JAMA dermatology最新文献

Importance: Cancer is a life-threatening complication of dermatomyositis (DM) and contributes significantly to mortality. A validated association-based tool is urgently needed to optimize early cancer detection and reduce diagnostic delays.

Objective: To develop and validate a practical prediction model for cancer-associated likelihood in adult patients with DM.

Design, setting, and participants: A retrospective multicenter cohort study including adults with DM or clinically amyopathic DM was carried out. Participants were recruited from the Department of Dermatology at Ruijin Hospital (training cohort) and the Department of Rheumatology at Renji Hospital (validation cohort) over the period from 2015 to 2022. Multivariate logistic regression and machine learning techniques were employed for model development and validation. The analysis took place in 2024.

Exposure: DM and clinically amyopathic DM.

Main outcomes and measures: The primary outcome was the occurrence of cancer in patients with DM. Model performance was assessed using the area under the curve to evaluate predictive accuracy.

Results: A total of 546 adults with DM or clinically amyopathic DM were included, with a mean (SD) age of 49.8 (14.2) years, comprising 166 male individuals (30.4%) and 380 female individuals (69.6%). Five factors significantly associated with concomitant cancers in patients with DM were used to construct the TIP-CA model: anti-transcriptional intermediary factor 1-γ (TIF1-γ) antibody (positive scored as 1; negative scored as 0), interstitial lung disease (present scored as -1; absent scored as 0), poikiloderma (present scored as 1; absent scored as 0), DM subtypes (DM scored as 1; clinically amyopathic DM scored as 0), and anemia (present scored as 1; absent scored as 0). The model demonstrated good discriminatory capability, achieving an area under the curve of 0.809 and 0.808 in the derivation and validation cohorts, respectively.

Conclusions and relevance: This cohort study found that the TIP-CA model effectively stratified cancer-associated likelihood in patients with DM using routinely available clinical data. By using data from multidisciplinary patient cohorts and incorporating machine learning techniques, the model minimized referral bias. This proposed model may have the potential to guide clinicians in implementing targeted cancer screening strategies and improve patient outcomes.

Importance: Smoking and obesity are associated with risk of hidradenitis suppurativa, and both are considered important environmental risk factors. However, a causal relationship remains unproven.

Objective: To primarily investigate the relationship between body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) and smoking and HS, and secondarily to investigate potential relationships between 3 inflammatory diseases (psoriasis, inflammatory bowel disease [IBD], and systemic sclerosis [SSc]) and HS.

Design, setting, and participants: A mendelian randomization (MR) study conducted in 2024 on 5 exposure phenotypes (BMI, smoking, psoriasis, IBD, and SSc) on the outcome of phenotype HS was conducted. The MR analyses used large genetic White European cohorts from genome-wide association studies (GWAS) of each of the 6 phenotypes. Initial analyses were conducted May, 2024, and were updated in May, 2025.

Exposure: The 5 exposure phenotypes using predetermined genome-wide significant single-nucleotide variants as proxies for each particular exposure.

Results: The GWAS on HS included 4814 case patients and more than 1.2 million controls from Denmark, Iceland, Finland, the UK, and the US. The BMI GWAS involved 700 000 individuals from the UK Biobank and GIANT consortium. Smoking data were obtained from 1.23 million participants in an international consortium. The psoriasis GWAS analyzed 39 498 case patients and 286 769 controls from White European populations and a DNA genetic testing company. The IBD GWAS meta-analysis included 38 155 case patients and 48 485 controls from the International Inflammatory Bowel Disease (IBD) Genetics Consortium. The SSc GWAS included 9095 case patients and 17 584 controls from White European populations. Genetic correlations (rg) were found between HS and all exposure phenotypes except SSc (BMI: rg = 0.36, P < .001; smoking: rg = 0.33, P < .001; IBD: rg = 0.25, P < .001; psoriasis: rg = 0.34, P < .001; SSc: rg = 0.33, P = .22). MR analyses supported an effect of BMI on HS (β = 0.87; odds ratio [OR] per BMI unit, 1.20; 95% CI, 1.17-1.23; P < .001) without signs of pleiotropy (slope: β = 0.91, P < .001, P for intercept = .76). Smoking showed a significant causal estimate (β = 0.59, P < .001), but results became inconclusive in subsequent sensitivity analyses. Among IBD, psoriasis, and SSc, results supported a causal effect of IBD on HS (β = 0.18, OR = 1.20; 95% CI, 1.15-1.24; P < .001), without signs of pleiotropy.

Conclusions and relevance: These findings indicate causal effects of IBD and increased BMI on the risk of HS. This information may help physicians inform patients about disease risk contributed by modifiable lifestyle behaviors, which can be beneficial for planning lifestyle interventions.