The First-Ever Investigation of SNP rs119461977 in SECISBP2/SBP2 Gene and its Implications for Hypothyroidism: A Novel Case–Control Research

Abstract

Hypothyroidism is one of the most prevalent endocrine disorders worldwide. Various genes are involved in thyroid hormone production, regulation, and metabolism. SBP2 is required for the adequate assembly of selenoproteins, which are further required for thyroid hormone metabolism. Present case–control study aims to investigate the association of the K438X (rs119461977) variant situated on exon 10 of the gene with biochemical parameters and hypothyroidism. Our study comprises 253 subjects, 136 healthy control (Female 70, Male 66), and 117 case hypothyroid patients (Female 81, Male 36). Biochemical parameters were estimated using an automatic analyzer. PCR-RFLP method was used for genotyping. Heterozygous and homozygous mutant genotypes are higher among the cases as compared to control and shows significant association (p—0.0004). The frequency of minor alleles is significantly higher in cases than in control (p—0.0018). The dominant genetic model shows a two-fold disease risk (χ²—6.8505, OR—2.019, 95%CI 2.2779–1.74118, p value—0.008). Recessive and additive models also suggest a significant association with hypothyroidism (p—0.0005, 0.00066). This study indicates the novel association of rs119461977 of the SBP2 gene with hypothyroidism. This association underlines the importance of selenoproteins synthesis in the pathogenesis of hypothyroidism.

Introduction

Thyroid disorders are among the commonest endocrine disorders, worldwide. According to American Thyroid Association, more than 12% of the American population will develop a thyroid condition in their lifetime and around 20 million people already have some form of thyroid disease. Due to a lack of awareness, nearly 60% of people need to be made aware of their condition. India too bears the burden of thyroid disorders with 42 million people having a form of thyroid disorder [39]. According to a report named Status of Goitre or Thyroid Disorders in India posted on 8th February 2022 by the Ministry of Health and family welfare, the prevalence of self-reported thyroid disorders in National Family Health Survey IV [NFHS IV (2015–2016)] was 2.2% and as the age increased, the reported prevalence amongst the women increased. NFHS V survey for women was conducted in 2019–2020 in different states/cities according to which Kerala shows highest with 8696 cases and Nagaland shows least cases with 505 per 100,000. At the same time, Haryana state possessing 2701 cases per 100,000 women [24].

Genetic and environmental risk factors influence the production of serum Thyroid Stimulating Hormone (TSH), over and under-production of which can cause hyperthyroid and hypothyroid conditions respectively. It has been estimated that although the population reference range for TSH, free thyroxine (fT4), and free triiodothyronine (fT3) is wide, each individual has their own set point for them [3]. Various studies either twin or family-based have been performed to evaluate the genetic contribution to this individual set point. In a study performed on a large U.K. twin cohort the hereditable contribution for serum TSH, Ft4, and fT3 is 65%, 39%, and 23% respectively [29]. There are approximately 10 million single nucleotide variations spread throughout the genome which are very useful to study the genotype–phenotype association studies [27]. Various studies have been conducted to find out the genetic variations which show an association with thyroid hormone concentration. TSH value has been found to be associated with variants of phosphodiesterase 8 B gene (PDE8B) (rs4704397), F-actin-capping subunit protein beta (CAPZB) gene (rs10917469), Thyroid stimulating hormone receptor (TSHR) gene (rs1991517), and DIO1 gene (rs2235544) [4, 14, 28, 30].

Selenocysteine insertion sequence binding protein, which is encoded by the gene SECISBP2 or SBP2 located on chromosome 9(9q22.2), is essential for the synthesis of selenoproteins [31]. This gene is ubiquitously expressed in the testis, thyroid, and 25 other tissues. To metabolize the thyroid hormones, selenoproteins are required such as Iodothyronine deiodinases (DIO1, DIO2, and DIO3) which are well-characterized selenoenzymes among the 25 selenoproteins identified in the human genome.

Several family-based studies have shown altered physiological and biochemical values with subjects having mutations in this particular gene but no study has been conducted to establish the effect of genetic variations of the SBP2/SECISBP2 gene on the metabolism of thyroid hormone among the general population. Through this paper, an attempt has been made to establish the correlation between single nucleotide polymorphism of Selenocysteine insertion sequence (SECIS) binding protein 2 (SECISBP2/SBP2) gene and circulating thyroid hormones and also to find out their association with other biochemical parameters for the first time.

