Identification of basement membrane markers in diabetic kidney disease and immune infiltration by using bioinformatics analysis and experimental verification

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease worldwide. Basement membranes (BMs) are ubiquitous extracellular matrices which are affected in many diseases including DKD. Here, the authors aimed to identify BM-related markers in DKD and explored the immune cell infiltration in this process. The expression profiles of three datasets were downloaded from the Gene Expression Omnibus database. BM-related differentially expression genes (DEGs) were identified and Kyoto encyclopaedia of genes and genomes pathway enrichment analysis were applied to biological functions. Immune cell infiltration and immune function in the kidneys of patients with DKD and healthy controls were evaluated and compared using the ssGSEA algorithm. The association of hub genes and immune cells and immune function were explored. A total of 30 BM-related DEGs were identified. The functional analysis showed that BM-related DEGs were notably associated with basement membrane alterations. Crucially, BM-related hub genes in DKD were finally identified, which were able to distinguish patients with DKD from controls. Moreover, the authors observed that laminin subunit gamma 1(LAMC1) expression was significantly high in HK2 cells treated with high glucose. Immunohistochemistry results showed that, compared with those in db/m mouse kidneys, the levels of LAMC1 in db/db mouse kidneys were significantly increased. The biomarkers genes may prove crucial for DKD treatment as they could be targeted in future DKD treatment protocols.

Disabilities and Medicine: From Stairs to Stares

Description The current state of medicine has done little to correct the imbalance of underrepresented groups in the physician workforce. In this editorial, a psychiatric resident physician retells his experience as an individual who is completing his medical training while being affected by a neuromuscular disorder and how it impacts his daily life. The primary goal of this paper is to elucidate the need for greater representation and understanding of the disabled physician workforce.

Exploring Kinetic and Kinematic Finger Individuation Capability in Children With Hemiplegic Cerebral Palsy

While fine manual dexterity develops over time, the extent to which children show independent control of their digits in each hand and the impact of perinatal brain injury on this individuation have not been well quantified. Our goal in this study was to assess and compare finger force and movement individuation in 8-14 year old children with hemiplegic cerebral palsy (hCP; n = 4) and their typically developing peers (TD; n = 10). We evaluated finger force individuation with five independent load cells and captured joint movement individuation with video tracking. We observed no significant differences in individuation indices between the dominant and non-dominant hands of TD children, but individuated force and movement were substantially reduced in the paretic versus non paretic hands of children with hCP (p < 0.001). In TD participants, the thumb tended to have the greatest level of independent control. This small sample of children with hCP showed substantial loss of individuation in the paretic hand and some deficits in the non-paretic hand, suggesting possible benefit from targeted training of digit independence in both hands for children with CP.

Orthodontics in patients with significant medical co-morbidities

A wide variety of patients with medical co-morbidities may present to general orthodontic practice. It is important for the treating clinician to have a general understanding of key medical conditions that may impact upon the treatment and management options. This clinical supplement provides a treatment-focused summative update for the orthodontist regarding significant medical co-morbidities, their general prevalence and an exploration of potential impacts upon orthodontic treatment. This review also discusses the significance of key medications and provides suggestions for the safe provision of orthodontic treatment.

