Introduction
Patients with type 2 diabetes report sleep disturbances more frequently than individuals from the general population [
1,
2]. This is relevant for glucoregulation, as epidemiological [
3‐
5] and experimental studies [
6‐
8] have shown that reduced sleep duration and/or decreased sleep quality markedly reduce glucose tolerance and insulin sensitivity.
There are only a few studies on sleep characteristics in patients with type 1 diabetes.
Jauch-Chara et al. [
9] reported a trend towards less slow wave sleep in 14 non-hypoglycaemic adult patients with type 1 diabetes. Children with type 1 diabetes had more disrupted sleep [
10,
11] and more sleep disorders [
12] than healthy children. Conversely, in adult patients with type 1 diabetes, partial sleep deprivation, even during only a single night, reduced peripheral insulin sensitivity by 21% [
13]. Subjective sleep characteristics and their relation with glucoregulation have not been studied in adult patients with type 1 diabetes.
We hypothesised that adult patients with type 1 diabetes may have alterations in subjective sleep characteristics, assessed by validated sleep questionnaires, compared with healthy controls. In addition, we hypothesised that subjective sleep disturbances would be associated with impaired glucoregulation. Therefore, the aim of the present study was: (1) to assess subjective sleep characteristics by validated sleep questionnaires in adult patients with type 1 diabetes, compared with age-, sex- and BMI-matched non-diabetic controls; (2) to relate sleep characteristics to the quality of glycaemic control, i.e. HbA1c values; and (3) to assess possible risk factors for impaired sleep characteristics in adult patients with type 1 diabetes.
Methods
Participants
We included 99 consecutive patients with type 1 diabetes mellitus (55 men, 44 women) attending the outpatient clinic of the Leiden University Medical Center, and 99 age-, sex- and BMI-matched non-diabetic controls recruited by advertisement. Every patient with type 1 diabetes was individually matched with one non-diabetic healthy control for age, sex and BMI.
Exclusion criteria for both groups were: (1) previously diagnosed sleep disorders; (2) psychiatric disorders and/or use of psychotropic medication; (3) pregnancy or lactation; (4) working in nights shifts in the last 3 months; (5) travelling across time zones in the previous month; (6) age <18 years; (7) other endocrine disorders; (8) neuropathy caused by other conditions than type 1 diabetes; (9) chronic co-morbidity, other than peripheral neuropathy, associated with pain; and (10) chronic use of glucocorticoids.
The study was approved by the medical ethical committee of Leiden University Medical Centre and written informed consent was obtained from all participants prior to the study.
Study design
Patients and controls were asked to complete three validated sleep questionnaires—the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Berlin Questionnaire (BQ)—that provide data on sleep quality, daytime sleepiness and the presence of sleeping disorders [
14‐
16]. An additional questionnaire focused on duration and insulin management of type 1 diabetes, use of medication, co-morbidity, current smoking status and use of alcohol and coffee. Four questions aimed to identify restless legs syndrome (RLS), using the minimum criteria defined by the International RLS Study Group [
17]. As depressive feelings may also affect sleep characteristics, anxiety and depression scores were assessed by the Hospital Anxiety and Depression scale (HADS) [
18].
Sleep disturbances were identified by the answers of the ten possible encountered sleep disturbances of the PSQI questionnaire. The option ‘three times or more per week’ was taken as the affirmative answer. Habitual snoring was defined as a ‘yes’ answer to the three-item question ‘Do you snore’ in the BQ. The participants who answered ‘not known’ to this question were classified as non-snorers. We also included additional questions: ‘Has your sleep been disturbed by hypoglycaemia in the past month? Has your sleep been disturbed by hyperglycaemia in the past month?’ The following four options were provided: ‘never’; ‘less than once per week’; ‘once or twice per week’; and ‘three times or more per week’.
The following data were obtained from medical records for the 12 months preceding the current study. Microvascular complications were defined by the presence of: (1) retinopathy identified by retinal photography, detailed ophthalmologic examination and/or previous laser therapy; or (2) nephropathy identified by increased urinary albumin-to-creatinine ratios (men >2.5 μg/μmol, women >3.5 μg/μmol). Macrovascular complications were defined by objective documentation of coronary artery disease (diagnostic cardiac exercise test, coronary angiography, documented myocardial infarction and/or coronary artery bypass surgery or percutaneous coronary interventions), cerebral vascular disease (documented focal neurological findings supported by appropriate imaging studies) or peripheral vascular disease defined by reduced ankle–arm index or angiography. Hypertension was defined as systolic blood pressure ≥135 mmHg or diastolic blood pressure ≥85 mmHg at multiple occasions or treatment with antihypertensive medication. All patients had HbA1c data obtained within the previous 3 months. An HbA1c value of 7.5% (58 mmol/mol) was taken as the cut-off point dividing well-controlled vs moderate-to-poorly controlled patients.
