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. 2011 May 18;34(3):527–537. doi: 10.1007/s11357-011-9251-3

Association between sleep quality and body mass index among Chinese nonagenarians/centenarians

Zhou Yan 1, Huang Chang-Quan 1,2,3, Lu Zhen-Chan 1, Dong Bi-Rong 1,✉
PMCID: PMC3337939  PMID: 21590342

Abstract

We examined the individual association between body mass index (BMI) and sleep quality among the very elderly. The present study analyzed data from survey that was conducted on all residents aged 90 years or more in a district, there were 2,311,709 inhabitants in 2005. Subjects were divided into four groups according to quartile of BMI (<16.6, 16.6–18.9, 18.9–21.1, >21.1 kg/m2) and according to classification criteria of underweight, normal weight, overweight, and obesity in BMI (<18.5, 18.5–23.0, 23.0–27.5, >27.5 kg/m2), respectively. Sleep quality was measured using The Pittsburgh Sleep Quality Index (PSQI). Sleep quality included quality classification and scores, sleep duration, sleep latency, and sleep efficiency. The subjects included in the statistical analysis were 216 men and 444 women. According to quartile of BMI or classification criteria of underweight, normal weight, overweight, and obesity in BMI, none of the differences in sleep quality scores, sleep latency, sleep duration, sleep efficiency percentage, and prevalence of poor sleep quality was significant among different BMI groups. The difference in BMI between subjects with good and poor sleep quality was non-significant. Unadjusted and adjusted multiple logistic regression showed that none of the BMI groups had a function of decreasing the risk for poor quality. Among longevity Chinese, there is no association between BMI and sleep quality.

Keywords: Sleep quality, Body mass index, Nonagenarians/Centenarians

Keywords: Life Sciences, Molecular Medicine, Geriatrics/Gerontology, Cell Biology

Introduction

Sleep is an important modulator of neuroendocrine function and glucose metabolism in children as well as in adults (Van Cauter and Knutson 2008; Keith et al. 2006; Spiegel et al. 1999). In recent years, sleep curtailment has become a hallmark of modern society with both children and adults having shorter bedtimes than a few decades ago. This trend for shorter sleep duration has developed over the same time period as the dramatic increase in the prevalence of obesity (Van Cauter and Knutson 2008; Prevalence and trends in obesity among US adults 2002). There is rapidly accumulating evidence from both laboratory and epidemiological studies to indicate that chronic partial sleep loss may increase the risk of obesity and weight gain (Patel and Hu 2008; Zee and Turek 2006; Lopez-Garcia et al. 2008; Rao et al. 2009). The previous studies provided laboratory and clinical evidence indicating that sleep curtailment in young adults results in a constellation of metabolic and endocrine alterations, including decreased glucose tolerance, decreased insulin sensitivity, elevated sympathovagal balance, increased evening concentrations of cortisol, increased levels of ghrelin, decreased levels of leptin, and increased hunger and appetite (Patel and Hu 2008; Zee and Turek 2006; Lopez-Garcia et al. 2008; Rao et al. 2009; Spiegel et al. 2004; Littman et al. 2006; Gangwisch et al. 2005; Erik Landhuis et al. 2008).

It is well known that aging is associated with changes in body composition, including an increase of fat mass and a decline in lean mass. Abdominal fat, largely caused by the accumulation of visceral fat, increases proportionally more with age than peripheral fat (Horber et al. 1997; Snijder et al. 2006; Wang et al. 1994; Zamboni et al. 1997). Measurement of body mass index (BMI) in older individuals may not adequately reflect abdominal fat accumulation because of the concurrent decrease in muscle mass, and on the other hand, sleep is also a vital physiologic process with important restorative functions (Nevitt et al. 1989; Ohayon et al. 2004; Redline et al. 2004). Notable qualitative and quantitative changes in sleep occur with age (Nevitt et al. 1989; Ohayon et al. 2004; Redline et al. 2004). Aging is associated with several well-described changes in patterns of sleep with age (Jean-Louis et al. 2000). It is well known that both sleep quality and BMI are strongly influenced by lifestyles (Lamberg 2006; Bauer et al. 2010; French et al. 2010; Shinba et al. 1994). Less exercise and smoking are linked with the risk for obesity or chronic insomnia (Lamberg 2006; Bauer et al. 2010). It has been confirmed that there is a U-shaped association between alcohol consumption and obesity or chronic insomnia (French et al. 2010; Shinba et al. 1994). Both sleep disorders and obesity are associated with the high mortality risk (Grandner et al. 2010; Karra and Batterham 2010).

