A Comparison of Glycemic Control, Sleep, Fatigue, and Depression in Type 2 Diabetes with and without Restless Legs Syndrome

Abstract

Objective:

The purpose of this pilot study was to determine how sleep quality, glycemic control, sleepiness, fatigue, and depression differ in persons with type 2 diabetes with and without restless legs syndrome (RLS).

Research Designs/Methods:

The design was a descriptive, case-control study of participants with type 2 diabetes with and without RLS at the University of Pennsylvania, Rodebaugh Diabetes Center. Thirty-nine participants (adults over 21 years of age who had been diagnosed with type 2 diabetes and had a HbA_1c in the last 3 months) were stratified based on RLS diagnostic criteria. Exclusion criteria included severe hypoglycemia identified by seizures or coma related to hypoglycemia or known sleep disorder other than RLS. The primary outcome of sleep was measured by self-report sleep quality (Pittsburgh Sleep Quality Index) with secondary outcomes including HbA_1c, sleepiness (Epworth Sleepiness Scale), fatigue (Fatigue Severity Scale), and depression (Center for Epidemiologic Studies-Depression Scale).

Results:

Participants with type 2 diabetes with RLS reported a significant difference in quality of sleep (p = .001), sleep latency (p = .04), sleep efficiency (p = .035), use of sleep medications (p < .001), and daytime dysfunction (p = .005). In the total group, higher HbA_1c levels were positively correlated with sleepiness (p = .038). Global Pittsburgh Sleep Quality Index scores were positively correlated with fatigue (r = .58, p = .002) and depression (r = .74, p < .001). As well, fatigue and sleepiness were positively correlated (r = .36, p = .04).

Conclusions:

RLS is a significant sleep disorder that may have an impact on diabetes management and health outcomes. More research should be conducted on the impact of RLS in sleep to improve diabetic management.

Citation:

Cuellar NG; Ratcliffe SJ. A comparison of glycemic control, sleep, fatigue, and depression in type 2 diabetes with and without restless legs syndrome. J Clin Sleep Med 2008;4(1):50-56.

Diabetes is the fifth leading cause of death in America, affecting 17 million people or 6.2% of the population. In 2002, the direct cost of diabetes was $91.8 billion dollars in healthcare costs, with indirect costs of disability, work loss, and premature mortality at $40.2 billion dollars.¹ These alarming statistics are due in part to the rising problem of obesity and chronic diseases in our population, resulting in increased insulin resistance and increased rates of type 2 diabetes, which is most often diagnosed after the age of 40. Sleep disturbances and sleep loss are implicated in insulin resistance, a precursor to type 2 diabetes. Individuals who sleep more than 8 hours per day or less than 7 hours per day are at modestly increased risk of all-cause mortality, cardiovascular disease, and developing symptomatic diabetes.²

One sleep disorder that may affect the management of diabetes, yet is unrecognized as a significant contributor to diabetes health outcomes, is restless legs syndrome (RLS) increasingly seen in type 2 diabetes.³ However, the association between RLS and diabetes has not been studied thoroughly, and the effects of RLS on diabetes are unknown. In the results of the National Sleep Foundation Sleep in America 2005 Poll, adults with diabetes were more likely to be at risk for RLS (p < 0.05). The adults who were at risk for RLS were also at increased risk for obstructive sleep apnea (OSA) and insomnia (p < 0.05), sleep problems often reported in diabetes.²

Although the cause of RLS remains unknown, current hypotheses implicate abnormalities in central nervous system iron and dopamine metabolism.⁴ RLS may affect up to 15% of the population^5,6 with debilitating symptoms of sleep deprivation, discomfort, and fatigue that can interfere with family life and occupational and social activities.⁷ The prevalence of RLS is difficult to determine because it is often misdiagnosed or not diagnosed due to the myriad of symptoms with which patients may present. RLS symptoms worsen with age and may occur daily, despite the use of pharmacologic agents that temporarily treat the symptoms.^5,8 A higher prevalence of women is seen more frequently than men in the older population.^3,8 Many individuals that are affected by the syndrome do not report it to their healthcare providers, even when their symptoms are severe because they do not realize that it can be treated or is a problem worth mentioning.⁹

Secondary RLS is associated with comorbid disease, including diabetes. In persons with RLS, 21% report having diabetes,⁸ a prevalence more than 3 times that of the general population; however, there is no report found in the literature that provides the incidence or prevalence of RLS in the diabetic population in the US. RLS is significantly associated with self-reported diminished general health and poor mental health and correlates with age, increasing body mass index, sedentary lifestyle, and low exercise, all factors associated with type 2 diabetes.⁸

