{"doi":"10.69734/crvc7g84","title":"Genetic Risk Scores in Diabetes: Potential for Disease Prediction, Classification, and Precision Medicine","abstract":"Genome-wide association studies have discovered a large number of susceptibility variants for type 1 (T1DM) and type 2 diabetes mellitus (T2DM). This has facilitated numerous studies exploring the potential of genetic risk scores (GRS) to improve disease prediction and diabetes classification. Given the unique genetic architecture of T1DM, in which genetic variants explain ~90% of the heritability, GRS for T1DM are highly predictive for disease development, alone and in combination with clinical factors. T1DM GRS also effectively distinguish T1DM from other types of diabetes. Though composed of a greater number of variants, T2DM GRS have more modest ability to predict and classify diabetes. On the other hand, T2DM variants have been classified into subclusters that reflect diverse pathophysiologic processes underlying T2DM. GRS based on these clusters have been used to dissect the underpinnings not only of T2DM but also of related disorders such as polycystic ovary syndrome and pancreatogenic diabetes. They may also one day prove useful in precision medicine, allowing selection of drug therapy targeted to each patient’s underlying physiologic deficits. However, much work validating use of GRS in the clinic will need to be accomplished before the full potential of GRS can be realized.","journal":"SMART-MD journal of precision medicine.","year":2025,"id":587645,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9518,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1210784,"name":"Mark Goodarzi","orcid":null,"position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:59:39.958043Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}