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Hidden impact: How subclinical hypothyroidism influences renal outcomes in type 2 diabetes mellitus
*Corresponding author: Rahul Garg, Department of Medicine, Farukh Hussain Medical College, Agra, Uttar Pradesh, India gargrahul27@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Garg R, Thakre A. Hidden impact: How subclinical hypothyroidism influences renal outcomes in type 2 diabetes mellitus. Med India. 2025;4:74-81. doi: 10.25259/MEDINDIA_18_2025
Abstract
Objectives:
Type 2 diabetes mellitus (T2DM) frequently presents with multiple complications, including subclinical hypothyroidism (SCH) and diabetic kidney disease. The purpose of this research was to examine whether subclinical thyroid dysfunction influences the onset and advancement of diabetic nephropathy (DN) among individuals with type 2 diabetes.
Materials and Methods:
A cross-sectional study was conducted with 368 patients with T2DM. Thyroid function tests, urinary albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate (eGFR), and other relevant clinical parameters were assessed. Patients were categorized based on thyroid status (euthyroid or SCH) and nephropathy status. The association between SCH and DN was analyzed using multivariate logistic regression models.
Results:
Among 368 T2DM patients, 61 (16.6%) had SCH, whereas 275 (74.7%) were euthyroid. DN was significantly more prevalent in patients with SCH compared to euthyroid patients (54.1% vs. 25.8%, P < 0.001). Following statistical correction for potential confounders such as patient age, gender, diabetes duration, blood pressure status, body mass index, and glycemic control, subclinical thyroid dysfunction continued to demonstrate an independent relationship with diabetic kidney disease (adjusted odds ratio: 4.30, 95% confidence interval: 2.25–8.20, P < 0.001). Patients with SCH showed significantly higher UACR levels (62.45 ± 48.25 vs. 28.15 ± 24.80 mg/g, P < 0.001) and lower eGFR (78.35 ± 16.42 vs. 89.78 ± 14.25 mL/min/1.73 m2, P < 0.001) compared to euthyroid patients. Significant correlations were observed between thyroid-stimulating hormone (TSH) levels and renal function parameters (r = 0.487 for UACR, r = −0.398 for eGFR, both P < 0.001), with a clear dose–response relationship across TSH quartiles.
Conclusion:
SCH is significantly associated with an increased prevalence of DN in patients with T2DM. The strength of association increases after adjusting for traditional risk factors, with particularly strong effects in hypertensive patients and those with longer diabetes duration. These findings suggest that thyroid function assessment may be warranted in T2DM patients with renal impairment; however, prospective studies are needed to determine whether SCH management can improve renal outcomes.
Keywords
Albuminuria
Diabetic nephropathy
Renal function
Subclinical hypothyroidism
Type 2 diabetes mellitus
INTRODUCTION
Diabetic nephropathy (DN) is a major microvascular complication of diabetes mellitus, typified by persistent albuminuria, progressive decline in glomerular filtration rate, and elevated blood pressure. It remains the principal cause of end-stage renal disease worldwide.[1] According to the International Diabetes Federation, approximately 40% of patients with diabetes develop kidney disease, with substantial variations across populations and geographic regions.[2]
Thyroid disorders are commonly observed in patients with type 2 diabetes mellitus (T2DM), with subclinical hypothyroidism (SCH) being particularly prevalent.[3] SCH is defined as an elevated serum thyroid-stimulating hormone (TSH) level with normal-free thyroxine (FT4) levels, and it has been reported to affect approximately 3–16.9% of the general population according to various Indian studies[4-8] with a higher prevalence in diabetic patients (4.69–23%).[9-12]
The interconnection between thyroid dysfunction and diabetes has been well established, with both conditions influencing each other through various metabolic pathways.[13] However, the specific association between SCH and DN remains incompletely understood, with mixed findings reported in the literature. Some studies have suggested that SCH might accelerate the development and progression of DN[14-17] while others have found no significant association.[18] A study showed that SCH is independently associated with albuminuria.[19] A meta-analysis by Han et al. concluded that SCH in Type 2 DM is associated with increased risk of DN (odds ratio [OR]: 1.7, 95% confidence interval [CI]: 1.34–2.28).[10]
Several possible mechanisms have been put forward to explain how SCH might contribute to renal dysfunction in diabetic patients. These include hemodynamic alterations, increased systemic and intraglomerular pressure, direct effects on the renin-angiotensin-aldosterone system, endothelial dysfunction, and increased oxidative stress.[3,20,21] In addition, thyroid hormones play a key role in kidney development, physiology, and function by influencing renal blood flow, glomerular filtration rate, and tubular function.[22]
Given the potential implications for clinical practice and the conflicting evidence in the literature, this study focused on exploring the association between SCH and DN in patients with T2DM, with particular focus on albuminuria and estimated glomerular filtration rate (eGFR) as markers of renal function, and to explore potential dose–response relationships between TSH levels and renal parameters.
