Background Rheumatoid arthritis (RA) patients have increased risk for cardiovascular disease (CVD). Accurate CVD risk prediction could improve care for RA patients. Our goal is to develop and validate a biomarker-based model for predicting CVD risk in RA patients. Methods Medicare claims data were linked to multi-biomarker disease activity (MBDA) test results to create an RA patient cohort with age ≥ 40 years that was split 2:1 for training and internal validation. Clinical and RA-related variables, MBDA score, and its 12 biomarkers were evaluated as predictors of a composite CVD outcome: myocardial infarction (MI), stroke, or fatal CVD within 3 years. Model building used Cox proportional hazard regression with backward elimination. The final MBDA-based CVD risk score was internally validated and compared to four clinical CVD risk prediction models. Results 30,751 RA patients (904 CVD events) were analyzed. Covariates in the final MBDA-based CVD risk score were age, diabetes, hypertension, tobacco use, history of CVD (excluding MI/stroke), MBDA score, leptin, MMP-3 and TNF-R1. In internal validation, the MBDA-based CVD risk score was a strong predictor of 3-year risk for a CVD event, with hazard ratio (95% CI) of 2.89 (2.46–3.41). The predicted 3-year CVD risk was low for 9.4% of patients, borderline for 10.2%, intermediate for 52.2%, and high for 28.2%. Model fit was good, with mean predicted versus observed 3-year CVD risks of 4.5% versus 4.4%. The MBDA-based CVD risk score significantly improved risk discrimination by the likelihood ratio test, compared to four clinical models. The risk score also improved prediction, reclassifying 42% of patients versus the simplest clinical model (age + sex), with a net reclassification index (NRI) (95% CI) of 0.19 (0.10–0.27); and 28% of patients versus the most comprehensive clinical model (age + sex + diabetes + hypertension + tobacco use + history of CVD + CRP), with an NRI of 0.07 (0.001–0.13). C-index was 0.715 versus 0.661 to 0.696 for the four clinical models. Conclusion A prognostic score has been developed to predict 3-year CVD risk for RA patients by using clinical data, three serum biomarkers and the MBDA score. In internal validation, it had good accuracy and outperformed clinical models with and without CRP. The MBDA-based CVD risk prediction score may improve RA patient care by offering a risk stratification tool that incorporates the effect of RA inflammation.

Aim: Evaluate the accuracy of a 23-gene expression signature in differentiating benign nevi from melanoma by comparing test results with clinical outcomes. Materials & methods: Seven dermatopathologists blinded to gene expression test results and clinical outcomes examined 181 lesions to identify diagnostically uncertain cases. Participants independently recorded diagnoses and responses to questions quantifying diagnostic certainty. Test accuracy was determined through comparison with clinical outcomes (sensitivity and percent negative agreement). Results: Overall, 125 cases fulfilled criteria for diagnostic uncertainty (69.1%; 95% CI: 61.8–75.7%). Test sensitivity and percent negative agreement in these cases were 90.4% (95% CI: 79.0–96.8%) and 95.5% (95% CI: 87.3–99.1%), respectively. Conclusion: The 23-gene expression signature has high diagnostic accuracy in diagnostically uncertain cases when evaluated against clinical outcomes.

Background The multi-biomarker disease activity (MBDA) test measures 12 serum protein biomarkers to quantify disease activity in RA patients. A newer version of the MBDA score, adjusted for age, sex, and adiposity, has been validated in two cohorts (OPERA and BRASS) for predicting risk for radiographic progression. We now extend these findings with additional cohorts to further validate the adjusted MBDA score as a predictor of radiographic progression risk and compare its performance with that of other risk factors. Methods Four cohorts were analyzed: the BRASS and Leiden registries and the OPERA and SWEFOT studies (total N = 953). Treatments included conventional DMARDs and anti-TNFs. Associations of radiographic progression (ΔTSS) per year with the adjusted MBDA score, seropositivity, and clinical measures were evaluated using linear and logistic regression. The adjusted MBDA score was (1) validated in Leiden and SWEFOT, (2) compared with other measures in all four cohorts, and (3) used to generate curves for predicting risk of radiographic progression. Results Univariable and bivariable analyses validated the adjusted MBDA score and found it to be the strongest, independent predicator of radiographic progression (ΔTSS > 5) compared with seropositivity (rheumatoid factor and/or anti-CCP), baseline TSS, DAS28-CRP, CRP SJC, or CDAI. Neither DAS28-CRP, CDAI, SJC, nor CRP added significant information to the adjusted MBDA score as a predictor, and the frequency of radiographic progression agreed with the adjusted MBDA score when it was discordant with these measures. The rate of progression (ΔTSS > 5) increased from < 2% in the low (1–29) adjusted MBDA category to 16% in the high (45–100) category. A modeled risk curve indicated that risk increased continuously, exceeding 40% for the highest adjusted MBDA scores. Conclusion The adjusted MBDA score was validated as an RA disease activity measure that is prognostic for radiographic progression. The adjusted MBDA score was a stronger predictor of radiographic progression than conventional risk factors, including seropositivity, and its prognostic ability was not significantly improved by the addition of DAS28-CRP, CRP, SJC, or CDAI.

