Skip to main content

Some NLM-NCBI services and products are experiencing heavy traffic, which may affect performance and availability. We apologize for the inconvenience and appreciate your patience. For assistance, please contact our Help Desk at info@ncbi.nlm.nih.gov.

Scientific Reports logoLink to Scientific Reports
. 2017 Apr 19;7:954. doi: 10.1038/s41598-017-01073-z

Effect of handgrip on coronary artery disease and myocardial infarction: a Mendelian randomization study

Lin Xu 1,2,, Yuan Tao Hao 1
PMCID: PMC5430422  PMID: 28424468

Abstract

Observational studies have reported an association of handgrip strength with risk of cardiovascular disease. However, residual confounding and reverse causation may have influenced these findings. A Mendelian randomization (MR) study was conducted to examine whether handgrip is causally associated with cardiovascular disease. Two single nucleotide polymorphisms (SNPs), rs3121278 and rs752045, were used as the genetic instruments for handgrip. The effect of each SNP on coronary artery disease/myocardial infarction (CAD/MI) was weighted by its effect on handgrip strength, and estimates were pooled to provide a summary measure for the effect of increased handgrip on risk of CAD/MI. MR analysis showed that higher grip strength reduces risk for CAD/MI, with 1-kilogram increase in genetically determined handgrip reduced odds of CAD by 6% (odds ratio (OR) = 0.94, 95% confidence interval (CI) 0.91–0.99, P = 0.01), and reduced odds of MI by 7% (OR = 0.93, 95% CI 0.89–0.98, P = 0.003). No association of grip strength with type 2 diabetes, body mass index, LDL- and HDL-cholesterol, triglycerides and fasting glucose was found. The inverse causal relationship between handgrip and the risk of CAD or MI suggests that promoting physical activity and resistance training to improve muscle strength may be important for cardiovascular health.

Introduction

Handgrip strength, a prognostic marker for healthy aging, has been associated with a number of chronic disease outcomes in observational studies. Specifically, greater grip strength was associated with lower risks of diabetes1, metabolic syndrome2, cardiovascular disease and mortality3. However, observational studies on grip strength may be subject to residual confounding such as body size and underlying illnesses, and reverse causality. Grip strength is well correlated with measures of body size especially body mass index, and also reflects functional capacity and frailty4, which could be affected by chronic disease, malnutrition, falls and hospitalization in older people. Traditional observational studies cannot account for all possible confounders. Thus, it is unclear whether grip strength, as a marker of muscle strength, causes the metabolic abnormalities or cardiovascular disease per se, or is only a predictor of underlying health conditions.

A large randomized controlled trial (RCT) on resistance training to improve grip strength with cardiovascular events as the primary endpoint would be definitive, but will take several years and might be difficult to conduct because of poor compliance. Moreover, whether the effects, if any, are due to the improvement in grip strength or other intervention efforts (i.e. changes in diet) is unclear. Mendelian randomization (MR) studies make use of genetic variants as instrumental variables to investigate the effect of environmental exposures on health outcomes. Since alleles are randomly allocated after conception and do not change during lifetime, MR studies are less vulnerable to confounding from non-genetic factors and to reverse causality. Thus it can be used to infer causality as further extensions to observational studies5. Many MR studies have been successfully conducted in cardiovascular research to investigate potential etiological mechanisms, prioritize drug targets and increase understanding of current therapies6.

Here, single nucleotide polymorphisms (SNPs) identified in a recent genome wide association study (GWAS)7 were used as genetic instrumental variables, to examine the causal effect of handgrip on coronary artery disease (CAD) and cardiovascular risk factors.

Methods

Data sources

Genetic instrumental variable for handgrip

From the most updated GWAS from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium) consortium on handgrip, 2 SNPs [i.e. rs3121278 in BMS1L (BMS1-like ribosome biogenesis protein) and rs752045 in CSMD1 (CUB and Sushi multiple domains 1)] independently contributing to grip strength (kg) at genome wide significance level (p < 5 * 10−8) in the discovery stage were used as genetic instrumental variables in the Mendelian randomization analysis (Table 1)7. To fully take advantage of all data available in the CHARGE, statistics of these two SNPs were obtained from the combined discovery and replication set. The pleiotropic effects of these 2 SNPs were identified from Ensembl (Homo sapiens – phenotype) (http://grch37.ensembl.org/Homo_sapiens/Info/Index), a comprehensive genotype to phenotype cross-reference. As no other phenotypes were reported for these 2 SNPs except for handgrip, indicating pleiotropy was unlikely, both of them were included in the current analysis.

Table 1.

Characteristics of the SNPs used as genetic instrumental variables of handgrip strength (kg).

Nearest gene SNP Effect (beta) SE Effect allele Other allele P-value EAF Sample size
BMS1L rs3121278 −0.26 0.06 T G 6.18e-5 0.18 34,910
CSMD1 rs752045 0.47 0.08 G A 5.20e-10 0.18 34,910

BMS1L: BMS1-like ribosome biogenesis protein; CSMD1: CUB and Sushi multiple domains 1; SNP: single-nucleotide polymorphisms; SE; standard error; EAF: effect allele frequency.

Effect on handgrip per kilogram per copy of the effect allele.

