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Clonal hematopoiesis of indeterminate potential and risk of autoimmune thyroid disease

BMC Medicine volume 23, Article number: 237 (2025) Cite this article

Abstract

Background

Autoimmune thyroid disease (AITD) is the most common organ-specific autoimmune disease, often remaining asymptomatic until the thyroid is significantly affected. Clonal hematopoiesis of indeterminate potential (CHIP) has been reported to drive many inflammatory diseases and autoimmune diseases. The association between CHIP and AITD is scarcely reported. This study aims to investigate whether CHIP is associated with the risk of AITD.

Methods

We conducted a prospective community-based cohort study at the UK Biobank. CHIP, defined as the exposure, was identified using whole-exome sequencing (WES) data. AITD was sourced from the inpatient hospitalization register, the death register, and the primary healthcare register. Cox regression models were utilized to estimate the hazard ratio (HR) and 95% confidence interval (CI) for the association between CHIP and AITD. Next, we conducted a subgroup analysis to investigate the role of specific gene mutations (DNMT3A, TET2, ASXL1, PPM1D, SRSF2, and JAK2) in the investigated association. Finally, we assessed the association across small CHIP clones (variant allele frequency, VAF: 2–10%) and large CHIP clones (VAF ≥ 10%). All models were adjusted for sex, age, ethnicity, education, Townsend deprivation index, body mass index, smoking status, and drinking status.

Results

A total of 454,618 individuals were included in the final analysis. We identified 14,059 (3.1%) participants with CHIP. Compared with individuals without CHIP, those with CHIP were generally older and more likely to be smokers. Over a median follow-up of 12.7 years (interquartile range, IQR: 11.9–13.5), 21,708 cases with AITD were diagnosed. CHIP was associated with an increased risk of AITD (HR 1.11, 95% CI 1.03–1.19). Specifically, individuals with TET2-mutant CHIP (HR 1.23, 95% CI 1.07–1.41) had an elevated risk of AITD. A large CHIP clone (HR 1.17, 95% CI 1.08–1.27) was associated with an increased risk of AITD. Focusing on large CHIP clone, we also observed an association between TET2-mutant (HR 1.27, 95% CI 1.10–1.47) and ASXL1-mutant (HR 1.33, 95% CI 1.02–1.73) CHIP and risk of AITD.

Conclusions

Individuals with CHIP were associated with a modestly increased risk of AITD, especially TET2-mutant CHIP. Future studies are needed to verify current findings and elaborate potential mechanisms.

Peer Review reports

Background

Autoimmune thyroid disease (AITD) is the most common organ-specific autoimmune disease, is characterized by a strong genetic predisposition, accounts for approximately 90% of all thyroid diseases, and affects 2–5% of the general population [1,2,3,4,5]. AITD is caused by a complex interplay of genetic susceptibility and environmental exposures, with the breakdown of the immune tolerance and overexpression of the autoimmune antibodies leading to a spectrum of phenotypes, such as thyroid hormone excess (hyperthyroidism) or deficiency (hypothyroidism) [2, 6, 7]. Most cases of AITD remain asymptomatic until the thyroid is significantly affected [8]. There is an unmet need for early diagnosis of AITD. Therefore, additional risk factors must be identified for early intervention and potential targeted treatment.

Clonal hematopoiesis of indeterminate potential (CHIP) is defined as the presence of a somatic leukemia-associated mutation in the peripheral blood or bone marrow with a variant allele frequency (VAF) ≥ 2% in otherwise healthy people [9, 10]. It is a common, age-related state affecting more than 10% of people over 70 years old [9,10,11]. Individuals with CHIP showed an increased risk of hematologic malignancy, cardiovascular disease, and all-cause mortality [9]. It is well documented that CHIP might evoke pro-inflammatory effects and alter the immune effector cells’ function [12,13,14,15,16,17]. Thus, CHIP has been reported to be the cause of many inflammatory diseases, including chronic liver disease, chronic kidney disease, and chronic obstructive pulmonary disease [18,19,20]. CHIP also demonstrated an increased risk in some autoimmune diseases, like inflammatory bowel disease [21]. Therefore, we hypothesized that the inflammatory and immune changes in CHIP might be involved in the process of AITD. However, the association between CHIP and AITD is scarcely reported. This study aims to investigate whether CHIP is associated with the risk of AITD.

