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Association between waist circumference and sleep disorder in the elderly: Based on the NHANES 2005–2018
Waist circumference and sleep disorder
Zhong Yuting Data curation Formal analysis Investigation Writing – original draft 1
Li Ying Funding acquisition Methodology Validation 1
Zhong Maolin Data curation Validation Visualization 1
Peng Cheng Data curation Visualization 2
Zhang Hui Data curation Software 3
https://orcid.org/0009-0001-6825-4483
Tian Kejun Conceptualization Data curation Formal analysis Investigation Methodology Project administration Writing – review & editing 4 *
1 Department of Anesthesiology, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
2 Department of Anesthesiology, Ganzhou People’s Hospital, Ganzhou, Jiangxi, China
3 Department of Experimental Medicine, University of Rome Tor Vergata, Rome, Italy
4 Department of Cardiology, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
Khashayar Patricia Editor
Gent University, BELGIUM
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: tiankejun371@163.com
23 9 2024
2024
19 9 e030886011 4 2024
31 7 2024
© 2024 Zhong et al
2024
Zhong et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The existing data do not consistently support the link between elderly adults’ waist circumferences and sleep disorders. This study aimed to evaluate whether waist circumference was connected with sleep disorder in the elderly. This cross-sectional study utilized data from the 2005–2018 National Health and Nutrition Examination Survey (NHANES) regarding waist circumference, sleep disorders, and confounding factors. Included in the study were participants older than 60 who completed sleep questionnaires and waist circumference measurements. Using a multivariate logistic regression model and subgroup analyses, the relationship between waist circumference and sleep disorder was evaluated. To explore the non-linear relationship, restricted cubic spline (RCS) with three knots coupled with a logistic regression model to assess the dose-response relationship between waist circumference (continuous variables) and sleep disorder. A total of 2,545 (Weighted 14,682,916.3) elderly participants with complete information were included in the analysis and 312 (Weighted 1,777,137.8) subjects met the definition of sleep disorder. Compared with participants without sleep disorder, those with sleep disorder had a higher waist circumference (100.80 cm vs. 108.96 cm, P< 0.001). The results of the multivariable adjusted logistic regression model suggested that those in quartiles 4 (≥ 75th percentile) for their waist circumference had higher odds of sleep disorder [adjusted odds ratio (AOR) = 2.75, 95% confidence interval (CI) = 1.66–4.54, P < 0.001] compared with those in quartile 1. The RCS result showed that the OR of sleep disorder and waist circumference displayed a linear relationship (P <0.001, Non-linear P = 0.642). Age and gender subgroup analysis revealed comparable relationships between waist circumference and sleep disorder among elderly individuals. Waist circumference was associated with sleep disorders in the elderly. There was a dose-response relationship between waist circumference and the likelihood of sleep disorder. Those with a larger waist circumference were more likely to have a sleep disorder than those with a smaller waist circumference.

The author(s) received no specific funding for this work. Data AvailabilityThe datasets given in this investigation are accessible through the online repositories (https://www.cdc.gov/nchs/nhanes/index.htm).
Data Availability

The datasets given in this investigation are accessible through the online repositories (https://www.cdc.gov/nchs/nhanes/index.htm).
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pmcIntroduction

Sleep disorders are prevalent among the elderly, and they can have a substantial impact on their quality of life and overall health [1, 2]. Approximately fifty percent of the elderly, according to statistics, frequently experience sleep problems in their daily lives [3]. Previous studies have shown that sleep disorders are associated with a range of adverse outcomes, including cognitive impairment [4], depression [5, 6], cardiovascular disease [7], and lower urinary tract symptoms [8]. In addition, the proportion of elderly people is increasing due to increased life expectancy and enhanced socioeconomic development [9]. Examining prospective risk factors for sleep disorders may therefore facilitate sleep disorder prevention and intervention, thereby enhancing the quality of life of the elderly and decreasing the disease burden on society.

Recent interest in the potential function of body composition measures in the development of sleep disorders has increased [10]. A substantial amount of research has focused on exploring the relationship between obesity and sleep disorder, highlighting the bidirectional nature of this association [11, 12]. Although body mass index (BMI) has been used as an important indicator for assessing obesity, waist circumference is a more sensitive predictor of health risks associated with obesity than BMI [13]. Waist circumference is a common indicator of central adiposity and has been associated with a variety of adverse health outcomes, such as cardiovascular disease, metabolic syndrome, and type 2 diabetes [14]. However, the association between waist circumference and sleep disorder in the elderly population remains ambiguous.

Given the high prevalence of sleep disorders in the geriatric population and the potential influence of waist circumference on sleep, additional research is required in this area. National Health and Nutrition Examination Survey (NHANES) is a complex, multistage probability design sample of the noninstitutionalized U.S. population. Using NHANES data, the purpose of this investigation was to evaluate the association between waist circumference and sleep disorder in the elderly. This study’s findings could have significant implications for the prevention and treatment of sleep disorders in the geriatric population.

Methods

Study population

The National Health and Nutrition Examination Survey (NHANES) is a nationally representative survey of the U.S. population that provides extensive information about the nutrition and health of the general U.S. population. The NHANES uses a complex, multistage probability sampling design to ensure that the data collected is representative of the civilian, non-institutionalized population of the U.S. [15]. The NHANES survey data are publicly accessible to data researchers and consumers. The National Center for Health Statistics (NCHS) collects its data in biennial cycles [16]. To acquire large samples for analysis, seven cycles of continuous NHANES data from 2005 to 2018 were combined. The protocol for the survey was endorsed by the NCHS Research Ethics Review Board, and all participants provided written informed consent. There is more information about the NHANES available at http://www.cdc.gov/nhanes.

Of 70,190 participants extracted from the NHANES database, we excluded those with age < 60 years (n = 56,710), missing information on sleep disorder (n = 4,071), and other covariates (n = 6,864). Finally, a total of 2,545 participants were included in this study. The flow chart of the systematic selection process is shown in Fig 1.

10.1371/journal.pone.0308860.g001 Fig 1 Screening of admissions for inclusion.

