Prevalence Rates of Arthritis Among US Older Adults with Varying Degrees of Depression: Findings from the 2011 to 2014 National Health and Nutrition Examination Survey

Arthritis and depressive symptoms often interact and negatively influence one another to worsen mental and physical health outcomes. Better characterization of arthritis rates among older adults with different levels of depressive symptoms is an important step toward informing mental health professionals of the need to detect and respond to arthritis and related mental health complications. The primary objective is to determine arthritis rates among US older adults with varying degrees of depression. Using National Health and Nutrition Examination Survey 2011 to 2014 data (N = 4792), we first identified participants aged ≥50 years. Measures screened for depressive symptoms and self‐reported doctor‐diagnosed arthritis. Weighted logistic regression models were conducted. Prevalence of arthritis was 55.0%, 62.9%, and 67.8% in participants with minor, moderate, and severe depression, respectively. In both unadjusted and adjusted regression models, a significant association between moderate depression and arthritis persisted. There were also significant associations between minor and severe depression with arthritis. Arthritis is commonly reported in participants with varying degrees of depression. This study highlights the importance of screening for and treating arthritis‐related pain in older adults with depressive symptoms and the need for future geriatric psychiatry research on developing integrated biopsychosocial interventions for these common conditions

Prevalence Rates of Arthritis Among US Older Adults with Varying Degrees of Depression: Findings from the 2011 to 2014 National Health and Nutrition Examination Survey

Arthritis and depressive symptoms often interact and negatively influence one another to worsen mental and physical health outcomes. Better characterization of arthritis rates among older adults with different levels of depressive symptoms is an important step toward informing mental health professionals of the need to detect and respond to arthritis and related mental health complications. The primary objective is to determine arthritis rates among US older adults with varying degrees of depression. Using National Health and Nutrition Examination Survey 2011 to 2014 data (N = 4792), we first identified participants aged ≥50 years. Measures screened for depressive symptoms and self‐reported doctor‐diagnosed arthritis. Weighted logistic regression models were conducted. Prevalence of arthritis was 55.0%, 62.9%, and 67.8% in participants with minor, moderate, and severe depression, respectively. In both unadjusted and adjusted regression models, a significant association between moderate depression and arthritis persisted. There were also significant associations between minor and severe depression with arthritis. Arthritis is commonly reported in participants with varying degrees of depression. This study highlights the importance of screening for and treating arthritis‐related pain in older adults with depressive symptoms and the need for future geriatric psychiatry research on developing integrated biopsychosocial interventions for these common conditions

Microbiome dysbiosis is associated with disease duration and increased inflammatory gene expression in systemic sclerosis skin

Abstract Background Infectious agents have long been postulated to be disease triggers for systemic sclerosis (SSc), but a definitive link has not been found. Metagenomic analyses of high-throughput data allows for the unbiased identification of potential microbiome pathogens in skin biopsies of SSc patients and allows insight into the relationship with host gene expression. Methods We examined skin biopsies from a diverse cohort of 23 SSc patients (including lesional forearm and non-lesional back samples) by RNA-seq. Metagenomic filtering and annotation was performed using the Integrated Metagenomic Sequencing Analysis (IMSA). Associations between microbiome composition and gene expression were analyzed using single-sample gene set enrichment analysis (ssGSEA). Results We find the skin of SSc patients exhibits substantial changes in microbial composition relative to controls, characterized by sharp decreases in lipophilic taxa, such as Propionibacterium, combined with increases in a wide range of gram-negative taxa, including Burkholderia, Citrobacter, and Vibrio. Conclusions Microbiome dysbiosis is associated with disease duration and increased inflammatory gene expression. These data provide a comprehensive portrait of the SSc skin microbiome and its association with local gene expression, which mirrors the molecular changes in lesional skin