MATERIALS AND METHODS
Study design and participants
This cross-sectional study was conducted at a tertiary care hospital from July 2024 to May 2025. A total of 368 patients with diagnosed T2DM were enrolled. The diagnosis of T2DM was based on the American Diabetes Association criteria. Patients with known thyroid disorders, those on medications affecting thyroid function (including levothyroxine, antithyroid drugs, amiodarone, lithium, or glucocorticoids), pregnant women, and patients with acute illness, overt renal failure (eGFR <30 mL/min/1.73 m2), urinary tract infection, or other causes of proteinuria were excluded from the study. The research methodology received approval from the institutional review board, and all participants provided written consent before enrollment.
Clinical and laboratory assessments
Demographic data and relevant clinical information, including age, sex, duration of diabetes, smoking status, and medication history, were recorded for all participants. Physical examination included measurements of height, weight, body mass index (BMI), and blood pressure.
Blood samples were collected after an overnight fast of at least eight hours. Laboratory investigations included fasting plasma glucose, glycated hemoglobin (HbA1c), serum creatinine, lipid profile (total cholesterol, triglycerides, high-density lipoprotein-cholesterol, and low-density lipoproteincholesterol [LDL-C]), thyroid function tests (TSH, free T3, and free T4), and urinary albumin-to-creatinine ratio (UACR).
Thyroid function tests were performed using chemiluminescence immunoassay. SCH was defined as a TSH level above the upper limit of the reference range (>4.5 μIU/mL) with normal FT4 levels (0.8–1.8 ng/dL). This cutoff was chosen based on the laboratory reference range and is consistent with several major endocrine society guidelines, though we acknowledge that some guidelines suggest lower thresholds (>4.0 μIU/mL). Euthyroid status was defined as normal TSH (0.5–4.5 μIU/mL) and normal FT4 levels.
The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation. Albuminuria was assessed by measuring the UACR in a spot urine sample. DN was defined according to the American Diabetes Association criteria as UACR ≥30 mg/g and/or eGFR <60 mL/min/1.73 m2.
Statistical analysis
Statistical analysis was performed using the Statistical Package for the Social Sciences Software (Version 26.0). A post hoc power analysis was conducted to assess the adequacy of sample size for detecting the observed effect sizes. Continuous variables were expressed as mean ± standard deviation or median (interquartile range), depending on the distribution of data. Categorical variables were presented as frequencies and percentages. The Kolmogorov–Smirnov test was used to assess the normality of data distribution. For comparison between groups (SCH vs. euthyroid), Student’s t-test or Mann–Whitney U test was used for continuous variables, and Chi-square test or Fisher’s exact test for categorical variables, as appropriate. Pearson’s or Spearman’s correlation analysis was performed to evaluate the relationship between TSH levels and parameters of renal function. Multivariate logistic regression analysis was conducted to determine the independent association between SCH and DN, after adjusting for potential confounding factors including age, sex, duration of diabetes, BMI, HbA1c, hypertension, and dyslipidemia. Multicollinearity was assessed using variance inflation factors (VIF). Results were expressed as OR with 95% CI.
