Association Between Thyroid Hormone Levels with Diabetic Kidney Disease Among Euthyroid Participants with Type 2 Diabetes Mellitus: A Cross-Sectional Study

Abstract

Purpose

Thyroid hormones are linked to prognostic outcomes for a range of diseases. The link between thyroid hormone levels and diabetic kidney disease (DKD) risk in euthyroid individuals with type 2 diabetes mellitus (T2DM) was investigated in this study.

Patients and Methods

Overall, 2945 T2DM cases were enrolled and grouped as with or without DKD. Thyroid hormones were analyzed both as continuous variables and by quartiles. Logistic regression models and smooth curve fitting were applied to assess associations. Stratified analyses and interaction tests were also conducted to examine effect modification.

Results

Patients with DKD had significantly decreased free triiodothyronine (FT3) levels compared to those without DKD, while free thyroxine (FT4) and thyroid-stimulating hormone (TSH) were comparable across groups. Relative to the bottom FT3 quartile (Q1), the respective adjusted odds ratios (ORs) for DKD in Q2, Q3, and Q4 were 0.63 (95% CI: 0.50–0.80, p < 0.001), 0.60 (95% CI: 0.47–0.77, p < 0.001), and 0.63 (95% CI: 0.49–0.82, p = 0.001). The relationship between FT3 and DKD followed a nonlinear, L-shaped pattern (p for nonlinearity < 0.001), with a threshold at approximately 3.09 pg/mL. Below this inflection point, an adjusted OR for DKD of 0.622 (95% CI: 0.515–0.751, p < 0.001) was evident. Subgroup analysis suggested that this association was generally consistent across most subsets of study subjects, but was not statistically significant in several strata.

Conclusion

Lower FT3 levels are independently linked to greater prevalence of DKD in euthyroid individuals with T2DM. The observed L-shaped association suggests a threshold effect, with 3.09 pg/mL as the approximate inflection point.

Introduction

Diabetes mellitus (DM) represents a significant global issue, with its prevalence increasing steadily in recent decades. Currently, the global adult diabetes prevalence stands at 10.5%, affecting approximately 780 million individuals, and is projected to rise by 46% by 2045.¹ Among all diabetes types, type 2 diabetes mellitus (T2DM) accounts for the vast majority of cases. Diabetic kidney disease (DKD) is a frequent serious DM complication and also a major contributor to end-stage renal disease worldwide.² Between 20 and 40% of diabetic individuals are likely to develop DKD, and nearly 40% of these may progress to end-stage renal disease (ESRD).³ Once ESRD develops, renal damage becomes irreversible,⁴ necessitating renal replacement therapies such as dialysis or transplantation,⁵ which impose significant financial burdens on both healthcare systems and families and profoundly reduce patients’ quality of life.⁶ Identifying modifiable factors related to DKD progression is therefore critical for early detection, preventive strategies, and improved clinical outcomes.

Thyroid hormones, specifically, thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4), play a vital role in regulating energy metabolism, glucose homeostasis, and insulin sensitivity.⁷ These hormones influence not only systemic metabolic processes, growth, and development, but are also linked to prognostic outcomes for a range of diseases.⁸ They are also closely tied to renal development, glomerular filtration, renal blood flow, and fluid-electrolyte balance.⁹ Both hypothyroidism and hyperthyroidism can impair renal function through direct effects on the kidney and indirect effects on cardiovascular and metabolic homeostasis.¹⁰ Although several studies have linked thyroid dysfunction to an increased risk of DKD,^11,12 limited evidence is available regarding associations linking thyroid hormone levels and DKD in euthyroid individuals.

Thus, this study was formulated in an effort to clarify the link between thyroid hormone levels, particularly those of FT3, and DKD in euthyroid individuals with T2DM.

