Abstract
Objectives
To elucidate the relationship between various sleep-wake-related indicators and nutritional status.
Design
Cross-sectional study.
Setting
Community-based survey between 2017 and 2022 in Yilan City, Taiwan.
Participants
1,905 community-dwelling older adults aged ≥65 years.
Measurements
Nutritional status was evaluated using the Mini Nutritional Assessment, and participants were classified into normal nutritional status and undernutrition groups. Regarding sleep-wake-related indicators, specific items or component scores of the Pittsburgh Sleep Quality Index were used to assess sleep-wake schedule, subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, presence of sleep disturbances, hypnotic use, and dysfunction in maintaining enthusiasm. The 5-item Athens Insomnia Scale and the Epworth Sleepiness Scale were used to evaluate insomnia and excessive daytime sleepiness, respectively.
Results
Of the 1,905 participants, the mean age was 75.6±7.1, with 52.2% being ≥75 years old, 58.9% were women, and 11.4% had undernutrition. After controlling for covariates, short sleepers were less likely to have undernutrition (OR: 0.63; 95% CI: 0.41–0.97); in contrast, long sleepers were more likely to exhibit undernutrition (OR: 1.52; 95% CI: 1.06–2.17). In addition, poor habitual sleep efficiency (OR:1.69; 95% CI:1.15–2.50), taking hypnotics in the past month (OR: 1.58; 95% CI: 1.12–2.24), and dysfunction in maintaining enthusiasm (OR: 1.93; 95% CI: 1.24–2.99) were associated with increased risk of undernutrition.
Conclusions
Among older adults, various sleep-wake-related indicators differed in their relationships with nutritional status. Specific sleep-wake disturbances may indicate undernutrition in this population.
Key words: Malnutrition, nutritional status, older adults, sleep-wake disturbance
Abbreviations
- AIS
Athens Insomnia Scale
- BMI
Body Mass Index
- CI
confidence interval
- EDS
Excessive Daytime Sleepiness
- ESS
Epworth Sleepiness Scale
- MNA
Mini Nutritional Assessment
- OR
odds ratio
- PSQI
Pittsburgh sleep quality index
Introduction
Maintaining an adequate nutritional status is essential for healthy aging. However, older adults are especially susceptible to malnutrition (1). Malnutrition predisposes older adults to frailty and low quality of life and is associated with higher mortality (2). Thus, identifying the correlates of malnutrition is crucial in older adults.
Numerous factors affect nutritional status in older adults, including comorbidities, immobility, and sleep-wake disturbances (2, 3). As sleep-wake disturbances are common in older adults, their relationship with nutritional status deserves attention. In older population, poor sleep quality (3, 4, 5, 6), insomnia (7), and daytime sleepiness (8) were more consistently associated with elevated risk for malnutrition. However, associations with other sleep-wake-related indicators, such as sleep duration and sleep-wake schedule, remain inconsistent or unexplored (9, 10). Because various sleep-wake-related indicators differ in their relationship with other health outcomes (11), the respective associations between each sleep-wake-related indicator and malnutrition may also be differential and warrant disentanglement.
Furthermore, sleep-wake-related physiology and pathology vary with age, and some changes might be a part of normal aging without adverse health consequences (12). Therefore, by simultaneously assessing the individual relationship between various sleep-wake-related indicators and malnutrition, we could identify the most relevant indicators and contribute to precision community medicine for older adults. To the best of our knowledge, only a few studies have examined the relationship between a comprehensive set of sleep-wake-related indicators and malnutrition in older adults, which examined the correlations between the subcomponents of the Pittsburgh Sleep Quality Index and nutritional status in specific populations (13, 14). However, the limited number and specific characteristics of participants, inadequate domains, and the less stringent definition of some sleep wake-related indicators have constrained their ability to report unbiased, rigorous, and generalizable findings in the general older population.
Although nutritional status profoundly affects the health of older adults, the relationship between various sleep-wake-related indicators and nutritional status remains unclear. Using a large older community cohort and specifying adequate potential confounders, this study aimed to comprehensively and simultaneously investigate the independent associations between various sleep-wake-related indicators and nutritional status. We hypothesize that only specific sleep-wake-related indicators correlate with nutritional status in older adults.
