Front. Public Health Frontiers in Public Health Front. Public Health 2296-2565 Frontiers Media S.A. 10.3389/fpubh.2022.1072238 Public Health Original Research Association between sleep duration and quality with rapid kidney function decline and development of chronic kidney diseases in adults with normal kidney function: The China health and retirement longitudinal study Xu Sujuan 1 2 3 † Jin Jifu 4 † Dong Qi 2 3 † Gu Chenjie 5 † Wu Yong 6 7 Zhang Haibo 8 Yin Yingchao 2 3 Jia Huiyang 2 3 Lei Mingcheng 9 Guo Junfei 2 3 Xu Haixia 10 Chang Suchi 11 Zhang Feng 9 Hou Zhiyong 2 3 Zhang Liping 12 * 1Department of Nephrology, Third Hospital of Hebei Medical University, Shijiazhuang, China 2Department of Orthopaedical Surgery, Third Hospital of Hebei Medical University, Shijiazhuang, China 3Orthopaedic Research Institute of Hebei Province, Hebei Medical University, Shijiazhuang, China 4Department of Cardiovascular Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China 5The First Affiliated Hospital of Dalian Medical University, Dalian, China 6Department of Nephrology, Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China 7Department of Nephrology, Huashan Hospital, Fudan University, Shanghai, China 8Department of Liver Disease, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China 9Department of Rehabilitation Medicine, Third Hospital of Hebei Medical University, Shijiazhuang, China 10Department of Cardiology, Affiliated Hospital of Nantong University, Jiangsu, China 11Department of Cardiology, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China 12Department of Physiology, Hebei Medical University, Shijiazhuang, Hebei, China

Edited by: Cheng Yang, Fudan University, China

Reviewed by: Yifei Miao, Cincinnati Children's Hospital Medical Center, United States; Weili Yang, Capital Medical University, China

 *Correspondence: Liping Zhang ✉ zhanglp616@163.com

This article was submitted to Aging and Public Health, a section of the journal Frontiers in Public Health

†These authors have contributed equally to this work and share first authorship

 18 01 2023 2022 10 1072238 17 11 2022 21 12 2022 Copyright © 2023 Xu, Jin, Dong, Gu, Wu, Zhang, Yin, Jia, Lei, Guo, Xu, Chang, Zhang, Hou and Zhang. 2023 Xu, Jin, Dong, Gu, Wu, Zhang, Yin, Jia, Lei, Guo, Xu, Chang, Zhang, Hou and Zhang

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Research have shown that sleep is associated with renal function. However, the potential effects of sleep duration or quality on kidney function in middle-aged and older Chinese adults with normal kidney function has rarely been studied. Our study aimed to investigate the association of sleep and kidney function in middle-aged and older Chinese adults. Four thousand and eighty six participants with an eGFR ≥60 ml/min/1.73 m2 at baseline were enrolled between 2011 and 2015 from the China Health and Retirement Longitudinal Study. Survey questionnaire data were collected from conducted interviews in the 2011. The eGFR was estimated from serum creatinine and/or cystatin C using the Chronic Kidney Disease Epidemiology Collaboration equations (CKD-EPI). The primary outcome was defined as rapid kidney function decline. Secondary outcome was defined as rapid kidney function decline with clinical eGFR of <60 ml/min/1.73 m2 at the exit visit. The associations between sleep duration, sleep quality and renal function decline or chronic kidney disease (CKD) were assessed based with logistic regression model. Our results showed that 244 (6.0%) participants developed rapid decline in kidney function, while 102 (2.5%) developed CKD. In addition, participants who had 3–7 days of poor sleep quality per week had higher risks of CKD development (OR 1.86, 95% CI 1.24–2.80). However, compared with those who had 6–8 h of night-time sleep, no significantly higher risks of rapid decline in kidney function was found among those who had <6 h or >8 h of night time sleep after adjustments for demographic, clinical, or psychosocial covariates. Furthermore, daytime nap did not present significant risk in both rapid eGFR decline or CKD development. In conclusion, sleep quality was significantly associated with the development of CKD in middle-aged and older Chinese adults with normal kidney function.

 aging renal function decline glomerular filtration rate sleep quality sleep duration Introduction

Chronic kidney disease (CKD) is a detrimental public health issue with an increasing prevalence and complications worldwide (1). In 2012, the overall prevalence of CKD was 11% in Chinese adults (2, 3). As CKD is closely linked to the increased risk of various disease, such as diabetes mellitus (DM), hypertension, metabolic disorders, and cardiovascular disease (2), early identification and intervention of modifiable lifestyle-related risk factors for CKD are recognized as an effective option for preventing the development of this disease (4).

Sleep is an indispensable element for optimal heath and quality of life. In recent years, accelerated aging in China raises serious concerns for middle-aged and older persons, where the circadian mechanisms increasingly become less efficient. Consequently, older people tend to sleep less and have poor sleep quality, which may lead to multiple chronic diseases, such as depression, headache, memory loss, CKD, obesity, DM, and hypertension (5–9). Epidemiological studies demonstrated that prevalence of sleep disturbances in CKD was ~80% (10), and sleep duration and quality were modifiable risk factors that could effectively prevent CKD. Mechanistically, this relationship is associated with sympathetic overreaction, circadian rhythm and metabolic disorders (11, 12). Several studies showed that inadequate duration and poor quality of sleep were increasingly associated with decline of kidney function and development of proteinuria (13–15). Furthermore, a recent study revealed that short or long sleep duration were related to the increased risk of CKD when compared with intermediate sleep duration (8). In contrary, previous meta-analysis indicated that short sleep duration was closely related to proteinuria rather than CKD development (16). Inconsistent findings such as these indicate that further studies focused on the association between sleep disturbances and CKD development needs to be evaluated.

To address the above inconsistencies, this study explored whether sleep duration, quality were deleterious factors for rapid decline of renal function and the development of CKD in middle-aged and older Chinese adults within The China Health and Retirement Longitudinal Study (CHARLS) database, a nationally representative, longitudinal cohort with the measurements of serum creatinine and cystatin C.

 Materials and methods Study participants and design

The China Health and Retirement Longitudinal Study (CHARLS) (17) was a project implemented using a multistage, stratified and proportionate-to-size sampling method. CHARLS included 17,708 participants from 150 counties and 450 villages within 28 provinces in mainland China. The baseline survey was carried out from June 2011 to March 2012. The detailed design and methods on the demographic, lifestyle factors, clinical or biochemical measurements and blood samples in the study were reported previously (17, 18). CHARLS data, which were collected from representative participants of 45 years old and above from among the Chinese population, aimed to establish a higher quality database.

