Genes and early treatment clues reveal which Graves’ patients will defy standard therapy

Graves’ disease is the most common cause of hyperthyroidism, and for most newly diagnosed patients the first prescription is methimazole, an antithyroid drug that dials down the runaway hormone production of an overactive thyroid. Yet the treatment has a stubborn weakness: roughly half of patients relapse after stopping the medication, and a substantial fraction never reach the point where therapy can be safely withdrawn at all. These patients, whose disease resists standard courses of antithyroid drugs, often end up needing radioactive iodine treatment or thyroid surgery. A new study published in iScience now offers clinicians a way to spot them early, combining genetic markers with the first three months of treatment response to predict who will follow the difficult path before years of ineffective therapy have elapsed.

The research, led by a team at the First Affiliated Hospital with Nanjing Medical University, built on a prospective randomized trial comparing two antithyroid drug strategies. The investigators enrolled 578 treatment-naive adults with Graves’ disease across two cohorts: a development cohort of 216 patients drawn from the randomized trial conducted between 2018 and 2020, and a temporal validation cohort of 208 patients treated between 2021 and 2023. Rather than focusing only on relapse after drug withdrawal, the team adopted a broader definition of refractory Graves’ disease that also captured patients whose thyroid-stimulating hormone receptor antibody, or TRAb, remained positive for more than two years, keeping them ineligible to stop treatment. By this composite measure, 42.6 percent of the development cohort was refractory, with about two-thirds of those cases arising from post-withdrawal recurrence and the remainder from persistent antibody positivity despite ongoing therapy.

The baseline characteristics of refractory patients told part of the story. They were significantly younger at diagnosis, averaging 34 years compared with 39 years among non-refractory patients, and they had larger thyroid glands, higher levels of free thyroxine and free triiodothyronine, and elevated thyroid peroxidase antibody levels. But static measurements taken before treatment began could not fully explain why some patients respond poorly to standardized therapy while others sail through it. The researchers therefore turned to a dynamic dimension: how quickly each patient’s thyroid function and antibody levels responded to a given cumulative dose of medication during the critical first three months of treatment.

To compare responses fairly across two different drug regimens, the team developed a pharmacological dose-correction method that converted levothyroxine doses into equivalent methimazole exposure, using a correction coefficient of −0.12 derived by grid search and validated against observed changes in thyroid hormones. With doses normalized, they computed relative dose-effect values, the percentage change in each laboratory parameter divided by the adjusted cumulative drug dose. Refractory patients showed significantly poorer early responsiveness across the board, with lower dose-effect values for thyroid hormones, thyroid-stimulating hormone, and both major autoantibodies. A machine learning pipeline using least absolute shrinkage and selection operator regression with ten-fold cross-validation distilled the clinical predictors down to eight variables: age, baseline thyroid volume, baseline free thyroxine, baseline thyroid peroxidase antibody, baseline TRAb, and the three-month relative dose-effect values of free triiodothyronine, TRAb, and thyroid peroxidase antibody.

Genetics provided the second pillar. Because Graves’ disease has a well-established hereditary component, and because existing genetic prognostic tools such as the GREAT+ score were built on European populations, the researchers screened 13 susceptibility variants previously reported in East Asian and cross-ethnic genome-wide association studies. Four loci emerged as significantly associated with refractory disease: CD40, HLA-DPB1, LPP, and VANGL2. Each contributes to autoimmune biology through a distinct route. CD40 drives B cell activation and antibody production, potentially shaping the generation of TRAb itself. HLA-DPB1, a major histocompatibility complex class II gene, governs antigen presentation. LPP and VANGL2 influence immune cell migration and signal transduction. The strongest single signal came from the C allele of CD40 rs1883832, which was carried by 78 percent of refractory patients versus 60 percent of non-refractory patients.