Methods
Study design and participants
This was a community-based, cross-sectional study under the umbrella “Yilan study,” wherein study design has been detailed elsewhere (15). Between June 2017 and August 2022, adults aged ≥65 years residing in Yilan City were randomly recruited and interviewed by trained research assistants. Sociodemographic (age, sex, educational status, and living status), medical history (diabetes, hypertension, cardiac disease, hyperlipidemia, stroke, and cancer), lifestyle (smoking and drinking status), and sleep-wake-related indicators were investigated using physical examinations and questionnaires. Participants who were physically disabled in a way that prevented the collection of anthropometric measurements, such as height and weight, were excluded. In addition, individuals with significant cognitive impairment that hindered their comprehension of the questionnaires were also excluded. This study was approved by the Institutional Review Boards of National Yang-Ming University Hospital (IRB No. 2011A016) and National Taiwan University Hospital (IRB No. 201512116RINC). Written informed consent was obtained from all the participants.
Assessment of nutritional status
Nutritional status was evaluated using the Mini Nutritional Assessment (MNA), which has been validated in the older population (16). The MNA consists of 18 questions and evaluates four domains: anthropometric measurements, global assessment, dietary questionnaire, and subjective assessment. Participants with a total MNA score <17 were classified as malnutrition, those with scores ranging from 17 to 23.5 were classified as “at risk of malnutrition,” and those with a score ≥24 were classified as having normal nutritional status. Due to the relatively small proportion of participants with malnutrition and “at risk of malnutrition” in this study, we collapsed the malnutrition and “at risk of malnutrition” groups into a single group defined as “undernutrition.”
Sleep-wake-related indicators
Specific items or component scores of the Pittsburgh Sleep Quality Index were used to assess sleep-wake-related indicators, including sleep-wake schedule, subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, presence of sleep disturbances, hypnotic use, and dysfunction in maintaining enthusiasm. The Chinese version of the PSQI has been validated (17). Specifically, the bedtime component of the sleep-wake schedule was evaluated by asking the participants, “During the past month, what time have you usually gone to bed at night?” The responses were categorized by tertiles: <21:30, 21:30–22:30, and >22:30. Subjective sleep quality was assessed by asking participants, “During the past month, how would you rate your sleep quality overall?” The response options included “very good,” “fairly good,” “fairly bad,” and “very bad,” which were scored from 0 to 3, respectively. In this study, a score of ≥2 was defined as poor subjective sleep quality. Sleep latency was determined using component 2 of the PSQI, with a score of ≥1 defined as prolonged sleep latency.
Sleep duration was evaluated by asking participants, “During the past month, how many hours of actual sleep did you get at night?” In the literature, short sleep duration (≤5 hours) is associated with comorbidities and mortality (18), while among older adults in Taiwan, longer sleep duration (≥8 hours) is associated with an increased risk of mortality (19). Accordingly, we categorized subjective sleep duration as short sleep duration (≤5 hours), moderate sleep duration (6–7 hours), and long sleep duration (≥8 hours). Habitual sleep efficiency was assessed using component 4 (sleep efficiency) of the PSQI, with a cutoff of <85% defining poor sleep efficiency. Based on selected items from component 5 (sleep disturbance) of the PSQI, participants were considered to have sleep disturbances if they reported being affected by any of the following conditions during the past month: “wake up in the middle of the night or early morning,” “have to get up to use the bathroom,” “cannot breathe comfortably,” “cough or snore loudly,” “feel too cold,” “feel too hot,” “have bad dreams,” “have pain,” or “other reasons (leading to trouble sleeping).” Hypnotic use was determined by asking the participants, “During the past month, how often have you taken medicine to help you sleep?” Participants who responded, “not during the past month” were classified as non-users, while the rest were classified as hypnotic users. Dysfunction in maintaining enthusiasm was evaluated using the question from the PSQI: “During the past month, how much of a problem has it been for you to keep up enough enthusiasm to get things done?” The options of the response included “no problem at all,” “only a very slight problem,” “somewhat of a problem,” and “a very big problem,” which were scored from 0 to 3. A score of ≥1 was defined as dysfunction in maintaining enthusiasm.