Methods

Study population

Our study adopted data from the UK Biobank (https://www.ukbiobank.ac.uk/), a large prospective cohort with over 500,000 participants aged 40–69 across 22 centers located in England, Scotland, and Wales between 2006 and 2010 [22]. It is continuing to collect phenotypic and genotypic details about its participants, including data from questionnaires, physical measures, sample assays, accelerometry, multimodal imaging, and genome-wide genotyping [23]. By linking to the national health records, it can conduct a continuous follow-up for a wide range of health-related outcomes [24].

We found 469,877 individuals with whole-exome sequencing (WES) information and excluded 355 participants for informed consent withdrawal or with missing information on blood sampling. Then, we excluded 3296 participants with a diagnosis of any prevalent hematologic malignancy to preclude secondary CHIP. Those with a diagnosis of AITD were further excluded (N = 12,058), leaving 454,168 individuals in the final analysis (Additional file 2: Fig. S1). To minimize the impact of reverse causation, we conducted the follow-up from 1 year after initial blood sampling until a diagnosis of AITD, death, or December 2022, whichever occurred first.

Definition of exposure

CHIP was ascertained as recently described [25]. CRAM files from the UK Biobank were aligned to the GRCh38 reference genome and filtered against the Genome Aggregation Database (gnomAD) to exclude germline variants [26]. Briefly, we first used 74 genes associated with myeloid malignancy to identify CHIP [27]. Somatic mutations were identified using Mutect2 (Genome Analysis Toolkit v. 4.2.4.0) in “tumor-only” mode, focusing on the exons in 73 of the 74 genes in relation to myeloid malignancy (except for U2AF1) [27, 28]. Raw Mutect2 outputs were filtered via FilterMutectCalls, utilizing probabilities derived from LearnReadOrientationModel [28, 29]. We ran the Rust-HTSLIB binary (https://github.com/weinstockj/pileup_region) to identify U2AF1 variant reads [27]. Gene annotation was performed on ANNOVAR [30]. Variants were removed if: a total read depth of ≤ 20, minimum read depth for the alternate allele < 5, and VAF < 2%, or no evidence on both forward and reverse sequencing reads. Variants not associated with either age or telomerase reverse transcriptase promoter were removed to minimize potential artifacts [27]. In addition, genes that were not observed in myeloid malignancy in one previous study were further removed [27]. After filtering and excluding the above gene variants, there were 55 genes in the final list (Additional file 1: Table S1).

Potential clonal cytopenia of undetermined significance (CCUS) was defined as the presence of CHIP and cytopenia, specifically anemia (hemoglobin < 12 g/dL in females and < 13 g/dL in males), neutropenia (absolute neutrophil count < 1.8 × 109/L), and/or thrombocytopenia (platelet count < 150 × 109/L), in the absence of an alternative explanation [31,32,33].

Any CHIP was identified as the aforementioned CHIP somatic mutations with a VAF of 2% or greater, and the large CHIP had a VAF of 10% or greater. We separately examined gene-specific CHIP subtypes in the common driver genes DNMT3A, TET2, ASXL1, PPM1D, SRSF2, and the prior reported autoimmune diseases associated with mutation JAK2 [34, 35].

Definition of outcome

The study outcome was the incident diagnosis of AITD, ascertained by the 10th edition of the International Classification of Diseases (ICD-10) from the inpatient records and death register (Additional file 1: Table S2) [36]. We also identified AITD using read codes from the primary healthcare register.

