Heart rate variability and atrial fibrillation in the general population: a longitudinal and Mendelian randomization study

Background Sex differences and causality of the association between heart rate variability (HRV) and atrial fibrillation (AF) in the general population remain unclear. Methods 12,334 participants free of AF from the population-based Rotterdam Study were included. Measures of HRV including the standard deviation of normal RR intervals (SDNN), SDNN corrected for heart rate (SDNNc), RR interval differences (RMSSD), RMSSD corrected for heart rate (RMSSDc), and heart rate were assessed at baseline and follow-up examinations. Joint models, adjusted for cardiovascular risk factors, were used to determine the association between longitudinal measures of HRV with new-onset AF. Genetic variants for HRV were used as instrumental variables in a Mendelian randomization (MR) analysis using genome-wide association studies (GWAS) summary-level data. Results During a median follow-up of 9.4 years, 1302 incident AF cases occurred among 12,334 participants (mean age 64.8 years, 58.3% women). In joint models, higher SDNN (fully-adjusted hazard ratio (HR), 95% confidence interval (CI) 1.24, 1.04–1.47, p = 0.0213), and higher RMSSD (fully-adjusted HR, 95% CI 1.33, 1.13–1.54, p = 0.0010) were significantly associated with new-onset AF. Sex-stratified analyses showed that the associations were mostly prominent among women. In MR analyses, a genetically determined increase in SDNN (odds ratio (OR), 95% CI 1.60, 1.27–2.02, p = 8.36 × 10–05), and RMSSD (OR, 95% CI 1.56, 1.31–1.86, p = 6.32 × 10–07) were significantly associated with an increased odds of AF. Conclusion Longitudinal measures of uncorrected HRV were significantly associated with new-onset AF, especially among women. MR analyses supported the causal relationship between uncorrected measures of HRV with AF. Our findings indicate that measures to modulate HRV might prevent AF in the general population, in particular in women. Graphical abstract AF; atrial fibrillation, GWAS; genome-wide association study, IVW; inverse variance weighted, MR; Mendelian randomization, MR-PRESSO; MR-egger and mendelian randomization pleiotropy residual sum and outlier, RMSSD; root mean square of successive RR interval differences, RMSSDc; root mean square of successive RR interval differences corrected for heart rate, SDNN; standard deviation of normal to normal RR intervals, SDNNc; standard deviation of normal to normal RR intervals corrected for heart rate, WME; weighted median estimator. aRotterdam Study n=12,334 bHRV GWAS n=53,174 cAF GWAS n=1,030,836 Supplementary Information The online version contains supplementary material available at 10.1007/s00392-022-02072-5.


Introduction
Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with substantial morbidity and mortality and represents a significant burden on healthcare [1][2][3][4]. The exact AF pathogenesis remains to be identified. It has recently been suggested that cardiac autonomic imbalance could play a role in AF pathophysiology by promoting a decline in cardiac function [5][6][7][8][9][10].
Heart rate variability (HRV) is considered a non-invasive, accessible measure that may reflect the complex interaction between the autonomic nervous system and the heart [11,12]. A complex relationship between HRV and AF has been suggested [5][6][7][8][9][10]13]. Specifically, lower and higher levels of HRV may lead to decline in cardiac function and subsequently give rise to AF [5][6][7][8][9][10]13]. Recent evidence shows that sex differences with regard to AF burden, pathophysiology, and prognosis exist [14]. However, the previous observational studies have been limited to either a cross-sectional design or a single measurement of HRV and did not evaluate sex differences. In addition, observational studies are prone to residual confounding and reverse causality [15].
Genome-wide association studies (GWAS) have identified genetic variants/single nucleotide polymorphisms (SNPs) for multiple assessments of HRV [16] and AF [17,18]. Pathway and tissue enrichment analyses suggest that HRV SNPs are preferentially expressed within the sinoatrial node [16]. Moreover, AF SNPs have been suggested to affect the cardiac ion channels, cardiac calcium signaling, and the heart and skeletal muscles [17,18]. This suggests that there may be a genetic foundation underlying the association between HRV and AF.
We aimed to investigate the association between longitudinal measures of HRV and heart rate with the risk of new-onset AF in the general population. Additionally, we used a comprehensive Mendelian randomization (MR) analysis using summary-level data from GWAS on measures of HRV and AF to investigate the potential causal relationship between HRV and AF.