The CHARLS prospective longitudinal cohort included data collected from two time-points (2011, 2015). The exclusion criteria for this study were as follow: participants whose ages were under 45 years old; participants with missing information of baseline sleep duration, baseline sleep quality, baseline kidney functions, exit kidney outcomes and related information such as demographic, lifestyle factors, clinical or biochemical measurements. Based on these criteria, data from 7,761 participants were excluded, and a total of 4,086 participants with eGFR ≥60 ml/min 1.73 m2 at baseline were included (Supplementary Figure 1).

Ethical approval of CHARLS was authorized by the Biomedical Ethics Review Committee of Peking University (IRB00001052–11015) (17). All participants have signed and provided written informed consent before participating in the survey. Information on the materials for this study are available on the CHARLS project website.

 Assessment of sleep duration and quality

Sleep duration and quality were collected from the baseline survey carried out in 2011 (17). The standardized question used was, “How many hours of sleep did you get per night (average hours per night-time sleep) during the past month?” The night-time sleep duration were stratified into three categories: short (< 6 h/night), intermediate (6–8 h/night), and long (>8 h/night) (8, 14, 19). We selected < 6 h as the definition for short sleep in the analysis to include those who have short sleeping duration despite self-reported sleep duration.

Daytime nap duration was self-reported by participants with the question: “How long did a nap last on average during the past month?.” Consistent with previous literatures (20, 21), participants were categorized into four groups: non-nappers (0 h), short nappers (< 0.5 h), moderate nappers (0.5–1.5 h) and extended nappers (>1.5 h).

Sleep quality was assessed by “How many days of restless sleep in a week?.” We classified sleep quality into two categories: rarely or a little (0–2 days/week); and occasionally, most or all of the time (3–7 days/week).

 Assessment of kidney function

The estimated glomerular filtration rate (eGFR) was calculated from serum creatinine and/or cystatin C using the Chronic Kidney Disease Epidemiology Collaboration equations (CKD-EPI)(22). The eGFRcr − cys was calculated using the CKD-EPI creatinine-cystatin C equation:

eGFRcr-cys = 135×min(Scr/k,1)α×max(Scr/k,1)−0.601                    × min(Scys/0.8,1)−0.375×max(Scys/0.8,1)−0.711                    × 0.995age[×1.08 if  black]×0.969[if female];

where Scr is serum creatinine in mg/dl, Scys is serum cystatin C in mg/l, k is 0.7 for females and 0.9 for males, and α is −0.248 for females and −0.207 for males.

The eGFRcr was calculated using the CKD-EPI creatinine equation: eGFRcr = 141 × min (Scr /k, 1) α × max (Scr /k, 1) −1.209 × 0.993age × 1.159 [if black] × 1.018 [if female];

Where, k is 0.7 for females and 0.9 for males, α is −0.329 for females and −0.411 for males.

The eGFRcys was calculated by CKD-EPI cystatin C equation: eGFRcys = 133 × min (Scys /0.8, 1) −0.499 × max (Scys /0.8, 1) −1.328 × 0.996age × 0.932 [if female].

 Study outcomes

The primary outcome was rapid kidney function decline, which was defined as an annualized decline in eGFRcr − cys or eGFRcr or eGFRcys of ≥5 ml/min/ 1.73 m2 (23). Annualized eGFR decline was estimated by the formula of (eGFR at baseline - eGFR at exit)/follow-up time (4 years).

The secondary outcome was the progression to CKD, which was defined by an annualized decline in eGFR of ≥5 ml/min/1.73 m2 and final eGFR < 60 ml/min/1.73 m2 at exit.

 Assessment of covariates

Participants voluntarily provided their demographic information, health related data and laboratory results at baseline from the questionnaires in CHARLS survey. Marital status was categorized into two groups: unmarried and married, unmarried included never married, separated, divorced and widowed. Educational level was categorized into four groups: illiterate, literate, primary school and middle school or above. Blood pressure, height and weight were measured with calibrated equipment. Body mass index (BMI) was calculated as weight/height2. Health-related factors, such as smoking, drinking, diabetes and heart disease were self-reported. Diabetes was defined as random glucose ≥200 mg/dl, fasting glucose ≥126 mg/dl, hemoglobin A1c ≥7% and physician-diagnosed diabetes or the use of hypoglycemic drugs.

 Statistical analyses

Baseline characteristics of the population are shown as means ± standard deviations (SD) for continuous variables and as numbers and proportions for categorical variables in the categories of sleep duration or quality. One-way ANOVA analysis of variance, student's t-test or chi-squared tests were used to compare the characteristics of participants based on the categories of sleep duration or quality.

Univariate and multivariable logistic regression models were used to investigate the association between sleep duration, quality and kidney outcomes with adjustments for baseline eGFR in model 1 and model 2. In addition, adjusted covariates in model 2 included age, sex, BMI, smoking status, living residence, blood pressure, self-reported heart disease, glucose, total cholesterol, triglycerides, high-density lipoprotein (HDL) cholesterol and uric acid. These were presented as adjusted odds-ratios (ORs) with 95% confidence interval (95% CI).

Furthermore, potential modifications of the relationship among sleep duration, quality and rapid kidney function decline were investigated for the following variables: age, sex, BMI, smoking status, living residence, marital status, educational level, diabetes, total cholesterol, uric acid and high-sensitivity C-reactive protein (CRP) via stratified analyses and interaction testing. These variables were either suspected or traditional risk factors for kidney function decline.

IBM SPSS version 23.0 (IBM Corporation, Armonk, NY, USA) was used for statistical analyses in our study. P < 0.05 was considered as statistically significant in all analyses.

 Results Participants baseline characteristics

Supplementary Figure 1 illustrated the baseline characteristics of participants, where a total of 4,086 analyzed participants with eGFRcr − cys ≥60 ml/min/ 1.73 m2 at baseline, their sleep duration and quality from CHARLS were included. The mean age of the included participants was 58.9 ± 8.7 years, 1,755 (43.0%) were male, which is shown in Supplemental Table 1, the mean eGFRcr − cys was 86.2 ± 14.4 ml/min/1.73 m2 at baseline.