The presence of peripheral polyneuropathy in the lower extremities was assessed by a single researcher (M. van Dijk), both in all type 1 diabetes patients and all controls, using the modified Toronto Clinical Neuropathy scale (mTCNs, see below) [
19] and a neurothesiometer (Scientific Laboratory Supplies, Nottingham, UK). For the present study, peripheral polyneuropathy was considered to be present using a 2.5 percentile cut-off point for abnormality using data from the matched healthy controls of the present study, when either mTCNs score was >5 points or the vibration perception threshold was >18.4 V.
Assessment of subjective sleep characteristics
Assessment of peripheral polyneuropathy
Statistical analysis
Data were analysed using PASW Statistics version 17.0.2 (SPSS, Chicago, IL, USA).
Continuous variables were described as mean±SEM; categorical variables were expressed as proportions. We used the paired t test and the McNemar test for differences in means and proportions for continuous and categorical paired variables, respectively, and the two-tailed independent t test and the χ
2 test for unpaired data.
In patients with type 1 diabetes, logistic regression analysis was performed to examine the association of each sleep characteristic with poor glycaemic control (yes/no) adjusting for age, sex, BMI, use of alcohol (>1 glass/day, yes/no), and anxiety and depression scores according to the HADS.
To investigate variables for impaired sleep quality (PSQI > 5) in the diabetic population, we fitted logistic regressions separately for all possible variables that could affect sleep quality: total exogenous insulin dose (units per kilogram per day), use of beta-blockers (yes/no), use of ACE inhibitors (yes/no), anxiety and depression score according to HADS, presence of hypertension (yes/no), nephropathy (yes/no), peripheral polyneuropathy (yes/no), macrovascular disease (yes/no), uncomfortable temperatures (yes/no), pain (yes/no), polyuria (yes/no), other sleep disturbances (e.g. metabolic dysregulation), habitual snoring (yes/no), high risk for OSA (yes/no), and RLS (yes/no). In these analyses, we adjusted for the confounders age, sex and BMI by including them as covariates. Subsequently, we performed multivariate logistic regression analysis—including age, sex, BMI, HbA1c, duration of the diabetes and the risk factors that showed a p value of <0.2 in the preceding separate analyses.
Discussion
The aim of this study was to assess subjective sleep characteristics in adult patients with long-standing type 1 diabetes, and to relate sleep variables to HbA1c values. Although sleep duration did not differ between patients and controls, more patients had poor sleep quality compared with non-diabetic, age-, sex- and BMI-matched controls. Patients with type 1 diabetes reported more sleep disturbances and daytime dysfunction. A higher proportion of the patients with type 1 diabetes were at increased risk for OSA. There was no association between subjective sleep characteristics and impaired glucoregulation. These observations indicate that type 1 diabetes is associated with an increased prevalence of disturbed subjective sleep characteristics, which do not relate to glucoregulation.
Previous studies on the relation between diabetes and sleep characteristics mainly focussed on patients with type 2 diabetes [
1,
2]. Only a few studies have assessed sleep characteristics in patients with type 1 diabetes [
9‐
12]. Those studies investigated relatively few individuals and children [
10‐
12] with type 1 diabetes. The present study extends those observations in showing that in a large group of adult patients with a long history of type 1 diabetes subjective sleep characteristics are impaired, compared with a carefully matched control group, controlling for potential confounding factors such as age, sex and BMI.
This decrease in sleep quality and increased prevalence of sleep disturbances in patients with long-standing type 1 diabetes may have important implications, as previous studies showed that reduction of sleep duration and/or decreased sleep quality impair glucose tolerance and reduce insulin sensitivity in healthy controls [
6‐
8]. Sleep disturbances might have a similar negative effect on glucose metabolism in patients with type 1 diabetes, resulting in worse diabetic control. However, this presumed relationship between sleep disturbances and impaired glucose metabolism, assessed by HbA
1c values, was not detectable in the current study. Nonetheless, it is still possible that disturbed sleep characteristics influence glucose metabolism in these patients. However, the effects of impaired sleep characteristics may not simply be reflected in HbA
1c values because intensive glucose control and frequent, appropriate adjustments of insulin doses in patients at risk might have obtunded the effects of impaired sleep characteristics on glucoregulation.
Various aspects of diabetes could be linked to disturbed sleep quality, including physical complications of the disease, psychological factors, metabolic fluctuations and high prevalence of sleep disorders. In the patients with type 1 diabetes in our study, disturbed sleep quality was independently associated with habitual snoring, higher depression scores according to the HADS questionnaire, presence of polyneuropathy and other sleep disturbances, mainly by hypoglycaemia.
Previous studies showed a high prevalence of depression in diabetes [
20] and chronic pain conditions [
21]. Although we excluded patients with a known depression, use of psychotropic drugs, and co-morbid disorders (other than neuropathy) associated with pain, in our study higher depression scores were independently associated with impaired sleep quality.