Study population
The current study was embedded within the Rotterdam Study [19,20]. The Rotterdam Study is a prospective populationbased cohort study that aims to assess the occurrence and progression of risk factors for chronic diseases in middle-age and elderly persons. During 1990During -1993, all inhabitants of the Ommoord district in the city of Rotterdam in The Netherlands aged ≥ 55 years were invited for the study. A total of 7983 (78% of all invitees) agreed to participate (RS-I). In 2000, the cohort was extended with 3011 participants who had become ≥ 55 years or had migrated into the research area (RS-II). In 2006, the cohort was again extended with 3932 participants who were ≥ 45 years (RS-III). The overall response rate at baseline was 72%. Participants attended follow-up examinations every 3-5 years. Outcome data on morbidity and mortality were continuously collected through linkage with digital files from general practitioners in the study area [19,20].
For the present study, we included participants at study entry of the three recruitment waves. Participants with prevalent AF at baseline (n = 559), no informed consent for follow-up data collection (n = 305), no follow-up time (n = 6) or no measures of HRV (n = 1722) were excluded. Among the 12,334 free of AF included participants, 12,334 had at least 1 measurement for standard deviation of normal RR intervals (SDNN), SDNN corrected for heart rate (SDNNc), RR interval differences (RMSSD), RMSSD corrected for heart rate (RMSSDc), and heart rate. 8,832 participants had 2 measurements, 3837 had 3 measurements, 1817 had 4 measurements, and 787 participants had 5 measurements that were available during follow-up (before date of onset of AF, date of death, loss to follow-up, or to January 1, 2014, whichever occurred first).

Assessment of heart rate variability
Participants underwent a 10-s 12-lead resting electrocardiogram (ECG) using an ACTA Gnosis IV ECG recorder (Esaote Biomedica, Florence, Italy) and the ECG records were digitally stored. Subsequently, Modular ECG Analysis System (MEANS) was used to interpret the ECGs [21]. ECGs of individuals with a pacemaker, < 5 RR intervals between normal beats or > 5 premature supra-and/or ventricular complexes were excluded for the assessment of HRV [22]. In addition, the remaining ECGs marked as non-sinus arrhythmia and sinus arrhythmia by MEANS were manually assessed by 2 medical doctors to rule out and exclude atrial fibrillation/flutter, other arrhythmias, and ECGs with poor signal quality. Sinus rhythm (including sinus arrhythmia) is based on the detection by MEANS of regular P waves that have a fixed coupling interval with the following QRS complexes [21]. Furthermore, a random sample of 200 ECGs marked as sinus rhythm by MEANS were also manually checked by 2 medical doctors and 199 ECGs were found to be in sinus rhythm during the manual assessment indicating a very high positive predictive value of MEANS which was also demonstrated in earlier work [21]. RR intervals between 2 adjacent normal beats were used to compute the mean heart rate and time-domain indices of HRV; SDNN and RMSSD. Moreover, as HRV is potentially inversely and exponentially associated with heart rate, we additionally used heart rate corrected values of RMSSD (RMSSDc), and SDNN (SDNNc) using an exponential model [22][23][24][25][26]. The reproducibility of the HRV data was evaluated in a later cohort of the Rotterdam Study, in which ECG recordings of 3-5 min were made. From a sample of 310 3-5 min ECGs, we extracted from each recording two 10-s ECGs, one after the first minute of recording, the second after 2 minutes. The sample of 310 pairs of 10-s ECGs was also manually assessed by 2 medical doctors to rule out and exclude arrhythmias, and ECGs with poor signal quality. After exclusion, 211 ECG pairs remained and were used to calculate the HRV measures. Differences were examined using the paired T-test. The HRV measures did not statistically significantly differ from each other (p = 0.087 for RR, p = 0.415 for SDNN, and p = 0.427 for RMSSD).

