Association between waist circumference and sleep disorder in the elderly: Based on the NHANES 2005-2018.

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.

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. † Percentage and median (Q25, Q75) were weighted. * P <0.05 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 ). † Percentage and median (Q25, Q75) were weighted. * P <0.05 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). a Unadjusted b Adjusted for age, and gender c Adjusted for age, gender, race, marital status, alcohol intake, recreational physical activity, diabetes, and comorbidity index. 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 . 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).

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.

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 . 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. 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. 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 ]. 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 χ 2 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.