Main independent variable

All waist circumference is taken at the mobile examination center (MEC). We divided the waist circumference of the subjects into groups of Q1 (< 92.5cm), Q2 (92.5–101.3cm), Q3 (101.3–110.8cm) and Q4 (≥ 110.8cm) according to quartile.

Covariates

We incorporated the following covariates derived from NHANES interview, examination, laboratory, and questionnaire data: Age, gender, race, education level, marital status, family income (poverty income ratio, PIR), alcohol intake, smoking status, and recreational physical activity. Age was categorized into three groups: 60–69 years, 70–79 years, and 80+ years. Race was categorized into five groups: Mexican American, non-Hispanic Black, non-Hispanic White, other Hispanic, and other/multiracial. Education level was assessed by the question “What is the highest grade or level of school you have completed or the highest degree you have received?” [less than high school/high school graduate or above]. Participants were divided into two categories for marital status (have spouse/without spouse). Based on the original survey records, PIR was assessed. Participants were divided into four categories for alcohol intake (non-drinker, 1 to < 5 drinks/month, 5 to < 10 drinks/month, or 10+ drinks/month). Participants were asked whether they had ever smoked 100 cigarettes in their lifetime and whether they smoked currently to identify current and former smokers. Participants were defined as former smokers if they did not smoke currently but had ever smoked 100 cigarettes in the past. We also included total daily calories as a potential covariable. Recreational physical activity was assessed using the "Physical Activity" questionnaire. Within this questionnaire, participants were queried about the frequency of engaging in vigorous work activity, the number of days they participated in vigorous or moderate recreational activities, and the duration of vigorous-intensity work and moderate-intensity recreational activities in minutes. According to the Physical Activity Guidelines, it is recommended that individuals engage in at least 75 minutes of vigorous exercise per week or 150 minutes of moderate exercise per week. Doctors diagnosed diseases, including diabetes and stroke, basking participants “Have you ever been told by a doctor or health professional that you have __?”. Comorbidity included 1) congestive heart failure, 2) chronic obstructive pulmonary disease (emphysema and/or chronic bronchitis), 3) coronary artery disease, 4) cancer and 5) hypertension [17]. The comorbidity index indicates how many complications an individual has.

Outcome variable

The outcome variable was that the participant had sleep disorder. Data were collected in participants’ homes by interviewers using a computer-assisted personal interview (CAPI) system. Various questions concerning sleep patterns were asked, including usual sleep time on weekdays or workdays, usual wake time on weekdays or workdays, sleep hours, how often participants snore, snort, or stop breathing, if participants have ever told a doctor, they had trouble sleeping, and how often they feel overly sleepy during the day. In our study, the NHANES variable SLQ050 (ever told a doctor or other health professional that you had trouble sleeping) was used to determine if a participant had a sleep disorder [18].

Statistical analysis

The intricate sampling methodology employed, as detailed in reference [19], encompasses stratified, cluster, and multistage sampling, along with unequal probability sampling proportional to a measure of size (PPS). This approach necessitates the consideration of sampling weights. This design allows for the integration of additional cycles, thereby enhancing statistical reliability (WTDRD1/7). However, conventional regression methods are inadequate as they can yield incorrect inferential outcomes. Specifically, the standard error and confidence intervals of parameter estimates may be significantly underestimated, and the probability of Type I errors in hypothesis testing is substantially elevated. Consequently, we used SURVEYMEANS, SURVEYREG, and SURVEYLOGISTIC to perform accurate statistical descriptions and logistic regression analyses that account for complex sampling designs.

Continuous variables are presented as weighted medians with interquartile ranges (IQRs), while categorical variables are displayed as weighted numbers and weighted percentages. Group comparisons were performed using the χ2 test for categorical variables and the Mann-Whitney U test for continuous variables, as appropriate. Waist circumference was categorized into four groups. Weighted multivariate logistic regression analyses were conducted to explore the relationship between waist circumference and sleep disorders. In the unadjusted Model 1, no confounders were controlled. Model 2 adjusted for gender and age. Beyond the adjustments made in Model 2, additional potential confounding factors were accounted for in further analyses. To explore the non-linear relationship, weighted restricted cubic spline (RCS) with three knots coupled with a logistic regression model to assess the dose-response relationship between waist circumference (continuous variables) and sleep disorder. Subgroup analyses stratified by age, gender and race were performed. All statistical analyses were performed using R software (version 4.1.1), and P < 0.05 was considered statistically significant.

Results

Description of the study population

A total of 2,545 (Weighted 14,682,916.3) participants aged 60–85 years who had measured data for waist circumference and sleep disorder were included in this study. Of the included participants, 52.6% (weighted 7,725,328.4) were 60–69 years old, followed by 70–79 years old (27.1%, weighted 3,982,584.6), and 80+ years old (20.3%, weighted 2,975,003.3). The gender distribution was relatively equal. Participants with sleep problems made up more than a tenth of the study population (12.1%). Gender, waist circumference, smoking status, recreational physical activity, diabetes, stroke, and comorbidity index were significantly different in the sleep disorder group compared with the non-sleep disorder group (P <0.05, Table 1). Participants with sleep disorder were more likely to be male, larger waist circumference, higher percentage of current smokers, lower percentage of recreational physical activity, had a history of diabetes and stroke, and higher comorbidity index.

10.1371/journal.pone.0308860.t001 Table 1 Baseline characteristics of subjects in the different groups†.

