Association between sleep duration and estimated glomerular filtration rate in Chinese patients with diabetes: evidence from a cross-sectional study

Association between sleep duration and estimated glomerular filtration rate in Chinese patients with diabetes: evidence from a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between sleep duration and estimated glomerular filtration rate in Chinese patients with diabetes: evidence from a cross-sectional study Cishuang Fu, Zhiming Deng, Shenglian Gan, Haifeng Zhou, Quan Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4306709/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Previous epidemiological studies have demonstrated associations between sleep duration and diabetes, vascular complications and cardiovascular disease in diabetic patients. However, few studies have so far explored the effect of sleep time on estimated glomerular filtration rate (eGFR) in diabetic patients. This study was performed for the purpose of exploring the relationship between sleep duration and eGFR in diabetic patients. This study analyzed 1389 patients with diabetes. Sleep duration at night was categorized into four groups: very short ( 8h). The association of sleep duration with eGFR was analyzed using univariate linear regression, and generalized additive models were applied to assess the nonlinear relationship between sleep duration and eGFR. Compared to optimal sleep duration (7-8h), both long sleep duration (> 8h) and very short sleep duration (< 6h) were associated with level of eGFR (β = -3.63, 95%CI: -5.54 to -1.71, P = 0.0002 and β = -4.79, 95%CI: -8.55 to -1.03, P = 0.0126, respectively). The smooth curve showed there is a U-shaped relationship between sleep duration and eGFR levels. The results of this study show that both very short and long sleep durations were associated with low eGFR in diabetics. Health sciences/Endocrinology Health sciences/Endocrinology/Endocrine system and metabolic diseases sleep duration estimated glomerular filtration rate (eGFR) diabetes diabetic kidney disease (DKD) Figures Figure 1 Figure 2 Figure 3 Introduction Diabetes is a rapidly growing disease worldwide, projected to affect 693 million adults by 2045 [ 1 ] . Among diabetes patients, approximately 20–40% will develop diabetic kidney disease (DKD) [ 2 ] , which is the leading cause of end-stage renal disease (ESRD) globally [ 3 ] . DKD leads to a progressive decline in estimated glomerular filtration rate in estimated glomerular filtration rate (eGFR) and eventually results in ESRD which have significant financial and daily life impact on affected individuals [ 4 ] . Thus, better comprehension of modifiable risk factors for eGFR decline in diabetes patients could help delay the progression of DKD and enhance the quality of life for individuals with diabetes. Previous epidemiological studies have demonstrated substantial associations between sleep duration and diabetes, vascular complications of diabetes and cardiovascular disease in diabetic patients [ 5 – 7 ] . However, studies investigating the correlation between sleep duration and eGFR in patients with diabetes are limited. A cross-sectional study in China established an independent correlation between short sleep duration (< 6h) and DKD, but failed to further explore the association between sleep duration and eGFR [ 5 ] . Two other cross-sectional studies [ 8 , 9 ] further analyzed the relationship between sleep duration and eGFR, and found that long sleep duration was significantly correlated with low eGFR in patients with diabetes. In order to further understand the correlation between sleep duration and eGFR in diabetic patients, we performed this study. Material and methods Study population The National Metabolic Management Center (MMC) was founded in China in 2016 to establish a platform for the standardized diagnosis and treatment of metabolic diseases and long-term follow-up [ 10 ] . The First People's Hospital of Changde City (Hunan, China) is an MMC network hospital. Between May 2020 and January 2022, 1665 (961 men, 704 women) participants with diabetes were recruited to participate in the baseline survey and follow-up. We used only the baseline data of these participants in this study. Participants were T2DM patients at the MMC of the Changde First People's Hospital. According to the WHO 1999 diagnostic criteria for diabetes mellitus, the following diagnostic criteria were used: fasting blood glucose ≥ 7.0 mmol/L, or typical symptoms of diabetes mellitus with a random blood glucose ≥ 11.1 mmol/L, or 2h blood glucose in a glucose tolerance test ≥ 11.1 mmol/L. Participants were excluded if they: (1) had a history of cancer; (2) had a history of thyroid disease, including hyperthyroidism, hypothyroidism, thyroid cancer, and thyroiditis; (3) had major depression and anxiety, schizophrenia, or other psychiatric disorders; and (4) had missing data on sleep duration and variables included in our multivariable models. All participants signed informed consent forms and the study protocol was approved by the Ethics Committee of Changde First People's Hospital (Protocol Code YX-2023-193-02). All the procedures were conducted in accordance with the principles of the Declaration of Helsinki. Assessment of sleep duration Sleep duration was collected using a standardized interviewer-administered questionnaire. Every participant was aske, “During the past week, at what times did you normally go to bed at night and wake up in the morning?” Estimated nocturnal sleep duration was defined as the time space between bedtime and waking up. Sleep duration was categorized into four groups: very short ( 8h). The optimal sleep duration was defined as 7-8h according to previous studies [ 11 , 12 ] . Definition of estimated glomerular filtration rate (eGFR) Venous blood samples were collected to determine serum creatinine levels. eGFR (mL/min/1.73m 2 ) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [ 13 ] . The formulas for calculating the eGFR: (1) Female: Scr (mg/dl): ≤0.7 eGFR = 144 × (Scr/0.7) −0.329 × (0.993) Age > 0.7 eGFR = 144 × (Scr/0.7) −1.209 × (0.993) Age (2) Male: Scr (mg/dl): ≤0.9 eGFR = 141 × (Scr/0.9) −0.411 × (0.993) Age > 0.9 eGFR = 141 × (Scr/0.9) −1.209 × (0.993) Age Other assessments Standardized questionnaires were used to obtain information on demographic factors, lifestyle behaviours (smoking and alcohol consumption), disease history, and medication use at baseline. Napping duration was estimated using self-reported daytime sleep time, and the participants were asked the following question, “how long did you take naps after lunch during the past week?” The STOP-BANG Questionnaire [ 14 ] was used as a screening tool to identify individuals at high-risk of obstructive sleep apnea (OSA), which is defined by a score of > 3 in this study. For the measurement of height, we used a wall-mounted measuring tape, and participants took off their shoes and pressed their heads, buttocks and heels against the wall. Participants took off their shoes, put on thin clothes, and stood on a digital scale to measure their weight. Body mass index (BMI, kg/m 2 ) was calculated as weight divided by the square of height. Blood pressure (BP) was obtained by the mean of two measurements after a 10 min seated rest, using a digital automatic blood pressure monitor. Definition of hypertension was that a blood pressure ≥ 140/90 mmHg or currently taking antihypertension drugs. Venous blood samples, taken after a 12-hour fast, were used to measure levels of triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), hemoglobin A1c (HbA1c), and uric acid (UA). Untimed urine samples were collected for measurement of albumin and creatinine, then urine albumin creatinine ratio (UACR, mg/g) levels were calculated as urinary albumin divided by urinary creatinine. Statistical analysis All statistical analysis in this study was performed with the statistical software packages R ( http://www.R-project.org , The R Foundation R.4.2.0) and Empower Stats ( http://www.empowerstats.com , X&Y Solutions, Inc., Boston, MA, USA). Normal distributed continuous variables were expressed as mean ± standard deviation, while skewed distributed continuous variables were expressed as median (quartile 1–3). Categorical variables were presented as frequency (%). The ANOVA (for normal distribution), Kruskal-Wallis H test (skewed distribution) and Chi-Square test (for categorical variables) were used to analyze the statistical differences between the different categories of sleep duration. Linear regression was used to analyze the associations between very short, short, and long sleep duration (referenced against optimal sleep duration) and eGFR. Three models were used to determine the association between sleep duration and the eGFR.. In Model 1, we made no adjustments. Model 2 was adjusted for sex and age. In model 3, we further adjusted for smoking status, alcohol status, duration of diabetes, diabetic medication, history of coronary artery disease, history of hypertension, hypertensive medication use, systolic blood pressure (SBP), diastolic blood pressure (DBP), BMI, HBAIC, UA, TG, TC, HDL-C, LDL-C, UACR, high-risk of OSA and nap time based on clinical experience and previous researches [ 5 , 9 , 15 ] . Subgroup analyses were performed to further verify the stability of the results. Moreover, the interaction terms between sleep duration and the other independent variables in the models were tested. The nonlinear relationship between sleep duration and eGFR level was explored by a generalized additive model. Based on the smoothed curve, a two-piece linear regression model was used to determine the infection point. Statistical significance was set a value less than 0.05 considered significant. Results Participant flowchart Initially, 1665 patients with diabetes were recruited. We excluded individuals who had a history of cancer (n = 11), thyroid disease (n = 43), and missing data on sleep duration (n = 222). Therefore, 1389participants (819 men, 570 women) were included in the primary analysis. A flowchart summarizing the inclusion and exclusion criteria is shown in Fig. 1 . Characteristics of study participants The cross-sectional study included 1389 participants with diabetes; 819 (58.96%) were male and 570 (41.04%) were female. The mean age of participants was 52.32 ± 11.11 years. Table 1 displays the clinical characteristics of participants categorized based on sleep duration. There were no significant differences observed in smoking status, history of coronary artery disease, duration of diabetes, diabetic medication, history of hypertension, hypertensive medication use, BMI, SBP, DBP, HBAIC, UA, TG, TC, HDL-C, LDL-C, UACR and nap time among different sleep duration groups. However, significant differences were observed in terms of sex, eGFR, and high-risk of OSA among