Material and Methods

Study Design and Participants

This study enrolled 4018 diabetes individuals who were admitted to Taizhou Central Hospital (Taizhou University Hospital) between September 2017 and February 2024. Type 1 diabetes, a history of thyroid disorders, taking thyroid-related medications (including levothyroxine or antithyroid agents), evidence of abnormal thyroid function test results (ie, values of FT3, FT4, or TSH outside the reference range), or a lack of critical laboratory information were causes for patient exclusion. For further details, see the study flow diagram (Figure 1). The investigation was undertaken according to the principles of the Declaration of Helsinki and was approved by the institutional ethics committee of Taizhou Central Hospital. Written informed consent was provided before enrollment in all cases. This study was conducted following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.

Figure 1.

Study flowchart.

Data Collection

Trained medical personnel extracted demographic, clinical, and biochemical data from electronic medical records. These data comprised variables such as age, sex, vital signs, laboratory results, and existing comorbidities. Blood samples were obtained between 06:00 and 10:00 after a fasting period of at least eight hours and were processed by the hospital’s clinical laboratory. The laboratory analyses encompassed measurements of fasting blood glucose (FBG), fasting serum C-peptide (FCp), glycated hemoglobin (HbA1c), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (Scr), blood urea nitrogen (BUN), total triglycerides (TG), total cholesterol (TC), high- and low-density lipoprotein cholesterol (HDL-C and LDL-C), and thyroid function indicators including free FT3, FT4, and TSH, with respective reference ranges of 2.00–4.40 pg/mL, 0.93–1.70 ng/dL, and 0.27–4.20 uIU/mL. Urine specimens were collected under standardized, non-stress conditions from participants who were free from fever, infections, or other inflammatory illnesses. To confirm the presence of albuminuria, each patient underwent two separate urinary assessments. The urinary albumin-to-creatinine ratio (UACR) was computed, and kidney function was further evaluated using the estimated glomerular filtration rate (eGFR), which was derived using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation modified for individuals of Asian ethnicity.¹³ Following the criteria of the American Diabetes Association, DKD was diagnosed using a UACR value > 30 mg/g or eGFR < 60 mL/min/1.73 m².¹⁴ Body mass index (BMI) represented the weight divided by the height squared (kg/m²).

Statistical Analyses

Normally distributed continuous data are shown as means ± standard deviations, while non-normally distributed variables are given as medians and interquartile ranges (IQRs). Comparisons between the DKD and non-DKD groups were conducted using t-tests for normally distributed data, Wilcoxon rank-sum tests for skewed data, and chi-square tests for categorical variables. To clarify the interplay between thyroid hormone levels and DKD, participants were categorized into quartiles for FT3, FT4, and TSH, denoted as Q1 (lowest) through Q4 (highest). Three progressively adjusted multivariate logistic regression models were utilized: Model 1 had no adjustments; Model 2 had adjustments for age and sex; and Model 3 included additional adjustments for HbA1c, diabetes duration, educational attainment, BMI, smoking and alcohol use, hypertension, and hyperlipidemia. Restricted cubic spline (RCS) modeling was performed to characterize potential nonlinear relationships between thyroid hormone levels and DKD, with adjustments made based on the variables in Model 3. To identify a threshold effect of FT3 on DKD risk, a two-piecewise logistic regression model with a smoothing function was utilized. The likelihood ratio test and bootstrap resampling methods were applied for determination of the inflection point. To investigate potential effect modification, stratified analyses were conducted according to age group (18–50 vs >50 years), sex, and educational level (below high school vs high school and above). Heterogeneity across subgroups was assessed through multivariate logistic regression, and interaction terms were tested using likelihood ratio tests. Analysis was performed using R 4.2.2 (https://www.Rproject.org; The R Foundation, Vienna, Austria) and the Free Statistics software (version 2.1.1; Beijing Free Clinical Medical Technology Co., Ltd, Beijing, China). A two-sided P < 0.05 was deemed significant.

