One of the commonest endocrine diseases throughout the world is thyroid disease. This affects about 5-10% of all adults with a predominance of females and older people. There are both systemic effects of hypothyroidism and hyperthyroidism that are not limited to metabolic processes and which affect cardiovascular, neuromuscular and renal systems as well. The hormones of thyroid are important for cellular metabolism, cardiovascular system and renal blood flow; therefore, alterations in the function of the gland can change renal physiology significantly^1-2.

There exists a multifaceted reciprocal physiology between the thyroid and kidneys. The thyroid hormones are known to control the growth and development of kidneys, renal blood flow, glomerular filtration rate (GFR), secretion and absorption of fluids, electrolytes, and the renin-angiotensin-aldosterone system (RAAS). On the other hand, the kidneys are responsible for the breakdown and elimination of the thyroid hormones and iodine. As a result, any abnormality in either organ negatively affects the functioning of the other^3-4.

Hypothyroidism has been shown to be linked with low cardiac output, high peripheral vascular resistance, and low renal perfusion, causing low renal plasma flow and low GFR. Physiological changes such as these often result in high serum creatinine levels, low eGFR, poor free water excretion, and electrolyte imbalance, especially hyponatremia. What is important to mention is that a lot of these changes can be reversed once euthyroidism has been restored. This is proof that hypothyroidism causes functional but not structural changes in renal physiology^5-6.

On the other hand, hyperthyroidism leads to a hyperdynamic cardiovascular state where there is an increase in cardiac output, increased renal blood flow, and GFR. These factors often lead to reduced serum levels of creatinine and can, therefore, hide any form of kidney damage. Chronic thyrotoxicosis may also have effects on electrolyte balance, protein metabolism, and intraglomerular blood flow. The emerging scientific data indicates a strong correlation between thyroid disorders and chronic kidney disease (CKD). The high levels of TSH, especially in cases of overt and subclinical hypothyroidism, were shown to correlate with the decrease in eGFR, increase in serum creatinine, presence of proteinuria, and the development of CKD. Moreover, some scientific evidence suggests that the degree of thyroid dysfunction is positively correlated with the degree of renal impairment, thus implicating the progressive hormonal disorder in the progression of renal insufficiency. The exact pathophysiological mechanisms behind this phenomenon still require clarification⁷.

While there is an increased realization of the thyroid-kidney axis, there is a significant amount of heterogeneity in terms of the characteristics of the subjects studied, criteria for diagnosis, types of thyroid dysfunction studied, and methods used in assessing the renal function. Besides, most of the literature that is currently available has been generated by research carried out in developed nations, while data from South Asian communities are limited. Very few studies have examined the association between the degree of thyroid dysfunction and renal function in terms of serum creatinine and eGFR in subjects attending tertiary care facilities.

Hence, the current study was designed to identify the relation between the degree of thyroid dysfunctions and renal functions in adults. The current study will be able to provide an insight into the relation between the two that may help in early diagnosis of the disease along with optimal treatment of the patient.

