Abstract
Aims/Introduction
To investigate the correlation between snoring and chronic kidney disease (CKD), and explore whether metabolic syndrome (MetS) plays an important role in this relationship among middle‐aged and elderly Chinese.
Materials and Methods
The participants included in the present study were categorized into three subgroups based on self‐reported snoring frequency (regularly [≥3 times per week], occasionally [between ‘regularly’ and ‘never’] or never [<1 time per month]). An estimated glomerular filtration rate <60 mL/min/1.73 m2 was considered as CKD. We diagnosed MetS based on the 2004 Chinese Diabetes Society criteria. We explored the relationship between snoring and CKD by using multiple logistic regressions.
Results
The frequency of MetS, MetS components and CKD was dramatically higher in regular snorers than in non‐snorers and occasional snorers. The odds ratios for MetS and all the MetS elements, except for hyperglycemia, increased progressively with the snoring frequency (P < 0.001). Upon additional adjustment for other MetS components, snoring was not significantly related with hypertension; however, the associations between snoring frequency and overweight/obesity and dyslipidemia became attenuated, but still remained statistically significant (P < 0.01). Interestingly, odds ratios for CKD also increasingly augmented with snoring frequency (P < 0.001). Upon further adjustment for individual MetS components or MetS, regular snoring also resulted in a significantly increased odds ratio for CKD (odds ratio 1.72; P = 0.034) relative to non‐snoring.
Conclusions
Self‐reported snoring is closely associated with CKD independent of MetS among middle‐aged and elderly Chinese.
Keywords: Chronic kidney disease, Metabolic syndrome, Snoring
Introduction
Chronic kidney disease (CKD) is emerging as one of the biggest global public health issues with rapidly expanding prevalence1, 2. CKD contributes to the development of cardiovascular events and mortality3, 4. Metabolic syndrome (MetS), which includes hyperglycemia, hypertension, dyslipidemia and obesity, is a well‐known risk factor for CKD5. However, these metabolic disturbances do not sufficiently account for CKD progression; thus, novel risk factors for CKD must be identified for use in patient screening.
Obstructive sleep apnea (OSA), with the characteristic of upper airway obstruction, is the most common sleep problem leading to sleep fragmentation and intermittent hypoxia. OSA is closely related with an increased risk for metabolic disorders, including diabetes and hypertension, which can subsequently facilitate the progression of CKD6, 7, 8. In addition to indirect effects, a direct effect of OSA on CKD development and progression has been suggested9. However, the results are inconsistent, and additional investigation of the association between OSA and CKD is required.
Although snoring is a weakly specific manifestation of OSA, accumulating evidence has shown the association between regular snoring and various metabolic problems, including hypertension, type 2 diabetes and MetS10, 11, 12, 13, the most common CKD risk factors. Although the relationship between OSA and CKD has been well studied14, the specific relationship between snoring and CKD remains unclear. In addition, whether snoring is associated with CKD dependently or independently of metabolic disorders also remains unknown. Here, we carried out this cross‐sectional study in middle‐aged and elderly Chinese populations to address these issues.
Methods
Ethics statement
The present study was a component of the baseline Risk Evaluation of Cancers in Chinese Diabetic Individuals: A Longitudinal (REACTION) study, which recruited 259,657 participants (aged ≥40 years) from 25 communities in mainland China during 2011–201215. All of the participants signed informed consent. The Ruijin Hospital ethics committee of the Shanghai Jiao Tong University approved this study.
Study participants
10,028 adults (aged ≥40 years) from Shandong province were randomly recruited during January to April 2012, as described previously16. Briefly, the snoring frequency was recorded from the questionnaire ‘Did you snore in the last year?’, which included three levels (regularly [≥3 times per week], occasionally [between ‘regularly’ and ‘never’] or never [<1 time per month]), and 3,520 participants provided specific answers (6,508 participants did not provide specific answers). Then, the exclusion criteria of this study were as follows: (i) missing data for the estimated glomerular filtration rate (GFR; eGFR) calculations; (ii) missing data for diagnosis of MetS; (iii) previously diagnosed kidney disease, such as autoimmune or drug‐related kidney disease, nephritis, renal fibrosis or renal failure; (iv) previously diagnosed hepatic disease, such as fatty liver, liver cirrhosis and autoimmune hepatitis; and (v) any malignant diseases. Ultimately, 3,279 individuals (including 2,057 women) were included for analysis.