Compared with participants of 6–8 h of night-time sleep, majority of those in the category of < 6 h or >8 h of night-time sleep were unmarried, farmers, and less educated. Additionally, those in the category of < 6 h of night time sleep showed a trend toward higher HDL cholesterol levels and lower baseline eGFR (Table 1). According to the daytime nap duration (Table 2), participants in the extended nappers group were mostly males, with a trend toward lower HDL cholesterol levels. Table 3 showed the baseline characteristics of participants in sleep quality (0–2 or 3–7 days of poor sleep quality). Participants with poor sleep quality were older and less educated, mostly females, non-smokers and non-drinkers. There were also a higher percentage of heart disease observed in these participants, presenting with lower BMI and uric acid in serum.

Participants (n = 4,086) characteristics by night sleep duration categories.

Sleep duration per night in hours P-value
Overall <6 h 6–8 h >8 h
N 4,086 1,228 2,531 327
Sleep duration, hour 6.3 ± 1.9 4.1 ± 1.1 7.0 ± 0.8 9.6 ± 0.8 <0.001
Age, year 58.9 ± 8.7 60.7 ± 8.7 58.0 ± 8.5 59.2 ± 9.3 <0.001
Male, no. (%) 1,755 (43.0) 495 (40.0) 1,129 (51.0) 131 (40.1) 0.024
Body mass index, kg/m2 23.7 ± 3.9 23.2 ± 3.9 23.9 ± 3.8 23.7 ± 4.3 <0.001
Rural residence, no. (%) 3,419 (83.7) 1,050 (85.5) 2,075 (82.0) 294 (90.0) <0.001
Married, no. (%) 3,618 (88.5) 1,047 (85.2) 2,297 (90.8) 274 (83.8) <0.001
Smoking status, no. (%) 1,170 (28.6) 359 (29.2) 732 (28.9) 79 (24.1) 0.172
Drinking status, no. (%) 1,282 (31.4) 363 (29.6) 820 (32.4) 99 (30.3) 0.193
Systolic BP, mmHg 130.2 ± 21.4 130.0 ± 21.6 130.0 ± 21.1 131.4 ± 22.2 0.559
Diastolic BP, mmHg 75.6 ± 10.9 75.8 ± 11.0 75.5 ± 10.7 75.8 ± 11.2 0.725
Diabetes, no. (%) 326 (8.0) 102 (8.3) 192 (7.6) 32 (9.8) 0.495
Self-reported heart disease, no. (%) 500 (12.2) 180 (14.6) 293 (11.5) 27 (8.3) 0.002
Education, no. (%) <0.001
Illiteracy 1,207 (29.5) 442 (36.0) 649 (25.6) 116 (35.5)
Literate 779 (19.1) 275 (22.4) 448 (17.7) 56 (17.1)
Primary school 936 (22.9) 273 (22.2) 577 (22.8) 86 (26.3)
Middle school or above 1,164 (29.5) 238 (19.4) 858 (33.9) 68 (20.8)
Laboratory results
Total cholesterol, mg/dl 193.5 ± 37.9 194.1 ± 38.0 192.9 ± 37.6 196.2 ± 39.9 0.252
Triglycerides, mg/dl 131.7 ± 90.8 131.5 ± 93.5 130.7 ± 88.7 139.7 ± 96.7 0.243
HDL cholesterol, mg/dl 51.0 ± 15.1 52.4 ± 15.9 50.5 ± 14.7 50.1 ± 15.3 < 0.001
LDL cholesterol, mg/dl 116.6 ± 34.9 115.5 ± 34.7 117.0 ± 34.9 118.0 ± 35.4 0.397
Glucose, mg/dl 109.6 ± 34.2 109.3 ± 33.1 109.6 ± 34.5 110.7 ± 37.0 0.807
Hemoglobin A1c, % 5.3 ± 0.8 5.2 ± 0.7 5.3 ± 0.8 5.3 ± 1.0 0.018
eGFRcr-cys, ml/min per 1.73 m2 86.2 ± 14.4 84.7 ± 13.9 86.6 ± 14.5 88.4 ± 15.4 < 0.001
Uric acid, mg/dl 4.3 ± 1.1 4.3 ± 1.1 4.3 ± 1.2 4.2 ± 1.1 0.177
High-sensitivity CRP, mg/L 2.3 ± 5.5 2.4 ± 5.9 2.2 ± 5.5 1.8 ± 2.8 0.164

Variables are presented as mean ± standard deviation or numbers (percentages).

p-values were calculated with one-way ANOVA analysis for continuous variables and chi-squared test for categorical variables. BP, blood pressure; HDL Cholesterol, high density lipoprotein cholesterol; LDL Cholesterol, low density lipoprotein cholesterol; eGFRcr-cys, eGFR on the basis of a combination of serum creatinine and cystatin C; CRP, C-reactive protein. Bold values was considered as statistically significant.

Participants (n = 4,086) characteristics by daytime nap categories.

