The role of personality traits on self-medicated cannabis in rheumatoid arthritis patients: a multivariable analysis.

BACKGROUND: Rheumatoid arthritis (RA) patients commonly report medicinal cannabis use (MCU). Personality has been independently associated with both RA-related outcomes and MCU, but there is no information available on how they interact in RA patients. This study aimed to investigate a potential association between personality traits and MCU in RA outpatients, as well as to identify additional factors associated with its use. METHODS: This cross-sectional study was performed between June 2020 and August 2021. Consecutive RA outpatients had standardized evaluations using an interview format to collect sociodemographic information, comorbidities, risk of recreational substance use, RA-related disease activity/severity, health-related quality of life, depressive and anxiety symptoms, five personality traits, and MCU in the 12 months before the interview. Multivariable logistic regression estimated adjusted odds ratios (aOR). The study was IRB-approved. RESULTS: 180 patients were included; 160 (88.9%) were women with a mean age of 53.4 ± 13 years. Fifty-three (29.4%) patients reported MCU. Among them, 52 (98.1%) used topical formulations. Neuroticism had the highest overall score ([Formula: see text] = 3.47 ± 0.34). Openness to experience trait was higher in MCU patients in the comparative analysis (p = 0.007). In the multivariable regression, higher openness trait along with moderate risk in tobacco use and higher RA disease activity/severity were independently associated with MCU. CONCLUSIONS: In the current study, personality influenced the seeking of MCU for pain relief, associating dynamically with higher disease activity/severity and tobacco use. Contrary to other available information, it did not relate to psychopathology or the recreational use of cannabis. Proactive interdisciplinary clinical evaluations around MCU in RA outpatients should include personality, besides standard clinical assessments, to understand patients' motivations for its use as they may reveal important clinical information

The role of personality traits on self-medicated cannabis in rheumatoid arthritis patients: A multivariable analysis.

BackgroundRheumatoid arthritis (RA) patients commonly report medicinal cannabis use (MCU). Personality has been independently associated with both RA-related outcomes and MCU, but there is no information available on how they interact in RA patients. This study aimed to investigate a potential association between personality traits and MCU in RA outpatients, as well as to identify additional factors associated with its use.MethodsThis cross-sectional study was performed between June 2020 and August 2021. Consecutive RA outpatients had standardized evaluations using an interview format to collect sociodemographic information, comorbidities, risk of recreational substance use, RA-related disease activity/severity, health-related quality of life, depressive and anxiety symptoms, five personality traits, and MCU in the 12 months before the interview. Multivariable logistic regression estimated adjusted odds ratios (aOR). The study was IRB-approved.Results180 patients were included; 160 (88.9%) were women with a mean age of 53.4 ± 13 years. Fifty-three (29.4%) patients reported MCU. Among them, 52 (98.1%) used topical formulations. Neuroticism had the highest overall score ([Formula: see text] = 3.47 ± 0.34). Openness to experience trait was higher in MCU patients in the comparative analysis (p = 0.007). In the multivariable regression, higher openness trait (aOR: 2.81, 95%CI: 1.11-7.10) along with moderate risk in tobacco use (aOR: 3.36, 95%CI: 1.04-10.7) and higher RA disease activity/severity (aOR: 1.10, 95%CI: 1.01-1.19) were independently associated with MCU.ConclusionsIn the current study, personality influenced the seeking of MCU for pain relief, associating dynamically with higher disease activity/severity and tobacco use. Contrary to other available information, it did not relate to psychopathology or the recreational use of cannabis. Proactive interdisciplinary clinical evaluations around MCU in RA outpatients should include personality, besides standard clinical assessments, to understand patients' motivations for its use as they may reveal important clinical information

The role of personality traits on self-medicated cannabis in rheumatoid arthritis patients: A multivariable analysis

Background Rheumatoid arthritis (RA) patients commonly report medicinal cannabis use (MCU). Personality has been independently associated with both RA-related outcomes and MCU, but there is no information available on how they interact in RA patients. This study aimed to investigate a potential association between personality traits and MCU in RA outpatients, as well as to identify additional factors associated with its use. Methods This cross-sectional study was performed between June 2020 and August 2021. Consecutive RA outpatients had standardized evaluations using an interview format to collect sociodemographic information, comorbidities, risk of recreational substance use, RA-related disease activity/severity, health-related quality of life, depressive and anxiety symptoms, five personality traits, and MCU in the 12 months before the interview. Multivariable logistic regression estimated adjusted odds ratios (aOR). The study was IRB-approved. Results 180 patients were included; 160 (88.9%) were women with a mean age of 53.4 ± 13 years. Fifty-three (29.4%) patients reported MCU. Among them, 52 (98.1%) used topical formulations. Neuroticism had the highest overall score ( Conclusions In the current study, personality influenced the seeking of MCU for pain relief, associating dynamically with higher disease activity/severity and tobacco use. Contrary to other available information, it did not relate to psychopathology or the recreational use of cannabis. Proactive interdisciplinary clinical evaluations around MCU in RA outpatients should include personality, besides standard clinical assessments, to understand patients’ motivations for its use as they may reveal important clinical information

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Introduction Previous reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methods We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.Results SNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).Conclusions Diabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications