Thyroid hormones play a crucial role in metabolism (Mulluret al., 2014). Disruptions in their secretion can lead to Metabolic Syndrome (MetS), characterized by insulin resistance, hypertension, hyperglycemia and abnormal cholesterol levels (Fahedet al., 2022).

In the Western world, Hypothyroidism (HT) affects about 5% of its population, with another 5% potentially undiagnosed (Chiovatoet al., 2019; Garmendiaet al., 2014). In India, HT is the most prevalent thyroid disorder, affecting 10.9% of adults (Unnikrishnanet al., 2013; (Kumaret al., 2022).

HT is related to MetS, including dyslipidemia, hypertension, obesity and insulin resistance (Shaji and Joel, 2022; Mavromati and Jornayvaz, 2021). Studies indicate that 95% of newly detected hypothyroid cases exhibit elevated cholesterol levels, with 5% presenting with hypertriglyceridemia (Gluvicet al., 2015).

HT can present with vague and nonspecific symptoms that often resemble those of other conditions, making diagnosis difficult. Weight gain results from decreased fat burning, while cold intolerance stems from reduced heat production. This affects the Quality of Life (QOL) (Chakeret al., 2017).

The primary treatment for HT is levothyroxine, with dosage adjusted based on TSH levels and clinical symptoms (Razviet al., 2020). Special considerations are required for pregnant women, central hypothyroidism and those with alternative treatments such as liothyronine or supplements (Lee and Pearce, 2022; Jonklaaset al., 2014). Regular monitoring and dosage adjustments are essential to prevent drug-related problems.

Patients with HT experience poor QOL, particularly in the physical dimensions compared to social dimensions (Ghamret al., 2022). Physicians should regularly assess hypothyroid patients’ QOL and prioritize improving it as part of their management plan (Shivaprasadet al., 2018). With this background, the study assess the impact of hypothyroidism on metabolic components and QOL.

The study is part of a long-term study conducted in the Department of General Medicine of a tertiary care hospital, which analyses the impact of clinical pharmacy services in hypothyroid patients with MetS.

The sample size determination was based on a 5% level of significance, 80% power, a mean difference of 1.3 and an effect size of 0.435. The required sample size was calculated as 200, using nMaster software, version 2.0.

The Central Ethics Committee, Nitte (Deemed to be University), approved the study (Ref. No: NU/CEC/2022/317), prior to initiation. The investigator explained the study to the participants and obtained their written informed consent. Participants who gave voluntary consent were enrolled in the study. Privacy and confidentiality were maintained throughout the study.

The study included patients aged 18 years and above diagnosed with hypothyroidism with metabolic syndrome. MetS was determined using the modified NCEP ATP III criteria (2005 revision) (National Cholesterol Education Program (NCEP), 2002). Both Inpatients and Outpatients were included in the study.

Pediatric patients, Pregnant women, Patients who had undergone thyroid surgery, Type I Diabetic Mellitus, Psychiatric illness and Cancer patients were excluded from the study.

Based on the inclusion and exclusion criteria, eligible patients were enrolled randomly after obtaining written consent.

As part of their regular checkup, the patients gave their blood samples to the laboratory following the consultation with their physician. A qualified medical laboratory technician collects the blood sample and analyzes it for Thyroid function tests (TSH, T3, T4) and metabolic components (FBS, TG and HDL). A consultant pathologist verified the laboratory reports. The researcher collected the study subjects’ laboratory data and documented it for data analysis.

As per Modified NCEP ATP III (2005 revision) MetS is determined as the presence of three or more of the following five criteria: WC above 40 inches (men) or 35 inches (women), BP above 130/85 mmHg or on treatment, fasting TG above 150 mg/dL or on treatment, fasting HDL cholesterol levels less than 40 mg/dL (men) or 50 mg/dL (women) and FBS above 100 mg/dl or on treatment.

The BP, WC and BMI were assessed as a part of the regular checkup. The data were documented by the researcher for analysis.

The QOL was assessed using the WHOQOL BREF, a short version of the WHOQOL questionnaire. The questionnaire was translated from English to regional languages (Kannada and Malayalam). The researchers requested the study participants to complete the WHOQOL BREF Questionnaire within 30 min and the responses were collected and documented. The scores were converted to the WHOQOL questionnaire format and analyzed.

The collected data were documented using Microsoft Excel 2021 and statistically analyzed with SPSS version 29. The categorical variables were presented as frequency and percentage. Mean and standard deviation were used to illustrate the thyroid hormones, metabolic components and quality of life. The relationship between TSH with Metabolic Components and QOL was analyzed using Spearman’s Correlation. Multiple linear Regressions was used to analyze the effect of TSH on Metabolic Components and Quality of Life.