To further explore the dose–response relationship, we analyzed renal function parameters across quartiles of TSH levels. Trend analysis was performed to assess the significance of dose–response relationships. In addition, stratified analyses were conducted to evaluate the association between SCH and DN in various subgroups (hypertension status, diabetes duration, age, and sex). Sensitivity analyses were performed using different TSH cutoff values (>4.0 μIU/mL and >5.0 μIU/mL) to assess the robustness of our findings. A P < 0.05 was considered statistically significant for all analyses.
RESULTS
Baseline characteristics
A total of 368 patients with T2DM were included in the study. Based on thyroid function tests, 61 patients (16.6%) were classified as having SCH, and 275 patients (74.7%) were classified as euthyroid. The remaining 32 patients (8.7%) had other thyroid abnormalities (overt hypothyroidism, overt hyperthyroidism, subclinical hyperthyroidism, unclassified thyroid dysfunction) and were excluded from the comparative analysis. Post hoc power analysis revealed that with a sample size of 336 patients (61 SCH, 275 euthyroid), we had >90% power to detect the observed effect size (OR: 4.30) at a = 0.05.
The baseline characteristics of patients with SCH and euthyroid patients are presented in Table 1. There were no statistically significant differences between SCH and euthyroid groups in terms of age (57.74 ± 3.63 vs. 56.72 ± 3.45 years), diabetes duration (10.66 ± 2.93 vs. 10.45 ± 2.65 years), or BMI (27.95 ± 1.14 vs. 27.81 ± 1.09 kg/m2). Sex distribution was similar between groups, with females comprising 63.9% of the SCH group and 52.4% of the euthyroid group. Smoking status was also comparable (36.1% vs. 47.6%), as was the prevalence of hypertension (63.9% vs. 66.9%). As expected, the mean TSH level was significantly higher in the SCH group compared to the euthyroid group (6.80 ± 0.68 vs. 2.76 ± 0.52 μIU/mL, P < 0.001), while FT4 levels were slightly lower in the SCH group but still within normal range (1.09 ± 0.10 vs. 1.15 ± 0.15 ng/dL, P > 0.05).
| Characteristic | SCH (n=61) | Euthyroid (n=275) | P-value |
|---|---|---|---|
| Age (years) | 57.74±3.63 | 56.72±3.45 | 0.178 |
| Sex (female), n(%) | 39 (63.9) | 144 (52.4) | 0.095 |
| Duration of diabetes (years) |
10.66±2.93 | 10.45±2.65 | 0.942 |
| BMI (kg/m2) | 27.95±1.14 | 27.81±1.09 | 0.903 |
| Smoking, n(%) | 22 (36.1) | 131 (47.6) | 0.095 |
| Hypertension, n(%) | 39 (63.9) | 184 (66.9) | 0.654 |
| ACE inhibitor/ ARB use, n(%) |
28 (45.9) | 137 (49.8) | 0.587 |
| Metformin use, n(%) | 54 (88.5) | 251 (91.3) | 0.502 |
| SBP (mmHg) | 138.20±7.06 | 137.17±6.58 | 0.882 |
| DBP (mmHg) | 83.52±4.07 | 82.85±3.86 | 0.866 |
| FPG (mg/dL) | 155.51±13.96 | 152.31±11.90 | 0.180 |
| HbA1c (%) | 8.15±0.62 | 7.96±0.50 | 0.172 |
| Total cholesterol (mg/dL) |
194.70±13.90 | 192.25±11.85 | 0.846 |
| Triglycerides (mg/dL) | 169.59±13.85 | 167.20±11.78 | 0.851 |
| HDL-C (mg/dL) | 40.85±2.87 | 40.96±3.17 | 0.974 |
| LDL-C (mg/dL) | 121.07±10.25 | 119.92±9.61 | 0.909 |
| TSH (μIU/mL) | 6.80±0.68 | 2.76±0.52 | <0.001 |
| FT4 (ng/dL) | 1.09±0.10 | 1.15±0.15 | 0.168 |
P-value <0.05 is statistically significant. Values are presented as mean±standard deviation or n(%). T2DM: Type 2 diabetes mellitus, SCH: Subclinical hypothyroidism, BMI: Body mass index, ACE: Angiotensin-converting enzyme, ARB: Angiotensin receptor blocker, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, FPG: Fasting plasma glucose, HbA1c: Glycated hemoglobin, HDL-C: High-density lipoprotein cholesterol, LDL-C: Low-density lipoprotein cholesterol, TSH: Thyroid-stimulating hormone, FT4: Free thyroxine.
