Quantity and Quality of Sleep and Incidence of Type 2 Diabetes

University of Warwick, Warwick Medical School, Clinical Sciences Research Institute, Coventry, UK.
Diabetes care (Impact Factor: 8.42). 11/2009; 33(2):414-20. DOI: 10.2337/dc09-1124
Source: PubMed


To assess the relationship between habitual sleep disturbances and the incidence of type 2 diabetes and to obtain an estimate of the risk.
We conducted a systematic search of publications using MEDLINE (1955-April 2009), EMBASE, and the Cochrane Library and manual searches without language restrictions. We included studies if they were prospective with follow-up >3 years and had an assessment of sleep disturbances at baseline and incidence of type 2 diabetes. We recorded several characteristics for each study. We extracted quantity and quality of sleep, how they were assessed, and incident cases defined with different validated methods. We extracted relative risks (RRs) and 95% CI and pooled them using random-effects models. We performed sensitivity analysis and assessed heterogeneity and publication bias.
We included 10 studies (13 independent cohort samples; 107,756 male and female participants, follow-up range 4.2-32 years, and 3,586 incident cases of type 2 diabetes). In pooled analyses, quantity and quality of sleep predicted the risk of development of type 2 diabetes. For short duration of sleep (< or =5-6 h/night), the RR was 1.28 (95% CI 1.03-1.60, P = 0.024, heterogeneity P = 0.015); for long duration of sleep (>8-9 h/night), the RR was 1.48 (1.13-1.96, P = 0.005); for difficulty in initiating sleep, the RR was 1.57 (1.25-1.97, P < 0.0001); and for difficulty in maintaining sleep, the RR was 1.84 (1.39-2.43, P < 0.0001).
Quantity and quality of sleep consistently and significantly predict the risk of the development of type 2 diabetes. The mechanisms underlying this relation may differ between short and long sleepers.

