Longer leukocyte telomere length increases the odds of premature rupture of membranes: a cross-sectional study based on UK Biobank

Abstract

This study aimed to explore the association between leukocyte telomere length (LTL) and premature rupture of membranes (PROM). This cross-sectional study included a total of 170,841 participants from the UK Biobank. Restricted cubic spline model (RCS) and logistic regression were used to evaluate the relationship between LTL and PROM. Subgroup analysis and interaction tests were conducted to test the stability of the results. RCS model showed a notable non-linear association between LTL and PROM. The logistic regression model further revealed that longer LTL was associated with an increased odds of developing PROM after fully adjusting for confounding factors. For every 1 unit increase in the original LTL, the odds of PROM significantly increases by 1.97 times (OR = 1.97, 95% CI:1.30–2.95). Compared with the Q1 of LTL, the PROM odds of Q3 and Q4 increased by 33% (OR = 1.33, 95% CI: 1.11–1.59) and 31% (OR = 1.31, 95% CI: 1.09–1.56). Finally, subgroup analysis found that age and education level play an important role in regulating the relationship between LTL and the odds of PROM. The research results indicate a non-linear relationship between LTL and PROM, and longer LTL is associated with higher PROM odds, which is influenced by age and education level.

Introduction

Telomeres are protective structures located at the ends of chromosomes, whose main function is to protect the integrity of chromosomes by preventing fusion and degradation. They play a crucial role in DNA repair and maintaining genomic stability^1,2. Leukocyte telomere length (LTL) is an easily measurable indicator in peripheral blood, and due to its correlation with telomere length in most tissues, it is often used as a surrogate marker to reflect the overall telomere status of the body^2,3. At present, LTL, as a marker of cellular aging and gene stability, has become a potential indicator of a healthy system^4,5. Research shows that short LTL is associated with a variety of pathological conditions, including coronary heart disease^6,7, diabetes⁸, Alzheimer’s disease⁹, etc. Long LTL will increase the risk of cancer¹⁰, female reproductive system disease², and female cancer¹¹. In addition, in a study on the association of LTL phenotype range, the association between LTL and 67 phenotypes was determined, and it was shown that both longer and shorter LTL were associated with increased mortality¹².

In recent years, the role of LTL in the field of reproductive health has also gradually received attention. Research has found a significant correlation between LTL and adverse pregnancy outcomes. On the one hand, shorter LTL increases the risk of premature birth and spontaneous abortion^13,14, while on the other hand, longer LTL is associated with an increased risk of preeclampsia¹⁵. However, although the relationship between LTL and various adverse pregnancy outcomes has been revealed, the relationship between LTL and premature rupture of membranes has not been explored. Premature rupture of membranes (PROM) refers to the premature rupture of fetal membranes before delivery, which is an obstetric complication¹⁶. The global incidence rate is between 10% and 20%¹⁷. PROM can lead to serious fetal complications such as umbilical cord compression, respiratory distress syndrome, premature birth, and brain injury^16,18,19. Mothers also face an increased risk of amniocentesis, placental abruption, umbilical cord prolapse, sepsis, and even death^20,21, which poses a significant threat to maternal and neonatal health. Given that the relationship between LTL and PROM is not yet clear, it is crucial to explore the potential association between the two in depth. This not only helps to elucidate the pathogenesis of PROM, but may also provide new biological markers for clinical risk prediction.

Based on this, we conducted a cross-sectional design using baseline data from the UK Biobank from 2006 to 2010 to analyze the association between LTL and PROM, providing a basis for revealing PROM biomarkers.

Methods

Study design and population

This study adopts a cross-sectional research design, and the data is sourced from the UK Biobank. The UK Biobank is a large-scale prospective cohort study that recruited approximately 500,000 participants in the UK between 2006 and 2010. All participants completed a detailed touchscreen questionnaire, standardized physical measurements, and provided blood, urine, and saliva samples for biomarker testing at baseline visits. For more information about the UK Biobank, please visit https://www.ukbiobank.ac.uk/.