Assessment of atrial fibrillation
AF was defined in accordance with the European Society of Cardiology (ESC) guidelines [4]. The methods on event adjudication for prevalent and incident AF within the Rotterdam Study have been described in detail earlier [20]. In short, AF was assessed at baseline and follow-up examinations using a 10-s 12-lead ECG with an ACTA Gnosis IV ECG recorder (Esaote Biomedica, Florence, Italy). The ECG records were then stored digitally and analyzed with MEANS [21]. Thereafter, 2 medical doctors validated the diagnosis of AF and in case of disagreement a cardiologist was consulted [3]. Additional follow-up data were obtained from medical files of participating general practitioners, hospitals, outpatient clinics, national registration of all hospitals discharge diagnoses, and follow-up examinations at the research center. The date of incident AF was defined as the date of the first occurrence of symptoms suggestive of AF with subsequent ECG verification obtained from the medical records. Participants were followed from the date of enrollment in the Rotterdam Study until the date of onset of AF, date of death, loss to follow-up, or to January 1, 2014, whichever occurred first.

Assessment of cardiovascular risk factors
The cardiovascular risk factors included in this study were body mass index (BMI), total cholesterol, high-density lipoprotein cholesterol, hypertension, smoking status, history of diabetes mellitus, history of coronary heart disease, history of heart failure, left ventricular hypertrophy on the ECG, use of cardiac medication, use of antihypertensive medication, use of beta blockers, use of calcium blockers, and use of lipid lowering medication. Methods for measurements of cardiovascular risk factors are explained in detail in the Methods S1 [3,19,20].

Selection of genetic variants on heart rate variability and atrial fibrillation
Genetic variants associated with HRV were used as instrumental variables for the MR analyses. The genetic variants were retrieved from publically available summary statistics from 2 GWAS [16][17][18]. Details regarding these study populations are depicted in Tables S4 and S5. For HRV, we retrieved independent genetic variants from a GWAS on HRV that assessed SDNN and RMSSD as log transformed continuous measures. This GWAS meta-analysis on HRV included 53,174 participants from European descent [16]. In addition, we retrieved independent genetic variants that were associated with AF from a GWAS that included 1,030,836 European participants (60,620 AF cases and 970,216 controls) [17]. We only included independent genetic variants in the subsequent MR analyses (p-value < 5.0 × 10 -08 genomewide significant and r 2 < 0.1).

Joint model analyses
The baseline characteristics of the study population are presented as mean with standard deviation (SD) or number (n) with percentages as appropriate. Differences between men and women were examined by Student's T-test (normal distribution) or the Mann Whitney U-test (skewed distribution) for continuous variables, and Chi-Square Test for categorical variables. The distributions of the different HRV measures Flow chart for the conducted analyses. Abbreviations: AF, atrial fibrillation; GWAS, genome-wide association study; HRV, heart rate variability; SDNN, standard deviation of normal to normal RR intervals; SDNNc, standard deviation of normal to normal RR intervals corrected for heart rate; RMSSD, root mean square of successive RR interval differences; RMSSDc, root mean square of successive RR interval differences corrected for heart rate; SNPs, single-nucleotide polymorphisms and heart rate were skewed. Therefore, a natural logarithmic transformation was used to obtain a normal distribution.
Competing risk analyses were employed using joint models for longitudinal and time-to-event data. To investigate the association between longitudinal measures of HRV with the risk of new-onset AF with mortality as a competing event, cause-specific hazard ratios (HRs) with their 95% confidence intervals (CIs) were calculated to quantify the associations (Fig. 1). See the Methods S2 for more details on the rationale, imputation and sensitivity analyses of the joint model analyses [27,28].
The analyses were done in the total study population and for men and women separately. Additionally, we reported the p-values of sex interaction from the joint model. All models (mixed-and survival models) were adjusted for age, sex (if applicable), and cohort (model 1), and additionally for cardiovascular risk factors including body mass index, total cholesterol, high-density lipoprotein cholesterol, hypertension, smoking status, history of diabetes mellitus, history of coronary heart disease, history of heart failure, left ventricular hypertrophy on the ECG, cardiac medication, beta blockers, calcium blockers, and use of lipid lowering medication (model 2). Time was measured in years after baseline and the variables from model 1 and 2 were treated as covariates in the subsequent models.

Mendelian randomization analyses
We conducted two-sample MR analyses to examine the potential causal association between HRV and AF. The inverse variance weighted (IVW) method is the main method used in our analyses [29]. MR estimates were presented as odds ratios (ORs) with corresponding 95% CIs (Fig. 1). See the Methods S3, for more details on the rationale, assumptions and sensitivity analyses of the MR analyses [15,[29][30][31][32][33][34][35].
A two-tailed p < 0.05 was considered statistically significant. The data management was done using IBM SPSS Statistics version 25.0 for Windows (IBM Corp, Armonk, New York). The statistical analyses were done using the R packages "JMbayes2", [36] and "TwoSampleMR" (30,34,35) in R software (R 4.0.2; R Foundation for Statistical Computing, Vienna, Austria) [37].