Data collection
Demographic and lifestyle information was collected from a standard questionnaire by face‐to‐face interview. Current smoking status and alcohol intake were reported as binary variables (yes, no). Body mass index (BMI) was defined as weight (kg) divided by squared body height (m2). Blood pressure was measured for all participants using OMRON Model HEM‐752 FUZZY (Omron Company, Dalian, China) from the left arm three consecutive times after they were seated for >5 min. The mean of three readings was used for further statistics. Fasting blood glucose, triglyceride, high‐density lipoprotein cholesterol and creatinine were measured with overnight fasting venous blood samples. The 2‐h plasma glucose was assayed when the participants underwent a 75‐g oral glucose tolerance test. High‐performance liquid chromatography (VARIANT II and D‐10 Systems; Bio‐Rad, Hercules, California, USA) was taken to assay hemoglobin A1c. eGFR was calculated by the Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) equation based on creatinine levels17. All relevant data are presented in Supplemental Files‐data.xls.
Definitions
eGFR < 60 mL/min/1.73 m2 was considered as CKD based on the Kidney Disease Outcomes Quality Initiative provided by the USA National Kidney Foundation18. Serum creatinine levels were assayed with the picric acid method using the automatic analyzer (ARCHITECT ci16200 Integrated System; Abbott Laboratories, Abbott Park, Illinois, USA) at the standardized laboratory of Ruijin Hospital in Shanghai, China.
According to the Chinese Diabetes Society criteria in 200419, MetS was diagnosed when at least three of the following disorders occurred: (i) overweight and/or obesity – BMI ≥ 25.0 kg/m2; (ii) hyperglycemia – fasting plasma glucose ≥6.1 mmol/L and/or 2‐h plasma glucose ≥7.8 mmol/L or diagnosed as type 2 diabetes mellitus before or received medicine; (iii) hypertension – systolic/diastolic blood pressure ≥140/90 mmHg or diagnosed hypertensive before and received medicine; and (iv) dyslipidemia – triglyceride level ≥1.7 mmol/L and/or high‐density lipoprotein cholesterol level <0.9 mmol/L (men) or <1.0 mmol/L (women).
Statistical analysis
Continuous variables showing normal distributions were presented as the mean ± standard deviation, whereas variables with non‐normal distributions were shown as medians (interquartile range). Values (%) were used to express categorical variables. One‐way anova (least significant difference; continuous variables showing normal distributions), the Kruskal–Wallis H‐test (skewed continuous variables) and χ2‐test (categorical variables) were carried out to compare the differences in the three groups. The relationships between the snoring frequency and MetS, MetS components, and CKD were statistically evaluated by multiple logistic regressions with adjustments of relevant covariates as follows: model 1 was not adjusted; model 2 was adjusted with sex and age; and model 3 was adjusted with age, sex, smoking status and alcohol intake. Subsequent models were further adjusted for different MetS components in addition to the model 3 covariates. The linear trends in the relationship between the snoring frequency and MetS, MetS components, and CKD were calculated by considering the snoring frequency as continuous variables. Statistical significance was defined as P < 0.05. All of the statistics were taken by SPSS 16.0 (SPSS Inc., Chicago, Illinois, USA).
Results
Study participant characteristics based on snoring frequency
A total of 3,279 adults including 2,057 women were divided into three groups accorded with the snoring frequency. As shown in Table 1, the prevalence of MetS and CKD was 34.0 and 4.9%, respectively. Compared with non‐snorers and occasional snorers, regular snorers were older, male, smokers and alcohol drinkers. Additionally, regular snorers showed an increased incidence of MetS, MetS components and CKD.
Table 1.