the four groups. Furthermore, individuals with very short or long sleep durations, compared to those with an optimal sleep duration, were more likely to be older and have lower levels of eGFR (all p < 0.05), and participants with very short sleep duration were found to have a higher risk of OSA (p < 0.05). According to the violin picture (Fig. 2 ), eGFR was positively skewed in all four groups. The level of eGFR was found to be significantly lower in individuals with very short or long sleep duration, compared to those with optimal or short sleep duration (all p 0.05). Table 1 Baseline characteristics of participants by sleep duration Sleep Duration 8h P P* No. of participants 76 154 742 417 Sex 0.003 - Male 47 (61.84%) 109 (70.78%) 439 (59.16%) 224 (53.72%) Female 29 (38.16%) 45 (29.22%) 303 (40.84%) 193 (46.28%) Age, year 55.53 ± 9.51 51.71 ± 8.67 51.80 ± 10.76 52.87 ± 12.60 0.024 0.006 Smoking status 0.522 - Never 43 (56.58%) 86 (55.84%) 463 (62.40%) 267 (64.03%) Ever 8 (10.53%) 13 (8.44%) 61 (8.22%) 28 (6.71%) Everyday 25 (32.89%) 55 (35.71%) 218 (29.38%) 122 (29.26%) Alcohol status 0.030 - Never 52 (68.42%) 91 (59.09%) 495 (66.71%) 292 (70.02%) Ever 5 (6.58%) 7 (4.55%) 63 (8.49%) 33 (7.91%) Everyday 19 (25.00%) 56 (36.36%) 184 (24.80%) 92 (22.06%) History of coronary artery disease 0.459 - No 73 (96.05%) 151 (98.05%) 714 (96.23%) 407 (97.60%) Yes 3 (3.95%) 3 (1.95%) 28 (3.77%) 10 (2.40%) Duration of diabetes, month 39.00(5.00–96.00) 37.50(4.25-78.00) 37.00 (3.00–96.00) 38.00 (6.00-108.00) 0.255 0.483 Diabetic medication 0.962 - No 10 (13.16%) 24 (15.58%) 106 (14.29%) 61 (14.63%) Yes 66 (86.84%) 130 (84.42%) 636 (85.71%) 356 (85.37%) History of hypertension 0.707 - No 48 (63.16%) 103 (66.88%) 513 (69.14%) 288 (69.06%) Yes 28 (36.84%) 51 (33.12%) 229 (30.86%) 129 (30.94%) Hypertensive medication use 0.947 - No 54 (71.05%) 112 (72.73%) 544 (73.32%) 300 (71.94%) Yes 22 (28.95%) 42 (27.27%) 198 (26.68%) 117 (28.06%) BMI, kg/m 2 26.12 ± 3.85 26.05 ± 3.52 25.58 ± 3.56 25.53 ± 3.53 0.250 0.271 SBP, mmHg 137.24 ± 19.00 134.16 ± 18.99 135.09 ± 19.77 134.57 ± 18.95 0.681 0.643 DBP, mmHg 83.0 ± 12.0 84.7 ± 11.9 82.9 ± 10.5 82.0 ± 11.4 0.070 0.039 HbA1c, % 8.29 ± 2.35 8.28 ± 2.10 8.35 ± 2.15 8.42 ± 2.31 0.903 0.930 Uric acid, µmol/L 337.17 ± 108.17 343.66 ± 81.96 333.44 ± 85.75 329.80 ± 83.41 0.384 0.245 TG, mmol/l 1.98 (1.36–3.02) 1.90(1.41–3.10) 1.86(1.27–2.94) 1.93 (1.32–2.73) 0.712 0.935 TC, mmol/l 4.99 ± 1.22 5.03 ± 1.23 4.89 ± 1.23 4.97 ± 1.19 0.502 0.315 HDL-C, mmol/l 1.23 ± 0.30 1.24 ± 0.41 1.23 ± 0.30 1.26 ± 0.32 0.329 0.293 LDL-C, mmol/l 2.81 ± 0.92 2.88 ± 0.90 2.73 ± 0.90 2.83 ± 0.95 0.154 0.185 UACR, mg/g 15.26 (7.91–32.98) 12.25 (6.72–33.93) 14.16(7.35–31.41) 15.65 (8.31–42.08) 0.417 0.146 eGFR, mL/min/1.73m 2 100.15 (89.19-114.82) 109.44 (99.46-119.84) 109.41 (96.79-119.59) 105.35 (90.22-116.23) < 0.001 < 0.001 High-risk of OSA 0.002 - No 47 (61.84%) 75 (48.70%) 469 (63.21%) 276 (66.19%) Yes 29 (38.16%) 79 (51.30%) 273 (36.79%) 141 (33.81%) Nap time, min/day 0.141 - No 29 (38.16%) 42 (27.27%) 258 (34.77%) 156 (37.41%) Yes 47 (61.84%) 112 (72.73%) 484 (65.23%) 261 (62.59%) P* indicated U test. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; TC, Total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein; UACR, urinary albumin-to-creatinine ratio; eGFR, estimated glomerular filtration rate; OSA, obstructive sleep apnea. The results of relationship between sleep duration and eGFR Because eGFR was a continuous variable, linear regression model be used to determine the associations between sleep duration categories and level of eGFR. As shown in Table 2 , both long sleep duration and very short sleep duration were found to be associated with eGFR levels. (β = -3.63, 95%CI: -5.54 to -1.71, P = 0.0002 and β = -4.79, 95%CI: -8.55 to -1.03, P = 0.0126, respectively). Compared to optimal sleep duration, eGFR decreased by 3.63 ml/min/1.73m 2 and 4.79 ml/min/1.73m 2 respectively in those participants with long sleep duration and very short sleep duration after multivariate adjustments. 3.74% (n = 52) of the participants had been diagnosed diabetes nephropathy in this study. To assess the potential impact of patients with diabetes nephropathy on the study results, we conducted an analysis specifically among participants without diabetes nephropathy to examine the association between sleep duration and eGFR. In Table 3 , both long and very short sleep durations were still associated with eGFR levels in participants without diabetes nephropathy. Table 2 Associations of sleep duration with eGFR in diabetic patients Sleep duration N Model 1 Model 2 Model 3 β, 95%CI, P β, 95%CI, P β, 95%CI, P 7–8 742 Ref Ref Ref 8h 417 -4.63 (-7.14, -2.13) 0.0003 -3.39 (-5.44, -1.34) 0.0012 -3.63 (-5.54, -1.71) 0.0002 Model 1 adjust for: None. Model 2 adjust for: Sex; Age. Model 3 adjust for: Sex; Age; Smoking status; Alcohol status; Duration of diabetes; Diabetic medication; History of coronary artery disease; History of hypertension; Hypertensive medication use; BMI; SBP; DBP; HBAIC; UA; TG; TC; HDL-C; LDL-C; UACR; High-risk of OSA; Nap time. Table 3 Associations of sleep duration with eGFR in the participants without DKD Sleep duration N Model 1 Model 2 Model 3 β, 95%CI, P β, 95%CI, P β, 95%CI, P 7–8 723 Ref Ref Ref 8h 391 -2.68 (-4.87, -0.50) 0.0161 -1.96 (-3.73, -0.20) 0.0292 -2.30 (-3.97, -0.63) 0.0071 Model 1 adjust for: None. Model 2 adjust for: Sex; Age. Model 3 adjust for: Sex; Age; Smoking status; Alcohol status; Duration of diabetes; Diabetic medication; History of coronary artery disease; History of hypertension; Hypertensive medication use; BMI; SBP; DBP; HBAIC; UA; TG; TC; HDL-C; LDL-C; UACR; High-risk of OSA; Nap time. The analyses of non-linear relationship The non-linear relationship between sleep duration and eGFR was analyzed (Fig. 3 ). After making adjustments for multiple variables, the analysis revealed a U-shaped relationship between sleep duration and eGFR level, as demonstrated by the smooth curve. Using a two-piecewise linear regression model, the inflection point was 7h. On the left side of the inflection point, there was a positive relationship observed between sleep duration and eGFR levels (β = 1.79, 95%CI: 0.36 to 3.22, P = 0.0142). However, on the right side of the inflection point, a negative relationship was observed between sleep duration and eGFR levels (β = -2.30, 95%CI: -3.24 to -1.37, P < 0.0001) (Table 4 ). The P value for the log-likelihood ratio test was < 0.001. Table 4 Non-linear relationship between sleep duration and eGFR level. Outcome: β, 95%CI, P Model I Fitting model by standard linear regression -0.87 (-1.51, -0.24) 0.0071 Model II Fitting model by two piecewise linear regression Inflection point 7 7 -2.30 (-3.24, -1.37) < 0.0001 P for log likelihood ratio test < 0.001 Adjusted for Sex; Age; Smoking status; Alcohol status; Duration of diabetes; Diabetic medication; History of coronary artery disease; History of hypertension; Hypertensive medication; BMI; SBP; DBP; HBAIC; UA; TG; TC; HDL-C; LDL-C; UACR; High-risk of OSA; Nap time. The results of subgroup analysis To determine whether the association between sleep duration and eGFR was stable in the different subgroups, subgroup and interaction analyses were performed (Table 5 ). Participants were classified into subgroups based on various factors including sex, age, history of hypertension, high-risk of obstructive sleep apnea (OSA), nap time, BMI, HbA1c, UA, and TG. We found that Interaction tests were not statistically significant in any subgroups, which implied that sleep duration was independently and stably associated with eGFR levels. Table 5 The results of subgroup analysis and interaction analysis subgroup N 7-8h 8h β, 95%CI P for interaction Sex 0.7924 male 819 Ref -5.24 (-10.36, -0.11) -0.04 (-3.62, 3.55) -4.58 (-7.34, -1.82) female 570 Ref -4.17 (-9.06, 1.25) 2.34 (-2.10, 6.78) -3.08 (-5.68, -0.49) Age, year 0.2442 < 60 1070 Ref -8.89 (-13.84, -3.94) -1.60 (-4.97, 1.78) -3.53 (-6.05, -1.00) ≥ 60 319 Ref -3.31 (-10.47, 3.86) 6.92 (-0.77, 14.61) -0.72 (-4.53, 3.09) BMI, Kg/m 2 Ref 0.2240 < 24 462 Ref -10.62 (-17.24, -3.99) 0.55 (-4.32, 5.43) -2.51 (-5.55, 0.53) ≥ 24 927 Ref -3.35 (-7.85, 1.15) 0.90 (-2.44, 4.25) -4.18 (-6.57, -1.78) History of hypertension Ref 0.4960 No 952 Ref -5.38 (-9.77, -0.98) 0.44 (-2.71, 3.59) -4.62 (-6.76, -2.48) Yes 437 Ref -4.98 (-12.08, 2.13) 2.84 (-2.64, 8.32) -1.69 (-5.59, 2.21) High-risk of OSA Ref 0.4425 No 867 Ref -6.70 (-11.17, -2.23) -1.05 (-4.68, 2.58) -4.08 (-6.30, -1.86) Yes 522 Ref -1.64 (-8.37, 5.09) 1.97 (-2.39, 6.33) -3.83 (-7.42, -0.23) Nap time, min/day Ref 0.3791 No 485 Ref -5.99 (-12.04, 0.06) 0.25 (-4.88, 5.38) -5.51 (-8.63, -2.39) Yes 904 Ref -4.91 (-9.64, -0.17) 1.46 (-1.83, 4.75) -2.10 (-4.50, 0.29) HBAIC, % Ref 0.6709 < 7 485 Ref -5.83 (-12.08, 0.43) 1.79 (-3.06, 6.64) -2.11 (-5.47, 1.25) ≥ 7 904 Ref -4.07 (-8.82, 0.68) 0.86 (-2.57, 4.29) -4.31 (-6.66, -1.95) Uric acid, µmol/L Ref 0.6009 < 420 1185 Ref -5.30 (-9.27, -1.34) 0.71 (-2.24, 3.66) -3.96 (-5.94, -1.97) ≥ 420 204 Ref 0.03 (-12.16, 12.21) 3.21 (-5.67, 12.09) -0.06 (-6.99, 6.87) TG, mmol/l Ref 0.1193 =1.7 799 Ref -5.34 (-10.48, -0.20) 1.34 (-2.56, 5.23) -5.35 (-8.00, -2.69) Note 1 : adjusting for Sex; Age; Smoking status; Alcohol status; Duration of diabetes; Diabetic medication; History of coronary artery disease; History of hypertension; Hypertensive medication use; BMI; SBP; DBP; HBAIC; UA, TG, TC, HDL-C, LDL-C, UACR, High-risk of OSA, Nap time. Discussion We found that both long and very short sleep durations correlated with eGFR in patients with diabetes. The association between sleep duration and eGFR levels displayed a reverse U-shaped curve. On the left side of the inflection point, a 1-hour increase in sleep duration was associated with a 1.79 ml/min/1.73m 2 increase in eGFR. Conversely, on the right side of the inflection point, a 1-hour increase in sleep duration was associated with a 2.30 ml/min/1.73m 2 decrease in eGFR. Moreover, subgroup and interaction analyses revealed that sleep duration was independently and stably associated with eGFR. There were few researches on the relationship between sleep duration and eGFR, most of which were conducted in non-diabetic populations, and even fewer were conducted in diabetic population. Some studies have explored the correlation between sleep duration and CKD /eGFR in the general population, but their conclusion have been inconsistent. A retrospective cohort study [ 16 ] conducted in Japan involved 82,001 participants who visited primary healthcare centers. The study revealed that in comparison to participants who slept for 7 hours, those with long sleep duration (≥ 8 hours) exhibited a significant correlation with a decline in eGFR. Xu S et al [ 17 ] found that participants with 8 h of nighttime sleep were at a similar risk for both rapid eGFR decline and CKD