The 5-item Athens Insomnia Scale (AIS) was used to assess insomnia, with a total score of ≥5 indicating clinically significant insomnia. The Chinese version of the AIS has been previously validated (20). Finally, the validated Chinese version of the Epworth Sleepiness Scale (ESS) was used to evaluate subjective daytime sleepiness in participants, with a total score of ≥11 defining excessive daytime sleepiness (EDS) (21).
Other variables
Sociodemographic characteristics, including age (categorized into two groups: <75 years old, and≥75 years old (22)), sex, educational status (illiterate vs. literate), living status (living alone vs. living with others) and lifestyles (smoking and drinking status) were collected. Medical history was assessed to determine whether the participants had the following comorbidities: diabetes, hypertension, cardiac disease, hyperlipidemia, stroke, and cancer. Participants were coded as having a specific disease only if they self-reported diagnosis and received respective treatment (23). The total number of morbidities was used as an indicator of disease burden.
Statistical analysis
IBM SPSS Statistics version 20 was used for data analysis. A Goodness-of-fit test was used to compare the differences in demographic characteristics between the study participants and all registered individuals in Yilan City. Student's t-test and chi-square analysis were performed to explore the univariate relationships between sociodemographic and clinical characteristics and nutritional status. Multivariable logistic regression analysis was used to examine the independent relationship between sleep-wake-related indicators and nutritional status. To assess potential collinearity, polychoric correlation analyses were conducted to estimate the extent of correlations among sleep-wake-related indicators. Sensitivity analyses that used the raw scores of the MNA as the dependent variable were performed by linear regression analyses. P <0.05 was considered statistically significant.
Results
A total of 1,905 older adults were included in this study. Table 1 summarizes the sociodemographic and clinical characteristics of the participants as well as the distribution of sleep-wake-related indicators. Among the participants, the mean age was 75.6±7.1, with 52.2% being ≥75 years old, and 57.7% were women. Regarding nutritional status, 11.4% of the participants had undernutrition (10.9% with an MNA score of 17–23.5 and 0.5% with a score <17). Compared with the demographic data of the registered residents of Yilan City in 2017, the participants in this study did not show significant differences in age (χ2= 0.04, df = 1, p = 0.85), sex (χ2= 0.23, df = 1, p = 0.63), or educational status (χ2= 1.18, df = 1, p = 0.28). Polychoric correlation analyses showed that the correlation coefficients among all sleep-wake-related indicators ranged from ϕ= −0.07 to 0.
Table 1.
Sociodemographic and clinical characteristics of participants (n=1905)
| Total | |
|---|---|
| n (%) | |
| Age (years) | |
| < 75 | 911 (47.8) |
| ≥ 75 | 994 (52.2) |
| Sex | |
| Female | 1100 (57.7) |
| Male | 805 (42.3) |
| Educational status | |
| Literate | 1740 (91.3) |
| Illiterate | 157 (8.2) |
| Living status | |
| Alone | 190 (10.0) |
| With others | 1714 (90.0) |
| Smoking status | |
| Non-smoker | 1523 (79.9) |
| Ex-smoker | 252 (13.2) |
| Current smoker | 130 (6.8) |
| Drinking status | |
| Non-drinker | 1590 (83.5) |
| Ex-drinker | 82 (4.3) |
| Current drinker | 233 (12.2) |
| Number of morbidities (mean±SD) | 1.8±1.4 |
| Sleep-wake-related indicators | |
| Bedtime (military time) | |
| < 21:30 | 584 (30.7) |
| 21:30–22:30 | 553 (29.0) |
| > 22:30 | 768 (40.3) |
| Athens Insomnia Scale ≥ 5 | 363 (19.1) |
| Poor subjective sleep quality | 415 (21.8) |
| Prolonged sleep latency | 950 (49.9) |
| Sleep duration (hours) | |
| ≤ 5 | 426 (22.2) |
| 6–7 | 975 (51.2) |
| ≥ 8 | 507 (26.6) |
| Habitual sleep efficiency < 85% | 502 (26.4) |
| Presence of sleep disturbances | 833 (43.7) |
| Taking hypnotics in the past month | 418 (21.9) |
| Epworth Sleepiness Scale ≥ 11 | 68 (3.6) |
| Dysfunction in maintaining enthusiasm | 1169 (8.9) |
| Nutritional status | |
| Normal | 1687 (88.6) |
| Undernutrition | 218 (11.4) |
Numbers of subjects do not equal because of missing values in variables.