Characteristics of the study participants based on snoring frequency
| Characteristic | Snoring frequency | P‐value | ||
|---|---|---|---|---|
| Never n = 877 | Occasionally n = 1,596 | Regularly n = 806 | ||
| Female (%) | 614 (70.0) | 1,053 (66.0) | 390 (48.4) | <0.001 |
| Age (years) | 59.33 ± 9.97 | 58.98 ± 9.62 | 60.42 ± 8.87 | 0.002 |
| BMI (kg/m2) | 25.72 ± 3.29 | 26.26 ± 3.43 | 27.05 ± 3.51 | <0.001 |
| SBP (mmHg) | 139.06 ± 20.26 | 140.02 ± 21.22 | 142.52 ± 19.26 | 0.002 |
| DBP (mmHg) | 79.65 ± 11.26 | 79.92 ± 11.47 | 81.65 ± 11.46 | <0.001 |
| FPG (mmol/L) | 5.98 ± 1.72 | 5.98 ± 1.81 | 6.34 ± 1.90 | <0.001 |
| 2hPG (mmol/L) | 7.30 (4.90–8.00) | 7.34 (4.92–8.17) | 7.79 (5.00–9.00) | 0.002 |
| HbA1c (%) | 6.18 ± 1.19 | 6.17 ± 1.10 | 6.34 ± 1.19 | 0.002 |
| TG (mmol/L) | 1.51 (0.91–1.79) | 1.59 (0.93–1.89) | 1.73 (1.06–2.07) | <0.001 |
| HDL‐C (mmol/L) | 1.50 ± 0.35 | 1.49 ± 0.32 | 1.44 ± 0.30 | <0.001 |
| eGFR (mL/min/1.73 m2) | 89.79 ± 13.94 | 88.79 ± 15.03 | 84.37 ± 14.39 | <0.001 |
| Smoking (%) | 82 (9.4) | 174 (10.9) | 156 (19.4) | <0.001 |
| Drinking (%) | 62 (7.1) | 127 (8.0) | 144 (17.9) | <0.001 |
| Overweight/obesity (%) | 482 (55.0) | 990 (62.0) | 580 (72.0) | <0.001 |
| Hyperglycemia (%) | 325 (37.1) | 574 (36.0) | 357 (44.3) | <0.001 |
| Hypertension (%) | 497 (56.7) | 921 (57.7) | 543 (67.4) | <0.001 |
| Dyslipidemia (%) | 261 (29.8) | 517 (32.4) | 326 (40.4) | <0.001 |
| MetS (%) | 268 (30.6) | 499 (31.3) | 347 (43.1) | <0.001 |
| CKD (%) | 29 (3.3) | 77 (4.8) | 54 (6.7) | 0.005 |
Data are expressed as the mean ± standard deviation, median (interquartile range) or n (%). 2hPG, 2‐h plasma glucose; BMI, body mass index; CKD, chronic kidney disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; HDL‐C, high‐density lipoprotein cholesterol; MetS, metabolic syndrome; SBP, systolic blood pressure; TG, triglyceride.
Relationship between snoring frequency and MetS by multiple logistic regression analysis
We next investigated the relationships between the snoring frequency and MetS or each MetS component by using different models. As presented in Table 2, the odds ratios (ORs) of MetS showed a progressive increase along with the snoring frequency (P < 0.001), and regular snorers showed an OR of 1.70 (P < 0.001) relative to non‐snorers with adjustment of age, sex, smoking status and alcohol drinking (model 3). The MetS components showed similar trends, except for hyperglycemia, after adjusting for the aforementioned covariates (model 3). However, when further adjustments for other components in MetS were included (model 4), the associations between snoring frequency overweight/obesity and dyslipidemia were attenuated, but still remained significant (P < 0.01). No significant differences were observed in the relationship between snoring frequency and hypertension after these additional adjustments based on model 4.
Table 2.