This study included women who had been pregnant before the baseline assessment (2006–2010) as research subjects, and all participants provided informed consent. We excluded participants who lacked LTL and covariate data at baseline. Moreover, to ensure temporal consistency between exposure measurements and outcome status, we excluded individuals who were first diagnosed with PROM after baseline assessment. In addition, all options for “don’t want to answer” and “don’t know” in the variables are treated as missing values. After the above screening, a total of 170,841 participants were included in the cross-sectional analysis. The detailed screening process for research subjects is shown in Fig. 1. The study was approved by the UK Biobank Ethics Committee (Approval No.: 103654). All procedures and methods conform to the ethical guidelines defined by the World Medical Association’s Declaration of Helsinki and its subsequent revisions.

Fig. 1.

The flowchart of participant selection from UK Biobank. Abbreviations: LTL, Leukocyte telomere length; PROM, Premature Rupture of Membranes.

Leukocyte telomere length

In the baseline survey, the UK Biobank collected DNA samples from peripheral blood white blood cells of participants and tested them using quantitative polymerase chain reaction (qPCR). The ratio of telomere repeat copy number (T) to single copy gene (S) copy number (T/S ratio) was ultimately reported as the result of LTL²². The complete testing process and quality control procedures for LTL in the UK Biobank have been reported in relevant literature²³. During implementation, the UK Biobank optimized and adjusted key technical parameters such as enzymes, PCR machines, primers, operators, reaction temperature, and DNA purity, and implemented strict quality control for LTL measurement at the sample and detection batch levels²⁴. All samples that did not meet the quality control standards were retested until valid results were obtained. If the samples were exhausted or confirmed as unqualified, they were excluded; At the same time, the repeatability and stability of LTL measurement were verified through deliberate repeated testing and blind sample repeated testing²⁵. In this study, we used the adjusted T/S ratio (UKB data field 22191).

Premature rupture of membranes

The diagnosis of PROM is defined by self-report by participants during baseline assessment or the “first occurrence field” provided by UK Biobank (data category: 2415). The ‘first occurrence field’ includes data from primary healthcare, hospital admission records, self-reported medical conditions, and death registers, primarily using the 10th edition of the International Classification of Diseases (ICD10) for disease diagnosis. In this study, the UK Biobank data field of the PROM we used was 132,230.

Covariates

Through touch screen questionnaires and body measurements during baseline surveys, participants provided personal information about age, race, body mass index (BMI), waist circumference, hip circumference, alcohol consumption status, smoking status, health score, education level, and basal metabolic rate as covariates. The age at which the patient first participated in the evaluation center was selected as the age variable for this study. Race includes white, mixed, Asian or Asian British mixed race, black or British black, Chinese and other. BMI is classified according to international standards into underweight (BMI < 18.5 kg/m²), normal (18.5 kg/m²≤BMI<25 kg/m²), overweight (25 kg/m²≤BMI<30 kg/m²) and obesity (BMI ≥ 30/kg/m²). The drinking status is divided into never, previous and current. Smoking status is divided into never, previous and current. The health score is divided into excellent, good, fair and poor. The education level is divided into College or University degree, A levels/AS levels or equivalent, O levels/GCSEs or equivalent, CSEs or equivalent, NVQ or HND or HNC or equivalent and other professional qualifications. We also collected waist circumference, hip circumference and basal metabolic rate as continuous variables for analysis.

Statistical analysis

This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for cross-sectional studies²⁶. We first performed descriptive analyses of covariates, LTL and PROM in the total study population. Continuous variables with a normal distribution were presented as mean±standard deviation (SD) and compared employing analysis of variance (ANOVA). Those with a non-normal distribution were reported as median and interquartile range (IQR) and compared using Kruskal-Wallis H tests. Categorical variables were expressed as frequency and percentage (n, %) and analyzed using the chi-square test or Fisher’s exact test, as appropriate.

To accurately explore the potential nonlinear relationship between LTL and PROM and reveal their association pattern, this study employed the Restricted Cubic Spline (RCS) method for analysis. As a classic approach for fitting nonlinear associations in epidemiological observational studies, RCS flexibly capture subtle nonlinear trends, enhancing the reliability and interpretability of the results^27,28. Subsequently, we employed logistic regression analysis to evaluate the association between LTL and PROM. In logistic regression analysis, we constructed two adjusted regression models. The Crude Model is an unadjusted rough analysis model that directly evaluates the original correlation between LTL and the odds of PROM. The Fully Adjusted Model adjusted for all covariates such as age, race, and education level to more comprehensively control for confounding factors and accurately assess the independent association between LTL and the odds of PROM. We analyzed LTL as both a continuous variable and a categorical variable (divided into quartiles 1–4), with the lowest exposure group (Quartile1) as the reference category. All results were quantified using odds ratios (OR) and their 95% confidence intervals (CI) to assess the strength of the association between LTL and PROM. Finally, to further validate the robustness of the results, we conducted subgroup analysis and interaction tests based on all categorical covariates to evaluate the stability and consistency of the association between LTL and PROM across different population characteristics.