Study Design This unicentric analytical cross-sectional analysis was carried out at the department of otorhinolaryngology Lady Reading Hospital Peshawar for a period of 6 months from 1st January 2026 till 30th June 2026, to determine the relationship between the severity of thyroid function and renal function among adult patients. All the patients were selected based on non-probability consecutive sampling techniques. Ethical approval was given by the Institutional Research and Ethics Committee of [Hospital Name] prior to the study (Approval No. [Reference Number]). All participants gave written informed consent prior to enrollment. The confidentiality of the participants was ensured by using unique identification codes and the data were analyzed anonymously following the principles of the Declaration of Helsinki. Sample Size The sample size was calculated using the Open-EPI sample size calculator for correlation studies at a 95% confidence level and 80% study power, considering the expected correlation reported in previous studies at 10%9. A minimum sample of 140 participants was required. To compensate for incomplete records or missing data, additional participants were enrolled where feasible. Statistical details are shown below. Population size (for finite population correction factor or fpc) (N): 1000000 Hypothesized % frequency of outcome factors in the population (p): 10%+/-5 Confidence limits as % of 100(absolute +/- %) (d): 5% Design effect (for cluster surveys-DEFF): 1 Sample Size(n) for Various Confidence Levels 140 Inclusion criteria  Patients above 18 years old.  Patients with biochemical signs of thyroid dysregulation (thyroid function tests such as TSH, FT3, FT4).  Patients who are willing to take part in the research and have signed the consent form. Exclusion criteria  Patients with history of chronic kidney disease (CKD stage 3 or higher).  Patients undergoing dialysis or renal transplantation.  Pregnant patients.  Patients with acute kidney injury.  Patients with history of chronic liver disease, congestive heart failure, nephrotic syndrome, or active malignancy.  Patients under any medication that is known to alter thyroid function or renal function (e.g., amiodarone, lithium, glucocorticoids at high dose, nephrotoxic chemotherapy) except if it was the cause of thyroid dysregulation.  Patients with incomplete medical records. Data Collection Procedure Eligible patients were recruited consecutively after obtaining approval from the Institutional Research and Ethics Committee. Written informed consent was obtained prior to participation. Demographic data (age, sex, body mass index (BMI), history of diabetes mellitus, and hypertension) were collected using a structured data collection proforma. The clinical information and laboratory findings were extracted from patient interviews and hospital records. Standardized automated analyzers were used in the hospital's central lab to analyze venous blood samples. Thyroid function tests included free thyroxine (FT4), free triiodothyronine (FT3) and serum thyroid-stimulating hormone (TSH). Serum creatinine and estimated glomerular filtration rate (eGFR) were used to measure renal function. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was used to calculate the eGFR (mL/min/1.73 m²). Based on thyroid function test results, participants were categorized into the following groups according to standard laboratory reference ranges: • Euthyroid • Hypothyroidism • Hyperthyroidism The severity of thyroid dysfunction was determined according to serum TSH concentration in conjunction with FT4 and FT3 values. Study Variables Independent Variable • Severity of thyroid dysfunction (categorized as euthyroid, subclinical hypothyroidism, overt hypothyroidism, subclinical hyperthyroidism, and overt hyperthyroidism) Dependent Variables • Serum creatinine (mg/dL) • Estimated glomerular filtration rate (eGFR; mL/min/1.73 m²) Covariates • Age • Sex • Diabetes mellitus • Hypertension Statistical Analysis IBM SPSS Statistics version 26.0.was used to enter and analyze data. The Shapiro–Wilk test was used to test continuous variables for normality. The data were normally distributed and presented as mean ± standard deviation (SD) and non-normally distributed data were presented as median and interquartile range (IQR). Categorical variables were presented in terms of frequencies and percentages. The comparison of renal function parameters was carried out by one-way analysis of variance (ANOVA) for normally distributed data and Kruskal-Wallis test for non-parametric data. The Chi-square test and/or Fisher's exact test was used to determine the association between categorical variables as appropriate. Pearson's correlation coefficient was used as correlation coefficient for variables to determine the relationship between thyroid function parameters (TSH, FT3, and FT4) and renal function indices (serum creatinine and eGFR). After adjusting for potential confounding factors such as age, sex, diabetes mellitus and hypertension, multiple linear regression analysis was performed to identify independent predictors of renal function. Model fitting was preceded by an evaluation of regression assumptions. A p value of <0.05 for two-tailed was set as statistically significant.

A total of 140 adult patients were included in the study. The mean age of the participants was 37.14 ± 14.56 years, with a mean body mass index (BMI) of 27.89 ± 3.20 kg/m². Demographic characteristics of the study population are presented in Table 1.

Table 1. Baseline Demographic and Clinical Characteristics of the Study Participants (n = 140)

For the thyroid hormone analysis, the median (IQR) levels of T3, T4, and TSH were 1.42 (1.10-1.42), 1.17 (1.06-1.17), and 3.59 (1.32-9.17) mIU/L, respectively. The respective mean ± standard deviations were 1.42 ± 1.20, 1.17 ± 0.52, and 9.17 ± 16.30, respectively. There was notable variability in the levels of TSH hormones as shown in table 2.

Table 2. Thyroid Hormone Profile of the Study Participants (n = 140)

In relation to kidney function, the median (IQR) for serum urea level was 37.31 (31.00-37.31) mg/dL, serum creatinine was 1.02 (0.75-1.31) mg/dL, and the eGFR was 88.12 (83.98-88.45) mL/min/1.73 m2. On the other hand, the mean ± SD was 37 as shown in table 3.