Shorter sleep duration as a risk for increased BMI (Van Cauter and Knutson 2008; Patel and Hu 2008), obesity individuals more likely with sleep disorders (Vgontzas 2008), some lifestyles (exercise, smoking, alcohol consumption, and so on) relating with the risk factors for both sleep disorders and obesity (Lamberg 2006; Bauer et al. 2010; French et al. 2010; Shinba et al. 1994), both sleep disorders and obesity as risk factors for high mortality in the elderly (Grandner et al. 2010; Karra and Batterham 2010), and BMI and sleep disorders both relating with age have all been confirmed (Horber et al. 1997; Snijder et al. 2006; Wang et al. 1994; Zamboni et al. 1997; Nevitt et al. 1989; Ohayon et al. 2004; Redline et al. 2004). From all of these, we can conclude that in the long-lived subjects (aged 90 years or more), there is a close association between sleep quality and BMI, which may be different from that in general population. However, to our knowledge, no population-based study has yet evaluated the association between sleep quality and BMI in the long-lived subjects. To do it, using data from a sample of Chinese nonagenarians and centenarians, we examined the association between sleep quality and BMI in the long-lived subjects.

Subjects and methods

Study subjects

The methods were reported previously (Huang et al. 2009a, b) In brief, on the basis of the Dujiangyan (located in Sichuan province, southwest China) 2005 census, a cross-sectional study for age-related diseases was conducted in 870 long-lived subjects (>90 years), which was a part of the Project of Longevity and Aging in Doujiangyan (PLAD). The PLAD aimed to investigate the relationship between environments, lifestyle, genetic, cognitive function, sleep quality, longevity, and age-related diseases among long-lived subjects in the community. Volunteers were examined by trained professional physicians according to basic health criteria in their home, and the results were filled in the standard form, especially questionnaire on lifestyles and sleep quality (measured using The Pittsburgh Sleep Quality Index (PSQI)). In this analysis, the subjects were bedridden, or with cancer, a history or clinical evidence of stroke, terminal stage of physical disease such as respiratory system disease, cardiovascular disease, kidney diseases and so on, were excluded. Overall, 21 men and 26 women were not eligible for the study because they had already died or moved away from the area. Of 262 men and 561 women who were interviewed, 46 men and 117 women were excluded for the reason above. The study population ultimately consisted of 660 long-lived subjects. Informed consents were obtained from all participants (as well as their legal proxies). The Research Ethics Committee of the Sichuan University approved the study. To assure reliability of this information, during the course of interviewing, at least one of the family members, who usually lived with the participant, took part in the interviewing and checked the filled questionnaire.

Data collection and measurements

Measurement of sleep quality

The Pittsburgh Sleep Quality Index (PSQI) was used for the subjective assessment of sleep quality (Buysse et al. 1989). To decrease methodological errors and assure methodological reliability, the administrator reviewed the PSQI procedure and grading system outlined in a short booklet and video, observed a psychogeriatrician conducting the PSQI on residents not part of the study and was supervised when conducting the PSQI on residents not part of the study. The PSQI is a questionnaire consisting of 19 items which are coded on a 4-point scale (0–3) to obtain seven subcategories, including sleep duration, sleep disturbances, sleep latency, daytime dysfunction, sleep efficiency, sleep satisfaction, and medication use. The sum of all subscores represents the total sleep quality score, ranging between 0–21, with higher scores representing lower sleep quality. The individuals were categorized as follows: good sleep quality (scores between 0 and 5) and poor sleep quality (scores between 6 and 21). Respondents are asked to rate their sleep reflecting on the past month. Psychometric properties have demonstrated good reliability (internal consistency 0.89; test retest reliability 0.85) and good construct validity for the English language version (Shochat et al. 2007). The PSQI is a widely used tool in research studies and clinical trials and has been translated to several languages including Chinese, Spanish, German, and Hebrew, with comparable reliability and validity values. Internal consistency in the present study was α = 0.69. When excluding the medication use subscale due to a low rate of medication users in this sample, internal consistency increased to α = 0.76 (Zisberg et al. 2010). Outcome measures included the total PSQI score as well as self report of sleep latency (SL) (question 2: how long (in minutes) has it usually taken you to fall asleep each night?), sleep duration (total time in bed: hours computed based on reported bedtime in question 1: what time have you usually gone to bed at night? and reported wake-time in question 3: what time have you usually gotten up in the morning?) and sleep efficiency percentage (SE) computed as the ratio between the hours of actual sleep (question 4: how many hours of actual sleep did you get at night?) and total time in bed, multiplied by 100 (Shochat et al. 2007; Zisberg et al. 2010). Forty-six men and 117 women were bedridden, or with cancer, a history or clinical evidence of stroke, terminal stage of physical disease such as respiratory system disease, cardiovascular disease, kidney disease, and so on. These subjects usually had longer bed time and poor sleep quality, and sleep quality in them could not be accurately calculated. To address this, these subjects were excluded when the data were analyzed.

