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Abstract

Objective

To investigate the association between hypothyroidism and incident chronic kidney disease and identify potential mediators.

Methods

This study included a retrospective cohort and Mendelian randomization analysis. Data were obtained from the UK Biobank and FinnGen via the Integrative Epidemiology Unit Open Genome-Wide Association Studies Project. After exclusions, 439,381 participants were included. The risk of incident chronic kidney disease was the primary outcome. Associations were assessed using Fine and Gray models as well as stratified and sensitivity analyses, and the cumulative risk was presented using Kaplan–Meier curves. For Mendelian randomization analyses, causal effects were estimated using the inverse-variance weighted method. Mendelian randomization–Egger regression, Mendelian randomization–pleiotropy residual sum and outlier test, weighted median method, and weighted mode method were used to assess the robustness of the causal estimates. Mediation analysis was conducted to explore the effects of body mass index, inflammatory factors, and senescence-associated secretory phenotype proteins.

Results

Hypothyroidism was associated with a higher risk of incident chronic kidney disease (hazard ratio: 1.35, 95% confidence interval: 1.19–1.53). Mendelian randomization analysis supported a causal effect (odds ratio: 8.23, 95% confidence interval: 3.22–21.00). Absolute risk differences increased over time (5 years, 1.10; 8 years, 1.80; 10 years, 2.40; and 12 years, 3.13). Body mass index and obesity partially mediated this effect. Senescence-associated secretory phenotype proteins were also identified as important mediators, including transforming growth factor-alpha, transforming growth factor-beta, transforming growth factor-beta receptors 2 and 3, growth differentiation factor 15, C-X-C motif chemokine-10, matrix metalloproteinase 1, and matrix metalloproteinase 9.

Conclusions

Hypothyroidism was associated with incident chronic kidney disease. Obesity and senescence-associated secretory phenotype proteins may mediate this relationship.

Keywords

Hypothyroidism chronic kidney disease senescence-associated secretory phenotype protein UK Biobank cohort study Mendelian randomization mediation analysis

Introduction

Chronic kidney disease (CKD) is characterized by progressive and irreversible deterioration of renal function, representing the advanced stages of various chronic kidney disorders. As a major global health concern, CKD significantly contributes to the cardiovascular disease (CVD) burden.^1,2 The prevalence of CKD continues to rise across all age groups, driven by demographic aging and increasing rates of diabetes, obesity, and hypertension.³

Hypothyroidism, resulting from various etiologies that impair thyroid hormone production, typically requires lifelong exogenous hormone replacement to manage clinical symptoms; however, this treatment does not address the root cause. The pathophysiological relationship between hypothyroidism and CKD has historically received limited research attention.

Substantial evidence indicates that thyroid hormones play a direct role in renal growth and development, glomerular filtration rate (GFR), renal transport systems, and sodium–water homeostasis.⁴ The effects of hypothyroidism on renal function may be mediated through several mechanisms, including reduced renal perfusion pressure (due to decreased cardiac output and increased vascular resistance),⁵ diminished sensitivity to sympathetic drive, and suppressed renin–angiotensin–aldosterone system activity.⁶ A recent study suggested that both elevated and reduced thyroid hormone levels are associated with an increased risk of CKD.⁵ Furthermore, CKD patients with concomitant hypothyroidism exhibit a higher likelihood of progression to kidney failure and increased all-cause mortality than those without hypothyroidism.^7,8 However, it remains unclear whether these renal effects stem from direct hormonal actions or secondary physiological and biochemical alterations induced by hypothyroidism.

Hypothyroidism exerts considerable effects on hepatic function, including alterations in liver metabolism and the promotion of hepatic fibrosis.^9,10 A recent study by Kiourtis et al. demonstrated renal p21 expression and functional impairment in a model of CCl₄-induced liver injury and aging.¹¹ Notably, even in the absence of direct liver damage—when aging was induced solely through oncogene activation—similar renal p21 upregulation and dysfunction were observed.¹⁰ These findings suggest that interorgan senescence signaling contributes to cross-tissue pathological effects. Consequently, hypothyroidism could influence CKD progression through mechanisms extending beyond direct hormonal actions.

Recent studies have highlighted the significant involvement of inflammation in CKD development.^12,13 Moreover, senescence-associated secretory phenotype (SASP) proteins play a crucial role in the onset and progression of the disease.^14,15 Based on this, we analyzed a broad range of inflammatory markers and SASP components to identify potential mediators.

This retrospective cohort study based on data from the UK Biobank (UKB) aimed to elucidate the causal relationship between hypothyroidism and CKD while identifying potential mediating pathways, revealing findings that could inform preventive strategies for CKD in hypothyroidism patients.