Renal function parameters
Renal function parameters according to thyroid status are shown in Table 2. UACR was significantly higher in patients with SCH compared to euthyroid patients (62.45 ± 48.25 vs. 28.15 ± 24.80 mg/g, P < 0.001), representing a 121.8% increase. Similarly, serum creatinine was 15.4% higher in the SCH group (1.08 ± 0.18 vs. 0.94 ± 0.16 mg/dL, P < 0.001), whereas eGFR was 12.7% lower (78.35 ± 16.42 vs. 89.78 ± 14.25 mL/min/1.73 m2, P < 0.001).
| Parameter | SCH (n=61) | Euthyroid (n=275) | Percentage difference | P-value |
|---|---|---|---|---|
| UACR (mg/g) | 62.45±48.25 | 28.15±24.80 | +121.8 | <0.001 |
| Serum creatinine (mg/dL) | 1.08±0.18 | 0.94±0.16 | +15.4 | <0.001 |
| eGFR (mL/min/1.73 m2) | 78.35±16.42 | 89.78±14.25 | −12.7 | <0.001 |
| Diabetic nephropathy, n(%) | 33 (54.1) | 71 (25.8) | +109.7 | <0.001 |
P-value <0.05 is statistically significant. Values are presented as mean±standard deviation or n(%). T2DM: Type 2 diabetes mellitus, SCH: Subclinical hypothyroidism, UACR: Urinary albumin-to-creatinine ratio, eGFR: Estimated glomerular filtration rate.
Overall, the prevalence of DN was significantly higher in patients with SCH compared to euthyroid patients (54.1% vs. 25.8%, P < 0.001), as shown in Figure 1.
- Prevalence of diabetic nephropathy (DN) in Subclinical hypothyroidism versus euthyroid patients. Bar chart showing the prevalence of DN: 54.1% in subclinical hypothyroidism group versus 25.8% in euthyroid group (P < 0.001).
Correlation analysis
Correlation analysis showed significant relationships between TSH levels and renal function parameters [Table 3]. There was a moderate positive correlation between TSH levels and UACR (r = 0.487, P < 0.001), and a weak to moderate positive correlation with serum creatinine (r = 0.342, P < 0.001). Conversely, a moderate negative correlation was observed between TSH and eGFR (r = −0.398, P < 0.001) in the entire study population. These correlations remained statistically significant after adjustment for potential confounding variables.
| Parameter | Correlation coefficient (r) | P-value |
|---|---|---|
| UACR | 0.487 | <0.001 |
| Serum creatinine | 0.342 | <0.001 |
| eGFR | −0.398 | <0.001 |
P-value <0.05 is statistically significant. Pearson correlation coefficients between TSH levels and renal function parameters in the combined SCH and euthyroid groups. TSH: Thyroid-stimulating hormone, UACR: Urinary albumin-to-creatinine ratio, eGFR: Estimated glomerular filtration rate.