For checking the distribution of continuous variables, the Shapiro-Wilk test was applied. All the thyroid hormones as well as renal function markers showed a non-normal distribution (p<0.05). Consequently, for further statistical analysis, non-parametric tests were employed, and for assessing the correlation between thyroid hormones and renal function parameters, the Spearman’s rank correlation coefficient was chosen.

Table 3. Renal Function Profile of the Study Participants (n = 140)

According to Spearman’s rank correlation, there was a weak negative correlation of TSH and serum urea (ρ = −0.269, p = 0.001) which indicated that TSH levels were high with low levels of serum urea. There was also a weak negative correlation of T3 with serum urea (ρ = −0.272, p = 0.001).

There was a very weak positive correlation of TSH with serum creatinine (ρ = 0.188, p = 0.026). Nevertheless, there was no correlation between T4 and serum urea, serum creatinine, or eGFR (p > 0.05). Also, there was no association of TSH with eGFR (ρ = −0.046, p = 0.588), T3 with eGFR (ρ = 0.076, p = 0.375), or T4 with eGFR (ρ = −0.011, p = 0.902). Details of the above given information is more clearly stated in scatterplots (Figures 1,2 and 3 below).

Table 4. Spearman Correlation Between Thyroid Hormone Levels and Renal Function Parameters

In order to determine whether independent predictors for renal function included levels of thyroid hormones, a multiple linear regression analysis with eGFR as the dependent variable and age, BMI, T3, T4, and TSH levels as independent variables was performed. The regression model as a whole was not statistically significant (F(5,134) = 1.054, p = 0.389) and accounted for 3.8% of variation in eGFR (R² = 0.038, adjusted R² = 0.002).

None of the independent variables had a statistically significant relationship with eGFR when adjusted for other predictors. Namely, age (β = −0.069, p = 0.423), BMI (β = −0.109, p = 0.207), T3 (β = 0.022, p = 0.869), T4 (β = −0.154, p = 0.253), and TSH (β = −0.069, p = 0.430) were not independent predictors for eGFR. The assessment of multicollinearity revealed tolerance values exceeding 0.40 and variance inflation factors (VIFs) varying between 1.024 and 2.490, thus implying no evidence of multicollinearity among the predictors.

Therefore, the results of the regression analysis imply that, accounting for age and BMI, thyroid hormone levels were not independently associated with eGFR in this study population.

Table 5. Multiple Linear Regression Analysis for Predictors of eGFR

Figure 1 Scatter plot: Scatter plot illustrating the relationship between serum TSH levels and serum creatinine concentrations. The plot demonstrates a weak positive association consistent with the Spearman correlation analysis.

Figure 2 Scatter plot: Scatter plot showing the relationship between serum T3 levels and serum urea concentrations. A weak negative correlation was observed.

Figure 4. Scatter plot demonstrating the relationship between serum TSH levels and estimated glomerular filtration rate (eGFR). No significant association was identified.