Many patients with type 1 diabetes in our study used ACE inhibitors, statins and/or beta-blockers, which might interfere with sleep characteristics. The effects of beta-blockers on sleep are not equivocal. A previous study showed that the use of beta-blockers could positively or negatively affect sleep [
22]. A case report by Cicolin et al. suggested that ACE inhibitors may contribute to OSA by inducing upper airway inflammation [
23]. Therefore, we have considered that the use of beta-blockers and/or ACE inhibitors might affect sleep quality in patients with type 1 diabetes. However, in univariate logistic regression analysis we did not find an association between the use of beta-blockers or ACE inhibitors and impaired sleep quality. In univariate logistic regression analysis there was an association between the use of ACE inhibitors and high risk of OSA. However, this association was no longer significant after correction for the confounders age, sex, BMI and hypertension. There are conflicting data on sleep disturbances in patients treated with statins. Some studies reported higher prevalence of sleep disturbances in patients treated with lipophilic statins than with pravastatin [
24,
25] whereas other studies did not find an increased prevalence of sleep disturbances in patients treated with different statins compared with placebo [
26,
27]. In the present study, there was no difference in the use of statins between patients with a poor sleep quality (PSQI > 5) and patients with a good sleep quality (PSQI ≤ 5). The use of statins was even higher in the group with good sleep quality. In accordance, in univariate analysis, use of statins was not associated with impaired sleep quality. Therefore, our conclusions are not likely to be merely explained by the use of medications in our patients.
The clinical assessment of peripheral polyneuropathy according to strict criteria in individuals with type 1 diabetes is a major strength of our study, as our study shows diabetic polyneuropathy was a major determinant of impaired sleep. Polyneuropathy contributes to impaired sleep via several potential mechanisms. First, neuropathic pain may lead to disturbed sleep [
28]. Second, polyneuropathy can impair thermoregulation. It has been proposed that autonomic changes in skin temperature modulate the neuronal activity of the thermosensitive neurons in the pre-optic area/anterior hypothalamus, which, in turn, regulate vigilance and sleepiness [
29]. This hypothesis is supported by a report showing that diabetic patients, even those without evidence of clinical neuropathy, show impaired thermoregulation during sleep [
30].
The relatively high prevalence of type 1 diabetic patients with a high risk for OSA, according to the BQ, suggests the potential of a high burden of unrecognised OSA in people with type 1 diabetes. This is a relatively new finding, in accordance with a recent pilot study of Borel et al., which observed a prevalence of OSA of 40% in 37 non-obese adult patients with type 1 diabetes [
31]. In accordance with our data, this observation is remarkable, as the BMI, which is a risk factor for OSA in the general population, of our patients with type 1 diabetes was matched to that of the healthy controls. Several studies in patients with type 2 diabetes have shown that OSA is associated with the presence of autonomic neuropathy [
32,
33], which might also be involved in patients with type 1 diabetes. Unfortunately, our current study was not designed to elucidate underlying mechanisms of disturbed sleep, and we did not include assessments of autonomic neuropathy. Nonetheless, there was no association between poor glycaemic control (HbA
1c ≥ 7.5%) and a ‘high’ risk for OSA in our study, despite the association between sleep-disordered breathing, glucose intolerance and insulin resistance in patients with type 2 diabetes [
34,
35]. There was also no association between the occurrence of hypoglycaemia and the risk for OSA in our patients [
36].
Sleep characteristics were assessed by validated questionnaires in the present study. The PSQI and ESS have been developed to measure sleep quality and daytime sleepiness, respectively [
14,
15], and reflect stable measures of sleep quality and sleepiness over the past year [
37]. The PSQI has a diagnostic sensitivity of 89.6% and a specificity of 86.5% for identifying cases with poor sleep quality, using a cut-off score of 5. This questionnaire has been validated by polysomnographic measurements [
38].The BQ is a screening tool widely used to differentiate between ‘high-’ and ‘low-risk’ groups for OSA. This risk grouping was useful in the prediction of respiratory disturbances in consecutive participants, who visited internists for any reason. For example, being in the ‘high-risk’ group defined by the BQ predicted more than five respiratory events per hour with a sensitivity of 86%, and a specificity of 77% [
16]. In view of the current data, polysomnography is required to objectively assess the sleep quality and OSA in patients with type 1 diabetes mellitus at high risk for OSA according to the BQ.
The current cross-sectional study was designed to assess subjective sleep variables in patients with type 1 diabetes mellitus and, therefore, we cannot elucidate from the data which chain(s) of events lead from type 1 diabetes to disturbed sleep. In particular, the links between peripheral polyneuropathy and disturbed sleep and between type 1 diabetes and the risk of OSA are not fully clear. Another matter is whether disturbed sleep leads to further impairment of glucose metabolism, with the effect that sleep disturbances and glycaemic control can interact in a vicious circle. Additional studies with objective sleep measurements are warranted to assess these relations in more detail.
In conclusion, the present study demonstrated that adult patients with long-standing type 1 diabetes mellitus have altered self-reported sleep characteristics compared with sex-, age- and BMI-matched non-diabetic controls. Therefore, disturbed subjective sleep characteristics are part of the complex syndrome of long-standing type 1 diabetes mellitus.