Dose–response relationship
To further explore the relationship between TSH levels and renal function, we analyzed renal parameters across TSH quartiles [Table 4 and Figure 2]. Mean UACR values showed a clear progressive increase across TSH quartiles: Q1 (<2.40 μIU/mL): 18.65 mg/g; Q2 (2.40–2.90 μIU/mL): 24.78 mg/g; Q3 (2.90–3.40 μIU/mL): 35.92 mg/g; Q4 (>3.40 μIU/mL): 58.43 mg/g; p-trend < 0.001. Similarly, serum creatinine increased across TSH quartiles (Q1: 0.89 mg/dL; Q2: 0.92 mg/dL; Q3: 0.98 mg/dL; Q4: 1.06 mg/dL; p-trend = 0.003), whereas eGFR decreased (Q1: 95.2 mL/min/1.73 m2; Q2: 91.8 mL/min/1.73 m2; Q3: 86.4 mL/min/1.73 m2; Q4: 79.6 mL/min/1.73 m2; p-trend < 0.001). The prevalence of DN also increased progressively across TSH quartiles: Q1: 12.5%; Q2: 18.2%; Q3: 31.8%; Q4: 48.3% (p-trend < 0.001).
| TSH quartile | TSH range (μIU/mL) | UACR (mg/g) | Serum creatinine (mg/dL) | eGFR (mL/min/1.73 m2) | DN prevalence (%) |
|---|---|---|---|---|---|
| Q1 | <2.40 | 18.65 | 0.89 | 95.2 | 12.5 |
| Q2 | 2.40–2.90 | 24.78 | 0.92 | 91.8 | 18.2 |
| Q3 | 2.90–3.40 | 35.92 | 0.98 | 86.4 | 31.8 |
| Q4 | >3.40 | 58.43 | 1.06 | 79.6 | 48.3 |
| p for trend | - | <0.001 | 0.003 | <0.001 | <0.001 |
TSH: Thyroid-stimulating hormone, UACR: Urinary albumin-to-creatinine ratio, eGFR: Estimated glomerular filtration rate, DN: Diabetic nephropathy.
- Dose-response relationship between thyroid-stimulating hormone (TSH) quartiles and renal function parameters: (a) Urinary albumin-to-creatinine ratio, (b) Serum creatinine, (c) Estimated glomerular filtration rate. Values represent means across TSH quartiles.
Multivariate analysis
Multivariate logistic regression analysis was conducted to evaluate the independent association between SCH and DN after adjusting for potential confounding variables [Table 5]. VIFs for all variables were <2.0, indicating no significant multicollinearity. In the crude model, SCH was associated with a significantly increased risk of DN (OR: 3.42, 95% CI: 1.95–6.00, P < 0.001). After adjusting for age and sex, the association remained strong (adjusted OR: 3.28, 95% CI: 1.85–5.82, P < 0.001). When adjusting for hypertension, the OR increased substantially (adjusted OR: 4.85, 95% CI: 2.63–8.95, P < 0.001), suggesting potential effect modification. Similar increases in OR were observed when adjusting for diabetes duration (adjusted OR: 4.72, 95% CI: 2.55–8.74, P < 0.001) and HbA1c (adjusted OR: 4.18, 95% CI: 2.28–7.65, P < 0.001). In the fully adjusted model, accounting for age, sex, duration of diabetes, BMI, HbA1c, hypertension, smoking, and dyslipidemia, SCH remained strongly and independently associated with DN (adjusted OR: 4.30, 95% CI: 2.25–8.20, P < 0.001). In a separate analysis evaluating TSH as a continuous variable, each 1 μIU/mL increase in TSH was associated with a 15% increased risk of DN (adjusted OR: 1.15, 95% CI: 1.07–1.24, P < 0.001) after adjusting for the same confounding factors.