Covariates	No sleep disorder group	Sleep disorder group	P	
Unweighted number	2233	312		
Weighted number	12905778.5	1777137.8		
Age			0.145	
60–69 years	6685232.6 (51.8%)	1040095.8 (58.5%)		
70–79 years	3514605.7 (27.2%)	467978.9 (26.3%)		
80+ years	2705940.2 (21.0%)	269063.1 (15.1%)		
Gender			0.009	
Female	7069189.2 (54.8%)	787358.0 (44.3%)		
Male	5836589.3 (45.2%)	989779.7 (55.7%)		
Race			0.950	
Mexican American	465484.6 (3.6%)	61731.6 (3.5%)		
Non-Hispanic Black	1089543.8 (8.4%)	139634.4 (7.9%)		
Non-Hispanic White	10137245.4 (78.5%)	1412115.5 (79.5%)		
Other Hispanic	500958.3 (3.9%)	60609.0 (3.4%)		
Other/multiracial	712546.4 (5.5%)	103047.3 (5.8%)		
Education			0.883	
Less than high school	4985773.9 (38.6%)	676993.7 (38.1%)		
More than high school	7920004.7 (61.4%)	1100144.1 (61.9%)		
Waist circumference (cm%)	100.80 (92.00, 110.00)	108.96 (100.14, 121.30)	< 0.001	
Marital Status			0.300	
Have spouse	8331435.0 (64.6%)	1217568.2 (68.5%)		
Without spouse	4574343.6 (35.4%)	559569.5 (31.5%)		
PIR	3.09 (1.63, 5.00)	2.97 (1.61, 4.94)	0.584	
Alcohol intake			0.267	
Non-drinker	3595277.3 (27.9%)	454446.7 (25.6%)		
1–5 drinks/month	6053762.6 (46.9%)	983093.1 (55.3%)		
5–10 drinks/month	2581095.4 (20.0%)	271132.7 (15.3%)		
10+ drinks/month	675643.3 (5.2%)	68465.3 (3.9%)		
Smoking status			0.036	
Never	11688709.0 (90.6%)	1517530.1 (85.4%)		
Current smoker	1174006.2 (9.1%)	258725.3 (14.6%)		
Former smoker	43063.4 (0.3%)	882.3 (0.0%)		
Total calories (cal)	1798.47 (1411.43, 2261.06)	1791.00 (1396.98, 2223.51)	0.628	
Recreational physical activity	6107095.6 (47.3%)	683207.9 (38.4%)	0.038	
Diabetes	2234468.2 (17.3%)	609371.4 (34.3%)	< 0.001	
Stroke	738868.6 (5.7%)	233567.4 (13.1%)	0.001	
Comorbidity index			< 0.001	
1	5444623.6 (42.2%)	589068.4 (33.1%)		
2	3935332.8 (30.5%)	303065.5 (17.1%)		
3 or greater	3525822.2 (27.3%)	885003.8 (49.8%)		
†Percentage and median (Q25, Q75) were weighted.

*P<0.05

Baseline comparison of different waist circumference groups

Of the participants included in the study, 632 participants had a waist circumference < 92.5 cm (Q1 group), 639 participants were between 92.5 and 101.3 cm (Q2 group), 636 participants were between 92.5 and 110.8 cm (Q3 group), and 638 patients were ≥ 110.8 cm (Q4 group). Table 2 lists the weighted demographic features of all participants between different waist circumference groups. In different groups of waist circumference, age, gender, race, marital status, alcohol intake, recreational physical activity, diabetes, and comorbidity index are significantly different (P <0.05, Table 2).

10.1371/journal.pone.0308860.t002 Table 2 Baseline characteristics of subjects in the different waist circumstance groups.

Covariates	Q1	Q2	Q3	Q4	P	
Unweighted number	632	639	636	638		
Weighted number	3591579.4	3494513.6	3759868.2	3836955.1		
Age					< 0.001	
60–69 years	1907208.5 (53.1%)	1718810.8 (49.2%)	1889078.5 (50.2%)	2210230.6 (57.6%)		
70–79 years	756646.3 (21.1%)	1019364.4 (29.2%)	1109470.7 (29.5%)	1097103.3 (28.6%)		
80+ years	927724.6 (25.8%)	756338.4 (21.6%)	761319.0 (20.2%)	529621.3 (13.8%)		
Gender					< 0.001	
Female	2554924.3 (71.1%)	2004008.3 (57.3%)	1724916.4 (45.9%)	1572698.2 (41.0%)		
Male	1036655.1 (28.9%)	1490505.3 (42.7%)	2034951.8 (54.1%)	2264256.9 (59.0%)		
Race					< 0.001	
Mexican American	89999.3 (2.5%)	154649.3 (4.4%)	123907.9 (3.3%)	158659.7 (4.1%)		
Non-Hispanic Black	297184.7 (8.3%)	282430.3 (8.1%)	279783.1 (7.4%)	369780.1 (9.6%)		
Non-Hispanic White	2692869.8 (75.0%)	2678024.3 (76.6%)	3104775.9 (82.6%)	3073690.9 (80.1%)		
Other Hispanic	133552.5 (3.7%)	173892.2 (5.0%)	159441.9 (4.2%)	94680.7 (2.5%)		
Other/multiracial	377973.0 (10.5%)	205517.5 (5.9%)	91959.6 (2.4%)	140143.7 (3.7%)		
Education					0.853	
Less than high school	1322423.2 (36.8%)	1346357.7 (38.5%)	1478791.7 (39.3%)	1515194.9 (39.5%)		
More than high school	2269156.1 (63.2%)	2148155.9 (61.5%)	2281076.5 (60.7%)	2321760.2 (60.5%)		
Marital Status					0.046	
Have spouse	2174768.6 (60.6%)	2360078.2 (67.5%)	2623667.1 (69.8%)	2390489.2 (62.3%)		
Without spouse	1416810.8 (39.4%)	1134435.4 (32.5%)	1136201.1 (30.2%)	1446465.9 (37.7%)		
PIR	3.22 (1.70, 5.00)	3.04 (1.70, 5.00)	3.30 (1.58, 5.00)	2.77 (1.53, 4.76)	0.412	
Alcohol intake					< 0.001	
Non-drinker	1100069.4 (30.6%)	1048987.3 (30.0%)	987660.3 (26.3%)	913006.9 (23.8%)		
1–5 drinks/month	1573788.1 (43.8%)	1406924.7 (40.3%)	1802775.0 (47.9%)	2253367.8 (58.7%)		
5–10 drinks/month	765387.6 (21.3%)	823183.4 (23.6%)	773406.0 (20.6%)	490251.1 (12.8%)		
10+ drinks/month	152334.3 (4.2%)	215418.1 (6.2%)	196026.9 (5.2%)	180329.3 (4.7%)		
Smoking status					0.667	
Never	3189607.6 (88.8%)	3119617.6 (89.3%)	3430094.7 (91.2%)	3466919.1 (90.4%)		
Current smoker	394164.7 (11.0%)	365557.0 (10.5%)	321622.1 (8.6%)	351387.7 (9.2%)		
Former smoker	7807.1 (0.2%)	9339.0 (0.3%)	8151.4 (0.2%)	18648.3 (0.5%)		
Total calories (cal)	1695.49 (1355.80, 2140.65)	1764.06 (1410.50, 2227.71)	1823.38 (1403.04, 2262.81)	1894.45 (1460.45, 2321.33)	0.055	
Recreational physical activity	2093124.8 (58.3%)	1831674.4 (52.4%)	1484616.5 (39.5%)	1380887.9 (36.0%)	< 0.001	
Diabetes	339178.7 (9.4%)	475228.8 (13.6%)	777117.8 (20.7%)	1252314.3 (32.6%)	< 0.001	
Stroke	214433.5 (6.0%)	231651.6 (6.6%)	232204.8 (6.2%)	294146.0 (7.7%)	0.740	
Comorbidity index					< 0.001	
1	1339656.2 (37.3%)	1374742.6 (39.3%)	1592486.3 (42.4%)	1726806.9 (45.0%)		
2	1468050.1 (40.9%)	1117512.2 (32.0%)	982940.8 (26.1%)	669895.2 (17.5%)		
3 or greater	783873.0 (21.8%)	1002258.8 (28.7%)	1184441.1 (31.5%)	1440253.0 (37.5%)		
Sleep disorder	220942.9 (6.2%)	288324.0 (8.3%)	452613.4 (12.0%)	815257.5 (21.2%)	< 0.001	
†Percentage and median (Q25, Q75) were weighted.