Methods

Source population

After obtaining approval by the North West Multi-center Research Ethics Committee, the UKB database enrolled more than 500,000 volunteers aged 40–69 years from 22 assessment centers across England, Scotland, and Wales between 2006 and 2010. Participants were excluded if they met any of the following criteria: (a) pre-existing CKD diagnosis per the International Classification of Diseases, 10th Revision (ICD-10) criteria prior to baseline assessment; (b) baseline urinary albumin-to-creatinine ratio (UACR) >30 mg/g; (c) estimated GFR (eGFR) <60 mL/min/1.73 m²; (d) CKD diagnosis occurring either within 1 year of cohort entry or within 1 year following hypothyroidism onset; and (e) development of hypothyroidism after cohort entry (Supplementary Figure 1). All participants included in the cohort were of White ethnicity. We calculated the UACR using urinary microalbumin and creatinine data from the UKB. The eGFR was calculated using the CKD-creatinine Epidemiology Collaboration equation.¹⁶

Exposure and outcome ascertainment

Clinical diagnoses were systematically coded in compliance with the diagnostic standards outlined in the International Statistical Classification of Diseases and Related Health Problems. The exposure in our study was defined as a history of hypothyroidism diagnosed after the year 2000 and prior to cohort inclusion, specifically coded as E03 (first-reported other hypothyroidism). This definition excluded patients with subclinical iodine-deficiency hypothyroidism as well as those diagnosed with hypothyroidism before 2000 or after cohort inclusion. The risk of incident CKD was the primary outcome, with the corresponding ICD-10 code N18. The follow-up end date was 1 December 2022, which was used as the censoring date for other participants if no CKD, no death, or loss to follow-up had been recorded.

Covariates and laboratory data

Covariates included age, sex, body mass index (BMI), hypertension, diabetes and CVD, ever smoked, alcohol consumption, sleep status, and Townsend deprivation index. The diagnosis of hypertension was defined by the presence of ICD-10 codes for essential (primary) hypertension, hypertensive heart disease with (congestive) heart failure, and hypertensive heart disease without (congestive) heart failure or by a systolic blood pressure level ≥140 mmHg, diastolic blood pressure level ≥90 mmHg, or the presence of hypertension-related treatment. The diagnostic criteria for CVD included the presence of any of the following in the ICD-10: atherosclerotic heart disease, atrial fibrillation, heart failure, stroke, occlusion and stenosis of the carotid artery, cerebral atherosclerosis, cerebrovascular disease (unspecified), peripheral vascular disease (unspecified), angina pectoris, acute myocardial infarction, hypertensive heart disease with (congestive) heart failure, hypertensive heart disease without (congestive) heart failure, other venous embolism and thrombosis, or abdominal aortic aneurysm (without mention of rupture). The covariate ever smoked was classified into the following two types: “yes” and “no.” Alcohol consumption was classified as “never,” “previous,” or “current.” Sleep status was categorized into the following three groups based on the hours of sleep: <6 h as short sleep, >9 h as long sleep, and the rest as normal sleep. The ICD-10 ID and field ID of the above covariates in the UKB are presented in Supplementary Table 1.

The protein mediators analyzed in this study included interleukin (IL)-1 alpha (1-α), IL-1-β, IL-6, IL-11, IL-33, IL-1-receptor-1, IL-1-receptor-2, transforming growth factor-α (TGF-α), TGF-β, TGF-β-receptor-1, TGF-β-receptor-2, TGF-β-receptor-3, growth differentiation factor 15 (GDF15), hepatocyte growth factor (HGF), matrix metalloproteinase (MMP)-1, MMP3, MMP9, C-C motif chemokine 2 (CCL-2), CCL-5, C-X-C motif chemokine (CXCL)-1, CXCL-3, CXCL-5, and CXCL-10. All abovementioned data correspond to NPX values for each participant, as measured by Olink. Other inflammatory indicators analyzed in this study included C-reactive protein, platelet-to-lymphocyte ratio, and neutrophil-to-lymphocyte ratio. All laboratory data included in the study passed the quality control process. The BMI in the aforementioned data also served as a mediator in our study. Obesity was defined based on whether the BMI was greater than 28 kg/m², which was also considered a mediator.

Mendelian randomization (MR)

For MR analysis, the genetic data related to hypothyroidism and CKD for participants of White ethnicity were obtained from the FinnGen and UKB, respectively. In addition, genetic data related to hypothyroidism and CKD in Asians were obtained from the Genome-Wide Association Studies (GWAS) database. The outcomes included CKD, eGFR based on creatinine values, and eGFR based on cystatin C level. We used several commonly employed methods to estimate the causal effect of hypothyroidism on CKD risk.