Overall Daytime nap time P-value
Non nappers Short nappers Moderate nappers Extended nappers
N 4,086 1,882 388 1,244 572
Nap time, % 100.0 46.1 9.5 30.4 14.0 <0.001
Age, year 58.9 ± 8.7 58.6 ± 8.5 58.8 ± 8.3 59.3 ± 9.0 9.1 0.141
Male, no. (%) 1,755 (43.0) 667 (35.4) 190 (49.0) 604 (48.4) 294 (51.4) < 0.001
Body mass index, kg/m2 23.7 ± 3.9 23.5 ± 4.1 23.9 ± 3.7 23.7 ± 3.8 23.9 ± 3.7 0.044
Rural residence, no. (%) 3419 (83.7) 1609 (86.4) 311 (80.2) 1018 (81.8) 481 (84.1) 0.001
Married, no. (%) 3,618 (88.5) 1,663 (88.4) 354 (91.2) 1,100 (88.4) 501 (87.6) 0.338
Smoking status, no. (%) 1,170 (28.6) 501 (26.6) 104 (26.8) 358 (28.8) 204 (35.7) 0.001
Drinking status, no. (%) 1,282 (31.4) 511 (27.2) 139 (35.8) 418 (33.6) 214 (37.4) 0.001
Systolic BP, mmHg 130.2 ± 21.4 127.4 ± 21.5 129.0 ± 19.2 132.4 ± 20.6 135.2 ± 22.4 < 0.001
Diastolic BP, mmHg 75.6 ± 10.9 74.2 ± 11.0 75.8 ± 10.1 76.7 ± 10.3 77.7 ± 11.5 <0.001
Diabetes, no. (%) 326 (8.0) 133 (7.1) 33 (8.5) 115 (9.2) 45 (7.9) 0.172
Self-reported heart disease, no. (%) 500 (12.2) 209 (11.1) 57 (14.7) 169 (13.6) 65 (11.4) 0.074
Education, no. (%) <0.001
Illiteracy 1,207 (29.5) 633 (33.8) 90 (23.2) 341 (27.4) 143 (25.0)
Literate 779 (19.1) 365 (19.5) 59 (15.2) 244 (19.6) 111 (19.4)
Primary school 936 (22.9) 403 (21.5) 100 (25.8) 290 (23.3) 143 (25.0)
Middle school or above 1,164 (29.5) 471 (25.2) 139 (35.8) 369 (29.7) 175 (30.6)
Laboratory results
Total cholesterol, mg/dl 193.5 ± 37.9 193.2 ± 37.0 196.4 ± 41.7 193.5 ± 38.0 192.7 ± 38.2 0.440
Triglycerides, mg/dl 131.7 ± 90.8 128.9 ± 88.1 130.4 ± 81.4 132.5 ± 94.7 139.8 ± 96.7 0.094
HDL cholesterol, mg/dl 51.0 ± 15.1 52.3 ± 15.2 50.0 ± 14.0 50.6 ± 15.8 48.7 ± 13.8 < 0.001
LDL cholesterol, mg/dl 116.6 ± 34.9 115.9 ± 34.6 121.0 ± 37.6 116.5± 34.0 116.3 ± 32.9 0.073
Glucose, mg/dl 109.6 ± 34.2 108.0 ± 32.8 109.5 ± 32.3 111.1 ± 37.4 111.6 ± 37.0 0.037
Hemoglobin A1c, % 5.3 ± 0.8 5.2 ± 0.8 5.3 ± 0.8 5.3 ± 0.8 5.3 ± 0.8 0.133
eGFRcr-cys, ml/min per 1.73 m2 86.2 ± 14.4 86.6 ± 14.3 85.2 ± 14.2 85.6 ± 15.4 86.5 ± 14.8 0.110
Uric acid, mg/dl 4.3 ± 1.1 4.2 ± 1.1 4.3 ± 1.1 4.2 ± 1.2 4.2 ± 1.2 0.020
High-sensitivity CRP, mg/L 2.3 ± 5.5 2.1 ± 5.0 2.3 ± 5.1 2.2 ± 5.6 2.9 ± 6.9 0.018

Variables are presented as mean ± standard deviation or numbers (percentages).

p-values were calculated with one-way ANOVA analysis for continuous variables and chi-squared test for categorical variables. BP, blood pressure; HDL, Cholesterol, high density lipoprotein cholesterol; LDL, Cholesterol, low density lipoprotein cholesterol; eGFRcr-cys, eGFR on the basis of a combination of serum creatinine and cystatin C; CRP, C-reactive protein. Bold values was considered as statistically significant.

Participants (n = 4,086) characteristics by poor sleep quality categories.

Overall Days of poor sleep quality in the past week P-value
0–2 d 3–7 d
N 4,086 2,630 1,456
Poor sleep quality, day 2.1 ± 1.2 1.3 ± 0.4 3.6 ± 0.5 <0.001
Age, year 58.9 ± 8.7 58.6 ± 8.7 59.7 ± 8.7 <0.001
Male, no. (%) 1755 (43.0) 1244 (47.3) 511 (35.1) <0.001
Body mass index, kg/m2 23.7 ± 3.9 23.8 ± 3.9 23.4 ± 3.8 <0.001
Rural residence, no. (%) 3,419 (83.7) 2,174 (82.7) 1,245 (84.0) 0.001
Married, no. (%) 3,618 (88.5) 2,355 (89.5) 1,263 (86.7) 0.007
Smoking status, no. (%) 1,170 (28.6) 800 (28.4) 367 (25.2) < 0.001
Drinking status, no. (%) 1,282 (31.4) 363 (33.3) 820 (28.0) < 0.001
Systolic BP, mmHg 130.2 ± 21.4 130.1 ± 21.1 130.1 ± 21.8 0.990
Diastolic BP, mmHg 75.6 ± 10.9 75.6 ± 10.8 75.6 ± 11.0 0.971
Diabetes, no. (%) Self- 326 (8.0) 200 (7.6) 126 (8.7) 0.236
Reported heart disease, no. (%) 500 (12.2) 278 (10.6) 222 (15.2) < 0.001
Education, no. (%) < 0.001
Illiteracy 1207 (29.5) 683 (26.0) 524 (36.2)
Literate 779 (19.1) 484 (18.4) 295 (20.4)
Primary school 936 (22.9) 613 (23.3) 323 (22.3)
Middle school or above 1,164 (29.5) 850 (32.3) 858 (21.0)
Laboratory results
Total cholesterol, mg/dl 193.5 ± 37.9 193.4 ± 38.4 193.8 ± 37.6 0.757
Triglycerides, mg/dl 131.7 ± 90.8 132.4 ± 91.1 130.4 ± 90.2 0.496
HDL cholesterol, mg/dl 51.0 ± 15.1 50.6 ± 15.1 51.8 ± 15.2 0.015
LDL cholesterol, mg/dl 116.6 ± 34.9 116.8 ± 35.4 116.3 ± 33.9 0.655
Glucose, mg/dl 109.6 ± 34.2 109.6 ± 33.8 109.4 ± 35.1 0.852
Hemoglobin A1c, % 5.3 ± 0.8 5.3 ± 0.8 5.3 ± 0.8 0.789
eGFRcr-cys, ml/min per 1.73 m2 86.2 ± 14.4 86.3 ± 14.6 86.0 ± 14.1 0.458
Uric acid, mg/dl High- 4.3 ± 1.1 4.3 ± 1.2 4.2 ± 1.1 <0.001
sensitivity CRP, mg/L 2.3 ± 5.5 2.3 ± 5.8 2.2 ± 4.8 0.661

Variables are presented as mean ± standard deviation or numbers (percentages).

p-values were calculated with two-sample t tests for continuous variables and chi-squared test for categorical variables.

BP, blood pressure; HDL Cholesterol, high density lipoprotein cholesterol; LDL Cholesterol, low density lipoprotein cholesterol; eGFRcr-cys, eGFR on the basis of a combination of serum creatinine and cystatin C; CRP, C-reactive protein. Bold values was considered as statistically significant.

Supplementary Table 1 showed the baseline characteristics of excluded participants. Compared with those who were included, participants excluded were more commonly urban men, smokers, non-drinkers, more educated and had higher triglycerides and uric acid.

 Association between sleep duration, quality, and study outcomes

Based on data followed up for a median of 4 years, 244 (6.0%) participants developed rapid declines in kidney function, and 102 (2.5%) progressed to CKD.