In the univariate analysis, participants who were ≥ 75-year-old (p < 0.001), illiterate (p = 0.004), ex-drinker (p = 0.04), and had a higher number of comorbidities (p < 0.001), were more likely to exhibit undernutrition. Regarding univariate analysis of individual comorbidities, participants with diabetes (p = 0.04), cardiac disease (p < 0.001), stroke (p = 0.002) or cancer (p = 0.03) had a higher risk of undernutrition. Among the sleep-wake-related indicators, insomnia (p < 0.001), poor subjective sleep quality (p < 0.001), prolonged sleep latency (p = 0.002), poor habitual sleep efficiency (p < 0.001), presence of sleep disturbances (p < 0.001), taking hypnotics in the past month (p < 0.001), and dysfunction in maintaining enthusiasm (p < 0.001) were associated with an increased risk of undernutrition. Additionally, older adults with undernutrition tended to have earlier bedtimes (<21:30) (p < 0.001) and longer sleep durations (p = 0.04) (Table 2).
Table 2.
Univariate analyses for factors associated with nutritional status
| Nutritional status | p-value for χ2 / t test | ||
|---|---|---|---|
| Normal | Undernutrition | ||
| Age (years) | |||
| < 75 | 844 (50.0) | 67 (30.7) | <0.001 |
| ≥ 75 | 843 (50.0) | 151 (69.3) | |
| Sex | |||
| Female | 967 (57.3) | 133 (61.0) | 0.30 |
| Male | 720 (42.7) | 85 (39.0) | |
| Educational status | |||
| Literate | 1551 (92.4) | 189 (86.7) | 0.004 |
| Illiterate | 128 (7.6) | 29 (13.3) | |
| Living status | |||
| Alone | 161 (9.5) | 29 (13.3) | 0.08 |
| With others | 1525 (90.5) | 189 (86.7) | |
| Smoking status | |||
| Non-smoker | 1345 (79.7) | 178 (81.7) | 0.58 |
| Ex- smoker | 228 (13.5) | 24 (11.0) | |
| Current smoker | 114 (6.8) | 16 (7.3) | |
| Drinking status | |||
| Non-drinker | 1397 (82.8) | 193 (88.5) | 0.04 |
| Ex-drinker | 72 (4.3) | 10 (4.6) | |
| Current drinker | 218 (12.9) | 15 (6.9) | |
| Number of morbidities | 1.8±1.4 | 2.2±1.4 | <0.001 |
| Sleep-wake-related indicators | |||
| Bedtime (military time) | |||
| < 21:30 | 465 (27.6) | 88 (40.4) | <0.001 |
| 21:30–22:30 | 517 (30.6) | 67 (30.7) | |
| > 22:30 | 705 (41.8) | 63 (28.9) | |
| Athens Insomnia Scale ≥ 5 | 290 (17.2) | 73 (33.5) | <0.001 |
| Poor subjective sleep quality | 339 (20.1) | 76 (34.9) | <0.001 |
| Prolonged sleep latency | 820 (48.6) | 130 (59.6) | 0.002 |
| Sleep duration (hours) | |||
| ≤ 5 | 375 (22.2) | 48 (22.0) | 0.04 |
| 6–7 | 878 (52.0) | 97 (44.5) | |
| ≥ 8 | 434 (25.7) | 73 (33.5) | |
| Habitual sleep efficiency < 85% | 412 (24.4) | 90 (41.3) | <0.001 |
| Presence of sleep disturbances | 708 (42.0) | 125 (57.3) | <0.001 |
| Taking hypnotics in the past month | 337 (20.0) | 81 (37.2) | <0.001 |
| Epworth Sleepiness Scale ≥ 11 | 58 (3.4) | 10 (4.6) | 0.39 |
| Dysfunction in maintaining enthusiasm | 125 (7.4) | 44 (20.2) | <0.001 |
Table 3 shows the independent relationship between each sleep-wake-related indicator and nutritional status in the multivariable logistic regression analysis. After adjusting for covariates, short sleepers were less likely to have undernutrition [odds ratio [OR]: 0.63; 95% confidence interval [CI]: 0.41–0.97], whereas long sleepers were prone to having undernutrition (OR: 1.52; 95% CI: 1.06–2.17). Poor habitual sleep efficiency (OR: 1.69; 95% CI: 1.15–2.50), taking hypnotics in the past month (OR: 1.58; 95% CI: 1.12–2.24), and dysfunction in maintaining enthusiasm (OR: 1.93; 95% CI: 1.24–2.99) were also indicators of undernutrition. Supplement Table 1 summarized the results of sensitivity analyses that treated raw scores of the MNA as the dependent variables. In the multiple linear regression analysis, except for poor sleep quality that turned out to correlate with lower scores of the MNA, all the other correlation patterns between sleep-wake-related indicators and nutritional status remained the same.