Statistical analyses

Baseline characteristics were compared using chi-square tests for categorical variables and unpaired t-tests for continuous variables. This study was designed as a retrospective cohort study using data from the UKB. The association of hypothyroidism with incident CKD was assessed using the Fine and Gray model and presented as hazard ratios (HRs) with 95% confidence intervals (CIs). Three models were generated for the analysis in the UKB cohort: model 1 was adjusted for age, sex, and BMI; model 2 was further adjusted for hypertension, diabetes, and CVD; and model 3 was further adjusted for Thomson’s Deservability score, sleep duration, alcohol consumption, and smoking status. We conducted stratified analyses to evaluate the association between hypothyroidism and incident CKD. Individuals were stratified by age (<65 and ≥65 years), BMI (<28 and ≥28 kg/m²), hypertension (yes or no), CVD (yes or no), and diabetes (yes or no). The association of hypothyroidism with CKD risk was evaluated in the main-effect and interaction models. The Fine and Gray model was performed using the fastcmprsk package. The convergence tolerance level was set to 1e-6, and the maximum number of iterations for the algorithm was set to 1000. Missing values in the analysis were omitted.

The cumulative risk of incident CKD between the hypothyroidism and non-hypothyroidism groups was presented using Kaplan–Meier (KM) curves. The absolute risk was calculated to determine the risk of CKD occurrence within a specific time. We excluded patients diagnosed with CKD within the first 3 and 5 years of follow-up to perform a sensitivity analysis. The KM curve and absolute risk were calculated using the survival and rms packages, respectively.

In the MR analysis, we used odds ratios and 95% CIs to estimate the CKD risk attributable to hypothyroidism. For the primary MR analysis, we utilized the inverse-variance weighted (IVW) method to estimate the causal effect. Several other MR analyses, including MR-Egger regression, MR PRESSO, weighted median, and weighted mode, were performed to assess the robustness of the results. We used the MR-Egger intercept test and global test to assess for horizontal pleiotropy and Cochran’s Q statistic to assess for population heterogeneity. To confirm the causal effect of any single-nucleotide polymorphism (SNP), we conducted a leave-one-out analysis by discarding each exposure-associated SNP and repeatedly performing IVW analysis. MR analysis was performed using the TwoSampleMR package. We selected SNPs with a p-value threshold of 5 × 10⁻⁸ to ensure strong associations with the exposure variable and applied a less strict linkage disequilibrium threshold of r² < 0.001. To remove correlated SNPs, we applied a clumping window of 10,000 kilobases.

We performed causal mediation analysis using structural equation modelling for a Cox proportional hazards model to investigate whether the association between hypothyroidism and CKD was mediated by BMI, inflammatory markers, and SASP proteins. We used counterfactual modelling and calculated CIs by employing bootstrapping (nboot = 500) using the ‘CMAverse’ package in R software (version 4.4.1; R Foundation for Statistical Computing). The mediation effect was estimated using parametric function method, and multiple imputation was utilized to handle missing data. The overall study design is shown in Supplementary Figure 1.

Statistical analyses were performed using R software (version 4.4.1; R Foundation for Statistical Computing); ggplot2 package was used for data visualization.

Patient and public involvement

Patients and the public were not involved in the design, implementation, reporting, or dissemination plans of this research. Patient consent for publication was not required. All procedures were performed in accordance with the ethical principles of the Declaration of Helsinki (1975), as revised in 2024.

Ethical approval

The UKB has received ethical approval from the North West Multi-centre Research Ethics Committee (REC reference: 21/NW/0157). Our project ID was 134892. All studies incorporated in this research adhered to the ethical standards and guidelines delineated in the Declaration of Helsinki (1975), as revised in 2024. Similarly, the GWAS database used for MR analysis was accompanied by pertinent ethical compliance.

The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.¹⁷

Results

Baseline characteristics

The baseline characteristics of the participants from the UKB are presented in Table 1. In total, 439,381 participants were included in the present study, of whom 14,535 had hypothyroidism and 242,846 did not. Except for smoking status, all other variables showed statistically significant differences. The average age of the population was 56.5 years, and 53.3% of the participants were female. Most individuals with hypothyroidism were women (81.6%). Hypothyroidism patients were more likely to have a higher BMI (28.42 ± 5.36 vs. 27.29 ± 4.69 kg/m²) and suffer from hypertension (63.5% vs. 60.7%), CVD (15.7% vs. 13.1%), and diabetes (6.6% vs. 4.4%). The Thomson’s Deprivation Index (−1.27 ± 3.05 vs. −1.47 ± 2.99) and sleep duration (7.20 ±1.20 vs. 7.16 ± 1.08 h) were also higher in individuals with hypothyroidism. Individuals with hypothyroidism had lower eGFR than those without hypothyroidism (92.25 ± 13.42 vs. 95.21 ± 12.42 mL/min/1.73 m²). Furthermore, the overall incidence of CKD was 4.42%, with the incidences being 7.35% in those with hypothyroidism and 4.31% in those without hypothyroidism.

Table 1.

Baseline characteristics grouped by hypothyroidism in the UKB cohort.