Results

Baseline Features

The detailed information of the 2945 participants is provided in Table 1. 851 participants had DKD, while 2094 did not. Compared with non-DKD participants, those with DKD were generally older, had lower levels of formal education, and had suffered from diabetes for longer. Furthermore, they exhibited higher rates of comorbid hypertension and hyperlipidemia. Clinically, the DKD group demonstrated markedly raised SBP and DBP, BMI, FBG, FCp, UA, Cr, TG, HbA1c, and UACR, alongside lower eGFR, all with P-values less than 0.05. Notably, FT3 levels were markedly decreased in DKD relative to non-DKD individuals, while FT4 and TSH levels were comparable between the groups.

Table 1.

Baseline Characteristics

Variables	All participants (n = 2945)	Patients Without DKD (n = 2094)	Patients with DKD (n = 851)	P-value
Age (y)	51.5 ± 12.1	51.1 ± 11.9	52.6 ± 12.6	0.003
Males, n (%)	2126 (72.2)	1520 (72.6)	606 (71.2)	0.449
Education, n (%)				0.015
Below high school	1898 (64.4)	1321 (63.1)	577 (67.8)
High school education and above	1047 (35.6)	773 (36.9)	274 (32.2)
Duration of diabetes (y)	4.7 (0.5, 10.2)	4.0 (0.2, 9.8)	6.1 (1.5, 11.8)	< 0.001
Smoking, n (%)	784 (26.6)	558 (26.6)	226 (26.6)	0.96
Drinking, n (%)	482 (16.4)	346 (16.5)	136 (16)	0.718
History of hypertension, n (%)	1424 (48.4)	883 (42.2)	541 (63.6)	< 0.001
History of hyperlipidemia, n (%)	2104 (71.4)	1455 (69.5)	649 (76.3)	< 0.001
SBP (mmHg)	130.0 ± 14.6	128.0 ± 13.4	134.8 ± 16.3	< 0.001
DBP (mmHg)	80.1 ± 9.8	79.2 ± 9.2	82.3 ± 10.8	< 0.001
BMI (kg/m2)	25.3 ± 3.8	25.1 ± 3.7	25.8 ± 4.2	< 0.001
FBG (mmol/L)	9.1 ± 3.4	8.9 ± 3.2	9.6 ± 3.7	< 0.001
FCp (ng/mL)	1.9 ± 1.2	1.8 ± 1.0	2.1 ± 1.5	< 0.001
HbA1c (%)	9.6 ± 2.5	9.5 ± 2.5	9.9 ± 2.4	< 0.001
ALT (IU/L)	24.0 (16.0, 37.0)	23.0 (16.0, 37.0)	24.0 (16.0, 39.0)	0.936
AST (IU/L)	20.0 (16.0, 27.0)	20.0 (16.0, 27.0)	20.0 (16.0, 28.0)	0.971
BUN (mmol/L)	5.3 ± 1.9	5.1 ± 1.6	5.9 ± 2.3	< 0.001
Scr (μmol/L)	68.8 ± 23.4	64.8 ± 14.0	78.7 ± 35.8	< 0.001
e-GFR (mL/min per 1.73 m2)	106.9 ± 31.3	110.3 ± 26.9	98.5 ± 38.8	< 0.001
UA (μmol/L)	339.7 ± 99.8	331.7 ± 95.1	359.4 ± 108.1	< 0.001
TG (mmol/L)	1.8 (1.2, 2.7)	1.7 (1.2, 2.5)	2.0 (1.4, 3.2)	< 0.001
TC (mmol/L)	4.9 ± 1.2	4.9 ± 1.1	4.9 ± 1.4	0.147
HDL-C (mmol/L)	1.0 ± 0.3	1.0 ± 0.3	1.0 ± 0.3	< 0.001
LDL-C (mmol/L)	2.9 ± 0.9	2.9 ± 0.9	2.8 ± 0.9	0.038
UACR (mg/g)	12.8 (6.7, 33.7)	8.7 (5.6, 14.8)	74.8 (43.0, 212.0)	< 0.001
FT3 (pg/ml)	3.1 ± 0.5	3.1 ± 0.4	3.0 ± 0.5	< 0.001
FT4 (ng/dl)	1.3 ± 0.2	1.3 ± 0.2	1.3 ± 0.2	0.801
TSH (μIU/ml)	1.7 ± 0.8	1.7 ± 0.8	1.7 ± 0.8	0.247

Notes: Data are presented as median (IQR) or numbers (%).