Among 200 study subjects, the majority of the patients were females. The mean age of the subjects were 51.93+10.09. The majority of the subjects (60.5%) were overweight. Based on the level of education, 58.5% had completed primary school, 19% middle school, 19.5% secondary school and 3% were graduates. Among the study subjects, Diabetes mellitus was present in 23.5% of the patients, hypertension in 21.5% and dyslipidemia in 10.5% of the patients. Moreover, 25.5% of patients have both diabetes mellitus and hypertension, 7% have diabetes mellitus and dyslipidemia, 6.5% have hypertension and dyslipidemia and 5.5% have all three disease conditions. Based on socioeconomic status, 33% of the patients were in the upper-lower class, 41% in the lower-middle class and 26% in the upper-middle class (Table 1).

In the study subjects, the mean TSH, T3 and T4 were 13.59+32.69, 1.65+6.88 and 8.42+9.15, respectively. Among the metabolic components, the mean BMI, WC, SBP and DBP, TG, HDL and FBS were 25.75±3.36 kg/m², 94.32±5.22 cm, 139.70±10.53 mmHg, 88.07±6.88 mmHg, 159.20±57.04 mg/dL, 40.83±8.1 mg/dL, 139.26±43.72 mg/dL respectively.

Across the four domains of physical, psychological, social and environmental QOL, the mean scores were 48.34±12.84, 51.46±10.58, 49.78±17.00 and 49.29±12.63, respectively (Table 2), (Figure 1).

Figure 1:
Levels of Thyroid Hormones, Metabolic Components and Quality of Life Metabolic Components and Quality of Life.

A significant weak positive correlation (R: 0.070, p: 0.02) was observed between TSH and BMI. This suggests that as the TSH levels increase, BMI also increases, indicates a direct potential relationship between TSH and BMI.

The linear regression analysis shows a weak, significant positive correlation between TSH and BMI (R: 0.070, p: 0.02). The R² value is 0.005, indicates that the 0.5% variation in TSH is contributed by the BMI. The F value is 0.948 and the p-value is 0.33, shows that the regression model is not significant. The beta coefficient for BMI indicates that as BMI increases, TSH increases by 0.010 units; however, this effect is not statistically significant (p: 0.331).

A weak positive association was observed between TSH and WC (R: 0.068) but not statistically significant (p: 0.86). TSH may not be a direct factor affecting WC in this specific population.

The linear regression analysis shows a weak positive correlation between TSH and WC (R: 0.068) but not significant (p: 0.86). The R² value of 0.005 implies that the WC contributes to the 0.5% variation in TSH. The F-value of 0.922 and the p-value of 0.338 shows that the regression model is not significant. While the beta coefficient for WC suggests a potential increase in TSH by 0.006 units with increasing WC, but this trend is not statistically significant (p=0.338).

A significant positive association was found between TSH and SBP (R: 0.187, p: 0.008). This suggests that as the TSH increases, SBP tends to increase, indicating a potential association between TSH levels and SBP.

The R² value of 0.040 implies that SBP contributes to 4% of the variation in TSH. The F value of 8.064 and the p-value of 0.005 imply that the regression model is statistically significant. The beta coefficient for SBP suggests that SBP increases, TSH increases by 0.009 units and this effect is statistically significant.

TSH is weakly correlated with DBP (R: 0.082) but was not statistically significant (p: 0.28). The findings suggest that TSH levels do not significantly influence DBP.

The linear regression analysis shows a very weak, non-significant correlation between DBP and TSH (R: 0.082, p: 0.28). The R² value of 0.007 implies that DBP contributes to 0.7% of the variation in TSH. The F value of 1.409 and the p-value of 0.237 indicated >that the regression model is not statistically significant. The beta coefficient for DBP suggests that as DBP increases, TSH increases by 0.006 units; but this effect is not statistically significant.

A weak positive association was found between TSH and TG (R: 0.154), but the correlation was not significant (p: 0.030). There may be an association between TSH and TG, but it is not strong enough to be considered a reliable predictor of TG based on TSH.

The R² value of 0.024 implies that TG contributes to 2.4% of the variation in TSH. The regression model is statistically significant, as indicated by the F value of 4.750 and the p-value of 0.030. The beta coefficient for TG suggests that as TG increase, TSH increases by 0.001 units, which is statistically significant.

A positive correlation was obtained between TSH and FBS (R: 0.136) but not statistically significant (p: 0.05). The observation suggests that while there is a slight tendency for FBS to increase as TSH increases, the relationship is very weak.

The R² value of 0.018 implies that FBS contributes to 1.8% of the variation in TSH. The F value of 3.677 and the p-value of 0.05 indicated that the regression model is statistically significant. The beta coefficient for FBS suggests that as FBS increases, TSH increases by 0.001 units.

A significant negative association was found between TSH and HDL levels (R:-0.140, p: 0.04). This suggests there is an inverse relationship between TSH and HDL

The R² value of 0.020 implies that HDL contributes to 2.0% of the variation in TSH. The regression model is statistically significant, as indicated by the F value of 3.933 and the p-value of 0.04. The beta coefficient for HDL suggests that as HDL increases, TSH decreases by 0.008 units.

