Background: Diabetes mellitus, classified into types 1 and 2, is a chronic disease that shows high comorbidity with psychiatric disorders. Insulin-dependent patients show a higher prevalence of psychiatric disorders than do patients with type 2 diabetes.
Methods: This research involved the participation of 200 subjects divided into 2 groups: 100 patients with diabetes type 1 and 100 patients with diabetes type 2. This study used the Mini International Neuropsychiatric Interview for the identification of psychiatric disorders.
Results: Of the 200 participants, 85 (42.5%) were found to have at least 1 psychiatric disorder. The most prevalent disorders were generalized anxiety disorder (21%), dysthymia (15%), social phobia (7%), current depression (5.5%), lifelong depression (3.5%), panic disorder (2.5%), and risk of suicide (2%). Other disorders with lower prevalence were also identified. The groups showed a statistically significant difference in the presence of dysthymia, current depression, and panic disorder, which were more prevalent in patients with diabetes type 1.
Conclusion: The high prevalence of psychiatric disorders in diabetic patients points to the need for greater investment in appropriate diagnostic evaluation of patients that considers mental issues. The difference identified between the groups shows that preventive measures and therapeutic projects should consider the specific demands of each type of diabetes.
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http://dx.doi.org/10.1016/j.comppsych.2012.03.011 | DOI Listing |
Int J Bipolar Disord
December 2024
Department for Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt-Goethe University, Frankfurt am Main, Germany.
Background: Attention-deficit/hyperactivity disorder (ADHD) is a common neuro-developmental disorder that often persists into adulthood. Moreover, it is frequently accompanied by bipolar disorder (BD) as well as borderline personality disorder (BPD). It is unclear whether these disorders share underlying pathomechanisms, given that all three are characterized by alterations in affective states, either long or short-term.
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December 2024
The Department of Mechanical Engineering and Mechatronics, Ariel University, Ariel, Israel.
Autism spectrum disorder (ASD) involves challenges in communication and social interaction, including challenges in recognizing emotions. Existing technological solutions aim to improve social behaviors in individuals with ASD by providing learning aids. This paper presents a real-time environmental translator designed to enhance social behaviors in individuals with ASD using sensory substitution.
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December 2024
Department of Neurology, Union Hospital of Jilin University, Changchun, 130000, China.
Alzheimer's disease (AD) is a severe neurodegenerative disease, and the most common type of dementia, with symptoms of progressive cognitive dysfunction and behavioral impairment. Studying the pathogenesis of AD and exploring new targets for the prevention and treatment of AD is a very worthwhile challenge. Accumulating evidence has highlighted the effects of fatty acid metabolism on AD.
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December 2024
Department of Pharmacology, University of the Basque Country, UPV/EHU, Sarriena S/N, 48940, Leioa, Bizkaia, Spain.
Cannabis use disorder affects up to 42% of individuals with schizophrenia, correlating with earlier onset, increased positive symptoms, and more frequent hospitalizations. This study employed an untargeted lipidomics approach to identify biomarkers in plasma samples from subjects with schizophrenia, cannabis use disorder, or both (dual diagnosis), aiming to elucidate the metabolic underpinnings of cannabis abuse and schizophrenia development. The use of liquid chromatography-high resolution mass spectrometry enabled the annotation of 119 metabolites, with the highest identification confidence level achieved for 16 compounds.
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December 2024
Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il, Ha'il, 81481, Saudi Arabia.
Alzheimer's disease (AD) is a brain disorder that causes memory loss and behavioral and thinking problems. The symptoms of Alzheimer's are similar throughout its development stages, which makes it difficult to diagnose manually. Therefore, artificial intelligence (AI) techniques address the limitations of manual diagnosis.
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