Background Although several hereditary cancer predisposition genes have been implicated in pancreatic ductal adenocarcinoma (PDAC) susceptibility, gene-specific risks are not well defined, and are potentially biased due to the design of previous studies. More precise and unbiased risk estimates can result in screening and prevention better tailored to genetic findings. Methods This is a retrospective analysis of 676,667 individuals, 2,445 of whom had a personal diagnosis of PDAC, who received multigene panel testing between 2013–2020 from a single laboratory. Clinical data were obtained from test requisition forms. Multivariable logistic regression models determined the increased risk of PDAC due to pathogenic variants (PVs) in various genes as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Multivariable odds ratios were adjusted for age, personal/family cancer history, and ancestry. Results Overall, 11.1% of patients with PDAC had a PV. Significantly elevated PDAC risk (two-sided P &lt; .05) was observed for CDK2NA (p16INK4a) (OR 8.69, 95% CI 4.69–16.12), ATM (OR 3.44, 95% CI 2.58–4.60), MSH2 (OR 3.17, 95% CI 1.70–5.91), PALB2 (OR 3.09, 95% CI 2.02–4.74), BRCA2 (OR 2.55, 95% CI 1.99–3.27), and BRCA1 (OR 1.62, 95% CI 1.07–2.43). Conclusions This study provides PDAC risk estimates for 6 genes commonly included in multigene panel testing for hereditary cancer risk. These estimates are lower than those from previous studies, possibly due to adjustment for family history, and support current recommendations for germline testing in all PDAC patients, regardless of a personal or family history of cancer.

PURPOSE PTEN-associated clinical syndromes such as Cowden syndrome (CS) increase cancer risk and have historically been diagnosed based upon phenotypic criteria. Because not all patients clinically diagnosed with CS have PTEN pathogenic variants (PVs), and not all patients with PTEN PVs have been clinically diagnosed with CS, the cancer risk conferred by PTEN PVs calculated from cohorts of patients with clinical diagnoses of CS/CS-like phenotypes may be inaccurate. METHODS We assessed a consecutive cohort of 727,091 individuals tested clinically for hereditary cancer risk, with a multigene panel between September 2013 and February 2022. Multivariable logistic regression models accounting for personal and family cancer history, age, sex, and ancestry were used to quantify disease risks associated with PTEN PVs. RESULTS PTEN PVs were detected in 0.027% (193/727,091) of the study population, and were associated with a high risk of female breast cancer (odds ratio [OR], 7.88; 95% CI, 5.57 to 11.16; P = 2.3 × 10−31), endometrial cancer (OR, 13.51; 95% CI, 8.77 to 20.83; P = 4.2 × 10−32), thyroid cancer (OR, 4.88; 95% CI, 2.64 to 9.01; P = 4.0 × 10−7), and colon polyposis (OR, 31.60; CI, 15.60 to 64.02; P = 9.0 × 10−22). We observed modest evidence suggesting that PTEN PVs may be associated with ovarian cancer risk (OR, 3.77; 95% CI, 1.71 to 8.32; P = 9.9 × 10−4). Among patients with similar personal/family history and ancestry, every 5-year increase in age of diagnosis decreased the likelihood of detecting a PTEN PV by roughly 60%. CONCLUSION We demonstrate that PTEN PVs are associated with significantly increased risk for a range of cancers. Together with the observation that PTEN PV carriers had earlier disease onset relative to otherwise comparable noncarriers, our results may guide screening protocols, inform risk-management strategies, and warrant enhanced surveillance approaches that improve clinical outcomes for PTEN PV carriers, regardless of their clinical presentation.