The current study assessed the relationship between levels of thyroid hormones and parameters of renal functions in adults admitted at a tertiary hospital. The study revealed a weak but statistically significant correlation between TSH and serum urea, T3 and serum urea, and TSH and serum creatinine, through non-parametric correlation and multivariate linear regression. On the other hand, there was no significant correlation between thyroid hormones and eGFR and none of the thyroid hormones were able to independently predict eGFR after adjusting for age and body mass index. This implies that while thyroid hormones can have minimal effects on specific biochemical indicators of renal function, their independent effect on glomerular filtration is insignificant. The link between thyroid and renal physiology has been well-documented. There is evidence for an impact of thyroid hormones on renal blood flow, cardiac output, systemic vascular resistance, sodium and water balance, as well as the renin-angiotensin-aldosterone system. The contribution of kidneys to the metabolism and elimination of thyroid hormones leads to the creation of an interrelation between the two organs. Modern reviews stress the importance of the effect of thyroid disorders on serum creatinine, blood urea nitrogen, and glomerular filtration rate via hemodynamic and metabolic pathways9-10. In the present case, elevated levels of TSH have shown a weak inverse relationship with serum urea levels and a very weak direct relationship with serum creatinine, whereas elevated levels of T3 have shown a weak inverse relationship with serum urea. Despite achieving statistical significance, these relationships were of low magnitude, suggesting little clinical relevance. In fact, such findings have been observed in recent literature where there is a possibility of an increase in serum creatinine level with an increase in TSH level because of the decrease in renal plasma flow and glomerular filtration in cases of hypothyroidism. On the other hand, a number of studies have shown that increased free T3 levels are generally linked with good renal health, although the strength of these relationships diminishes with adjustments for covariates11-12. One noteworthy observation from this research is the lack of correlation between the level of thyroid hormones and the eGFR. In addition, through multiple linear regression, it was found that age, BMI, T3, T4, and TSH only accounted for 3.8% of the variance in eGFR, with none of these parameters having any independent contribution towards renal function. It aligns with recent literature where there have been suggestions that while there is an influence of thyroid dysfunction on renal function, there are other parameters like age, hydration, body muscle, cardiac function, diabetes, hypertension, and kidney disease that contribute to eGFR13-14. In addition, the absence of any correlation between thyroid hormone concentration and eGFR in the multivariate analysis may also be due to the fact that renal function of most subjects analyzed in the current study was preserved to some extent. The lack of correlation could be attributed to the fact that in many cases eGFR values were close to or even within the normal range, so that the effect of the thyroid hormones would not have been great enough to show any differences after taking into consideration age and BMI. Another factor that might explain the lack of correlation is the fact that serum creatinine and blood urea levels are affected by more physiological processes than just filtration through the glomeruli15-16. It is notable that there were no significant correlations of FT4 with any parameters of renal function in our study. It is supported by the recent data showing that the association of FT4 with renal function is less clear-cut than the link between TSH or FT3. Indeed, although several cohort studies have found the association between high TSH or low FT3 levels and chronic kidney disease, data for FT4 are inconsistent and do not provide an independent link to renal dysfunction17-18. This new research has to be considered with respect to the recent evidence as well. A systematic review and meta-analysis conducted recently found that overt and subclinical hypothyroidism have been found to have an association with a high prevalence of chronic kidney disease and low eGFR among the population. At the same time, the review pointed out the heterogeneity among various studies concerning the strength of these associations. It was emphasized that observational studies could not prove any causal relationships between these two conditions 15. Clinically speaking, one can say that in light of the current study, it becomes apparent that regular testing for levels of thyroid hormones in addition to traditional renal functional test can offer additional insights into kidney functioning yet cannot be viewed as an independent predictor of glomerular filtration rate in adult patients regardless of other clinical factors. Renal function tests need to be interpreted by clinicians taking into consideration the fact that thyroid abnormalities can affect the levels of creatinine and urea in the blood without causing the changes in eGFR19-20. The strengths of the study include continuous variables, thus no loss of information through arbitrary categorization of the variables. Since the study variables are not normally distributed, the use of Spearman’s rank correlation was justified. Multivariate linear regression was done for the adjustment of confounders, including age and body mass index (BMI). However, several weaknesses of the study need to be pointed out. First, the cross-sectional design prevents causal inference. Second, the predominance of women in the study sample limits its applicability to men. Third, residual confounding due to variables such as diabetes, hypertension, medications, protein diet, and duration of thyroid disease cannot be ruled out because they were not measured. Fourth, the assessment of renal function involved only a single biochemical measurement and not follow-up data. LIMITATIONS/STRENGHTS: This study has some limitations that should be considered while analyzing the results. Firstly, the cross-sectional design of the study does not allow for establishing any causation between thyroid hormones and renal function and, thus, limits the results only to association. Secondly, the study has been conducted in one specific tertiary care setting, which may narrow down the possibility of generalization of the findings to other settings. Thirdly, the sample was comprised mostly of females (95%), which may affect the relevance of the findings to male subjects. Additionally, while adjusting for age and body mass index in the multivariable analysis, other factors such as hypertension, diabetes mellitus, medication intake, duration of the condition, etiology of the thyroid condition, dehydration, dietary protein intake, and chronic kidney disease have not been taken into account. Lastly, the assessment of renal function is based on a one-time set of biochemistry tests; therefore, there is no possibility to trace the dynamics of the two parameters over time.