The review, authored by Abhisheka Lenin and Jasmin Sajini Rajayan of Sri Ramachandra Institute of Higher Education and Research in Chennai, evaluates pharmacogenomic evidence across monogenic and polygenic diabetes subtypes and positions a single underexplored gene, GLIS3, as a potential future pharmacogenomic target with cross-disease relevance spanning neonatal diabetes, type 1 diabetes, and type 2 diabetes. The authors frame genotype-guided therapy as an emerging clinical priority rather than a distant aspiration, particularly for genetically diverse populations where standardized treatment protocols built on Western genomic data may be suboptimal.

Diabetes mellitus is among the most genetically complex diseases of modern medicine, yet it continues to be managed through standardized protocols that do not account for individual genetic differences. While lifestyle factors undeniably contribute to disease risk, that picture is dangerously incomplete. A significant proportion of patients, particularly in South Asian populations, develop type 2 diabetes despite normal body weight, healthy diets, and active lifestyles, because the real driver in many cases is genetic and operates independently of behavior. Current treatment systems rely on standardized guidelines, doses, and drugs that assume population homogeneity, but two patients can present with the same HbA1c, the same BMI, and the same symptoms while carrying completely different genetic profiles driving their disease.

Pharmacogenomics offers a potential solution by changing the fundamental clinical question from what drug works for most people to what drug works for this person, based on their genetic makeup. In monogenic diabetes, particularly MODY subtypes, this revolution has already arrived with clear evidence. A patient with an HNF1A mutation responds extraordinarily well to low-dose sulfonylureas. A patient with a GCK mutation may need no pharmacotherapy at all, only lifestyle management. A patient with KCNJ11 or ABCC8 mutations can often switch from insulin injections to oral sulfonylureas. The genotype tells the clinician exactly what to do, and replicated clinical evidence confirms it works.

Type 2 diabetes presents a far more complicated pharmacogenomic story. It is not driven by one gene but by hundreds of common variants, each contributing a small effect, all interacting with lifestyle, environment, and each other. This polygenic complexity is precisely why no single genetic marker has yet been identified that reliably predicts drug response across all type 2 diabetes patients. Several variations have emerged as clinically significant nonetheless. Reduced-function SLC22A1 variants impede hepatic OCT1-mediated metformin uptake, diminishing the glucose-lowering efficacy of the global first-line therapy. Polymorphisms in PPARG influence insulin sensitivity and thiazolidinedione response, though effect sizes remain modest and replication across diverse cohorts has been inconsistent. Variants in KCNJ11 and ABCC8 affect sulfonylurea sensitivity in type 2 diabetes, though with far less predictive clarity than in monogenic disease.

This matters enormously for India. A comprehensive pharmacogenomic analysis of Indian populations has catalogued 76 known variants affecting non-insulin antidiabetic drug responses, including reduced-function alleles in SLC22A1 that impair metformin hepatic uptake. These reduced-function alleles occur at higher frequencies in Indian cohorts compared to European populations, suggesting population-specific differences in metformin transport efficiency. In practical terms, a drug given as standard first-line therapy to hundreds of millions of people may be working less effectively in a large proportion of Indian patients, not because of anything they did wrong, but because of genetic variations in their drug transport system that no standard clinical assessment would ever detect.

India presents a uniquely important context for pharmacogenomic research given its extraordinary population stratification across tribal, linguistic, religious, and geographic groups. Ancient admixture events involving Iranian farmers, Central Asian steppe pastoralists, and indigenous Dravidian populations have created a genetic landscape of complexity that no other country quite replicates. Allele frequencies for drug-response variants can differ significantly between a tribal community in Odisha and an urban population in Chennai. When clinicians prescribe metformin using dose guidelines developed from European cohorts to patients from Indian tribal or rural populations, the desired response may not occur. Approximately 80 percent of all genome-wide association study participants to date are of European ancestry, despite this group representing only around 16 percent of the global population, meaning pharmacogenomic guidelines applied to Indian patients were built on data that does not represent them.