| Model | Odds ratio | 95% confidence interval | P-value |
|---|---|---|---|
| Crude | 3.42 | 1.95–6.00 | <0.001 |
| Adjusted for age and sex | 3.28 | 1.85–5.82 | <0.001 |
| Adjusted for hypertension | 4.85 | 2.63–8.95 | <0.001 |
| Adjusted for diabetes duration | 4.72 | 2.55–8.74 | <0.001 |
| Adjusted for HbA1c | 4.18 | 2.28–7.65 | <0.001 |
| Adjusted for BMI | 4.16 | 2.27–7.61 | <0.001 |
| Multivariate adjusted* | 4.30 | 2.25–8.20 | <0.001 |
P-value <0.05 is statistically significant. Adjusted for hypertension, diabetes duration, HbA1c, BMI, age, sex, smoking, and LDL-C. SCH: Subclinical hypothyroidism, HbA1c: Glycated hemoglobin, BMI: Body mass index.
Sensitivity analysis
Sensitivity analyses using different TSH cutoffs showed consistent results: TSH >4.0 μIU/mL (adjusted OR: 3.87, 95% CI: 2.05–7.32, P < 0.001) and TSH >5.0 μIU/mL (adjusted OR: 4.65, 95% CI: 2.38–9.08, P < 0.001), supporting the robustness of our findings across different threshold definitions.
Stratified analysis
We conducted stratified analyses to evaluate whether the association between SCH and DN differed across various subgroups [Table 6]. The association was particularly strong in hypertensive patients (OR: 5.84, 95% CI: 2.95–11.56, P < 0.001), compared to non-hypertensive patients (OR: 2.45, 95% CI: 1.02–5.88, P = 0.045). The association was stronger in patients with longer diabetes duration (≥10 years: OR: 5.55, 95% CI: 2.84–10.84, P < 0.001) compared to those with shorter duration (<10 years: OR: 2.85, 95% CI: 1.25–6.49, P = 0.013), although both associations were statistically significant. Stratification by age reveals a stronger association in older patients (≥60 years: OR: 4.73, 95% CI: 2.14–10.45, P < 0.001) compared to younger patients (<60 years: OR: 2.96, 95% CI: 1.42–6.18, P = 0.004). When stratified by sex, the association was significant in both females (OR: 4.82, 95% CI: 2.36–9.84, P < 0.001) and males (OR: 2.38, 95% CI: 1.08–5.24, P = 0.031), although it was stronger in females.
| Subgroup | Odds ratio | 95% CI | P-value |
|---|---|---|---|
| Hypertension | |||
| Yes | 5.84 | 2.95–11.56 | <0.001 |
| No | 2.45 | 1.02–5.88 | 0.045 |
| Diabetes duration | |||
| ≥10 years | 5.55 | 2.84–10.84 | <0.001 |
| <10 years | 2.85 | 1.25–6.49 | 0.013 |
| Age | |||
| ≥60 years | 4.73 | 2.14–10.45 | <0.001 |
| <60 years | 2.96 | 1.42–6.18 | 0.004 |
| Sex | |||
| Male | 2.38 | 1.08–5.24 | 0.031 |
| Female | 4.82 | 2.36–9.84 | <0.001 |
P-value <0.05 is statistically significant. SCH: Subclinical hypothyroidism, CI: Confidence interval.
DISCUSSION
In this cross-sectional study, we found a significant and independent association between SCH and DN in patients with T2DM. Patients with SCH had a higher prevalence of DN, higher UACR levels, higher serum creatinine, and lower eGFR compared to euthyroid patients. This association remained robust and actually strengthened after adjusting for potential confounding factors, suggesting that SCH may be an important and previously underrecognized independent risk factor for the development and progression of DN in T2DM patients.
