Sleep Disorders and Medical Conditions in Women

Abstract Sleep disorders affect women differently than they affect men and may have different manifestations and prevalences. With regard to obstructive sleep apnea (OSA), variations in symptoms may cause misdiagnoses and delay of appropriate treatment. The prevalence of OSA appears to increase markedly after the time of menopause. Although OSA as defined by the numbers of apneas/hypopneas may be less severe in women, its consequences are similar and perhaps worse. Therapeutic issues related to gender should be factored into the management of OSA. The prevalence of insomnia is significantly greater in women than in men throughout most of the life span. The ratio of insomnia in women to men is approximately 1.4:1.0, but the difference is minimal before puberty and increases steadily with age. Although much of the higher prevalence of insomnia in women may be attributable to the hormonal or psychological changes associated with major life transitions, some of the gender differences may result from the higher prevalence of depression and pain in women. Insomnia's negative impact on quality of life is important to address in women, given the high relative prevalence of insomnia as well as the comorbid disorders in this population. Gender differences in etiology and symptom manifestation in narcolepsy remain understudied in humans. There is little available scientific information to evaluate the clinical significance and specific consequences of the diagnosis of narcolepsy in women. Restless legs syndrome (RLS) is characterized by an urge to move the legs or other limbs during periods of rest or inactivity and may affect as much as 10% of the population. This condition is more likely to afflict women than men, and its risk is increased by pregnancy. Although RLS is associated with impaired quality of life, highly effective treatment is available.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63103/1/jwh.2007.0561.pd

Insulin resistance and circadian rhythm of cardiac autonomic modulation

<p>Abstract</p> <p>Background</p> <p>Insulin resistance (IR) has been associated with cardiovascular diseases (CVD). Heart rate variability (HRV), an index of cardiac autonomic modulation (CAM), is also associated with CVD mortality and CVD morbidity. Currently, there are limited data about the impairment of IR on the circadian pattern of CAM. Therefore, we conducted this investigation to exam the association between IR and the circadian oscillations of CAM in a community-dwelling middle-aged sample.</p> <p>Method</p> <p>Homeostasis models of IR (HOMA-IR), insulin, and glucose were used to assess IR. CAM was measured by HRV analysis from a 24-hour electrocardiogram. Two stage modeling was used in the analysis. In stage one, for each individual we fit a cosine periodic model based on the 48 segments of HRV data. We obtained three individual-level cosine parameters that quantity the circadian pattern: mean (M), measures the overall average of a HRV index; amplitude (Â), measures the amplitude of the oscillation of a HRV index; and acrophase time (θ), measures the timing of the highest oscillation. At the second stage, we used a random-effects-meta-analysis to summarize the effects of IR variables on the three circadian parameters of HRV indices obtained in stage one of the analysis.</p> <p>Results</p> <p>In persons without type diabetes, the multivariate adjusted β (SE) of log HOMA-IR and M variable for HRV were -0.251 (0.093), -0.245 (0.078), -0.19 (0.06), -4.89 (1.76), -3.35 (1.31), and 2.14 (0.995), for log HF, log LF, log VLF, SDNN, RMSSD and HR, respectively (all <it>P </it>< 0.05). None of the IR variables were significantly associated with  or θ of the HRV indices. However, in eight type 2 diabetics, the magnitude of effect due to higher HOMA-IR on M, Â, and θ are much larger.</p> <p>Conclusion</p> <p>Elevated IR, among non-diabetics significantly impairs the overall mean levels of CAM. However, the  or θ of CAM were not significantly affected by IR, suggesting that the circadian mechanisms of CAM are not impaired. However, among persons with type 2 diabetes, a group clinically has more severe form of IR, the adverse effects of increased IR on all three HRV circadian parameters are much larger.</p

Clinical and polysomnographic predictors of the Natural History of poor sleep in the general population