Table 3.
Logistic regression analyses for factors associated with undernutrition
| Undernutrition | ||
|---|---|---|
| Crude odds ratio (95% CI) | Adjusted odds ratioa(95% CI) | |
| Age(years) (≥ 75 vs. < 75) | 2.26 (1.67–3.06) | 1.83 (1.33–2.53) |
| Sex (Male vs. Female) | 0.86 (0.64–1.15) | 1.07 (0.73–1.57) |
| Educational status (Illiterate vs. Literate) | 1.86 (1.21–2.86) | 1.10 (0.68–1.78) |
| Living status (With others vs. Alone) | 0.69 (0.45–1.05) | 0.77 (0.49–1.20) |
| Smoking status | ||
| Ex- smoker vs. Non-smoker | 0.80 (0.51–1.25) | 0.79 (0.46–1.38) |
| Current smoker vs. Non-smoker | 1.06 (0.61–1.83) | 1.32 (0.70–2.49) |
| Drinking status | ||
| Ex- drinker vs. Non-drinker | 1.01 (0.51–1.98) | 0.82 (0.37–1.79) |
| Current drinker vs. Non-drinker | 0.50 (0.29–0.86) | 0.55 (0.31–0.99) |
| Number of morbidities | 1.24 (1.13–1.37) | 1.12 (1.00–1.24) |
| Sleep-wake-related indicators | ||
| Bedtime (military time) | ||
| < 21:30 vs. 21:30–22:30 | 1.46 (1.04–2.06) | 1.14 (0.79–1.64) |
| > 22:30 vs. 21:30–22:30 | 0.69 (0.48–0.99) | 0.72 (0.49–1.06) |
| Athens Insomnia Scale (≥ 5 vs. < 5) | 2.43 (1.78–3.30) | 1.37 (0.84–2.23) |
| Poor subjective sleep quality (Yes vs. No) | 2.13 (1.57–2.88) | 1.17 (0.74–1.85) |
| Prolonged sleep latency (Yes vs. No) | 1.56 (1.17–2.08) | 0.93 (0.65–1.35) |
| Sleep duration (hours) | ||
| ≤ 5 vs. 6–7 | 1.16 (0.80–1.67) | 0.63 (0.41–0.97) |
| ≥ 8 vs. 6–7 | 1.52 (1.10–2.11) | 1.52 (1.06–2.17) |
| Habitual sleep efficiency < 85% (Yes vs. No) | 2.18 (1.63–2.91) | 1.69 (1.15–2.50) |
| Presence of sleep disturbances (Yes vs. No) | 1.86 (1.40–2.47) | 1.19 (0.83–1.72) |
| Taking hypnotics in the past month (Yes vs. No) | 2.37 (1.76–3.20) | 1.58 (1.12–2.24) |
| Epworth Sleepiness Scale (≥ 11 vs. < 11) | 1.35 (0.68–2.68) | 1.27 (0.61–2.66) |
| Dysfunction in maintaining enthusiasm (Yes vs. No) | 3.16 (2.17–4.61) | 1.93 (1.24–2.99) |
a: Covariates entered the model including age, sex, education status, living status, smoking status, drinking status, number of morbidities, bedtime, Athens Insomnia Scale, poor subjective sleep quality, prolonged sleep latency, sleep duration, habitual sleep efficiency, presence of sleep disturbances, taking hypnotics in the past month, Ep-worth Sleepiness Scale, and dysfunction in maintaining enthusiasm.