Contraceptive practices in women with chronic medical conditions

Contraception in women with severe medical conditions is a potential measure to reduce maternal mortality. We sought to determine the contraceptive use in women with medical conditions at the University Hospital of the West Indies (UHWI) in Jamaica to determine if there is room for improvement in contraceptive use. Participants were identified from the medical out-patient departments and questionnaires administered. Two hundred and sixty females between 18 and 44 years with varied chronic medical conditions were included. Those included were systemic lupus erythematosus (SLE), diabetes, hypertension (HTN), thyroid disease, cardiac and renal disease. The total current use of contraception was 58.4%, while 41.6% were not on contraceptives. The use of barrier methods and long-acting reversible contraceptives (LARCs) was 71% and 10%, respectively. The current use of contraception in patients with sickle cell disease (SCD) was 84% (p=.004) and in rheumatoid arthritis (RA), 14% (p=.028). Fifty-eight (58, 24.2%) of the women were using two or more methods of contraception. There is a role for improving contraceptive use among women with medical conditions as they are at increased risk of pregnancy complications.IMPACT STATEMENTWhat is already known on this subject? Women with medical comorbidities significantly contribute to both direct and indirect causes of maternal mortality. Contraception may play an integral role in reducing the risk of dying in chronically ill women; however, the use of contraception in this group is often suboptimal.What the results of this study add? This study adds to the literature that in this high-risk group, there is an underuse of long-acting reversible contraceptives, which is ideal for this population.What the implications are of these findings for clinical practice or further research? The results will provide evidence that this high-risk group of women should be targeted and counselled regarding their risk of morbidity and mortality in pregnancy as well as contraception use while their condition is optimised. From this evidence, services may be put in place in institutions, especially in low-resource settings.

Network-based computational approach to identify genetic links between cardiomyopathy and its risk factors

Cardiomyopathy (CMP) is a group of myocardial diseases that progressively impair cardiac function. The mechanisms underlying CMP development are poorly understood, but lifestyle factors are clearly implicated as risk factors. This study aimed to identify molecular biomarkers involved in inflammatory CMP development and progression using a systems biology approach. The authors analysed microarray gene expression datasets from CMP and tissues affected by risk factors including smoking, ageing factors, high body fat, clinical depression status, insulin resistance, high dietary red meat intake, chronic alcohol consumption, obesity, high-calorie diet and high-fat diet. The authors identified differentially expressed genes (DEGs) from each dataset and compared those from CMP and risk factor datasets to identify common DEGs. Gene set enrichment analyses identified metabolic and signalling pathways, including MAPK, RAS signalling and cardiomyopathy pathways. Protein-protein interaction (PPI) network analysis identified protein subnetworks and ten hub proteins (CDK2, ATM, CDT1, NCOR2, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E and HIST1H4L). Five transcription factors (FOXC1, GATA2, FOXL1, YY1, CREB1) and five miRNAs were also identified in CMP. Thus the authors' approach reveals candidate biomarkers that may enhance understanding of mechanisms underlying CMP and their link to risk factors. Such biomarkers may also be useful to develop new therapeutics for CMP.

Selection of optimum frequency bands for detection of epileptiform patterns

The significant research effort in the domain of epilepsy has been directed toward the development of an automated seizure detection system. In their usage of the electrophysiological recordings, most of the proposals thus far have followed the conventional practise of employing all frequency bands following signal decomposition as input features for a classifier. Although seemingly powerful, this approach may prove counterproductive since some frequency bins may not carry relevant information about seizure episodes and may, instead, add noise to the classification process thus degrading performance. A key thesis of the work described here is that the selection of frequency subsets may enhance seizure classification rates. Additionally, the authors explore whether a conservative selection of frequency bins can reduce the amount of training data needed for achieving good classification performance. They have found compelling evidence that using spectral components with <25 Hz frequency in scalp electroencephalograms can yield state-of-the-art classification accuracy while reducing training data requirements to just a tenth of those employed by current approaches.

Influence of mental stress on the pulse wave features of photoplethysmograms

Mental stress is a major burden for our society. Invasive and non-invasive methods have been proposed to monitor and quantify it using various sensors on and off body. In this Letter, the authors investigated the use of the arm photoplethysmogram (PPG) to assess mental stress in laboratory conditions. Results were in correspondence with their previous in-silico study which guided the present study. Three wave shape parameters were identified for stress assessment from the PPG signal: (i) the time from dicrotic notch to end diastole; (ii) the time from pulse onset to systolic peak; and (iii) the ratio of diastolic to systolic area. The proposed in-vivo results showed that the two first parameters responded significantly to increased mental stress and to a breathing relaxation procedure, complementing heart rate, heart rate variability, and pulse transit time as indices of stress.

Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal

Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may be found in 5%-8% of the children. Early diagnosis of ADHD is crucial for treating the disease and reducing its harmful effects on education, employment, relationships, and life quality. On the other hand, non-linear analysis methods are widely applied in processing the electroencephalogram (EEG) signals. It has been proved that the brain neuronal activity and its related EEG signals have chaotic behaviour. Hence, chaotic indices can be employed to classify the EEG signals. In this study, a new approach is proposed based on the combination of some non-linear features to distinguish ADHD from normal children. Lyapunov exponent, fractal dimension, correlation dimension and sample, fuzzy and approximate entropies are the non-linear extracted features. For computing, the chaotic time series of obtained EEG in the brain frontal lobe (FP1, FP2, F3, F4, and Fz) need to be analysed. Experiments on a set of EEG signal obtained from 50 ADHD and 26 normal cases yielded a sensitivity, specificity, and accuracy of 98, 92.31, and 96.05%, respectively. The obtained accuracy provides a significant improvement in comparison to the other similar studies in identifying and classifying children with ADHD.

Detection of focal electroencephalogram signals using higher-order moments in EMD-TKEO domain

Detection of epileptogenic focus based on electroencephalogram (EEG) signal screening is an important pre-surgical step to remove affected regions inside the human brain. Considering the fact above, in this work, a novel technique for detection of focal EEG signals is proposed using a combination of empirical mode decomposition (EMD) and Teager–Kaiser energy operator (TKEO). EEG signals belonging to focal (Fo) and non-focal (NFo) groups were at first decomposed into a set of intrinsic mode functions (IMFs) using EMD. Next, TKEO was applied on each IMF and two higher-order statistical moments namely skewness and kurtosis were extracted as features from TKEO of each IMF. The statistical significance of the selected features was evaluated using student's t-test and based on the statistical test, features from first three IMFs which show very high discriminative capability were selected as inputs to a support vector machine classifier for discrimination of Fo and NFo signals. It was observed that the classification accuracy of 92.65% is obtained in classifying EEG signals using a radial basis kernel function, which demonstrates the efficacy of proposed EMD-TKEO based feature extraction method for computer-based treatment of patients suffering from focal seizures.

Data mining process for predicting diabetes mellitus based model about other chronic diseases: a case study of the northwestern part of Nigeria

To predict diabetes mellitus model data mining (DM) based approaches on the dataset collected from the seven northwestern states of Nigeria. Data were collected from both primary and secondary sources through questionnaires and verbal interviews from patients with diabetic mellitus and other chronic diseases. Some hospital data were also used from the records of patients involved in this work. The dataset comprises 281 instances with 8 attributes. R programming software (version 5.3.1) was used in the experiments. The DM techniques used in this research were binomial logistic regression, classification, confusion matrix and correlation coefficient. The data were partitioned into training and testing sets. Training data were used in building the model while testing data were used to validate the model. The algorithm for the best-fitted model converges with null deviance: 281.951, residual deviance: 16.476 and AIC: 30.476. The significance variables are AGE, GLU, DBP and KDYP with 0.025, 0.01, 0.05 and 0.025 P values, respectively. The predicted model accounted for the accuracy of ∼97.1%. The correlation analysis results revealed that diabetic patients are more likely to be hypertensive than patients with other chronic diseases considered in the research.

Automatic detection of sleep apnea events based on inter-band energy ratio obtained from multi-band EEG signal

Sleep apnea is a potentially serious sleep disorder characterised by abnormal pauses in breathing. Electroencephalogram (EEG) signal analysis plays an important role for detecting sleep apnea events. In this research work, a method is proposed on the basis of inter-band energy ratio features obtained from multi-band EEG signals for subject-specific classification of sleep apnea and non-apnea events. The K-nearest neighbourhood classifier is used for classification purpose. Unlike conventional methods, instead of classifying apnea patient and healthy person, the objective here is to differentiate apnea and non-apnea events of an apnea patient, which makes the task very challenging. Extensive experimentation is carried out on EEG data of several subjects obtained from a publicly available database. Comprehensive experimental results reveal that the proposed method offers very satisfactory classification performance in terms of sensitivity, specificity and accuracy.