Material and Methods

Subjects

The present study involves 117 clinically proven hypothyroid patients and 136 healthy individuals from various regions of Haryana, a northern state in India with their ages between 18 and 65 years. A pre-informed consent was obtained from all the participants. The research work was conducted according to the guidelines of the Indian Council of Medical Research (ICMR) and was approved by the Institutional Human Ethical Committee (IHEC) of Kurukshetra University, Kurukshetra.

Inclusion criteria: Test subjects were chosen according to their thyroid hormone profile test with serum thyroid stimulating hormone (TSH) value ˃ 5.5 μIU/L. The control group includes persons with a normal range of thyroid hormones, TSH (between 0.35μIU/L and 5.5 μIU/L), and free thyroxine (fT4) (between 5.2 and 12.5 μg/dl).

Exclusion criteria: Subjects without complete information were excluded. Subjects suffering from other metabolic disorders, infectious diseases, pregnant women, and subjects receiving ongoing treatment with drugs that can alter the thyroid hormones were also excluded.

A standard questionnaire was prepared to assess the demographic characteristics of subjects such as age, sex, and consumption habits (diet, smoking, alcohol intake, etc.). BMI was also calculated for all the subjects by dividing weight in (Kg) by height in meter square (m2).

Sample Collection

Blood samples (3–5 ml) were collected in K2 EDTA vials from subjects on an empty stomach under the supervision of a trained technician. Blood serum was used to measure the biochemical parameters. Thyroid profile was estimated by using the chemiluminescence method on an immunoassay analyzer (Maglumi 800). Other biochemical parameters were analyzed using a fully automated biochemistry analyzer (Backman AU 680).

Genomic DNA Isolation

Genomic DNA was isolated from the whole blood by using the salting out method with slight modifications [23]. Isolated genomic DNA was run on 1% agarose gel electrophoresis in a TBE (Tris borate) buffer and then visualized on a UV transilluminator to check the integrity of genomic DNA. The purity of DNA was assessed by checking the absorbance at 260/280 nm. DNA was considered pure and stored at − 20 °C for further use for those samples, whose ratio was between 1.6 and 2.0.

Primer Designing and Optimisation of rs119461977

Primers were designed using Primer 3 web, an online tool, by inserting the target sequence in FASTA format in order to amplify the sequence at SNP 119461977 of the SBP2 gene [18]. A set of primers were selected from the output of software and further used to check the virtual amplified product in UCSC In-Silico PCR online software and verify the results [16]. To get the specific restriction enzyme that would recognize the targeted SNP, GenScript Restriction Enzyme Map Analysis Tools was used. Restriction Mapper version 3 software was used for virtual restriction digestion with Dra1 enzyme and to check the digested products. PCR product with A allele was recognized and cleaved while the final amplified product with mutant allele was unable to be digested by the enzyme. As the PCR product contains two recognition sites for the Dra1 enzyme an extra digested product of 50 bp size was obtained.

PCR and RFLP

Isolated and purified DNA was amplified using PCR. 50–100 ng of DNA was amplified in a 25 μl reaction mixture containing 10 pm μl⁻¹ (0.5 μl) of each of the following primers (forward primer: 5’-TGGCAGATAATGTGGGATTGATT-3) and (reverse primer:5’-ACTGGTTTGGAGGACTGCTT-3’), 11.5 μl of master mix (2X) (GeneDireX) and nuclease-free water to make the final volume 25 ul.PCR was initiated with an initial denaturation temperature of 94 °C for 5 min followed by 35 cycles of amplification(denaturation at 94 °C for 1 min, annealing at 61 °C for 1 min and, extension at 72 °C for 1 min. The process was terminated by a final 10 min extension at 72 °C. PCR products were visualized on stained 1.5% agarose gel which was stained by ethidium bromide. A 100 bp ladder (Genei) was used to ascertain and visualize the PCR product (Fig. 1).

Fig. 1.

PCR products in Lane 2–8 and 100bp ladder in Lane 1

PCR products were digested by Dra 1 restriction enzyme (New England Biolabs) using the RFLP method. For the RFLP reaction mixture was prepared to contain 10 μl of PCR product, 1 μl of restriction enzyme (10 units), and 2 μl of 10X buffer solution, and the final volume was made 30 μl with nuclease-free water. Samples were incubated at 37 °C for 4 h. After digestion bands of digested PCR products were visualized on ethidium bromide-stained 2.5% agarose gel. To confirm the size of bands 100 bp and 50 bp ladders were used.