Discussion
This study examined the relationship between various sleep-wake-related indicators and nutritional status by including a large community-based older adult cohort. Our findings suggest that specific sleep-wake domains are explicitly indicative of nutritional status when various sleep-wake-related indicators are considered simultaneously. By comprehensively considering sleep-wake-related indicators and rigorously controlling for confounding factors, this study stands out.
Opposite association between two ends of sleep duration with undernutrition
Long sleep duration was found to be associated with undernutrition in our study, which is consistent with the literature (9). Long sleep durations may increase the risk of malnutrition through several mechanisms. First, long sleepers are more likely to eat during unconventional hours and replace meals with snacks, leading to poorer diet quality (24). Second, long sleep duration is associated with lower olfactory sensitivity (25), which subsequently decreases appetite and food intake (26). Furthermore, long sleep duration is associated with chronic inflammation (27), in which anorexia, reduced food intake, increased resting energy expenditure, and muscle catabolism result in malnutrition (9, 28).
On the other hand, older adults with short sleep duration were less likely to exhibit undernutrition in this study. However, the relationship between short sleep duration and nutritional status in older adults is inconsistent in the literature. For example, short sleep duration was not associated with malnutrition risk in a study conducted in Finland (10) but was associated with a higher risk of malnutrition in China (9). In older adults, the relationships between short sleep duration and adverse health outcomes have been regarded as an epiphenomenon of comorbidities (29), suggesting that short sleep duration may be reflective of the consequences of normal aging (12). Because the participants in this study were relatively healthy community-dwelling older adults and potential confounding effects were rigorously controlled, the observed lower risk of undernutrition in short sleepers was not unexpected.
Independent relationships between specific sleep-wake-related indicators with undernutrition
In addition to sleep duration, several sleep-wake domains were associated with undernutrition. First, poor habitual sleep efficiency was associated with undernutrition, which is consistent with the results reported in hemodialysis patients (30). Poor sleep efficiency is related to increased sympathetic activity, which may increase energy expenditure (31, 32). Additionally, poor sleep efficiency is associated with poor diet quality and higher levels of inflammatory cytokines (33, 34), which may contribute to a higher risk of undernutrition. Second, older adults using hypnotics are more likely to have undernutrition, which has also been reported in older Iranian adults (14). In contrast, hypnotic use was not associated with a risk of malnutrition in Chinese centenarians (13). This inconsistency may be due to the differences in the age range of the study participants, as there may be selective survival among centenarians. Regarding the underlying mechanism, dysphagia that is secondary to reduced muscle strength or reduced arousal level may mediate the association between hypnotic use and undernutrition (35, 36). Finally, older adults with a dysfunction in maintaining enthusiasm had a higher likelihood of undernutrition. These older people may lack the motivation to perform activities of daily living, including preparing or eating meals, which can impact their nutritional status. This finding suggests that not only nocturnal sleep symptoms, but also daytime function have comparable imperativeness to healthy aging.
Null findings between other sleep-wake domains with undernutrition
In contrast to the null findings between insomnia, subjective sleep quality, excessive daytime sleepiness, and nutritional status in the present study, previous studies have reported relationships between these sleep-wake domains and malnutrition risk in older adults (7, 8, 13, 14, 37). The sleep-wake schedule, sleep latency, and sleep disturbances were not associated with nutritional status in this study. When compared with the literature, to the best of our knowledge, our study is the first to examine the association between sleep-wake schedule with nutritional status in older adults. In contrast, findings regarding the associations between sleep latency, sleep disturbances, and nutritional status have been inconsistent (13, 14, 30).