Joint model analyses
A total of 12,334 participants, 5140 men (41.7%) and 7194 women (58.3%), were eligible for the analyses. Baseline characteristics for the total study population and stratified by sex are presented in Table 1. The mean age of the total study population was 64.8 ± 9.5 years and 58.3% were women. Median values for SDNN, SDNNc, RMSSD, RMSSDc, and heart rate were 16.2 ms, 27.0 ms, 16.3 ms, 33.7 ms, and 69.0 beats/min, respectively. See Table 1 for more details.
During a median follow-up of 9.4 years (interquartile range (IQR), 6.2-15.1), 1302 incident AF cases (10.6%) (613 in men and 691 in women) and 4004 mortality cases (32.5%) (1,740 in men and 2264 in women) occurred. The incidence rate of AF was 9.6 per 1000 person-years in the total study population (11.5 per 1000 person-years in men, 8.4 per 1000 person-years in women) and the incidence rate of mortality was 29.5 per 1000 person-years in the total study population (32.6 per 1000 person-years in men, 27.5 per 1000 person-years in women).
All results of the joint model sensitivity analyses are depicted in Results S1.

Mendelian randomization analyses
A total of 33 genome-wide significant independent genetic variants were associated with HRV represented by SDNN (n = 16) and RMSSD (n = 17), respectively. A total of 5 SNPs for SDNN and 6 SNPs for RMSSD were available in the AF GWAS and were used for the MR analyses after removal of potential outliers. All individual genetic instruments for SDNN and RMSSD had a F statistic > 10 (median for SDNN, 53.2 (IQR, 51.8-70.8) and median for RMSSD, 54.7 (IQR, 43.1-69.3)) and were, therefore, considered to be of sufficient strength to be used in the MR analyses. The effect estimates of the genetic variants associated with SDNN, RMSSD and AF that were used in the MR analyses are presented in Table S4.
The results of the MR sensitivity analyses are depicted in Results S2 [33].

