A groundbreaking study spearheaded by researchers at the University of California San Diego has now introduced a novel machine learning apparatus, termed T1GRS, which redefines the accuracy and breadth of genetic risk prediction for T1D. This sophisticated tool delves into the intricate interplay between numerous genetic variants beyond the scope of conventional models, capturing a more comprehensive genomic landscape linked to T1D susceptibility. As revealed in a recent publication in the distinguished journal Nature Genetics, the T1GRS model can identify individuals—both pediatric and adult—at elevated risk for T1D significantly earlier than previous methodologies. This advance paves the way for proactive interventions potentially capable of delaying or even preventing disease onset.

The foundation of this innovation lies in an extensive genomic analysis encompassing over 20,000 individuals of European ancestry diagnosed with T1D, contrasted against nearly 800,000 unaffected controls. Through high-resolution genome-wide association studies (GWAS), the researchers affirmed 79 loci previously implicated in T1D risk and identified 13 novel loci. These newly discovered regions influence gene regulation, immune modulation, and metabolic processes essential for glucose homeostasis, broadening the genetic context of the disease.

A particular focal point of the investigation was the major histocompatibility complex (MHC) on chromosome 6, known for its dense clusters of immune-related genes strongly correlated with T1D. Leveraging data from more than 29,000 individuals, the team unraveled additional novel single nucleotide polymorphisms (SNPs) within the MHC that modulate immune pathways and gene activation patterns. These discoveries underscore the complexity of the genetic architecture underpinning autoimmune responses in T1D and set the stage for more nuanced risk stratification.

Emily Griffin, PhD, postdoctoral fellow and co-first author, emphasizes the significance of MHC haplotypes: “Within the MHC region, blocks of co-inherited genetic information are significantly enriched among individuals with Type 1 diabetes. Their presence elevates risk, but absence correlates with an exceedingly low probability of disease manifestation.” This insight inspired the integration of non-linear interactions among 199 identified risk variants across the genome into the T1GRS model, allowing it to account for complex epistatic relationships that traditional linear risk scores often miss.

An essential advantage of T1GRS lies in its robustness across a wide spectrum of genetic risk profiles. As noted by co-first author TJ Sears, PhD, “Prior genetic risk scores excel at pinpointing only those with the highest risk alleles. In contrast, T1GRS achieves high predictive accuracy across individuals with varied and intricate genetic backgrounds, including those lacking canonical high-risk variants.” This marks a pivotal enhancement, enabling a more inclusive and precise identification of at-risk populations.

Beyond risk quantification, the researchers discerned four genetically distinct T1D subtypes by analyzing the dominant genetic features influencing individual T1GRS scores. These subpopulations exhibit unique clinical trajectories and pathological mechanisms. The MHC-driven subtype reflects classical high-risk alleles and is typically associated with early-onset juvenile diabetes. The MHC-enriched group demonstrates a mixed genetic influence and a delayed onset compared to the first group. The T-cell-enriched subtype is characterized largely by immune-regulatory non-MHC variants affecting adaptive immunity, while the pancreas-enriched subtype involves mutations affecting pancreatic cell function, correlating with the highest incidence of severe complications such as nephropathy and cardiovascular disease.

To validate the generalizability of T1GRS, the team applied the model to external genetic datasets from the NIH All of Us Research Program and the nPOD biobank. Despite a comparatively smaller sample size, the model robustly predicted T1D risk with approximately 87% accuracy. Importantly, these independent cohorts recapitulated the four genetic subtypes, reinforcing the reproducibility and clinical relevance of these classifications in diverse populations.

Interestingly, Griffin notes the model’s translational potential beyond European populations: “Even though T1GRS was developed using European ancestry datasets, it maintained notable accuracy in non-European groups. This opens avenues toward equitable screening applications worldwide.” Such inclusivity is imperative given the global burden of T1D and the disparities often seen in genetic studies.

The clinical implications of this research are profound. Early identification of high-risk individuals allows for intensified monitoring and timely initiation of therapeutic interventions, potentially forestalling the disease’s progression. As elucidated by co-first author Carolyn McGrail, PhD, “Capturing a broader pool of susceptible individuals enhances surveillance strategies to mitigate acute complications like diabetic ketoacidosis at diagnosis and facilitates enrollment in preventative trials, including therapies such as teplizumab.” This immunotherapeutic agent has shown promise in delaying onset in high-risk patients, underscoring the imperative for precise predictive tools.

Furthermore, the delineation of distinctive T1D subtypes may revolutionize personalized medicine approaches. Tailoring interventions based on the unique genetic and immunological profile of each patient could augment efficacy and minimize adverse effects. For instance, immune-modulatory treatments might be prioritized in the T-cell-enriched group, whereas metabolic support could be emphasized for pancreas-enriched individuals.

The study also highlights the transformative potential of machine learning in genetic medicine. By modeling the complex, non-linear gene-gene interactions inherent in human disease, machine learning transcends traditional analytic frameworks, offering unprecedented granularity and predictive power.