development as those with 6–8 h of nighttime sleep. Some studies [ 18 , 19 ] showed that the eGFR levels exhibited a U-shaped association with sleep duration. Little research has been conducted on the relationship between sleep duration and eGFR in the population with diabetes. A cross-sectional study [ 5 ] in China, where DKD was defined as the presence of albuminuria (urinary levels of 24h microalbumin > 30mg/24h) and/or eGFR < 60mL/min/1.73m 2 , indicated that short sleep duration ( 9 hours) did not show a significant correlation with DKD in this study. However, this study did not further explore the correlation between sleep duration and eGFR. Another cross-sectional study [ 8 ] found that low eGFR had a relationship to long sleep duration(≥ 8.5h) in patients with type 2 diabetes. Tan et al. found that compared to normal sleep duration (7-8h), diabetic patients with very short ( 9h) duration of sleep had lower levels of eGFR, but only long duration of sleep was associated with low eGFR after adjusting for multiple potential confounders [ 9 ] . Results of this study demonstrate a U-shaped correlation between sleep duration and eGFR, which aligns with the aforementioned research findings. Our research primarily investigates the association between nighttime sleep duration and eGFR. However, previous studies have indicated that nap duration and OSA may also be linked to eGFR in both healthy individuals and those with diabetes [ 18 , 20 – 22 ] . It is also found that OSA and nap time may affect sleep duration [ 23 , 24 ] . Therefore, both OSA and nap time were potential confounding factors. Although polysomnography was not performed in our study to further confirm OSA, the STOP-BANG questionnaire was utilized to identify individuals who were at a high risk of OSA in our study. In Table 2 , we seen that there is still a correlation between long and very short sleep duration and eGFR after adjusting for high-risk of OSA and nap time. In addition, we conducted an interaction analysis and found that neither high-risk of OSA nor nap time interacted with the correlation between sleep duration and eGFR. This indicates that sleep duration was independently and stably associated with eGFR. Because our study was a cross-sectional study, we could not show the causal link and possible mechanisms between sleep duration and eGFR in patients with diabetes, but the correlation can be explained as follows. First, the secretion of inflammatory mediators, such as FGF23 and ICAM-1, can be stimulated by very short and long sleep durations, and these inflammatory mediators may contribute to lower eGFR levels [ 15 , 25 ] . Second, some researches [ 26 – 29 ] had shown insufficient sleep time can increase insulin resistance, which can induce oxidative stress and further aggravates the renal damage of diabetes, leading to the decline of eGFR. Third, a disordered sleep duration can lead to high blood pressure, which may further damage the kidneys. A study from China discovered that long sleep duration was significantly associated with hypertension; however, this association was not obvious between short sleep duration and hypertension [ 30 ] . He et al. found that a short sleep duration was related to an increased risk of hypertension [ 31 ] . These are the possible indirect factors of the relationship between sleep and eGFR in diabetic patients. The direct cause of the association is not yet clear, and further research is required. However, our study is not without limitations. First, sleep duration was assessed using a self-reported questionnaire rather than objective measurements. In future research, it would be beneficial to incorporate objective measures, such as polysomnography, to provide a more accurate assessment of sleep quality and duration. Second, because our study was a cross-sectional study, it was not possible to assess the causal relationship between sleep duration and eGFR. Third, while we considered several potential confounders in our analysis, there may still exist other unidentified factors that could have influenced the results, which highlights the need for additional studies with comprehensive control of confounding variables to further explore the relationship between sleep duration and eGFR in diabetic patients. Forth, the number of patients with sleep duration less than 6 hours was small in this study, which may have resulted in unstable results when comparing between groups. It would be very necessary to conduct larger cohort studies to further investigate the association between sleep duration less than 6 hours and eGFR in diabetic patients. Finally, it is important to note that our study participants were all Chinese, therefore, caution should be exercised when generalizing our findings to other ethnicities. Conclusion In summary, we found a U-shaped correlation between sleep duration and eGFR in patients with diabetes, in which both very short and long sleep durations were associated with a low eGFR. This result indicates that sleep duration may be a potentially modifiable risk factor for a decline in eGFR in patients with diabetes and that sleep duration should be considered when implementing strategies to prevent DKD. In addition, we need to use objective indicators such as polysomnography to conduct a prospective Cohort study to further investigate the causal relationship and potential mechanism. Declarations Data availability According to ethical guidelines in China, we cannot provide individual data due to participant privacy considerations. In addition, the informed consent obtained does not include a provision for publicly sharing data. Qualified researchers may apply to access a minimal dataset by contacting Professor Shenglian Gan, head of MMC of the First People's Hospital of Changde City, Changde, China at [email protected] . Ethics statement All participants in this study have signed informed consent, and the Ethics Committee of Changde First People's Hospital has approved this study protocol (Protocol Code YX-2023-193-02). All procedures were conducted in accordance with the Declaration of Helsinki. Funding The authors declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the research project of the Hunan Provincial Health Commission (NO. D202303068892). Acknowledgments The authors would like to thank all the patients and staff who participated in this study. The authors also would like to thank the editors and the reviewers for their valuable comments and suggestions to improve the quality of the paper. Author contributions FCS and DZM designed the study. GSL and ZHF organized the database. FCS performed the statistical analyses and wrote the manuscript. ZQ supervised data analysis and reviewed the manuscript. All authors read and approved the final manuscript. 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. References Cole J B, Florez J C. Genetics of diabetes mellitus and diabetes complications[J]. Nat Rev Nephrol, 2020,16(7):377–390. Afkarian M, Zelnick L R, Hall Y N, et al. Clinical Manifestations of Kidney Disease Among US Adults With Diabetes, 1988–2014[J]. JAMA, 2016,316(6):602–610. Lytvyn Y, Bjornstad P, van Raalte D H, et al. The New Biology of Diabetic Kidney Disease Mechanisms and Therapeutic Implications[J]. Endocr Rev, 2020,41(2):202–231. Thomas M C, Brownlee M, Susztak K, et al. Diabetic kidney disease[J]. Nat Rev Dis Primers, 2015, 1: 15018. Meng L L, Liu Y, Geng R N, et al. Association of diabetic vascular complications with poor sleep complaints[J]. Diabetol Metab Syndr, 2016,8:80. Han H, Wang Y, Li T, et al. Sleep Duration and Risks of Incident Cardiovascular Disease and Mortality Among People With Type 2 Diabetes[J]. Diabetes Care, 2023,46(1):101–110. Shan Z, Ma H, Xie M, et al. Sleep duration and risk of type 2 diabetes: a meta-analysis of prospective studies[J]. Diabetes Care, 2015,38(3):529–537. Ohkuma T, Fujii H, Iwase M, et al. Association between sleep duration and urinary albumin excretion in patients with type 2 diabetes: the Fukuoka diabetes registry[J]. PLoS One, 2013, 8 (11): e78968. Tan N, Chan J, Cheng C Y, et al. Sleep Duration and Diabetic Kidney Disease[J]. Front Endocrinol (Lausanne), 2018,9:808. Zhang Y, Wang W, Ning G. Metabolic Management Center: An innovation project for the management of metabolic diseases and complications in China[J]. J Diabetes, 2019,11(1):11–13. Choi H, Kim H C, Lee J Y, et al. Sleep duration and chronic kidney disease: The Korean Genom and Epidemiology Study (KoGES)-Kangwha study[J]. Korean J Intern Med, 2017,32(2):323–334. Zhou L, Yu K, Yang L, et al. Sleep duration, midday napping, and sleep quality and incident stroke: The Dongfeng-Tongji cohort[J]. Neurology, 2020,94(4):e345-e356. Levey A S, Stevens L A, Schmid C H, et al. A new equation to estimate glomerular filtration rate[J]. Ann Intern Med, 2009,150(9):604–612. Nagappa M, Liao P, Wong J, et al. Validation of the STOP-Bang Questionnaire as a Screening Tool for Obstructive Sleep Apnea among Different Populations: A Systematic Review and Meta Analysis[J]. PLoS One, 2015,10(12):e143697. Meng L, Ding Y, Li J, et al. Impact of inflammatory markers on the relationship between sleep quality and diabetic kidney disease[J]. Sleep Breath, 2022,26(1):157–165. Hirano K, Komatsu Y, Shimbo T, et al. Longitudinal relationship between long sleep duration and future kidney function decline[J]. Clin Kidney J, 2022,15(9):1763–1769. Xu S, Jin J, Dong Q, et al. 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[J]. Front Public Health, 2022,10:1072238. Ye Y, Zhang L, Yan W, et al. Self-reported sleep duration and daytime napping are associated with renal hyperfiltration and microalbuminuria in an apparently healthy Chinese population[J]. PLoS One, 2019,14(8):e214776. Bo Y, Yeoh E K, Guo C, et al. Sleep and the Risk of Chronic Kidney Disease: A Cohort Study[J]. J Clin Sleep Med, 2019,15(3):393–400. Tahrani A A, Ali A, Raymond N T, et al. Obstructive sleep apnea and diabetic nephropathy: a cohort study[J]. Diabetes Care, 2013,36(11):3718–3725. Zamarron E, Jaureguizar A, Garcia-Sanchez A, et al. Obstructive sleep apnea is associated with impaired renal function in patients with diabetic kidney disease[J]. Sci Rep, 2021,11(1):5675. Franke F J, Arzt M, Kroner T, et al. Daytime napping and diabetes-associated kidney disease[J]. Sleep Med, 2019,54:205–212. Jordan A S, McSharry D G, Malhotra A. Adult obstructive sleep apnoea[J]. Lancet, 2014,383(9918):736–747. Carrier J, Semba K, Deurveilher S, et al. Sex differences in age-related changes in the sleep-wake cycle[J]. Front Neuroendocrinol, 2017,47:66–85. Matoba K, Takeda Y, Nagai Y, et al. Unraveling the Role of Inflammation in the Pathogenesis of Diabetic Kidney Disease[J]. Int J Mol Sci, 2019,20(14). Reutrakul S, Van Cauter E. Sleep influences on obesity, insulin resistance, and risk of type 2 diabetes[J]. Metabolism, 2018,84:56–66. Tanase D M, Gosav E M, Anton M I, et al. Oxidative Stress and NRF2/KEAP1/ARE Pathway in Diabetic Kidney Disease (DKD): New Perspectives[J]. Biomolecules, 2022,12(9). Jung C Y, Yoo T H. Pathophysiologic Mechanisms and Potential Biomarkers in Diabetic Kidney Disease[J]. Diabetes Metab J, 2022,46(2):181–197. Vasavada N, Agarwal R. Role of oxidative stress in diabetic nephropathy[J]. Adv Chronic Kidney Dis, 2005,12(2):146–154. Chang X, Chen X, Ji J S, et al. Association between sleep duration and hypertension in southwest China: a population-based cross-sectional study[J]. BMJ Open, 2022,12(6):e52193. He J, He Q. Association between Sleep Duration and Hypertension among Adults in Southwest China[J]. Glob Heart, 2022,17(1):10. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4306709","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":296482758,"identity":"c7c80966-17d0-4b7a-9743-c58ba856d48c","order_by":0,"name":"Cishuang Fu","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Cishuang","middleName":"","lastName":"Fu","suffix":""},{"id":296482759,"identity":"8928bbcb-43a5-472e-ba09-d0e88cba8a4a","order_by":1,"name":"Zhiming Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3PsWoCQRCA4QkLm2bw2pEc2lmPBIKF4AvkIXZRrlKwvIBFYOUsRNLmMSwtlYO12VhfaR4hnYWo10duL12K/er5mRmAIPiHZGT2J0r7ONib3VGlM3/SIKuo55IYnB3y0Vl/0oIx01uW96F47TS/56LGYeCYCynw4RNlqt8lRIulqk5ENtXlNIqnlS30JgZyX2vPlnydEyHK+JAU2klgmvgS1TVnLhsav0x1Juoko2cgxUhlAvUSsgnQViGjHZJyFr2/tD+MheblOuBHs/s5pbNWtFhVJ7/g38aDIAiCu25G5Ucfkw4tYgAAAABJRU5ErkJggg==","orcid":"","institution":"Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Zhiming","middleName":"","lastName":"Deng","suffix":""},{"id":296482760,"identity":"fc190e7a-9f9c-41dd-934b-568617e2c5dd","order_by":2,"name":"Shenglian Gan","email":"","orcid":"","institution":"The First People’s Hospital of Changde City","correspondingAuthor":false,"prefix":"","firstName":"Shenglian","middleName":"","lastName":"Gan","suffix":""},{"id":296482761,"identity":"1e8235e5-c161-45ac-92f8-000cfb66ef4f","order_by":3,"name":"Haifeng Zhou","email":"","orcid":"","institution":"The First People’s Hospital of Changde City","correspondingAuthor":false,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Zhou","suffix":""},{"id":296482762,"identity":"69c519be-97b1-4cf1-9a53-ef03abccc41c","order_by":4,"name":"Quan Zhou","email":"","orcid":"","institution":"The First People’s Hospital of Changde City","correspondingAuthor":false,"prefix":"","firstName":"Quan","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-04-22 14:57:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4306709/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4306709/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55764009,"identity":"f4608756-c450-4d71-b143-bd7d6e84f080","added_by":"auto","created_at":"2024-05-02 19:50:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":289031,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the inclusion and exclusion of participants for this study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4306709/v1/f923446a7f5f5bbdf5a05eb3.png"},{"id":55763661,"identity":"f265c30a-466c-4a6e-a45b-003acfc17452","added_by":"auto","created_at":"2024-05-02 19:42:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172202,"visible":true,"origin":"","legend":"\u003cp\u003eeGFR distribution in the groups classified by sleep duration\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4306709/v1/1aceca628185423d287135fa.png"},{"id":55763663,"identity":"20c07fa6-831b-41be-aca6-f79b379ce7de","added_by":"auto","created_at":"2024-05-02 19:42:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":192445,"visible":true,"origin":"","legend":"\u003cp\u003eNon-linear relationship between sleep duration and eGFR level. A nonlinear relationship was detected after adjusting for Sex, Age, Smoking status, Alcohol status, Duration of diabetes, Diabetic medication, History of coronary artery disease, History of hypertension, Hypertensive medication use, BMI, SBP, DBP, HBAIC, UA, TG, TC, HDL-C, LDL-C, UACR, High-risk of OSA, Nap time.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4306709/v1/111a57671853e3b8d1bbb8e7.png"},{"id":71951824,"identity":"036124d5-989d-4917-980e-cf001540d746","added_by":"auto","created_at":"2024-12-20 04:39:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1492142,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4306709/v1/44787309-4d7f-4fab-be01-da8adf65a128.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between sleep duration and estimated glomerular filtration rate in Chinese patients with diabetes: evidence from a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes is a rapidly growing disease worldwide, projected to affect 693\u0026nbsp;million adults by 2045\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Among diabetes patients, approximately 20\u0026ndash;40% will develop diabetic kidney disease (DKD)\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, which is the leading cause of end-stage renal disease (ESRD) globally\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. DKD leads to a progressive decline in estimated glomerular filtration rate in estimated glomerular filtration rate (eGFR) and eventually results in ESRD which have significant financial and daily life impact on affected individuals\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Thus, better comprehension of modifiable risk factors for eGFR decline in diabetes patients could help delay the progression of DKD and enhance the quality of life for individuals with diabetes.\u003c/p\u003e \u003cp\u003ePrevious epidemiological studies have demonstrated substantial associations between sleep duration and diabetes, vascular complications of diabetes and cardiovascular disease in diabetic patients\u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. However, studies investigating the correlation between sleep duration and eGFR in patients with diabetes are limited. A cross-sectional study in China established an independent correlation between short sleep duration (\u0026lt;\u0026thinsp;6h) and DKD, but failed to further explore the association between sleep duration and eGFR\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Two other cross-sectional studies\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e further analyzed the relationship between sleep duration and eGFR, and found that long sleep duration was significantly correlated with low eGFR in patients with diabetes. In order to further understand the correlation between sleep duration and eGFR in diabetic patients, we performed this study.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy population\u003c/h2\u003e\n\u003cp\u003eThe National Metabolic Management Center (MMC) was founded in China in 2016 to establish a platform for the standardized diagnosis and treatment of metabolic diseases and long-term follow-up\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The First People's Hospital of Changde City (Hunan, China) is an MMC network hospital. Between May 2020 and January 2022, 1665 (961 men, 704 women) participants with diabetes were recruited to participate in the baseline survey and follow-up. We used only the baseline data of these participants in this study. Participants were T2DM patients at the MMC of the Changde First People's Hospital. According to the WHO 1999 diagnostic criteria for diabetes mellitus, the following diagnostic criteria were used: fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, or typical symptoms of diabetes mellitus with a random blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L, or 2h blood glucose in a glucose tolerance test\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L. Participants were excluded if they: (1) had a history of cancer; (2) had a history of thyroid disease, including hyperthyroidism, hypothyroidism, thyroid cancer, and thyroiditis; (3) had major depression and anxiety, schizophrenia, or other psychiatric disorders; and (4) had missing data on sleep duration and variables included in our multivariable models. All participants signed informed consent forms and the study protocol was approved by the Ethics Committee of Changde First People's Hospital (Protocol Code YX-2023-193-02). All the procedures were conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eAssessment of sleep duration\u003c/h2\u003e\n\u003cp\u003eSleep duration was collected using a standardized interviewer-administered questionnaire. Every participant was aske, \u0026ldquo;During the past week, at what times did you normally go to bed at night and wake up in the morning?\u0026rdquo; Estimated nocturnal sleep duration was defined as the time space between bedtime and waking up. Sleep duration was categorized into four groups: very short (\u0026lt;\u0026thinsp;6h), short (6-6.9h), optimal (7-8h), and long (\u0026gt;\u0026thinsp;8h). The optimal sleep duration was defined as 7-8h according to previous studies\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eDefinition of estimated glomerular filtration rate (eGFR)\u003c/h2\u003e\n\u003cp\u003eVenous blood samples were collected to determine serum creatinine levels. eGFR (mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The formulas for calculating the eGFR:\u003c/p\u003e\n\u003cp\u003e(1) Female:\u003c/p\u003e\n\u003cp\u003eScr (mg/dl): \u0026le;0.7 eGFR\u0026thinsp;=\u0026thinsp;144 \u0026times; (Scr/0.7)\u003csup\u003e\u0026minus;0.329\u003c/sup\u003e \u0026times; (0.993)\u003csup\u003eAge\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;0.7 eGFR\u0026thinsp;=\u0026thinsp;144 \u0026times; (Scr/0.7)\u003csup\u003e\u0026minus;1.209\u003c/sup\u003e \u0026times; (0.993)\u003csup\u003eAge\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(2) Male:\u003c/p\u003e\n\u003cp\u003eScr (mg/dl): \u0026le;0.9 eGFR\u0026thinsp;=\u0026thinsp;141 \u0026times; (Scr/0.9)\u003csup\u003e\u0026minus;0.411\u003c/sup\u003e \u0026times; (0.993)\u003csup\u003eAge\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;0.9 eGFR\u0026thinsp;=\u0026thinsp;141 \u0026times; (Scr/0.9)\u003csup\u003e\u0026minus;1.209\u003c/sup\u003e \u0026times; (0.993)\u003csup\u003eAge\u003c/sup\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eOther assessments\u003c/h2\u003e\n\u003cp\u003eStandardized questionnaires were used to obtain information on demographic factors, lifestyle behaviours (smoking and alcohol consumption), disease history, and medication use at baseline. Napping duration was estimated using self-reported daytime sleep time, and the participants were asked the following question, \u0026ldquo;how long did you take naps after lunch during the past week?\u0026rdquo; The STOP-BANG Questionnaire\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e was used as a screening tool to identify individuals at high-risk of obstructive sleep apnea (OSA), which is defined by a score of \u0026gt;\u0026thinsp;3 in this study.