AMH protects the ovary from doxorubicin by regulating cell fate and the response to DNA damage.

Anti-Müllerian hormone (AMH) protects the ovarian reserve from chemotherapy, and this effect is most pronounced with Doxorubicin (DOX). However, the mechanisms of DOX toxicity and AMH rescue in the ovary remain unclear. Herein, we characterize these mechanisms in various ovarian cell types using scRNAseq. In the mesenchyme, DOX activates the intrinsic apoptotic signaling pathway through p53 class mediators, particularly affecting theca progenitors, while co-treament with AMH halts theca differentiation and reduces apoptotic gene expression. In preantral granulosa cells, DOX upregulates the cell cycle inhibitor Cdkn1a and dysregulates Wnt signaling, which are ameliorated by AMH co-treatment. Finally, in follicles, AMH induces Id3 , a protein involved in DNA repair, which is necessary to prevent the accumulation of DNA lesions marked by γ-H2AX in granulosa cells. Altogether this study characterizes cell, and follicle stage-specific mechanisms of AMH protection of the ovary, offering promising new avenues for fertility preservation in cancer patients undergoing chemotherapy. Highlights: Doxorubicin treatment induces DNA damage that activates the p53 pathway in stromal and follicular cells of the ovary.AMH inhibits the proliferation and differentiation of theca and granulosa cells and promotes follicle survival following Doxorubicin insult.AMH treatment mitigates Doxorubicin-induced DNA damage in the ovary by preventing the accumulation of γ-H2AX-positive unresolved foci, through increased expression of ID3, a protein involved in DNA repair.

Imputation of plasma lipid species to facilitate integration of lipidomic datasets.

Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass spectrometric resolution of newer platforms, has made it challenging to align datasets created at different times, or on different platforms. Here we present a framework for harmonising such plasma lipidomic datasets with different levels of granularity in their lipid measurements. Our method utilises elastic-net prediction models, constructed from high-resolution lipidomics reference datasets, to predict unmeasured lipid species in lower-resolution studies. The approach involves (1) constructing composite lipid measures in the reference dataset that map to less resolved lipids in the target dataset, (2) addressing discrepancies between aligned lipid species, (3) generating prediction models, (4) assessing their transferability into the targe dataset, and (5) evaluating their prediction accuracy. To demonstrate our approach, we used the AusDiab population-based cohort (747 lipid species) as the reference to impute unmeasured lipid species into the LIPID study (342 lipid species). Furthermore, we compared measured and imputed lipids in terms of parameter estimation and predictive performance, and validated imputations in an independent study. Our method for harmonising plasma lipidomic datasets will facilitate model validation and data integration efforts.

Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention.

BACKGROUND: Accurate risk stratification is vital for primary prevention of cardiovascular disease (CVD). However, traditional tools such as the Framingham Risk Score (FRS) may underperform within the diverse intermediate-risk group, which includes individuals requiring distinct management strategies. OBJECTIVES: This study aimed to develop a lipidomic-enhanced risk score (LRS), specifically targeting risk prediction and reclassification within the intermediate group, benchmarked against the FRS. METHODS: The LRS was developed via a machine learning workflow using ridge regression on the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab; n = 10,339). It was externally validated with the Busselton Health Study (n = 4,492), and its predictive utility for coronary artery calcium scoring (CACS)-based outcomes was independently validated in the BioHEART cohort (n = 994). RESULTS: LRS significantly improved discrimination metrics for the intermediate-risk group in both AusDiab and Busselton Health Study cohorts (all P < 0.001), increasing the area under the curve for CVD events by 0.114 (95% CI: 0.1123-0.1157) and 0.077 (95% CI: 0.0755-0.0785), with a net reclassification improvement of 0.36 (95% CI: 0.21-0.51) and 0.33 (95% CI: 0.15-0.49), respectively. For CACS-based outcomes in BioHEART, LRS achieved a significant area under the curve improvement of 0.02 over the FRS (0.76 vs 0.74; P < 1.0 × 10-5). A simplified, clinically applicable version of LRS was also created that had comparable performance to the original LRS. CONCLUSIONS: LRS, augmenting the FRS, presents potential to improve intermediate-risk stratification and to predict atherosclerotic markers using a simple blood test, suitable for clinical application. This could facilitate the triage of individuals for noninvasive imaging such as CACS, fostering precision medicine in CVD prevention and management.