Multiple logistic regression analysis of the association between snoring frequency and metabolic syndrome
| Never | Occasionally Odds ratio (95% CI), P‐value | Regularly Odds ratio (95% CI), P‐value | P for trend | |
|---|---|---|---|---|
| MetS | ||||
| Model 1 | 1 | 1.03 (0.87–1.24), 0.716 | 1.72 (1.41–2.10), <0.001a | <0.001a |
| Model 2 | 1 | 1.05 (0.88–1.26), 0.579 | 1.69 (1.38–2.08), <0.001a | <0.001a |
| Model 3 | 1 | 1.05 (0.88–1.26), 0.575 | 1.70 (1.38–2.09), <0.001a | <0.001a |
| Overweight/obesity | ||||
| Model 1 | 1 | 1.34 (1.13–1.58), 0.001a | 2.10 (1.72–2.58), <0.001a | <0.001a |
| Model 2 | 1 | 1.34 (1.13–1.58), 0.001a | 2.03 (1.65–2.49), <0.001a | <0.001a |
| Model 3 | 1 | 1.34 (1.13–1.58), 0.001a | 2.04 (1.66–2.51), <0.001a | <0.001a |
| Model 4 | 1 | 1.34 (1.12–1.59), 0.001 | 1.84 (1.49–2.28), <0.001 | <0.001 |
| Hypertension | ||||
| Model 1 | 1 | 1.04 (0.88–1.23), 0.618 | 1.58 (1.29–1.93), <0.001a | <0.001a |
| Model 2 | 1 | 1.05 (0.89–1.25), 0.567 | 1.42 (1.15–1.74), 0.001a | <0.001a |
| Model 3 | 1 | 1.05 (0.88–1.25), 0.572 | 1.40 (1.14–1.73), 0.002a | <0.001a |
| Model 4 | 1 | 0.99 (0.83–1.19), 0.926 | 1.17 (0.94–1.45), 0.157 | 0.667 |
| Hyperglycemia | ||||
| Model 1 | 1 | 0.95 (0.80–1.13), 0.589 | 1.35 (1.11–1.64), 0.003a | 0.667 |
| Model 2 | 1 | 0.97 (0.81–1.15), 0.697 | 1.29 (1.06–1.58), 0.013a | 0.667 |
| Model 3 | 1 | 0.97 (0.81–1.15), 0.706 | 1.32 (1.08–1.61), 0.008a | 0.667 |
| Model 4 | 1 | 0.93 (0.78–1.11), 0.400 | 1.16 (0.94–1.42), 0.168 | 0.667 |
| Dyslipidemia | ||||
| Model 1 | 1 | 1.13 (0.95–1.35), 0.177 | 1.60 (1.31–1.96), <0.001a | <0.001a |
| Model 2 | 1 | 1.14 (0.95–1.36), 0.152 | 1.62 (1.32–1.99), <0.001a | <0.001a |
| Model 3 | 1 | 1.14 (0.95–1.36), 0.153 | 1.61 (1.31–1.98), <0.001a | <0.001a |
| Model 4 | 1 | 1.10 (0.91–1.32), 0.316 | 1.40 (1.13–1.73), 0.002a | <0.001a |
P < 0.05. Model 1: not adjusted; model 2: adjusted for age and sex; model 3: adjusted for age, sex, smoking status and drinking status; and model 4: adjusted as described for model 3 in addition to other metabolic syndrome (MetS) components. CI, confidence interval.
Relationship between snoring frequency and CKD by multiple logistic regressions analysis
To elucidate the specific relationship between snoring frequency and CKD, and whether MetS plays a role in this relationship, we used different models. As presented in Table 3, the ORs for CKD increased progressively with the snoring frequency (P < 0.001), and an OR of 1.79 was shown in regular snorers (P = 0.022) relative to non‐snorers after adjusting for age, sex, smoking and alcohol drinking (model 3). When we adjusted of MetS components or MetS, regular snorers showed significantly greater ORs for CKD (models 4–8).
Table 3.