In the demographic, clinical, or psychosocial covariates adjusted model (Model 2), participants in the category of < 6 h or >8 h of night-time sleep were similar in their risks for both rapid eGFR decline and CKD development compared to those with 6–8 h of nighttime sleep (Supplementary Table 2). The effects of day time nap on kidney function is shown in Supplementary Table 3. Non-nappers, short-time nappers and extended-time nappers were similar in their risks for both rapid eGFR decline and CKD development compared with moderate nappers in this analysis. When sleep qualities were assessed, the adjusted ORs for participants with 3–7 days of poor sleep quality who developed CKD was 1.86 (95% CI, 1.24 to 2.80) compared with those with 0–2 days of poor sleep quality (Table 4).

The association between sleep quality and CKD outcomes.

CKD outcomes Restless sleep (days/week) Events/N (%) Odds ratio (95% CI) P-value
Model 1 Model 2
Rapid decline in kidney function
0–2 148/2,630 (5.6%) Reference Reference
3–7 96/1,456 (6.6%) 1.24 (0.95–1.63) 0.119 1.31 (0.98–1.76) 0.068
Progression to CKD
0–2 51/2,630 (1.9%) Reference Reference
3–7 51/1,456 (3.5%) 1.83 (1.24–2.72) 0.003 1.86 (1.24–2.80) 0.003

Model 1 was adjusted for eGFRcr-cys at baseline.

Model 2 was adjusted for age, gender, body mass index, smoking status, rural residence, systolic BP, diastolic BP, self-reported heart disease, glucose, total cholesterol, triglycerides, HDL cholesterol, eGFRcr-cys, uric acid.

BP, blood pressure; HDL Cholesterol, high density lipoprotein cholesterol; eGFRcr-cys, eGFR on the basis of a combination of serum creatinine and cystatin C; CKD, chronic kidney disease; CI, confidence interval. Bold values was considered as statistically significant.

The associations of sleep duration, quality and the kidney function were further investigated in Supplementary Tables 4–9. Similar trends were observed in the association between sleep duration, quality, kidney primary and secondary outcomes defined by eGFRcr (Supplementary Tables 4–6) or eGFRcys (Supplementary Tables 7–9), though some of the comparisons were not statistically significant.

 Stratified analyses by potential effect modifiers

Stratified logistic regression analysis for associations between sleep quality and rapid eGFRcr − cys decline through the adjustment of several variables are shown in Table 5 and Supplementary Tables 10–13.

Stratified analyses for the association between days of poor sleep quality and rapid kidney function decline.

Subgroup N Days of poor sleep quality OR(95%CI) P for interaction
0–2 d 3–7 d
Age, years 0.18
< 65 3,020 110 (5.6) 59 (5.6) 1.15 (0.80–1.64)
≥65 1,066 38 (5.8) 37 (9.1) 1.70 (1.00–2.91)
Body mass index, kg/m2 0.46
< 24 2,370 76 (5.1) 55 (6.2) 1.28 (0.86–1.90)
≥24 1,708 72 (6.3) 41 (7.3) 1.45 (0.95–2.23)
Sex 0.01
Male 1,755 75 (6.0) 41 (8.0) 1.32 (0.85–2.03)
Female 2,331 73 (5.3) 55 (5.8) 1.26 (0.85–1.88)
Smoking status 0.03
No 2,916 99 (5.4) 72 (6.6) 1.43 (1.01–2.03)
Yes 1,170 49 (6.1) 24 (6.5) 1.06 (0.61–1.84)
Drinking status 0.69
No 2,804 100 (5.7) 68 (6.5) 1.35 (0.95–1.92)
Yes 1,282 48 (5.5) 28 (6.9) 1.33 (0.78–2.27)
Marital status 0.78
Married 3,618 126 (5.4) 77 (6.1) 1.29 (0.93–1.78)
Unmarried 468 22 (8.0) 19 (9.8) 1.60 (0.74–3.45)
Diabetes 0.09
No 3,760 129 (5.3) 79 (5.9) 1.23 (0.89–1.69)
Yes 326 19 (9.5) 17 (13.5) 2.16 (0.93–5.03)
Educational level 0.25
Illiteracy or literate 1,986 65 (5.6) 58 (7.1) 1.50 (1.01–2.24)
Primary school or above 2,100 83 (5.7) 38 (6.0) 1.13 (0.73–1.75)
Total cholesterol, mg/dl 0.06
< 200 2,438 78 (4.9) 51 (5.9) 1.36 (0.92–2.01)
≥200 1,648 70 (6.6) 45 (7.6) 1.26 (0.81–1.96)
Uric acid, mg/dl 0.15
< 4.2 2,116 69 (5.0) 39 (4.8) 1.07 (0.69–1.65)
≥4.2 1,970 79 (2.5) 57 (8.8) 1.52 (1.02–2.27)
High-sensitivity CRP, mg/L 0.35
< 1.0 2,079 67 (5.0) 42 (5.6) 1.28 (0.83–1.98)
≥1.0 2,007 81 (6.2) 54 (7.6) 1.33 (0.89–1.99)

The model was adjusted, if not stratified, for age, gender, body mass index, smoking status, rural residence, systolic BP, diastolic BP, self-reported heart disease, glucose, total cholesterol, triglycerides, HDL cholesterol, eGFRcr-cys, uric acid.

BP, blood pressure; HDL Cholesterol, high density lipoprotein cholesterol; eGFRcr-cys, eGFR on the basis of a combination of serum creatinine and cystatin C; CKD, chronic kidney disease; CI, confidence interval.

None of the variables such as age, BMI, drinking, marital status, diabetes, education level, total cholesterol, uric acid, high sensitivity CRP or uric acid significantly modified the associations between quality and rapid eGFRcr − cys decline (P > 0.05 for all).

 Discussion

To the best of our knowledge, the present prospective longitudinal study was the first study to demonstrate that poor sleep quality was associated with increased risk of CKD development in Chinese middle-aged and older people with normal kidney function, which provided clues to the risk factors affecting kidney function.

In recent years, the phenomenon of accelerated aging in China raises serious concerns for middle-aged and older people. As the circadian mechanisms becomes less efficient in the elderly, they tend to sleep less and have poor sleep quality, which may contribute to a series of health problems, such as cardiovascular diseases, depression, headache and memory loss (9). Some studies showed that poor sleep quality was associated with higher risk of coronary heart disease (24–26). In addition, higher proportion of depressive symptoms was associated with higher risk of rapid eGFR decline or CKD development in Chinese middle-aged or older adults with normal kidney function (27). Consistent with our study, another study from CHARLS suggested that long night-time sleep duration and poor sleep quality were associated with increased risk of CKD in middle-aged and older Chinese (18). This study highlighted the significant association between poor sleep duration and quality with the risks of CKD development in Chinese middle-aged or older people.