Comparison of interactive and automatic segmentation of stereoelectroencephalography electrodes on computed tomography post-operative images: preliminary results

Stereoelectroencephalography is a surgical procedure used in the treatment of pharmacoresistant epilepsy. Multiple electrodes are inserted in the patient's brain in order to record the electrical activity and detect the epileptogenic zone at the source of the seizures. An accurate localisation of their contacts on post-operative images is a crucial step to interpret the recorded signals and achieve a successful resection afterwards. In this Letter, the authors propose interactive and automatic methods to help the surgeon with the segmentation of the electrodes and their contacts. Then, they present a preliminary comparison of the methods in terms of accuracy and processing time through experimental measurements performed by two users, and discuss these first results. The final purpose of this work is to assist the neurosurgeons and neurologists in the contacts localisation procedure, make it faster, more precise and less tedious.

Is insomnia a risk factor for new-onset asthma? A population-based study in Taiwan

OBJECTIVES

To determine whether insomnia at baseline is a risk factor for new-onset asthma.

METHODS

We recruited 48 871 patients with insomnia (insomnia group) newly diagnosed between 2002 and 2007, and 97 742 matched controls without insomnia (control group) from Taiwan's Longitudinal Health Insurance Database 2000. All of the patients were followed up for 4 years to see whether new-onset asthma developed. Patients with previous asthma or insomnia were excluded. The Poisson regression was used to estimate the incidence rate ratios (IRRs) and 95% CIs of asthma. Cox proportional hazard regression was used to calculate the risk of asthma between the two groups.

RESULTS

After a 4-year follow-up, 424 patients in the insomnia group and 409 in the control group developed asthma. The incidence rate of asthma was significantly higher in the insomnia group (22.01vs10.57 per 10 000 person-years). Patients with insomnia have a higher risk of developing new-onset asthma during the 4-year follow-up (HR: 2.08, 95% CI 1.82 to 2.39). The difference remained significant after adjustment (adjusted HR: 1.89, 95% CI 1.64 to 2.17).

CONCLUSIONS

This large population-based study suggests that insomnia at baseline is a risk factor for developing asthma.

Speech reconstruction using a deep partially supervised neural network

Statistical speech reconstruction for larynx-related dysphonia has achieved good performance using Gaussian mixture models and, more recently, restricted Boltzmann machine arrays; however, deep neural network (DNN)-based systems have been hampered by the limited amount of training data available from individual voice-loss patients. The authors propose a novel DNN structure that allows a partially supervised training approach on spectral features from smaller data sets, yielding very good results compared with the current state-of-the-art.

Analysis of physiological signals using state space correlation entropy

In this letter, the authors propose a new entropy measure for analysis of time series. This measure is termed as the state space correlation entropy (SSCE). The state space reconstruction is used to evaluate the embedding vectors of a time series. The SSCE is computed from the probability of the correlations of the embedding vectors. The performance of SSCE measure is evaluated using both synthetic and real valued signals. The experimental results reveal that, the proposed SSCE measure along with SVM classifier have sensitivity value of 91.60%, which is higher than the performance of both sample entropy and permutation entropy features for detection of shockable ventricular arrhythmia.

Impact of Randomization, Clinic Visits, and Medical and Psychiatric Cormorbidities on Continuous Positive Airway Pressure Adherence in Obstructive Sleep Apnea

STUDY OBJECTIVES

To evaluate factors associated with continuous positive airway pressure (CPAP) adherence in patients with obstructive sleep apnea (OSA) in the Apnea Positive Pressure Long-term Efficacy Study (APPLES) cohort.

