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. 2022 Sep 29;2(6):430–437. doi: 10.1007/s43657-022-00072-z

Causal Association of Cardiac Function by Magnetic Resonance Imaging with Frailty Index: A Mendelian Randomization Study

Hui Zhang 1,2,#, Meng Hao 1,#, Zixin Hu 1, Yi Li 1, Xiaoxi Hu 1, Xiaoyan Jiang 3, Zuyun Liu 4, Xuehui Sun 1,2,✉, Xiaofeng Wang 1,2,✉
PMCID: PMC9712899  PMID: 36939795

Abstract

Owing to the susceptibility of conventional observational studies to confounding factors and reverse causation, the causal association between cardiac function and frailty is unclear. We aimed to investigate whether cardiac function has causal effects on frailty. In this study, a two-sample Mendelian randomization (MR) study was conducted using genetic variants associated with cardiac function assessed by magnetic resonance imaging phenotypes as instrumental variables. Genetic variants associated with cardiac function by magnetic resonance imaging (including seven cardiac function phenotypes) and the frailty index (FI) were obtained from two large genome-wide association studies. MR estimates from each genetic instrument were combined using inverse variance weighted (IVW), weighted median, and MR‒Egger regression methods. We found that the increase in genetically determined stroke volume (beta − 0.13, 95% CI − 0.16 to − 0.10, p = 1.39E−6), rather than other cardiac phenotypes, was associated with lower FI in MR analysis of IVW after Bonferroni correction. Sensitivity analyses examining potential bias caused by pleiotropy or reverse causality revealed similar findings (e.g., intercept [SE], − 0.008 [0.011], p = 0.47 by MR‒Egger intercept test). The leave-one-out analysis indicated that the association was not driven by single nucleotide polymorphisms. No evidence of heterogeneity was found among the genetic variants (e.g., MR‒Egger: Q statistic = 14.4, p = 0.156). In conclusion, we provided evidence that improved cardiac function could contribute to reducing FI. These findings support the hypothesis that enhancing cardiac function could be an effective prevention strategy for frailty.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00072-z.

Keywords: Cardiac function, Frailty, Mendelian randomization, Genetics

Introduction

Frailty is a major phenotype of accelerated ageing, and it is characterized by a loss of physiologic reserve and resistance to stressors caused by cumulative declines in multiple physiological systems throughout the life course (Clegg et al. 2013). Previous studies demonstrated that the disturbance/ageing of single or multiple organ systems might contribute to frailty, such as the immune system (Samson et al. 2020; Sayed et al. 2021; Zhang et al. 2022), kidney (Dalrymple et al. 2013; Guerville et al. 2019; Liu et al. 2017), and cardiac function (Nadruz et al. 2017; Tan et al. 2021; Yao et al. 2019). Among the multiple organ systems, abnormalities in cardiac structure and function were the most widespread factors independently associated with frailty (Nadruz et al. 2017; Tan et al. 2021; Yao et al. 2019). In particular, heart failure (HF), the main consequence of a disturbance/ageing of cardiac function, is intertwined with frailty (Gude et al. 2018; Lakatta and Levy 2003; Pandey et al. 2019). Observational studies indicated that the prevalence of frailty is almost 50% in patients with HF (Denfeld et al. 2017; Dewan et al. 2020; Marengoni et al. 2020), and the hypotheses from previous studies suggested a potential bidirectional relationship between frailty and HF by sharing common inflammatory origins (Bellumkonda et al. 2017). However, due to the presence of reverse causation and confounding factors, the causal association of cardiac function with frailty might have been misinterpreted in conventional epidemiological studies (Davey Smith and Ebrahim 2005).

Mendelian randomization (MR) analysis is an emerging method using exposure-related genetic variants as instrumental variables (IVs) to infer the causal relationship between modified exposures (i.e., cardiac function) and outcomes (i.e., frailty) (Davey Smith and Hemani 2014; Lawlor et al. 2008). It is conducted after applying the following assumptions: genetic variants are randomly allocated at gametogenesis (Smith et al. 2007), genetically predicted exposures are not generally liable to confounding factors, and the exposures are not consequentially influenced by outcomes through reverse causality (Davey Smith and Ebrahim 2005). Hence, MR analysis could overcome the limitations of traditional epidemiological studies, improve causal inference, and thereby identify causal associations between exposures and outcomes.

Considering the complex associations of cardiac function with frailty, the inevitable limitations in conventional observational studies, and the outstanding strengths of MR analysis to infer causality, we conducted a two-sample MR study to investigate the potential causal effect of cardiac function by magnetic resonance imaging (MRI) on frailty risk in this research.

Methods

Genetic Data on Cardiac MRI Phenotypes

In this study, the exposure variable of interest was cardiac function measured by MRI phenotypes. The summary statistics were from a recent genome-wide association study (GWAS) of cardiac MRI phenotypes by Pirruccello and his colleagues (Pirruccello et al. 2020). This GWAS was conducted among UK Biobank participants (n = 36,041) who did not have cardiac disease, including a diagnosis of congestive HF, coronary artery disease, or dilated cardiomyopathy at the time of enrolment (Pirruccello et al. 2020). It examined seven cardiac MRI phenotypes, including left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), stroke volume (SV), body-surface-area (BSA) indexed versions of each of the three traits (LVEDVi, LVESVi, and SVi), and left ventricular ejection fraction (LVEF). At the time of MRI measurement, participants had a mean age of 64 (7.5) years. Among them, 19,075 (52.93%) were women aged 58–69 years, and 16,966 (47.07%) were men aged 58–70 years. Additionally, 35,407 (98.24%) were Europeans, and the rest were from Africa (0.26%), East Asia (0.30%), South Asia (0.84%), or had other ancestry (0.36%).

Each of the seven cardiac MRI phenotypes (LVEDV, LVESV, LVEF, SV, LVEDVi, LVESVi, and SVi) was standardized by rank-based inverse normal transformation. GWAS was adjusted for ancestry, sex, year of birth, age at the time of MRI, and the MRI scanner’s unique identifier to account for batch effects. The independent genome-wide significant variants were those with a p on cardiac MRI phenotypes less than 5.00E−8 and linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) r2 less than 0.2 after excluding SNPs with minor allele frequency (MAF) less than 0.01 (Pirruccello et al. 2020). Finally, we identified 22 loci associated with LVEDV, 14 loci with LVEDVi, 22 loci with LVEF, 32 loci with LVESV, 28 loci with LVESVi, 12 loci with SV, and eight loci with SVi. The detailed information is presented in Table S1.

Genetic Data on the Frailty Index

The outcome of interest, the frailty index (FI), was assessed based on the accumulation of deficits model (Searle et al. 2008). The summary statistics were collected from a recent GWAS of FI conducted among the participants of UK Biobank Study by Atkins and his colleagues (Atkins et al. 2021). In detail, this study included two populations (European descent UK Biobank participants and Swedish TwinGene participants). Atkins’s analysis was restricted to European descent (n = 451,367), and 164,610 participants aged 60–70 with complete FI data were ultimately included. In this specific population, the FI was constructed based on 49 items (including information on sensory, cranial, mental well-being, infirmity, cardio-metabolic, respiratory, musculoskeletal, immunological, cancer, pain, and gastrointestinal function) and calculated as a proportion of the sum of all deficits. The FI GWAS analysed 16,446,667 autosomal variants with MAF > 0.1%, Hardy–Weinberg p > 1.00E−9, and imputation quality > 0.3. Software BOLT-LMM (v2.3.2) was applied for the GWAS (Loh et al. 2015). GWAS was conducted by linear mixed-effects modelling with adjustments for age, sex, assessment centre (22 categories), and genotyping array (two categories: Axiom or BiLEVE). The mean age of the included participants was 64.1 (2.8) years, and the mean FI was 0.129 (0.075). The detailed demographic information of this population was presented in Atkins’s research (Atkins et al. 2021). Full GWAS summary statistics are freely available to download directly from (https://doi.org/10.6084/m9.figshare.9204998). Due to the skewness of the untransformed traits, the FI was quantile normalized (i.e., transformed into a normal distribution) prior to GWAS. Detailed information on the relationship between IVs and FI is presented in Table S2. The questionnaire items from the baseline UK Biobank assessment used to compose the FI are also presented in Table S3.

Statistical Analysis

We conducted a two-sample MR study using summary statistics for SNPs related to cardiac MRI phenotypes as IVs. Statistical tests were two-tailed, and the analyses were conducted by the R-based package “TwoSampleMR”. Excluding the SNPs that were not available in the summary statistics data for FI, the SNPs used for the MR analyses were 19, 29, 21, 12, 13 and seven for the association of LVEDV, LVESV, LVEF, SV, LVEDVi, LVESVi, and SVi with FI, respectively. In the main MR analysis, the random-effects inverse-variance weighted (IVW) model, which can provide an accurate estimate in the absence of directional pleiotropy (Burgess et al. 2015), was used to assess the association between cardiac MRI phenotypes and FI. The significance level of p in the main MR analysis was set as 7.14E−3 (0.05/7) after applying Bonferroni correction.

Furthermore, we conducted sensitivity analyses to validate the estimates using the following approaches. The MR weighted median method allows up to 50% of IVs to be invalid (Bowden et al. 2016). MR‒Egger regression can provide unbiased results in the presence of pleiotropic instruments (Bowden et al. 2015). Because the primary analysis essentially focussed on testing the hypothesis that cardiac MRI phenotypes were associated with frailty risk and, therefore, no correction for multiple comparisons was performed, a threshold of p < 0.05 was used in sensitivity analyses. Heterogeneity of individual genetic variants was evaluated by scatterplots of the SNP-exposure and SNP-outcome associations as well as Cochran’s Q test, which was able to detect moderate to weak pleiotropy (Greco et al. 2015). To assess the pleiotropic effect of the IVs, MR‒Egger regressions that allow the intercept to be freely estimated as an indicator of average pleiotropic bias were conducted (Bowden et al. 2015). The MR‒Egger intercept can be interpreted as a test of overall unbalanced horizontal pleiotropy, for which a p greater than 0.05 suggested that there was no significant pleiotropy present in the IVs. The leave-one-out analyses were performed to assess the sensitivity of each genetic variant in our study (Burgess and Thompson 2017).

Results

Main Analyses

The associations between genetic cardiac MRI phenotypes and FI are presented in Table 1. Causal estimates are displayed as coefficients (beta) and 95% confidence intervals (CIs). No evidence was found for causal associations of six cardiac MRI phenotypes (LVEDV, LVESV, LVEF, LVEDVi, LVESVi, and SVi) with FI in MR analyses of IVW, MR‒Egger, and weighted median, where all p values were greater than 7.14E−3 after Bonferroni correction. However, we found a protective causal effect of SV on FI, showing that the increase in genetically determined SV was associated with lower FI in the MR analyses by the IVW method (beta − 0.13, 95% CI − 0.16 to − 0.11, p = 1.39E−6) with 12 SNPs meeting the relaxed threshold (Table 1 and Fig. 1). Additionally, the association of individual and combined SNPs with the frailty index indicated similar results (Fig. 2 and Table S4).

Table 1.

Mendelian randomization estimates of the association of cardiac MRI phenotypes with frailty index

Exposure Method SNPs (N) Beta (95% CI) p value
SVi MR–Egger 7 0.515 (0.136, 0.895) 0.233
SVi Weighted median 7 − 0.027(− 0.069, 0.015) 0.519
SVi IVW 7 0 (− 0.037, 0.036) 0.989
LVEDV MR–Egger 19 0.058 (− 0.15, 0.266) 0.785
LVEDV Weighted median 19 − 0.044 (− 0.078, − 0.01) 0.191
LVEDV IVW 19 − 0.079 (− 0.117, − 0.041) 0.037
LVEDVi MR–Egger 13 0.131 (− 0.064, 0.325) 0.515
LVEDVi Weighted median 13 − 0.007 (− 0.039, 0.025) 0.835
LVEDVi IVW 13 − 0.018 (− 0.051, 0.014) 0.575
LVESVi MR–Egger 26 − 0.001 (− 0.058, 0.057) 0.991
LVESVi Weighted median 26 0.039 (0.017, 0.061) 0.071
LVESVi IVW 26 0.05 (0.031, 0.07) 0.011
SV MR–Egger 12 0.149 (− 0.085, 0.384) 0.538
SV Weighted median 12 − 0.142 (− 0.178, − 0.106) 7.45E−05
SV IVW 12 − 0.132 (− 0.16, − 0.105) 1.39E−06
LVESV MR–Egger 29 0.121 (0.045, 0.198) 0.126
LVESV Weighted median 29 0.038 (0.016, 0.061) 0.092
LVESV IVW 29 0.018 (− 0.004, 0.041) 0.416
LVEF MR–Egger 21 − 0.072 (− 0.148, 0.004) 0.353
LVEF Weighted median 21 − 0.05 (− 0.073, − 0.028) 0.025
LVEF IVW 21 − 0.055 (− 0.077, − 0.033) 0.013
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Causal effects are estimated using three MR models. The cardiac MRI phenotypes including left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), stroke volume (SV), the body-surface-area (BSA) indexed versions of each of these traits (LVEDVi, LVESVi, and SVi), and left ventricular ejection fraction (LVEF)

IVW inverse variance-weighted model

Fig. 1.

Fig. 1

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Scatter plots of the estimated SNP effects on SV plotted against the estimated SNP effects on the frailty index. The Mendelian randomization (MR) inverse-weighted (IVW), MR–Egger and weighted median method lines are plotted with red, blue and green, respectively

Fig. 2.

Fig. 2

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Forest plot of the association of individual and combined SNPs with frailty index. Effects of SNPs were expressed as Beta values (95% CI). IVW inverse variance-weighted method

Sensitivity Analyses

Sensitivity analyses were performed to assess the plausibility of the assumption that genetic variants influence FI through their association with SV. The nonsignificant p value of the MR‒Egger intercept test (intercept [SE], − 0.008 [0.011], p = 0.47) suggested that the causal association of SV with FI may not be attributable to pleiotropy. Meanwhile, the Cochran Q statistic of 14.4 with an associated p of 0.156 implies no heterogeneity in the effect estimates. In particular, one of the variants (rs143384) was reported to be associated with knee osteoarthritis (p = 4.2E−23) in a previous study (Styrkarsdottir et al. 2019) and therefore potentially influenced FI by underlying pleiotropy independent of SV. However, the leave-one-out analysis (Fig. 3; Table S5) illustrated that the SNP (rs143384) did not drive the effect size estimates (beta = − 0.14 [0.029], p = 3.78E−6). Additionally, the weighted median method (Table 1) also indicated a statistically significant causal relationship between SV and FI (beta = − 0.14, 95% CI − 0.18 to − 0.11, p = 7.45E−5).

Fig. 3.

Fig. 3

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Forest plot of the results of the leave-one-out sensitivity analysis of causal association, where each SNP in the instrument was iteratively removed from the instrument. Effects of SNPs were expressed as Beta values (95% CI)

Discussion

To the best of our knowledge, for the first time, a two-sample MR study was conducted to evaluate the causal association between cardiac function assessed by MRI and frailty. Overall, our principal findings demonstrated that the genetic predisposition to higher SV had a statistically significant association with decreased FI, suggesting a protective causal effect of cardiac function on frailty.

Several studies have investigated the aetiology of frailty and found many factors causally associated with FI using MR analyses, such as low-density lipoprotein cholesterol, body mass index, waist-hip ratio, inflammatory bowel disease, and handgrip strength (Atkins et al. 2021; Wang et al. 2019). However, no studies have been conducted to evaluate the causal association between cardiac function and FI by MR analyses. Therefore, we compared our results with the findings of previous observational studies. HF is one of the most common consequences of cardiac ageing (Gude et al. 2018), and the prevalence of frailty was approximately 50% in patients with HF using various assessments of frailty (Denfeld et al. 2017; Dewan et al. 2020; Marengoni et al. 2020). For example, the pooled prevalence of frailty based on all assessments was 0.40 (95% CI 0.31–0.48) (Marengoni et al. 2020), while the prevalence of frailty was 0.47 (95% CI 0.31–0.64) using multidimensional frailty (Denfeld et al. 2017) and 0.45 (95% CI 0.36–0.53) using physical frailty (Denfeld et al. 2017) in meta-analyses. Dewan et al. also found that the proportion of frail individuals was approximately 63% using cumulative deficits models employing 42 items (FI > 0.210) in 13,625 HF patients with reduced ejection fraction (Dewan et al. 2020). In brief, frailty commonly existed in HF patients and was intertwined with HF (Pandey et al. 2019).

Additionally, observational studies found an independent association of abnormalities in cardiac structure and function with frailty (Nadruz et al. 2017; Tan et al. 2021; Yao et al. 2019). Our previous study also provided evidence that elevated B-type natriuretic peptide, one of the most commonly used biomarkers in the diagnosis of HF (Goetze et al. 2020), was significantly related to prefrailty (odds ratio [OR] 1.61, 95% CI 1.13–2.29) and frailty (OR 2.63, 95% CI 1.61–4.32) after adjustment for confounders (Yao et al. 2019). Tan et al. conducted a longitudinal analysis of 1431 participants without prevalent CVD in the Cardiovascular Health Study, suggesting that left ventricular early diastolic strain was associated with incident frailty (OR 1.58, 95% CI 1.11–2.27, p = 0.013) (Tan et al. 2021). Additionally, Nadruz et al. showed a significant association between abnormal left ventricular structures (for left ventricular hypertrophy: OR 1.72, 95% CI 1.30–2.40, p = 0.002; for abnormal global longitudinal strain: OR 1.68, 95% CI 1.16–2.44, p = 0.006; for abnormal left atrial volume index: OR 1.60, 95% CI 1.13–2.27, p = 0.008; respectively) and frailty in the Atherosclerosis Risk in Communities Study (Nadruz et al. 2017). In particular, a prospective cohort study found that for elderly individuals with advanced HF, their FI decreased after six months of left ventricular assist device support (Maurer et al. 2017). In summary, evidence from observational studies indicated that abnormal cardiac structure and function might play an important role in frailty, which is consistent with our findings.

The mechanisms underlying the association of cardiac function with FI are not clearly understood. Several potential mechanisms are involved in the association between cardiac function and the FI. The FI was constructed based on the accumulation of deficits/functional losses in various items, including information about functional disability, cardiovascular disease (CVD), chronic kidney disease (CKD), and cognition (Atkins et al. 2021; Searle et al. 2008). Cardiac function decline is one of the most important factors in cardiac ageing that could lead to diseases, such as CVD (Gude et al. 2018; Lakatta and Levy 2003), CKD (Bansal et al. 2015; Park et al. 2014; Schefold et al. 2016), and neurodegenerative diseases (de Bruijn et al. 2015; Jefferson et al. 2015; Moazzami et al. 2018). Additionally, cardiac function declines could also result in functional disability, which is one of the components of the FI (Kuh et al. 2019; Leibowitz et al. 2018, 2011). In brief, cardiac function decline contributes to dysfunction of organ systems and/or functional losses and thereby leads to a gradual increase in the FI.

The present two-sample MR analysis has several strengths. First, we used the two-sample MR approach to assess the association of cardiac function assessed by MRI with frailty. The MR approach would be less susceptible to confounding by unmeasured or unknown factors due to the independent assortment of the IV risk alleles with confounding factors than conventional observational analysis, and it could not be influenced by reverse causation. The two-sample MR design allowed us to use a large sample size, maximizing our statistical power and providing evidence of causality. Second, we applied three MR methods in this study, including IVW, weighted median, and MR‒Egger. The weighted median approach gives more weight to the precise instrumental variables, and the estimate is consistent even when up to 50% of the information comes from invalid or weak instruments (Yavorska and Burgess 2017). The results from the weighted median approach were consistent with those from the IVW, which excludes the possibility that the causal effect of SV on FI was affected by invalid or weak instruments. Last, a multiple-testing-adjusted significance threshold of p < 7.14E−3 (Bonferroni correction p) was considered, which could provide robust evidence that inherited exposure to higher SV was associated with decreased FI.

Several limitations may exist in our analyses. First, only UK Biobank participants were selected in our MR study to investigate the causal association of cardiac function with frailty, which points out the necessity of using a dataset from different populations to verify our findings. Second, genetic pleiotropy cannot be completely ruled out in any MR studies (Larsson et al. 2017). One variant (rs143384) was reported to be associated with knee osteoarthritis (p = 4.2E−23) in a previous study (Styrkarsdottir et al. 2019) and therefore potentially influenced the FI by underlying pleiotropy independent of SV. However, our leave-one-out analysis illustrated that this SNP did not drive the effect size estimates (beta = − 0.14 [0.029], p = 3.78E−6) in the MR analyses. Last, genetic heterogeneity in different ethnicities or genetic ancestries would affect the coefficient estimates in MR analyses. The genetic association should be considered and investigated in future studies.

Conclusions

Using genetic instruments identified from large-scale GWAS, our findings provided evidence that inherited exposures that improve cardiac function were associated with decreased FI. Our findings pointed out the importance of objective measurement of cardiac function in epidemiologic studies and supported the hypothesis that enhancing cardiac function may be an effective prevention strategy for frailty. Further research is necessary to help understand the underlying mechanisms of this association.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 78 kb) (78KB, docx)

Acknowledgements

The data and samples used for this research were obtained from UK biobank. Data on cardiac function assessed by MRI is contributed by Pirruccello and his colleagues. Data on frailty index is contributed by Atkins and his colleagues. We would like to thank Pirruccello and his colleagues, Atkins and his colleagues, and UK Biobank participants and coordinators for their contribution to this dataset.

Authors' Contributions

HZ, MH and XW designed the research; HZ, MH, YL, XH, ZH and XJ conducted the research; HZ, MH, ZH and YL analysed the data and performed the tactical analyses. HZ and XW wrote the paper; ZL and XW supervised the study, and all authors read and approved the final manuscript.

Funding

This work was supported by National Key Research and Development Programme of China (2018YFC2000400, 2018YFC2000400-3, 2018YFC2002000) and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01).

Data Availability

The full GWAS summary statistics of cardiac function by MRI from the main analysis for each of the seven traits are available for download from the Broad Institute Cardiovascular Disease Knowledge Portal under the ‘Downloads’ (http://www.broadcvdi.org/). Full GWAS summary statistics of gene-frailty index are freely available for download via the GWAS catalogue (https://www.ebi.ac.uk/gwas/downloads/summary-statistics; study accession GCST90020053) or directly from (https://doi.org/10.6084/m9.figshare.9204998).

Declarations

Conflict of Interest

The authors declare no conflicts of interest.

Consent to Participate

The present research used publicly available summary data and did not contact any participants, where no extra ethical approval is required.

Consent for Publication

All the participants approved to publish the paper.

Footnotes

Hui Zhang and Meng Hao contributed equally to this work.

Contributor Information

Xuehui Sun, Email: xhsun@fudan.edu.cn.

Xiaofeng Wang, Email: wangxiaofeng@fudan.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 78 kb) (78KB, docx)

Data Availability Statement

The full GWAS summary statistics of cardiac function by MRI from the main analysis for each of the seven traits are available for download from the Broad Institute Cardiovascular Disease Knowledge Portal under the ‘Downloads’ (http://www.broadcvdi.org/). Full GWAS summary statistics of gene-frailty index are freely available for download via the GWAS catalogue (https://www.ebi.ac.uk/gwas/downloads/summary-statistics; study accession GCST90020053) or directly from (https://doi.org/10.6084/m9.figshare.9204998).

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