Covariates

We collected data on demographic factors, including sex (male and female), ethnicity (white, other, and unknown), and education (college/university degree, other degree, and unknown) at recruitment. Age was calculated according to dates of birth and time of baseline assessment. Height and body weight were measured by trained nurses at enrollment [22]. Body mass index (BMI) was calculated as (weight/height2) and classified into four categories using WHO criteria: underweight (≤ 18.5), normal weight (18.5–25), overweight (25–30), and obese (≥ 30). Townsend deprivation index (TDI) was obtained from the postcode of residence by the integration of unemployment, car and home ownership, and household overcrowding [37]. Lifestyle factors, smoking, and drinking statuses were collected at recruitment and categorized into never, previous, current, and unknown. We used the ICD-10 codes to identify myeloid malignancy (i.e., acute myeloid leukemia, AML; myelodysplastic syndromes, MDS; myeloproliferative neoplasms, MPN) in both the Cancer Register and Inpatient Register (Additional file 1: Table S3).

Statistics analysis

Cox proportional hazard models were used to investigate the association between CHIP and the risk of AITD. The analysis was adjusted for sex, ethnicity, education, TDI, smoking, and drinking. Age and BMI were fitted as a natural cubic spline in Cox regression models to reduce potential residual confounding. Stratified analyses were conducted by sex, age (< 60 years, ≥ 60 years), BMI, ethnicity, education level, TDI categories, smoking, and drinking. We also performed subgroup analyses in several common mutations of CHIP (DNMT3A, TET2, ASXL1, PPM1D, SRSF2, and JAK2). The association of small CHIP and large CHIP with incident AITD was also evaluated.

To evaluate the robustness of our findings, we conducted a series of sensitivity analyses using multiple approaches. First, we implemented lag-time analyses with 3-year and 5-year intervals between CHIP and AITD onset to account for potential reverse causality. Second, we replicated our primary analyses using propensity score matching (PSM) to address potential confounding. Propensity scores for CHIP were calculated using logistic regression, in which exposure status was regressed on age, sex, BMI, ethnicity, education, TDI, smoking, and alcohol drinking. For each individual with CHIP, one matched individual without CHIP was randomly selected based on their propensity scores. In addition, we also perform a sensitivity analysis using inverse probability weighting (IPW). IPW was conducted using the propensity scores obtained from the PSM procedure, weighting CHIP carriers by the inverse of their propensity score and non-carriers by the inverse of 1 minus their propensity score. Third, we performed an additional analysis by excluding participants with rheumatoid arthritis to minimize potential confounding from autoimmune comorbidities. Fourth, we further explore the dose–response association of VAF of any CHIP mutation and specific CHIP mutations with risk of AITD. Fifth, to investigate the different effects between CHIP and CCUS, we performed a sensitivity analysis by dividing exposed individuals into two groups: (1) CHIP without CCUS and (2) CCUS. Sixth, to relieve potential concern of the effect of myeloid malignancy in the association between TET2-CHIP and AITD, we performed a sensitivity analysis by excluding individuals with a diagnosis of myeloid malignancy from the exposed group, regardless of the diagnostic time of myeloid malignancy.

The analyses were performed in SAS 9.4 and R version 4.1.3. UK Biobank has approval from the Northwest Multi-centre Research Ethics Committee, with all participants given written informed consent (reference 21/NW/0157). This work was approved by the Ethical Review Board in Nanfang Hospital, Southern Medical University in China (NFEC-2023–559).

Results

Among the 454,168 individuals in the final analysis, 14,059 (3.1%) individuals had any CHIP. The median age of participants with or without CHIP was 58 and 53, respectively. Individuals with CHIP exhibited a higher percentage of being elderly (67.9 to 44.2%) and smoking (51.4 to 44.7%) than those without CHIP. Sex, BMI, education, TDI, and drinking status were comparable between participants with and without CHIP (Table 1).

