Men and women with chronic insomnia disorder and OSAS: Different responses to CPAP

Toward personalized care for insomnia in the US Army: development of a machine-learning model to predict response to pharmacotherapy

STUDY OBJECTIVES

Although many military personnel with insomnia are treated with prescription medication, little reliable guidance exists to identify patients most likely to respond. As a first step toward personalized care for insomnia, we present results of a machine-learning model to predict response to insomnia medication.

METHODS

The sample comprised n = 4,738 nondeployed US Army soldiers treated with insomnia medication and followed 6-12 weeks after initiating treatment. All patients had moderate-severe baseline scores on the Insomnia Severity Index (ISI) and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble machine-learning model was developed in a 70% training sample to predict clinically significant ISI improvement, defined as reduction of at least 2 standard deviations on the baseline ISI distribution. Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample.

RESULTS

21.3% of patients had clinically significant ISI improvement. Model test sample area under the receiver operating characteristic curve (standard error) was 0.63 (0.02). Among the 30% of patients with the highest predicted probabilities of improvement, 32.5.% had clinically significant symptom improvement vs 16.6% in the 70% sample predicted to be least likely to improve (χ21 = 37.1, P < .001). More than 75% of prediction accuracy was due to 10 variables, the most important of which was baseline insomnia severity.

CONCLUSIONS

Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment, but parallel models will be needed for alternative treatments before such a system is of optimal value.

CITATION

Gabbay FH, Wynn GH, Georg MW, et al. Toward personalized care for insomnia in the US Army: development of a machine-learning model to predict response to pharmacotherapy. J Clin Sleep Med. 2023;19(8):1399-1410.

Is there a relationship between the ketogenic diet and sleep disorders?

Sleep disorders are very often underestimated and, consequently, not treated with due priority. Common sleep disorders include insomnia disorders, sleep-related breathing disorders, central disorders of hypersomnolence, circadian rhythm sleep-wake disorders, sleep-related movement disorders, parasomnias, and other sleep disorders. The ketogenic diet (KD) is rich in fat, low in carbohydrates (CHO), and adequate in protein. The KD has shown several applications in treating medical conditions, such as epilepsy, neurodegenerative disorders, obesity with its comorbidities, and sleep disorders, with encouraging results. Therefore, the purpose of this review is to address the primary sleep disorders and their respective standard therapeutic approaches, analyse the effect of ketone bodies (KBs) on sleep homeostasis, and the effects of KD on sleep disorders and in particular on obstructive sleep apnoea (OSA) syndrome. The goal is to summarise the evidence existing up to now on the subject, to provide a starting point for further investigations.

Association between Obstructive Sleep Apnea and Type 2 Diabetes Mellitus: A Dose-Response Meta-Analysis

Materials and Methods

We screened four databases (PubMed, Embase, Cochran Library, and CNKI) for the observational studies about the OSA and T2DM. Studies were collected from database establishment to October 2020. We performed a traditional subgroup meta-analysis. What is more, linear and spline dose-response models were applied to assess the association between apnea-hypopnea index (AHI), an indicator to evaluate the severity of OSA, and the risk of T2DM. Review Manager, version 5.3, software and Stata 16.0 were used for the analysis.

Result

Seven observational studies were included in the research. We excluded a study in the conventional meta-analysis. In the subgroup analysis, mild-dose AHI increased the risk of T2DM (odds ratio = 1.23, 95% confidence interval = 1.06–1.41, P < 0.05). Moderate-dose AHI increased the risk of T2DM with a higher odds ratio (OR = 1.35, 95% CI = 1.13–1.61, P < 0.05). Moderate-to-severe-dose AHI increased the risk of T2DM with a higher odds ratio (OR = 2.14, 95% CI = 1.72–2.67, P < 0.05). Severe-dose AHI increased the risk of T2DM with a higher odds ratio (OR = 2.19 95% CI = 1.30–3.68, P < 0.05). Furthermore, the spline and linear dose-response meta-analysis results revealed that the risk of T2DM increased with increasing AHI values.

Conclusion

Through the dose-response meta-analysis, we found a potential dose-response relationship existed between the severity of OSA and the risk of T2DM. This relationship in our passage should be considered in the prevention of T2DM in the future.

Bi-directional relationships between co-morbid insomnia and sleep apnea (COMISA)

Insomnia and obstructive sleep apnea (OSA) commonly co-occur. Approximately 30-50% of patients with OSA report clinically significant insomnia symptoms, and 30-40% of patients with chronic insomnia fulfil diagnostic criteria for OSA. Compared to either insomnia or OSA alone, co-morbid insomnia and sleep apnea (COMISA) is associated with greater morbidity for patients, complex diagnostic decisions for clinicians, and reduced response to otherwise effective treatment approaches. Potential bi-directional causal relationships between the mechanisms and manifestations of insomnia and OSA could play an integral role in the development and management of COMISA. A greater understanding of these relationships is required to guide personalized diagnostic and treatment approaches for COMISA. This review summarizes the available evidence of bi-directional relationships between COMISA, including epidemiological research, case studies, single-arm treatment studies, randomized controlled treatment trials, and objective sleep study data. This evidence is integrated into a conceptual model of COMISA to help refine the understanding of potential bi-directional causal relationships between the two disorders. This theoretical framework is essential to help guide future research, improve diagnostic tools, determine novel therapeutic targets, and guide tailored sequenced and multi-faceted treatment approaches for this common, complex, and debilitating condition.