Statistical Analysis

All the statistical analysis of data was done using IBM SPSS statistic version 22.0 software. For comparison between the two groups a student t test was used for continuous data and the result is presented as mean ± SD. The chi-square test was used for the comparison of groups in the case of categorical data as well as for genotype and allele frequency comparison. Regression analysis was performed to calculate the OR (odd ratio), and CI (confidence interval) and to measure the proportion of variance in the dependent variable accounted by predictors. Statistical significant value is expressed by p value < 0.05.

Result

Demographic Characteristics and Biochemical Parameters Analysis

The demographic characteristics of the studied population are summarized in Table 1. The population includes 117 diseased case and 136 healthy control individuals. The sex of subjects among both groups shows a significant difference with p value < 0.05. There are 69.23% females, 30.76% males, and 51.47% females, 48.52% males in case and control groups respectively. The average age of control subjects were found 43.10 ± 12.43 years and, of hypothyroid patients were 45.97 ± 13.76 years. Individuals were grouped according to their smoking and alcohol consumption status. Consumption habits do not show any significant difference between the control and exposed groups. Workout habits were found to be significantly associated (p < 0.00001) (Table 1).

Table 1.

Comparative analysis of demographic characteristics

Variables	Control	Hypothyroid	χ2 /t value	p value
Age (years)	43.100 ± 12.435	45.975 ± 13.766	1.708	0.089
	M = 42.06 ± 12.523	M = 43.87 ± 14.289	0.572	0.568
	F = 44.15 ± 11.689	F = 47.92 + 13.752	1.800	0.0739
Gender
Female	70 (51.47%)	81 (69.23%)	8.244	0.004*
Male	66 (48.52%)	36 (30.76%)
Smoking status
Never	85 (62.5%)	77 (65.81%)	0.581	0.747
Former	18 (13.23%)	12 (10.26%)
Current	33 (24.26%)	28 (23.93%)
Alcohol intake status
Never	77 (56.62%)	58 (49.57%)	5.0941	0.165
Former	12 (8.82%)	18 (15.38%)
Moderately	26 (19.11%)	29 (24.78%)
Frequently	21 (15.44%)	12 (10.26%)
Exercise
No exercise	23 (16.91%)	51 (43.59%)	26.486	< 0.00001*
Low exercise	49 (36.02%)	23 (19.66%)
Moderate exercise	53 (38.97%)	41 (35.04%)
Intense exercise	11 (8.08%)	2 (1.70%)

Data is represented as percentages

*p < 0.05 is statistically significant

Analysis of different biochemical parameters reveals that levels of parameters of lipid profile such as total cholesterol (TC), Triglycerides (TG), and VLDL cholesterol are higher in the diseased group as compared to control subjects except for HDL cholesterol which is low and shows a significant difference with p value = 0.000. Triglyceride level also shows an increment in the test group but the difference is not significant among both groups (p = 0.101). Serum glutamic-oxaloacetic acid (SGOT) and serum glutamic pyruvic acid (SGPT) values are also significantly different between both the groups (p = 0.000, 0.029) with their values increased among the test subjects. Serum calcium and uric acid values tend to be decreased in the patient biochemical parameters analysis with p values 0.000 and 0.008 respectively. Thyroid function tests reveal a significant increase in the value of Thyroid stimulating hormone (TSH) and decreased level of T3 and T4 hormones in the test group with p value = 0.000. Comparing the mean value of TSH for both the sexes reveal the same result with p values < 0.0001. (Table 2) When divided into different age groups, females show greater incidence of disease as compared to males in almost all the age groups. There is a significant tendency of hypothyroidism incidence with increased age as well with p values 0.0367, and 0.0469 for age groups 21–40 years and 41–60 years respectively. There is slight but not significant change in TSH value with the increased age in both the groups (Table 3).

Table 2.