METHODS

The data from a prospective 6-mo multicenter randomized controlled trial with 558 subjects randomized to active CPAP and 547 to sham CPAP were analyzed to assess adherence to CPAP during first 2 mo (early period) and during months 5-6 (late period).

RESULTS

Participants randomized to active CPAP had higher hours of nightly adherence compared to the sham CPAP group at both 2 (4.9 ± 2.0 h versus 4.07 ± 2.14 h, p < 0.001) and 6 mo (4.70 ± 2.08 h versus 3.41 ± 2.19 h, p < 0.001). Those assigned to sham CPAP were more likely to correctly identify their treatment group (70.0% versus 55.2%, p < 0.001). Irrespective of treatment group assignment, those who believed they were receiving active CPAP had higher hours of adherence than those who thought they were in the sham CPAP group at both 2 mo (4.91 ± 2.01 versus 4.17 ± 2.17, p < 0.001) and 6 mo (4.65 ± 2.10 versus 3.65 ± 2.22, p < 0.001). Among those randomized to active CPAP, older age was significantly related to CPAP use > 4 h per night. Presence of cardiovascular disorders was associated with higher hours of CPAP use, whereas presence of anxiety was associated with a trend toward lower hours of CPAP use. Presence of nasal congestion was associated with a decrease in mean daily CPAP use between the early and the late adherence period. The adherence during the week prior to a clinic visit was higher than the average adherence during the 2-mo period prior to the visit.

CONCLUSIONS

Randomization to active therapy, belief that one is in the active treatment group, older age, and possibly presence of cardiovascular disorders are positively linked to CPAP adherence. Nasal congestion and anxiety are negatively associated with CPAP adherence. CPAP nightly usage increases as clinic visits approach.

Characterisation of rollator use using inertial sensors

The use of walking aids is prevalent among older people and people with mobility impairment. Rollators are designed to support outdoor mobility and require the user to negotiate curbs and slopes in the urban environment. Despite the prevalence of rollators, analysis of their use outside of controlled environments has received relatively little attention. This Letter reports on an initial study to characterise rollator movement. An inertial measurement unit (IMU) was used to measure the motion of the rollator and analytical approaches were developed to extract features characterising the rollator movement, properties of the surface and push events. The analytics were tested in two situations: first, a healthy participant used a rollator in a laboratory using a motion capture system to obtain ground truth. Second, the IMU was used to measure the movement of a rollator being used by a user with multiple sclerosis on a flat surface, cross-slope, up and down slopes and up and down a step. The results showed that surface inclination and distance travelled measured by the IMU have close approximation to the results from ground truth; therefore, demonstrating the potential for IMU-derived metrics to characterise rollator movement and user's pushing style in the outdoor environment.

Disrupting the world of Disability: The Next Generation of Assistive Technologies and Rehabilitation Practices

Designing, developing and deploying assistive technologies at a scale and cost which makes them accessible to people is challenging. Traditional models of manufacturing would appear to be insufficient at helping the world's 1 billion disabled people in accessing the technologies they require. In addition, many who receive assistive technologies simply abandon them as they do not meet their needs. In this study the authors explore the changing world of design for disability. A landscape which includes the rise of the maker movement, the role of ubiquitous sensing and the changing role of the 'user' to one of designer and maker. The authors argue they are on the cusp of a revolution in healthcare provision, where the population will soon have the ability to manage their own care with systems in place for diagnosis, monitoring, individualised prescription and action/reaction. This will change the role of the clinician from that of diagnostician, gatekeeper and resource manager/deliverer to that of consultant informatics manager and overseer; perhaps only intervening to promote healthy behaviour, prevent crisis and react at flash moments.

