Biomedical subjects

Michael Y Mi

Publications and source records attributed to Michael Y Mi.

3 recordsLinked to original sources

PubMed2026

Longitudinal Repeated Protein Measurements in a Multiethnic Cohort Identify Novel Diabetes Biomarkers That Reveal Unique Disease Pathways.

There is up to a fourfold increase in diabetes biomarkers identified with longitudinal repeated versus single time point proteomic measurements. The increase in biomarkers identified with longitudinal repeated measurements is supported by a similar proportion being nominated as causal for type 2 diabetes with Mendelian randomization. Proteins unique to the longitudinal repeated analyses highlighted biological pathways (e.g., posttranslational protein modification and cellular structure and cycle regulation) that were distinct from pathways enriched among the shared proteins (e.g., small-molecule metabolic and catabolic processes). Longitudinal protein measurements identify additional novel disease biomarkers and disparate biological pathways compared with single measurement analyses.

Journal Article↗

PubMed2026

A validated, modifiable proteomic score from the EXSCEL trial predicts cardiovascular events in diabetes.

BACKGROUNDAdults with type 2 diabetes mellitus (T2DM) are at increased risk for stroke, myocardial infarction, and cardiovascular death, yet individual risk is heterogeneous and incompletely captured by clinical models.METHODSIn the Exenatide Study of Cardiovascular Event Lowering (EXSCEL), adults with T2DM were randomized to a GLP-1 RA (exenatide) or a placebo and followed longitudinally for major adverse cardiovascular events (MACE). High-throughput discovery proteomics was done in plasma collected at baseline and 12 months. Proteins associated with time to MACE were identified using multivariable regression and incorporated into supervised machine learning models. A multi-protein score was developed and externally validated in 2 independent population-based and trial cohorts.RESULTSThe proteomic score showed incremental improvement in cardiovascular risk discrimination beyond clinical factors alone, and several proteins were consistently prioritized across modeling approaches. The protein score and a top-ranked protein, tetranectin, were modified by GLP-1 RA treatment, and a decrease in protein score was associated with improved outcomes, supporting modifiability of MACE risk.CONCLUSIONExternal validation confirmed generalizability across cohorts with and without diabetes. Together, these findings demonstrate that plasma proteomic signatures can enhance cardiovascular risk stratification and identify treatment-responsive biomarkers in T2DM, supporting their potential role in precision prevention strategiesFUNDINGThe EXSCEL study was funded by Amylin Pharmaceuticals. This research was supported by contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006, and grants R01HL146145, U01HL080295, U01HL130114, R01HL172803, and R01HL144483 from the National Heart, Lung, and Blood Institute, with additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided by R01AG023629 from the National Institute on Aging.

Aged↗

PubMed2026

Multi-omic signatures of genetic mechanisms inform on type 2 diabetes biology and patient heterogeneity.

Type 2 diabetes (T2D) is a heterogeneous disease shaped by genetic pathways related to insulin resistance and β-cell dysfunction, but how this heterogeneity is reflected molecularly remains unclear. We integrated partitioned polygenic scores (pPS) with proteomic and metabolomic profiling to define molecular signatures of T2D and their clinical relevance. We analyzed UK Biobank participants with genomic, proteomic, and metabolomic data. In a disease-free training subset, we used LASSO regression to identify multi-omic signatures associated with each pPS by jointly modeling proteins and metabolites. In an independent testing set, we constructed multi-omic scores and examined their associations with clinical traits and diabetes-related outcomes. Mediation analyses were used to investigate putative causal pathways. Key findings were evaluated in the Multi-Ethnic Study of Atherosclerosis (MESA). We identified distinct multi-omic signatures that capture the molecular architecture of T2D genetic risk across physiological subtypes. Compared with genetic scores alone, multi-omic pPS showed larger effect sizes and better disease discrimination. These scores recapitulated subtype-specific physiology and were associated with T2D risk. The Beta-Cell 2 multi-omic score showed marked stratification for insulin use, which was replicated in MESA, where it also predicted future insulin use. Mediation analyses implicated lipoprotein remodeling and fatty acid metabolism in the Lipodystrophy 1 cluster, accounting for 30-45% of the total effect of pPS on T2D risk. Integrating process-specific genetic risk with circulating multi-omic profiles reveals biologically distinct endotypes of T2D and supports a framework for improved patient stratification and risk assessment.

Journal Article↗