*P<0.05

Association of waist circumference with sleep disorder

The incidence of sleep disorder was significantly higher among patients with Q4 group (21.2%; weighted 815,257.5), compared with patients with Q3 group (12.0%; weighted 452,613.4), Q2 group (8.3%; weighted 288,324.0), and Q1 group (6.2%; weighted 220,942.9; P <0.001, Table 2). We have used three weighted multivariate logistic regression models to show the relationship between waist circumference with sleep disorder in Table 3: model 1, no covariate was adjusted; model 2, Gender, waist circumference, smoking status, recreational physical activity, diabetes, stroke, and comorbidity index were adjusted. We found a significantly positive association between waist circumference with the incidence of sleep disorder in the unadjusted model (Model 1). In model 2, the ORs (95% CIs) after adjusting for age and gender for incidence of sleep disorder in participants with Q4 group compared with those with Q1 group was 3.79 (2.28–6.33) (Table 3, P < 0.001). In model 3, the ORs (95% CIs) after adjusting for related indexes for incidence of sleep disorder in participants with Q3 group compared with those with Q1 group was 2.75 (1.66–4.54) (Table 3, P < 0.001).

10.1371/journal.pone.0308860.t003 Table 3 Weighted logistic regression model of the effect of different levels of waist circumstance on sleep disorder.

Groups	SE	Wald	OR (95%CI)	P	P for trend	
Model 1a					< 0.001	
Q1			1.00			
Q2	0.367	0.743	1.37 (0.65–2.91)	0.396		
Q3	0.351	4.404	2.09 (1.02–4.28)	0.045		
Q4	0.234	36.529	4.12 (2.55–6.64)	< 0.001		
Model 2b					< 0.001	
Q1			1.00			
Q2	0.376	0.595	1.34 (0.62–2.90)	0.447		
Q3	0.360	3.615	1.98 (0.95–4.15)	0.068		
Q4	0.249	28.713	3.79 (2.28–6.33)	< 0.001		
Model 3c					< 0.001	
Q1			1.00			
Q2	0.348	0.334	1.22 (0.58–2.58)	0.572		
Q3	0.331	2.276	1.65 (0.81–3.35)	0.154		
Q4	0.234	18.639	2.75 (1.66–4.54)	< 0.001		
aUnadjusted

bAdjusted for age, and gender

cAdjusted for age, gender, race, marital status, alcohol intake, recreational physical activity, diabetes, and comorbidity index.

The dose-response relationship of waist circumference and the risk of sleep disorder

A dose-response relationship was examined between waist circumference and the risk of sleep disorder. The result showed that the OR of sleep disorder and waist circumference displayed a linear relationship (P <0.001, Non-linear P = 0.642), as shown in Fig 2.

10.1371/journal.pone.0308860.g002 Fig 2 Dose–response relationship between waist circumference and sleep disorder.

Subgroup analyses

Subgroup analyses for the association between different waist circumstance levels and incidence of sleep disorder. The participants were divided into subgroups according to age, gender and race. The results showed that the association between different waist circumference levels and incidence of sleep disorder stably existed in the different subgroups (Fig 3, P trend < 0.05).

10.1371/journal.pone.0308860.g003 Fig 3 Relationship between waist circumference and sleep disorder in subgroups of potential effect modifiers.

Discussion

Using NHANES data, the present study sought to investigate the association between waist circumference and sleep disorder in the elderly population. Our research revealed that elderly participants with a sleep disorder had a substantially greater waist circumference than those without a sleep disorder. The results of this study revealed association between waist circumference and sleep disorder in older adults, providing support for the hypothesis that central adiposity may be a risk factor for sleep disturbances [3, 20, 21].

Moreover, our study revealed that participants in the higher quartiles of waist circumference had substantially greater odds of suffering from a sleep disorder than those in the lowest quartile. This suggests that central obesity may have a cumulative influence on the risk of sleep disturbances in the elderly. Even after controlling for potential confounding factors such as age, gender, and lifestyle, the observed association between waist circumference and sleep disorder remained significant. The results also showed a linear dose-response relationship between waist circumference and sleep disorder. With the increase of waist circumference, the OR of sleep disorder increased. This strengthens the reliability of our findings and provides support for the independent function of waist circumference in predicting sleep disorders in the elderly. Age and gender-based subgroup analyses did not reveal any significant differences in the association between waist circumference and sleep disorder among the elderly. This indicates that the association holds true across all demographic subgroups of the elderly population.