	All	Hypothyroidism	Non-hypothyroidism	p-value
Age at recruitment (years), mean ± SD	56.54 ± 8.04	57.98 ± 7.49	56.49 ± 8.06	<0.001
Sex				<0.001
Female	234,111 (53.3%)	11,856 (81.6%)	222,255 (52.3%)
Male	205,270 (46.7%)	2679 (18.4%)	202,591 (47.7%)
BMI (kg/m²), mean ± SD	27.32 ± 4.72	28.42 ± 5.36	27.29 ± 4.69	<0.001
Hypertension				<0.001
Yes	253,991 (60.9%)	8789 (63.5%)	245,202 (60.7%)
No	163,231 (29.1%)	5051 (26.5%)	158,180 (29.3%)
CVD				<0.001
Yes	58,128 (13.2%)	2281 (15.7%)	55,847 (13.1%)
No	381,253 (86.8%)	12,254 (84.3%)	368,999 (86.9%)
Diabetes				<0.001
Yes	19,694 (4.5%)	957 (6.6%)	18,737 (4.4%)
No	418,688 (95.5%)	13,539 (93.4%)	405,149 (95.6%)
Thomson’s Deservability score, mean ± SD	−1.47 ± 2.99	−1.27 ± 3.05	−1.47 ± 2.99	<0.001
Sleep duration (h), mean ± SD:	7.16 ± 1.08	7.20 ± 1.20	7.16 ± 1.08	<0.001
Ever smoked (N)				0.3898
Yes	265,906 (60.5%)	8746 (60.2%)	257,160 (60.5%)
No	173,475 (39.5%)	5789 (39.8%)	167,686 (39.5%)
Alcohol consumption status (N)				<0.001
Never	13,524 (3.0%)	732 (5.0%)	12,792 (3.0%)
Previous	14,714 (3.4%)	728 (5.0%)	13,986 (3.3%)
Current	410,771 (93.6%)	13,066 (90.0%)	397,705 (93.7%)
eGFR (mL/min/1.73 m²)	95.11 ± 12.46	92.25 ± 13.42	95.21 ± 12.42	<0.001
CKD	4.42%	7.35%	4.31%	<0.001

BMI: body mass index; CVD: cardiovascular diseases; eGFR: estimated glomerular filtration rate; CKD: chronic kidney disease; UKB: UK Biobank.

Hypothyroidism and incident CKD: results from the UKB cohort

The results of the multivariate Fine and Gray model are shown in Table 2. Compared with participants without hypothyroidism, those with hypothyroidism had a significantly higher risk of CKD: model 1, HR: 1.49, 95% CI: 1.40–1.59; model 2, HR: 1.37, 95% CI: 1.29–1.46; and model 3, HR: 1.35, 95% CI: 1.19–1.53. After performing stratified analysis, hypothyroidism remained a significant risk factor for CKD (Supplementary Figure 2). In the stratified analyses, hypothyroidism demonstrated the strongest association with CKD risk in the following specific population strata: participants aged <60 years with BMI ≥28 kg/m² (HR: 1.96, 95% CI: 1.68–2.27) and normal blood pressure (HR: 1.95, 95% CI: 1.64–2.32). Notably, the effect size was more modest in the diabetic population stratum (HR: 1.23, 95% CI: 1.06–1.43).

Table 2.

Fine and Gray model for the associations of hypothyroidism with the risk of incident CKD.

	Model 1		Model 2		Model 3
	HR (95% CI)	p-value	HR (95% CI)	p-value	HR (95% CI)	p-value
Non-hypothyroidism	ref		ref		ref
Hypothyroidism	1.49 (1.40–1.59)	<0.001	1.37 (1.29–1.46)	<0.001	1.35 (1.19–1.53)	<0.001

Model 1: adjusted for age, sex, and BMI; model 2: adjusted for age, sex, BMI, hypertension, CVD, and diabetes; model 3: adjusted for age, sex, BMI, hypertension, CVD, diabetes, Thomson’s Deservability score, sleep duration, alcohol consumption, and smoking status.

BMI: body mass index; CVD: cardiovascular diseases; CKD: chronic kidney disease.

Subgroup analyses, sensitivity analyses, and absolute risk: results from the UKB cohort

Subgroup analyses for the association of hypothyroidism with CKD risk are presented in Supplementary Figure 3. Significant interactions were observed between hypothyroidism and all covariates for the risk of CKD (p_interaction < 0.001). In the sensitivity analyses, excluding participants diagnosed with CKD within the first 3 or 5 years, the association between hypothyroidism and CKD remained significant (p < 0.001) (Supplementary Table 2). The interaction between hypothyroidism and enhanced polygenic risk score (PRS) for eGFR (creatinine based) was not significant (p > 0.05). Thus, the impact of hypothyroidism on CKD did not change with alterations in the PRS.