Multiple logistic regression analysis of the association between the snoring frequency and chronic kidney disease
| Never | Occasionally Odds ratio (95% CI), P‐value | Regularly Odds ratio (95% CI), P‐value | P for trend | |
|---|---|---|---|---|
| Model 1 | 1 | 1.48 (0.96–2.29), 0.076 | 2.18 (1.38–3.46), 0.001a | <0.001a |
| Model 2 | 1 | 1.59 (0.99–2.55), 0.053 | 1.78 (1.08–2.93), 0.023a | <0.001a |
| Model 3 | 1 | 1.59 (0.99–2.54), 0.055 | 1.79 (1.09–2.94), 0.022a | <0.001a |
| Model 4 | 1 | 1.55 (0.97–2.49), 0.068 | 1.69 (1.03–2.80), 0.039a | <0.001a |
| Model 5 | 1 | 1.59 (0.99–2.56), 0.054 | 1.81 (1.10–2.99), 0.020a | <0.001a |
| Model 6 | 1 | 1.59 (0.99–2.55), 0.053 | 1.78 (1.08–2.93), 0.024a | <0.001a |
| Model 7 | 1 | 1.57 (0.98–2.52), 0.062 | 1.70 (1.03–2.81), 0.038a | <0.001a |
| Model 8 | 1 | 1.59 (0.99–2.55), 0.056 | 1.72 (1.04–2.84), 0.034a | <0.001a |
P < 0.05. Model 1: not adjusted; model 2: adjusted for age and sex; model 3: adjusted for age, sex, smoking status and drinking status; model 4: adjusted as described for model 3 in addition to overweight/obesity; model 5: adjusted as described for model 3 in addition to hypertension; model 6: adjusted as described for model 3 in addition to hyperglycemia; model 7: adjusted as described for Model 3 in addition to dyslipidemia; and model 8: adjusted as described for model 3 in addition to metabolic syndrome. CI, confidence interval.
Discussion
The main findings of the present study were the close relationships between snoring, a common but weakly specific marker of OSA, and CKD independent of MetS. The associations between sleep disturbances and the development or progression of CKD have recently been well studied9. These results are inconsistent; however, it appears that sleep disturbances exert a negative effect on CKD. In addition, polysomnography is the gold standard of evaluating sleeping quality. It combines whole‐night recordings with a multiple‐lead electroencephalogram of muscle tones or eyes movement measurements; however, polysomnography is too complicated and time‐consuming to be carried out in typical clinical practice. Therefore, wrist actigraphy and several validated questionnaires are used to assess sleep duration and quality; these techniques were used in most studies to investigate the correlations between sleep disturbances and CKD9. However, these methods are not easily implemented for disease prevention among the many individuals who might not undergo the examinations described above. As a common manifestation of the sleep disturbance disease, OSA, snoring can be easily monitored and improved for disease prevention. Only a few studies recently analyzed the correlation between breathing disorder during sleep and CKD independent of other disorders, such as diabetes and hypertension20. Therefore, we explored this issue in the present survey and discovered regular snoring was closely related to CKD independent of MetS.
Sleep disorders can indirectly or directly impact CKD. Their indirect impact might be attributed to the following parameters. Hypertension is the most well recognized risk factor contributing to the development or progression of CKD21. Given the well‐established relationship between snoring and hypertension by previous studies22, 23, 24, whether snoring affects CKD development by promoting hypertension remains unclear. Therefore, we analyzed the correlation between snoring frequency and hypertension and CKD in the present study. Unexpectedly, our data showed that the snoring frequency was not related with hypertension after multiple adjustments (Table 2; model 4). Our finding is in contrast with previous studies, which might be due to the different populations recruited in these studies. Lindberg et al.23 found snoring was a risk factor for the development of hypertension in men aged <50 years, but there was no such relationship detected in men aged ≥50 years. The present study primarily consisted of middle‐aged and older individuals (age of regular snorers 60.42 ± 8.87 years), and most of the participants were aged >50 years. Thus, no correlation between snoring frequency and hypertension was found. Furthermore, the close relationship between snoring frequency and CKD was independent of hypertension (Table 3; model 5). Hyperglycemia and, thus, diabetes, is the most well‐known risk factor for CKD25, 26. Additionally, a significant association between snoring and diabetes has been suggested by many studies13, 27, 28. Therefore, we analyzed the effect of snoring on hyperglycemia and further investigated the possible role of hyperglycemia in the relationship between snoring frequency and CKD. Surprisingly, we did not detect a significant association between snoring frequency and hyperglycemia with other MetS components adjustment (Table 2; model 4); this result was similar to the results of a Korean population‐based study29. Furthermore, snoring resulted in increased ORs of CKD independent of hyperglycemia (Table 3; model 6). Accumulating evidence has shown that obesity is an independent risk factor of CKD, as well as for the progression from CKD to kidney failure and the need for dialysis30, 31. Positive associations between snoring and abdominal obesity12, 32 and BMI have been observed33. As expected, snoring was positively associated with BMI in the present study (Table 2; model 4). However, on further adjustment for overweight/obesity, snoring frequency remained closely associated with CKD (Table 3; model 4).