Due to our larger sample size, it is less likely that the null findings in this study were attributable to inadequate statistical power. In addition, collinearity analysis did not support the conclusion that these null findings were a consequence of statistical overadjustment. Accordingly, three possible explanations are favored. First, the null findings may be a result of adequately partial confounding effects, which were achieved by the inclusion of various covariates and a comprehensive set of sleep-wake-related indicators. Second, the disparity in the characteristics of the study participants may underlie the inconsistency between our null findings and those in the literature. Associations between insomnia and excessive daytime sleepiness and malnutrition in older adults have been identified in clinical individuals (8, 37) but not in general community older adults, such as those dwelling in Yilan city. Third, the accuracy of measurements may have influenced the validity of the findings. Definitions of sleep disorders, such as insomnia and excessive daytime sleepiness, were more stringent in our study, which used standardized instruments with valid cutoffs (7).
Limitation
This study had several limitations. First, owing to the nature of this cross-sectional study, causal inferences were not allowed. Second, the time frame for recalling sleep-wake-related indicators was only one month, and it is uncertain whether it could reflect genuine long-term conditions. Third, objective sleep measurements such as polysomnography were not used in this study. However, it has been suggested that subjective and objective measures of sleep-wake indicators may reflect distinct aspects and are not comparable in older adults (38). On the other hand, objective sleep measurements are less practical in large community-based studies. Fourth, details of hypnotics, such as dosage, frequency of use and half-life, which may also have differential correlation patterns with nutritional status were not collected. Fifth, a comprehensive assessment of nutritional status in older adults included anthropometric, laboratory, clinical, and dietary assessments; however, laboratory data were not obtained. Nevertheless, the MNA score was highly correlated with serum albumin and pre-albumin levels (16). Finally, despite controlling for many covariates, residual confounders may introduce bias. For example, chronic pain may contribute to both sleep-wake disturbance and undernutrition (39, 40). Additionally, factors, such as depression, dementia, mobility, and psychological stress, are important confounders. However, the MNA incorporates items that share similar constructs with these potential confounders. To prevent self-prediction, we did not include these factors in our statistical model.
Conclusions
Nutritional status is fundamental to healthy aging. Therefore, it is important to identify clinically significant sleep-wake-related indicators. Our findings suggest that specific sleep-wake domains may be used as risk indicators of undernutrition in older adults, which may benefit the implementation of geriatric community medicine. However, the relationship between sleep-wake indicators and nutritional status is complex. Longitudinal studies and randomized controlled trials are warranted to examine the pathophysiological mechanisms underlying the association between specific sleep-wake-related indicators and nutritional status.
Acknowledgments
The authors would like to thank Dr. Pesus Chou for helping with the study design, Yang-Ming Crusaders, Mr. Da-Wei Lin, Ms. Yu-Hui Lin, Mr. Chia-Hsiang Lin, and Ms. Tzu-Chun Lo for their help with data collection, and Chia-Ling Liao for her assistance with statistical analyses.
Author's contribution:
R.A.L. and H.C.C. prepared the first draft of this manuscript. H.C.C., P.J.P. and N.W.H. designed the study. H.C.C. performed statistical analyses. All authors participated in the drafting and revision of the manuscript. All the authors have read and approved the manuscript.
Funding:
This work was supported by the National Science and Technology Council of Taiwan (MOST-108-2314-B-002 -110 -MY2) and (MOST-110-2314-B-002 -096 -MY3). The funding agency had no role in the preparation of this manuscript, including data collection, writing, and analysis.
Ethical Standards:
This study was approved by the Institutional Review Boards of National Yang-Ming University Hospital (IRB No. 2011A016) and National Taiwan University Hospital (IRB No. 201512116RINC). Written informed consent was obtained from all the participants.
Conflict of Interest:
None declared.
Electronic Supplementary Material
Supplementary material is available for this article at https://doi.org/10.1007/s12603-023-1984-z and is accessible for authorized users.
Supplement Table 1. Linear regression analyses for factors associated with nutritional status
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Supplementary Materials
Supplement Table 1. Linear regression analyses for factors associated with nutritional status