Measurement of the BMI

Height and weight were measured according to standardized procedures. BMI was calculated as weight in kilograms divided by height in meters-squared (kg/m2). Using BMI cutoffs for Asian populations recommended by WHO, we categorized BMI into four categories: underweight (<18.5 kg/m2), normal weight (18.5–23.0 kg/m2), overweight (23.0–27.5 kg/m2), and obesity (≥27.5 kg/m2) (WHO Expert Consultation 2004). Considering the association changes in body composition with aging, cutoff points for underweight, normal weight, overweight, and obesity might not be suitable for the long-lived sample, we also categorized BMI into four groups using quartile cutoff points: the first (<16.6 kg/m2), the second (16.6–18.9 kg/m2), the third (18.9–21.1 kg/m2), and the fourth (≥21.1 kg/m2). In the sample, there were 6 men and 17 women bedridden, unable to stand, who could not be exactly measured in weight and height, and BMI could not be accurately calculated. To address this, these subjects were excluded when the data were analyzed (Drøyvold et al. 2006; Scholtens et al. 2007).

Assessment of covariates

The baseline examination included information on age (Years), gender (Male/Female), smoking habits (Yes or No), alcohol consumption (Yes or No), tea consumption (Yes or No), exercise (Yes or No), nap in daytime (Eve/Usu/Occ/Never), cognitive function, waist circumference, serum lipid/lipoprotein levels (including serum triglyceride (TG), total cholesterol (TC), high-density-lipoprotein (HDL) cholesterol, and low-density-lipoprotein (LDL) cholesterol), fasting blood glucose (FBG), serum uric acid (SUA) (mole per liter) and blood pressure (Huang et al. 2009a, b). Right arm blood pressure (sitting or recumbent position) was measured twice to the nearest 2 mmHg using a standard mercury sphygmomanometer (Korotkoff phases I and V) by trained nurses or physicians. Waist circumference was measured about 1 cm below the belly button, stand up straight, breathe out, do not hold your breath or tuck in your stomach; make sure the measuring tape is not too loose or too tight around your waist; when take reading, make sure it was not held the tape at an angle. Serum lipid/lipoprotein levels, FBG, and SUA were determined by standard laboratory techniques (performed by a technician in the biochemistry laboratory of Sichuan University). Cognitive function was measured using the 30-item Mini-Mental State Examination (MMSE), which was a global test with components of orientation, attention, calculation, language, and recall. The other covariates were collected by using a general question.

Habits of smoking, alcohol consumption, tea consumption, and exercise, which included former and current, were collected by using a general questionnaire. In the questionnaire, every item had two options (yes or no). We defined subjects with such habit as doing it almost everyday. Subjects were asked whether they had ever had habits of smoking, alcohol consumption, tea consumption, and exercise, and one of three answers were recorded: never, did in the past, or currently. Among those who did, currently or in the past, information was obtained on the average frequency of smoking, using alcohol, drinking tea, and doing exercise and on the number of years they had did for. The subjects, who did almost everyday during the recent 1 year, were classified as those with these habits current, otherwise as without. The subjects, who had done almost everyday for more than 2 years as of a year before, were classified as those with these habits previously, otherwise as without. Alcohol consumption included spirits, liqueurs, wine, sherry, martini, beer, lager, cider, stout, and so on. Tea consumption included all types of tea.

Statistical analysis

All of the statistical analyses for this study were performed with the SPSS for Windows software package, version11.5 (SPSS Inc, Chicago, Illinois, USA). Baseline characteristics were compared among different BMI subgroups using one-way analysis of variance for continuous variable and Pearson Chi-Square or Fisher’s exact test (whereas an expected cell count was <5) for categorical variables. Baseline characteristics were also compared between good and poor sleep quality using unpaired Student’s t test for continuous variables and Pearson Chi-Square or Fisher’s exact test (whereas an expected cell count was <5) for categorical variables. Multiple logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI) of BMI as a function of increased risk for poor sleep quality. Considering general or central adiposity, elevated blood pressure, dyslipidemia, hyperglycemia, and hyperuricemia were viewed as the components of metabolic syndrome, and these components might be with the similar mechanism in relation with sleep quality; thus, we adjusted FBG, waist circumference, serum lipid/lipoprotein, blood pressure, and SUA in mode 1. Lifestyles (including smoking, alcohol consumption, tea consumption, and exercise) might relate with sleep quality and BMI, so we adjusted them in mode 2. Finally, we adjusted age, gender, nap in daytime, cognitive function, and all factors above in model 3. P value <0.05 was considered to be statistically significant, and all of the P values have two sides.