Characterisation of the complexity of intracranial pressure signals measured from idiopathic and secondary normal pressure hydrocephalus patients

Hydrocephalus is a condition characterised by enlarged cerebral ventricles, which in turn affects intracranial pressure (ICP); however, the mechanisms regulating ICP are not fully understood. A nonlinear signal processing approach was applied to ICP signals measured during infusion studies from patients with two forms of hydrocephalus, in a bid to compare the differences. This is the first study of its kind. The two forms of hydrocephalus were idiopathic normal pressure hydrocephalus (iNPH) and secondary normal pressure hydrocephalus (SH). Following infusion tests, the Lempel-Ziv (LZ) complexity was calculated from the iNPH and SH ICP signals. The LZ complexity values were averaged for the baseline, infusion, plateau and recovery stages of the tests. It was found that as the ICP increased from basal levels, the LZ complexities decreased, reaching their lowest during the plateau stage. However, the complexities computed from the SH ICP signals decreased to a lesser extent when compared with the iNPH ICP signals. Furthermore, statistically significant differences were found between the plateau and recovery stage complexities when comparing the iNPH and SH results (p = 0.05). This Letter suggests that advanced signal processing of ICP signals with LZ complexity can help characterise different types of hydrocephalus in more detail.

Development of a prototype of portable FES rehabilitation system for relearning of gait for hemiplegic subjects

This study aimed at developing a prototype of portable FES rehabilitation system for relearning gait pattern of healthy subjects, which can measure gait information during walking applying electrical stimulation for foot drop correction or providing timing information. A gait event detection method using an inertial sensor attached on the foot was determined based on gait of healthy subjects from simultaneous measurements with pressure sensors. From the result of comparing the detected gait event timings with EMG signal of the tibialis anterior muscle during walking of healthy subjects, the toe off and the foot flat timings detected by the inertial sensor were suggested to be useful to determine the stimulation timing for the foot drop correction. The gait event detection method was implemented in a prototype of portable FES rehabilitation system consisting of an 8-inch tablet-type device, 2 inertial sensors and an electrical stimulator. The portable system was examined with hemiplegic subjects under the conditions of FES foot drop correction and inducing voluntary effort to develop ankle dorsiflexion at the timing given by electrical stimulation with small stimulation intensity. The system was considered to be useful for gait rehabilitation of hemiplegia using FES foot drop correction or inducing voluntary effort.

Optical wireless connected objects for healthcare

In this Letter the authors explore the communication capabilities of optical wireless technology for a wearable device dedicated to healthcare application. In an indoor environment sensible to electromagnetic perturbations such as a hospital, the use of optical wireless links can permit reducing the amount of radio frequencies in the patient environment. Moreover, this technology presents the advantage to be secure, low-cost and easy to deploy. On the basis of commercially available components, a custom-made wearable device is presented, which allows optical wireless transmission of accelerometer data in the context of physical activity supervision of post-stroke patients in hospital. Considering patient mobility, the experimental performance is established in terms of packet loss as a function of the number of receivers fixed to the ceiling. The results permit to conclude that optical wireless links can be used to perform such mobile remote monitoring applications. Moreover, based on the measurements obtained with one receiver, it is possible to theoretically determine the performance according to the number of receivers to be deployed.

Electrical bioimpedance measurement as a tool for dysphagia visualisation

A non-invasive and portable bioimpedance method and a device for detecting superior to inferior closure of the pharynx during swallowing have been developed. The 2-channel device measures electric impedance across the neck at two levels of the pharynx via injected currents at 40 and 70 kHz. The device has been trialled on both healthy and dysphagic subjects. Results from these trials revealed a relationship (r = 0.59) between the temporal separation of the second peaks in the bioimpedance waveforms and descending pressure sequence in the pharynx as measured by pharyngeal manometry. However, these features were only clearly visible in the bioimpedance waveforms for 64% of swallows. Further research is underway to improve the bioimpedance measurement reliability and validate waveform feature correlation to swallowing to maximise the device's efficacy in dysphagia rehabilitation.

