Abstract Background Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome comprising multiple pathophysiological phenotypes. HFpEF trials have largely enrolled diverse populations and reported average treatment effects, consistently yielding neutral results that may obscure drug-specific benefits within distinct subgroups. To address this issue, we employ an interaction-based that incorporates treatment-by-variable interactions to uncover drug-specific responses. Methods We leveraged four HFpEF clinical trials (TOPCAT, RELAX, NEAT-HFpEF, INDIE-HFpEF) and developed a framework comprising two complementary approaches. The first employed a prognostic responder model to evaluate whether conventional responder definitions reflect treatment-specific benefit or instead capture favorable clinical trajectories common to both treatment and placebo groups. The second used an interaction-based individual treatment effect (ITE) modeling to identify baseline variables that modify therapy effect, distinguishing drug-specific response from prognostic phenotypes. Results Although the prognostic responder model demonstrated good discrimination, further analisys suggested it primarily captured a prognostic signal associated with favorable clinical trajectories common to both treatment and placebo arms. In contrast, the ITE model identified distinct, drug-specific effect modifiers across trials (cardiorenal-inflammatory for spironolactone (TOPCAT), NO-mediated anti-inflammatory for isosorbide mononitrate (NEAT-HFpEF), afterload-reducing for inorganic nitrite (INDIE-HFpEF), and anti–volume-overload for sildenafil (RELAX). Each ITE model demonstrated significance only within its own trial suggesting drug-specific signal. Conclusions The proposed method identifies mechanism-specific effect modifiers, and uncovers clinically meaningful heterogeneity in treatment response, which is not captured by conventional MCID-based approaches. Although exploratory, these findings support phenotype-guided therapy in HFpEF and argue for phenotype-informed trial design to enhance treatment-effect detection and therapy targeting. Graphical Abstract
Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)
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AbstractWe previously demonstrated that cancer‐associated fibroblasts (CAFs) promoted the proliferation of gallbladder cancer (GBC) cells, but the mechanism is not clear. Neuropilin‐1 (NRP‐1) plays an important role in various malignancies as transmembrane glycoprotein. Our goal was to reveal the relationship between CAFs and NRP‐1 and their potential functions in GBC. In this study, we found NRP‐1 was overexpressed in GBC tissue, associated with poor survival and was up‐regulated by CAFs. The cytokine array cluster analysis revealed IL‐8 secreted by CAFs facilitated the up‐regulation of NRP‐1 in tumour cells. NRP‐1 knockdown suppressed tumour growth in vivo. Gene expression microarray analysis showed 581 differentially regulated genes under NRP‐1 knockdown conditions. Ingenuity pathway analysis demonstrated that NRP‐1 knockdown may inhibit tumour progression by affecting cell proliferation. We then confirmed that NRP‐1 knockdown in NOZ and GBC‐SD cells significantly inhibited cell proliferation. Additionally, the IL‐8 mediated MDM2 and CCNA2 expression were affected by NRP‐1 knockdown. Our findings suggested that NRP‐1 was up‐regulated by CAF‐secreted IL‐8, which subsequently promoted GBC cell proliferation, and these molecules may serve as useful prognostic biomarkers and therapeutic targets for GBC.