Comparative analysis of biochemical parameters

Variables	Control	Hypothyroid	t value	p value
BMI (Kg/m2)	22.330 ± 2.641	26.880 ± 3.347	8.507	0.000*
Total cholesterol (mg/dl)	184.845 ± 42.485	218.577 ± 46.431	6.043	0.000*
Triglyceride (mg/dl)	160.560 ± 76.131	176.122 ± 73.660	1.648	0.101
HDL cholesterol (mg/dl)	51.843 ± 9.654	47.828 ± 8.264	3.571	0.000*
VLDL cholesterol (mg/dl)	28.632 ± 10.545	40.450 ± 13.231	7.917	0.000*
SGOT(AST)(IU/L)	28.668 ± 10.859	38.102 ± 16.121	5.384	0.000*
SGPT(ALT)(IU/L)	31.597 ± 16.378	36.003 ± 15.406	2.198	0.029*
Uric acid (mg/dl)	4.883 ± 1.433	4.410 ± 1.354	2.684	0.008*
Serum Calcium (mg/dl)	9.161 ± 0.715	8.477 ± 0.910	6.545	0.000*
T3 (ng/ml)	1.264 ± 0.543	0.932 ± 0.375	5.710	0.000*
T4 (μg/dl)	8.164 ± 2.543	6.340 ± 2.053	6.226	0.000*
TSH (μIU/ml)	2.191 ± 1.185 M = 2.08 ± 1.014 F = 2.31 ± 1.192	12.953 ± 6.364 M = 11.895 ± 5.914 F = 13.779 ± 6.324	18.098 12.111 16.258	0.000*  < 0.0001*  < 0.0001*

Data shown as Mean ± standard deviation

BMI Body mass index, HDL High density lipoprotein, VLDL Very low density lipoprotein, SGOT (AST) Serum glutamic-oxaloacetic transaminase (aspartate aminotransferase), SGPT (ALT) Serum glutamic pyruvic transaminase (alanine transaminase), T3 Triiodothyronine, T4 Thyroxine, TSH Thyroid stimulating hormone

*p < 0.05 statistically significant

A p-value less than 0.05 indicates a statistically significant result, meaning the observed data provide strong evidence against the null hypothesis. Statistically significant values have been kept bold

Table 3.

Comparative analysis according to gender and different age groups

Age groups (years)	Gender	Control	TSH (μIU/ml)	Hypothyroid	TSH (μIU/ml)	Chi square value	p value
< 20	M	7 (5.14%)	1.98 ± 1.023	4 (3.41%)	11.214 ± 4.984	0.625	0.438
	F	9 (6.61%)	2.16 ± 1.154	5 (4.27%)	12.871 ± 5.987
21–40	M	25 (18.38%)	1.93 ± 1.102	15 (12.82%)	11.224 ± 5.326	4.361	0.0367*
	F	32 (23.53%)	2.09 ± 1.302	44 (38.60%)	12.751 ± 6.104
41–60	M	23 (16.91%)	2.18 ± 1.101	12 (10.25%)	12.331 ± 5.987	3.9461	0.0469*
	F	20 (14.70%)	2.40 ± 1.058	26 (22.22%)	13.952 ± 5.947
> 60	M	11 (8.08%)	2.22 ± 1.130	5 (4.27%)	12.672 ± 5.109	0.433	0.610
	F	9 (6.61%)	2.58 ± 1.102	6 (5.12%)	14.582 ± 6.547
Total		136		117

Genotype Comparison Between Both Groups

Restriction digestion of amplified PCR products produced bands of 127 bp, 113 bp, and 50 bp shows AA genotype; bands of 240 bp and 50 bp shows TT genotype while bands of size 240 bp, 127 bp, 113 bp, and 50 bp show AT genotype (Fig. 2).

Fig. 2.

Restriction digestion product, lane 3,5,7: TT genotype(Homozygous for minor allele), lane 2: AA genotype(Homozygous for major allele), lane 4 and 6: AT genotype(heterozygous), lane 1: 100 bp ladder and lane 8: 50 bp ladder

It was observed that the frequency of the AA genotype is higher in the control group (45.59%) as compared to the case (29.91%). The frequency of AT and TT genotypes is higher among the cases, 59.82% and 10.25% respectively as compared to the control where the frequency of AT genotype is 54.41% and of the TT genotype is 0% (Table 4).

Table 4.