A continuous point measure for quantifying skull deformation in medical diagnostics

Deformational plagiocephaly (DP) manifests in a deformed skull primarily caused by retaining a constant sleeping position in infants. Manual measures of skull asymmetry based on MRI or CT scans combined with the cranial vault asymmetry index (CVAI) provides information on the extent of asymmetry. CVAI uses four points on the skull as markers for the asymmetry index but tends to underestimate the deformity because of the lack of sampling points. Computer-based continuous-point methods may be a more objective measure with better sensitivity for the skull contour. The outline of the skull circumference of infants with confirmed cranial deformity was obtained from the literature and analysed applying the mean bending energy (MBE) obtained from the Hermitian wavelet. MBE was shown to correlate with CVAI in the current sample and has the potential to add both quantitative and visual information in 2D or 3D space for the clinician to diagnose DP. Wavelet-based continuous-point estimation of skull asymmetry is a useful method as it is more sensitive to mild deformation anywhere along the skull outline and in assessing slow but progressive improvement as a result of treatment. The broader significance is that this method can be applied to other structural pathology analysis in clinical practice.

Temporal epilepsy seizures monitoring and prediction using cross-correlation and chaos theory

Temporal seizures due to hippocampal origins are very common among epileptic patients. Presented is a novel seizure prediction approach employing correlation and chaos theories. The early identification of seizure signature allows for various preventive measures to be undertaken. Electro-encephalography signals are spectrally broken down into the following sub-bands: delta; theta; alpha; beta; and gamma. The proposed approach consists of observing a high correlation level between any pair of electrodes for the lower frequencies and a decrease in the Lyapunov index (chaos or entropy) for the higher frequencies. Power spectral density and statistical analysis tools were used to determine threshold levels for the lower frequencies. After studying all five sub-bands, the analysis has revealed that the seizure signature can be extracted from the delta band and the high frequencies. High frequencies are defined as both the gamma band and the ripples occurring within the 60-120 Hz sub-band. To validate the proposed approach, six patients from both sexes and various age groups with temporal epilepsies originating from the hippocampal area were studied using the Freiburg database. An average seizure prediction of 30 min, an anticipation accuracy of 72%, and a false-positive rate of 0% were accomplished throughout 200 h of recording time.

Prevalence and polysomnographic correlates of insomnia comorbid with medical disorders

STUDY OBJECTIVES

To determine the prevalence and polysomnographic correlates of insomnia in subjects with self-reported medical disorders.

DESIGN

Prospective cross-sectional study.

PARTICIPANTS

Community-based sample of 3282 men and women aged 18 to 65 years old, with a subset who underwent polysomnography.

MEASUREMENTS

Self-reported measures of sleep habits and current health, and polysomnographic sleep variables.

RESULTS

The prevalence of insomnia was 21.4%. The adjusted odds of insomnia were 2.2 times as high in persons with any medical disorders as in those without medical disorders. Specifically, odds of insomnia were higher in people with heart disease (OR = 1.6 [95% CI: 1.2-23], P = 0.004), hypertension (1.5 [12-18], P < 0.001), diabetes (1.4 [105-20], P = 0.04), stomach ulcers (2.1 [1.6-2.7], P < 0.001), arthritis (1.8 [1.5-2.2], P < 0.001), migraine (1.8 [1.5-2.1], P < 0.001), asthma (1.6 [1.3-2.0], P = 0.04), COPD (1.9 [1.5-2.5], P < 0.001), neurological problems (2.0 [1.5-2.7], P < 0.001), and menstrual problems (1.7 [1.3-2.1], P < 0.001) than in people without these disorders. Prevalence of insomnia increased with increasing number of medical disorders. However, polysomnographic sleep was not significantly different in persons with or without medical disorders for most disorders assessed.

CONCLUSION

This large population-based study suggests that insomnia is highly prevalent in diverse chronic medical disorders. However, polysomnographic evidence of disturbed sleep is present in only a subset of comorbid insomnia populations.