Background: Thyroidectomized patients need variable doses of levothyroxine (LT₄) to obtain target thyroid-stimulating hormone (TSH) levels. Individual feedback set-points have been hypothesized and the influence of several genes in the regulation of the pituitary-thyroid axis has been demonstrated. Objectives: We hypothesized that genetic variants of the TRHR gene could be associated with a different hypothalamo-pituitary sensitivity to thyroid hormone feedback. Methods: We retrospectively analyzed 84 thyroidectomized patients with no residual thyroid function and undetectable thyroglobulin levels. Patients were evaluated under LT₄ resulting in TSH levels detectable but <0.5 μIU/ml. The two SNPs rs3134105 and rs3110040 were identified as informative markers of the TRHR gene. Genotyping was performed using high-resolution melting technology. Genotype distribution was compared between the patients and 99 euthyroid controls. Results: The selected SNPs were in linkage disequilibrium and only rs3134105 was further considered. A significant difference between the three possible genotypes for rs3134105 was found for TSH (p = 0.04) and free thyroxine (fT₄)/TSH ratio (p = 0.02). Moreover, despite similar serum concentrations of free triiodothyronine (fT₃) and fT₄, carriers of at least one A allele of rs3134105 had significantly lower serum TSH levels (p = 0.01) as well as higher fT₃/TSH (p = 0.01) and fT₄/TSH ratios (p < 0.01). Conclusions: We demonstrated an association between serum TSH levels and discrete alleles of the TRHR gene in totally thyroidectomized patients under LT₄ therapy. Therefore, the TRHR gene seems to be a determinant of hypothalamo-pituitary sensitivity to LT₄.

To identify a peculiar genetic combination predisposing to differentiated thyroid carcinoma (DTC), we selected a set of single nucleotide polymorphisms (SNPs) associated with DTC risk, considering polygenic risk score (PRS), Bayesian statistics and a machine learning (ML) classifier to describe cases and controls in three different datasets. Dataset 1 (649 DTC, 431 controls) has been previously genotyped in a genome-wide association study (GWAS) on Italian DTC. Dataset 2 (234 DTC, 101 controls) and dataset 3 (404 DTC, 392 controls) were genotyped. Associations of 171 SNPs reported to predispose to DTC in candidate studies were extracted from the GWAS of dataset 1, followed by replication of SNPs associated with DTC risk (P < 0.05) in dataset 2. The reliability of the identified SNPs was confirmed by PRS and Bayesian statistics after merging the three datasets. SNPs were used to describe the case/control state of individuals by ML classifier. Starting from 171 SNPs associated with DTC, 15 were positive in both datasets 1 and 2. Using these markers, PRS revealed that individuals in the fifth quintile had a seven-fold increased risk of DTC than those in the first. Bayesian inference confirmed that the selected 15 SNPs differentiate cases from controls. Results were corroborated by ML, finding a maximum AUC of about 0.7. A restricted selection of only 15 DTC-associated SNPs is able to describe the inner genetic structure of Italian individuals, and ML allows a fair prediction of case or control status based solely on the individual genetic background.