CKD were previously reported to be associated with night-time sleep duration and quality in middle-aged or older adults (18). However, previous studies demonstrated inconsistent results regarding the relationship between sleep duration and kidney function decline or CKD progression. An observed cohort study of 502,505 UK Biobank participants through clinical and genetic analyses indicated that either < 6 or ≥9 h of sleep duration was associated with a higher risk of CKD. Moreover, only short sleep duration was associated with the risk of end-stage kidney disease (ESKD) outcome when their study population was limited to males. While short sleep duration was associated with higher odds for CKD in genetic analysis (8). McMullan et al. (19) found that < 6 h sleep duration was significantly associated with higher risk of a faster eGFR decline in over 4,000 females. Moreover, a previous study of 3,600 Japanese workers indicated that short sleep duration (5 h/night) increased the risk of CKD in shift workers instead of non-shift workers, while long sleep duration was not a risk factor for CKD in shift and non-shift workers (28). Nevertheless, Nakajima et al. (29) showed that shorter sleep duration reduced the risk of CKD in Japanese males. The possible explanations for the inconsistency across different studies is probably because of the different reference groups used in the comparison and the different classifications of sleep duration, for example, 6–7 h (30), 6–8 h (14), and 7–8 h (29) were set as reference groups. In addition, < 6h, ≤ 5h and < 4h of nighttime sleep were regarded as short sleep duration in the different studies (14, 29, 30). Overall, the various studies showed that the relationships between sleep duration and the risk of kidney function decline remained unclear. In this study, neither night-time sleep duration nor daytime naps had any significant effect on rapid kidney function decline and the development of CKD in middle-aged and older adults with normal renal function. More studies are still needed to explore the reason for the inconsistencies.

In the older population, creatinine-based eGFR was inaccurate because diet, physical activity, and muscle mass could affect the creatinine levels (31). Cystatin Cis a cysteine protease inhibitor produced by nucleated cells. The serum cystatin C may vary due to insulin resistance or inflammation (32, 33). The kidney outcomes were assessed by eGFRcr or eGFRcys alone in previous studies (18, 34). Therefore, taking both cystatin C and creatinine measurement into consideration to determine the eGFR could improve the accuracy (35). The associations between sleep duration, quality and kidney outcomes defined by eGFRcr − cys were not fully explored in previous studies. Some interesting findings were observed in our clinical analysis. The use of eGFRcr − cys to evaluate kidney outcomes was more accurate compared to eGFRcr or eGFRcys alone (22, 36). The serum creatinine and cystatin C measurements across a longitudinal study (27) provided an opportunity to explore the associations between sleep duration, quality and rapid decline of kidney function among middle-aged or older population, which could be adjusted for known co-variables and stratified by various clinical characteristics. Multi-center sleep and CKD studies could be conducted to further investigate the findings of our study and to confirm the reciprocal relationship between them in the future.

Moreover, although analysis showed that no variables significantly modified the association between sleep quality and rapid eGFR decline (P interactions values > 0.05 for all), While BMI < 24 kg/m2, non-smoking, non-diabetic, married female participants strongly affected the associations between sleep quality and kidney function. However, given the multiple testing and similar directionality of most associations, these results may not have significant clinical impact. Meanwhile, the ability to detect moderate interactions was limited in the current sample size and larger number of samples are needed to verify the lack of influence by these variables in the future.

The mechanisms underlying the relationship between sleep and renal function need to be investigated further. We speculated that several potential mechanisms may participate in sleep duration and quality which affect renal function. Firstly, growing evidence showed that sleep duration was associated with the upregulation of inflammatory markers such as IL-6,TNF-a,CRP,AP-1 and STAT protein families which may activate immune response, aggravate kidney fibrosis and accelerate the decline of kidney function (37–44). However, there were no significant differences in high-sensitivity CRP levels between the different groups with different sleep durations and restlessness. Secondly, poor quality of nighttime sleep may disrupt circadian rhythms, which cause changes in serum hormone levels, insulin resistance, inverted cortisol rhythms and increased blood pressure (45, 46). These showed that sleep duration and quality were modifiable determinants of these established CKD risk factors (47–49). However, adjusting for blood pressure, glucose or self-reported heart disease did not affect the estimates for the risk of rapid renal function decline or developing to CKD in relation to sleep duration and quality. This suggested that either short sleep duration or quality are associated with rapid decline of renal function or progression to CKD via mechanisms independent of these known risk factors; or these endpoints did not eventually capture the vascular and metabolic consequences related to alterations in sleep duration and quality. Overall, these findings need to be verified, and their mechanisms investigated in the further studies.

This study has several limitations. Firstly, sleep duration and quality were self-reported, which may cause recall bias of sleep duration and quality. Self-reported sleep duration and quality is different from objectively measured sleep. In a study of 669 individuals, those with objective sleep measured as 5 h per night may overestimate their sleep duration by 1.3 h. While those participants with objective sleep measured as 7 h per night self-reported their sleep duration accurately (50). Secondly, the measurements of eGFR were only assessed at baseline and at the exit visit. If eGFR was changed due to other factors, the decline of eGFR from 2011 to 2015 would not accurately reflect the underlying change of eGFR during that period. More frequent measures of eGFR would improve accuracy for evaluating the progression of CKD over time. Thirdly, urine albumin was not measured at baseline in our cohort so neither adjustment for albuminuria nor analysis of the influence of urinary albumin secretion on the relationship between sleep duration, quality and kidney function could be performed. Fourthly, all of our study participants aged 45 years old and above were from China. Thus, it is unclear whether these findings can be applied to younger individuals or other ethnic groups. Fifthly, the number of participants whose sleep duration were >8 h was limited, so we were unable to assess whether there were associations between sleep duration and the decline of creatinine or cystatin C based on eGFR. Finally, in this observational study, some of the covariates used in the analyses were self-reported values. Hence, we are unable to rule out the possibility that our findings were confounded by unidentified factors.

 Conclusions

In summary, our analysis demonstrated that poor sleep quality was significantly associated with progression to CKD among Chinese middle-aged or older adults with normal kidney function. These findings paved the way for finding evidence toward potential therapeutic interventions to improve primary prevention of CKD.

 Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: http://charls.pku.edu.cn/, The China Health and Retirement Longitudinal Study (CHARLS) database.

