DSP AAV gene therapy (Renovacor)

Anticholinergic medications (ACMs) are associated with poorer age‐related outcomes, including falls and frailty. We investigate associations between ACM use and recurrent falls and frailty among older (aged ≥50 years) people with HIV in the POPPY study.

Anticholinergic potential of co‐medications at study entry was coded using the anticholinergic burden score, anticholinergic risk score, and Scottish Intercollegiate Guidelines Network score; drugs scoring ≥1 on any scale were defined as ACM. Associations with recurrent falls (two or more falls in the previous 28 days) and frailty (modified Fried's) were assessed using logistic regression adjusting for (1) possible demographic/lifestyle confounders and (2) clinical factors and depressive symptoms (Patient Health Questionnaire‐9).

ACM use was reported by 193 (28%) of 699 participants, with 64 (9%) receiving two or more ACM; commonly prescribed ACMs were codeine (12%), citalopram (12%), loperamide (9%), and amitriptyline (7%). Falls were reported in 63/673 (9%), and 126/609 (21%) met the frailty criteria. Both recurrent falls and frailty were more common in ACM users than in non‐users (recurrent falls: 17% in users vs. 6% in non‐users, p < 0.001; frailty: 32% vs. 17%, respectively, p < 0.001). Use of two or more ACMs was associated with increased odds of falls after adjustment for demographic/lifestyle factors (odds ratio [OR] 4.53; 95% confidence interval [CI] 2.06–9.98) and for clinical factors (OR 3.58; 95% CI 1.37–9.38). Similar albeit weaker associations were seen with frailty (OR 2.26; 95% CI 1.09–4.70 and OR 2.12; 95% CI 0.89–5.0, respectively).

ACM are commonly prescribed for people living with HIV, and evidence exists for an association with recurrent falls and frailty. Clinicians should be alert to this and reduce ACM exposure where possible.

It is difficult to distinguish between arrhythmogenic cardiomyopathy (ACM) and dilated cardiomyopathy (DCM) because of their similar clinical manifestations. This study aimed to develop a novel diagnostic algorithm for distinguishing ACM from DCM.

Two public datasets containing human ACM and DCM myocardial samples were used. Consensus clustering, non-negative matrix factorization and principal component analysis were applied. Weighted gene co-expression network analysis and machine learning methods, including random forest and the least absolute shrinkage and selection operator, were used to identify candidate genes. Receiver operating characteristic curves and nomograms were performed to estimate diagnostic efficacy, and Spearman's correlation analysis was used to assess the correlation between candidate genes and cardiac function indices.

Both ACM and DCM showed highly similar gene expression patterns in the clustering analyses. Hub gene modules associated with cardiomyopathy were obtained using weighted gene co-expression network analysis. Thirteen candidate genes were selected using machine learning algorithms, and their combination showed a high diagnostic value (area under the ROC curve = 0.86) for distinguishing ACM from DCM. In addition, TATA-box binding protein associated factor 15 showed a negative correlation with cardiac index (R = -0.54, p = 0.0054) and left ventricular ejection fraction (R = -0.48, p = 0.0015).

Our study revealed an effective diagnostic model with key gene signatures, which indicates a potential tool to differentiate between ACM and DCM in clinical practice. In addition, we identified several genes that are highly related to cardiac function, which may contribute to our understanding of ACM and DCM.