Biomedical subjects

Shuo Chen

Publications and source records attributed to Shuo Chen.

3 recordsLinked to original sources

PubMed2026

Assessing the potential of wastewater-based epidemiology to evaluate the impact of COVID-19 policy changes on stimulant use across 17 countries.

BACKGROUND AND AIMS: A limited number of studies have employed wastewater-based epidemiology (WBE) to assess the impact of specific COVID-19 public health directives on stimulant consumption. This study investigates the potential of WBE as a complementary information source to support decision-making, by examining drug use changes during the pandemic. METHODS: WBE data on stimulant use from 2019 through 2022 was obtained from 17 countries covering a total of 47 wastewater treatment plants worldwide. The Oxford Coronavirus Government Response Tracker stringency index was used to standardize the severity of the COVID-19 interventions across different countries. A multiple linear regression model was fitted for the population-normalized mass loads of amphetamine, cocaine (through its metabolite benzoylecgonine), MDMA, and methamphetamine to investigate whether changes in COVID-19 restrictions influenced stimulant use, controlling for possible week-weekend and spatial effects. RESULTS: In most locations, WBE suggests that stimulant use was not significantly affected by the COVID-19 interventions, or even increased under the stricter measures. Methamphetamine use showed the largest decrease with increasing policy stringency, with a negative linear relationship found in 19% of cities, followed by MDMA (18%), cocaine (15%) and amphetamine (6%). Social gathering restrictions mainly impacted cocaine and MDMA use, while methamphetamine consumption declined most under stringent travel restrictions. CONCLUSIONS: This study highlights the heterogeneous effects of the COVID-19 policy changes on stimulant use, even within countries. The ability of WBE to compile consecutive daily estimates proves to be particularly useful to assess the direct effect of specific policy changes on the consumption of different stimulants.

COVID-19 interventions↗

PubMed2026

FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) integrate genome wide association studies with expression quantitative trait locus reference panels to identify genes associated with traits of interest. However, linkage disequilibrium and correlated gene expression can induce spurious TWAS signals, motivating fine mapping methods to prioritize putatively causal genes within associated loci. The rapid growth of large-scale phenomic resources (e.g. electronic health records (EHRs)) has shifted genetic studies from single-trait analyses to phenome-wide investigations that jointly evaluate many closely related phenotypes. We introduce FM-GPT (Fine-mapping of causal Genes for Phenome-wide Transcriptome-wide association studies), a novel Bayesian fine mapping method for prioritizing causal genes across multiple correlated phenotypes with potentially mixed outcome types (e.g., binary, count or continuous) in phenome-wide TWAS. FM-GPT performs gene-guided dimension reduction of the phenotypes and reveals pleiotropic or phenotype-specific effects of the identified genes. In simulations, FM-GPT identified true causal genes more accurately than other fine mapping methods while controlling false positives. We applied FM-GPT to two applications using data from UK Biobank: a brain-wide genetic analysis of MRI data derived regional cortical thickness measures and a phenome-wide genetic analysis of clinical phenotypes derived from EHR data. FM-GPT greatly narrowed down the set size of putatively causal genes and identified: 1. genes with pleiotropic effects on regional cortical thickness across the cerebral cortex, including five genes BCAS3, LRRC37A, NOS2P3, ARL17B and UBB on chromosome 17 regulating neuronal morphology and cortical organization; and 2. genes that influence multiple medical conditions across the circulatory, metabolic, digestive, respiratory and genitourinary systems, revealing two major axes of variation among these conditions that point to a potential trade-off in gene regulation between immune and metabolic functions. These results highlight FM-GPT's power to disentangle complex gene-phenotype relationships in large-scale phenome-wide studies, uncovering shared biological mechanisms across diverse human traits and advancing translational and comorbidity research.

Bayesian fine mapping↗

PubMed2025

The Effect of Alcohol Intake on Brain White Matter Microstructural Integrity: A New Causal Inference Framework for Incomplete Phenomic Data.

Although substance use, such as alcohol intake, is known to be associated with cognitive decline during aging, its direct influence on the central nervous system remains incompletely understood. In this study, we investigate the influence of alcohol intake frequency on reduction of brain white matter microstructural integrity in the fornix, a brain region considered a promising marker of age-related microstructural degeneration, using a large UK Biobank (UKB) cohort with extensive phenomic data reflecting a comprehensive lifestyle profile. Two major challenges arise: (a) potentially nonlinear confounding effects from phenomic variables and (b) a limited proportion of participants with complete phenomic data. To address these challenges, we develop a novel ensemble learning framework tailored for robust causal inference and introduce a data integration step to incorporate information from UKB participants with incomplete phenomic data, improving estimation efficiency. Our analysis reveals that daily alcohol intake may significantly reduce fractional anisotropy, a neuroimaging-derived measure of white matter structural integrity, in the fornix and increase systolic and diastolic blood pressure levels. Moreover, extensive numerical studies demonstrate the superiority of our method over competing approaches in terms of estimation bias, while outcome regression-based estimators may be preferred when minimizing mean squared error is prioritized. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Brain aging↗