Genotype and allele distribution

Variant	Model		Control n (%)	Cases n (%)	χ2	OR	95% CI	p value
rs119461977	Genotype	AA AT TT	62 (45.59) 74 (54.44) 0 ()	35 (29.91) 70 (59.82) 12 (10.25)	15.611			0.0004*
	Allele	A T	198 (72.79) 74 (27.20)	140 (59.82) 94 (40.17)	9.6674	1.792	1.9807–1.6037	0.0018*
	Additive model	AT AA	74 62	70 35	3.7002			0.544
		TT AA	0 62	35 12	11.698			0.0006*
	Dominant model	AA AT + TT	62 (45.58) 74 (54.41)	35 (29.91) 82 (70.08)	6.8505	2.019	2.2779–1.74118	0.008*
	Recessive model	TT AA + AT	0 136	12 105	11.7936			0.0005*
	Heterozygous model	AT AA + TT	74 62	70 47	0.7527	0.801	1.3018–0.3008	0.385

OR Odd ratio, CI Confidence interval

*p < 0.05 is statistically significant

A p-value less than 0.05 indicates a statistically significant result, meaning the observed data provide strong evidence against the null hypothesis. Statistically significant values have been kept bold

Association of SBP2 Gene Variant rs119461977 with Hypothyroidism

Genotype distributions under different genetic models were analyzed using logistic regression to find the association of SBP2 variant rs119461977 with hypothyroidism risk. Under the dominant genetic model, T allele was assumed to increase the risk of disease. It was observed that AT and TT genotypes under this model were significantly correlated with the hypothyroid condition and assuming T allele as a risk factor, a two-fold risk for the disease was observed(χ²—6.8505, OR—2.019, 95%CI 2.2779–1.74118, p value—0.008). While Additive and recessive genetic models show significant association (p values 0.0006 and 0.0005 respectively), the Heterozygous genetic model did not show significant association (p value 0.385) (Table 4).

Comparative Analysis of rs119461977 Genotype with Clinical Parameters in Control and Disease Samples under Dominant Genetic Model

Biochemical parameters were compared between control and disease subjects using the dominant genetic model (AA vs AT + TT). BMI is substantially different among both control and hypothyroid groups in the AT + TT genotype compared to the AA genotype (p-value: 0.034, 0.036). There is a statistically notable difference in hypothyroid subjects having AA + AT genotype with a p-value of 0.022 regarding TSH level. The value of total cholesterol, triglycerides, and VLDL is slightly elevated in the AT + TT genotype group in both control and case subjects as compared to the AA genotype, but not significantly different (p > 0.05). The rest of the biochemical parameters also do not show any significant difference in AA vs AT + TT comparison (Table 5).

Table 5.

Inter genotypic comparison of control and hypothyroid subjects

Clinical parameters	Control		p value	Hypothyroid		p value
	AA	AT + TT		AA	AT + TT
BMI	22.062 ± 2.507	25.672 ± 2.810	0.034*	26.184 ± 2.805	28.6875 ± 3.934	0.036*
Total cholesterol	182.99 ± 44.515	186.454 ± 40.967	0.643	216.647 ± 48.195	222.943 ± 41.237	0.481
Triglyceride	152.253 ± 84.610	167.180 ± 68.895	0.272	168.877 ± 72.603	192.436 ± 74.867	0.127
HDL cholesterol	48.358 ± 8.730	47.698 ± 8.223	0.654	52.752 ± 10.114	49.517 ± 7.847	0.069
VLDL cholesterol	26.985 ± 8.687	29.842 ± 11.629	0.102	39.216 ± 14.207	40.935 ± 12.884	0.530
SGOT(AST)	27.995 ± 10.271	28.951 ± 11.257	0.605	38.784 ± 16.283	38.443 ± 16.450	0.919
SGPT(ALT)	30.745 ± 14.439	32.023 ± 17.595	0.641	35.610 ± 15.369	36.415 ± 15.604	0.801
Uric acid	5.120 ± 1.341	5.219 ± 1.377	0.675	4.575 ± 1.281	4.341 ± 1.376	0.389
Serum Calcium	9.314 ± 0.640	9.103 ± 0.797	0.105	8.456 ± 1.007	8.479 ± 0.864	0.903
T3	1.269 ± 0.561	1.260 ± 0.534	0.929	1.001 ± 0.507	0.906 ± 0.308	0.219
T4	8.196 ± 2.777	8.141 ± 2.379	0.904	6.461 ± 1.963	6.028 ± 2.270	0.305
TSH	2.009 ± 1.189	2.323 ± 1.173	0.130	11.462 ± 4.704	13.998 ± 6.602	0.022*