Our findings extend and strengthen previous research reporting associations between SCH and DN. The crude OR we observed (OR: 3.42, 95% CI: 1.95–6.00) is comparable to that reported by Chen et al., who found an OR of 3.15 (95% CI: 1.48–6.69).[15] However, our fully adjusted OR of 4.30 (95% CI: 2.25–8.20) is notably higher, which may reflect more comprehensive adjustment for confounding factors or differences in population characteristics. Our results are also consistent with findings from Nsr-Allah, who reported that SCH predicted DN with an OR of 1.86 (95% CI: 1.01–3.41), and Mansournia et al., who found an OR of 3.23 (95% CI: 1.42–7.37) after multivariable adjustment.[16,17]
A particularly important finding of our study is the clear dose–response relationship between TSH levels and renal function parameters. We observed that UACR and serum creatinine progressively increased, while eGFR decreased across TSH quartiles, with all trend analyses showing statistical significance (P < 0.001 for UACR and eGFR, P = 0.003 for serum creatinine). The prevalence of DN also increased progressively across TSH quartiles (12.5–48.3%, p-trend < 0.001). Each 1 μIU/mL increase in TSH was associated with a 15% increased risk of DN, independent of traditional risk factors. This dose–response relationship provides stronger evidence for a potential causal link between thyroid function and renal outcomes in diabetic patients, consistent with findings reported by Zhou et al.[22] The moderate correlations we found between TSH and renal function parameters (r = 0.487 for UACR, r = 0.342 for serum creatinine, r = −0.398 for eGFR) further support the biological plausibility of this association.
Our stratified analyses revealed important insights into the potential altering effects of clinical factors on the relationship between SCH and DN. The association was particularly strong in hypertensive patients (OR: 5.84, 95% CI: 2.95–11.56, P < 0.001) compared to non-hypertensive patients (OR: 2.45, 95% CI: 1.02–5.88, P = 0.045), suggesting a potential synergistic effect between SCH and hypertension on renal damage. This finding is especially relevant considering that hypertension is a common comorbidity in diabetic patients and a well-established risk factor for DN.
Similarly, the stronger association observed in patients with longer diabetes duration (≥10 years: OR: 5.55 vs. <10 years: OR: 2.85) suggests that the negative effects of SCH on renal function may accumulate over time or that longer diabetes duration creates a more vulnerable renal milieu in which the effects of SCH become more pronounced. Importantly, unlike our previous observation, the association between SCH and DN was significant in both males and females, although it was stronger in females (OR: 4.82 vs. 2.38), indicating that the relationship is not sex-specific but may be more pronounced in women.
Several mechanisms have been suggested to explain the potential link between SCH and renal dysfunction in diabetic patients. Thyroid hormones play crucial roles in kidney development, physiology, and function. Even subtle alterations in thyroid function, as seen in SCH, might affect renal hemodynamics, glomerular filtration, and tubular function. Mild thyroid hormone deficiency alters cardiovascular hemodynamics, resulting in reduced cardiac output and increased vascular resistance, which in turn impairs renal blood flow and glomerular filtration rate.[20] SCH dysregulates the renin–angiotensin–aldosterone system, leading to increased intraglomerular pressure and albuminuria.[3] It also promotes endothelial dysfunction by reducing nitric oxide production, while increasing oxidative stress and inflammation, thereby compromising the kidney’s filtration barrier. The dyslipidemia often seen in SCH patients, with elevated LDL-cholesterol, may accelerate renal disease progression through lipotoxicity and foam cell formation in glomerular structures.[20] Additionally, autoimmune mechanisms may be involved in linking thyroid dysfunction to kidney disease. Autoimmune thyroiditis and certain glomerulonephropathies share pathogenic pathways, suggesting a common immunological dysregulation that affects both organs.[21]
The clinical implications of our findings are substantial. Given the significant and independent association between SCH and DN, routine screening for thyroid dysfunction might be beneficial in T2DM patients, particularly those with signs of renal impairment. Our findings of increased risk even with modest TSH elevations, and the clear dose– response relationship observed, suggest that current cutoff values for defining SCH may need reconsideration in diabetic populations, especially those with or at risk for microvascular complications.
Early detection and management of SCH might potentially help in reducing the risk or slowing the progression of DN, although prospective intervention studies are needed to confirm this hypothesis. The stronger association observed in hypertensive patients and those with longer diabetes duration suggests that these subgroups might particularly benefit from thyroid function screening and potential intervention.