Discussion
Our study shed light on the complex interaction between HRV and AF. Our joint model analyses showed that longitudinal measures of SDNN, and RMSSD were significantly associated with new-onset AF in the general population while SDNNc, RMSSDc, and heart rate were not significantly associated. Sex-stratified analyses showed that RMSSD among men, and SDNN, RMSSD, and heart rate among women were significantly associated with new-onset AF. MR analyses supported the causal association between SDNN, and RMSSD with AF. Our findings indicate that treatment to modulate HRV might prevent AF in the general population, in particular in women.
The exact mechanism that underlies the relationship between HRV and AF remains incompletely understood. Shared underlying risk factors, such as obesity, diabetes mellitus, and coronary heart disease, could influence HRV and are also implicated in AF pathophysiology [1,5,12,38,39]. In our study, however, the associations of HRV with incident AF slightly attenuated, but remained significant after extensive adjustment for shared cardiovascular risk factors. The increase in left atrial size that has been associated with HRV could suggest a role for HRV in AF pathogenesis that is mediated by the left atrium [13]. Moreover, autonomic imbalance could trigger an inflammatory response that can subsequently lead to AF [12]. Finally, the effect of the GWAS-identified HRV SNPs on the genes (especially, GNG11, RGS6) that are preferentially expressed within the Table 2 Association between longitudinal measures of heart rate variability with the risk of new-onset atrial fibrillation in the total study population and stratified by sex CI, confidence interval; HR, hazard ratio; RMSSD, root mean square of successive RR interval differences; RMSSDc, root mean square of successive RR interval differences corrected for heart rate; SDNN, standard deviation of normal to normal RR intervals; SDNNc, standard deviation of normal to normal RR intervals corrected for heart rate a Adjusted for age, sex (if applicable), and cohort b Adjusted for age, sex (if applicable), cohort, body mass index, total cholesterol, high-density lipoprotein cholesterol, hypertension, smoking status, history of diabetes mellitus, history of coronary heart disease, history of heart failure, left ventricular hypertrophy on the electrocardiogram, cardiac medication, beta blockers, calcium blockers, and use of lipid lowering medication c Hazard ratios represent 1 unit increase of ln(SDNN), ln(SDNNc), ln(RMSSD), ln(RMSSDc), and 1 unit decrease of ln(heart rate) with the risk of new-onset atrial fibrillation sinoatrial node underlines the genetic basis that potentially underlies the association between HRV and AF. In short, these genes may affect acetylcholine release of the vagal nerves within the sinoatrial node and thereby influence HRV [16]. More specifically, GNG11 codes for the γ11 subunit of the heterotrimeric G-protein complex Gαβγ and may cause a decreased expression of this subunit [16]. This lower availability of this subunit may then reduce Gβγ induced GIRK activation. This potentially blunts heart rate changes caused by oscillatory changes in cardiac vagal activity, ultimately decreasing HRV [16]. Furthermore, RGS6 regulates the heterotrimeric G-protein complex signaling type 6 and may increase its availability. This leads to a decreased GIRK activation and potentially blunts the effects in cardiac vagal activation, and may thereby decrease HRV [16]. Subsequently, it has been suggested that sinus node disease (SND) may cause AF by promoting atrial extrasystoles, and re-entry [40,41]. Atrial extrasystoles may occur during the slow atrial cycle in the presence of SND. Atrial extrasystoles are mostly followed by a compensatory pause. The pause may then be prolonged which allows other atrial ectopic activity to arise which possibly triggers AF [40]. Early premature beats that originate from areas other than the sinus node may result in conduction block and initiate re-entry, which may be a mechanism underlying AF [40]. Furthermore, stenosis in the sinus nodal artery is also common in patients with AF which implies that ischemic damage to the sinus node alone without atrial fibrosis, stretch or muscle loss may result in AF [40]. Overall, a combination of atrial extrasystoles, reentry, and ischemia to the sinus node are mechanisms by which SND may cause and promote AF. We investigated the longitudinal measures of HRV during a long follow-up time in relation to new-onset AF. Taking into account repeated measurements of HRV in relation to new-onset AF may provide more insight and prognostic information over a single baseline measurement that has been done by most of the previous studies [5][6][7][8][9][10]13]. Longitudinal measures of HRV during follow-up were associated with an increased risk of incident AF, especially among women. These findings extend previous evidence by simultaneously evaluating the repeated measurements of uncorrected and corrected HRV, heart rate, and sex differences while investigating the link between HRV and AF [5][6][7][8][9][10]13]. To some extent our findings support the association between heart rate and AF that has been previously reported in observational, [6,7,42] and Mendelian randomization studies [43]. However, we only found a significant association for heart rate in association with AF among women. One potential explanation could be differences in sex hormones. It has been demonstrated that an acute ovarian hormone withdrawal induced by oophorectomy leads to decline in different measures of HRV (SDNN, RMSSD), and an increase in heart rate in women [44]. The same study also showed that estrogen replacement therapy for three months within the oophorectomized women restored the HRV and heart rate to a pre-surgery level [44]. This might explain why uncorrected HRV and heart rate were only associated with incident AF in women, and not in men, in our study. We further hypothesize that competing risk of death is a possible explanation for the observed sex differences. AF is strongly associated with age, [1][2][3] so it is likely that men die of other (cardiovascular) diseases before development of AF. This hypothesis was supported by our competing risk analyses which showed that SDNN, RMSSD, RMSSDc, and heart rate were significantly associated with mortality, especially among men. Nevertheless, we found a higher incidence of AF in men, than in women in our study.
