with

deteriorating outcomes observed in individuals with BMI < 25 kg/m2 and BMI ≥ 35 kg/m2; these data demonstrate that overweight or mild obesity (30–35 kg/m2) results in the most favorable

outcomes and that in advanced CKD stages (eGFR < 30 mL/min), even morbid obesity (BMI ≥ 35 kg/m2) is not associated with adverse outcomes. A similar U-shaped association between an

increased risk of progressive CKD and lowest BMI levels was noted in a large population-based cohort study in Israel45. A retrospective study in the Taiwanese general population reported

that waist-to-height ratio (WHtR), rather than BMI, increased as the prevalence of CKD increased46. Other studies have reported that WHtR and waist circumference, but not BMI, were

associated with mortality in patients with CKD and ESRD47,48. Although the recent global definition uses BMI as a standard measure of obesity, obesity is affected by muscle mass, peripheral

and abdominal adipose tissue mass, and bone; thus, the results should be considered with the condition49. Central obesity has been proved to be more vulnerable to metabolic syndrome and

obesity-related diseases, whereas peripheral obesity and higher muscle mass appear protective50,51,52. BMI failed to represent central obesity because of the variation in individual body

composition and contribution. This explains the nonsignificant association between BMI and CKD progression through all stages of CKD in our study. Our study has several limitations. Because

we studied a prevalent cohort of patients with CKD, we could not determine the effects of obesity on incident CKD. We used the study participants’ personal identities to link health care

databases, and because the NHI database is based on the reporting data system and does not include the population not under medical health care, the study result may not represent the whole

population; however, the missing population is negligible. We used only BMI to determine obesity, which may not be an ideal marker of obesity among our cohort; nevertheless, because BMI is

generally accepted as a predominant index to establish obesity in clinical practice, our results have direct clinical relevance. Because the blood and urine samples of study participants

were collected from individual hospitals and sent to the research center, the use of different equipment and personnel of individual hospitals may have resulted in measurement errors.

Furthermore, we did not determine the influence of low and high BMI on mortality outcomes. MATERIALS AND METHODS ETHICS STATEMENT The study was reviewed and approved by the institutional

ethical committee of Taipei Medical University - Shuang Ho Hospital (TMU-JIRB 201204036), Tri-Service General Hospital (TSGHIRB100-05-197), Cardinal Tien Hospital (TMU-JIRB 201204035),

Changhua Christian Hospital (CCHIRB 20405), Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUHIRB 20120019), Kaohsiung Chang Gung Memorial Hospital (101-1096B), National Cheng

Kung University Hospital (A-ER-101-117) and China Medical University Hospital (DMR101-IRB2-273(CR-1)). After a complete explanation of the study, written informed consent was obtained from

all participants. All clinical and biological samples were collected after patient consent. All the study methods were in accordance with the guidelines approved by the joint institutional

review board and aforementioned governmental regulations. STUDY POPULATION We conducted a multicenter, longitudinal cohort study using data from the Epidemiology and Risk Factors

Surveillance of CKD database (2008–2013) managed by the Bureau of Health Promotion, Ministry of Health and Welfare, Taiwan. After excluding patients with incomplete or missing data, we

linked the biochemical laboratory data to the NHIRD from 2001 to 2013. The same medical laboratory criteria and protocol have been used in our study hospitals, and the serum creatinine

levels derived from different hospitals can be compared and standardized with each other. In this study, we measured CKD progression at the individual level. In addition, the patients were

reexamined in the same hospital to control the individual variation. All patients provided informed consent before data collection. MEASUREMENTS AND VARIABLE DEFINITIONS The patients’

demographic, clinical, and health-related behavior data were collected using a structured questionnaire. The questionnaire collected data on age, sex, cigarette smoking, alcohol consumption,

betel nut chewing, personal and family comorbid conditions, and medication use. Physical examination included anthropometry, blood pressure measurement, pulse rate measurement, and systemic

examination. Height was measured in centimeters by using a wall-mounted measuring tape, and weight was measured in kilograms by using a digital scale (SECA, model 782 2321009; Vogel &

Halke, Germany). BMI was classified into the following groups: <18.5 kg/m2 (underweight), 18.5–23.9 kg/m2 (normal), 24–26.9 kg/m2 (overweight), and ≥27 kg/m2 (obesity). Glycemia, blood

pressure, and lipid control conditions were classified as intensive and poor. Proteinuria status was determined using the urine protein-to-creatinine ratio (UPCR). CKD was defined according

to the Kidney Disease Outcomes Quality Initiative guidelines53 and was evaluated using the estimated glomerular filtration rate (eGFR), which was calculated using the Chronic Kidney

Disease-Epidemiology Collaboration equation: eGFR (mL/min/1.73 m2) = 141 × min (SCr/ƙ, 1)α × max (serum creatinine/ƙ, 1) − 1.209 × 0.993Age × 1.018 (if female) and × 1.159 (if black), where