Table 3 shows the absolute risk of CKD at 5, 8, 10, and 12 years. The absolute risks (95% CIs) were higher in those with hypothyroidism than in those without: 5 years, 2.62 (2.56–2.67) vs. 1.52 (1.52–1.53); 8 years, 4.33 (4.24–4.42) vs. 2.53 (2.52–2.54); 10 years, 5.85 (5.74–5.97) vs. 3.45 (3.43–3.46); 12 years, 7.72 (7.57–7.86) vs. 4.59 (4.58–4.61), respectively. Additionally, the gap in the risk between the two groups also widened over time: risk difference (95% CIs); 5 years, 1.10 (1.04–1.14); 8 years, 1.80 (1.72–1.88); 10 years, 2.40 (2.31–2.51); and 12 years, 3.13 (2.99–3.25). Similarly, the Kaplan–Meier curve indicated that the risk of developing CKD increases over time, and the gap between individuals with and without hypothyroidism widens progressively (Supplementary Figure 4).

Table 3.

Absolute risk for the associations of hypothyroidism with CKD risk.

	Absolute risk, % (95% CI)
5 years
Hypothyroidism	2.62 (2.56–2.67)
Non-hypothyroidism	1.52 (1.52–1.53)
Risk difference	1.10 (1.04–1.14)
8 years
Hypothyroidism	4.33 (4.24–4.42)
Non-hypothyroidism	2.53 (2.52–2.54)
Risk difference	1.80 (1.72–1.88)
10 years
Hypothyroidism	5.85 (5.74–5.97)
Non-hypothyroidism	3.45 (3.43–3.46)
Risk difference	2.40 (2.31–2.51)
12 years
Hypothyroidism	7.72 (7.57–7.86)
Non-hypothyroidism	4.59 (4.58–4.61)
Risk difference	3.13 (2.99–3.25)

The analysis was adjusted for age, sex, BMI, hypertension, CVD, and diabetes.

CKD: chronic kidney disease; CI: confidence interval; BMI: body mass index; CVD: cardiovascular disease.

Hypothyroidism and incident CKD: results from MR analysis

The results of MR analyses are shown in Supplementary Table 3. In the two-sample MR analyses based on data from the UKB, GWAS, and Finnish GWAS databases for CKD, hypothyroidism was associated with an increased risk of CKD (p_ivw < 0.05). Moreover, genetic data from both Asian and White populations yielded concordant results, further supporting the robustness of this association. All Egger’s tests indicated that the heterogeneity was not significant (p_{intercept Egger} > 0.05). However, Cochran’s Q and the global test showed significant heterogeneity (p_{Q IVW} < 0.05, p_{global test} < 0.05).

In the two-sample MR analyses for CKD in Asian populations, hypothyroidism was also associated with an increased risk of CKD (p_ivw < 0.05). Moreover, both Egger’s tests, global test, and Cochran’s Q indicated that the heterogeneity was not significant (p_{intercept Egger} = 0.427, p_Q IVW =0.138, p_{global test} = 0.166).

Mediation analysis

After adjusting for age, sex, hypertension, CVD, and diabetes, the pure natural indirect effect of BMI yielded an HR of 1.06, with a proportion mediated (PM) of 18% (p < 0.05). For obesity (BMI ≥ 28 kg/m²), the HR was 1.02, with a PM of 12% (p < 0.05) (Table 4). In addition, we investigated several SASP proteins. Multiple transforming growth factors exerted significant mediating effects, including TGF-α (HR, 1.03; 95% CI, 1.01–1.05), TGF-β (HR, 1.01; 95% CI, 1.00–1.02), TGF-β receptor 2 (HR, 1.05; 95% CI, 1.02–1.09), TGF-β receptor 3 (HR, 1.04; 95% CI, 1.02–1.07), and GDF15 (HR, 1.04; 95% CI, 1.02–1.07). Notably, CXCL10 (HR, 1.02; 95% CI, 1.01–1.03), MMP1 (HR, 1.01; 95% CI, 1.00–1.02), and MMP9 (HR, 1.04; 95% CI, 1.04–1.05) also showed a significant mediation effect. Detailed results of the mediation analysis for SASP proteins and inflammatory markers are presented in Table 5 and Supplementary Tables 4–7.

Table 4.

Mediation analysis of BMI and obesity in the association between hypothyroidism and risk of incident CKD.

Mediator	Rcde		Rpnie		PM
Mediator	HR (95% CI)	p-value	HR (95% CI)	p-value	PM (95% CI)	p-value
BMI	1.42 (1.31–1.52)	<0.001	1.06 (1.06–1.07)	<0.001	0.160 (0.12–0.21)	<0.001
obesity	1.37 (1.24–1.51)	<0.001	1.04 (1.04–1.05)	<0.001	0.14 (0.10–0.19)	<0.001

The analysis was adjusted for age, sex, BMI, hypertension, CVD, and diabetes.

BMI: body mass index; CKD: chronic kidney disease; PM: overall proportion mediated; Rcde: controlled direct effect; Rpnie: pure natural indirect effect; HR: hazard ratio; CI: confidence interval.

Table 5.

Mediation analysis of TGFs in the association between hypothyroidism and risk of incident CKD.