Studies specifically examining the symptomatology of RLS in diabetes have not been done in the US. In Brazil, RLS was found in 27% of patients with diabetes. In 45% of these patients, poor sleep quality was associated with age (p = 0.04), peripheral neuropathy (p = 0.001), and RLS (p = 0.000). Logistic regression analysis revealed an association between RLS and peripheral neuropathy (p = 0.001).¹⁰ In Italy, RLS was diagnosed in 18% of patients with type 2 diabetes and was independently associated with diabetes (p < .04). Polyneuropathy was the main risk factor for RLS but only partially explained the increased prevalence.¹¹ In another study recently conducted in the same country, RLS was present in 33 of 99 patients with neuropathy associated with diabetes mellitus, impaired glucose tolerance, and impaired fasting glucose. The patients with RLS were more commonly diagnosed with small fiber sensory neuropathy and more often reported symptoms of burning feet.¹²

It is unknown if the presence of RLS in patients with type 2 diabetes contributes to sleep disturbances that may affect glycemic control, in turn, exacerbating the severity of symptoms of RLS or contributing to diabetic complications. Poor diabetic control with RLS may also impact the associated consequences of diabetes, including sleep quality, sleepiness, fatigue, and depression. Sleep disorders in diabetes have been reported primarily as sleep-disordered breathing or OSA associated with obesity, body mass index, diet, and exercise. The impact of sleep loss related to other sleep disorders, besides OSA, should be considered in patients with type 2 diabetes. Therefore, the purpose of this pilot study was to determine how sleep quality, glycemic control, sleepiness, fatigue, and depression differ in persons with type 2 diabetes with and without RLS. It was hypothesized that participants with type 2 diabetes with RLS would have worse self-reported sleep quality (the primary outcomes measure of the study) as well as higher levels of HbA_1c, sleepiness, fatigue, and depression than those participants with type 2 diabetes without RLS.

METHODS

Research Design

The design of this pilot study was a descriptive, comparative, case-control study. The target population was patients with type 2 diabetes with and without RLS. Thirty-nine participants were stratified to 2 groups (with or without RLS). All participants continued their standard care for diabetes and RLS, including any pharmacologic interventions.

Participants

Participants (n = 39) with type 2 diabetes were recruited from the Penn Rodebaugh Diabetes Center at the University of Pennsylvania in Philadelphia between June 2004 and December 2005. One participant was excluded after data collection due to the misreported diagnosis of type 1 diabetes mellitus. A second participant was reassigned to the non-RLS group after reporting only 3 out of the 4 criteria for RLS on the survey. Institutional Review Board approval was received from the Office of Regulatory Affairs at the University of Pennsylvania.

Procedures

Patients who had been seen at the Diabetes Center in the previous year were sent letters asking for participation in a study examining sleep in type 2 diabetics. The letters provided a brief explanation of the study, and participants were asked to call for more information. The letter stated that patients could be included if they did not have sleep problems. Participants were asked to contact the principal investigator if interested in participating in the study.

When contact was made with the principal investigator, verbal consent over the phone was given to provide the principal investigator with information needed to screen the participant and determine if the participant qualified for the study and had symptoms of RLS. The principal investigator obtained verbal consent over the phone to obtain healthcare information related to inclusion and exclusion criteria. Inclusion criteria were men and women older than 21 years of age who had been diagnosed with type 2 diabetes and had a HbA_1c in the last 3 months. Exclusion criteria included persons with severe hypoglycemia, defined as ever having seizures or coma related to hypoglycemia, or a sleep disorder other than RLS (e.g., OSA, narcolepsy, other sleep-disordered breathing).

The participants were screened for RLS based on the 4 diagnostic criteria developed in 1995, including (1) an urge to move legs due to discomfort, (2) temporary relief with movement, (3) worsening symptoms at rest, and (4) worsening symptoms at night¹³ and were assigned to 1 of the 2 groups: with or without RLS. At this time, they were asked if they would like to participate in the study.

While the participants were being enrolled in the study, records were collected based on meeting the 4 criteria and were documented to estimate the prevalence of RLS in type 2 diabetes. If the participants agreed to be in the study, participants were mailed the consent, demographic survey, and questionnaires with a 100% return rate. A $25.00 gift card from a local pharmacy was mailed to participants when the surveys were returned to the principal investigator in a stamped envelope provided.