All statistical analyses were conducted using R software (version 4.3.3), and a two-sided test with a P value < 0.05 was considered statistically significant.

Results

Baseline characteristics of participants

The basic demographic characteristics of the study participants are shown in Table 1. This study included a total of 170,841 participants, of which 1170 participants had experience with PROM. Compared to participants without PROM experience, participants with PROM experience are younger and have a lower proportion of white people. In terms of metabolism, participants who have experienced PROM had lower BMI, waist circumference, and hip circumference compared to the non-PROM group. In addition, no significant differences were observed in smoking status, drinking status, and health scores between the PROM group and non-PROM group. It is worth noting that participants with PROM experience had significantly higher LTL than those without PROM experience (0.84 vs. 0.87, P < 0.001), suggesting a possible association between LTL and PROM.

Table 1.

Baseline characteristics of the UK Biobank participants. (N = 170841).

Characteristic	Non-PROM	PROM	P
N	169,671	1170
Age, years (median [IQR])	57.00 [49.00, 62.00]	43.00 [41.00, 45.00]	< 0.001
Age, years (n, %)
<45	17,581 (10.4)	786 (67.2)	< 0.001
≥ 45	152,090 (89.6)	384 (32.8)
Race (n, %)
White	160,761 (94.7)	1051 (89.8)	< 0.001
Mixed	1189 (0.7)	15 (1.3)
Asian or Asian British	2634 (1.6)	42 (3.6)
Black or Black British	2895 (1.7)	35 (3.0)
Chinese	606 (0.4)	9 (0.8)
Other	1586 (0.9)	18 (1.5)
BMI, km/m2 (mean ± SD)	26.85 (5.02)	25.68 (4.59)	< 0.001
BMI, km/m2 (n, %)
Underweight	1123 (0.7)	15 (1.3)	< 0.001
Normal	69,006 (40.7)	586 (50.1)
Overweight	62,428 (36.8)	382 (32.6)
Obesity	37,114 (21.9)	187 (16.0)
Waist circumference, cm (median [IQR])	82.00 [75.00, 91.00]	79.00 [73.00, 88.00]	< 0.001
Hip circumference, cm (median [IQR])	102.00 [96.00, 108.00]	100.00 [95.00, 106.00]	< 0.001
Drinking status (n, %)
Never	8036 (4.7)	58 (5.0)	0.938
Previous	5339 (3.1)	37 (3.2)
Current	156,296 (92.1)	1075 (91.9)
Smoking status (n, %)
Never	102,311 (60.3)	725 (62.0)	0.097
Previous	53,572 (31.6)	338 (28.9)
Current	13,788 (8.1)	107 (9.1)
Health score (n, %)
Excellent	31,434 (18.5)	229 (19.6)	0.171
Good	103,466 (61.0)	725 (62.0)
Fair	29,551 (17.4)	191 (16.3)
Poor	5220 (3.1)	25 (2.1)
Education level (n, %)
College or University degree	61,075 (36.0)	557 (47.6)	< 0.001
A levels/AS levels or equivalent	24,315 (14.3)	188 (16.1)
O levels/GCSEs or equivalent	50,206 (29.6)	264 (22.6)
CSEs or equivalent	11,686 (6.9)	87 (7.4)
NVQ or HND or HNC or equivalent	9933 (5.9)	40 (3.4)
Other professional qualifications	12,456 (7.3)	34 (2.9)
Basal metabolic rate, KJ (median [IQR])	5561.00 [5205.00, 5996.00]	5680.00 [5339.00, 6088.00]	< 0.001
LTL (median [IQR])	0.84 [0.76, 0.92]	0.87 [0.79, 0.95]	< 0.001

Abbreviations: PROM, Premature Rupture of Membranes; IQR, Interquartile Range; BMI, Body Mass Index; SD, Standard Deviation; cm, centimeter; LTL, Leukocyte telomere length.