A negative significant correlation was observed between TSH and Physical Domain of QOL (R: -0.187 p:0.008). This suggests an inverse relationship between TSH and Physical QOL domain. When the TSH levels increase, hypothyroid symptoms intensify and the patients become weaker physically.

The R² value of 0.035 implies that the physical domain of quality of life contributes to 3.5% of the variation in TSH. The regression model is statistically significant, as indicated by the F value of 7.141 and the p-value of 0.008. The beta coefficient for the physical domain of QOL suggests that as the physical domain of QOL increases, TSH decreases by 0.007 units.

A weak positive correlation was observed between TSH and psychological Domain (R: 0.055) but not statistically significant (p: 0.172). Even though there is a weak positive relationship between TSH and the psychological domain, the data are not sufficient to substantiate a strong significant association between TSH and the psychological domain of QOL.

The linear regression analysis shows a very weak positive correlation between the psychological domain of QOL and TSH (R: 0.055), but not statistically significant (p: 0.443). The R² value of 0.003 implies that the psychological domain of quality of life contributes 0.3% of the variation in TSH. The F value is 0.591 and the p-value is 0.443 implies that the regression model is not significant.

A weak positive correlation coefficient exists between TSH and the social domain of QOL (R: 0.002) but is not statistically significant (p: 0.98).

The regression analysis shows an extremely weak correlation between the social domain of quality of life and TSH levels (R: 0.002) but not statistically significant (p: 0.980). The F value of 0.001 and p-value of 0.980 imply that the regression model is not significant. The beta coefficient for the social domain of QOL suggests that the social domain of QOL increases, as the TSH decreases by 1.98 units.

The correlation analysis of TSH and the Environmental Domain of QOL showed a very weak positive correlation (R: 0.048) but not statistically significant (p=0.49) (Table 3), (Figure 2).

Figure 2:
Association of TSH with Metabolic Components and Quality of Life.

The regression analysis shows a weak positive correlation between the environmental domain of QOL and TSH levels (R: 0.058) but is not statistically significant (p: 0.412). The R² value of 0.003 implies that the environmental domain of QOL contributes 0.3% of the variation in TSH. The F value of 0.676 and p-value of 0.412 indicate that the regression model is not significant. The beta coefficient for the environmental domain of QOL suggests that as the environmental domain of QOL increases, TSH decreases by 0.001 units (Table 4, Figures 3 and 4).

Figure 3:
Regression Standardized Residual.

Figure 4:
Normal P-Plot of Regression Standardized Residual.

Thyroid hormones play a critical role in regulating metabolism. Since thyroid hormones have an important role in metabolism, HT increases the risk for metabolic syndrome (Iwenet al., 2013).

In this study, TSH was found to have a significant positive correlation with BMI (R: 0.07, p: 0.02), SBP (R: 0.187, p: 0.008), TG (R: 0.154, p: 0.03) and FBS (R: 0.136, p: 0.05) and a negative significant correlation with HDL (R: -0.140, p: 0.04). These findings were similar to the study conducted by Khatiwada S et al. and Bensenor IM et al., where a positive correlation was observed for DBP, blood glucose and TG with TSH. However, HDL and TSH had a negative correlation (Khatiwadaet al., 2016; Bensenñor et al., 2015). Similarly, a negative correlation between SBP and WC with TSH was observed in their study, but this correlation was not significant, which contradicts the findings of the present study. In contrast to the findings of this study, Huang CY et al. found no correlation between serum TSH levels and MetS, but strong correlations with high serum T3 levels and no clear correlations with low serum T4 (Huang and Hwang, 2016).

The current study found that females are at a greater risk of developing metabolic syndrome (76%). The findings can be compared with the study conducted by He J et al., where metabolic syndrome is more prevalent in women with hypothyroidism than in men (Heet al., 2021).

Regarding QOL, hypothyroid patients with MetS experience poor QOL, with the psychological domain better than the physical, social and environmental domains. A negative correlation was observed between TSH and the physical domain of QOL, indicating that higher TSH levels are associated with a decline in QOL. These findings are comparable to studies by Ghamri R et al. and Kelderman-Bolk et al., which also observed a decline in QOL with increasing TSH (Ghamret al., 2022; Kelderman-Bolket al., 2015).

The current study provides comprehensive insights into the impact of HT on metabolic diseases and overall QOL. Through a multifaceted analysis, the research highlights the association of hypothyroidism with metabolic components and QOL.

Hypothyroidism is associated with BMI, WC, BP and TG; however, it has a negative relationship with HDL. Hypothyroidism patients with metabolic syndrome have a poor quality of life, particularly in the physical domain. Screening of MetS in hypothyroid patients is important for the early detection and prevention of disease-related complications.

We would like to thank the faculties of Department of Pharmacy Practice, NGSM Institute of Pharmaceutical Sciences and Department of General Medicine, KS Hegde Medical Academy, Justice K S Hegde Charitable Hospital for their valuable guidance throughout the research process. The authors would like to express their sincere gratitude to Nitte (Deemed to be University), Mangaluru, for providing the necessary facilities to carry out the research work.