Measures

Demographic data were collected on age, sex, marital status, ethnicity, employment status, education, socioeconomic status, comorbid conditions, pharmacologic interventions, and nonconventional treatments. The use of all pharmacologic medications prescribed, natural products, and vitamins with name, dose, and frequency were listed on the demographic data form. Objective measures were collected by self-report on HbA_1c height, weight, and blood pressure. Information collected on the participants with RLS included family history and age of onset of RLS. Common comorbid conditions associated with RLS that were listed included iron deficiency, vitamin deficiencies, neuropathy, rheumatoid arthritis, Parkinson disease, emphysema, hypothyroidism, kidney failure, fibromyalgia, hypertension, hypotension, chronic fatigue syndrome, irritable bowel syndrome, and other.

The primary outcome of the study was sleep quality measured by the Pittsburgh Sleep Quality Index (PSQI) examining 7 components (sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction) and a global score. With a total of 19 questions, participants rate the components on a scale of 0 to 3 with a possible range from 0 to 21 and with higher scores indicating worse sleep quality (> 6 = poor sleepers). Reliability of the instrument is .83, with strong correlates between sleep logs and PSQI (r = .81).^14,15

The Epworth Sleepiness Scale (ESS) is the gold standard for subjectively measuring daytime sleepiness in adults. Participants are asked to rate the chances of falling asleep in 8 situations encountered in daily life on a scale of 0 to 3, with scores ranging from 0 to 24 (> 10 indicates excessive daytime sleepiness). Reliability of the instrument is .88, with validity correlated with the mean sleep latency scale at −.30 (p < .0001).^16,17

The Fatigue Severity Scale (FSS) is designed to rate statements distinguishing fatigue from depression. With 9 items rated on a scale of 1 to 7, possible scores range from 7 to 63, with 63 representing the most fatigue. Reliability is reported at .88, with strong convergent validity (≥ 0.6-0.7).¹⁸

The Center for Epidemiologic Studies Depression Scale (CES-D) is a self-report measure of depressive symptomatology developed for nonpsychiatric populations aged 18 and older. The CES-D provides an index of cognitive, affective, and behavioral features of depression and the frequency of symptoms. Participants rate each item on a scale of 0 to 3, with a possible score of 0 to 60 (higher scores being the most depressed). A score of 16 or above indicates depressive symptomatology. Every fourth item is reversed scoring. Reliability is reported at .90, with a sensitivity of 77.8% and specificity of 84.7%.^{19, 20}

Sample Size/Power Analysis

Sample-size calculations were based on the differences in PSQI scores in primary and secondary RLS participants, since sleep outcomes are a major complication of RLS and diabetes. This study was powered to find a difference of 4.0 in the average PSQI score between groups, assuming 80% power, α of 0.5, and standard error of 4.5 (from previous studies) under a Student t-test. A total of 40 participants was required for enrollment with a 10% drop-out rate anticipated requiring a minimum of 18 participants in each group.

Statistical Methods

The prevalence of RLS in the diabetic population was estimated based upon the proportion of screened diabetic participants found to have RLS. Descriptive statistics were used to explore the demographic characteristics and outcome variables in the case and control groups. Nonnormally distributed continuous variables were transformed to normality, where possible. Comparisons between groups were conducted using χ², Fisher exact, or t-tests. Additionally, Pearson correlations were used to determine the strength of the relationships between HbA_1c and sleep outcomes. All analyses were conducted in SPSS 14.0 (SPSS, Inc., Chicago, Ill).

RESULTS

One hundred twenty-one participants were screened for the study from June 2004 to December 2005. The participants' flowchart using the CONSORT statement can be seen in Figure 1. The estimate prevalence of RLS from this sample was 44.6% (95% confidence interval [CI] = 38.3 – 50.4).

Figure 1.

CONSORT Flowchart. DM refers to diabetes mellitus; RLS, restless legs syndrome.

Of the 39 participants included in the analysis sample, the average participant was female (56.4%), married or partnered (59.0%), and Caucasian (46.2%) or African American (38.5%). Participants with and without RLS were similar across all demographic characteristics except ethnicity (Table 1). Caucasians were 5 times more likely to have RLS in this sample (odds ratio [OR] = 5.0, 95% CI = 1.3–19.6, p = 0.026). Comorbid conditions reported in participants with and without RLS can be seen in Figure 2. Participants reported that symptoms of RLS developed a mean of 2 years after the diagnosis of type 2 diabetes was confirmed. Of the 18 participants with RLS, only 5 were being treated for RLS with gabapentin (4), pramipexole (4), clonazepam (2), and temazepam (1).