Table 1 Characteristics of the individuals included
Full size table

Over a median follow-up of 12.7 years (IQR 11.9–13.5), we identified 817 (5.8%) incident cases of AITD with CHIP and 20,891 (4.7%) without CHIP, with an incident rate of 497.8 and 391.5 per 100,000 person-years, respectively (Table 2). Individuals with CHIP carried a statistically significantly increased risk of AITD (HR 1.11, 95% CI 1.03–1.19). The association did not change largely by sex, age, BMI, race, education, TDI, smoking status, and drinking status (Table 2). The robustness of this association was further supported by multiple sensitivity analyses. Lag-time analyses with 3-year (HR 1.11, 95% CI 1.02–1.19) and 5-year (HR 1.10, 95% CI 1.01–1.20) intervals between CHIP and AITD diagnosis yielded consistent results. Furthermore, the association persisted after PSM (HR 1.08, 95% CI 1.01–1.16) and IPW (HR 1.09, 95% CI 1.01–1.18) analyses. Additionally, exclusion of participants with rheumatoid arthritis did not substantially alter the observed association (HR 1.10, 95% CI 1.03–1.18). Individuals with CCUS had a higher risk increase of AITD (HR 1.47, 95% CI 1.18–1.83) than individuals with CHIP but without CCUS (HR 1.08, 95% CI 1.01–1.17) (P for difference: 0.001).

Table 2 HRs with 95% CI of AITD among individuals with CHIP, compared to individuals without CHIPa
Full size table

In the analysis by gene-specific CHIP mutations, TET2-mutant CHIP was found to be associated with risk of AITD (HR 1.23, 95% CI 1.07–1.41) at any VAF level. Other CHIP mutations, such as DNMT3A (HR 1.04, 95% CI 0.95–1.15), ASXL1 (HR 1.22, 95% CI 0.98–1.53), PPMID (HR 1.12, 95% CI 0.72–1.74), SRSF2 (HR 1.55, 95% CI 0.93–2.57), and JAK2 (HR 1.40, 95% CI 0.67–2.94), did not associate with risk of AITD (Fig. 1). We observed a linear association of VAF of any CHIP, DNMT3A-mutant CHIP, ASXL1-mutant CHIP, or JAK2-mutant CHIP with risk of AITD (Additional file 2: Fig. S2). The association was also noted for CHIP with VAF > 0.1 (HR 1.17, 95% CI 1.08–1.27) with AITD, but not for small CHIP clone (HR 0.98, 95% CI 0.85–1.11) with incident AITD (Table 3). Focusing on large CHIP clone, we also observed an association between TET2-mutant (HR 1.27, 95% CI 1.10–1.47) and ASXL1-mutant (HR 1.33, 95% CI 1.02–1.73) CHIP and risk of AITD. We further evaluated the results after excluding CHIP individuals who were diagnosed with MDS, MPN, or AML. The analysis revealed that TET2-mutant CHIP carriers continued to exhibit a significantly elevated risk of AITD incidence (HR 1.22, 95% CI 1.06–1.40).

Fig. 1
figure 1

HRs with 95% CI of AITD incidence among individuals with gene-specific CHIP, compared to individuals without CHIP* 

HR, hazards ratio; CI, confidence interval; AITD, autoimmune thyroid disease; CHIP, clonal hematopoiesis of indeterminate potential. *Cox regression adjusted for sex (female or male), age (as natural cubic spline), BMI (as natural cubic spline), ethnicity, education, TDI, smoking statuses, and drinking statuses

Full size image
Table 3 HRs with 95% CI of AITD among individuals with large CHIP and small CHIP, compared to individuals without CHIPa
Full size table

Discussion

In this study, we revealed an association between CHIP and risk of AITD. CHIP carriers demonstrated a modestly increased risk of AITD. This association showed minimal heterogeneity when stratified by sex, age, BMI, race, education, TDI, smoking status, and drinking status. In gene-specific analyses, the association was observed for TET2-mutant CHIP. The association was particularly obvious in individuals with a higher VAF (> 10%), with pronounced association observed for TET2- and ASXL1-mutant CHIP in large CHIP clones.