 Ethics statement

The studies involving human participants were reviewed and approved by the Biomedical Ethics Review Committee of Peking University. The patients/participants provided their written informed consent to participate in this study.

 Author contributions

LZ and SX designed the study. LZ, SX, QD, FZ, YW, JJ, CG, JG, ML, and HZ analyzed the data. SX, YY, HJ, HX, SC, and HZ made the figures. ZH, SX, FZ, and JJ drafted and revised the paper. All authors approved the final version of the manuscript.

 Funding

This work was supported by National Natural Science Foundation of China (82072523 to ZH), National Natural Science Foundation of China (82200753 to SX).

We thank the CHARLS research team for collecting and providing the data in this study. The analysis was on the basis of the data provided by the CHARLS.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2022.1072238/full#supplementary-material

 References Collaboration GBDCKD. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet. (2020) 395:709–33. 10.1016/S0140-6736(20)30045-332061315 Zhang L Wang F Wang L Wang W Liu B Liu J . Prevalence of chronic kidney disease in China: a cross-sectional survey. Lancet. (2012) 379:815–22. 10.1016/S0140-6736(12)60033-624559433 Wang S Chen R Liu Q Shu Z Zhan S Li L. Prevalence, awareness and treatment of chronic kidney disease among middle-aged and: the china health and retirement longitudinal study. Nephrology. (2015) 20:474–84. 10.1111/nep.1244925773384 Shlipak MG Tummalapalli SL Boulware LE Grams ME Ix JH Jha V . The case for early identification and intervention of chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. (2021) 99:34–47. 10.1016/j.kint.2020.10.01233127436 Patel SR Hayes AL Blackwell T Evans DS Ancoli-Israel S Wing YK . The association between sleep patterns and obesity in older adults. Int J Obes. (2014) 38:1159–64. 10.1038/ijo.2014.1324458262 Xu Q Song Y Hollenbeck A Blair A Schatzkin A Chen H. Day napping and short night sleeping are associated with higher risk of diabetes in older adults. Diabetes Care. (2010) 33:78–83. 10.2337/dc09-114319825823 Gangwisch JE Heymsfield SB Boden-Albala B Buijs RM Kreier F Pickering TG . Short sleep duration as a risk factor for hypertension: analyses of the first national health and nutrition examination survey. Hypertension. (2006) 47:833–9. 10.1161/01.HYP.0000217362.34748.e016585410 Park S Lee S Kim Y Lee Y Kang MW Kim K . Short or long sleep duration and CKD: a mendelian randomization study. J Am Soc Nephrol. (2020 D) 31:2937–47. 10.1681/ASN.202005066633004418 Gulia KK Kumar VM. Sleep disorders in the: a growing challenge. Psychogeriatrics. (2018) 18:155–65. 10.1111/psyg.1231929878472 Turek NF Ricardo AC Lash JP. Sleep disturbances as nontraditional risk factors for development and progression of CKD: review of the evidence. Am J Kidney Dis. (2012) 60:823–33. 10.1053/j.ajkd.2012.04.02722727724 Vink EE de Jager RL Blankestijn PJ. Sympathetic hyperactivity in chronic kidney disease: pathophysiology and (new) treatment options. Curr Hypertens Rep. (2013) 15:95–101. 10.1007/s11906-013-0328-523354877 Ohdo S. Chrono-Drug Discovery and Development Based on Circadian Rhythm of Molecular, Cellular and Organ Level. Biol Pharm Bull. (2021) 44:747–61. 10.1248/bpb.b21-0027734078807 Li J Huang Z Hou J Sawyer AM Wu Z Cai J . Sleep and CKD in Chinese adults: a cross-sectional study. Clin J Am Soc Nephrol. (2017) 12:885–92. 10.2215/CJN.0927081628389618 Bo Y Yeoh EK Guo C Zhang Z Tam T Chan TC . Sleep and the risk of chronic kidney disease: a cohort study. J Clin Sleep Med. (2019) 15:393–400. 10.5664/jcsm.766030853043 Yamamoto R Nagasawa Y Iwatani H Shinzawa M Obi Y Teranishi J . Self-reported sleep duration and prediction of proteinuria: a retrospective cohort study. Am J Kidney Dis. (2012) 59:343–55. 10.1053/j.ajkd.2011.08.03222019276 Cheungpasitporn W Thongprayoon C Gonzalez-Suarez ML Srivali N Ungprasert P Kittanamongkolchai W . The effects of short sleep duration on proteinuria and chronic kidney disease: a systematic review and meta-analysis. Nephrol Dial Transplant. (2017) 32:991–6. 10.1093/ndt/gfw07227190375 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. 10.1093/ije/dys20323243115 Sun H Qin K Zou C Wang HH Lu C Chen W . The association of nighttime sleep duration and quality with chronic kidney disease in middle-aged and older Chinese: a cohort study. Sleep Med. (2021 O) 86:25–31. 10.1016/j.sleep.2021.08.00734455367 McMullan CJ Curhan GC Forman JP. Association of short sleep duration and rapid decline in renal function. Kidney Int. (2016) 89:1324–30. 10.1016/j.kint.2015.12.04827165820 Fang W Li Z Wu L Cao Z Liang Y Yang H . Longer habitual afternoon napping is associated with a higher risk for impaired fasting plasma glucose and diabetes mellitus in older adults: results from the Dongfeng-Tongji cohort of retired workers. Sleep Med. (2013) 14:950–4. 10.1016/j.sleep.2013.04.01523831240 Li J Cacchione PZ Hodgson N Riegel B Keenan BT Scharf MT . Afternoon napping and cognition in Chinese older adults: findings from the China health and retirement longitudinal study baseline assessment. J Am Geriatr Soc. (2017) 65:373–80. 10.1111/jgs.1436827995615 Inker LA Schmid CH Tighiouart H Eckfeldt JH Feldman HI Greene T . Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. (2012) 367:20–9. 10.1056/NEJMoa111424822762315 Inker LA Astor BC Fox CH Isakova T Lash JP Peralta CA . KDOQI US commentary on the 2012 KDIGO clinical practice guideline for the evaluation and management of CKD. Am J Kidney Dis. (2014) 63:713–35. 10.1053/j.ajkd.2014.01.41624647050 Lao XQ Liu X Deng HB Chan TC Ho KF Wang F . Sleep quality, sleep duration, and the risk of coronary heart disease: a prospective cohort study with 60,586 adults. J Clin Sleep Med. (2018) 14:109–17. 