\u003c/p\u003e\n\u003cp\u003eFor the measurement of height, we used a wall-mounted measuring tape, and participants took off their shoes and pressed their heads, buttocks and heels against the wall. Participants took off their shoes, put on thin clothes, and stood on a digital scale to measure their weight. Body mass index (BMI, kg/m\u003csup\u003e2\u003c/sup\u003e) was calculated as weight divided by the square of height. Blood pressure (BP) was obtained by the mean of two measurements after a 10 min seated rest, using a digital automatic blood pressure monitor. Definition of hypertension was that a blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg or currently taking antihypertension drugs.\u003c/p\u003e\n\u003cp\u003eVenous blood samples, taken after a 12-hour fast, were used to measure levels of triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), hemoglobin A1c (HbA1c), and uric acid (UA). Untimed urine samples were collected for measurement of albumin and creatinine, then urine albumin creatinine ratio (UACR, mg/g) levels were calculated as urinary albumin divided by urinary creatinine.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eAll statistical analysis in this study was performed with the statistical software packages R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003c/span\u003e, The R Foundation R.4.2.0) and Empower Stats (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.empowerstats.com\u003c/span\u003e\u003c/span\u003e, X\u0026amp;Y Solutions, Inc., Boston, MA, USA). Normal distributed continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while skewed distributed continuous variables were expressed as median (quartile 1\u0026ndash;3). Categorical variables were presented as frequency (%). The ANOVA (for normal distribution), Kruskal-Wallis H test (skewed distribution) and Chi-Square test (for categorical variables) were used to analyze the statistical differences between the different categories of sleep duration.\u003c/p\u003e\n\u003cp\u003eLinear regression was used to analyze the associations between very short, short, and long sleep duration (referenced against optimal sleep duration) and eGFR. Three models were used to determine the association between sleep duration and the eGFR.. In Model 1, we made no adjustments. Model 2 was adjusted for sex and age. In model 3, we further adjusted for smoking status, alcohol status, duration of diabetes, diabetic medication, history of coronary artery disease, history of hypertension, hypertensive medication use, systolic blood pressure (SBP), diastolic blood pressure (DBP), BMI, HBAIC, UA, TG, TC, HDL-C, LDL-C, UACR, high-risk of OSA and nap time based on clinical experience and previous researches\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Subgroup analyses were performed to further verify the stability of the results. Moreover, the interaction terms between sleep duration and the other independent variables in the models were tested.\u003c/p\u003e\n\u003cp\u003eThe nonlinear relationship between sleep duration and eGFR level was explored by a generalized additive model. Based on the smoothed curve, a two-piece linear regression model was used to determine the infection point. Statistical significance was set a value less than 0.05 considered significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eParticipant flowchart\u003c/h2\u003e \u003cp\u003eInitially, 1665 patients with diabetes were recruited. We excluded individuals who had a history of cancer (n\u0026thinsp;=\u0026thinsp;11), thyroid disease (n\u0026thinsp;=\u0026thinsp;43), and missing data on sleep duration (n\u0026thinsp;=\u0026thinsp;222). Therefore, 1389participants (819 men, 570 women) were included in the primary analysis. A flowchart summarizing the inclusion and exclusion criteria is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of study participants\u003c/h2\u003e \u003cp\u003eThe cross-sectional study included 1389 participants with diabetes; 819 (58.96%) were male and 570 (41.04%) were female. The mean age of participants was 52.32\u0026thinsp;\u0026plusmn;\u0026thinsp;11.11 years. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the clinical characteristics of participants categorized based on sleep duration. There were no significant differences observed in smoking status, history of coronary artery disease, duration of diabetes, diabetic medication, history of hypertension, hypertensive medication use, BMI, SBP, DBP, HBAIC, UA, TG, TC, HDL-C, LDL-C, UACR and nap time among different sleep duration groups. However, significant differences were observed in terms of sex, eGFR, and high-risk of OSA among the four groups. Furthermore, individuals with very short or long sleep durations, compared to those with an optimal sleep duration, were more likely to be older and have lower levels of eGFR (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and participants with very short sleep duration were found to have a higher risk of OSA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). According to the violin picture (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), eGFR was positively skewed in all four groups. The level of eGFR was found to be significantly lower in individuals with very short or long sleep duration, compared to those with optimal or short sleep duration (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, there was no significant difference observed between the very short sleep duration group and the long sleep duration group (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants by sleep duration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep Duration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6-6.9h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7-8h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (61.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (70.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e439 (59.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e224 (53.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (38.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (29.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303 (40.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193 (46.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.53\u0026thinsp;\u0026plusmn;\u0026thinsp;9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.71\u0026thinsp;\u0026plusmn;\u0026thinsp;8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.80\u0026thinsp;\u0026plusmn;\u0026thinsp;10.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.87\u0026thinsp;\u0026plusmn;\u0026thinsp;12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (56.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (55.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e463 (62.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e267 (64.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (10.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (8.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (8.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (6.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEveryday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (32.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (35.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e218 (29.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 (29.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (68.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (59.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e495 (66.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e292 (70.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (6.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (4.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (8.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (7.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEveryday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (25.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (36.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184 (24.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92 (22.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of coronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (96.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151 (98.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e714 (96.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e407 (97.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (3.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (2.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of diabetes, month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.00(5.00\u0026ndash;96.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.50(4.25-78.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.00 (3.00\u0026ndash;96.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.00 (6.00-108.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (13.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (15.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (14.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (14.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (86.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (84.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e636 (85.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e356 (85.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (63.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (66.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e513 (69.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e288 (69.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (36.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (33.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e229 (30.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129 (30.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertensive medication use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (71.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (72.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e544 (73.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300 (71.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (28.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (27.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198 (26.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117 (28.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.53\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.24\u0026thinsp;\u0026plusmn;\u0026thinsp;19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.16\u0026thinsp;\u0026plusmn;\u0026thinsp;18.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.09\u0026thinsp;\u0026plusmn;\u0026thinsp;19.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134.57\u0026thinsp;\u0026plusmn;\u0026thinsp;18.