Abbreviations: DKD, diabetic kidney disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; FBG, fasting blood glucose; FCp, fasting serum C peptide; HbA1c, glycated hemoglobin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; Scr, serum creatinine; eGFR, estimated glomerular filtration rate; UA, uric acid; TG, triglycerides; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; UACR, urinary albumin to creatinine ratio; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone.

Relationships Between Thyroid Hormone Levels and DKD

Multivariable logistic regression was utilized to probe the interplay between thyroid hormone levels and DKD (Table 2). Across all three progressively adjusted models, FT3 levels were independently linked to DKD risk. In contrast, FT4 and TSH levels were not significantly linked to DKD in any model. Relative to those in the bottom FT3 quartile (Q1), those in Q2, Q3, and Q4 exhibited markedly less likelihood of DKD, with adjusted ORs of 0.63 (95% CI: 0.50–0.80, p < 0.001), 0.60 (95% CI: 0.47–0.77, p < 0.001), and 0.63 (95% CI: 0.49–0.82, p = 0.001), respectively (Table 2). Further RCS regression indicated a non-linear, L-shaped association between FT3 and DKD (p for non-linearity < 0.001), as depicted in Figure 2. Threshold effect analysis identified an inflection point at approximately 3.09 pg/mL. Below this threshold, every 1 pg/mL increase in FT3 was related to a 37.8% reduction in the likelihood of DKD (OR = 0.622, 95% CI: 0.515–0.751, p < 0.001) (Table 3). However, no association was evident when FT3 levels were at or above 3.09 pg/mL, suggesting a plateau effect at higher concentrations.

Table 2.

Multivariate Regression Evaluation of Links Between Thyroid Hormone Levels and DKD

Variables	Model 1		Model 2		Model 3
	OR (95% CI)	P-value	OR (95% CI)	P-value	OR (95% CI)	P-value
FT3 (pg/ml)	0.62 (0.52–0.74)	<0.001	0.63 (0.53–0.76)	<0.001	0.65 (0.53–0.81)	<0.001
Q1	Reference		Reference		Reference
Q2	0.63 (0.5–0.79)	<0.001	0.64 (0.51–0.8)	<0.001	0.63 (0.5–0.8)	<0.001
Q3	0.57 (0.46–0.72)	<0.001	0.58 (0.46–0.73)	<0.001	0.6 (0.47–0.77)	<0.001
Q4	0.58 (0.47–0.72)	<0.001	0.6 (0.48–0.76)	<0.001	0.63 (0.49–0.82)	0.001
FT4 (ng/dl)	0.94 (0.58–1.52)	0.801	1.12 (0.68–1.83)	0.664	1.04 (0.62–1.75)	0.876
Q1	Reference		Reference		Reference
Q2	0.94 (0.75–1.18)	0.609	0.97 (0.77–1.21)	0.774	0.98 (0.78–1.25)	0.895
Q3	0.86 (0.69–1.09)	0.215	0.91 (0.72–1.14)	0.407	0.94 (0.74–1.2)	0.605
Q4	0.93 (0.74–1.17)	0.529	1 (0.79–1.26)	0.979	0.98 (0.77–1.25)	0.864
TSH (μIU/ml)	1.06 (0.96–1.17)	0.247	1.06 (0.96–1.17)	0.236	1.04 (0.93–1.15)	0.507
Q1	Reference		Reference		Reference
Q2	0.83 (0.66–1.04)	0.111	0.83 (0.66–1.04)	0.11	0.81 (0.64–1.03)	0.082
Q3	0.96 (0.77–1.2)	0.728	0.96 (0.77–1.21)	0.755	0.94 (0.74–1.18)	0.58
Q4	1.12 (0.9–1.4)	0.323	1.12 (0.89–1.4)	0.329	1.06 (0.84–1.33)	0.651

Notes: Model 1: unadjusted; Model 2: Adjusted for sex and age; Model 3: Model 2 + HbA1c, diabetes duration, BMI, education, drinking, smoking, hypertension, hyperlipidemia.