In addition to the aforementioned metabolic disorders, dyslipidemia is always observed in CKD patients, and thus might facilitate the development of cardiovascular complications and many other complications of CKD34. Furthermore, our present study showed that snoring was significantly associated with dyslipidemia (Table 2; model 4), consistent with a previous study12. However, snoring remained significantly associated with CKD after adjusting for dyslipidemia (Table 3; model 7). Even upon adjustment for MetS rather than any individual MetS component, snoring resulted in significantly increased ORs for CKD (Table 3; model 8).
All of the data described above showed that snoring is closely associated with MetS, primarily through the associations between snoring and overweight/obesity and dyslipidemia in the present study. In addition, snoring is associated with CKD independent of its indirect effects on MetS. The additional indirect or direct effects of snoring on CKD require further investigation. In fact, the direct impact of sleep disorders on CKD have been supported by two lines of evidence that have been discussed in detail9. First, sleep exerts an important effect on the regulation of kidney function; thus, reduced sleep quality and duration might negatively affect kidney function. Second, recent studies suggest that OSA is an independent risk factor for CKD by inducing endothelial injury or vessel stiffness. Therefore, as a common manifestation of OSA, snoring can also directly affect the development and progression to CKD.
Of course, several limitations existed in the present study. First, the study was cross‐sectional, so it cannot avoid causality between snoring and CKD. A prospective study is required to analyze the association between snoring and CKD development or progression. Second, the data about snoring frequency was acquired by a self‐reported questionnaire rather than a more accurate assay by polysomnography. Furthermore, other characteristics of snoring, such as loud and disturbing noises, were not considered in the present study. Additionally, other symptoms of OSA, such as daytime sleepiness and fatigue, were not considered here. Third, without measuring the urinary protein levels, eGFR based on creatinine levels using the CKD‐EPI equation might not accurately reflect kidney function, and thus affect the accurate evaluation of CKD. However, the gold standard to measure GFR (isotope clearance measurement) was too expensive and time‐consuming. Thus, the equation based on creatinine to evaluate GFR is logical in epidemiological surveys. Furthermore, the CKD‐EPI equation was more accurate as compared with the Modification of Diet in Renal Disease study equation and the Cockcroft–Gault equation35. Thus, the CKD‐EPI equation might be the optimal method to estimate the GFR in the present study. Fourth, as there is a high prevalence of hypothyroidism in middle‐aged and elderly people, especially in women36, 37, further studies are required to test thyroid function and analyze the relationship between snoring and hypothyroidism. Fifth, the quantity of smoking or drinking is the key to its correlation with hypertension, hyperlipidemia, diabetes and so on. The present study could not classify the smoking and drinking state according to quantity. Despite these limitations, our study revealed an association between disordered breathing during sleep, as defined as regular snoring, and CKD.
In conclusion, the present study shows that snoring is closely associated with CKD independent of MetS. Early detection of patients showing habitual/regular snoring and interventions for the associated MetS could have important preventive implications.
Disclosure
The authors declare no conflict of interest.
Acknowledgments
The present study was supported by the National Natural Science Foundation of China (No. 81100617), the National Science and Technology Support Plan (2009BAI80B04), Distinguished Middle‐Aged and Young Scientist Encourage and Reward Foundation of Shandong Province (BS2013YY033), China National Natural Science Foundation (No. 81370943), China Postdoctoral Science Foundation (2015M580334), and Shanghai Municipal Health and Family Planning Commission Project (20134189).