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e337.17\u0026thinsp;\u0026plusmn;\u0026thinsp;108.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e343.66\u0026thinsp;\u0026plusmn;\u0026thinsp;81.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333.44\u0026thinsp;\u0026plusmn;\u0026thinsp;85.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e329.80\u0026thinsp;\u0026plusmn;\u0026thinsp;83.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98 (1.36\u0026ndash;3.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90(1.41\u0026ndash;3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86(1.27\u0026ndash;2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.93 (1.32\u0026ndash;2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mmol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mmol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUACR, mg/g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.26 (7.91\u0026ndash;32.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.25 (6.72\u0026ndash;33.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.16(7.35\u0026ndash;31.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.65 (8.31\u0026ndash;42.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.15 (89.19-114.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.44 (99.46-119.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109.41 (96.79-119.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.35 (90.22-116.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-risk of OSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (61.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (48.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e469 (63.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e276 (66.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (38.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (51.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e273 (36.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141 (33.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNap time, min/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (38.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (27.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258 (34.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156 (37.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (61.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (72.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e484 (65.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e261 (62.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eP* indicated U test. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; TC, Total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein; UACR, urinary albumin-to-creatinine ratio; eGFR, estimated glomerular filtration rate; OSA, obstructive sleep apnea.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe results of relationship between sleep duration and eGFR\u003c/h2\u003e \u003cp\u003eBecause eGFR was a continuous variable, linear regression model be used to determine the associations between sleep duration categories and level of eGFR. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, both long sleep duration and very short sleep duration were found to be associated with eGFR levels. (β = -3.63, 95%CI: -5.54 to -1.71, P\u0026thinsp;=\u0026thinsp;0.0002 and β = -4.79, 95%CI: -8.55 to -1.03, P\u0026thinsp;=\u0026thinsp;0.0126, respectively). Compared to optimal sleep duration, eGFR decreased by 3.63 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e and 4.79 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e respectively in those participants with long sleep duration and very short sleep duration after multivariate adjustments. 3.74% (n\u0026thinsp;=\u0026thinsp;52) of the participants had been diagnosed diabetes nephropathy in this study. To assess the potential impact of patients with diabetes nephropathy on the study results, we conducted an analysis specifically among participants without diabetes nephropathy to examine the association between sleep duration and eGFR. In Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, both long and very short sleep durations were still associated with eGFR levels in participants without diabetes nephropathy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of sleep duration with eGFR in diabetic patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSleep duration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ, 95%CI, P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ, 95%CI, P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ, 95%CI, P\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.09 (-14.02, -4.17) 0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.18 (-9.23, -1.14) 0.0121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.79 (-8.55, -1.03) 0.0126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (-2.34, 4.90) 0.4881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (-2.02, 3.93) 0.5288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05 (-1.73, 3.83) 0.4587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.63 (-7.14, -2.13) 0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.39 (-5.44, -1.34) 0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.63 (-5.54, -1.71) 0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1 adjust for: None.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2 adjust for: Sex; Age.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 3 adjust for: Sex; Age; Smoking status; Alcohol status; Duration of diabetes; Diabetic medication; History of coronary artery disease; History of hypertension; Hypertensive medication use; BMI; SBP; DBP; HBAIC; UA; TG; TC; HDL-C; LDL-C; UACR; High-risk of OSA; Nap time.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of sleep duration with eGFR in the participants without DKD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSleep duration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ, 95%CI, P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ, 95%CI, P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ, 95%CI, P\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.56 (-10.92, -2.21) 0.0032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.41 (-6.93, 0.11) 0.0581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.37 (-6.70, -0.04) 0.0478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05 (-3.14, 3.05) 0.9771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.21 (-2.71, 2.29) 0.8683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23 (-2.16, 2.61) 0.8513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.68 (-4.87, -0.50) 0.0161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.96 (-3.73, -0.20) 0.0292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.30 (-3.97, -0.63) 0.0071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1 adjust for: None.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2 adjust for: Sex; Age.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 3 adjust for: Sex; Age; Smoking status; Alcohol status; Duration of diabetes; Diabetic medication; History of coronary artery disease; History of hypertension; Hypertensive medication use; BMI; SBP; DBP; HBAIC; UA; TG; TC; HDL-C; LDL-C; UACR; High-risk of OSA; Nap time.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe analyses of non-linear relationship\u003c/h2\u003e \u003cp\u003eThe non-linear relationship between sleep duration and eGFR was analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After making adjustments for multiple variables, the analysis revealed a U-shaped relationship between sleep duration and eGFR level, as demonstrated by the smooth curve. Using a two-piecewise linear regression model, the inflection point was 7h. On the left side of the inflection point, there was a positive relationship observed between sleep duration and eGFR levels (β\u0026thinsp;=\u0026thinsp;1.79, 95%CI: 0.36 to 3.22, P\u0026thinsp;=\u0026thinsp;0.0142). However, on the right side of the inflection point, a negative relationship was observed between sleep duration and eGFR levels (β = -2.30, 95%CI: -3.24 to -1.37, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The P value for the log-likelihood ratio test was \u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNon-linear relationship between sleep duration and eGFR level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ, 95%CI, P\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting model by standard linear regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.87 (-1.51, -0.24) 0.0071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel II Fitting model by two piecewise linear regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.79 (0.36, 3.22) 0.0142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.30 (-3.24, -1.37)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for log likelihood ratio test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAdjusted for Sex; Age; Smoking status; Alcohol status; Duration of diabetes; Diabetic medication; History of coronary artery disease; History of hypertension; Hypertensive medication; BMI; SBP; DBP; HBAIC; UA; TG; TC; HDL-C; LDL-C; UACR; High-risk of OSA; Nap time.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe results of subgroup analysis\u003c/h2\u003e \u003cp\u003eTo determine whether the association between sleep duration and eGFR was stable in the different subgroups, subgroup and interaction analyses were performed (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Participants were classified into subgroups based on various factors including sex, age, history of hypertension, high-risk of obstructive sleep apnea (OSA), nap time, BMI, HbA1c, UA, and TG. We found that Interaction tests were not statistically significant in any subgroups, which implied that sleep duration was independently and stably associated with eGFR levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of subgroup analysis and interaction analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003esubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7-8h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6h\u003c/p\u003e \u003cp\u003eβ, 95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6-6.9h\u003c/p\u003e \u003cp\u003eβ, 95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8h\u003c/p\u003e \u003cp\u003eβ, 95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-5.24 (-10.36, -0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e-0.04 (-3.62, 3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-4.58 (-7.34, -1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-4.17 (-9.06, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2.34 (-2.10, 6.