Table 1.

Comparison of Demographic Characteristics Between Diabetic Participants with and without Restless Legs Syndrome

Characteristica	Cases (n = 18)	Controls (n = 21)	Effect Sizeb	95% CI
Female	10 (55.6)	12 (57.1)	OR = 0.94	0.26 – 3.34
Age, y	59.5 (± 11.6)	62.1 (± 10.8)	D = −2.55	−9.96 – 4.86
Married or partnered	11 (61.1)	12 (60.0)	OR = 1.05	0.28 – 3.86
Caucasian	12 (66.7)	6 (28.6)	OR = 5.00c	1.28 – 19.61
Years in school	13.6 (± 2.5)	15.2 (± 4.7)	D = −1.58	−4.00 – 0.84
Employment status
Full-time	2 (11.1)	6 (28.6)	(ref)
Retired	7 (38.9)	6 (28.6)	OR = 3.50	0.51 – 24.27
Other	9 (50.0)	9 (52.8)	OR = 3.00	0.47 – 19.04
Socioeconomic status ($1,000)
0–20	5 (31.3)	8 (42.1)	(ref)
20–40	5 (31.3)	4 (21.1)	OR = 2.00	0.36 – 11.23
40–60	5 (31.3)	3 (15.8)	OR = 2.67	0.43 – 16.39
60+	1 (6.3)	4 (21.1)	OR = 0.40	0.03 – 4.68
Cups of coffee/day
0	7 (38.9)	7 (33.3)	(ref)
1	4 (22.2)	7 (33.3)	OR = 0.57	0.11 – 2.87
2+	7 (38.9)	7 (33.3)	OR = 1.00	0.23 – 4.40
1+ Other caffeinated drinks/day	13 (72.2)	12 (57.1)	OR = 1.95	0.51 – 7.49
Smokes	2 (11.1)	2 (9.5)	OR = 1.19	0.15 – 9.41
Exercise / week, h	3.9 (± 3.3)	3.7 (± 2.8)	D = 0.18	−1.82 – 2.18
body mass index, kg/m2	32.4 (± 7.1)	33.8 (± 8.8)	D = −1.42	−6.66 – 3.82

Data are reported as frequencies (%) or mean (± SD).

^aResponses within a characteristic may not sum to 100% due to missing values.

^bOdds ratio (OR) for cases (with restless legs syndrome) versus controls (without restless legs syndrome), difference (D) for cases-controls. CI refers to confidence interval.

^cp < 0.05

Figure 2.

Report of comorbid conditions in participants with type 2 diabetes with and without restless legs syndrome (RLS). Iron def refers to iron deficiency; RA, rheumatoid arthritis; HTN, hypertension; IBS, irritable bowel syndrome.

Participants with type 2 diabetes with RLS were found to have significantly worse sleep outcomes. Participants with RLS had approximately twice the global PSQI score as those without RLS (12.8 vs 6.7, p = 0.002). Further, in the individual PSQI component scores, participants with type 2 diabetes with RLS had significantly worse subjective sleep quality (p = 0.001), sleep latency (p = 0.040), habitual sleep efficiency (p = 0.035), and daytime dysfunction (p = 0.005) and took more sleep medications (p < 0.001). For secondary outcomes, the only significant difference between patients with and without RLS was seen in the FSS scores. Participants with type 2 diabetes with RLS had significantly higher FSS scores than participants without RLS (3.8 vs. 2.6, p = 0.028). Comparison of sleep outcomes between diabetic participants with and without RLS can be found in Table 2.

Table 2.