SCr denotes the serum creatinine level (mg/dL), ƙ = 0.7 (for women) and 0.9 (for men), α = −0.329 (for women) and −0.411(for men), min denotes the minimum of SCr/ƙ or 1, and max denotes the

maximum of SCr/ƙ or 154. CKD was classified as follows: CKD stage 1, eGFR ≥ 90 mL/min/1.73 m2 and the presence of kidney damage (i.e., proteinuria dipsticks ≥1+, UPCR ≥ 150, or urine

albumin-to-creatinine ratio [UACR] ≥30); CKD stage 2, eGFR = 60–89 mL/min/1.73 m2 and the presence of kidney damage (i.e., proteinuria dipsticks ≥1+, UPCR ≥ 150, or UACR ≥ 30); CKD stage 3a,

eGFR = 45–59 mL/min/1.73 m2; CKD stage 3b, eGFR = 30–44 mL/min/1.73 m2; CKD stage 4, eGFR = 15–29 mL/min/1.73 m2; and CKD stage 5, eGFR < 15 mL/min/1.73 m2 55. Renal progression was

defined as an average eGFR decline by more than 5 mL/min/1.73 m2 per year or into the dialysis stage56. STATISTICAL ANALYSIS Consistent with the study hypothesis, all analyses were

stratified according to BMI. We examined BMI as quartiles: <18.5, 18.5–23.9, 24–26.9, and ≥27 kg/m2. The characteristics of different BMI groups were compared using the chi-squared test

for categorical variables and ANOVA for continuous variables. The odds ratio (OR) (95% confidence interval) of CKD was calculated for each BMI category. Next, we explored the data for

confounding and effect modification in stratified analyses. After adjusting for all covariates, we used the multivariate logistic model with stepwise variable selection models to evaluate

the association between BMI and eGFR decline. In our subsequent multivariate modeling, we considered covariates including age; sex; comorbid conditions such as diabetes mellitus (DM),

stroke, and cancer; the Charlson comorbidity index; use of antihypertensive medications (e.g., ACEI/ARB and loop diuretics) within the previous 1 year; and baseline CKD stage. The SAS

statistical package (Version 9.3, SAS Institute Inc., Cary, NC, USA) was used for all statistical tests. Results with P < 0.05 were considered statistically significant. CONCLUSIONS In

conclusion, the definition and classification of obesity among patients with CKD should be intensively re-determined, because misdiagnosis can lead to inappropriate clinical decisions and

might deteriorate patients’ prognosis. The anthropomorphic measures alternate to BMI should be established from randomized controlled clinical trials among the CKD population. CHANGE HISTORY

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(2013). Article  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS This study was supported by grants from the Health Promotion Administration, Ministry of Health and Welfare,

Institute for Biotechnology and Medicine Industry, Taiwan, ROC (MOHW104-HPA- H-114-134101). AUTHOR INFORMATION Author notes * Senyeong Kao and Yuh-Feng Lin contributed equally to this work.

AUTHORS AND AFFILIATIONS * Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan Tian-Jong Chang, Jing-Quan Zheng, Senyeong Kao & Yuh-Feng Lin *

Performance Appraisal Section, Secretary Office, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan Tian-Jong Chang * Division of Nephrology, Department of Internal Medicine,

School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan Cai-Mei Zheng, Mei-Yi Wu, Yung-Ho Hsu & Yuh-Feng Lin * Division of Nephrology, Department of Internal

Medicine, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan Cai-Mei Zheng, Mei-Yi Wu, Yung-Ho Hsu & Yuh-Feng Lin * Graduate Institute of Clinical Medicine, College of

Medicine, Taipei Medical University, Taipei, Taiwan Cai-Mei Zheng & Yuh-Feng Lin * Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan

University, Taipei, Taiwan Tzu-Ting Chen & Yun-Chun Wu * School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan Yi-Lien Wu * Kidney Disease Prevention

Foundation, Taipei, Taiwan Yi-Lien Wu & Yuh-Feng Lin * Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan Hsin-Ting Lin *

Department of Critical Care Medicine, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan Jing-Quan Zheng * School of Public Health, National Defense Medical Center, Taipei, Taiwan

Nain-Feng Chu, Sui-Lung Su & Senyeong Kao * Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan Nain-Feng Chu * School of Public Health,

College of Public Health and Nutrition, Taipei Medical University, Taipei, Taiwan Yu-Me Lin & Hung-Yi Chiou * Division of Nephrology, Department of Medicine, Fu-Jen Catholic University

Hospital, School of Medicine, Fu-Jen Catholic University, Taipei, Taiwan Kuo-Cheng Lu * Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical

Center, Taipei, Taiwan Jin-Shuen Chen & Yuh-Feng Lin * School of Public Health, Graduate Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan Fung-Chang

Sung * Division of Nephrology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung Medical University, Kaohsiung, Taiwan Chien-Te Lee * The Division of Nephrology, Changhua Christian

Hospital, Changhua, Taiwan Yu Yang * Division of Nephrology, Department of Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan Shang-Jyh Hwang * Division of Nephrology,

Department of Internal Medicine, Cheng Kung University Medical Center, Tainan, Taiwan Ming-Cheng Wang Authors * Tian-Jong Chang View author publications You can also search for this author

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publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS For research articles with several authors, a short paragraph specifying their individual contributions

must be provided. The following statements should be used “Senyeong Kao and Cai-Mei Zheng conceived and designed the experiments; Cai-Mei Zheng, Hsin-Ting Lin, Jing-Quan Zheng, Nain-Feng

Chu, Yu-Me Lin, Sui-Lung Su, Kuo-Cheng Lu, Jin-Shuen Chen, Fung-Chang Sung, Chien-Te Lee, Yu Yang, Shang-Jyh Hwang, Ming-Cheng Wang, Yung-Ho Hsu and Hung-Yi Chiou performed the experiments;

Tian-Jong Chang, Mei-Yi Wu, Tzu-Ting Chen, Yun-Chun Wu and Yi-Lien Wu analyzed the data; Yuh-Feng Lin contributed reagents/materials/analysis tools and coordinate experiment performance;

Tian-Jong Chang wrote the paper”. Authorship must be limited to those who have contributed substantially to the work reported. CORRESPONDING AUTHORS Correspondence to Senyeong Kao or

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renal function deterioration among the Taiwanese chronic kidney disease population. _Sci Rep_ 8, 6908 (2018). https://doi.org/10.1038/s41598-018-24757-6 Download citation * Received: 12

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