Despite these advances, the authors acknowledge limitations, including the need for larger, ethnically diverse cohorts to refine and validate the T1GRS tool further. There is also the challenge of integrating genetic risk scores with environmental and lifestyle factors, which collectively influence disease manifestation.

In sum, this research marks a paradigm shift in understanding and predicting Type 1 diabetes. Through the innovative fusion of genomics and machine learning, T1GRS emerges as a potent instrument poised to enhance early diagnosis, prevention, and personalized treatment strategies for T1D, promising to transform care for millions at risk worldwide.

Subject of Research: People

Article Title: Genetic association and machine learning improves prediction of type 1 diabetes risk

News Publication Date: 30-Apr-2026

Web References: https://www.nature.com/articles/s41588-026-02578-y

References: DOI: 10.1038/s41588-026-02578-y

Image Credits: Kyle Dykes | UC San Diego Health Sciences

Keywords: Type 1 diabetes, Genetic screening, Genetic medicine, Machine learning, Autoimmune disorders, Disease susceptibility

Over the past decade, the Human Islet Research Network (HIRN), established by the National Institute of Diabetes and Digestive and Kidney Diseases in 2014, has made substantial strides in unraveling the complexities of type 1 diabetes pathogenesis. Spearheaded by City of Hope’s John Kaddis, the interdisciplinary HIRN consortium has developed innovative in vitro and in vivo systems that elucidate beta cell behavior within their native microenvironment. These advances allow scientists to model disease progression more accurately, employing novel technologies to dissect beta cell loss, immune interactions, and mechanisms of cell replacement. Despite these advancements, critical gaps remain in understanding the precise triggers for autoimmune beta cell destruction and how best to intercept these processes before the onset of clinical disease. The network emphasizes shared data platforms and collaborative training to foster cross-disciplinary solutions aimed at preventing and ultimately curing type 1 diabetes.

Emerging epidemiological evidence highlights the impact of obesity on multiple myeloma progression, particularly in individuals with the precursor lesion monoclonal gammopathy of undetermined significance (MGUS). Research led by Lawrence Liu at City of Hope meticulously analyzed longitudinal body mass index (BMI) data from nearly 22,500 MGUS patients to quantify the risk attributed to sustained elevated BMI. Their findings reveal a compelling correlation between prolonged exposure to overweight or obese BMI ranges and an increased likelihood of progression to full-blown multiple myeloma. Participants maintaining a BMI above 25 after diagnosis demonstrated a significantly elevated risk of malignancy evolution, underscoring the crucial importance of weight management in mitigating cancer risk. This study represents a paradigm shift in recognizing metabolic factors as modifiable determinants in hematologic cancer progression.

In the realm of metastatic colorectal cancer (mCRC), precision medicine continues to evolve with sophisticated biomarker-driven approaches to guide therapeutic decisions. Investigators at City of Hope, led by Ajay Goel, validated a revolutionary liquid biopsy platform known as EXONERATE to predict patient responses to epidermal growth factor receptor (EGFR) inhibitors, specifically panitumumab and cetuximab. Employing genome-wide small RNA sequencing of circulating exosomes and cell-free microRNAs, the assay identifies molecular signatures indicative of therapeutic efficacy. Crucially, this technology accounts for tumor heterogeneity associated with primary tumor sidedness, a known determinant of EGFR inhibitor response. The assay demonstrated robust predictive value for progression-free and overall survival across diverse patient populations, offering an unprecedented non-invasive tool for real-time treatment stratification in mCRC management.

Immune checkpoint blockade has transformed oncologic care, yet the heterogeneity of patient response remains a formidable challenge. Targeting this, Kelly Mahuron and colleagues at City of Hope embarked on an intricate molecular characterization of tumor infiltrating lymphocytes (TILs) within advanced melanoma samples to identify biomarkers predictive of anti-PD-1 antibody efficacy. Their seminal work employing single-cell RNA sequencing delineated a unique CD8+ TIL subset expressing high levels of PD-1 and CTLA-4 receptors, termed CP^Hi TILs. Patients harboring ≥20% CP^Hi TILs exhibited markedly improved objective response rates and survival outcomes following PD-1 monotherapy. This pioneering biomarker assay holds transformative potential for refining patient selection in immunotherapy, enabling clinicians to tailor treatments based on intratumoral immune cell phenotypes and thereby maximizing therapeutic benefit while minimizing unnecessary toxicity.

Cardiotoxicity remains a critical long-term concern for pediatric cancer survivors, whose growing population faces significant risks for treatment-related cardiovascular disease. A recent scientific statement from the American Heart Association, co-authored by City of Hope’s Saro Armenian, addresses emerging cardio-oncology challenges in this vulnerable group. Reflecting on decades of research, the statement underscores the deleterious effects of anthracycline chemotherapy and chest radiotherapy—cornerstones of pediatric oncology—on cardiac function. Advances in dose optimization, cardioprotective agents, and modern radiotherapy techniques have ameliorated some risks but novel therapies, including small-molecule inhibitors and immunotherapies, introduce new cardiotoxicity profiles. The comprehensive review advocates for equitable long-term surveillance, rehabilitation through structured physical activity, and seamless transition from pediatric to adult cardiology care to improve cardiovascular outcomes in childhood cancer survivors.