Mediator	Rcde		Rpnie		PM
Mediator	HR (95% CI)	p-value	HR (95% CI)	p-value	PM (95% CI)	p-value
TGFA	1.43 (1.03–1.88)	0.028	1.03 (1.01–1.06)	<0.001	0.10 (0.02–0.33)	0.004
TGFB	1.37 (1.00–1.77)	0.048	1.01 (1.00–1.02)	0.024	0.06 (0.00–0.22)	0.048
TGFBR1	1.41 (1.13–1.69)	<0.001	1.00 (1.00–1.02)	0.324	0.02 (−0.01 to 0.07)	0.324
TGFBR2	1.36 (0.94–1.80)	0.108	1.08 (1.04–1.12)	<0.001	0.22 (0.100–0.73)	0.016
TGFBR3	1.38 (1.02–1.77)	0.04	1.04 (1.02–1.08)	0.004	0.14 (0.05–0.35)	0.008
HGF	1.34 (1.09–1.59)	<0.001	1.01 (1.00–1.03)	0.056	0.04 (−0.00 to 0.15)	0.056
GDF15	1.45 (1.04–1.81)	0.024	1.04 (1.02–1.07)	<0.001	0.12 (0.05–0.34)	0.008

The analysis was adjusted for age, sex, BMI, hypertension, CVD, and diabetes.

CKD: chronic kidney disease; GDF15: growth differentiation factor 15; HGF: hepatocyte growth factor; TGF: transforming growth factor; TGFA: transforming growth factor-α; TGFB: transforming growth factor-β; TGFBR1: transforming growth factor-β-receptor-1; TGFBR2: transforming growth factor-β-receptor-2; TGFBR3: transforming growth factor-β-receptor-3; PM: overall proportion mediated; Rcde: controlled direct effect; Rpnie: pure natural indirect effect; BMI: body mass index.

The TGF family is closely associated with CVDs; therefore, we re-evaluated the mediating role of TGF after excluding CVD patients.^18,19 As shown in Supplementary Table 8, the results obtained are consistent with those reported previously: TGF-α: model 2, HR: 1.03, 95% CI: 1.01–1.06; TGF-β: model 2, HR: 1.01, 95% CI: 1.00–1.02; TGF-β-receptor-2: model 2, HR: 1.08; 95% CI: 1.04–1.12; TGF-β-receptor-3: model 2, HR: 1.05, 95% CI: 1.01–1.07; and GDF15: model 2, HR: 1.04, 95% CI: 1.02–1.07.

Discussion

The key findings from this prospective cohort study can be summarized as follows: hypothyroidism was found to be associated with a higher risk of developing CKD. Two-sample MR analysis provided evidence supporting a causal link between hypothyroidism and incident CKD. Mediation analysis demonstrated that elevated BMI and obesity act as significant mediators in the causal pathway connecting hypothyroidism to CKD. Additionally, multiple members of the SASP proteins, particularly TGFs, were identified as important mediating factors in this pathological cascade.

The association of hypothyroidism with the risk of CKD has been previously explored. In 2024, You et al. found that TSH levels in the upper reference range or higher and below the reference range were associated with incident kidney dysfunction or CKD progression.⁷ In addition, Cheng et al. conducted a retrospective cohort study using data from the China Renal Data System and demonstrated that hypothyroidism could accelerate CKD progression, while both hypo- and hyperthyroidism increase mortality risk in CKD patients.⁸ Although existing studies provided novel insights into the association of hypothyroidism with CKD risk, some limitations remain. For example, previous studies mainly used cohort settings, which limited causal inference and were susceptible to confounding biases. Moreover, previous studies have only indicated that hypothyroidism leads to CKD, without exploring its underlying mechanisms. In the current study, we aimed to address these limitations. In addition to cohort analysis, we used two-sample MR analyses to investigate the causal relationship between hypothyroidism and CKD, which can overcome the issue of reverse causality, offer stronger causal inference capabilities, and minimize the impact of confounding biases. In addition to these robust methodological enhancements, our study identified several mediators through mediation analysis.

Previous studies have shown that patients with hypothyroidism are more prone to metabolic problems, such as obesity, than healthy individuals.^20–22 As mentioned previously, obesity is a well-established risk factor for CKD.^23,24 Therefore, we investigated the role of BMI in the progression of CKD associated with hypothyroidism. The results indicated that BMI is a significant mediator. Subsequently, we stratified the population into obese and non-obese groups based on a BMI threshold of 28 kg/m² and conducted a mediation analysis for obesity. Similarly, obesity was found to be an important mediator. Previous studies have suggested that obesity contributes to CKD through multiple mechanisms, including renal fat accumulation, hemodynamic alterations, and insulin resistance with metabolic dysregulation.^25–27 These findings suggest that metabolic disorders play a significant mediating role in the development of CKD caused by hypothyroidism. Therefore, patients with hypothyroidism who have a high BMI or are obese should be particularly vigilant in monitoring their renal function to prevent or manage CKD at an early stage.