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-3.08 (-5.68, -0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-8.89 (-13.84, -3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e-1.60 (-4.97, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-3.53 (-6.05, -1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-3.31 (-10.47, 3.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e6.92 (-0.77, 14.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.72 (-4.53, 3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, Kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-10.62 (-17.24, -3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e0.55 (-4.32, 5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-2.51 (-5.55, 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-3.35 (-7.85, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e0.90 (-2.44, 4.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-4.18 (-6.57, -1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-5.38 (-9.77, -0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e0.44 (-2.71, 3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-4.62 (-6.76, -2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-4.98 (-12.08, 2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2.84 (-2.64, 8.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-1.69 (-5.59, 2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-risk of OSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-6.70 (-11.17, -2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e-1.05 (-4.68, 2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-4.08 (-6.30, -1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-1.64 (-8.37, 5.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e1.97 (-2.39, 6.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-3.83 (-7.42, -0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNap time, min/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-5.99 (-12.04, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e0.25 (-4.88, 5.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-5.51 (-8.63, -2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-4.91 (-9.64, -0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e1.46 (-1.83, 4.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-2.10 (-4.50, 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBAIC, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-5.83 (-12.08, 0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e1.79 (-3.06, 6.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-2.11 (-5.47, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-4.07 (-8.82, 0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e0.86 (-2.57, 4.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-4.31 (-6.66, -1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-5.30 (-9.27, -1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e0.71 (-2.24, 3.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-3.96 (-5.94, -1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e0.03 (-12.16, 12.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e3.21 (-5.67, 12.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.06 (-6.99, 6.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-7.15 (-12.42, -1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e-0.48 (-4.28, 3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-1.53 (-4.18, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-5.34 (-10.48, -0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e1.34 (-2.56, 5.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-5.35 (-8.00, -2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e1\u003c/span\u003e: adjusting for Sex; Age; Smoking status; Alcohol status; Duration of diabetes; Diabetic medication; History of coronary artery disease; History of hypertension; Hypertensive medication use; BMI; SBP; DBP; HBAIC; UA, TG, TC, HDL-C, LDL-C, UACR, High-risk of OSA, Nap time.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe found that both long and very short sleep durations correlated with eGFR in patients with diabetes. The association between sleep duration and eGFR levels displayed a reverse U-shaped curve. On the left side of the inflection point, a 1-hour increase in sleep duration was associated with a 1.79 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e increase in eGFR. Conversely, on the right side of the inflection point, a 1-hour increase in sleep duration was associated with a 2.30 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e decrease in eGFR. Moreover, subgroup and interaction analyses revealed that sleep duration was independently and stably associated with eGFR.\u003c/p\u003e \u003cp\u003eThere were few researches on the relationship between sleep duration and eGFR, most of which were conducted in non-diabetic populations, and even fewer were conducted in diabetic population. Some studies have explored the correlation between sleep duration and CKD /eGFR in the general population, but their conclusion have been inconsistent. A retrospective cohort study\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e conducted in Japan involved 82,001 participants who visited primary healthcare centers. The study revealed that in comparison to participants who slept for 7 hours, those with long sleep duration (\u0026ge;\u0026thinsp;8 hours) exhibited a significant correlation with a decline in eGFR. Xu S et al\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e found that participants with \u0026lt;\u0026thinsp;6 h or \u0026gt;\u0026thinsp;8 h of nighttime sleep were at a similar risk for both rapid eGFR decline and CKD development as those with 6\u0026ndash;8 h of nighttime sleep. Some studies\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e showed that the eGFR levels exhibited a U-shaped association with sleep duration.\u003c/p\u003e \u003cp\u003eLittle research has been conducted on the relationship between sleep duration and eGFR in the population with diabetes. A cross-sectional study\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e in China, where DKD was defined as the presence of albuminuria (urinary levels of 24h microalbumin\u0026thinsp;\u0026gt;\u0026thinsp;30mg/24h) and/or eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e, indicated that short sleep duration (\u0026lt;\u0026thinsp;6h) remained independently associated with DKD after adjustments. Additionally, long sleep duration (\u0026gt;\u0026thinsp;9 hours) did not show a significant correlation with DKD in this study. However, this study did not further explore the correlation between sleep duration and eGFR. Another cross-sectional study \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003efound that low eGFR had a relationship to long sleep duration(\u0026ge;\u0026thinsp;8.5h) in patients with type 2 diabetes. Tan et al. found that compared to normal sleep duration (7-8h), diabetic patients with very short (\u0026lt;\u0026thinsp;5h) or long (\u0026gt;\u0026thinsp;9h) duration of sleep had lower levels of eGFR, but only long duration of sleep was associated with low eGFR after adjusting for multiple potential confounders\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Results of this study demonstrate a U-shaped correlation between sleep duration and eGFR, which aligns with the aforementioned research findings.\u003c/p\u003e \u003cp\u003eOur research primarily investigates the association between nighttime sleep duration and eGFR. However, previous studies have indicated that nap duration and OSA may also be linked to eGFR in both healthy individuals and those with diabetes\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. It is also found that OSA and nap time may affect sleep duration\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Therefore, both OSA and nap time were potential confounding factors. Although polysomnography was not performed in our study to further confirm OSA, the STOP-BANG questionnaire was utilized to identify individuals who were at a high risk of OSA in our study. In Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we seen that there is still a correlation between long and very short sleep duration and eGFR after adjusting for high-risk of OSA and nap time. In addition, we conducted an interaction analysis and found that neither high-risk of OSA nor nap time interacted with the correlation between sleep duration and eGFR. This indicates that sleep duration was independently and stably associated with eGFR.\u003c/p\u003e \u003cp\u003eBecause our study was a cross-sectional study, we could not show the causal link and possible mechanisms between sleep duration and eGFR in patients with diabetes, but the correlation can be explained as follows. First, the secretion of inflammatory mediators, such as FGF23 and ICAM-1, can be stimulated by very short and long sleep durations, and these inflammatory mediators may contribute to lower eGFR levels\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Second, some researches\u003csup\u003e[\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e had shown insufficient sleep time can increase insulin resistance, which can induce oxidative stress and further aggravates the renal damage of diabetes, leading to the decline of eGFR. Third, a disordered sleep duration can lead to high blood pressure, which may further damage the kidneys. A study from China discovered that long sleep duration was significantly associated with hypertension; however, this association was not obvious between short sleep duration and hypertension\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. He et al. found that a short sleep duration was related to an increased risk of hypertension\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. These are the possible indirect factors of the relationship between sleep and eGFR in diabetic patients. The direct cause of the association is not yet clear, and further research is required.\u003c/p\u003e \u003cp\u003eHowever, our study is not without limitations. First, sleep duration was assessed using a self-reported questionnaire rather than objective measurements. In future research, it would be beneficial to incorporate objective measures, such as polysomnography, to provide a more accurate assessment of sleep quality and duration. Second, because our study was a cross-sectional study, it was not possible to assess the causal relationship between sleep duration and eGFR. Third, while we considered several potential confounders in our analysis, there may still exist other unidentified factors that could have influenced the results, which highlights the need for additional studies with comprehensive control of confounding variables to further explore the relationship between sleep duration and eGFR in diabetic patients. Forth, the number of patients with sleep duration less than 6 hours was small in this study, which may have resulted in unstable results when comparing between groups. It would be very necessary to conduct larger cohort studies to further investigate the association between sleep duration less than 6 hours and eGFR in diabetic patients. Finally, it is important to note that our study participants were all Chinese, therefore, caution should be exercised when generalizing our findings to other ethnicities.