Abbreviations: DKD, diabetic kidney disease; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone; OR, odds ratio; CI, confidence interval; T, tertile; BMI, body mass index; HbA1c, glycated hemoglobin.

Figure 2.

Restricted cubic spline analyses of thyroid hormone levels and DKD in euthyroid participants with T2DM. Binomial regression analyses with a restricted cubic spline analysis were conducted, and the results are presented as odds ratio (solid lines) and 95% confidence interval (curved lines). Analyses included adjustment for age, sex, HbA1c, drinking, smoking, diabetes duration, BMI, hypertension, and hyperlipidemia.

Table 3.

Two-Piecewise Regression Model-Based Analysis of the Association Between FT3 and DKD

FT3 (pg/ml)	Adjusted Model
	OR (95% CI)	P-value
< 3.09	0.622 (0.515–0.751)	< 0.001
≥ 3.09	1.621 (0.378–6.941)	0.515
Likelihood Ratio test		0.001

Notes: Analyses included adjustments for age, sex, diabetes duration, HbA1c, drinking, smoking, BMI, hypertension, and hyperlipidemia.

Abbreviations: DKD, diabetic kidney disease; FT3, free triiodothyronine; OR, odds ratio; CI, confidence interval.

Stratified Analyses

To explore potential modifiers of the association linking FT3 levels and DKD, subgroup analyses were conducted according to age, sex, and education. The association between FT3 and DKD was generally consistent across most clinically relevant subgroups, although the effect did not reach statistical significance in certain strata, particularly among younger participants (< 50 years) and those with higher education levels (high school and above), where the confidence intervals crossed the null value, with no statistically significant interactions detected (Figure 3).

Figure 3.

Subgroup analyses of the interplay between FT3 and DKD in euthyroid participants with T2DM. Analyses were adjusted for all of the following other than the variable being used for stratification: age, sex, diabetes duration, HbA1c, drinking, smoking, BMI, hypertension, and hyperlipidemia.

Discussion

This cross-sectional analysis of euthyroid individuals with T2DM revealed a significant inverse and non-linear relationship between FT3 levels and the presence of DKD, while no such association was found for FT4 or TSH. Specifically, FT3 displayed an L-shaped relationship with DKD risk, with a threshold inflection point at approximately 3.09 pg/mL. Below this level, a higher FT3 level was associated with a substantially lower odds of DKD, however, beyond this point, the protective effect plateaued. These findings was generally consistent across most subsets of study subjects.

Previous research investigating the link between thyroid function and DKD has yielded inconsistent findings. Zheng et al¹² reported that hypothyroidism is associated with albuminuria and reduced eGFR in T2DM patients, which indirectly supports a protective role of higher thyroid hormone levels. However, a propensity score-matched study found no differences in FT3, FT4, or TSH between DKD and non-DKD groups,¹⁵ a discrepancy that may stem from insufficient statistical power or selective matching that inadvertently balanced thyroid hormone levels.¹⁵ Regarding FT4, the evidence remains contradictory. A large-scale investigation involving over 7000 patients reported that elevated FT4 levels may offer protection against DKD,¹⁶ while a separate cross-sectional study of more than 4000 individuals found that higher FT4 levels were positively correlated with DKD incidence.¹⁷ These opposing findings highlight the possibility that FT4 may play a context-dependent or indirect role, whereas the role of FT3 appears more consistent. Indeed, the present findings align more closely with those studies emphasizing the role of FT3 in renal function.