Results

Baseline characteristics, sleep quality, and BMI

Among the 660 participants, mean age was 93.52 years, 69 were centenarians and 444 were women. Ninety percent of participants lived in the country side. There were 513 (77.8%) and 147 (22.2%) with good and poor sleep quality, respectively. The mean sleep quality score was 6.84 ± 2.15, the mean SL was 45.89 ± 16.72 min, the mean SE was 76.53 ± 8.78%, the mean sleep duration was 10.21 ± 1.55 h, and of them, 0.91%, 1.97%, 27.73%, and 69.39% were in <6/6–7/8–9/>10(h) sleep duration subgroups, respectively (see Table 1).

Table 1.

Baseline characteristics according to sleep quality (n = 660)

Characteristics All Good sleep quality Poor sleep quality
(n = 660) (n = 513) (n = 147) χ2 or t P value
Age (years) 93.52 ± 3.37 93.48 ± 3.40 93.63 ± 3.14 0.499 0.618
Gender
Male/Female 216/444 174/339 42/105 0.219 0.803
Nap in daytime
Eve/Usu/Occ/Never 186/101/80/264 134/80/63/214 52/21/17/50 4.035 0.258
MMSE scores 15.48 ± 5.44 15.85 ± 5.41 14.24 ± 5.27 3.156 0.002**
BMI kg/m2 19.05 ± 3.64 18.99 ± 3.76 19.20 ± 3.85 0.593 0.554
<18.5/18.5–23/23–27.5/>27.5 300/286/62/12 238/217/50/8 61/71/12/3 1.922 0.589
<16.6/16.6–18.9/18.9–21.1/>21.1 166/166/166/162 126/126/120/120 33/32/39/33 1.023 0.796
Waist circumference cm 76.60 ± 9.51 76.81 ± 9.23 76.05 ± 10.43 0.851 0.359
DBP(mmHg) 72.59 ± 12.06 72.70 ± 11.96 72.23 ± 12.65 0.408 0.683
SBP(mmHg) 139.71 ± 23.31 139.75 ± 22.96 140.37 ± 22.78 0.286 0.775
TG (mmol/L) 1.22 ± 0.64 1.22 ± 0.66 1.25 ± 0.58 0.445 0.657
TC (mmol/L) 4.15 ± 0.85 4.16 ± 0.84 3.97 ± 0.88 0.567 0.561
HDL (mmol/L) 1.58 ± 0.59 1.56 ± 0.38 1.63 ± 1.02 1.335 0.182
LDL (mmol/L) 2.28 ± 0.97 2.25 ± 0.60 2.40 ± 0.97 1.666 0.096
FBG(mmol/L) 4.46 ± 1.45 4.43 ± 1.36 4.57 ± 1.68 1.000 0.318
SUA (μmol/L) 318.72 ± 87.01 317.95 ± 85.70 321.45 ± 93.10 0.424 0.672
Smoking habits
Former (Yes/No) a 242/395 192/302 50/93 1.007 0.605
Current (Yes/No) 361/289 277/229 84/60 0.585 0.647
Alcoholic
Former (Yes/No) a 379/262 295/202 84/60 0.048 0.848
Current(Yes/No) a 176/473 143/361 33/112 1.796 0.180
Tea habits
Former (Yes/No) a 287/349 224/271 63/78 0.014 0.904
Current (Yes/No) a 275/376 217/289 58/87 0.385 0.535
Exercise habits
Former (Yes/No) a 214/420 167/327 47/93 0.003 0.999
Current (Yes/No) a 265/380 206/296 59/84 0.002 1.000
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Baseline characteristics were compared between different sleep quality groups

SUA serum uric acid, BMI body mass index, FBG fasting blood glucose, HDL high-density lipoprotein, LDL low-density lipoprotein, TC total cholesterol, TG triglyceride, SBP systolic blood pressure, DBP diastolic blood pressure, MMSE Mini-Mental State Examination

