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Genetically predicted dietary macronutrient intakes and atrial fibrillation risk: a Mendelian randomization study

European Journal of Medical Research volume 29, Article number: 227 (2024) Cite this article

Abstract

Background and aim

Previous observational investigations have indicated a potential association between relative dietary macronutrient intakes and atrial fibrillation and flutter (AF) risk. In this study, we employed Mendelian Randomization (MR) to evaluate the presence of causality and to elucidate the specific causal relationship.

Methods

We employed six, five, and three single nucleotide polymorphisms (SNPs) as instrumental variables for relative carbohydrate, protein, and fat intake, identified from a genome-wide association study that included 268,922 individuals of European descent. Furthermore, we acquired summary statistics for genome-wide association studies on AF from the FinnGen consortium, which involved 22,068 cases and 116,926 controls. To evaluate the causal estimates, we utilized the random effect inverse variance weighted method (IVW) and several other MR methods, including MR-Egger, weighted median, and MR-PRESSO, to confirm the robustness of our findings.

Results

Our analysis indicates a convincing causal relationship between genetically predicted relative carbohydrate and protein intake and reduced AF risk. Inverse variance weighted analysis results for carbohydrates (OR = 0.29; 95% CI (0.14, 0.59); P < 0.001) and protein (OR = 0.47; 95% CI (0.26, 0.85); P = 0.01) support this association. Our MR analysis did not identify a significant causal relationship between relative fat intake and AF risk.

Conclusion

Our study provides evidence supporting a causal relationship between higher relative protein and carbohydrate intake and a lower risk of atrial fibrillation (AF).

Introduction

Atrial fibrillation (AF) is a common arrhythmia typically caused by electrical abnormalities within the heart, leading to irregular contractions of the upper chambers (atria) and impaired pumping function [1]. AF affects approximately 33 million people globally and is the most common cardiac arrhythmia. According to estimates, by 2050, 6–12 million people worldwide will be affected by this disease, and by 2060, Europe will have 17.9 million people suffering from this condition [2]. AF is a significant risk factor for stroke, with AF patients having more than a fivefold increased risk compared to non-AF patients [3]. Its etiology may be related to hypertension, coronary artery disease, diabetes, thyroid disorders, pulmonary disease, and certain cardiac conditions [4]. Lifestyle factors such as excessive alcohol intake, smoking, physical inactivity, and obesity have also been implicated in AF development [5]. Although the pathogenesis of AF is partly understood, a definitive cure for AF has not been identified, making prevention and risk factor control crucial.

Dietary macronutrients refer to three major nutrients: carbohydrates, proteins, and fats [6, 7]. The proportions and amounts of macronutrient intakes can influence health and the risk of diseases. A balanced diet with appropriate intakes of macronutrients is key to maintaining health and reducing disease risk [8]. Many studies have shown a close association between macronutrient intakes and cardiovascular risk. A traditional Mediterranean diet emphasizing high-quality carbohydrates and proteins and a controlled intake of high fats benefits cardiovascular protection [9, 10]. Although higher carbohydrate intake seems associated with increased cardiovascular risks in some Asian populations, the evidence is inconsistent [11]. While low-carbohydrate diets effectively induce short-term weight loss, their long-term safety and effects remain unclear [12]. Recent studies report conflicting results regarding how carbohydrate intake influences atrial fibrillation (AF) risk [13]. For example, some show that reducing carbohydrate intake may increase AF risk by 16–18%, whereas others indicate that high carbohydrate intake at breakfast could decrease cardiovascular disease risk [14]. Regular consumption of low-carbohydrate and high-protein diets without accounting for carbohydrate quality or protein sources is associated with elevated cardiovascular risk [15]. Furthermore, some studies have found that low saturated fat intake can lower the incidence of atrial fibrillation and other cardiovascular diseases [16, 17]. However, other studies have presented opposing results [18, 19]. Above all, numerous observational studies have explored the association between macronutrient intakes and the risk of AF, but their results are not entirely consistent. Due to the inability of observational studies to control for confounding factors, it is difficult to establish a causal relationship between macronutrient intakes and the risk of atrial fibrillation. Therefore, it is essential to employ Mendelian Randomization (MR) to investigate the genuine causal relationship between macronutrient intakes and the risk of AF.

Mendelian Randomization (MR) is an epidemiological methodology that utilizes genetic variants as instrumental variables to circumvent confounding factors and reverse causality. [20] The three core assumptions of MR are instrumental variable assumption, independence assumption and exclusion restriction assumption. Similar to a randomized trial, genotypes are randomized at conception, making MR immune to confounding and reverse causality as per Mendel's second law [21]. Consequently, MR is a robust tool for predicting causal associations, as illustrated in Fig. 1. This study employs MR analysis to explore the relationship between macronutrient intakes and AF by utilizing genetically predicted relative macronutrient intakes as the exposure variable.

Fig. 1
figure 1

Mendelian randomization analyses to investigate associations between relative macronutrient intakes and the risk of atrial fibrillation and flutter. The broken lines signify potential causal effects (pleiotropic or direct) between variables that would violate the assumptions of Mendelian randomization

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Materials and methods

Study design

Our study utilized a two-sample MR approach to examine the causal association between the risk of atrial fibrillation and flutter and the proportion of macronutrients consumed. This study adhered to the guidelines for the Preferred Reporting of Observational Studies in Epidemiology Using Mendelian Randomization (The STROBE-MR Statement) [22]. Our investigations relied on publicly accessible GWAS summary data, which had already undergone ethical review board approvals before their inclusion in our study.

Instruments selection for relative macronutrients intake

We obtained GWAS summary statistics from a large genome-wide association study that included 268,922 individuals of European ancestries aged 27–71 years for relative intake of macronutrients [23], the most recent GWAS on relative macronutrients intake based on the European populations when we started the MR analysis. In the original GWAS study, relative macronutrients intake was expressed as % of total energy intake (E%) and assessed using comprehensive food-item questionnaires including previous-day or habitual dietary intake.

As genetic instruments, we selected nucleotide polymorphisms (SNPs) that demonstrated a strong association with relative macronutrients intake at a genome-wide significance level (p < 5 × 10−8). We, respectively, used 6 SNPs strongly associated with relative carbohydrate intake, 5 SNPs with relative protein intake, and 3 SNPs with relative fat intake as instrumental variables for the corresponding exposures. To maintain independence, we performed a linkage disequilibrium (LD) test (r2 < 0.001 and distance > 10 000 kb) [24]. We did not use palindromic SNPs with intermediate allele frequencies that pair with each other since they may invert the direction of a causal effect. We utilized proxy SNPs as substitutes for the corresponding SNPs only if they were not present in the outcome data. The proxy SNPs were identified by searching the SNIPA database (https://snipa.helmholtz-muenchen.de/snipa3/index.php). We performed a manual inspection using PhenoScanner for screening association with confounder [25]to exclude confounders. In screening for confounders, we focused on factors known to influence AF pathogenesis, including anatomical deformities (e.g., left atrium dilation and hypertrophic cardiopathy), valvular heart diseases, hyperthyroidism, chronic inflammation, infections, familial predispositions, and the use of specific medications such as statins and ACEIs/ARBs. This meticulous process helped refine our SNP selection, aiming for a clearer understanding of macronutrient intake's impact on AF risk. The R2 was then utilized to evaluate the extent to which each SNP accounted for the variance in relative macronutrient intake. [26]. To evaluate the effectiveness of instrumental variables, we utilized the F statistic, considering an F value greater than 10 as indicative of strong instruments [27].

Genetic associations with atrial fibrillation and flutter

Our GWAS summary statistics for AF were obtained from the FinnGen project, which can be accessed at https://gwas.mrcieu.ac.uk/datasets/finn-b-I9_AF. There were 22,068 cases of atrial fibrillation and flutter and 116,926 people who served as controls in this study.

Statistical analysis

We conducted all MR analyses using R software (version 4.2.2), with the Mendelian Randomization [28], RadialMR [29], and MR-PRESSO [30] packages utilized for the MR analyses. The primary MR analyses were conducted using inverse variance weighted (IVW) analyses [31]. We used the Cochran Q test to assess the heterogeneity of IVW estimates, with P < 0.05 indicating significant heterogeneity. In addition, we conducted several sensitivity analyses to assess the robustness of the results. Specifically, we employed five different MR methods, namely MR-Egger [32], weighted median [33], and MR-PRESSO [30], which have been widely used in MR studies. The MR-Egger method, a popular approach for detecting and correcting pleiotropy, was utilized to estimate the potential impact of pleiotropy on the result. P < 0.05 from the MR-Egger intercept indicates the presence of directional pleiotropy, which may bias the MR estimates [32]. The weighted median approach was used to generate a more reliable estimate, even when up to 50% of the instruments were invalid, by taking the median of all possible pairwise IVW estimates [33]. To enhance the reliability and validity of our results, we employed the MR-PRESSO analysis, which detects and corrects for outliers and horizontal pleiotropy, was used to assess the potential impact of outliers on the results [30]. For each MR method, we estimated the causal effect of AF as odds ratios (OR) per one standard deviation (SD) increase in genetically predicted continuous traits, along with corresponding 95% confidence intervals (CI). A significance threshold of P < 0.05 was used to determine statistical significance. Additionally, we conducted leave-one-out analyses to assess the impact of each SNP on the overall causal estimates. This approach involves systematically removing one SNP at a time and re-analyzing the data to evaluate the sensitivity of the results to each SNP.

Results

Our analysis utilizing the inverse variance weighted (IVW) method revealed a robust causal association between genetically predicted relative carbohydrate and protein intake and a decreased risk of AF as illustrated in Fig. 2. For all three macronutrients, the F-values of the instrumental variables exceeded 10, indicating a robust association with relative macronutrient intake. Our results were unlikely confounded by heterogeneity or pleiotropic effects, as demonstrated by the absence of significant heterogeneity of inverse variance weighted (IVW) estimates (Q-test, P > 0.05) and directional pleiotropy (MR-Egger intercept, P > 0.05). Based on our leave-one-out analyses (Fig. 3), we found no significant differences in the estimated causal effects when individual SNPs were excluded, underscoring the robustness of our MR analysis. Consequently, the estimated effect cannot be ascribed to any particular SNP. Our study suggests that genetically predicted higher relative carbohydrate intake and protein intake may protect against the risk of AF. Our analysis did not identify a significant causal relationship between relative fat intake and AF risk.

Fig. 2
figure 2

The odds ratios (OR) depicting the associations between genetically predicted relative macronutrient intake and AF risk are presented. CI confidence interval; MR Mendelian randomization

Full size image
Fig. 3
figure 3

The leave-one-out analyses revealed no significant differences in the estimated causal effects when individual SNPs were removed from our MR analysis. A MR leave-one-out sensitivity analysis for ' carbohydrate ' on 'AF'. B MR leave-one-out sensitivity analysis for 'protein' on 'AF'. C MR leave-one-out sensitivity analysis for ' fat ' on 'AF'

Full size image

Discussion

We conducted a Mendelian Randomization (MR) analysis using a sample of 22,068 cases of atrial fibrillation and flutter and 116,926 controls to investigate the potential causal relationship between genetically predicted relative carbohydrate and protein intake and atrial fibrillation (AF), while minimizing confounding and reverse causation often observed in observational studies. Acknowledging the inherent limitations of our approach, it's important to note that despite employing various MR methods to mitigate the influence of pleiotropy, the potential for unmeasured confounding variables and pleiotropic effects persists. To account for potential pleiotropy, we employed various MR methods, each with different assumptions of pleiotropy, and the consistent results from these methods provided robust support for causal inference. Our analysis revealed a significant inverse association between relative carbohydrate and protein intake and AF risk. In contrast, our MR analysis did not identify a significant causal relationship between relative fat intake and AF risk. These findings suggest that increasing protein and carbohydrate intake from various nutrients may be a promising modifiable factor for preventing atrial fibrillation and highlight the importance of dietary modifications in preventing and managing AF. However, further studies are needed to fully elucidate the exact mechanisms underlying the protective effects of relative carbohydrate and protein intake on AF.

Multiple studies [34, 35] have demonstrated that lifestyle interventions can reduce AF incidence and mortality, particularly in high-risk populations. Recently, research has found that weight loss through enhanced management of risk factors can alleviate the symptom burden and severity of AF [36]. Therefore, lifestyle interventions [37] are crucial in reducing the risk of cardiovascular diseases, including AF. In addition to rhythm control, rate control, and anticoagulation therapy, changes in lifestyle and risk factors, including dietary structure and exercise, have been recognized as another pillar of AF treatment [38, 39].

Several studies have investigated the link between carbohydrates and AF. The latest research, published in J Am Heart Assoc [13], reveals that a carbohydrate-restricted diet may increase the risk of AF. Researchers evaluated the link between carbohydrate consumption and AF events in this large, community-based, prospective cohort study involving 13,384 participants. During a 22.4-year follow-up period, 1,808 AF cases (13.5%) were recorded. The study found that individuals on a low-carbohydrate diet may be associated with an increased risk of AF. Conversely, an increase in carbohydrate intake by 9.4% was associated with an 18% reduction in the incidence. Furthermore, we found some studies linking protein intake to cardiovascular disease. Higher plant protein consumption was associated with a modest reduction in overall and cardiovascular mortality risk [40]. Interestingly, a 2016 study [41] found that higher animal protein intake was positively associated with cardiovascular mortality. According to a study presented at the American College of Cardiology’s Annual Scientific Session and the World Congress of Cardiology,Footnote 1 compared with women who consumed less protein, women who consumed moderately higher than the recommended daily amount of protein had a significantly lower likelihood of developing atrial fibrillation.Footnote 2

Possible underlying mechanisms may explain that carbohydrate and protein intakes may be protective factors against AF risk. Irregular heartbeats characterize AF, and myocardial cells require more energy to maintain normal mechanical and electrical function. Mitochondria are essential organelles in cardiac cells responsible for converting nutrients into energy to meet the high energy demands of the heart. However, the increased metabolic rate of AF requires high energy metabolism, which may lead to mitochondrial overwork and oxidative stress, resulting in mitochondrial damage [42]. Recent research has indicated a close association between mitochondrial dysfunction and the development of AF [43]. Mitochondrial dysfunction may cause energy metabolism disorders in myocardial cells [44] and changes in calcium dynamics [45] and further exacerbate the development of AF. In addition, mitochondrial dysfunction may also lead to increased oxidative stress and inflammation reaction [46] and cell apoptosis in myocardial cells [47]. Studies have shown that improving myocardial mitochondrial function can alleviate the occurrence of AF [48]. Carbohydrates are the primary fuel for myocardial mitochondrial energy production. Carbohydrates are broken down into glucose in the body, then converted into adenosine triphosphate (ATP) through the glycolysis pathway, an important energy source supporting heart function. Amino acids are the basic units of proteins. They produce ATP in mitochondria by forming α-ketoglutarate or entering the tricarboxylic acid (TCA) cycle. In particular, the branched-chain amino acid leucine is an important energy source for the myocardium [49]. Therefore, appropriate carbohydrate and protein intakes may alleviate the progression of AF by improving myocardial mitochondrial function.

In addition, carbohydrate intake can also affect insulin levels, which is a hormone that regulates blood glucose. High insulin levels can promote glucose uptake and utilization, increasing the body's energy levels. Moderate carbohydrate intake can help maintain stable blood glucose levels, reducing the incidence of AF. However, excessive carbohydrate intake may lead to elevated blood glucose levels, negatively impacting cardiovascular health. Numerous studies have also confirmed the negative impact of carbohydrates on cardiovascular diseases [50]. High blood glucose levels can lead to cardiovascular diseases such as atherosclerosis, hypertension, and heart disease, which may also affect heart rate stability [51].

The source of carbohydrates and protein is also a noteworthy focus. High-quality carbohydrates refer to those that are abundant in nutrients such as dietary fiber, vitamins, and minerals [52]. These nutrients have the potential to decrease blood pressure, mitigate the risk of cardiovascular disease, and safeguard the cardiovascular system. Studies [53, 54] have shown that a moderate intake of whole grains, fruits, vegetables, and other carbohydrates can lower the risk of cardiovascular disease. Whole grain foods, such as oats, whole wheat bread, and brown rice, are healthy sources of carbohydrates. One important indicator is the low glycemic index (GI), meaning the digestion speed is slower and provides longer-lasting energy. Foods with a low GI can aid in managing and regulating blood glucose [55]. A study lasting from 12 to 26 weeks has shown that a low GI diet gradually enhances blood sugar control and insulin sensitivity, reducing the risk of heart attacks [56, 57]. Given that AF is a form of cardiovascular disease, it may benefit from adopting a low GI diet. Conversely, consuming ultra-processed foods (UPFs), including high-sugar foods, sweetened beverages, and poor-quality proteins, may harm cardiovascular health [58]. Excessive intake can cause a sharp rise and fall in blood sugar levels, increasing cardiovascular disease risk. UPFs usually contain many additives and high-calorie food ingredients, which may increase the release of inflammatory factors in the body with long-term consumption [59]. Furthermore, the source of protein emphasized the importance of high-quality protein. Red meat and other animal proteins may produce proinflammatory factors [60]. Chronic inflammation can damage the health of the heart and blood vessels, increasing the risk of cardiovascular disease.

Nonetheless, it is crucial to be mindful of the quantity and quality of carbohydrates and proteins. It is recommended that people choose high-quality sources [61]. Excessive intake of carbohydrates and consumption of low-quality proteins may adversely affect cardiovascular health [62]. This study found that increasing carbohydrate and protein intake can help protect against AF. However, this does not mean higher intake levels will not negatively affect health. Therefore, our research results cannot be generalized to higher intake levels. In future studies, we need to explore the effects of different carbohydrate and protein intake levels on health to better guide people's dietary choices. While our research has discussed potential mechanisms, it is important to acknowledge that these findings are speculative and lack robust evidence. Therefore, caution is necessary when interpreting and generalizing these results. Essential mechanistic studies are needed to investigate the effects of macronutrient intake, quantity, and quality on AF development and progression. Advancing our knowledge in this field will enable us to provide more precise dietary recommendations, empowering individuals to make informed choices for preventing and managing atrial fibrillation.

Our study acknowledges several limitations that merit attention. Firstly, the exploration into the relationship between relative fat intake and AF failed to produce significant findings, a constraint likely stemming from the insufficient number of instrumental variables pertaining to relative fat intake. This limitation restricted the depth of our analyses. Secondly, the absence of high-quality GWAS studies for specific subgroups of relative carbohydrate and protein intake prevented us from performing detailed subgroup analyses. Such analyses could have provided more nuanced insights into the differential effects of various macronutrients on AF risk. Thirdly, the study's focus on individuals of European ancestry limits its generalizability. The findings might not extend to other populations with different genetic backgrounds, lifestyles, and dietary habits, underscoring the need for further research in diverse cohorts to validate and broaden our understanding of the relationship between macronutrient intake and AF risk. Additionally, we recognize the need for robust evidence to support the biological plausibility and validate the speculative links between macronutrient intake and AF, which our study proposes. Future research is essential to explore these potential mechanisms more thoroughly and to address the gaps identified in our study, contributing to a more comprehensive understanding of AF pathogenesis.

Conclusion

Using MR analysis, we identified a causal relationship between higher predicted relative carbohydrate and protein intake based on genetics and a reduced risk of atrial fibrillation. Higher relative dietary macronutrient intakes were associated with a lower risk of atrial fibrillation. However, further research is needed to confirm these findings and elucidate their underlying mechanisms.

Data availability

The summary statistics for relative macronutrient intake and AF are available at the Social Science Genetic Association Consortium (SSGAC; https://www.thessgac.org/data) and IEU open gwas project, respectively.

Notes

  1. https://www.acc.org/latest-in-cardiology/articles/2020/03/18/15/42/could-eating-more-protein-reduce-risk-of-afib-in-women-acc-2020

  2. https://www.acc.org/about-acc/press-releases/2020/03/19/09/47/eating-more-protein-could-help-ward-off-atrial-fibrillation-in-women#.XqvrZWik070.twitter

References

  1. Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ, Gillum RF, Kim YH, McAnulty JH Jr, Zheng ZJ, Forouzanfar MH, Naghavi M, Mensah GA, Ezzati M, Murray CJ. Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study. Circulation. 2014;129(8):837–47. https://doi.org/10.1161/circulationaha.113.005119.

    Article  PubMed  Google Scholar 

  2. Lippi G, Sanchis-Gomar F, Cervellin G. Global epidemiology of atrial fibrillation: an increasing epidemic and public health challenge. Int J Stroke. 2021;16(2):217–21. https://doi.org/10.1177/1747493019897870.

    Article  PubMed  Google Scholar 

  3. Benjamin EJ, Wolf PA, D’Agostino RB, Silbershatz H, Kannel WB, Levy D. Impact of atrial fibrillation on the risk of death: the Framingham heart study. Circulation. 1998;98(10):946–52. https://doi.org/10.1161/01.cir.98.10.946.

    Article  CAS  PubMed  Google Scholar 

  4. Heeringa J, van der Kuip DA, Hofman A, Kors JA, van Herpen G, Stricker BH, Stijnen T, Lip GY, Witteman JC. Prevalence, incidence and lifetime risk of atrial fibrillation: the Rotterdam study. Eur Heart J. 2006;27(8):949–53. https://doi.org/10.1093/eurheartj/ehi825.

    Article  PubMed  Google Scholar 

  5. Middeldorp ME, Pathak RK, Meredith M, Mehta AB, Elliott AD, Mahajan R, Twomey D, Gallagher C, Hendriks JML, Linz D, McEvoy RD, Abhayaratna WP, Kalman JM, Lau DH, Sanders P. PREVEntion and regReSsive effect of weight-loss and risk factor modification on atrial fibrillation: the REVERSE-AF study. Europace. 2018;20(12):1929–35. https://doi.org/10.1093/europace/euy117.

    Article  PubMed  Google Scholar 

  6. Venn BJ. Macronutrients and human health for the 21st century. Nutrients. 2020. https://doi.org/10.3390/nu12082363.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Muth AK, Park SQ. The impact of dietary macronutrient intake on cognitive function and the brain. Clin Nutr. 2021;40(6):3999–4010. https://doi.org/10.1016/j.clnu.2021.04.043.

    Article  CAS  PubMed  Google Scholar 

  8. San-Cristobal R, Navas-Carretero S, Martínez-González M, Ordovas JM, Martínez JA. Contribution of macronutrients to obesity: implications for precision nutrition. Nat Rev Endocrinol. 2020;16(6):305–20. https://doi.org/10.1038/s41574-020-0346-8.

    Article  PubMed  Google Scholar 

  9. Widmer RJ, Flammer AJ, Lerman LO, Lerman A. The Mediterranean diet, its components, and cardiovascular disease. Am J Med. 2015;128(3):229–38. https://doi.org/10.1016/j.amjmed.2014.10.014.

    Article  PubMed  Google Scholar 

  10. Delgado-Lista J, Alcala-Diaz JF, Torres-Peña JD, Quintana-Navarro GM, Fuentes F, Garcia-Rios A, Ortiz-Morales AM, Gonzalez-Requero AI, Perez-Caballero AI, Yubero-Serrano EM, Rangel-Zuñiga OA, Camargo A, Rodriguez-Cantalejo F, Lopez-Segura F, Badimon L, Ordovas JM, Perez-Jimenez F, Perez-Martinez P, Lopez-Miranda J. Long-term secondary prevention of cardiovascular disease with a Mediterranean diet and a low-fat diet (CORDIOPREV): a randomised controlled trial. Lancet. 2022;399(10338):1876–85. https://doi.org/10.1016/s0140-6736(22)00122-2.

    Article  CAS  PubMed  Google Scholar 

  11. Mohammadifard N, Mansourian M, Firouzi S, Taheri M, Haghighatdoost F. Longitudinal association of dietary carbohydrate and the risk cardiovascular disease: a dose-response meta-analysis. Crit Rev Food Sci Nutr. 2022;62(23):6277–92. https://doi.org/10.1080/10408398.2021.1900057.

    Article  CAS  PubMed  Google Scholar 

  12. Dehghan M, Mente A, Zhang X, Swaminathan S, Li W, Mohan V, Iqbal R, Kumar R, Wentzel-Viljoen E, Rosengren A, Amma LI, Avezum A, Chifamba J, Diaz R, Khatib R, Lear S, Lopez-Jaramillo P, Liu X, Gupta R, Mohammadifard N, Gao N, Oguz A, Ramli AS, Seron P, Sun Y, Szuba A, Tsolekile L, Wielgosz A, Yusuf R, Hussein Yusufali A, Teo KK, Rangarajan S, Dagenais G, Bangdiwala SI, Islam S, Anand SS, Yusuf S. Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study. Lancet. 2017;390(10107):2050–62. https://doi.org/10.1016/s0140-6736(17)32252-3.

    Article  CAS  PubMed  Google Scholar 

  13. Zhang S, Zhuang X, Lin X, Zhong X, Zhou H, Sun X, Xiong Z, Huang Y, Fan Y, Guo Y, Du Z, Liao X. Low-carbohydrate diets and risk of incident atrial fibrillation: a prospective cohort study. J Am Heart Assoc. 2019;8(9): e011955. https://doi.org/10.1161/jaha.119.011955.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Du X, Yang R, Ma M, Ke S, Zheng J, Tan X. The association of energy and macronutrient intake at breakfast and cardiovascular disease in Chinese adults: from a 14-year follow-up cohort study. Front Nutr. 2023;10:1093561. https://doi.org/10.3389/fnut.2023.1093561.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Lagiou P, Sandin S, Lof M, Trichopoulos D, Adami HO, Weiderpass E. Low carbohydrate-high protein diet and incidence of cardiovascular diseases in Swedish women: prospective cohort study. BMJ. 2012;344: e4026. https://doi.org/10.1136/bmj.e4026.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Abbasoglu O, Hardy G, Manzanares W, Pontes-Arruda A. Fish oil-containing lipid emulsions in adult parenteral nutrition: a review of the evidence. JPEN J Parenter Enteral Nutr. 2019;43(4):458–70. https://doi.org/10.1177/0148607117721907.

    Article  PubMed  Google Scholar 

  17. Poddar KH, Sikand G, Kalra D, Wong N, Duell PB. Mustard oil and cardiovascular health: why the controversy? J Clin Lipidol. 2022;16(1):13–22. https://doi.org/10.1016/j.jacl.2021.11.002.

    Article  PubMed  Google Scholar 

  18. Albert CM, Cook NR, Pester J, Moorthy MV, Ridge C, Danik JS, Gencer B, Siddiqi HK, Ng C, Gibson H, Mora S, Buring JE, Manson JE. Effect of marine omega-3 fatty acid and vitamin D supplementation on incident atrial fibrillation: a randomized clinical trial. JAMA. 2021;325(11):1061–73. https://doi.org/10.1001/jama.2021.1489.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gencer B, Djousse L, Al-Ramady OT, Cook NR, Manson JE, Albert CM. Effect of long-term marine ɷ-3 fatty acids supplementation on the risk of atrial fibrillation in randomized controlled trials of cardiovascular outcomes: a systematic review and meta-analysis. Circulation. 2021;144(25):1981–90. https://doi.org/10.1161/circulationaha.121.055654.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89-98. https://doi.org/10.1093/hmg/ddu328.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Luijk R, Dekkers KF, van Iterson M, Arindrarto W, Claringbould A, Hop P, Boomsma DI, van Duijn CM, van Greevenbroek MMJ, Veldink JH, Wijmenga C, Franke L, t Hoen PAC, Jansen R, van Meurs J, Mei H, Slagboom PE, Heijmans BT, van Zwet EW. Genome-wide identification of directed gene networks using large-scale population genomics data. Nat Commun. 2018;9(1):3097. https://doi.org/10.1038/s41467-018-05452-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, Langenberg C, Golub RM, Loder EW, Gallo V, Tybjaerg-Hansen A, Davey Smith G, Egger M, Richards JB. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR statement. JAMA. 2021;326(16):1614–21. https://doi.org/10.1001/jama.2021.18236.

    Article  PubMed  Google Scholar 

  23. Meddens SFW, de Vlaming R, Bowers P, Burik CAP, Linnér RK, Lee C, Okbay A, Turley P, Rietveld CA, Fontana MA, Ghanbari M, Imamura F, McMahon G, van der Most PJ, Voortman T, Wade KH, Anderson EL, Braun KVE, Emmett PM, Esko T, Gonzalez JR, Kiefte-de Jong JC, Langenberg C, Luan J, Muka T, Ring S, Rivadeneira F, Snieder H, van Rooij FJA, Wolffenbuttel BHR, Smith GD, Franco OH, Forouhi NG, Ikram MA, Uitterlinden AG, van Vliet-Ostaptchouk JV, Wareham NJ, Cesarini D, Harden KP, Lee JJ, Benjamin DJ, Chow CC, Koellinger PD. Genomic analysis of diet composition finds novel loci and associations with health and lifestyle. Mol Psychiatry. 2021;26(6):2056–69. https://doi.org/10.1038/s41380-020-0697-5.

    Article  CAS  PubMed  Google Scholar 

  24. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC. The MR-base platform supports systematic causal inference across the human phenome. Elife. 2018. https://doi.org/10.7554/eLife.34408.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kamat MA, Blackshaw JA, Young R, Surendran P, Burgess S, Danesh J, Butterworth AS, Staley JR. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics. 2019;35(22):4851–3. https://doi.org/10.1093/bioinformatics/btz469.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Shim H, Chasman DI, Smith JD, Mora S, Ridker PM, Nickerson DA, Krauss RM, Stephens M. A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians. PLoS ONE. 2015;10(4): e0120758. https://doi.org/10.1371/journal.pone.0120758.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40(3):740–52. https://doi.org/10.1093/ije/dyq151.

    Article  PubMed  Google Scholar 

  28. Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734–9. https://doi.org/10.1093/ije/dyx034.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Bowden J, Spiller W, Del Greco MF, Sheehan N, Thompson J, Minelli C, Davey Smith G. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the radial plot and radial regression. Int J Epidemiol. 2018;47(4):1264–78. https://doi.org/10.1093/ije/dyy101.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8. https://doi.org/10.1038/s41588-018-0099-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37(7):658–65. https://doi.org/10.1002/gepi.21758.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. https://doi.org/10.1093/ije/dyv080.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14. https://doi.org/10.1002/gepi.21965.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chung MK, Eckhardt LL, Chen LY, Ahmed HM, Gopinathannair R, Joglar JA, Noseworthy PA, Pack QR, Sanders P, Trulock KM. Lifestyle and risk factor modification for reduction of atrial fibrillation: a scientific statement from the American heart association. Circulation. 2020;141(16):e750–72. https://doi.org/10.1161/cir.0000000000000748.

    Article  PubMed  Google Scholar 

  35. Pathak RK, Middeldorp ME, Meredith M, Mehta AB, Mahajan R, Wong CX, Twomey D, Elliott AD, Kalman JM, Abhayaratna WP, Lau DH, Sanders P. Long-term effect of goal-directed weight management in an atrial fibrillation cohort: a long-term follow-up study (LEGACY). J Am Coll Cardiol. 2015;65(20):2159–69. https://doi.org/10.1016/j.jacc.2015.03.002.

    Article  PubMed  Google Scholar 

  36. Abed HS, Wittert GA, Leong DP, Shirazi MG, Bahrami B, Middeldorp ME, Lorimer MF, Lau DH, Antic NA, Brooks AG, Abhayaratna WP, Kalman JM, Sanders P. Effect of weight reduction and cardiometabolic risk factor management on symptom burden and severity in patients with atrial fibrillation: a randomized clinical trial. JAMA. 2013;310(19):2050–60. https://doi.org/10.1001/jama.2013.280521.

    Article  CAS  PubMed  Google Scholar 

  37. Lee SR, Ahn HJ, Choi EK, Lee SW, Han KD, Oh S, Lip GYH. Improved prognosis with integrated care management including early rhythm control and healthy lifestyle modification in patients with concurrent atrial fibrillation and diabetes mellitus: a nationwide cohort study. Cardiovasc Diabetol. 2023;22(1):18. https://doi.org/10.1186/s12933-023-01749-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Yang L, Chung MK. Lifestyle changes in atrial fibrillation management and intervention. J Cardiovasc Electrophysiol. 2023. https://doi.org/10.1111/jce.15803.

    Article  PubMed  Google Scholar 

  39. Sabzwari SRA, Garg L, Lakkireddy D, Day J. Ten lifestyle modification approaches to treat atrial fibrillation. Cureus. 2018;10(5): e2682. https://doi.org/10.7759/cureus.2682.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Huang J, Liao LM, Weinstein SJ, Sinha R, Graubard BI, Albanes D. Association between plant and animal protein intake and overall and cause-specific mortality. JAMA Intern Med. 2020;180(9):1173–84. https://doi.org/10.1001/jamainternmed.2020.2790.

    Article  CAS  PubMed  Google Scholar 

  41. Song M, Fung TT, Hu FB, Willett WC, Longo VD, Chan AT, Giovannucci EL. Association of animal and plant protein intake with all-cause and cause-specific mortality. JAMA Intern Med. 2016;176(10):1453–63. https://doi.org/10.1001/jamainternmed.2016.4182.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Pool L, Wijdeveld L, de Groot NMS, Brundel B. The role of mitochondrial dysfunction in atrial fibrillation: translation to druggable target and biomarker discovery. Int J Mol Sci. 2021. https://doi.org/10.3390/ijms22168463.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Mason FE, Pronto JRD, Alhussini K, Maack C, Voigt N. Cellular and mitochondrial mechanisms of atrial fibrillation. Basic Res Cardiol. 2020;115(6):72. https://doi.org/10.1007/s00395-020-00827-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Garbern JC, Lee RT. Mitochondria and metabolic transitions in cardiomyocytes: lessons from development for stem cell-derived cardiomyocytes. Stem Cell Res Ther. 2021;12(1):177. https://doi.org/10.1186/s13287-021-02252-6.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Di Fonso A, Pietrangelo L, D’Onofrio L, Michelucci A, Boncompagni S, Protasi F. Ageing causes ultrastructural modification to calcium release units and mitochondria in cardiomyocytes. Int J Mol Sci. 2021. https://doi.org/10.3390/ijms22168364.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Bravo-San Pedro JM, Kroemer G, Galluzzi L. Autophagy and mitophagy in cardiovascular disease. Circ Res. 2017;120(11):1812–24. https://doi.org/10.1161/circresaha.117.311082.

    Article  CAS  PubMed  Google Scholar 

  47. Pohjoismäki JL, Goffart S. The role of mitochondria in cardiac development and protection. Free Radic Biol Med. 2017;106:345–54. https://doi.org/10.1016/j.freeradbiomed.2017.02.032.

    Article  CAS  PubMed  Google Scholar 

  48. Muszyński P, Bonda TA. Mitochondrial dysfunction in atrial fibrillation-mechanisms and pharmacological interventions. J Clin Med. 2021. https://doi.org/10.3390/jcm10112385.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Nie C, He T, Zhang W, Zhang G, Ma X. Branched chain amino acids: beyond nutrition metabolism. Int J Mol Sci. 2018. https://doi.org/10.3390/ijms19040954.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Yang Q, Zhang Z, Gregg EW, Flanders WD, Merritt R, Hu FB. Added sugar intake and cardiovascular diseases mortality among US adults. JAMA Intern Med. 2014;174(4):516–24. https://doi.org/10.1001/jamainternmed.2013.13563.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Dwivedi AK, Dubey P, Reddy SY, Clegg DJ. Associations of glycemic index and glycemic load with cardiovascular disease: updated evidence from meta-analysis and cohort studies. Curr Cardiol Rep. 2022;24(3):141–61. https://doi.org/10.1007/s11886-022-01635-2.

    Article  PubMed  Google Scholar 

  52. Liu RH. Health-promoting components of fruits and vegetables in the diet. Adv Nutr. 2013;4(3):384s–92s. https://doi.org/10.3945/an.112.003517.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Aune D, Keum N, Giovannucci E, Fadnes LT, Boffetta P, Greenwood DC, Tonstad S, Vatten LJ, Riboli E, Norat T. Whole grain consumption and risk of cardiovascular disease, cancer, and all cause and cause specific mortality: systematic review and dose-response meta-analysis of prospective studies. BMJ. 2016;353: i2716. https://doi.org/10.1136/bmj.i2716.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Benisi-Kohansal S, Saneei P, Salehi-Marzijarani M, Larijani B, Esmaillzadeh A. Whole-grain intake and mortality from all causes, cardiovascular disease, and cancer: a systematic review and dose-response meta-analysis of prospective cohort studies. Adv Nutr. 2016;7(6):1052–65. https://doi.org/10.3945/an.115.011635.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Venn BJ, Green TJ. Glycemic index and glycemic load: measurement issues and their effect on diet-disease relationships. Eur J Clin Nutr. 2007;61(Suppl 1):S122-131. https://doi.org/10.1038/sj.ejcn.1602942.

    Article  CAS  PubMed  Google Scholar 

  56. Thomas D, Elliott EJ. Low glycaemic index, or low glycaemic load, diets for diabetes mellitus. Cochrane Database Syst Rev. 2009. https://doi.org/10.1002/14651858.CD006296.pub2.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Solomon TP, Haus JM, Kelly KR, Cook MD, Filion J, Rocco M, Kashyap SR, Watanabe RM, Barkoukis H, Kirwan JP. A low-glycemic index diet combined with exercise reduces insulin resistance, postprandial hyperinsulinemia, and glucose-dependent insulinotropic polypeptide responses in obese, prediabetic humans. Am J Clin Nutr. 2010;92(6):1359–68. https://doi.org/10.3945/ajcn.2010.29771.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Mendonça RD, Lopes AC, Pimenta AM, Gea A, Martinez-Gonzalez MA, Bes-Rastrollo M. Ultra-processed food consumption and the incidence of hypertension in a mediterranean cohort: the seguimiento universidad de navarra project. Am J Hypertens. 2017;30(4):358–66. https://doi.org/10.1093/ajh/hpw137.

    Article  CAS  PubMed  Google Scholar 

  59. Martins G, França A, Viola P, Carvalho CA, Marques KDS, Santos AMD, Batalha MA, Alves JDA, Ribeiro CCC. Intake of ultra-processed foods is associated with inflammatory markers in Brazilian adolescents. Public Health Nutr. 2022;25(3):591–9. https://doi.org/10.1017/s1368980021004523.

    Article  PubMed  Google Scholar 

  60. Sun L, Yuan JL, Chen QC, Xiao WK, Ma GP, Liang JH, Chen XK, Wang S, Zhou XX, Wu H, Hong CX. Red meat consumption and risk for dyslipidaemia and inflammation: a systematic review and meta-analysis. Front Cardiovasc Med. 2022;9: 996467. https://doi.org/10.3389/fcvm.2022.996467.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Sievenpiper JL. Low-carbohydrate diets and cardiometabolic health: the importance of carbohydrate quality over quantity. Nutr Rev. 2020;78(Suppl 1):69–77. https://doi.org/10.1093/nutrit/nuz082.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Waldhart AN, Muhire B, Johnson B, Pettinga D, Madaj ZB, Wolfrum E, Dykstra H, Wegert V, Pospisilik JA, Han X, Wu N. Excess dietary carbohydrate affects mitochondrial integrity as observed in brown adipose tissue. Cell Rep. 2021;36(5): 109488. https://doi.org/10.1016/j.celrep.2021.109488.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

The work was supported by the Scientific and technological innovation project of the China Academy of Chinese Medical Sciences (CI2021A00307, CI2021A03607).

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  1. Zhuoya Zhang, Jiale Zhang and Haoyang Jiao have contributed equally to this work and shared the first authorship.

Authors and Affiliations

  1. Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China

    Zhuoya Zhang

  2. Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China

    Jiale Zhang

  3. Institute of Documentation, China Academy of Chinese Medical Sciences, Beijing, China

    Haoyang Jiao

  4. Gaoyang County Hospital, Baoding, 071599, Hebei Province, China

    Wei Tian

  5. Guanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China

    Xu Zhai

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Contributions

X Z and W T conceived the research idea. ZY Z and JL Z conducted the literature search, while JL Z wrote the manuscript. ZY Z performed software operations and created the figures. HY J and ZY Z contributed to the final version. All authors reviewed and edited the manuscript and contributed to the analysis through constructive discussions.

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Zhang, Z., Zhang, J., Jiao, H. et al. Genetically predicted dietary macronutrient intakes and atrial fibrillation risk: a Mendelian randomization study. Eur J Med Res 29, 227 (2024). https://doi.org/10.1186/s40001-024-01781-z

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European Journal of Medical Research

ISSN: 2047-783X

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