Indian clinical experience reinforces the consequences of genomic misclassification. A comprehensive genomic analysis of 289 individuals, including 152 clinically suspected MODY cases, confirmed HNF1A mutations as the most frequent at 7.2 percent and ABCC8 mutations at 3.3 percent, but the truly significant finding was the discovery of novel variants in RFX6, WFS1, AKT2, and NKX6-1, genes not previously linked to MODY in global literature. Western diagnostic criteria for MODY were built on Western genomic data, and when applied to Indian populations they are working with an incomplete map. At CMC Vellore, sequencing of 80 young-onset diabetes patients identified mutations across multiple MODY-associated genes including HNF4A, HNF1A, GCK, PDX1, NEUROD1, and HNF1B, with digenic mutations such as NEUROD1 and PDX1 representing the most striking finding. In these cases, standard therapies failed not because doctors chose the wrong drug but because the underlying genetic architecture was more complex than standard classifications assumed.

At the center of the emerging pharmacogenomic research stands GLIS3, GLI-Similar Zinc Finger Protein 3, a member of the C2H2-type zinc finger protein family containing five zinc finger domains. It functions as both a transcriptional activator and repressor, binding specific DNA sequences to regulate gene expression across multiple tissues including the pancreas, thyroid, and kidneys. In the pancreatic beta cell, GLIS3 regulates biology through three principal mechanisms. First, it drives the differentiation of pancreatic progenitor cells into mature insulin-producing beta cells by regulating key developmental transcriptional programs. Second, it directly activates insulin gene transcription through binding to promoter and enhancer regions of the insulin locus, thereby maintaining adequate insulin biosynthesis. Third, it protects beta cells against endoplasmic reticulum stress by preserving protein-folding homeostasis, which is essential given the high biosynthetic demand of continuous insulin production.

Loss of GLIS3 function disrupts beta-cell homeostasis through a defined pathophysiological cascade. Impaired insulin gene transcription reduces biosynthetic output and secretory capacity, while concomitant loss of ER stress protection permits accumulation of misfolded proinsulin within the endoplasmic reticulum. The resulting proteotoxic stress activates the unfolded protein response and downstream apoptotic signaling, including dysregulation of the BH3-only protein Bim, which ultimately drives beta-cell apoptosis. This mechanistic sequence directly links GLIS3 dysfunction to the progressive beta-cell loss observed in both type 1 and type 2 diabetes. Complete loss-of-function mutations cause neonatal diabetes with congenital hypothyroidism, confirming that GLIS3 is non-redundant for both beta-cell development and survival during the perinatal period, while partial loss-of-function or regulatory variants are associated with type 1 and type 2 diabetes susceptibility.

The pharmacogenomic role of GLIS3 in guiding existing antidiabetic therapy remains a biologically plausible but clinically unvalidated hypothesis. GLIS3 loss-of-function variants may theoretically influence responses to therapies dependent on residual beta-cell function, including GLP-1 receptor agonists and DPP-4 inhibitors, given their reliance on a functional incretin-beta-cell axis. Emerging evidence of population-specific variants in South Asian cohorts, including insights from the INSPIRED study published in 2023, positions GLIS3 as a compelling future pharmacogenomic target. However, no clinical study has stratified drug response by GLIS3 genotype, no pharmacogenomic guideline currently incorporates GLIS3 testing, and no GLIS3-directed clinical testing or therapy is currently available. The evidence supports GLIS3 as a susceptibility locus and mechanistic regulator, not as an established pharmacogenomic marker.

Beyond pharmacogenomics, GLIS3 represents a potential target for novel therapeutic development distinct from current drug-response stratification. Pharmacological augmentation of GLIS3 transcriptional activity or gene therapy approaches targeting beta-cell GLIS3 expression could theoretically protect against ER stress-mediated beta-cell failure. The goal would be upstream intervention protecting the beta cell before it fails, rather than compensating for failure after it has occurred. However, these possibilities remain at the preclinical stage. No GLIS3-targeted compound has entered clinical trials, and no gene therapy vector has been validated for this application. The development of small-molecule GLIS3 activators, beta-cell-specific delivery systems, and functional validation in human islet models represents a long-term research direction rather than a near-term clinical reality.