*P < 0.05, **P < 0.01 vs. good sleep quality (using χ2 or Fisher’s exact test (where an expected cell count was <5) for categorical variables and unpaired Student’s t test for continuous variables)

aFisher’s exact test

In the oldest–old sample, the mean of BMI was 19.05 (s.d. 3.64). Categorized using widely recognized cutoff points of BMI, 45.5%, 43.4%, 9.4%, and 1.7% were in underweight, normal weight, overweight, and obesity subgroups, respectively. There were different DBP (P = 0.061), SBP (P < 0.01), TG (P < 0.01), HDL (P < 0.01), and SUA (P < 0.01) levels among these different subgroups. Categorized using quartile cutoff points, there were different DBP (P = 0.040), SBP (P < 0.01), TG (P < 0.01), HDL (P < 0.01), and SUA (P < 0.01) levels among these different subgroups. Categorized using both widely recognized cutoff points and quartile cutoff points; there was no significant difference in lifestyles among these different subgroups in BMI (see Table 2).

Table 2.

Baseline characteristics according to BMI (kg/m2) (n = 660)

Characteristics All Diagnostic criteria in BMI Quartile of (BMI)
(n = 660) Underweight (18.5) Normal (18.5–23) Overweight (23–27.5) Obese (27.5) P value Under 16.6 16.6–18.9 18.9–21.1 Above 21.1 P value
Number (n, %) 660 300, 45.5% 286, 43.4% 62, 9.4% 12, 1.7% 166, 25.1% 166, 25.1% 166, 25.1% 162, 24.7%
Age (years) 93.52 ± 3.37 93.46 ± 3.26 93.30 ± 3.18 93.35 ± 2.90 92.55 ± 2.21 0.781 93.41 ± 3.09 93.56 ± 3.47 93.24 ± 3.20 93.21 ± 2.93 0.761
Gender
Male/Female 216/444 93/207 103/183 14/48 6/6 0.101 49/117 51/115 62/104 54/108 0.411
Sleep quality
Score 6.84 ± 2.15 6.61 ± 3.01 6.73 ± 1.58 8.42 ± 3.15 7.07 ± 2.26 0.253 6.69 ± 2.98 6.71 ± 2.29 7.13 ± 3.06 6.83 ± 2.45 0.566
Poor sleep quality (%) 21.78 20.00 24.09 18.64 27.27 0.589 20.75 20.25 24.53 21.57 0.796
SE (%) 76.53 ± 8.78 74.91 ± 9.01 79. 35 ± 6.64 70.43 ± 8.15 81.32 ± 9.65 0.337 74.82 ± 6.95 75.10 ± 7.91 78.65 ± 6.83 77.58 ± 10.01 0.826
SL (min) 45.89 ± 16.72 49.20 ± 14.35 42.65 ± 14.17 46.05 ± 13.58 39.09 ± 11.32 0.348 49.03 ± 14.21 49.31 ± 14.52 43.89 ± 15.67 41.18 ± 13.27 0.613
Duration(h) 10.21 ± 1.55 10.25 ± 1.49 10.19 ± 1.52 10.40 ± 1.72 9.65 ± 1.20 0.491 10.41 ± 1.58 10.02 ± 1.46 10.23 ± 1.46 10.25 ± 1.46 0.151
<6/6–7/8–9/>10(h) 6/13/183/458 2/5/86/207 3/6/78/199 1/1/14/46 0/1/5/6 0.970 2/1/44/119 2/6/50/108 0/3/46/117 2/2/43/115 0.552
Nap in daytime