A p-value less than 0.05 indicates a statistically significant result, meaning the observed data provide strong evidence against the null hypothesis. Statistically significant values have been kept bold

Discussion

Selenoproteins, Iodothyronine deiodinases are involved in the metabolism of the thyroid hormone axis, and selenocysteine insertion sequence binding protein 2 (SECISBP2 or SBP2) is essentially required for their synthesis as they contain an unusual amino acid selenocysteine at their active site [31]. Selenocysteine is encoded by a UGA codon which is otherwise a stop codon [25]. Selenoprotein genes usually code for a single selenocysteine residue within the protein's peptide sequence and comprise a single SECIS element situated in the 3′-UTR region [7]. For the binding of elongation factor EFsec on SECIS loop an auxiliary protein factor is required called SECIS-binding protein 2 coded by the gene SECISBP2 or SBP2 [9]. Although some family-based studies have been conducted to investigate the association of the SBP2 gene mutations with altered thyroid hormones [5, 10, 11, 13] yet there is not a single study that shows the association of genetic variation of this gene with thyroid disorders among the general population (Fig. 3).

Fig. 3.

Figure 3. Diagram showing the key players and the insertion of selenocysteine (SeCys). SeCys-specific codon is called UGA. SECIS binding protein 2 (SBP2) binds to the SECIS element and is associated with ribosomes. The SeCys-specific translation elongation factor (eEFSeCys) promotes the incorporation of SeCys into the growing polypeptide by interacting with SeCys-tRNA. Reprinted from “Selenium: An Element of Life Essential for Thyroid Function,” by Francesca Gorini, Laura Sabatino, Alessandro Pingitore, and Cristina Vassalle. 2021. Molecules 26(23), 7084. Copyright (2021) by MDPI

The present study tries to establish the association of rs119461977 variant of the SBP2 gene with biochemical parameters and hypothyroidism. Results of our study support that the polymorphic variant rs119461977 of the SBP2 gene is likely a susceptible variant to hypothyroidism in the Indian population.

Although no such study has been conducted, but a mutation, K438X of the SBP2 gene on exon 10 was identified in a family of Irish origin, leading to early truncation [11]. The child inherited this mutation showed abnormal TFT values which are in accordance with our study.

Even though the study performed by Dumitrescu [11] is the only one that shows altered thyroid hormone values due to the mutation at the same position of the rs119461977 variant on the SBP2 gene, several other family-based studies were conducted to investigate the mutations of this gene and their impact on thyroid hormones. Two more mutations were detected, a missense mutation in exon 12, resulting in the amino acid substitution R540Q and IVS8ds + 29G-Aon intron 8 resulting in partial alternative splicing and abnormal transcript [11]. Another mutation in the SBP2 gene was identified in a family of African origin. The patient was homozygous for an R128X mutation [10].

Researchers from Brazil reported a syndrome of abnormal thyroid metabolism, delayed bone maturation, congenital myopathy, and impaired mental and motor coordination development on a 12-year-old girl which was a compound heterozygous defect in SBP2 [5]. Recently two additional families with 4 novel compound heterozygous mutations, c.2037G > T, c.589C > T, c.2045_2048delAACA, and c.2344C > T in the SBP2 gene were reported [13]. Above Studied Mutations [5, 10, 11, 13] in the SBP2 gene show elevated TSH levels probably due to combined reduced enzymatic activities of deiodinases similar to our study where TSH is significantly elevated among population having polymorphic variant (TSH 13.998 ± 6.602, P 0.022).BMI values were also found to be significantly higher among AA + AT genotypes in control as well as diseased subjects (p—0.034, p—0.036). Various family based studies have been performed to verify the associations of SBP2 mutations, but to our knowledge, this is the first population based report of an association between hypothyroidism and the SBP2 variant.

Decreased DIO2 activity was observed due to mutation in the SBP2 gene because it is epistatic to selenoproteins synthesis [11]. Type 2 deiodinase regulates thyroid hormones by converting T4 into T3. This leads to a negative feedback loop that inhibits the TSH in the pituitary [12]. Decreased activity of DIO2 results in an increment of TSH and hence the hypothyroid condition.