Realizing the potential of precision medicine in diabetes requires intervention at multiple levels simultaneously. The authors propose integrating pharmacogenomic testing into the Ayushman Bharat scheme, which currently covers over 500 million Indians under its public health insurance framework, as the measure with the most immediate and scalable impact on ground-level patient care. Such integration represents a proposed policy direction rather than an implemented practice, and cost-effectiveness analyses and pilot implementation studies would be required before nationwide adoption. Additional priorities include an ICMR-led national pharmacogenomic database representing India’s full ethnic variety, AI-driven clinical decision support systems calibrated to Indian demographic data, and rural awareness campaigns reaching communities where diabetes goes undetected for years.

Artificial intelligence offers a particularly important opportunity for India given the extraordinary genetic diversity of the population and the scale of the diabetes burden. Machine learning models trained on large, diverse genomic datasets can identify patterns of gene-drug interaction that conventional statistical approaches miss, and can integrate genetic data with clinical parameters, laboratory values, medication history, and lifestyle information to generate personalized treatment recommendations in real time. AI-driven polygenic risk score techniques are already showing potential in stratifying patients based on their expected response to metformin, sulfonylureas, and GLP-1 receptor agonists. For Indian patients specifically, developing population-specific polygenic risk scores trained on Indian genomic data rather than applying European-derived scores is an urgent research priority, as the genetic architecture of type 2 diabetes in Indian populations is distinct enough that European polygenic risk scores show reduced predictive accuracy when applied across ethnicities.

The review acknowledges several limitations. As a narrative review, it does not employ systematic review methodology or meta-analysis, so quantitative effect estimates for pharmacogenomic associations could not be derived. Large-scale prospective pharmacogenomic validation studies in South Asian populations remain limited, and many population-specific claims rely on smaller cohort studies or biological plausibility rather than replicated clinical trials. No prospective GLIS3-directed clinical trials or genotype-stratified therapeutic studies have been conducted to date, and the translation of pharmacogenomic testing into national health schemes is presented as a policy proposal rather than an implemented intervention. The generalization of findings from monogenic diabetes to polygenic type 2 diabetes requires cautious interpretation given the differing genetic architectures involved.

Diabetes is not one disease and never was. It is a spectrum of conditions driven by different genes, different mechanisms, and different biological realities, and it demands different treatments. The Delhi girl who spent a decade on unnecessary insulin did not need more medication but one genetic test. The South India cohort that revealed entirely new MODY variants invisible to Western diagnostic criteria did not need better drugs but better tools. The CMC Vellore patients with digenic mutations who failed standard therapy had complex genetic architectures not captured by conventional diagnostic approaches. These are not isolated findings but warnings, repeated every day in clinics across India and the world. For India specifically, the review is a call to action, arguing that the country has the genetic diversity, the disease burden, the scientific talent, and the public health infrastructure to lead the world in precision diabetes medicine, provided political commitment connects these assets into a coordinated national strategy ensuring that a patient in rural Tamil Nadu has the same access to genotype-guided therapy as a patient in a Delhi research hospital.

Subject of Research: Pharmacogenomics and precision medicine in diabetes, focusing on MODY, type 2 diabetes, and GLIS3 gene variants

Article Title: Towards precision medicine in diabetes: pharmacogenomic approaches for MODY and type 2 diabetes with insights from GLIS3 gene variants

Article References: Towards precision medicine in diabetes: pharmacogenomic approaches for MODY and type 2 diabetes with insights from GLIS3 gene variants. (n.d.). https://doi.org/10.3389/fendo.2026.1871527

Image Credits: AI Generated

DOI: 10.3389/fendo.2026.1871527

Keywords: precision medicine, pharmacogenomics, diabetes, MODY, GLIS3, type 2 diabetes, genomics, India, Towards, precision, medicine, pharmacogenomic