Eve/Usu/Occ/Never 186/101/80/264 84/45/37120 78/39/36/122 19/15/6/19 5/2/1/3 0.549 53/21/23/61 41/32/16/72 48/20/21/71 44/28/20/60 0.492
MMSE scores 15.48 ± 5.44 15.73 ± 5.29 15.52 ± 5.53 14.70 ± 5.33 15.55 ± 5.99 0.350 15.59 ± 5.20 15.92 ± 5.44 16.37 ± 5.13 14.37 ± 5.71 <0.001**
Waist circumference (cm) 76.60 ± 9.51 72.50 ± 8.68 78.36 ± 7.21 87.08 ± 8.57 87.55 ± 11.25 <0.001** 71.63 ± 7.40 73.51 ± 9.97 78.45 ± 6.47 83.39 ± 8.33
DBP 72.59 ± 12.06 71.38 ± 11.31 73.33 ± 12.34 74.92 ± 12.90 76.36 ± 13.43 71.08 ± 12.14 71.60 ± 10.08 73.57 ± 12.79 74.41 ± 12.64 <0.001**
SBP 139.71 ± 23.31 136.36 ± 23.17 142.73 ± 22.13 143.43 ± 23.18 153.18 ± 11.46 0.061 135.25 ± 24.64 139.28 ± 22.81 142.33 ± 22.79 143.57 ± 20.06 0.040*
TG (mmol/L) 1.22 ± 0.64 1.12 ± 0.45 1.22 ± 0.71 1.57 ± 0.83 1.65 ± 1.06 <0.001** 1.12 ± 0.45 1.14 ± 0.48 1.19 ± 0.56 1.44 ± 0.92 0.001**
TC (mmol/L) 4.15 ± 0.85 4.17 ± 0.85 4.06 ± 0.81 4.48 ± 0.95 4.02 ± 0.94 <0.001** 4.20 ± 0.90 4.06 ± 0.84 4.07 ± 0.79 4.27 ± 0.86 <0.001**
HDL (mmol/L) 1.58 ± 0.59 1.70 ± 0.77 1.49 ± 0.38 1.42 ± 0.28 1.31 ± 0.24 0.006** 1.79 ± 0.99 1.55 ± 0.33 1.52 ± 0.42 1.43 ± 0.29
LDL (mmol/L) 2.28 ± 0.97 2.27 ± 1.28 2.28 ± 0.58 2.58 ± 0.69 2.29 ± 0.77 2.33 ± 1.66 2.17 ± 0.60 2.21 ± 0.59 2.42 ± 0.63 0.064
FBG(mmol/L) 4.46 ± 1.45 4.48 ± 1.58 4.36 ± 1.26 4.67 ± 1.54 4.70 ± 1.32 <0.001** 4.59 ± 1.69 4.37 ± 1.37 4.30 ± 1.21 4.54 ± 1.45 <0.001**
SUA (μmol/L) 318.72 ± 87.01 303.31 ± 90.94 327.19 ± 80.86 350.82 ± 89.47 334.18 ± 77.73 0.093 299.96 ± 94.43 304.55 ± 79.99 333.38 ± 84.73 337.65 ± 84.64 0.089
Smoking habits 0.422 0.242
Former (Yes/No) 242/395 124/167 97/179 18/40 3/7 <0.001** 66/92 71/92 58/104 47/107 <0.001**
Current (Yes/No) 361/289 166/130 155/128 34/26 6/5 88/74 98/69 89/74 85/72
Alcoholic
Former (Yes/No) 379/262 172/118 166/114 38/20 3/7 0.398 90/70 99/67 92/68 98/57 0.130
Current (Yes/No) 176/473 82/213 73/210 13/47 8/3 0.901 50/111 42/125 43/121 41/116 0.771
Tea habits
Former (Yes/No) 287/349 136/158 123/150 24/35 4/6 0.212 77/84 72/92 67/91 71/82 0.591
Current (Yes/No) 275/376 127/169 119/165 22/38 7/4 0.151 72/89 65/100 60/102 72/85 0.568
Exercise habits
Former (Yes/No) 214/420 101/188 101/186 19/40 3/6 0.873 47/111 64/99 55/104 48/106 0.788
Current (Yes/No) 265/380 126/168 108/172 26/34 5/6 0.408 60/102 77/87 64/97 64/94 0.311
0.952 0.310
0.743 0.274
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Baseline characteristics were compared between different body mass index (BMI) groups