Current guidelines provide limited recommendations regarding thyroid function testing in patients with diabetes. The American Diabetes Association recommends screening for thyroid dysfunction in patients with type 1 diabetes due to the high prevalence of autoimmune thyroid disease in this population, but there are no specific recommendations for routine screening in T2DM patients.[3] Our findings, along with accumulating evidence from previous studies, suggest that thyroid function testing may also be warranted in T2DM patients, particularly those with evidence of microvascular complications, such as nephropathy.
Furthermore, the optimal management of SCH in T2DM patients with DN remains an area of uncertainty. While levothyroxine therapy is generally recommended for patients with TSH levels >10 μIU/mL, the benefits of treatment for patients with mildly elevated TSH levels (4.6–10 μIU/mL) remain controversial.[3] Prospective randomized controlled trials are needed to determine whether levothyroxine therapy can improve renal outcomes in T2DM patients with SCH and DN.
Strengths and limitations of the study
Our study has several strengths, including a relatively large sample size, comprehensive assessment of thyroid function and renal parameters, detailed correlation and dose-response analyses, and careful adjustment for potential confounding factors using multiple statistical approaches. However, it also has some limitations that should be acknowledged. First, the cross-sectional design of our study precludes the establishment of a causal relationship between SCH and DN, and the possibility of reverse causation cannot be excluded. Prospective longitudinal studies are needed to determine whether SCH precedes the development of DN or occurs as a consequence of renal dysfunction. Second, we defined SCH based on a single measurement of thyroid function tests, which might not account for the potentially transient nature of thyroid dysfunction. TSH levels can fluctuate due to illness, medications, circadian rhythm, and other factors. Repeated measurements would have provided a more accurate assessment of thyroid status. Third, we did not assess thyroid autoantibodies, which might have provided insights into the underlying etiology of SCH in our study population. Future studies should include an assessment of thyroid autoimmunity to better characterize the relationship between autoimmune thyroid disease, diabetes, and renal dysfunction. Fourth, several potentially important factors were not assessed, including detailed medication history (specific dosages and duration of angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, diuretics), socioeconomic status and healthcare access, dietary factors and nutritional status, physical activity levels, other comorbidities (sleep apnea, chronic inflammatory conditions), duration and severity of hypertension, genetic factors predisposing to both thyroid dysfunction and nephropathy. Fifth, we used a broad definition of DN (UACR ≥30 mg/g and/or eGFR <60 mL/min/1.73 m2) without staging the severity of nephropathy. This may have masked potentially different associations between SCH and various stages of DN progression. Finally, our study was conducted in a tertiary care center, which might limit the generalizability of our findings to the broader population of T2DM patients in primary care settings.
CONCLUSION
Our study confirms a strong, independent association between SCH and DN in patients with T2DM. We observed a clear dose–response relationship, with worsening renal function across increasing TSH quartiles. This association was strongest in hypertensive patients, those with longer diabetes duration, and females. SCH patients demonstrated higher UACR, higher serum creatinine, and lower eGFR compared to euthyroid patients. These findings suggest thyroid dysfunction may significantly influence renal disease development in diabetic patients. Routine thyroid screening should be considered for T2DM patients, particularly those with renal impairment. Further research is needed to determine if early SCH management improves renal outcomes.
Acknowledgment:
We gratefully acknowledge the invaluable statistical support provided by the department of biostatistics for their expert guidance in study design, statistical analysis planning, and interpretation of results.
Author contributions:
RG is responsible for concept, organization, execution, and manuscript writing. AT is responsible for data collection.
Ethical approval:
The research/study approved by the Institutional Review Board at Farukh Hussain Medical College, number FHMC/IEC/R.Cell/2024/39, dated 11th July 2024.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent.
Conflicts of interest:
There are no conflicts of interest
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirms that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Financial support and sponsorship: Nil.
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