Decreased thyroid function is accompanied by low activity of LDL receptor which leads to decreased catabolism of LDL. Lipoprotein lipase (LPL) is also affected by thyroid hormone thus hypothyroid condition slows down the activity of LPL which results in decreased clearance of triglyceride-rich lipoproteins [2]. Therefore hypothyroidism can elevate the TG and VLDL levels [26]. Our results also reflect increased values of biochemical parameters of lipid profile except for HDL. However, there is a significant difference between the control and hypothyroid group with respect to their lipid profile showing dyslipidemia with high levels of total cholesterol, triglycerides, and VLDL while low level of HDL in hypothyroid patients. This result is quite similar to the results of studies performed by Marwaha and Tayal [22, 38]. Results of HDL values are contrary to a few studies while some have similar results. The study performed by Pearce [32] shows an elevated level of HDL, and a low level of HDL was observed among hypothyroid patients by Abdel-Gayoum [1] which is similar to the result of present study.

Thyroid hormones play a prominent role in regulation of basal metabolism, glucose metabolism and changes in body weight. Hypothyroidism has been found to be resulting in decreased metabolic, higher body mass index (BMI) and higher prevalence of obesity [33]. Present study is in concordance with these observations as a significant directly proportional trend can be seen between BMI and TSH when comparing control and hypothyroid group (p- 0.000) which is similar to the study conducted by Knudsen and Solanki [17, 37].

Further thyroid disorders have been found to be linked with non-alcoholic fatty liver disease (NAFLD), a lipid accumulating condition in liver resembling to the alcohol induced injury but occur in patients who do not consume alcohol which results in abnormal liver hormones [40]. Results of our study show significantly altered liver hormones (p—0.000, 0.029) which are consistent with the results of studies performed by chung and Ludwig [8, 20].

Renal functions, bone and purine metabolism is also influenced by thyroid hormones. Present study reveals a significantly lower level of serum calcium among hypothyroid patients. This finding is similar to the work of authors who find mean serum level of calcium higher in hyperthyroidism and low in hypothyroidism Compared to controls [36]. Previous studies conducted on hypothyroid patients have conflicting results with some of them showing decreased and rest of them showing normal level of serum calcium [21, 34]. This controversy indicates the complexity in cellular and hormonal mechanism involved in calcium metabolism which might be disturbed in thyroid dysfunction. A significant increase in uric acid is also observed in our study (p—0.008) which correlate well with studies conducted by Saini and Kaur [15, 35] which also show that mean uric acid level was high in hypothyroid subject when compared to control group.

In demographic characteristics, we can see the association of physical exercise with hypothyroidism and it has been evaluated by Bansal [6] also that regular physical exercise is helpful to improve thyroid status among hypothyroid patients.

In summary, SNP rs119461977 of SBP2 gene is found to be increasing the risk for predisposition to hypothyroidism in individuals harbouring the variant allele. The revelations of altered level of different biochemical parameters in hypothyroid subjects points towards the importance of assessment and management of them when dealing with thyroid dysfunctions and the condition can be made better by involving in regular physical activity. These results might be important to identify the individual who are at a risk of developing the hypothyroid condition.

Conclusion

This study reveals a novel finding as A/T SNP at exon 10 of the SBP2 gene is found to be associated with hypothyroidism in the Indian population with its variant allele affecting TSH level in cases. The significant change due to K438X variation is most probably due to its decreased activity which might lead to the low level of production of selenoproteins such as DIO1 and DIO2, which are important for the adequate production and regulation of thyroid hormones. This finding can open up new research strategies for the analysis and management of altered thyroid hormones and their pathogenesis. Sequencing of this variant may help in predicting the predisposition to hypothyroidism. This is the first study to elucidate this result, more studies can be performed to verify the results and make a more precise conclusion.

Author Contributions

Manpreet Kaur: Conceptualization, Methodology, data collection and analysis, writing of original draft for publication. Shama Tyagi: Methodology. Anita Yadav: Conceptualization, Methodology, resources, data analysis. Ranjan Gupta: Conceptualization, Methodology, resources, data analysis and editing of original draft. All authors have read and approved the final draft.

Funding

This study was funded by Department of Science and Technology, Haryana as JRF, Haryana State Council for Science Innovation and Technology (HSCSIT).

Declarations

Conflict of interest

Any potential conflict of interest is not reported by author.

Ethical Approval

The research protocol was approved by institutional Human Ethical Committee Kurukshetra University, Kurukshetra, Haryana.

Consent to Participation

All the participants were informed about the study and an informed consent was obtained from them.

Consent for Publication

A written consent form was provided by all the participants to publish the data.