Combining the four variants into a weighted candidate SNP risk score produced a clear dose-effect relationship: the proportion of refractory patients climbed steadily across score quartiles, and the score itself carried an odds ratio of 2.7 for refractory disease. When the researchers folded this genetic score into their clinical random forest model, the area under the receiver operating characteristic curve rose from 0.794 to 0.841 in the development cohort, a statistically significant improvement. The integrated model held up under bootstrap resampling and, crucially, in the independent temporal validation cohort, where it achieved an AUC of 0.805 compared with 0.755 for the clinical-only model. Decision curve analysis showed the integrated model delivered greater net clinical benefit across most relevant threshold probabilities, and calibration remained sound in both cohorts.

Perhaps the most clinically actionable finding concerned who benefits most from genetic testing. Risk reclassification analysis showed that the integrated model moved 17.6 percent of development-cohort patients into different risk strata, with the greatest net benefit concentrated in the intermediate-risk group, where 31.2 percent of patients were reclassified and nearly 27 percent moved in the clinically correct direction. The same pattern held in validation. This suggests a practical stepwise strategy: screen all newly diagnosed patients with baseline characteristics and early treatment responses, then reserve SNP testing for those the clinical model places in the ambiguous middle ground, where a genetic result can tip the balance toward earlier definitive therapy or extended medical treatment.

The study also yielded an unexpected insight about treatment duration. A restricted cubic spline analysis revealed a nonlinear relationship between how long patients stayed on antithyroid drugs and their recurrence risk, with a peak hazard around 2.5 years of therapy. Patients treated for two to three years had double the recurrence risk of those treated for two years or less, but those treated beyond three years showed a 69.5 percent lower recurrence risk than the two-to-three-year group, with risk stabilizing after roughly 3.8 years. The finding aligns with prior randomized evidence showing that 60 months of methimazole produced far lower relapse rates than 18 months. The authors caution, however, that treatment duration was not randomized, so residual confounding by disease severity, adherence, and physician decision-making cannot be excluded, and the three-year mark should not yet be treated as a validated rule.

On the question of drug regimens, the results were definitive in one direction. The modified partial block-and-replace regimen, which combines methimazole with levothyroxine throughout therapy, showed no advantage over conventional dose titration in refractory disease rates, relapse, or safety, with adverse events predominantly mild and transient in both arms. The finding reinforces current American Thyroid Association guidance, which recommends methimazole with dose titration for most patients and generally discourages block-and-replace therapy given its added complexity and cost without proven benefit.

The study has limitations the authors acknowledge candidly. It was conducted at a single center with patients of East Asian ancestry, so multicenter validation in broader populations is needed. The TRAb negativity threshold of 1.5 IU/L used in the trial protocol differs from the manufacturer’s diagnostic cutoff, which may affect classification of borderline patients. The dose-correction coefficient lacks external validation, and environmental factors such as iodine intake, smoking intensity, stress, and vitamin D status were not measured. Even so, the work delivers something Graves’ disease management has lacked: a validated, East Asian-adapted framework that unites static baseline risk, dynamic immunobiochemical response, and inherited immune architecture into a single early warning system. For the roughly two in five patients destined for a refractory course, that warning could arrive years before the failure becomes obvious, opening a window for individualized decisions about extended therapy, radioactive iodine, or surgery.

Subject of Research: Prediction of refractory Graves' hyperthyroidism using genetic polymorphisms and early antithyroid drug treatment responses

Article Title: Genetic polymorphisms and early responses predict refractory hyperthyroidism during antithyroid drug therapy

Article References: Wang, X., Gong, M., Wang, J., Wang, Q., Xu, K., Fu, Q., Cai, Y., Wang, Z., Liu, X., Xu, X., Yang, T., & Zheng, X. (2026). Genetic polymorphisms and early responses predict refractory hyperthyroidism during antithyroid drug therapy. iScience, 29(11), Article 117780. https://doi.org/10.1016/j.isci.2026.117780

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117780

Keywords: Graves' disease, hyperthyroidism, antithyroid drugs, methimazole, genetic polymorphisms, SNP risk score, TRAb, random forest model, treatment duration, relapse prediction, precision medicine, East Asian cohort