In efforts to counteract disease progression in chronic myeloid leukemia (CML), researchers at City of Hope led by Bin Zhang and Guido Marcucci have uncovered a novel immune-evasion mechanism operative during the transition to blast crisis (BC). Their study, published in Nature Communications, elucidates how acquired deficiency of microRNA miR-142 precipitates loss of cytotoxic T cells essential for anti-leukemic immunity. The miR-142 deficit simultaneously facilitates leukemic stem cell immune escape, thereby accelerating malignant transformation. Remarkably, the team developed a synthetic miR-142 mimic, M-miR-142, capable of restoring immune surveillance when administered alone or alongside monoclonal antibodies and tyrosine kinase inhibitors. Preclinical trials in murine models demonstrated prolonged survival, offering a promising therapeutic avenue to forestall BC progression and improve patient prognosis through immune modulation.

City of Hope also proudly celebrates the outstanding achievements of its scientific community. Notably, Dr. Ravi Salgia’s recognition as a Highly Ranked Scholar — Lifetime — for contributions to lung cancer research highlights the institution’s leadership. The 2025 American Association for Cancer Research (AACR) annual meeting further honored City of Hope researchers including Daniel D. Von Hoff for his unwavering dedication to cancer research and clinical care, Enrique Velazquez Villarreal for his minority scholar award recognition, and emerging scientists Kimya Karimi, Francisco Carranza, Eric Medina, and Isaac Bishara for their promising work in cancer research. These accolades attest to the vibrant and diverse scientific talent driving innovation at City of Hope.

In funding news, Ling Li, Ph.D., was awarded a prestigious $3.58 million National Cancer Institute grant for her innovative study targeting adenosine monophosphate (AMP) synthesis pathways to overcome resistance to BH3 mimetics in acute myeloid leukemia (AML). This research holds potential to surmount one of the major obstacles in AML therapy—the development of drug resistance—by exploiting metabolic vulnerabilities within leukemic cells, thereby enhancing the efficacy of pro-apoptotic agents.

City of Hope’s integrated research and clinical care model remains a cornerstone of its mission to revolutionize outcomes for patients afffected with cancer and diabetes. Its National Cancer Institute-designated comprehensive cancer center, consistently ranked among the top five in the United States, exemplifies excellence in multidisciplinary innovation, combining translational research, cutting-edge clinical trials, and an expansive network of care delivery. Through synergy with affiliated organizations such as the Translational Genomics Research Institute and AccessHope™, City of Hope continues to pioneer breakthroughs — from synthetic insulin production to monoclonal antibody therapeutics — redefining the standard of medical care.

Harnessing advanced molecular technologies, collaborative scientific inquiry, and clinical acumen, City of Hope’s researchers pave the way for precision medicine and immunotherapy strategies tailored to individual patient biology. Their recent discoveries in disease biomarkers, immune modulation, and metabolic regulation not only elucidate fundamental disease processes but also herald novel therapeutic paradigms. As the institution fuels this momentum, the prospects for improved prevention, diagnostic, and treatment modalities across oncology and metabolic disease grow exponentially, offering renewed hope for patients worldwide.

Subject of Research: Diabetes, Multiple Myeloma, Metastatic Colorectal Cancer, Immune Checkpoint Inhibitor Response, Pediatric Cardiovascular Toxicity, Chronic Myeloid Leukemia, Cancer Therapeutics, Biomarkers

Article Title: City of Hope Research Spotlight: Advances in Diabetes, Cancer Biology, Immunotherapy, and Survivorship

News Publication Date: 2025

Web References:

• City of Hope Research Spotlight Feed: https://www.cityofhope.org/about-city-of-hope/newsroom/research-spotlight/feed
• Diabetes Journal Article: https://doi.org/10.2337/db25-0097
• JAMA Network Open Paper: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2830028
• Clinical Cancer Research Paper: https://aacrjournals.org/clincancerres/article-abstract/31/6/1002/753268/An-Exosome-Based-Liquid-Biopsy-Predicts-Depth-of
• Cancer Research Paper: https://aacrjournals.org/cancerres/article/doi/10.1158/0008-5472.CAN-23-3918/753249/Single-Cell-Analyses-Reveal-a-Functionally
• Circulation Review Paper: https://www.ahajournals.org/doi/10.1161/CIR.0000000000001308
• Nature Communications Paper: https://www.nature.com/articles/s41467-025-56383-y

References: Included within the text as links to primary scientific publications.

Keywords: Cancer research, Diabetes research, Multiple Myeloma, Liquid biopsy, EGFR inhibitors, Immune checkpoint inhibitors, Pediatric cardio-oncology, Chronic myeloid leukemia, Biomarkers, Immunotherapy, Molecular diagnostics