Inflammation and immunity play a significant role in AITD provoking [1, 38,39,40,41,42,43]. What might be the underlying mechanisms behind CHIP and increased AITD risk? The recognition of somatic mosaicism as a cause of autoinflammatory diseases has been increasingly acknowledged recently. Somatic mutations in TET2 and UBA1 have been reported in adult-onset autoinflammatory disease [44, 45]. CHIP is a cause of inflammation [46]. Loss of TET2 results in elevated levels of pro-inflammatory cytokines release to the inflammation initiation, such as IL-1β, IL-6, and TNF-α [12, 47, 48]. TET2 is essential to suppress IL-6 and IL-1β expression during the resolution stage of inflammation via the HDAC-introduced histone deacetylation in innate myeloid cells and macrophages, respectively [49, 50]. Growing reports have identified TET2 mutation in thyroid diseases, including thyroid cancers and mucosa-associated lymphoid tissue lymphoma, although the specific reasons for these associations are not yet fully understood [51, 52]. Besides, CHIP mutation causes immune dysregulation. Evidence from in vivo and in vitro experiments in mice has revealed that TET-family enzymes suppress regulatory T-cell functions, alter T-cell polarization, and attenuate the inhibitory effects on pro-inflammatory Th17 activity [13,14,15,16, 53]. AITD, known as a “T cell-mediated organ-specific autoimmune disorder,” is characterized by a significant lymphocytic infiltration of the thyroid follicles, including both T cells and B cells [2, 49]. Studies have indicated that immune effector cells derived from hematopoietic stem cells, such as Th1 and Th17 cells, can modulate both adaptive and innate immune responses through the activation of inflammasomes (e.g., NLRP3) and the release of inflammatory cytokines (e.g., IL-1β) [1, 17, 42, 50,51,52]. These mechanisms may underlie the increased risk of AITD among individuals with CHIP. Future studies are needed to verify current findings and elaborate potential mechanisms.

Strengths of this study are the large sample size and prospective follow-up of the UK Biobank study. We included a thorough assessment of confounders, including sociodemographics, socioeconomic status, BMI, smoking, and alcohol consumption. Since we did adjust for multiple covariates in the individual gene analyses, there may be less associations with AITD due to confounding. Our study also has some limitations that should be acknowledged. Firstly, we only conducted this analysis in the UK Biobank, predominantly comprised of European ancestry individuals, impeding the broadening of the participant’s race into others. Secondly, exposure misclassification bias might exist in this study for unexposed participants at the beginning might have developed to have CHIP during the follow-up period. For the same reason, we also could inevitably misjudge the clone size. Thirdly, although the statistical power is enough for the main analysis, subgroup analyses suffered from the issue of insufficient power. Due to such issue, we did not further investigate the association by the subgroup of AITD. Fourthly, although important known confounders were adjusted in the study, there was still possible residual confounding. Fifthly, the UK Biobank dataset did not encompass data on the severity of AITD, precluding an investigation into the association between CHIP and AITD severity. Finally, we cannot fully exclude reverse causality between CHIP and AITD, though similar results were noted when excluding the first 3-year or 5-year follow-up.

Conclusions

In summary, individuals with CHIP were at a modestly increased risk of AITD, especially TET2-mutant CHIP. These data add to our growing understanding of AITD pathogenesis and provoke timely screening of AITD in CHIP populations. This study also highlights the importance of CHIP screening in AITD patients. Future studies are needed to verify current findings and elaborate potential mechanisms.

Data availability

This study utilized the UK Biobank Resource, under Application Number 106912. Researchers can request access data from the UK Biobank at www.ukbiobank.ac.uk.