Study Objectives: Approximately 8-10% of the general population suffers from chronic insomnia, whereas another 20-30% of the population has insomnia symptoms at any given time (i.e., poor sleep). However, few longitudinal studies have examined risk factors of the natural history of poor sleep, and none have examined the role of polysomnographic (PSG) variables. Design: Representative longitudinal study. Setting: Sleep laboratory. Participants: From a random, general population sample of 1,741 individuals of the adult Penn State Cohort, 1,395 were followed up after 7.5 yr. Measurements: Full medical evaluation and 1-night PSG at baseline and telephone interview at follow-up. Results: The rate of incident poor sleep was 18.4%. Physical (e.g., obesity, sleep apnea, and ulcer) and mental (e.g., depression) health conditions and behavioral factors (e.g., smoking and alcohol consumption) increased the odds of incident poor sleep as compared to normal sleep. The rates of persistent, remitted, and poor sleepers who developed chronic insomnia were 39%, 44%, and 17%, respectively. Risk factors for persistent poor sleep were physical health conditions combined with psychologic distress. Shorter objective sleep duration and a family history of sleep problems were risk factors for poor sleep evolving into chronic insomnia. Conclusions: Poor sleep appears to be primarily a symptom of physical and mental health conditions, whereas the persistence of poor sleep is associated with psychologic distress. Importantly, sleep apnea appears to be associated with incident poor sleep but not with chronic insomnia. Finally, this study suggests that objective short sleep duration in poor sleepers is a biologic marker of genetic predisposition to chronic insomniaThis research was funded in part by the National Institutes of Health grants RO1 51931, RO1 40916 (to Dr. Bixler), and RO1 64415 (to Dr. Vgontzas)

Personalized screening and risk profiles for Mild Cognitive Impairment via a Machine Learning Framework: Implications for general practice.

peer reviewedOBJECTIVES: Diagnosis of Mild Cognitive Impairment (MCI) requires lengthy diagnostic procedures, typically available at tertiary Health Care Centers (HCC). This prospective study evaluated a flexible Machine Learning (ML) framework toward identifying persons with MCI or dementia based on information that can be readily available in a primary HC setting. METHODS: Demographic and clinical data, informant ratings of recent behavioral changes, self-reported anxiety and depression symptoms, subjective cognitive complaints, and Mini Mental State Examination (MMSE) scores were pooled from two aging cohorts from the island of Crete, Greece (N = 763 aged 60-93 years) comprising persons diagnosed with MCI (n = 277) or dementia (n = 153), and cognitively non-impaired persons (CNI, n = 333). A Balanced Random Forest Classifier was used for classification and variable importance-based feature selection in nested cross-validation schemes (CNI vs MCI, CNI vs Dementia, MCI vs Dementia). Global-level model-agnostic analyses identified predictors displaying nonlinear behavior. Local level agnostic analyses pinpointed key predictor variables for a given classification result after statistically controlling for all other predictors in the model. RESULTS: Classification of MCI vs CNI was achieved with improved sensitivity (74 %) and comparable specificity (73 %) compared to MMSE alone (37.2 % and 94.3 %, respectively). Additional high-ranking features included age, education, behavioral changes, multicomorbidity and polypharmacy. Higher classification accuracy was achieved for MCI vs Dementia (sensitivity/specificity = 87 %) and CNI vs Dementia (sensitivity/specificity = 94 %) using the same set of variables. Model agnostic analyses revealed notable individual variability in the contribution of specific variables toward a given classification result. CONCLUSIONS: Improved capacity to identify elderly with MCI can be achieved by combining demographic and medical information readily available at the PHC setting with MMSE scores, and informant ratings of behavioral changes. Explainability at the patient level may help clinicians identify specific predictor variables and patient scores to a given prediction outcome toward personalized risk assessment

Effects of electrostatic therapy on nighttime sleep and daytime symptoms in patients with chronic insomnia: Evidences from an open label study

IntroductionTranscranial electric stimulation (TES) is a neuromodulation approach that applies low-intensity electrical current to the brain and has been proposed as a treatment for insomnia. Electrostatic therapy is a kind of TES and people do not have a feeling of electrical stimuli when the voltage of static electricity is lower than 2,000 volts. However, no studies have examined the effects of electrostatic therapy on objective sleep and daytime symptoms in patients with insomnia.Materials and methodsThirty chronic insomnia patients were included. All patients received a 6 week electrostatic therapy and three comprehensive assessments including two consecutive polysomnography (PSG) and daytime symptoms assessments, at pre-treatment, 3 week and 6 week of treatment. Insomnia Severity Index (ISI) was used to assess the severity of insomnia. Multiple sleep latency test (MSLT), Epworth Sleepiness Scale (ESS), and Flinders Fatigue Scale (FFS) were used to assess objective and self-reported daytime sleepiness and fatigue, respectively. Attention network test (ANT) was used to assess attention levels.ResultsTotal ISI scores decreased significantly at 3 weeks (p &lt; 0.001) and 6 weeks (p &lt; 0.001) after initiation of treatment. Furthermore, objective total sleep time (TST, p = 0.020) and sleep efficiency (SE, p = 0.009) increased and wake time after sleep onset (p = 0.012) decreased significantly after 6 weeks electrostatic therapy. Regarding daytime symptoms, ESS and FFS scores decreased significantly at 3 weeks (ESS, p = 0.047; FFS, p = 0.017) and 6 weeks (ESS, p = 0.008; FFS, p = 0.003) after initiation of treatment. Moreover, executive control improved significantly from pre-treatment to 3 weeks (p = 0.006) and 6 weeks (p = 0.013) and altering network improved significantly at 6 weeks (p = 0.003) after initiation of treatment. Secondary analyses showed that TST and SE improved significantly after electrostatic therapy in insomnia patients who slept &lt; 390 min (all p-value &lt; 0.05). However, no significant changes regarding TST and SE were observed in insomnia patients who slept ≥ 390 min.ConclusionElectrostatic therapy improves both nighttime sleep and daytime symptoms in patients with chronic insomnia. The effect on objective sleep appears to be stronger in patient with objective short sleep duration. Electrostatic therapy might be a therapeutic choice for insomnia patients with difficulty maintaining sleep and not responding to behavioral treatments.Clinical trial registration[www.clinicaltrials.gov], identifier [ChiCTR2100051590]