10.5664/jcsm.689429198294 Svensson AK Svensson T Kitlinski M Almgren P Engstrom G Nilsson PM . Incident diabetes mellitus may explain the association between sleep duration and incident coronary heart disease. Diabetologia. (2018) 61:331–41. 10.1007/s00125-017-4464-329103105 Strand LB Tsai MK Gunnell D Janszky I Wen CP Chang SS. Self-reported sleep duration and coronary heart disease mortality: a large cohort study of 400,000 Taiwanese adults. Int J Cardiol. (2016) 207:246–51. 10.1016/j.ijcard.2016.01.04426808986 Zhang Z He P Liu M Zhou C Liu C Li H . Association of depressive symptoms with rapid kidney function decline in adults with normal kidney function. Clin J Am Soc Nephrol. (2021) 16:889–97. 10.2215/CJN.1844112034052796 Sasaki S Yoshioka E Saijo Y Bannai A Kita T Tamakoshi A . A prospective cohort study of insomnia and chronic kidney disease in Japanese workers. Sleep Breath. (2018) 22:257–65. 10.1007/s11325-017-1508-328534280 Nakajima H Hashimoto Y Okamura T Obora A Kojima T Hamaguchi M . Association between sleep duration and incident chronic kidney disease: a population-based cohort analysis of the NAGALA study. Kidney Blood Press Res. (2020) 45:339–49. 10.1159/00050454532131067 Sasaki S Yoshioka E Saijo Y Kita T Tamakoshi A Kishi R. Short sleep duration increases the risk of chronic kidney disease in shift workers. J Occup Environ Med. (2014) 56:1243–8. 10.1097/JOM.000000000000032225479293 Hsu CY Chertow GM Curhan GC. Methodological issues in studying the epidemiology of mild to moderate chronic renal insufficiency. Kidney Int. (2002) 61:1567–76. 10.1046/j.1523-1755.2002.00299.x11967006 Shardlow A McIntyre NJ Fraser SDS Roderick P Raftery J Fluck RJ . The clinical utility and cost impact of cystatin C measurement in the diagnosis and management of chronic kidney disease: a primary care cohort study. PLoS Med. (2017) 14:e1002400. 10.1371/journal.pmed.100240029016597 Glassock RJ Rule AD. Optimally predicting mortality with kidney function markers is not the same as optimally determining how kidney function predicts mortality. Nephrol Dial Transplant. (2017) 32:585–7. 10.1093/ndt/gfx00728339938 Zhou W Zhou H Zhao S Li Y Shi Y Ding X. Association between muscle strength and cystatin C-based estimated glomerular filtration rate among middle-aged and population: findings based on the china health and retirement longitudinal study (CHARLS), 2015. Int J Gen Med. (2021) 14:3059–67. 10.2147/IJGM.S31706734234534 Guo X Qin Y Zheng K Gong M Wu J Shou W . Improved glomerular filtration rate estimation using new equations combined with standardized cystatin C and creatinine in Chinese adult chronic kidney disease patients. Clin Biochem. (2014) 47:1220–6. 10.1016/j.clinbiochem.2014.05.06024886770 Shlipak MG Matsushita K Arnlov J Inker LA Katz R Polkinghorne KR . Cystatin C versus creatinine in determining risk based on kidney function. N Engl J Med. (2013) 369:932–43. 10.1056/NEJMoa121423424004120 Sauvet F Leftheriotis G Gomez-Merino D Langrume C Drogou C Van Beers P . Effect of acute sleep deprivation on vascular function in healthy subjects. J Appl Physiol. (1985). (2010) 108:68–75. 10.1152/japplphysiol.00851.200919910332 Dungey M Hull KL Smith AC Burton JO Bishop NC. Inflammatory factors and exercise in chronic kidney disease. Int J Endocrinol. (2013) 2013:569831. 10.1155/2013/56983123737775 Akchurin OM Kaskel F. Update on inflammation in chronic kidney disease. Blood Purif. (2015) 39:84–92. 10.1159/00036894031943087 Irwin MR. Sleep and inflammation: partners in sickness and in health. Nat Rev Immunol. (2019) 19:702–15. 10.1038/s41577-019-0190-z31289370 Irwin MR Wang M Campomayor CO Collado-Hidalgo A Cole S. Sleep deprivation and activation of morning levels of cellular and genomic markers of inflammation. Arch Intern Med. (2006) 166:1756–62. 10.1001/archinte.166.16.175616983055 Irwin MR Wang M Ribeiro D Cho HJ Olmstead R Breen EC . Sleep loss activates cellular inflammatory signaling. Biol Psychiatry. (2008) 64:538–40. 10.1016/j.biopsych.2008.05.00418561896 Irwin MR Witarama T Caudill M Olmstead R Breen EC. Sleep loss activates cellular inflammation and signal transducer and activator of transcription (STAT) family proteins in humans. Brain Behav Immun. (2015) 47:86–92. 10.1016/j.bbi.2014.09.01725451613 Irwin MR Opp MR. Sleep Health: Reciprocal Regulation of Sleep and Innate Immunity. Neuropsychopharmacology. (2017) 42:129–55. 10.1038/npp.2016.14827510422 Scheer FA Hilton MF Mantzoros CS Shea SA. Adverse metabolic and cardiovascular consequences of circadian misalignment. Proc Natl Acad Sci U S A. (2009) 106:4453–8. 10.1073/pnas.080818010619255424 Huang W Ramsey KM Marcheva B Bass J. Circadian rhythms, sleep, and metabolism. J Clin Invest. (2011) 121:2133–41. 10.1172/JCI4604321633182 Haack M Serrador J Cohen D Simpson N Meier-Ewert H Mullington JM. Increasing sleep duration to lower beat-to-beat blood pressure: a pilot study. J Sleep Res. (2013) 22:295–304. 10.1111/jsr.1201123171375 Buxton OM Cain SW O'Connor SP Porter JH Duffy JF Wang W . Adverse metabolic consequences in humans of prolonged sleep restriction combined with circadian disruption. Sci Transl Med. (2012) 4:129ra43. 10.1126/scitranslmed.300320022496545 Leproult R Deliens G Gilson M Peigneux P. Beneficial impact of sleep extension on fasting insulin sensitivity in adults with habitual sleep restriction. Sleep. (2015) 38:707–15. 10.5665/sleep.466025348128 Lauderdale DS Knutson KL Yan LL Liu K Rathouz PJ. Self-reported and measured sleep duration: how similar are they? Epidemiology. (2008) 19:838–45. 10.1097/EDE.0b013e318187a7b018854708