Han et al¹⁸ observed that T2DM patients with DKD and lower FT3 levels had more pronounced proteinuria, reduced renal function, and more severe glomerular damage. Gao and Liu¹⁹ similarly identified a negative correlation between FT3 and UACR and a positive correlation with eGFR, suggesting that lower levels of FT3 levels are linked with greater renal impairment. Liu et al²⁰ observed that higher FT3 concentrations in the sera markedly reduced the odds of moderate to high-risk DKD. Yang et al²¹ confirmed that reduced FT3 is independently linked to DKD progression, with sex-specific thresholds. A study conducted in China found that FT3 was independently associated with UACR, and even in the euthyroid range, lower FT3 levels were more closely related to reduced eGFR in diabetic patients.¹⁷ Notably, Zou et al²² reported a decreasing trend in DKD prevalence across increasing FT3 quartiles, with this pattern seen in males rather than females. In contrast, our analysis did not detect a significant sex interaction, suggesting that the FT3–DKD relationship may be independent of sex in our population, possibly due to a larger sample size or differences in baseline characteristics.

A novel and clinically important finding of this study is the identification of an L-shaped association between FT3 and DKD, with an inflection point at approximately 3.09 pg/mL (equivalent to ~4.76 pmol/L). To our knowledge, few studies have explicitly examined threshold effects of FT3 on DKD in euthyroid T2DM populations. However, some indirect evidence supports the existence of such a threshold. Yang et al²¹ reported sex-specific FT3 cutoffs (≤4.30 pmol/L for males and ≤3.99 pmol/L for females) for predicting renal function decline, which are remarkably close to our inflection point when converted to similar units. This consistency across independent cohorts strengthens the validity of a threshold effect. The threshold of 3.09 pg/mL could serve as a practical reference for risk stratification in euthyroid T2DM patients. Individuals with FT3 levels below this value may warrant closer monitoring for early signs of DKD, such as microalbuminuria or a declining eGFR trajectory. From a therapeutic perspective, our findings suggest that simply increasing FT3 to supraphysiological levels is unlikely to provide additional renoprotective benefits. Instead, maintaining FT3 levels above the inflection point rather than achieving the highest possible levels may represent a reasonable clinical goal. This is particularly relevant given the potential adverse effects of excess thyroid hormone on cardiac function and bone metabolism. Thus, the identified threshold provides a potential target for future interventional studies aiming to normalize FT3 levels in high-risk euthyroid T2DM patients.

Based on the current findings, further exploration is warranted to clarify the mechanisms through which FT3 levels affect DKD risk. Thyroid hormones play critical roles in kidney development, regulate glomerular and tubular function, and modulate renal hemodynamics via their influence on renal blood flow and systemic circulation.^9,23 Additionally, these hormones can activate the renin-angiotensin-aldosterone system, thereby indirectly affecting renal function through cardiovascular and hemodynamic pathways.²⁴ Although the precise role of FT3 in DKD pathogenesis remains to be fully elucidated, several interconnected mechanisms have been proposed, progressing from vascular to cellular and anti-inflammatory pathways.

First, reduced FT3 levels have been linked to endothelial dysfunction.²⁵ As FT3 is involved in the modulation of endothelial activity and vasodilation, a deficiency may compromise vascular integrity, increase vascular permeability, and promote glomerular endothelial injury, thereby contributing to DKD progression.²⁶ Second, at the cellular level, FT3 has been shown to mitigate hyperglycemia-induced renal damage by enhancing phosphatidylinositol 3-kinase (PI3K) signaling and downregulating transforming growth factor-beta 1 (TGF-β1), which may collectively improve insulin sensitivity and reduce renal injury.²⁷ Third, FT3 metabolites may exert nephroprotective effects by activating sirtuin 1 (SIRT1), which helps lower albuminuria and alleviate renal damage.²⁸ Finally, emerging evidence suggests an inverse association between systemic inflammation and FT3 levels, implying that inflammatory processes could exacerbate DKD by modulating FT3 concentrations.²⁹ Taken together, these pathways suggest that low FT3 contributes to DKD through a vicious cycle involving endothelial damage, fibrotic signaling, mitochondrial dysfunction, and chronic inflammation. Despite these insights, the full spectrum of the involvement of FT3 in DKD development remains incompletely understood, underscoring the need for additional mechanistic studies.