Poor sleep quality: SL sleep latency, SE sleep efficiency percentage (SE), SUA serum uric acid, BMI body mass index, FBG fasting blood glucose, HDL high-density lipoprotein, LDL low-density lipoprotein, TC total cholesterol, TG triglyceride, MMSE Mini-Mental State Examination

*P < 0.05, **P < 0.01 (using one-way analysis of variance for continuous variable and Pearson Chi-Square or Fisher's exact test (whereas an expected cell count was <5) for categorical variables; in the testing, a P value <0.05 was considered to be statistically significant)

No association between BMI and quality of sleep

Between the different sleep quality groups, the difference in BMI was non-significant (18.99 ± 3.76 vs. 19.20 ± 3.85, between subjects with good and poor sleep quality, respectively, P = 0.554) (see Table 1). Categorized using widely recognized cutoff points in BMI or quartile cutoff points in BMI, among the different subgroups, none of the differences in sleep quality scores, sleep latency, sleep efficiency percentage, sleep duration, and prevalence of poor sleep quality was significant. Unadjusted and adjusted multiple logistic regression showed that none of the BMI groups had a function of decreasing the risk for poor quality. All of these showed that among long-lived subjects, there was no association between sleep quality and BMI (Table 3).

Table 3.

Effect of BMI on sleep quailty in long-lived subjects

Characteristics Diagnostic criteria in BMI Quartile of (BMI)
Underweight Normal weight Overweight Obese Under 16.6 16.6–18.9 18.9–21.1 Above 21.1
OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Unadjusted 1.500 (0.386–5.834) 1.182 (0.305–4.584) 1.636 (0.373–7).187 1.000 (reference) 1.050 (0.610–1.808) 1.083 (0.627–1.871) 0.846 (0.499–1.435) 1.000 (Reference)
Model 1 1.561 (0.383–6.212) 1.111 (0.278–4.259) 1.598 (0.376–7.412) 1.000 (reference) 1.210(0.658–2.213) 1.003(0.515–1.718) 0.801(0.464–1.371) 1.000 (Reference)
Model 2 3.082 (0.679–13.908) 2.375(0.514–10.003) 3.222(0.549–15.678) 1.000 (reference) 1.123(0.638–2.002) 1.167 (0.675–2.175) 0.953(0.562–1.675) 1.000 (Reference)
Model 3 2.765(0.514–12.789) 2.107 (0.421–9.505) 2.784(0.538–13.651) 1.000 (reference) 1.228 (0.646–2.307) 1.086(0.597–1.989) 0.887 (0.501–1.623) 1.000 (Reference)
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OR odds ratio, Unadjusted Wald Chi-square test with df = 1 was used, adjusted multiple logistic regression was used to adjust for covariates, model 1 adjustment made with the other components of metabolic syndrome (waist circumference, blood pressure, serum lipid/lipoprotein, blood sugar, uric acid), model 2 adjustment made with lifestyles (smoking habits, alcoholic, tea habits, exercise habits), model 3 adjustment made with age, gender, nap in daytime, cognitive function and all factors above

*P < 0.05, **P < 0.01

Discussion

This study evaluated the association between sleep quality and BMI in the long-lived subjects. In the present study, sleep quality included quality classification and scores, sleep duration, sleep latency, and sleep efficiency. According to cutoffs for Asian populations recommended by WHO and quartile cutoff points in BMI, the subjects were divided into four groups, respectively. In the cross-sectional observations, in the long-lived community dwellers, there was no association between sleep quality and BMI.

In the present study, we found that there was no association between sleep quality and BMI in the long-lived community dwellers, which was different from that in general population (Van Cauter and Knutson 2008; Spiegel et al. 1999; Prevalence and trends in obesity among US adults 2002; Patel and Hu 2008; Gangwisch et al. 2005; Erik Landhuis et al. 2008). This finding was interesting, and the difference between long-lived community dwellers and general population in association between sleep quality and BMI could be accounted for the changes in body composition with age and the changes in sleep patterns with age (Horber et al. 1997; Snijder et al. 2006; Wang et al. 1994; Zamboni et al. 1997; Nevitt et al. 1989; Ohayon et al. 2004; Redline et al. 2004).

In the present study, according to classification criteria (of underweight, normal weight, overweight, and obesity) in BMI in general population, there was a high prevalence of underweight (45.5%), and there were more underweight subjects than those with normal weight. The sample was from a community population, the prevalence of underweight in younger adults was less than 5% in Chinese community dwellers. Among elderly community dwellers (aged 60 years or above) in Chinese, the prevalence of underweight was lower than 10% (Lau et al. 2005; Chan et al. 1998). In the present study, BMI in many subjects is low, that about one fourth of them is lower than 16.6 kg/m2. Although symptoms of malnutrition do not be need to included in the survey, subjects being bedridden or with severe disease were excluded from our study. In fact, our sample was rural health longevity. So there was no subject with malnutrition caused by diseases. In the survey, the socio-economic parameters were included, and the incomes of the subjects were all 2,000–4,000 yuan (about 350–700 dollars)/month/person in family. So there was also no subject with malnutrition caused by the poor socio-economic parameters. Therefore, it is impossible for malnutrition to lead the low BMI in the sample. Obesity led to mortality through inducing cardiovascular disease and the other components of metabolic syndrome; however, the prospective studies showed that the elderly with body weight loss had higher mortality than those without, so the low BMI was not from high mortality in the long-lived subjects and more likely from changes in body composition (including a decrease of BMI) with aging. This finding was consistent with that of previous reports (Wang et al. 1994). In the present study, according to classification criteria widely recognized cutoff points in BMI or quartile cutoff points in BMI, there was non-significantly different sleep quality among BMI subgroups. This indicated that in the association of BMI with sleep quality, the difference between the long-lived subjects and general population was not from the classification criteria in BMI.