Abbreviations

AITD:

Autoimmune thyroid disease

AML:

Acute myeloid leukemia

BMI:

Body mass index

CCUS:

Clonal cytopenia of undetermined significance

CHIP:

Clonal hematopoiesis of indeterminate potential

CI:

Confidence interval

HR:

Hazard ratio

ICD- 10:

10Th version of the International Classification of Diseases

IPW:

Inverse probability weighting

IQR:

Interquartile range

MDS:

Myelodysplastic syndromes

MPN:

Myeloproliferative neoplasms

PSM:

Propensity score matching

TDI:

Townsend deprivation index

VAF:

Variant allele frequency

WES:

Whole-exome sequencing

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Acknowledgements

We appreciate the endocrinologist Dr. Shuang Wan for AITD consultation. This research has been conducted using the UK Biobank Resource under Application Number [106912].

Funding

Initial Founding for High Level Talented Scholars in Nanfang Hospital, Southern Medical University (2023G001); National Natural Science Foundation of China (82370183); Tian Jin Natural Science Foundation (23 JCZXJC00310); Haihe Laboratory of Cell Ecosystem Innovation Fund (HH22 KYZX0039).

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Author notes

  1. Xue Zhang, Yuqing Wang and Huiwen Xue contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 288 Nanjing Road, Tianjin, 300020, China

    Xue Zhang, Mingcheng Liu & Hui Wei

  2. Tianjin Institutes of Health Science, Tianjin, China

    Xue Zhang, Mingcheng Liu & Hui Wei

  3. Department of Hematology, Nanfang Hospital, Southern Medical University, No. 1838, North Guangzhou Avenue, Guangzhou, 510515, China

    Yuqing Wang, Huiwen Xue, Yingsuo Zhao & Qianwei Liu

  4. Clinical Medical Research Center of Hematological Diseases of Guangdong Province, Guangzhou, China

    Yuqing Wang, Huiwen Xue, Yingsuo Zhao & Qianwei Liu

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  1. Xue Zhang
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Contributions

Xue Zhang and Yuqing Wang drafted the manuscript. Qianwei Liu and Hui Wei designed the study. Qianwei Liu and Huiwen Xue analyzed the data. Xue Zhang, Yuqing Wang, Yingsuo Zhao, Mingcheng Liu, Hui Wei and Qianwei Liu interpreted the data and revised this manuscript. Xue Zhang, Yuqing Wang, and Huiwen Xue contributed equally to the manuscript. All authors read and approved the final manuscript.

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State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, China; Tianjin Institutes of Health Science. Xue Zhang, Mingcheng Liu, and Hui Wei. Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Clinical Medical Research Center of Hematological Diseases of Guangdong Province, Guangzhou, China. Yuqing Wang, Huiwen Xue, Yingsuo Zhao, and Qianwei Liu

Corresponding authors

Correspondence to Hui Wei or Qianwei Liu.

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Ethics approval and consent to participate

Data for this study were obtained from the UK Biobank. UK Biobank has approval from the Northwest Multi-centre Research Ethics Committee, with all participants given written informed consent (reference 21/NW/0157). This research has been conducted using the UK Biobank Resource under application number [106912]. This work was approved by the Ethical Review Board in Nanfang Hospital, Southern Medical University in China (NFEC-2023–559).

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Supplementary Information

12916_2025_4077_MOESM1_ESM.docx

Additional file 1. Table S1 Gene list of definition of CHIP. Table S2 The 10th version International Classification of Diseases (ICD) codes and the number of patients for AITD. Table S3 The 10th version International Classification of Diseases (ICD) codes for myeloid malignancy.

12916_2025_4077_MOESM2_ESM.docx

Additional file 2. Fig. S1 Flowchart of the study cohort. Fig. S2 Dose–response association between VAF of CHIP and risk of AITD.

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Zhang, X., Wang, Y., Xue, H. et al. Clonal hematopoiesis of indeterminate potential and risk of autoimmune thyroid disease. BMC Med 23, 237 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12916-025-04077-z

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BMC Medicine

ISSN: 1741-7015

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