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Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia.

ABSTRACT: Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may alter dementia onset. Mixed results are emerging from prior observational studies. To address this complexity, we deploy a causal inference approach accounting for the competing risk of death in emulated clinical trials using two distinct electronic health record systems. In intention-to-treat analyses, metformin use associates with lower hazard of all-cause mortality and lower cause-specific hazard of dementia onset, after accounting for prolonged survival, relative to sulfonylureas. In parallel systems pharmacology studies, the expression of two AD-related proteins, APOE and SPP1, was suppressed by pharmacologic concentrations of metformin in differentiated human neural cells, relative to a sulfonylurea. Together, our findings suggest that metformin might reduce the risk of dementia in diabetes patients through mechanisms beyond glycemic control, and that SPP1 is a candidate biomarker for metformin's action in the brain.

SUBMITTER: Charpignon ML

PROVIDER: S-EPMC9741618 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia.

Charpignon Marie-Laure ML Vakulenko-Lagun Bella B Zheng Bang B Magdamo Colin C Su Bowen B Evans Kyle K Rodriguez Steve S Sokolov Artem A Boswell Sarah S Sheu Yi-Han YH Somai Melek M Middleton Lefkos L Hyman Bradley T BT Betensky Rebecca A RA Finkelstein Stan N SN Welsch Roy E RE Tzoulaki Ioanna I Blacker Deborah D Das Sudeshna S Albers Mark W MW

Nature communications 20221210 1

Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may alter dementia onset. Mixed results are emerging from prior observational studies. To address this complexity, we deploy a causal inference approach accounting for the competing risk of death in emulated clinical trials using two distinct electronic health record systems. In intention-to-treat analyses, metformin use associates with lower hazard of all-cause mortality and lower cause-specific hazard of de ...[more]

PMID: 36496454

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Project description:Gene expression profiles that connect drug perturbations, disease gene expression signatures, and clinical data are important for discovering potential drug repurposing indications. However, the current approach to gene expression reversal has several limitations. First, most methods focus on validating the reversal expression of individual genes. Second, there is a lack of causal approaches for identifying drug repurposing candidates. Third, few methods for passing and summarizing information on a graph have been used for drug repurposing analysis, with classical network propagation and gene set enrichment analysis being the most common. Fourth, there is a lack of graph-valued association analysis, with current approaches using real-valued association analysis one gene at a time to reverse abnormal gene expressions to normal gene expressions. To overcome these limitations, we propose a novel causal inference and graph neural network (GNN)-based framework for identifying drug repurposing candidates. We formulated a causal network as a continuous constrained optimization problem and developed a new algorithm for reconstructing large-scale causal networks of up to 1,000 nodes. We conducted large-scale simulations that demonstrated good false positive and false negative rates. To aggregate and summarize information on both nodes and structure from the spatial domain of the causal network, we used directed acyclic graph neural networks (DAGNN). We also developed a new method for graph regression in which both dependent and independent variables are graphs. We used graph regression to measure the degree to which drugs reverse altered gene expressions of disease to normal levels and to select potential drug repurposing candidates. To illustrate the application of our proposed methods for drug repurposing, we applied them to phase I and II L1000 connectivity map perturbational profiles from the Broad Institute LINCS, which consist of gene-expression profiles for thousands of perturbagens at a variety of time points, doses, and cell lines, as well as disease gene expression data under-expressed and over-expressed in response to SARS-CoV-2.

Dataset Information

Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia.

Publications

Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia.

Similar Datasets

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