Investigating the Bidirectional Associations of Adiposity with Sleep Duration in Older Adults: The English Longitudinal Study of Ageing (ELSA)

Cross-sectional analyses of adiposity and sleep duration in younger adults suggest that increased adiposity is associated with shorter sleep. Prospective studies have yielded mixed findings, and the direction of this association in older adults is unclear. We examined the cross-sectional and potential bi-directional, prospective associations between adiposity and sleep duration (covariates included demographics, health behaviours, and health problems) in 5,015 respondents from the English Longitudinal Study of Ageing (ELSA), at baseline and follow-up. Following adjustment for covariates, we observed no significant cross-sectional relationship between body mass index (BMI) and sleep duration [(unstandardized) B?=??0.28?minutes, (95% Confidence Intervals (CI)?=??0.012; 0.002), p?=?0.190], or waist circumference (WC) and sleep duration [(unstandardized) B?=??0.10?minutes, (95% CI?=??0.004; 0.001), p?=?0.270]. Prospectively, both baseline BMI [B?=??0.42?minutes, (95% CI?=??0.013; ?0.002), p?=?0.013] and WC [B?=??0.18?minutes, (95% CI?=??0.005; ?0.000), p?=?0.016] were associated with decreased sleep duration at follow-up, independently of covariates. There was, however, no association between baseline sleep duration and change in BMI or WC (p?>?0.05). In older adults, our findings suggested that greater adiposity is associated with decreases in sleep duration over time; however the effect was very small

Prevalence of alexithymia and its association with anxiety and depression in a sample of Greek chronic obstructive pulmonary disease (COPD) outpatients

<p>Abstract</p> <p>Background</p> <p>Chronic obstructive pulmonary disease (COPD) is a major health problem, especially in adults over 40 years of age, and has a great social and economic impact. The psychological morbidity of COPD patients with regard to anxiety and depressive symptoms has been extensively studied in the past. However, few studies have investigated the prevalence of alexithymia in these patients, as well as its association with this comorbidity. Based on this fact, we studied the prevalence of alexithymia and its association with anxiety and depressive symptoms in COPD outpatients.</p> <p>Methods</p> <p>The present study included 167, randomly selected, outpatients diagnosed with COPD. Alexithymia, anxiety and depression were assessed using the Toronto Alexithymia Scale (TAS-20), Spielberger Trait Anxiety Inventory (STAI), and Beck Depression Inventory (BDI), respectively.</p> <p>Results</p> <p>The mean BDI score was 12.88 (SD: 7.7), mean STAI score 41.8 (SD: 11.0) and mean TAS-20 score 48.2 (SD: 11.5). No differences were observed between genders regarding age and alexithymia (t test <it>P </it>> 0.05), while female patients presented higher depression and trait anxiety scores than males (t test <it>P </it>< 0.05). Clinically significant levels of anxiety were present in 37.1% of men, and in 45.7% of women. The mean depression score was also higher than the corresponding mean score in the general population (one-sample t test <it>P </it>< 0.01), while 27.7% and 30.5% of the sample presented mild and moderate to severe depression, respectively. Finally, a strong correlation was observed between alexithymia, depression and anxiety.</p> <p>Conclusions</p> <p>This study confirms the high prevalence of anxiety and depression symptoms in Greek outpatients with COPD. The prevalence of alexithymia in COPD patients, contrary to what has been observed in patients with other chronic respiratory diseases, seem to be lower. However, we observed a strong association between alexithymia, depression and anxiety levels. This observation suggests that alexithymia should be taken into consideration when drafting specific psychotherapeutic interventions for these patients.</p

Acute effects of fine particulate air pollution on ST segment height: A longitudinal study