A non-linear correlation was observed between LTL and the PROM

To reveal the correlation pattern between LTL and PROM, this study used RCS regression analysis to determine whether there is a linear relationship. As shown in Fig. 2, the results showed a significant nonlinear correlation (P = 0.004) between LTL and PROM, and exhibited an anti J-shaped trend. After adjusting for all covariates, the odds of PROM showed a trend of first increasing and then decreasing with the increase of LTL. When LTL is less than 0.691, the PROM odds increases significantly, but remains lower than the baseline odds. When LTL is greater than 0.691, PROM odds first increases sharply, then slowly decreases and gradually becomes flat. This indicates that LTL may have different impacts on PROM odds within different ranges.

Fig. 2.

Nonlinear association between LTL and PROM. This RCS model has been adjusted for Age, Race, Education level, Basal metabolic rate, Waist circumference, Hip circumference, Drinking status, Smoking status, Health score and BMI. Abbreviations: LTL, Leukocyte telomere length; PROM, Premature Rupture of Membrane.

Longer LTL is associated with an increased odds of PROM

To further investigate the impact of LTL on PROM, we employed a logistic regression model to analyze the association between LTL and PROM. As shown in Table 2, after adjusting for all covariates, the odds of PROM significantly increased for every 1 unit increase in the original LTL (OR = 1.97, 95%CI: 1.30–2.95, P = 0.001). In addition, we divided LTL into quartiles (Q1–Q4) and found that in Fully Adjusted Model, compared with the Q1, the odds of PROM was significantly increased in the Q3 and Q4 of LTL (OR = 1.33, 95% CI: 1.11–1.59, P = 0.003; OR = 1.31, 95% CI: 1.09–1.56, P = 0.003). These results indicate that longer LTL increases the odds of developing PROM (Fig. 3).

Table 2.

Association between LTL and PROM among UK Biobank participants.

LTL	Crude Model		Fully Adjusted Model
	OR (95%CI)	p-value	OR (95%CI)	p-value
per unit increase in LTL	5.36(3.85, 7.40)	<0.001	1.97(1.30, 2.95)	0.001
Q1	—	—	—	—
Q2	1.38(1.14, 1.67)	<0.001	1.16(0.96, 1.41)	0.12
Q3	1.79(1.49, 2.14)	<0.001	1.33(1.11, 1.59)	0.003
Q4	2.15(1.80, 2.56)	<0.001	1.31(1.09, 1.56)	0.003

Crude Model: No covariates adjusted.

Fully Adjusted Model: Adjusted for Age, Race, Education level, Basal metabolic rate, Waist circumference, Hip circumference, Drinking status, Smoking status, Health score and BMI.

Abbreviations: LTL, Leukocyte telomere length; PROM, Premature Rupture of Membranes; OR, Odds Ratio; CI, Confidence Interval.

Fig. 3.

Forest plot of the association between LTL and the odds of PROM among UK Biobank participants. Crude Model: No covariates adjusted. Fully Adjusted Model: Adjusted for Age, Race, Education level, Basal metabolic rate, Waist circumference, Hip circumference, Drinking status, Smoking status, Health score and BMI. Abbreviations: LTL, Leukocyte telomere length; PROM, Premature Rupture of Membranes; OR, Odds Ratio.

Age and education level significantly affect the association between LTL and PROM

Next, in order to verify the stability of the above results, this study conducted subgroup analysis on categorical variables such as age, BMI, and smoking status, and adjusted the P-values for FDR to control for multiple test errors, as shown in Table 3. The subgroup analysis results showed that the positive association between LTL and the odds of PROM is significant among individuals aged 45 years or older, White race, never smokers, current drinkers, those with O levels/GCSEs or equivalent education, and those with other professional qualifications. In addition, we also conducted interaction tests to determine whether each subgroup variable has an effect modifying effect on the association between LTL and PROM. Product interaction terms between LTL and each subgroup variable were included, and their statistical significance was assessed using the likelihood ratio test (Table 3). The results showed that age (P for interaction < 0.001) and education level (P for interaction = 0.017) had a significant effect modifying effect on the association between LTL and PROM, while no significant interaction effect was detected in the other subgroup variables (P for interaction > 0.05), indicating that age and education level play an important role in regulating the relationship between LTL and the odds of PROM.