Comparison of Sleep Outcomes Between Diabetic Participants with and without Restless Legs Syndrome

Outcome Variable1	Cases (n = 18)	Controls (n = 21)	Effect Size2	95% CI or p value
Global PSQI score	12.9 (± 3.3)	6.7 (± 5.8)	D = 6.11d	2.49 – 9.73
Sleep category
Good	0 (0.0)	8 (44.4)	(ref)
Poor	17 (100.0)	10 (55.6)	OR = 28.3d	1.48 – 542.99
Component scores
1. Subjective sleep quality	2.2 (± 0.8)	1.0 (± 1.0)	Z = 3.22d	0.001
2. Sleep latency	1.9 (± 1.1)	1.0 (± 1.2)	Z = 2.14c	0.040
3. Sleep duration	2.1 (± 0.9)	1.5 (± 1.4)	Z = 1.07	0.308
4. Habitual sleep efficiency	1.9 (± 1.1)	1.1 (± 1.3)	Z = 2.22c	0.035
5. Sleep disturbance	1.7 (± 0.6)	1.3 (± 0.8)	Z = 1.33	0.229
6. Sleep medications	1.2 (± 1.6)	0.2 (± 0.7)	Z = 3.91e	< 0.001
7. Daytime dysfunction	1.6 (± 0.8)	0.7 (± 0.9)	Z = 2.85d	0.005
ESS	10.3 (± 5.1)	8.4 (± 6.0)	D = 1.94	−1.81 – 5.70
FSS	3.8 (± 1.2)	2.6 (± 1.6)	D = 1.19c	0.20 – 2.18
CES-D	18.4 (± 9.1)	12.1 (± 11.6)	D = 6.25	−0.47 – 12.96
HbA1c	7.1 (± 1.5)	7.4 (±1.5)	D = −0.30	−1.32 – 0.72

Data are reported as frequencies (%) or mean (± SD). PSQI refers to Pittsburgh Sleep Quality Index; ESS, Epworth Sleepiness Scale; FSS, Fatigue Severity Scale; CES-D, Center for Epidemiologic Studies-Depression Scale; CI, confidence interval.

^aResponses within a variable may not sum to 100% due to missing values.

^bCases and controls compared via a t-test, Fisher exact test, and Mann-Whitney U tests (component scores). Odds ratio (OR) for cases (with restless legs syndrome) versus controls (without restless legs syndrome), difference (D) for cases-controls, or z-values (Z) from nonparametric test.

^cp < 0.05;

^dp < 0.01;

^ep < 0.001

The relationship between the sleep outcomes and HbA_1c were examined in the entire sample (Table 3). Significant positive correlations were found between HbA_1c and ESS scores (r = 0.36, p = 0.037). Global PSQI scores had a strong positive correlation with both FSS (r = 0.58, p = 0.002) and CES-D (r = 0.74, p < 0.001) scores. These significant correlations persisted when RLS status was adjusted for via partial correlations (not shown).

Table 3.

Correlations Between Sleep Outcomes and HbA_1c in the Sample of 39 Subjects with Diabetes

	HbA1c	PSQI	ESS	FSS	CES-D
HbA1c		0.09	0.36a	0.06	0.22
PSQI Global Score	0.09		0.34	0.58b	0.74c
ESS	0.36a	0.34		0.36a	0.26
FSS	0.06	0.58b	0.36a		0.39a
CES-D	0.22	0.74c	0.26	0.39a

PSQI refers to Pittsburgh Sleep Quality Index; ESS, Epworth Sleepiness Scale; FSS, Fatigue Severity Scale; CES-D, Center for Epidemiologic Studies-Depression Scale.

^ap < 0.05;

^bp < 0.01;

^cp < 0.001

DISCUSSION

To promote better management of type 2 diabetes, sleep disorders must be addressed. Few studies have examined the sleep patters of persons with RLS and type 2 diabetes. This is the first study to look at outcomes of sleep in the US. This pilot shows that participants with type 2 diabetes with RLS have significantly worse sleep outcomes, reporting twice the global PSQI score as those without RLS, with no significant difference on glycemic control. More importantly, the RLS participants reported that their sleep quality was worse, that the time it took them to get to sleep was longer, and how much time they did sleep was less than that of the participants without RLS. The participants with RLS took more medications to help them sleep, despite the fact that they had worse sleep outcomes. These participants also reported that the lack of sleep affected their daytime functioning and that they had more fatigue than those without RLS, which could affect their personal self care, including diet, exercise, and social activities. It is also significant to note that less than one third of the participants with RLS were being treated for RLS.

