Background Migraine, tension‐type headache, and hypothyroidism constitute very common medical conditions. Headache is one of the most common symptoms of hypothyroidism, occurring in approximately one‐third of the patients. To date, data about the relationship between migraine and tension‐type headache and thyroid dysfunction, and in particular hypothyroidism have been contradictory, while the underlying pathophysiological basis explaining this association is still unclear. Objective In this review, we investigated the association between primary headaches and hypothyroidism, with the aim of shedding light on its pathophysiological basis. Methods We conducted a systematic search in the MEDLINE database using both subject headings and keywords for headache, migraine, tension‐type headache, thyroid hormones, and hypothyroidism, and we also examined manually the reference lists of all articles that met the inclusion criteria. Included studies were related to headache and thyroid disease comorbidity, with emphasis on hypothyroidism (ideally demonstrated by hormonal measurements), and with the term headache including migraine, tension‐type headache, and headache attributed to hypothyroidism (HAH) based on the International Classification of Headache Disorders IIIb. Quality of studies was assessed by the Newcastle‐Ottawa scale. Results Of a total of 640 identified articles, 9 studies were included. Overall, there was vast heterogeneity across the included studies concerning population, study design and outcomes. Two studies investigated the HAH, with emphasis on the clinical characteristics of headache (time of onset, localization, quality, intensity, and response to hormonal replacement treatment). Five studies investigated comorbidity between migraine and thyroid disorders, especially hypothyroidism, and in the majority of them a positive association was demonstrated. One study found that headache, and particularly migraine, may increase the risk of developing hypothyroidism. Finally, only 1 study on chronic tension‐type headache found coexistence of migraine and hypoactivity of the hypothalamus‐pituitary‐thyroid axis. The strengths and limitations of these studies are analyzed and possible pathophysiological mechanisms are suggested. Conclusions The existing data are considered inadequate to answer with certainty the relationship between headaches and thyroid disorders. According to our analysis, it seems that suggestions for a possible bidirectional association between headaches and especially migraine and hypothyroidism could exist. It hence lays the foundation for further research into the aforementioned association and its pathogenesis via large prospective multicenter studies.

Objective: Our aim was to develop a machine learning algorithm based only on non-invasively clinic collectable predictors, for the accurate diagnosis of these disorders. Methods: This is an ongoing prospective cohort study ( ClinicalTrials.gov identifier NCT number NCT04448340) of 78 PDD and 62 DLB subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. We used predictors such as clinico-demographic characteristics, 6 neuropsychological tests (mini mental, PD Cognitive Rating Scale, Brief Visuospatial Memory test, Symbol digit written, Wechsler adult intelligence scale, trail making A and B). We investigated logistic regression, K-Nearest Neighbors (K-NNs) Support Vector Machine (SVM), Naïve Bayes classifier, and Ensemble Model for their ability to predict successfully PDD or DLB diagnosis. Results: The K-NN classification model had an accuracy 91.2% of overall cases based on 15 best clinical and cognitive scores achieving 96.42% sensitivity and 81% specificity on discriminating between DLB and PDD. The binomial logistic regression classification model achieved an accuracy of 87.5% based on 15 best features, showing 93.93% sensitivity and 87% specificity. The SVM classification model had an accuracy 84.6% of overall cases based on 15 best features achieving 90.62% sensitivity and 78.58% specificity. A model created on Naïve Bayes classification had 82.05% accuracy, 93.10% sensitivity and 74.41% specificity. Finally, an Ensemble model, synthesized by the individual ones, achieved 89.74% accuracy, 93.75% sensitivity and 85.73% specificity. Conclusion: Machine learning method predicted with high accuracy, sensitivity and specificity PDD or DLB diagnosis based on non-invasively and easily in-the-clinic and neuropsychological tests.

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Stress has deleterious effects on brain health and yet, the prognostic value of psychosocial stress regarding the most common types of dementias, including Alzheimer disease, is still unclear. The primary aim of this systematic review was to explore the association between psychosocial stress and late onset dementia. We classified 24articles from Medline, PsycINFO, CINAHL, and Web of Science, as pertaining toxic categories of psychosocial and trauma-related stress (low socio-economic status [SES] related inequalities, marital status, posttraumatic stress disorder, work stress, “vital exhaustion” [VE], and, combined stressors). Using the Quality of Prognosis Studies in Systematic Reviews tool, we judged the quality of evidence to be low. This systematic review provided some non-robust, yet suggestive evidence that the above psychosocial types of stress are associated with increased risk of dementia in later life. Future robust, longitudinal studies with repeated validated measures of psychosocial stress and dementiaare required to strengthen or refute these findings.