Studies have shown that hypothyroidism is an important risk factor for liver fibrosis.^9,10 Recently, Almalki et al. demonstrated that hepatocyte senescence is closely related to liver fibrosis, with SASP proteins playing a significant role in this process.²⁸ Another study reported that senescent liver cells secrete SASP proteins.²⁹ Notably, previous research indicates that SASP proteins participate in the pathological process of CKD, and CKD correspondingly accelerates the progression of cell senescence and the secretion of SASP proteins.³⁰ Collectively, hypothyroidism promotes liver fibrosis, leading to SASP protein secretion that may subsequently contribute to CKD development. Therefore, we believe that it is meaningful to analyze whether SASP proteins play a mediating role in the development of CKD caused by hypothyroidism.

TGFs are the primary factors that drive fibrosis in the kidneys.³¹ Recently, Xia et al. found that protein L-isoaspartyl/D-aspartyl methyltransferase inhibits the excessive activation of the TGF-β1/Smad signaling pathway by regulating the deamidation level of asparagine at position 63 of TGFBR2.³² This finding highlights the critical role of TGFβ1 and TGFβ2 in driving renal fibrosis, thereby promoting CKD progression. Many TGFs have been found to play significant roles in our mediation analysis (p < 0.05). Therefore, we speculate that the secretion of TGFs in hypothyroidism patients affects CKD occurrence. Although direct evidence pinpointing the origin of these TGFs is lacking, the liver of hypothyroidism patients has been reported to secrete SASP proteins. Therefore, we hypothesize that these TGFs originate from hepatic sources.

Experimental studies have demonstrated significantly elevated MMP-1 levels in the ovarian tissue of hypothyroidism rats compared with those of euthyroid controls.³³ This finding is particularly clinically relevant in our study population where 81.6% of hypothyroidism patients were women (Table 1). Based on these observations, we propose a potential ovarian–renal axis in which hypothyroidism-induced alterations in ovarian MMP-1 secretion contribute to renal fibrogenesis and subsequent CKD development. However, this mechanistic hypothesis requires further investigation to establish causal relationships and elucidate the underlying molecular pathways.

The chemokine CXCL10 has been demonstrated to directly promote fibroblast proliferation and renal fibrosis.³⁴ In autoimmune thyroid diseases (AITD), immune dysregulation triggers thyroid autoimmunity through a well-characterized inflammatory cascade: recruited Th1 lymphocytes enhance interferon-gamma (IFN-γ) and tumor necrosis factor-alpha production, which subsequently stimulates thyroid follicular cells to secrete CXCL10, the prototypical IFN-γ–induced Th1 chemokine.³⁵ This establishes a pathogenic feedback loop that perpetuates autoimmune thyroid destruction. Importantly, our mediation analysis suggests that in AITD-induced hypothyroidism, thyroid-derived CXCL10 serves as a key mediator of CKD development by driving fibroblast proliferation within kidney tissue and eventually promoting renal fibrogenesis.

Inflammatory mediators are well-established as key contributors to CKD pathogenesis,³⁶ with leukocyte activity being particularly implicated in renal function decline.^37,38 Substantial evidence also demonstrates the significant involvement of inflammatory processes in hypothyroidism.^38,39 Although our mediation analysis did not identify inflammatory cytokines as significant mediators—potentially due to the limited panel of inflammatory markers assessed—this negative finding does not preclude the possible role of inflammation in hypothyroidism-related CKD progression. The current analysis was constrained by the availability of inflammatory biomarker data in our dataset. Future studies incorporating a more comprehensive inflammatory profile may provide further insight into this potential pathway.

The study is based on real-world data, features a large sample size, and includes participants presenting a wide spectrum of disease phenotypes from multiple British urban centers. However, our study also had several limitations. First, our dependence on ICD-10 diagnostic codes for defining clinical conditions did not fully leverage the clinical precision afforded by laboratory and imaging data. Second, unmeasured confounding factors, including dietary patterns and levels of physical activity, may have influenced the onset of CKD. Finally, due to the limitations of the available data, we could not examine the full spectrum of SASPs and inflammatory markers, despite the fact that many of these factors play important roles in both hypothyroidism and CKD.