Several plausible explanations can be proposed for the underlying mechanisms connecting waist circumference and sleep disorder, which are not completely understood. Previous studies have found that disrupted or disturbed sleep seems to contribute to the accumulation of body fat [22]. Visceral fat accumulation around the abdomen has been linked to an increased release of inflammatory cytokines and adipokines, which can disrupt sleep patterns and diminish sleep quality [23–26]. Central adiposity is also associated with metabolic abnormalities such as insulin resistance, dyslipidemia, and inflammation, which can disrupt the normal sleep-wake cycle and result in sleep disturbances [27, 28]. In addition, excess abdominal fat can exert mechanical pressure on the diaphragm and airways, impairing respiratory function during sleep and contributing to conditions like sleep apnea [29, 30].

This study’s findings have significant implications for the prevention and treatment of sleep disorders in the geriatric population. A straightforward and cost-effective method for identifying individuals at increased risk for sleep disturbances could be waist circumference measurement screening. Interventions that target abdominal obesity, such as lifestyle modifications and weight management programs, may enhance the quality of sleep and reduce the prevalence of sleep disorders in the elderly. Nonetheless, a number of limitations of this investigation must be acknowledged. Firstly, the study’s cross-sectional design hinders our ability to establish a causal link between waist circumference and sleep disorder. To further examine the temporal relationship between these variables, longitudinal studies are required. Secondly, the evaluation of sleep disorders relied on self-reported questionnaires, which are susceptible to recall bias and subjectivity. Future research integrating objective measures of sleep quality, such as polysomnography, would yield more convincing results. As with any observational study, the presence of unmeasured confounding variables cannot be ruled out entirely. Thirdly, We cannot capture all confounding factors, so the scientific nature of the conclusions still needs to be verified by more rigorous experimental design.

Conclusion

Our investigation demonstrates a significant correlation between waist circumference and sleep disorders in the elderly population. There was a dose-response relationship between waist circumference and the likelihood of suffering from a sleep disorder. These results highlight the significance of addressing central adiposity as a potential risk factor for sleep disturbances in the elderly. Further research is necessary to elucidate the underlying mechanisms and investigate effective interventions targeting abdominal adiposity to improve sleep quality and overall health in this vulnerable population.

10.1371/journal.pone.0308860.r001
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Khashayar Patricia Academic Editor
© 2024 Patricia Khashayar
2024
Patricia Khashayar
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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PONE-D-24-14233Association between Waist Circumference and Sleep Disorder in the Elderly: Based on the NHANES 2005–2018PLOS ONE

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Additional Editor Comments:

This is an interesting article but the reviewers have raised certain concerns. The authors should address these concerns before the article could be published

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1. The dose-response relationship was mentioned in the summary conclusion, but I did not see the corresponding result explanation in the results, please add.

2. Pay attention to check whether the format of the paper meets the requirements of the journal.

3. It is suggested to adjust the expression of the paper so that the context is smooth and there is no language disorder. There are both "()" and "[]" in the table, which must be unified.

4.NHANES data is required to be weighted in the analysis, but I did not see any words related to weighting in the whole text, please add clarification. The results of the full text are unreliable if they are not weighted during the analysis.

5. The introduction part covers the severity of sleep disorders and the function of waist circumference indicators, indicating that waist circumference is related to various adverse health outcomes, but it cannot lead to the scientific hypothesis for the study of the correlation between waist circumference and sleep disorders in this paper. It is suggested to supplement the possible correlation between the two.

6. Both hypertension and diabetes exist as independent covariates in previous papers. Why did the author choose diabetes and stroke as independent variables and put hypertension together with other comorbidities? Is this reasonable? Please explain.

7. Many studies have shown that there may be a correlation between sleep disorders and depression, suggesting that the author describe depression as a covariable.

8. Existing studies have shown that serum cotinine is a marker of tobacco exposure, and it is recommended to replace the smoking covariate with serum cotinine.

9. Are sleep disorders related to sleep duration? There is no description of sleep duration in the selection of variables, please add clarification.

10. Suggestions in the table, P< 0.05 to be * and explain.

11. It is recommended to convert Table 4 into a forest map format, as the table looks too messy.

12.Model 3 notes without adjustments for stroke, education level, and Total calories? Is there any adjustment for BMI? The full text does not see the description of BMI, but the Model 3 notes involve BMI, is there a commonality between BMI and waist circumference?

Reviewer #2: Comments to the authors:

This study addresses an important health problem in the elderly. The study evaluates whether waist circumference (WC) is related to sleep disorder. The authors report a significant association between WC and sleep disorder particularly in higher quartiles of WC. The authors concluded that people above 60 years with larger WC were more likely to have a sleep disorder. Whilst this study is valuable, the below weaknesses need to be addressed to strengthen it.

Methods

Study population: Please provide a summary of the sample design NHANES uses for its data collection and what makes it nationally representative.

Lines 15 – 19: What was the difference in the characteristics of those with missing information and those without missing information? Address how the missing information of nearly 80% of the eligible participants above 60 years could influence your results? Will the results change if you imputed?

Line 21 – 22: Waist circumference couldn’t be a covariate but the main independent variable. It would be important to clarify that by having a separate heading for waist circumference and be clear it is the independent variable. Also provide information on the units of measurement and if any recategorization was done and why

Line 23 – 30; 1- 12: More information on how some covariates were measured but nothing was provided on covariates such as age, race, family income, recreational physical activity. Overall, this section needs some improvement including stating how the covariates were measured and categorized/recategorized.

Outcome:

How was the outcome treated? Yes or No? It seems there was information on the types of sleep disorders. Any plans for subgroup analyses with the different sleep disorders?

Lines: 11 – 12: Is the complication index a standardized index? Please provide reference if it was previously standardized. Otherwise, address the issues of standardization and validity for this index.

Statistical analysis:

Line 22: What was the basis for showing continuous variables as medians and IQRs?

Line 26: Why did you choose four categories for the WC? Any precedence for this approach?

Line 27 – 30: How did you select the confounders? How many variables were adjusted in the final model?