Our findings are in agreement with other studies that have used objective sleep measures in patients with type 2 diabetes and RLS who reported poor sleep outcomes related to increased sleep latency. This interruption in sleep was based on the symptom severity of RLS, which was not measured in this study because all the participants did not have RLS. Nightly reduction of sleep of 2 to 4 hours affected sleep outcomes, including daytime functioning and fatigue. These findings were also consistent in our study, with significant differences reported in fatigue between the 2 groups. The demographics of the study are consistent with previous findings.^{21, 22}

Overall, all (n = 39) the participants with type 2 diabetes reported some sleep problems. Sleepiness and HbA_1c were positively correlated. As well, overall sleep quality was positively correlated with fatigue and depression. Few studies have examined the effect of sleepiness on self-care of patients with diabetes. It is uncertain if sleepiness is a result of the symptoms of type 2 diabetes (like fatigue or nocturia), comorbid conditions that may be associated with type 2 diabetes, polypharmacy, or obesity, which may contribute to inactivity, to name a few possibilities. In studies of patients with type 2 diabetes, excessive daytime sleepiness has been correlated with nocturia,²³ with habitual snoring,²⁴ and in RLS.¹⁰ Sleepiness has also been correlated with fatigue and depression, often seen in type 2 diabetes.²⁵ Although sleepiness or fatigue is a symptom of diabetes, the impact of sleepiness or fatigue on the management of type 2 diabetes has not been thoroughly examined.

Several hypotheses may explain the findings for our study. First, the misdiagnosis or lack of treatment for persons with RLS is documented. Therefore, the worsening sleep loss may be related to the management of RLS in persons with type 2 diabetes who have not been evaluated for this sleep disorder. RLS is treatable, and the symptoms that disrupt sleep can be managed with carbidopa-levodopa, dopamine agonists, opioids, benzodiazepines, and anticonvulsants. Although promising breakthroughs are being made in the treatment of RLS, it remains difficult to treat, as pharmacologic agents often stop working or cause augmentation or rebound. As well, many other pharmacologic agents exacerbate symptoms of RLS. Since participants in this study have many comorbid illnesses, the medications used to treat the chronic illness may in fact contribute to the development of RLS. For clinical practitioners, patients with RLS should be reevaluated at regular intervals to determine if their RLS symptoms remain under control or if the symptoms are worsening. The RLS Severity Scale is 1 instrument that can be used to measure symptom severity in these patients,²⁶ with the RLS Quality of Life Scale being a valid instrument to examine the effects of RLS in daily life.²⁷ Secondly, sleep disruption reported in the participants with RLS may also impact other metabolic disturbances found in this group of participants that, in turn, impact the negative symptoms of diabetes and RLS. Because a high proportion of these participants had comorbid conditions, all factors should be examined to determine their contribution to sleep disturbance. Thirdly, it is unknown if the negative health outcomes found in this study are the result of RLS or other factors that are associated with type 2 diabetes, such as depression, hypertension, or obesity. Because this is the first study examining RLS in type 2 diabetes, more research should be done on all of the variables that may impact health outcomes in diabetes and RLS.

Limitations

The limitations of this study include the lack of objective measures, including polysomnography, to evaluate sleep quality; the diagnostic criteria for RLS used for the study; and the ability to generalize to the population due to the small sample size, lack of clinical data, and the dependence on self-report for other sleep diagnoses, including OSA, which is thought to be common in type 2 diabetes.

The PSQI is commonly used to measure sleep quality in a variety of sleep disorders. The use of polysomnography or actigraphy could be used to determine objective sleep measures. The diagnostic criteria used to assess for symptoms of RLS have been used in the majority of RLS studies, developed by the International RLS Study Group.¹³ The use of the 4 criteria has been reported to have a false-positive rate. A new criterion has been developed by researchers at John Hopkins University, the Telephone Diagnostic Interview, for diagnosing RLS that should be used in studies that are screening for RLS.²⁸ A large sample size is needed to generalize these findings to the population.

Although this study is a pilot, it introduces healthcare providers to a possible cause of sleep disturbance that can impact diabetic healthcare outcomes. Future studies should examine the impact of RLS on sleep and health outcomes in the development and management of type 2 diabetes. Poor sleep quality in patients with type 2 diabetes in association with periodic limb movements as an independent cause for poor sleep quality should be examined.

Summary

This is one of the first studies examining the impact of RLS on outcomes in diabetes. RLS research has focused on primary RLS, which does not include participants who have diabetes. RLS has been found to be associated with a variety of medical and psychiatric conditions and can be diagnosed in conjunction with other sleep disorders. Patients with type 2 diabetes who develop RLS, and their healthcare providers, should understand that a variety of sleep disorders may be seen in diabetes and that treatment options are available that can improve sleep and thereby improve the management and health outcomes of diabetes and RLS. Healthcare providers must be able to identify and manage RLS, which is prevalent in type 2 diabetes and has been shown to be associated with increasing morbidity in this population.