This study has several strengths. It includes a relatively large sample of euthyroid T2DM participants (n = 2945), providing adequate statistical power to detect non-linear and threshold effects of FT3 on DKD. We employed robust analytical methods, including multivariable logistic regression, restricted cubic splines, and two-piecewise regression, which enhance the reliability of the inflection point estimate. Extensive subgroup analyses and interaction tests confirmed the general consistency of the FT3–DKD association across different strata. The lack of statistical significance in certain subgroups, such as younger participants and those with higher education levels, may be partially attributable to the relatively limited sample sizes and lower event rates in these strata, which reduced statistical power to detect a significant association. Additionally, restricting to euthyroid participants minimized confounding by overt thyroid dysfunction, allowing us to isolate the effect of physiological FT3 variations. Our findings have direct clinical implications for managing euthyroid T2DM patients. Routine FT3 assessment, even when TSH and FT4 are normal, may help identify individuals at higher DKD risk, and those with FT3 below 3.09 pg/mL should be prioritized for regular monitoring of UACR and eGFR. Although interventional evidence is lacking, our results suggest that maintaining or modestly increasing FT3 within the physiological range may confer renoprotective benefits, whereas aggressive elevation of FT3 appears unnecessary. Low-normal FT3 should not be dismissed as clinically irrelevant; rather, it may serve as a red flag for DKD risk. Collectively, this study supports incorporating FT3 into DKD risk prediction models and calls for future prospective studies to evaluate FT3-targeted interventions.

There are several limitations in this study. First, the cross-sectional and single-center design of this study preclude any inference of causality between thyroid hormone levels and DKD. While our cohort comprised stable T2DM patients without acute illness, the observed association could still be explained, at least in part, by reverse causation, unmeasured confounding, or low FT3 as a marker of non-thyroidal illness rather than a direct pathogenic factor. Future longitudinal and multicenter investigations are needed to confirm these associations. Second, the study population was drawn exclusively from a Chinese cohort, which may reduce generalizability to other ethnic or racial groups with different distributions of thyroid function and DKD risk factors. Third, although the analysis focused on T2DM patients with normal thyroid function, it did not include an assessment of thyroid autoantibodies, which may have introduced a degree of residual confounding.³⁰ Future studies should incorporate autoantibody measurements to more comprehensively characterize thyroid status. Finally, despite adjusting for numerous relevant covariates, the potential for unmeasured or unidentified confounders (such as trace element deficiencies, inflammatory markers, or nutritional status) cannot be entirely excluded.

Conclusion

In summary, lower FT3 in the sera were independently linked with a greater prevalence of DKD among euthyroid individuals with T2DM. The relationship between FT3 and DKD was characterized by a non-linear, L-shaped curve, with a notable inflection point at approximately 3.09 pg/mL. The results support further research into thyroid hormone modulation as a potential avenue for preventing or mitigating DKD progression.

Funding Statement

This study was supported by grants from Science and Technology Plan Project of Taizhou (No.24ywb44).

Data Sharing Statement

The datasets of the study are available from the corresponding author upon reasonable request.

Ethics Approval and Informed Consent

The study protocol was approved by the Ethics Committees of Taizhou Central Hospital (Taizhou University Hospital)(No. 202047) and adhered to the Declaration of Helsinki and STROBE guidelines. All participants provided written informed consent.

Author Contributions

MDC: Conceptualization, Funding acquisition, Methodology, Writing – original draft. NYC: Data curation, Writing – review & editing. PF: Data curation, Writing – review & editing. YZ: Formal analysis, Writing – review & editing. TL: Formal analysis, Writing – original draft. MF: Methodology, Project administration, Supervision, Writing – review & editing. All authors gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.