Line 1 – 2: How many age categories did you create for the subgroup analysis? How many racial groups did you use for subgroup analysis? And how many were collected? Any information on ethnicities?

The language in the analysis needs improvement.

Results:

Line 18: comorbidity index? Or complication index? You will need to provide more details in the methods section.

Lines 21 – 24: It is a better approach to present percentages than absolute numbers.

Association of WC with sleep disorder

Lines 17 – 21: There is lack of clarity on the sensitivity analysis. What were you testing for? Was there any suspected residual confounding by any unmeasured variable you used for the sensitivity analysis? P for trend is not a measure of sensitivity testing and not sure why that was mentioned here.

Subgroup analyses:

Lines 24 – 29: There was correlation analysis conducted here and so you may need to be mindful of the use of this term. Was there any evidence of effect modification by age, gender and race? You will need to present the results of the subgroup analyses other than the trend. Any evidence that the association differed by age, gender or race?

Discussion

Line 6 – 7: No correlation analysis was conducted, and the authors need to limit themselves to the correct terminology.

Lines 25 – 27: This is pointing to evidence of reverse causality. NHANES is a cross-sectional study and hence reverse causality is a big issue. How did you address this?

Table 3: No need to present both the betas and odds ratios information.

Table 4: Same comment as Table 4. Please present only the odds ratios. Also check the formatting of the Table

**********

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Reviewer #1: Yes: shan liu

Reviewer #2: No

**********

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Attachment Submitted filename: Comments to the authors.docx

10.1371/journal.pone.0308860.r002
Author response to Decision Letter 0
Submission Version1
12 Jun 2024

RESPOND TO REVIEWERS:

Reviewer 1

Comment 1: The dose-response relationship was mentioned in the summary conclusion, but I did not see the corresponding result explanation in the results, please add.

Reply 1: Thank you for your professional review. After consulting professional statisticians, we further evaluated the dose-response relationship by using restricted cubic spline plots, and the relevant results are shown in Figure 2. Dose-response relationship refers to that when the study factor can be quantified or graded, the change of the amount of the factor can affect the change of the morbidity of the population, and the possibility of causal relationship between the two is greater. That is, as the waist circumference of the study population increased, the incidence of sleep disorders also increased. (See page 8, line 25)

Comment 2. Pay attention to check whether the format of the paper meets the requirements of the journal.

Reply 2: Thank you for your professional review. We have revised the format of the manuscript to meet the requirements of the journal.

Comment 3. It is suggested to adjust the expression of the paper so that the context is smooth and there is no language disorder. There are both "()" and "[]" in the table, which must be unified.

Reply 3: Thanks for your careful review. We have revised this.

Comment 4.NHANES data is required to be weighted in the analysis, but I did not see any words related to weighting in the whole text, please add clarification. The results of the full text are unreliable if they are not weighted during the analysis.

Reply 4: Thank you for your professional review. We have consulted relevant literature in the previous research and found that similar literature has not carried out weighted analysis ([1] Hu PW, Yang BR, Zhang XL, Yan XT, Ma JJ, Qi C, Jiang GJ. The association between dietary inflammatory index with endometriosis: NHANES 2001-2006. PLoS One. 2023 Apr 26;18(4):e0283216. [2] Zheng D, Zhao C, Ma K, Ruan Z, Zhou H, Wu H, Lu F. Association between visceral adiposity index and risk of diabetes and prediabetes: Results from the NHANES (1999-2018). PLoS One. 2024 Apr 25;19(4):e0299285.). Therefore, we did not consider weighting in the analysis process, and future studies will definitely take this into account to make the conclusions of the paper more scientific. Hope to get your understanding.

Comment 5. The introduction part covers the severity of sleep disorders and the function of waist circumference indicators, indicating that waist circumference is related to various adverse health outcomes, but it cannot lead to the scientific hypothesis for the study of the correlation between waist circumference and sleep disorders in this paper. It is suggested to supplement the possible correlation between the two.

Reply 5: Thank you for your professional review. We have cited relevant literature in the Introduction part to prove the potential relationship between the two. Due to space problems, we focus on the relevant mechanism of the correlation between the two in the Discussion part. (See page 3, line 19)

Comment 6. Both hypertension and diabetes exist as independent covariates in previous papers. Why did the author choose diabetes and stroke as independent variables and put hypertension together with other comorbidities? Is this reasonable? Please explain.

Reply 6: Thank you for your professional review. What you said is very correct. When we consulted the relevant literature and analyzed, we accidentally found the comorbidities index, calculated the score and analyzed it. We consider that hypertension is very common and often co-exists with other comorbidities, such as cardiovascular disease, obesity, and metabolic syndrome. These conditions share common risk factors and may together contribute to the development of sleep disorders. Including high blood pressure as a separate variable may introduce redundancy, as it often accompanies diabetes and stroke. By grouping it with other comorbidities, the analysis can avoid multicollinearity and better isolate the unique effects of diabetes and stroke. Hope to get your approval.

Comment 7. Many studies have shown that there may be a correlation between sleep disorders and depression, suggesting that the author describe depression as a covariable.

Reply 7: Thank you for your professional review. Depression has been considered as one of the covariables in our analysis. Then, in the process of data cleaning, we found that there were many missing data of this index, so this index was abandoned for analysis. This will be taken into account in future studies. Thank you for your valuable advice.

Comment 8. Existing studies have shown that serum cotinine is a marker of tobacco exposure, and it is recommended to replace the smoking covariate with serum cotinine.

Reply 8: Thank you for your professional review. We acknowledge your comments. Firstly, Serum cotinine levels fluctuate based on recent nicotine exposure and have a relatively short half-life. This makes it a good marker for recent exposure but not for chronic exposure or long-term smoking behavior. Self-reported smoking status can give a more comprehensive picture of an individual's smoking history over months or years. Secondly, Collecting serum samples for cotinine analysis can be more invasive, costly, and logistically challenging than obtaining self-reported smoking information through questionnaires. This might not be feasible in all study settings, particularly in large-scale epidemiological studies or resource-limited settings. Finally, While serum cotinine is an objective measure and can help reduce misclassification due to underreporting or misreporting in self-reported data, it is not without limitations. Factors such as secondhand smoke exposure, nicotine replacement therapy, and individual metabolic differences can affect cotinine levels and potentially lead to misclassification of smoking status. However, in the future research, we will take your comments to improve the article. Hope you can understand.