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we found a U-shaped correlation between sleep duration and eGFR in patients with diabetes, in which both very short and long sleep durations were associated with a low eGFR. This result indicates that sleep duration may be a potentially modifiable risk factor for a decline in eGFR in patients with diabetes and that sleep duration should be considered when implementing strategies to prevent DKD. In addition, we need to use objective indicators such as polysomnography to conduct a prospective Cohort study to further investigate the causal relationship and potential mechanism.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to ethical guidelines in China, we cannot provide individual data due to participant privacy considerations. In addition, the informed consent obtained does not include a provision for publicly sharing data. Qualified researchers may apply to access a minimal dataset by contacting Professor Shenglian Gan, head of MMC of the First People\u0026apos;s Hospital of Changde City,\u0026nbsp;\u0026nbsp;Changde, China at \u0026nbsp;[email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants in this study have signed informed consent, and the Ethics Committee of Changde First People\u0026apos;s Hospital has approved this study protocol (Protocol Code YX-2023-193-02). All procedures were conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the research project of the Hunan Provincial Health Commission (NO. D202303068892).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the patients and staff who participated in this study.\u0026nbsp;The authors also would like to thank the editors and the reviewers for their valuable comments and suggestions to improve the quality of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFCS and DZM designed the study. GSL and ZHF organized the database. FCS performed the statistical analyses and wrote the manuscript. ZQ supervised data analysis and reviewed the manuscript. All authors read and approved the final manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCole J B, Florez J C. Genetics of diabetes mellitus and diabetes complications[J]. Nat Rev Nephrol, 2020,16(7):377\u0026ndash;390.\u003c/li\u003e\n\u003cli\u003eAfkarian M, Zelnick L R, Hall Y N, et al. Clinical Manifestations of Kidney Disease Among US Adults With Diabetes, 1988\u0026ndash;2014[J]. JAMA, 2016,316(6):602\u0026ndash;610.\u003c/li\u003e\n\u003cli\u003eLytvyn Y, Bjornstad P, van Raalte D H, et al. The New Biology of Diabetic Kidney Disease Mechanisms and Therapeutic Implications[J]. Endocr Rev, 2020,41(2):202\u0026ndash;231.\u003c/li\u003e\n\u003cli\u003eThomas M C, Brownlee M, Susztak K, et al. Diabetic kidney disease[J]. Nat Rev Dis Primers, 2015, 1: 15018.\u003c/li\u003e\n\u003cli\u003eMeng L L, Liu Y, Geng R N, et al. Association of diabetic vascular complications with poor sleep complaints[J]. Diabetol Metab Syndr, 2016,8:80.\u003c/li\u003e\n\u003cli\u003eHan H, Wang Y, Li T, et al. Sleep Duration and Risks of Incident Cardiovascular Disease and Mortality Among People With Type 2 Diabetes[J]. Diabetes Care, 2023,46(1):101\u0026ndash;110.\u003c/li\u003e\n\u003cli\u003eShan Z, Ma H, Xie M, et al. Sleep duration and risk of type 2 diabetes: a meta-analysis of prospective studies[J]. Diabetes Care, 2015,38(3):529\u0026ndash;537.\u003c/li\u003e\n\u003cli\u003eOhkuma T, Fujii H, Iwase M, et al. Association between sleep duration and urinary albumin excretion in patients with type 2 diabetes: the Fukuoka diabetes registry[J]. PLoS One, 2013, 8 (11): e78968.\u003c/li\u003e\n\u003cli\u003eTan N, Chan J, Cheng C Y, et al. Sleep Duration and Diabetic Kidney Disease[J]. Front Endocrinol (Lausanne), 2018,9:808.\u003c/li\u003e\n\u003cli\u003eZhang Y, Wang W, Ning G. Metabolic Management Center: An innovation project for the management of metabolic diseases and complications in China[J]. J Diabetes, 2019,11(1):11\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eChoi H, Kim H C, Lee J Y, et al. Sleep duration and chronic kidney disease: The Korean Genom and Epidemiology Study (KoGES)-Kangwha study[J]. Korean J Intern Med, 2017,32(2):323\u0026ndash;334.\u003c/li\u003e\n\u003cli\u003eZhou L, Yu K, Yang L, et al. Sleep duration, midday napping, and sleep quality and incident stroke: The Dongfeng-Tongji cohort[J]. Neurology, 2020,94(4):e345-e356.\u003c/li\u003e\n\u003cli\u003eLevey A S, Stevens L A, Schmid C H, et al. A new equation to estimate glomerular filtration rate[J]. Ann Intern Med, 2009,150(9):604\u0026ndash;612.\u003c/li\u003e\n\u003cli\u003eNagappa M, Liao P, Wong J, et al. Validation of the STOP-Bang Questionnaire as a Screening Tool for Obstructive Sleep Apnea among Different Populations: A Systematic Review and Meta Analysis[J]. PLoS One, 2015,10(12):e143697.\u003c/li\u003e\n\u003cli\u003eMeng L, Ding Y, Li J, et al. Impact of inflammatory markers on the relationship between sleep quality and diabetic kidney disease[J]. Sleep Breath, 2022,26(1):157\u0026ndash;165.\u003c/li\u003e\n\u003cli\u003eHirano K, Komatsu Y, Shimbo T, et al. Longitudinal relationship between long sleep duration and future kidney function decline[J]. Clin Kidney J, 2022,15(9):1763\u0026ndash;1769.\u003c/li\u003e\n\u003cli\u003eXu S, Jin J, Dong Q, et al. 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[J]. Front Public Health, 2022,10:1072238.\u003c/li\u003e\n\u003cli\u003eYe Y, Zhang L, Yan W, et al. Self-reported sleep duration and daytime napping are associated with renal hyperfiltration and microalbuminuria in an apparently healthy Chinese population[J]. PLoS One, 2019,14(8):e214776.\u003c/li\u003e\n\u003cli\u003eBo Y, Yeoh E K, Guo C, et al. Sleep and the Risk of Chronic Kidney Disease: A Cohort Study[J]. J Clin Sleep Med, 2019,15(3):393\u0026ndash;400.\u003c/li\u003e\n\u003cli\u003eTahrani A A, Ali A, Raymond N T, et al. Obstructive sleep apnea and diabetic nephropathy: a cohort study[J]. Diabetes Care, 2013,36(11):3718\u0026ndash;3725.\u003c/li\u003e\n\u003cli\u003eZamarron E, Jaureguizar A, Garcia-Sanchez A, et al. Obstructive sleep apnea is associated with impaired renal function in patients with diabetic kidney disease[J]. Sci Rep, 2021,11(1):5675.\u003c/li\u003e\n\u003cli\u003eFranke F J, Arzt M, Kroner T, et al. Daytime napping and diabetes-associated kidney disease[J]. Sleep Med, 2019,54:205\u0026ndash;212.\u003c/li\u003e\n\u003cli\u003eJordan A S, McSharry D G, Malhotra A. Adult obstructive sleep apnoea[J]. Lancet, 2014,383(9918):736\u0026ndash;747.\u003c/li\u003e\n\u003cli\u003eCarrier J, Semba K, Deurveilher S, et al. Sex differences in age-related changes in the sleep-wake cycle[J]. Front Neuroendocrinol, 2017,47:66\u0026ndash;85.\u003c/li\u003e\n\u003cli\u003eMatoba K, Takeda Y, Nagai Y, et al. Unraveling the Role of Inflammation in the Pathogenesis of Diabetic Kidney Disease[J]. Int J Mol Sci, 2019,20(14).\u003c/li\u003e\n\u003cli\u003eReutrakul S, Van Cauter E. Sleep influences on obesity, insulin resistance, and risk of type 2 diabetes[J]. Metabolism, 2018,84:56\u0026ndash;66.\u003c/li\u003e\n\u003cli\u003eTanase D M, Gosav E M, Anton M I, et al. Oxidative Stress and NRF2/KEAP1/ARE Pathway in Diabetic Kidney Disease (DKD): New Perspectives[J]. Biomolecules, 2022,12(9).\u003c/li\u003e\n\u003cli\u003eJung C Y, Yoo T H. Pathophysiologic Mechanisms and Potential Biomarkers in Diabetic Kidney Disease[J]. Diabetes Metab J, 2022,46(2):181\u0026ndash;197.\u003c/li\u003e\n\u003cli\u003eVasavada N, Agarwal R. Role of oxidative stress in diabetic nephropathy[J]. Adv Chronic Kidney Dis, 2005,12(2):146\u0026ndash;154.\u003c/li\u003e\n\u003cli\u003eChang X, Chen X, Ji J S, et al. Association between sleep duration and hypertension in southwest China: a population-based cross-sectional study[J]. BMJ Open, 2022,12(6):e52193.\u003c/li\u003e\n\u003cli\u003eHe J, He Q. Association between Sleep Duration and Hypertension among Adults in Southwest China[J]. Glob Heart, 2022,17(1):10.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sleep duration, estimated glomerular filtration rate (eGFR), diabetes, diabetic kidney disease (DKD)","lastPublishedDoi":"10.21203/rs.3.rs-4306709/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4306709/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious epidemiological studies have demonstrated associations between sleep duration and diabetes, vascular complications and cardiovascular disease in diabetic patients. However, few studies have so far explored the effect of sleep time on estimated glomerular filtration rate (eGFR) in diabetic patients. This study was performed for the purpose of exploring the relationship between sleep duration and eGFR in diabetic patients. This study analyzed 1389 patients with diabetes. Sleep duration at night was categorized into four groups: very short (\u0026lt;\u0026thinsp;6h), short (6-6.9h), optimal (7-8h), and long (\u0026gt;\u0026thinsp;8h). The association of sleep duration with eGFR was analyzed using univariate linear regression, and generalized additive models were applied to assess the nonlinear relationship between sleep duration and eGFR. Compared to optimal sleep duration (7-8h), both long sleep duration (\u0026gt;\u0026thinsp;8h) and very short sleep duration (\u0026lt;\u0026thinsp;6h) were associated with level of eGFR (β = -3.63, 95%CI: -5.54 to -1.71, P\u0026thinsp;=\u0026thinsp;0.0002 and β = -4.79, 95%CI: -8.55 to -1.03, P\u0026thinsp;=\u0026thinsp;0.0126, respectively). The smooth curve showed there is a U-shaped relationship between sleep duration and eGFR levels. The results of this study show that both very short and long sleep durations were associated with low eGFR in diabetics.\u003c/p\u003e","manuscriptTitle":"Association between sleep duration and estimated glomerular filtration rate in Chinese patients with diabetes: evidence from a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 19:42:36","doi":"10.21203/rs.3.rs-4306709/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7e2fbdeb-3699-4edf-a4b9-ebb98a76eb40","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31267677,"name":"Health sciences/Endocrinology"},{"id":31267678,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases"}],"tags":[],"updatedAt":"2025-08-04T03:23:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 19:42:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4306709","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4306709","identity":"rs-4306709","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}