Conclusion

Our study results indicate that hypothyroidism is associated with CKD, with obesity and SASP proteins potentially mediating this relationship. From the perspective of CKD prevention, regular renal function tests for patients with hypothyroidism may be beneficial. Further research on hypothyroidism and CKD is necessary.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605251387502 - Supplemental material for Association between a history of hypothyroidism and incident chronic kidney disease and potential mediators: A cohort study with Mendelian randomization analysis

Supplemental material, sj-pdf-1-imr-10.1177_03000605251387502 for Association between a history of hypothyroidism and incident chronic kidney disease and potential mediators: A cohort study with Mendelian randomization analysis by Yaofang Zhang, Yue Shen, Aiwen Shen, Yuqi Shen, Zhu Zhu, Yining He, Wenji Wang, Xuezhu Li, Xiao Bi and Feng Ding in Journal of International Medical Research

Supplemental Material

sj-pdf-2-imr-10.1177_03000605251387502 - Supplemental material for Association between a history of hypothyroidism and incident chronic kidney disease and potential mediators: A cohort study with Mendelian randomization analysis

Supplemental material, sj-pdf-2-imr-10.1177_03000605251387502 for Association between a history of hypothyroidism and incident chronic kidney disease and potential mediators: A cohort study with Mendelian randomization analysis by Yaofang Zhang, Yue Shen, Aiwen Shen, Yuqi Shen, Zhu Zhu, Yining He, Wenji Wang, Xuezhu Li, Xiao Bi and Feng Ding in Journal of International Medical Research

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sj-pdf-3-imr-10.1177_03000605251387502 - Supplemental material for Association between a history of hypothyroidism and incident chronic kidney disease and potential mediators: A cohort study with Mendelian randomization analysis

Supplemental material, sj-pdf-3-imr-10.1177_03000605251387502 for Association between a history of hypothyroidism and incident chronic kidney disease and potential mediators: A cohort study with Mendelian randomization analysis by Yaofang Zhang, Yue Shen, Aiwen Shen, Yuqi Shen, Zhu Zhu, Yining He, Wenji Wang, Xuezhu Li, Xiao Bi and Feng Ding in Journal of International Medical Research

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sj-pdf-4-imr-10.1177_03000605251387502 - Supplemental material for Association between a history of hypothyroidism and incident chronic kidney disease and potential mediators: A cohort study with Mendelian randomization analysis

Supplemental material, sj-pdf-4-imr-10.1177_03000605251387502 for Association between a history of hypothyroidism and incident chronic kidney disease and potential mediators: A cohort study with Mendelian randomization analysis by Yaofang Zhang, Yue Shen, Aiwen Shen, Yuqi Shen, Zhu Zhu, Yining He, Wenji Wang, Xuezhu Li, Xiao Bi and Feng Ding in Journal of International Medical Research

Supplemental Material

sj-pdf-5-imr-10.1177_03000605251387502 - Supplemental material for Association between a history of hypothyroidism and incident chronic kidney disease and potential mediators: A cohort study with Mendelian randomization analysis

Supplemental material, sj-pdf-5-imr-10.1177_03000605251387502 for Association between a history of hypothyroidism and incident chronic kidney disease and potential mediators: A cohort study with Mendelian randomization analysis by Yaofang Zhang, Yue Shen, Aiwen Shen, Yuqi Shen, Zhu Zhu, Yining He, Wenji Wang, Xuezhu Li, Xiao Bi and Feng Ding in Journal of International Medical Research

Supplemental Material

sj-pdf-6-imr-10.1177_03000605251387502 - Supplemental material for Association between a history of hypothyroidism and incident chronic kidney disease and potential mediators: A cohort study with Mendelian randomization analysis

Supplemental material, sj-pdf-6-imr-10.1177_03000605251387502 for Association between a history of hypothyroidism and incident chronic kidney disease and potential mediators: A cohort study with Mendelian randomization analysis by Yaofang Zhang, Yue Shen, Aiwen Shen, Yuqi Shen, Zhu Zhu, Yining He, Wenji Wang, Xuezhu Li, Xiao Bi and Feng Ding in Journal of International Medical Research

Footnotes

Acknowledgments

We gratefully acknowledge the contributions of research communities and individual investigators for making Genome-Wide Association Studies (GWAS) summary statistics openly accessible. We especially appreciate the Integrative Epidemiology Unit Open GWAS Project,UK Biobank,and FinnGen Consortium for providing the summary-level data utilized in this study. We further thank all researchers whose collective work has supported this research and enriched the scientific dialogue in the field of complex disease genetics.

Author contributions

FD and XB: designed the research;YZ,XB,and YH: analyzed the data;YZ and YQS: wrote the paper;YS,ZZ,and WW: contributed to the literature search;and XL and FD: critically reviewed the manuscript and took responsibility for the integrity of the data and accuracy of data analysis. FD is the guarantor. All authors have read and approved the final version of the manuscript.

Data availability statement

The data used in this article will be shared on reasonable request to the corresponding author.

Declaration of conflicting interests

None declared.

Funding

This study was supported by the National Natural Science Foundation of China (No.82200755,82400878,82070789,and 82470712),Collaborative Innovation Center for Clinical and Translational Science by Chinese Ministry of Education & Shanghai (CCTS-2022206),Clinical Research Program of Ninth People’s Hospital (JYLJ202218),and Fundamental research program funding of Ninth People’s Hospital (JYZZ249).

ORCID iD

Yaofang Zhang

Supplemental material

Supplemental material for this article is available online.

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