Background The mechanisms for the relationship between particulate air pollution and cardiac disease are not fully understood. Air pollution-induced myocardial ischemia is one of the potentially important mechanisms. Methods We investigate the acute effects and the time course of fine particulate pollution (PM2.5) on myocardium ischemic injury as assessed by ST-segment height in a community-based sample of 106 healthy non-smokers. Twenty-four hour beat-to-beat electrocardiogram (ECG) data were obtained using a high resolution 12-lead Holter ECG system. After visually identifying and removing all the artifacts and arrhythmic beats, we calculated beat-to-beat ST-height from ten leads (inferior leads II, III, and aVF; anterior leads V3 and V4; septal leads V1 and V2; lateral leads I, V5, and V6,). Individual-level 24-hour real-time PM2.5 concentration was obtained by a continuous personal PM2.5 monitor. We then calculated, on a 30-minute basis, the corresponding time-of-the-day specific average exposure to PM2.5 for each participant. Distributed lag models under a linear mixed-effects models framework were used to assess the regression coefficients between 30-minute PM2.5 and ST-height measures from each lead; i.e., one lag indicates a 30-minute separation between the exposure and outcome. Results The mean (SD) age was 56 (7.6) years, with 41% male and 74% white. The mean (SD) PM2.5 exposure was 14 (22) μg/m3. All inferior leads (II, III, and aVF) and two out of three lateral leads (I and V6), showed a significant association between higher PM2.5 levels and higher ST-height. Most of the adverse effects occurred within two hours after PM2.5 exposure. The multivariable adjusted regression coefficients β (95% CI) of the cumulative effect due to a 10 μg/m3 increase in Lag 0-4 PM2.5 on ST-I, II, III, aVF and ST-V6 were 0.29 (0.01-0.56) μV, 0.79 (0.20-1.39) μV, 0.52 (0.01-1.05) μV, 0.65 (0.11-1.19) μV, and 0.58 (0.07-1.09) μV, respectively, with all p < 0.05. Conclusions Increased PM2.5 concentration is associated with immediate increase in ST-segment height in inferior and lateral leads, generally within two hours. Such an acute effect of PM2.5 may contribute to increased potential for regional myocardial ischemic injury among healthy individuals

Sleep apnea is a manifestation of the metabolic syndrome. Sleep medicine reviews

Summary Obstructive sleep apnea (OSA) is a prevalent disorder particularly among middle-aged, obese men, although its existence in women as well as in lean individuals is increasingly recognized. Despite the early recognition of the strong association between OSA and obesity, and OSA and cardiovascular problems, sleep apnea has been treated as a &apos;local abnormality&apos; of the respiratory track rather than as a &apos;systemic illness.&apos; In 1997, we first reported that the pro-inflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor-a (TNFa) were elevated in patients with disorders of excessive daytime sleepiness (EDS) and proposed that these cytokines were mediators of daytime sleepiness. Also, we reported a positive correlation between IL-6 or TNFa plasma levels and the body-mass-index (BMI). In subsequent studies, we showed that IL-6, TNFa, and insulin levels were elevated in sleep apnea independently of obesity and that visceral fat, was the primary parameter linked with sleep apnea. Furthermore, our findings that women with the polycystic ovary syndrome (PCOS) (a condition associated with hyperandrogenism and insulin resistance) were much more likely than controls to have sleep disordered breathing (SDB) and daytime sleepiness, suggests a pathogenetic role of insulin resistance in OSA. Other findings that support the view that sleep apnea and sleepiness in obese patients may be manifestations of the Metabolic Syndrome, include: obesity without sleep apnea is associated with daytime sleepiness; PCOS and diabetes type 2 are independently associated with EDS after controlling for SDB, obesity, and age; increased prevalence of sleep apnea in post-menopausal women, with hormonal replacement therapy associated with a significantly reduced risk for OSA; lack of effect of continuous positive airway pressure (CPAP) in obese patients with apnea on hypercytokinemia and insulin resistance indices; and that the prevalence of the metabolic syndrome in the US population from the Third National Health and Nutrition Examination Survey (1988)(1989)(1990)(1991)(1992)(1993)(1994) parallels the prevalence of symptomatic sleep apnea in general random samples. Finally, the beneficial effect of a cytokine antagonist on EDS in obese, male apneics and that of exercise on SDB in a general random sample, supports the hypothesis that cytokines and insulin resistance are mediators of EDS and sleep apnea in humans. Sleep Medicine Reviews (2005) In conclusion, accumulating evidence provides support to our model of the bidirectional, feed forward, pernicious association between sleep apnea, sleepiness, inflammation, and insulin resistance, all promoting atherosclerosis and cardiovascular disease.