Comment 9. Are sleep disorders related to sleep duration? There is no description of sleep duration in the selection of variables, please add clarification.

Reply 9: Thank you for your professional review. We have supplemented this in the manuscript. There are items on sleep duration in the questionnaire of related sleep disorders, but we refer to relevant literature and adopt the items that are more suitable for diagnosis for definition [Rahman HH, Niemann D, Yusuf KK. Association of urinary arsenic and sleep disorder in the US population: NHANES 2015-2016. Environ Sci Pollut Res Int. 2022 Jan;29(4):5496-5504. doi: 10.1007/s11356-021-16085-6. Epub 2021 Aug 22. PMID: 34420169.]. (See page 6, line 12)

Comment 10. Suggestions in the table, P< 0.05 to be * and explain.

Reply 10: Thanks for your good advice. We have revised it.

Comment 11. It is recommended to convert Table 4 into a forest map format, as the table looks too messy.

Reply 11: Thanks for your good advice. We have revised it.

Comment 12. Model 3 notes without adjustments for stroke, education level, and Total calories? Is there any adjustment for BMI? The full text does not see the description of BMI, but the Model 3 notes involve BMI, is there a commonality between BMI and waist circumference?Please consider using widely accepted diagnostic criteria for BMI to change the continuous variable into categorical variables. I believe that during clinical practice, a clear conclusion based on already known criteria would be easier to access instead of quartile numbers, such as underweight, normal weight, overweight, and obesity.

Reply 12: Thanks for your careful review. This is a mistake we made in writing the paper. BMI was not included in the analysis in this study, because waist circumference was found to be collinearity with BMI in the previous analysis, and waist circumference was found to be a better indicator of central obesity in other studies, and it was simple and easy to measure daily, so waist circumference was selected as a variable.

Reviewer 2

This study addresses an important health problem in the elderly. The study evaluates whether waist circumference (WC) is related to sleep disorder. The authors report a significant association between WC and sleep disorder particularly in higher quartiles of WC. The authors concluded that people above 60 years with larger WC were more likely to have a sleep disorder. Whilst this study is valuable, the below weaknesses need to be addressed to strengthen it.

Methods

Comment 1. Study population: Please provide a summary of the sample design NHANES uses for its data collection and what makes it nationally representative.

Reply 1: Thank you for your professional review. We have supplemented the relevant information in the manuscript. (See page 4, line 14)

Comment 2. Lines 15 – 19: What was the difference in the characteristics of those with missing information and those without missing information? Address how the missing information of nearly 80% of the eligible participants above 60 years could influence your results? Will the results change if you imputed?

Reply 2: Thank you for your professional review. Indeed, in the process of data cleaning, we found a large number of data missing samples. We have consulted the relevant literature, and the basic processing method of the literature is to delete the samples of missing data. At that time, we also wanted to compare the feature data of the missing sample with that of the non-missing sample. However, due to the large number of missing data items, the scientific nature of the statistical results would be questioned. Therefore, we do not compare them. It must be that so many missing items have a great impact on the representativeness of the samples. However, as for the conclusions drawn from the included samples, it is hoped that more scientific methods will be adopted in the future to further promote the universality of the conclusions. Similar studies can be seen: Chen W, Sun X, Han J, Wu X, Wang Q, Li M, Lei X, Wu Y, Li Z, Luo G, Wei M. Joint effect of abnormal systemic immune-inflammation index (SII) levels and diabetes on cognitive function and survival rate: A population-based study from the NHANES 2011-2014. PLoS One. 2024 May 6;19(5):e0301300. doi: 10.1371/journal.pone.0301300. PMID: 38709763; PMCID: PMC11073711. Hu H, Wu Y, Zhao M, Liu J, Xie P. Sleep duration time and human papillomavirus infection risk: The U-shaped relationship revealed by NHANES data. PLoS One. 2024 Apr 5;19(4):e0301212. doi: 10.1371/journal.pone.0301212. PMID: 38578744; PMCID: PMC10997073. Hope to get your understanding.

Comment 3. Line 21 – 22: Waist circumference couldn’t be a covariate but the main independent variable. It would be important to clarify that by having a separate heading for waist circumference and be clear it is the independent variable. Also provide information on the units of measurement and if any recategorization was done and why

Reply 3: Thank you for your professional review. Learned a lot from your comments. We have revised it in the manuscript. (See page 5, line 1)

Comment 4. Line 23 – 30; 1- 12: More information on how some covariates were measured but nothing was provided on covariates such as age, race, family income, recreational physical activity. Overall, this section needs some improvement including stating how the covariates were measured and categorized/recategorized.

Reply 4: Thank you for your professional review. Learned a lot from your comments. We have revised it in the manuscript. (See page 5, line 8)

Outcome:

Comment 5. How was the outcome treated? Yes or No? It seems there was information on the types of sleep disorders. Any plans for subgroup analyses with the different sleep disorders?

Reply 5: Thank you for your professional question. This study referred to the relevant literature to set sleep disorders into two categories. Because we cannot accurately and effectively identify subgroups of sleep disorders from the raw data, it is not possible to perform a precise subgroup analysis ([1] Hu PW, Yang BR, Zhang XL, Yan XT, Ma JJ, Qi C, Jiang GJ. The association between dietary inflammatory index with endometriosis: NHANES 2001-2006. PLoS One. 2023 Apr 26;18(4):e0283216. [2] Zheng D, Zhao C, Ma K, Ruan Z, Zhou H, Wu H, Lu F. Association between visceral adiposity index and risk of diabetes and prediabetes: Results from the NHANES (1999-2018). PLoS One. 2024 Apr 25;19(4):e0299285.). However, your review has guided our approach, and we will work to improve the process in the future to ensure that the conclusions are scientific.

Comment 6. Lines: 11 – 12: Is the complication index a standardized index? Please provide reference if it was previously standardized. Otherwise, address the issues of standardization and validity for this index.