J Diabetes Investig 2019; 10: 124–130
Contributor Information
Li Chen, Email: chenliqilu@163.com.
Weiping Jia, Email: wpjia@sjtu.edu.cn.
References
- 1. Neuen BL, Chadban SJ, Demaio AR, et al Chronic kidney disease and the global NCDs agenda. BMJ Glob Health 2017; 2: e000380. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Zhang L, Wang F, Wang L, et al Prevalence of chronic kidney disease in China: a cross‐sectional survey. Lancet 2012; 379: 815–822. [DOI] [PubMed] [Google Scholar]
- 3. Di Lullo L, House A, Gorini A, et al Chronic kidney disease and cardiovascular complications. Heart Fail Rev 2015; 20: 259–272. [DOI] [PubMed] [Google Scholar]
- 4. Hallan SI, Matsushita K, Sang Y, et al Age and association of kidney measures with mortality and end‐stage renal disease. JAMA 2012; 308: 2349–2360. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Kang YU, Kim HY, Choi JS, et al Metabolic syndrome and chronic kidney disease in an adult Korean population: results from the Korean National Health Screening. PLoS One 2014; 9: e93795. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Knutson KL, Ryden AM, Mander BA, et al Role of sleep duration and quality in the risk and severity of type 2 diabetes mellitus. Arch Intern Med 2006; 166: 1768–1774. [DOI] [PubMed] [Google Scholar]
- 7. Ozkok A, Kanbay A, Odabas AR, et al Obstructive sleep apnea syndrome and chronic kidney disease: a new cardiorenal risk factor. Clin Exp Hypertens 2014; 36: 211–216. [DOI] [PubMed] [Google Scholar]
- 8. Peppard PE, Young T, Palta M, et al Prospective study of the association between sleep‐disordered breathing and hypertension. N Engl J Med 2000; 342: 1378–1384. [DOI] [PubMed] [Google Scholar]
- 9. Turek NF, Ricardo AC, Lash JP. Sleep disturbances as nontraditional risk factors for development and progression of CKD: review of the evidence. Am J Kidney Dis 2012; 60: 823–833. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Sabanayagam C, Zhang R, Shankar A. Markers of Sleep‐Disordered Breathing and Metabolic Syndrome in a Multiethnic Sample of US Adults: results from the National Health and Nutrition Examination Survey 2005‐2008. Cardiol Res Pract 2012; 2012: 630802. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Shivashankar R, Kondal D, Ali MK, et al Associations of sleep duration and disturbances with hypertension in metropolitan cities of Delhi, Chennai, and Karachi in South Asia: Cross‐Sectional Analysis of the CARRS Study. Sleep 2017; 40: zsx119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Sun L, Pan A, Yu Z, et al Snoring, inflammatory markers, adipokines and metabolic syndrome in apparently healthy Chinese. PLoS One 2011; 6: e27515. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Xiong X, Zhong A, Xu H, et al Association between self‐reported habitual snoring and diabetes mellitus: a systemic review and meta‐analysis. J Diabetes Res 2016; 2016: 1958981. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Yayan J, Rasche K, Vlachou A. Obstructive Sleep Apnea and Chronic Kidney Disease. Adv Exp Med Biol 2017; 1022: 11–18. [DOI] [PubMed] [Google Scholar]
- 15. Ning G, Reaction Study Group . Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study. J Diabetes 2012; 4: 172–173. [DOI] [PubMed] [Google Scholar]
- 16. Hou XG, Wang C, Ma ZQ, et al Optimal waist circumference cut‐off values for identifying metabolic risk factors in middle‐aged and elderly subjects in Shandong Province of China. Biomed Environ Sci 2014; 27: 353–359. [DOI] [PubMed] [Google Scholar]
- 17. Levey AS, Stevens LA, Schmid CH, et al A new equation to estimate glomerular filtration rate. Ann Intern Med 2009; 150: 604–612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. National Kidney Foundation . K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002; 39(2 Suppl 1): S1–S266. [PubMed] [Google Scholar]