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    • "Although there is a lack of epidemiologic studies linking long-term noise exposure to overweight or obesity, substantial evidence links noise to a stress response (Babisch 2003; Babisch et al. 2001; Ising and Braun 2000; Ising and Kruppa 2004; Persson Waye et al. 2003; Selander et al. 2009a; Spreng 2000a), and also links chronic stress to impaired metabolic functions (Björntorp and Rosmond 2000; Kyrou and Tsigos 2007; Rosmond 2003, 2005; Rosmond and Björntorp 2000; Spreng 2000b). In addition, noise exposure is commonly associated with sleep disturbances, which are known to have metabolic complications (Cappuccio et al. 2010; Chaput et al. 2007; Spiegel et al. 1999; Taheri et al. 2004; Van Cauter et al. 2008). As mentioned, we found an association between aircraft noise and increases in waist circumference; however, the findings for BMI were not as clear. "
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    ABSTRACT: Long-term aircraft noise exposure may increase the risk of cardiovascular disease, but no study has investigated chronic effects on the metabolic system. The aim of this study was to investigate effects of long-term aircraft noise exposure on body mass index (BMI), waist circumference, and Type 2 diabetes. Furthermore, we explored the modifying effects of sleep disturbance. This prospective cohort study of residents of Stockholm County, Sweden, followed 5,156 participants with normal baseline oral glucose tolerance tests (OGTT) for up to ten years. Exposure to aircraft noise was estimated based on residential history. Information on outcomes and confounders was obtained from baseline and follow-up surveys and examinations, and participants who developed prediabetes or Type 2 diabetes were identified by self-reported physician diagnosis or OGTT at follow-up. Adjusted associations were assessed by linear, logistic and random effects models. The mean increases in BMI and waist circumference during follow-up were 1.09 kg/m(2) ± 1.97 and 4.39 cm ± 6.39, respectively. The cumulative incidence of pre-diabetes and Type 2 diabetes was 8% and 3%, respectively. Based on an ordinal noise variable, a 5-dB(A) increase in aircraft noise was associated with a greater increase in waist circumference of 1.51 cm; 95% CI: 1.13, 1.89; fully adjusted. This association appeared particularly strong among those who did not change their home address during the study period, which may be a result of lower exposure misclassification. However, no clear associations were found for BMI or Type 2 diabetes. Furthermore, sleep disturbances did not appear to modify the associations with aircraft noise. Long-term aircraft noise exposure may be linked to metabolic outcomes, in particular increased waist circumference.
    Environmental Health Perspectives 05/2014; 122(7). DOI:10.1289/ehp.1307115 · 7.98 Impact Factor
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    • "Inadequate sleep duration, either in excess or deficit, has been associated with cardiovascular diseases and other non-communicable diseases (NCDs). A meta-analysis of prospective studies reported that either short- or long-sleep duration: (1) is a risk factor for dying of coronary heart disease or stroke (Cappuccio et al., 2011); (2) is associated with hypertension (Guo et al., 2013); (3) is associated with type-2 diabetes (Cappuccio et al., 2010a); (4) is associated with obesity (Marshall, Glozier & Grunstein, 2008). In general, individuals with short- or long-sleep patterns are at higher risk of all cause mortality (Cappuccio et al., 2010b; Gallicchio & Kalesan, 2009); yet, the evidence is not conclusive on this matter (Kurina et al., 2013). "
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    ABSTRACT: Background. Sleep duration, either short or long, has been associated with diseases such as obesity, type-2 diabetes and cardiovascular diseases. Characterizing the prevalence and patterns of sleep duration at the population-level, especially in resource-constrained settings, will provide informative evidence on a potentially modifiable risk factor. The aim of this study was to explore the patterns of sleep duration in the Peruvian adult and adolescent population, together with its socio-demographic profile. Material and Methods. A total of 12,424 subjects, mean age 35.8 years (SD ±17.7), 50.6% males, were included in the analysis. This is a cross-sectional study, secondary analysis of the Use of Time National Survey conducted in 2010. We used weighted means and proportions to describe sleep duration according to socio-demographic variables (area and region; sex; age; education attainment; asset index; martial and job status). We used Poisson regressions, taking into account the multistage sampling design of the survey, to calculate crude and adjusted prevalence ratios (PR) and 95% confidence intervals (95% CI). Main outcomes were short- (<6 h) and long-sleep duration (≥ 9 h). Results. On average, Peruvians slept 7.7 h (95% CI [7.4–8.0]) on weekdays and 8.0 h (95% CI [7.8–8.1]) during weekends. The proportions of short- and long-sleep, during weekdays, were 4.3% (95% CI [2.9%–6.3%]) and 22.4% (95% CI [14.9%–32.1%]), respectively. Regarding urban and rural areas, a much higher proportion of short-sleep was observed in the former (92.0% vs. 8.0%); both for weekdays and weekends. On the multivariable analysis, compared to regular-sleepers (≥ 6 to <9 h), short-sleepers were twice more likely to be older and to have higher educational status, and 50% more likely to be currently employed. Similarly, relative to regular-sleep, long-sleepers were more likely to have a lower socioeconomic status as per educational attainment. Conclusions. In this nationally representative sample, the sociodemographic profile of short-sleep contrasts the long-sleep. These scenarios in Peru, as depicted by sleeping duration, differ from patterns reported in other high-income settings and could serve as the basis to inform and to improve sleep habits in the population. Moreover, it seems important to address the higher frequency of short-sleep duration found in urban versus rural settings.
    PeerJ 04/2014; 2(1):e345. DOI:10.7717/peerj.345 · 2.11 Impact Factor
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    • "Additionally, sleep duration is only one component of sleep behavior; sleep quality may also impact health status independent of sleep duration [6], [18]. Sleep quality can be defined as difficulty in falling asleep and/or remaining asleep and is commonly assessed in population studies by self-report and user ratings [19], [20]. Up to 41% of adults report sleep difficulties or poor sleep quality [6], [21], yet the impact of sleep quality in combination with a wide variety of other lifestyle behaviors is infrequently examined as a potential influence of health status. "
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    ABSTRACT: The independent and combined influence of smoking, alcohol consumption, physical activity, diet, sitting time, and sleep duration and quality on health status is not routinely examined. This study investigates the relationships between these lifestyle behaviors, independently and in combination, and health-related quality of life (HRQOL). Adult members of the 10,000 Steps project (n = 159,699) were invited to participate in an online survey in November-December 2011. Participant socio-demographics, lifestyle behaviors, and HRQOL (poor self-rated health; frequent unhealthy days) were assessed by self-report. The combined influence of poor lifestyle behaviors were examined, independently and also as part of two lifestyle behavior indices, one excluding sleep quality (Index 1) and one including sleep quality (Index 2). Adjusted Cox proportional hazard models were used to examine relationships between lifestyle behaviors and HRQOL. A total of 10,478 participants provided complete data for the current study. For Index 1, the Prevalence Ratio (p value) of poor self-rated health was 1.54 (p = 0.001), 2.07 (p≤0.001), 3.00 (p≤0.001), 3.61 (p≤0.001) and 3.89 (p≤0.001) for people reporting two, three, four, five and six poor lifestyle behaviors, compared to people with 0-1 poor lifestyle behaviors. For Index 2, the Prevalence Ratio (p value) of poor self-rated health was 2.26 (p = 0.007), 3.29 (p≤0.001), 4.68 (p≤0.001), 6.48 (p≤0.001), 7.91 (p≤0.001) and 8.55 (p≤0.001) for people reporting two, three, four, five, six and seven poor lifestyle behaviors, compared to people with 0-1 poor lifestyle behaviors. Associations between the combined lifestyle behavior index and frequent unhealthy days were statistically significant and similar to those observed for poor self-rated health. Engaging in a greater number of poor lifestyle behaviors was associated with a higher prevalence of poor HRQOL. This association was exacerbated when sleep quality was included in the index.
    PLoS ONE 04/2014; 9(4):e94184. DOI:10.1371/journal.pone.0094184 · 3.23 Impact Factor
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