Table 3.

Subgroup analysis for the association between LTL and PROM.

Subgroup	OR (95%CI)	P-valuea	P for interactionb
Age, years			<0.001
<45	1.18(0.7, 1.99)	0.681
≥45	4.04(2.31, 7.07)	0.007
Drinking status			0.73
Never	2.83(0.62, 12.99)	0.388
Previous	4.74(0.4, 56.73)	0.392
Current	1.86(1.21, 2.87)	0.028
Smoking status			0.419
Never	2.39(1.46, 3.9)	0.007
Previous	1.38(0.6, 3.14)	0.627
Current	1.2(0.28, 5.18)	0.873
Health score			0.73
Excellent	2.89(1.13, 7.41)	0.095
Good	1.58(0.94, 2.68)	0.216
Fair	2.95(1.01, 8.6)	0.149
Poor	5.54(0.33, 91.59)	0.392
BMI, km/m2			0.809
Underweight	12.25(0.19, 787.55)	0.392
Normal	1.75(0.96, 3.16)	0.185
Overweight	2.2(1.11, 4.36)	0.095
Obesity	1.95(0.68, 5.57)	0.392
Race			0.086
White	2.29(1.49, 3.52)	0.007
Mixed	1.02(0.03, 41.26)	0.99
Asian or Asian British	1.38(0.16, 12.24)	0.865
Black or Black British	0.31(0.03, 3.06)	0.488
Chinese	0.18(0, 156.41)	0.719
Other	0.37(0.01, 14.85)	0.719
Education level			0.017
College or University degree	1.23(0.66, 2.3)	0.681
A levels/AS levels or equivalent	2.27(0.77, 6.69)	0.317
O levels/GCSEs or equivalent	2.8(1.32, 5.93)	0.033
CSEs or equivalent	1.17(0.23, 6.08)	0.880
NVQ or HND or HNC or equivalent	3.28(0.3, 36.08)	0.489
Other professional qualifications	49.77(6.86, 360.89)	0.007

^aP-value adjusted by False Discovery Rate (FDR) to control type I errors caused by multiple testing in subgroup analyses.^bP for interaction means P-value of interaction test, calculated by Likelihood Ratio Test (LRT) based on the logistic regression model including multiplicative interaction terms between LTL and subgroup variables, used to assess the effect modification of subgroup variables on the association between LTL and PROM.

Abbreviations: LTL, Leukocyte telomere length; PROM, Premature Rupture of Membranes; OR, Odds Ratio; CI, Confidence Interval.

Discussion

Although previous studies have revealed the association between LTL and adverse pregnancy outcomes such as preterm birth¹⁴ and spontaneous abortion¹³, there is currently no clear explanation of the relationship between LTL and PROM. Therefore, based on large-scale cross-sectional data from the UK Biobank, this study explores for the first time the association between LTL and PROM. We found a significant non-linear relationship between LTL and PROM. Further analysis suggests that longer LTL is associated with higher PROM odds, and this relationship is significantly influenced by age and education level. This study not only fills the gap in the research on the relationship between LTL and PROM, but also reveals its interaction with age and education level, providing new directions for future related research.

Telomeres are complexes composed of repeated non coding DNA sequences and associated binding proteins, forming a circular structure to protect the ends of chromosomes from degradation^29,30. LTL is generally considered a marker of biological and cellular aging³¹, associated with age-related diseases and overall health²⁵. The relationship between telomere biology and diseases is a complex topic. Long LTL and short LTL will increase the incidence rate of some diseases. In a follow-up study, it was found that subjects with longer LTL were more likely to develop benign tumors of varying degrees, such as thyroid tumors, melanoma, lymphoma, etc.³². In addition to tumors, other evidence also suggests a significant correlation between longer LTL and hematological diseases, such as lymphocytic leukemia, acute lymphoblastic leukemia, primary thrombocytopenia, malignant immune proliferative diseases, etc., which have high-risk causal effects³³. However, when LTL is too short, it can also cause a series of adverse health outcomes. A meta-analysis showed that LTL was negatively associated with diabetes. In other disease studies, it has been found that shorter LTL increases the risk of non-alcoholic fatty liver disease³⁴, digestive system diseases^12,30, cardiovascular disease³⁵, and deterioration of type 2 diabetes³⁶.