Reply 6: Thank you for your careful review. We obtained this indicator through relevant papers and applied it in this study, and relevant references have been supplemented in the manuscript. (See page 6, line 5)

Statistical analysis:

Comment 7. Line 22: What was the basis for showing continuous variables as medians and IQRs?

Reply 7: Thank you for your professional question. We refer to the statistics of similar studies and do some normal distribution tests. The continuity variables in the baseline indicators are basically skewed data, so they are presented in the form of median and quartile.

Comment 8. Line 26: Why did you choose four categories for the WC? Any precedence for this approach?

Reply 8: Thank you for your professional question. At present, we have not found a more suitable grouping standard for waist circumference indicators, so we refer to the statistical method of the corresponding article, divided the independent variables into four groups according to the quartile, and stratified waist circumference as far as possible to highlight the influence of waist circumference on the results.[Wang W, Lu X, Li Q, Chen D, Zeng W. The Relationship between Blood Lead Level and Chronic Pain in US Adults: A Nationwide Cross-Sectional Study. Pain Ther. 2023 Oct;12(5):1195-1208. doi: 10.1007/s40122-023-00535-9. Epub 2023 Jun 30. PMID: 37391620; PMCID: PMC10444925.]

Comment 9. Line 27 – 30: How did you select the confounders? How many variables were adjusted in the final model?

Reply 9: Thank you for your good question. Confounding factors were identified by reviewing the literature through a previous survey. The final model correction index was determined by the single-factor analysis results in Table 2, and the correction index was indicated in the annotations in Table 3.

Comment 10. Line 1 – 2: How many age categories did you create for the subgroup analysis? How many racial groups did you use for subgroup analysis? And how many were collected? Any information on ethnicities?

Reply 10: Thank you for good question. Nhanes included the race of the participants. We divided the age into three categories and the race into five categories for analysis.

Comment 11. The language in the analysis needs improvement.

Reply 11: Thank you for your careful review. We have revised it. (See page 6, line 24)

Results:

Comment 12. Line 18: comorbidity index? Or complication index? You will need to provide more details in the methods section.

Reply 12: Thank you for your careful review. It is comorbidity index. We have revised it. (See page 6, line 8)

Comment 13. Lines 21 – 24: It is a better approach to present percentages than absolute numbers.

Reply 13: Thank you for your good comment. We have not used percentages because the proportion of people in the groups is not very different because the classification is by quartile. Hope to get your understanding.

Association of WC with sleep disorder

Comment 14. Lines 17 – 21: There is lack of clarity on the sensitivity analysis. What were you testing for? Was there any suspected residual confounding by any unmeasured variable you used for the sensitivity analysis? P for trend is not a measure of sensitivity testing and not sure why that was mentioned here.

Reply 14: Thank you for your professional review. This is not our consideration, we have removed the

Attachment Submitted filename: Response to Reviewers.docx

10.1371/journal.pone.0308860.r003
Decision Letter 1
Khashayar Patricia Academic Editor
© 2024 Patricia Khashayar
2024
Patricia Khashayar
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
11 Jul 2024

PONE-D-24-14233R1Association between Waist Circumference and Sleep Disorder in the Elderly: Based on the NHANES 2005–2018PLOS ONE

Dear Dr. Tian,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Aug 25 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Patricia Khashayar

Academic Editor

PLOS ONE

Additional Editor Comments:

Many of the critical comments pointed out by reviewer 1 is either not addressed or the justification is not acceptable. I understand that there are some limitations but these limitations should be addressed in a scientific way or acknowledged in the limitation section. It is possible that some studies have used unweighted NHANES data but this is not a justification, you should clarify why weighing the data was not needed for your data.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1. For the issue of paper weighting, this is the requirement for the use of data on the NHANES website, and there are clear guidelines on how to weight the data.

2. Other questions I don't think the author did a good job of adjusting and revising my questions.

Reviewer #3: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

10.1371/journal.pone.0308860.r004
Author response to Decision Letter 1
Submission Version2
22 Jul 2024

RESPOND TO REVIEWERS:

Reviewer 1

Comment 1: For the issue of paper weighting, this is the requirement for the use of data on the NHANES website, and there are clear guidelines on how to weight the data.

Reply 1: Thank you for your professional review. We have appropriately weighted the NHANES data in our analysis in accordance with the guidelines provided on the NHANES website. Specific details on the weighting methodology used are included in the manuscript to ensure clarity and transparency. We have applied the recommended weights (e.g., WTDRD1/7) to account for the complex sampling design, which includes stratified, cluster, and multistage sampling with unequal probabilities. This approach ensures that our statistical analyses, including standard error and confidence interval estimates, are accurate and reliable.

Comment 2: Other questions I don't think the author did a good job of adjusting and revising my questions.

Reply 2: Thank you for your professional review. We appreciate your thorough review and the insights you have provided. We have done our best to make changes in accordance with your valuable comments, however, there are some limitations that cannot be well addressed. We hope to design more scientific experimental schemes in the future to further improve the scientificity of the research. Please kindly understand.

Additional Editor

Additional Editor Comments:

Many of the critical comments pointed out by reviewer 1 is either not addressed or the justification is not acceptable. I understand that there are some limitations but these limitations should be addressed in a scientific way or acknowledged in the limitation section. It is possible that some studies have used unweighted NHANES data but this is not a justification, you should clarify why weighing the data was not needed for your data.

Reply: Thank you. We have weighted and analyzed our nhanes data, making extensive revisions in the manuscript.

Attachment Submitted filename: Response to Reviewers.docx

10.1371/journal.pone.0308860.r005
Decision Letter 2
Khashayar Patricia Academic Editor
© 2024 Patricia Khashayar
2024
Patricia Khashayar
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version2
1 Aug 2024

Association between Waist Circumference and Sleep Disorder in the Elderly: Based on the NHANES 2005–2018

PONE-D-24-14233R2

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Reviewers' comments:

10.1371/journal.pone.0308860.r006
Acceptance letter
Khashayar Patricia Academic Editor
© 2024 Patricia Khashayar
2024
Patricia Khashayar
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
5 Aug 2024

PONE-D-24-14233R2

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