- 19. Expert Panel on Metabolic Syndrome of Chinese Diabetes Society . Recommendations on metabolic syndrome of Chinese diabetes society. Chin J Diab 2004; 12: 156–161 (Chinese). [Google Scholar]
- 20. Nicholl DD, Ahmed SB, Loewen AH, et al Clinical presentation of obstructive sleep apnea in patients with chronic kidney disease. J Clin Sleep Med 2012; 8: 381–387. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Chang CP, Li TC, Hang LW, et al The relationships of sleep apnea, hypertension, and resistant hypertension on chronic kidney disease. Medicine (Baltimore) 2016; 95: e3859. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Lindberg E, Berne C, Franklin KA, et al Snoring and daytime sleepiness as risk factors for hypertension and diabetes in women–a population‐based study. Respir Med 2007; 101: 1283–1290. [DOI] [PubMed] [Google Scholar]
- 23. Lindberg E, Janson C, Gislason T, et al Snoring and hypertension: a 10 year follow‐up. Eur Respir J 1998; 11: 884–889. [DOI] [PubMed] [Google Scholar]
- 24. Wang T, Lu J, Wang W, et al Sleep duration and snoring associate with hypertension and glycaemic control in patients with diabetes. Diabet Med 2015; 32: 1001–1007. [DOI] [PubMed] [Google Scholar]
- 25. Pyram R, Kansara A, Banerji MA, et al Chronic kidney disease and diabetes. Maturitas 2012; 71: 94–103. [DOI] [PubMed] [Google Scholar]
- 26. Saran R, Li Y, Robinson B, et al US Renal data system 2015 annual data report: epidemiology of kidney disease in the United States. Am J Kidney Dis 2016;67(3 Suppl 1): Svii, S1–S305. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Al‐Delaimy WK, Manson JE, Willett WC, et al Snoring as a risk factor for type II diabetes mellitus: a prospective study. Am J Epidemiol 2002; 155: 387–393. [DOI] [PubMed] [Google Scholar]
- 28. Elmasry A, Janson C, Lindberg E, et al The role of habitual snoring and obesity in the development of diabetes: a 10‐year follow‐up study in a male population. J Intern Med 2000; 248: 13–20. [DOI] [PubMed] [Google Scholar]
- 29. Cho N, Joo S, Kim J, et al Relation of habitual snoring with components of metabolic syndrome in Korean adults. Diabetes Res Clin Pract 2006; 71: 256–263. [DOI] [PubMed] [Google Scholar]
- 30. Lai YJ, Hu HY, Lee YL, et al Association between obesity and risk of chronic kidney disease: a nationwide Cohort study in Taiwan. Nutr Metab Cardiovasc Dis 2017; 27: 1008–1014. [DOI] [PubMed] [Google Scholar]
- 31. MacLaughlin HL, Hall WL, Sanders TA, et al Risk for chronic kidney disease increases with obesity: health Survey for England 2010. Public Health Nutr 2015; 18: 3349–3354. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Shin MH, Kweon SS, Choi BY, et al Self‐reported snoring and metabolic syndrome: the Korean Multi‐Rural Communities Cohort Study. Sleep Breath 2014; 18: 423–430. [DOI] [PubMed] [Google Scholar]
- 33. Wall H, Smith C, Hubbard R. Body mass index and obstructive sleep apnoea in the UK: a cross‐sectional study of the over‐50s. Prim Care Respir J 2012; 21: 371–376. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Vaziri ND, Norris K. Lipid disorders and their relevance to outcomes in chronic kidney disease. Blood Purif 2011; 31: 189–196. [DOI] [PubMed] [Google Scholar]
- 35. Jessani S, Levey AS, Bux R, et al Estimation of GFR in South Asians: a study from the general population in Pakistan. Am J Kidney Dis 2014; 63: 49–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Li TM. Hypothyroidism in elderly people. Geriatr Nurs 2002; 23: 88–93. [DOI] [PubMed] [Google Scholar]
- 37. Virta LJ, Eskelinen SI. Prevalence of hypothyroidism in Finland–a nationwide prescription study. Eur J Clin Pharmacol 2011; 67: 73–77. [DOI] [PubMed] [Google Scholar]