In the present study, the mean sleep duration was 10.21 ± 1.55 h, and of them, 0.91%, 1.97%, 27.73%, and 69.39% were in <6/6–7/8–9/>10(h) sleep duration subgroups, respectively. In Chinese, the mean sleep duration was less than 9 h in general population. In the present study, although we excluded the subjects being bedridden, or with cancer, a history or clinical evidence of stroke, terminal stage of physical disease, who had longer sleep duration, the sleep duration in the long-lived individuals was still longer than 10 h and more than 50% of them with the sleep duration longer than 10 h. This indicated the change in sleep quality with age, and this finding was consistent with that of previous reports (Ohayon et al. 2004; Redline et al. 2004). The longer sleep duration might be explained as follows: (1) they were all retired and no job to do; (2) any of the participants had underlying dementia; in the present study, there was a low mean MMSE score in the sample, and sleep quality was related with MMSE score; (3) primary sleep disorders, many of which, such as sleep apnea, restless legs syndrome, and rapid eye movement behavior disorder, tend to occur with increasing frequency in older adults.

In the present study, the differences in blood pressure, serum lipid/lipoprotein levels and SUA were all significant among the different BMI subgroups. Typically, general or central adiposity, elevated blood pressure, dyslipidemia, hyperuricemia, and hyperglycemia are thought to be part of this syndrome. The present study provided evidence from the long-lived subjects and showed that metabolic syndrome components in the long-lived subjects might be strongly correlated with each other. On the other hand, between the subjects with good and poor sleep quality, none of the difference in the other components of metabolic syndrome except obesity (including serum lipid/lipoprotein levels, FBG, SUA and blood pressure) was significant. The components of metabolic syndrome were related with sleep quality with the same mechanism (Hall et al. 2008). This indicated that in association of sleep quality with BMI, the difference between the general population and the long-lived subjects was not only from the changes in body composition with age and the changes in sleep patterns with age, but also from the metabolic changes with age.

In the present study, the lifestyles (including exercise, smoking, alcohol consumption, and so on), which had been confirmed factors related with both sleep and obesity, were not different among different BMI groups or between good and poor sleep quality groups. The association of BMI or sleep quality with these lifestyles also changes with age, the mechanism was unclear and should be further explored.

Our study had some limitations that deserve a mention. First, 870 subjects aged 90 years or older volunteered for the PLAD Study; among these 870 volunteers, only 660 had non-missing data for the two main variables involved in the current analyses. There might be selection biases. Since the many subjects excluded from the study were without the information on the two main variables, we could not compare the two main variables between the subjects included and excluded from the study, and the selection biases and its effect on the result of the study could not be confirmed. Because subjects are being bedridden or with cancer, a history or clinical evidence of stroke, terminal stage of physical disease such as respiratory system disease, cardiovascular disease, kidney disease, and so on, had longer sleep duration and poor sleep quality it was difficult to measure weight and height, and BMI could not be calculated because they were unable to stand. We excluded them. However, our sample was from community dwellers and the information from them (excluded from the study) might be unable to influence the practical implications of the present study. Second, one of the limitations of our study was that the survey did not ask about calories consumed by the participants, and our study did not specifically identify participants with malnutrition. However, our sample was from community dwellers, 90% of participants lived in the country side, and all subjects were interviewed in home. Subjects being bedridden or with severe disease were excluded from our study. In fact, our sample was rural health longevity, who were without disorder in intake nutrition and were similar in diet habits and socio-economic status. This might be unable to influence the practical implications of the present study. Third, in the study, sleep quality was measured using a questionnaire, not sleep studies or actigraphy, which measured quality using instrument and more sensitive and specific. However, it seemed unfeasible using this instrument in the subjects in their home. The previous study had showed the good reliability and good construct validity of PSQI in measuring sleep quality (Shochat et al. 2007). Fourth, there was a gender imbalance in our population, a common characteristic of very old populations. Of the 870 long-living individuals (≥90 years) in the Dujiangyan district (2005), 444 women and 216 men were included in this study. Finally, since this is a part of the PLAD, there might be a survival bias. However, this is inherent in a study of individuals of this age group.

In conclusion, among longevity Chinese, there is no association between BMI and sleep quality, which was different from that in general population.

Acknowledgements

This work was supported by the Discipline Construction Foundation of Sichuan University and by grants from the Project of Science and Technology Bureau of Sichuan Province (2006Z09-006-4), the Construction Fund for Subjects of West China Hospital of Sichuan University (XK05001) and the government of SiChuan province of China (2010FZ0061). The authors thank the staff of the Department of Geriatrics Medicine, West China Hospital and Dujiangyan Hospital, and all participants (as well as their legal proxies)for their great contribution.

Footnotes

H. Chang-Quan is the co-first author of this paper.

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