Our MR approach sheds light on the causality of the association between HRV and AF. Our effect estimates were more or less in line with previous observational studies. However, we were unable to assess the association between SDNNc, RMSSDc and AF, since not enough instrumental variables for SDNNc and RMSSDc were available to be used for the MR analyses. Future GWAS with a larger sample size could identify new additional genetic variants that could be used to assess the association between heart rate corrected HRV and AF. This could be of importance, because of the strong inverse association that exists between HRV and heart rate [24,25]. This relation is further underlined by Nolte et al. who showed attenuation in the HRV SNP associations when they corrected for heart rate [16]. This might imply that uncorrected measures of HRV may be, in part, confounded by heart rate. Although, we showed that heart rate itself was not significantly associated with newonset AF (except in women), we cannot rule out the possibility that heart rate is the overall determining factor instead of HRV after all. Since our uncorrected measures of HRV were indeed not significantly associated with new-onset AF. Further, as heart rate is also associated with AF and cardiovascular mortality proper adjustment for heart rate is of importance [26,43,45,46]. However, excluding a genetic variant that was also associated with heart rate, a potential confounder or horizontal mediator, did not substantially change our MR results. Future studies on HRV measures corrected for heart rate could further aid in elucidating the exact mechanisms underlying HRV and AF.
The major strengths of this study are its population-based nature, large sample size with detailed information on cardiovascular risk factors, meticulous adjudication of incident AF and long follow-up time, multiple sensitivity analyses including complete-case analyses, excluding prevalent and incident CHD prior to AF diagnosis, use of competing risk analyses to compute cause-specific hazards, and use of largescale GWAS summary statistics. The availability of repeated measurements for different HRV measures during followup also enabled us to investigate longitudinal measures of HRV in association with new-onset AF in a joint modeling approach which may provide more insight and give more prognostic information over a single baseline measurement. Moreover, using a MR approach we were able to gain more insight in the complex interaction between HRV and AF and to avoid certain biases that are more common in traditional observational epidemiological studies, such as residual confounding and reverse causation [15]. However, our study also has some limitations that should be taken into consideration. Our HRV measures were based on 10-s ECGs, although HRV guidelines recommend that HRV measures are based on preferably 5-min or 24-h ECG recordings [22]. Nevertheless, 10-s ECGs are more commonly performed in healthcare, are cheaper, are faster, and thereby more patient friendly than longer ECG recordings. Additionally, HRV measures from 10-s ECGs have already been associated with left ventricular function, [47] heart failure, [47,48] cardiac- [49] and all-cause mortality [50]. Additionally, other studies that investigated the reliability of 10-s ECGs in comparison to 5-min ECGs to assess HRV showed that 10-s ECGs are also a reliable tool for HRV risk assessment, in particular within population-based studies [51,52]. We could not distinguish between paroxysmal, persistent, longterm persistent, and permanent AF as Holter monitoring has not been done in this large population-based cohort. In our MR analyses, we cannot rule out unobserved horizontal pleiotropy, although we tried to address horizontal pleiotropy using multiple MR sensitivity analyses, such as MR-Egger, WME, MR-PRESSO, and sensitivity plots, to identify and correct for horizontal pleiotropy. Additionally, not enough sex-stratified SNPs were available in the publically available genetic dataset to perform the MR for men and women separately. Furthermore, there was partial overlap in the samples that were used to obtain the genetic instruments which may cause bias toward observational findings [33]. However, the potential bias was probably negligible given that the maximum potential overlap was 2.1%. Finally, our findings may not be generalizable to younger populations and other ethnicities, as our analysis included mainly older participants from European descent.
In conclusion, longitudinal measures of SDNN, RMSSD, but not SDNNc, RMSSDc, and heart rate, were significantly associated with new-onset AF. In sex-stratified analyses, RMSSD among men and SDNN, RMSSD, and heart rate among women were significantly associated with newonset AF. MR analysis confirmed the complex association between HRV and AF that has been indicated by our and previous observational studies. These findings indicate that measures to modulate HRV might prevent AF in the general population, especially among women, but future MR studies that investigate the causality between heart rate corrected measures of HRV and AF are warranted.

Institutional Review Board Statement
The Rotterdam Study complies with the Declaration of Helsinki and has been approved by the Medical Ethics Committee of the Erasmus MC (registration number MEC 02.1015) and by the Dutch Ministry of Health, Welfare and Sport (Population Screening Act WBO, license number 1071272-159521-PG). The Rotterdam Study has been entered into the Netherlands National Trial Register (NTR; www. trial regis ter. nl/ trials/) and into the WHO International Clinical Trials Registry Platform (ICTRP; https:// apps. who. int/ trial search/) under shared catalog number NL6645/NTR6831.

Informed Consent Statement
For the longitudinal study, all participants provided written informed consent to participate, prior to inclusion, in the study and to have their information obtained from treating physicians. The Mendelian randomization study has been conducted using publicly available summary statistics, so no original data were collected for this bidirectional MR study. Ethical approval and informed consent from each participant in the studies included in the investigation can be found in the original publications. The analysis of other publicly available summary statistics does not require additional ethical approval.
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