This study found that longer LTL is associated with an increased odds of PROM, and its potential mechanism still needs further clarification. Based on existing research, we speculate that the potential mechanism between LTL and PROM may involve the following pathways: on the one hand, there is evidence to suggest that longer LTL is associated with higher levels of oxidative stress and chronic inflammatory states³⁷. During pregnancy, an enhanced maternal inflammatory response can induce changes in the local microenvironment of the fetal membrane, thereby increasing the risk of PROM occurrence³⁸. On the other hand, according to the research of Menon Ramkumar et al., the increased activity of matrix degrading enzymes (such as matrix metalloproteinases) and the accelerated process of cell apoptosis are two key factors in PROM³⁹. Meanwhile, other studies have shown a positive correlation between LTL and estrogen levels⁴⁰. Based on this information, we propose a hypothesis that longer LTL may cause an increase in estrogen levels, thereby regulating the expression of matrix metalloproteinases and promoting apoptosis, ultimately weakening the structural integrity of the fetal membrane and triggering PROM. Therefore, we speculate that longer LTL may mediate membrane structural damage and PROM occurrence through a dual pathway of inflammatory activation and hormone protease regulation. In addition, the association between LTL and PROM may involve other mechanisms, and future research needs to further explore the specific mechanisms by which LTL affects PROM, which will provide theoretical basis for its potential as a biomarker or intervention target.

In previous studies, the relationship between LTL and some adverse pregnancy outcomes has been reported. Research has found that pregnant women with shorter LTL have a higher probability of premature birth¹⁴, which has been further confirmed in other analyses¹³. In addition, women with shorter LTL in early pregnancy are more likely to develop gestational diabetes, and are often associated with comorbidity depression of gestational diabetes⁴¹. Other studies have also found an association between shorter LTL and other adverse pregnancy outcomes, such as maternal and fetal mortality, intrauterine growth restriction, etc.⁴². It is worth noting that although longer LTL can reflect higher oocyte quality⁴³, it also increases the risk of preeclampsia¹⁵ and is associated with higher levels of oxidative stress and inflammation. Genetic studies have shown that longer LTL predicted by genetics significantly increases the risk of excessive menstruation and ovarian endometriosis². Similarly, other research findings have confirmed that longer LTL predicted by genes increases the risk of ovarian cancer, uterine leiomyoma, and ovarian cysts^11,44. However, the relationship between LTL and PROM has not been elucidated yet. Therefore, this study is the first to use the UK Biobank for cross-sectional analysis to reveal the relationship between LTL and PROM. Our analysis indicates that LTL significantly affects the odds of developing PROM. In addition, we also found that the relationship between LTL and PROM is influenced by age and education level, which has not been reported in previous studies. This suggests that in future research and practice, we need to pay more attention to individual differences in age and education level to comprehensively understand and apply the relationship between LTL and PROM. However, this study did not explore the mechanism of LTL and PROM, so subsequent research should combine animal experiments, multi omics analysis to further reveal the molecular pathway of LTL affecting PROM and its specific mechanism of action during pregnancy.

Conclusions

This study is based on participants with a history of pregnancy in the UK Biobank, revealing the relationship between LTL and PROM. The results showed that there is a non-linear correlation between LTL and PROM, and the odds of PROM increases with the increase of LTL. At the same time, this relationship will be influenced by age and education level. These findings provide clues for understanding the pathogenesis of PROM.

Author contributions

CX, YS, JH and RG had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.Concept and design: CX, YS, JH, RG.Acquisition, analysis, or interpretation of data: CX, YS, JH, RG.Drafting of the manuscript: CX, YS.Critical review of the manuscript for important intellectual content: All authors.Obtained funding: JH, RG.Administrative, technical, or material support: CP, WL, ZC, JL, QS, YR, YW.Final approval of the version to be submitted: JH, RG.

Funding

This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202300418), the National Natural Science Foundation of China(No.82201858), Outstanding Young Medical Talents Program of Chongqing (No.YXQN202401).

Data availability

The datasets supporting the conclusions of this study are available from theUK Biobank (https://www.ukbiobank.ac.uk/).

Declarations

Competing interests

The authors declare no competing interests.