All information was obtained from “Matteini, A. M. et al. GWAS analysis of handgrip and lower body strength in older adults in the CHARGE consortium. Aging Cell, doi:10.1111/acel.12468 (2016)”.

Coronary artery disease and its risk factors

Association of SNPs with the phenotypes were extracted from publicly available consortia. Data on coronary artery disease/myocardial infarction have been contributed by Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) plusC4D investigators and have been downloaded from www.CARDIOGRAMPLUSC4D.ORG8. The summary data on the gene-CAD association were obtained from the CARDIoGRAMplusC4D 1000 Genomes-based GWAS, a meta-analysis of GWAS studies of mainly European, South Asian, and East Asian, descent imputed using the 1000 Genomes phase 1 v3 training set with 38 million variants9. The study interrogated 9.4 million variants and involved 60,801 coronary artery disease (CAD) cases and 123,504 controls, and 43,676 myocardial infarction (MI) cases and 128,199 controls9. Data on T2DM was contributed by the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM, http://diagram-consortium.org/downloads.html), which includes 12,171 cases and 56,862 controls in Stage 1 GWAS10 and 26,488 cases and 83,964controls in the Trans-ethnic GWAS meta-analysis11. Genetic associations with BMI (kg/m2) have been contributed by The Genetic Investigation of ANthropometric Traits (GIANT) investigators and have been downloaded from https://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files which has BMI for 152,893 men and 171,977 women of European ancestry12.

Genetic associations with high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol, triglycerides, and total cholesterol in 188,577 people have been contributed by Global Lipids Genetics Consortium (GLGC) investigators and have been downloaded from http://csg.sph.umich.edu/abecasis/public/lipids2013/ 13. Genetic associations with fasting insulin (n = 38,238) and fasting glucose (n = 46,186) have been contributed by Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) investigators and have been downloaded from http://www.magicinvestigators.org/, which relates to people of European ancestry without diabetes14.

Statistical analysis

SNP-specific Wald estimates (ratio of SNP on outcome to SNP on handgrip) of the effect of handgrip on each outcome were combined using inverse-variance weighted (IVW) method giving an odds ratio (OR) for CAD and MI, and beta coefficients (log odds ratio of CAD/MI per 1 kg greater handgrip) for the other outcomes with 95% confidence interval (CI), based on the following formulas15:

βˆIVW=K=1KEkDkσDk2K=1KEk2σDk2 1
SEβˆIVW=1k=1kEK2σDk2 2

where EK is the mean change in exposure level (grip strength) per additional effect allele of SNP k and Dk is the mean change in disease outcomes (e.g. log odds of CAD or levels of other CVD risk factors) per additional effect allele of SNP k with standard error σDk. The weakness of the instruments was evaluated using the first-stage F-statistics calculated by

F=R2/K(1R2)/(nK1),

where R2 indicates the variance explained by each genetic instrument, K indicates the number of instrument, and n indicates the sample size of the first stage16. The R2 of each SNP was calculated using the effect allele frequency (f) and beta (β) from the results of the CHARGE consortium using the following formula12: R2 = β2 * (1 − f) * 2f. Statistical analysis was performed using STATA 14.0.

Results

The first-stage F-statistics for the IV including these 2 SNPs was 128. Tables 1 and 2 show the associations of 2 handgrip-associated SNPs, used as genetic instrumental variables in the Mendelian randomization analysis, with grip strength levels and CAD risk. Each handgrip increasing allele was associated with 5–7% reduction in CAD risk (OR 0.93, 95% CI 0.85–1.02 for rs3121278, and OR 0.95, 95% CI 0.9, 0.9995 for rs752045) and 6–10% reduction in MI risk (OR 0.90, 95% CI 0.81–0.99 for rs3121278 and OR 0.94, 95% CI 0.89–0.99 for rs752045). No association was found for type 2 diabetes, body mass index, HDL-cholesterol, LDL-cholesterol, triglycerides and fasting glucose. Table 3 shows that each kilogram increase in handgrip strength decreased CAD risk by 6% (odds ratio (OR) 0.94, 95% CI 0.91 to 0.99) and MI risk by 5% (OR 0.93, 95% CI 0.89 to 0.98).

Table 2.

Odds ratio (95% confidence interval) for coronary artery disease, myocardial infarction and type 2 diabetes, and mean difference (standard error) of cardiovascular risk factors per allele of SNPs used in Mendelian randomization analyses.

SNP rs3121278 rs752045
OR (95% CI) P-value OR (95% CI) P-value
Coronary artery disease 0.93 (0.85, 1.02) 0.12 0.95 (0.9, 0.9995) 0.047
Myocardial infarction 0.90 (0.81, 0.99) 0.03 0.94 (0.89, 0.99) 0.03
Type 2 diabetes 0.97 (0.86, 1.08) 0.57 1.00 (0.93, 1.08) 1.00
Mean difference (SD) P-value Mean difference (SD) P-value
Body mass index, SD 0.99 (0.96, 1.02) 0.50 1.01 (0.98, 1.03) 0.62
LDL-cholesterol, SD 0.99 (0.96, 1.03) 0.63 1.00 (0.97, 1.03) 0.88
HDL-cholesterol, SD 1.02 (0.98, 1.05) 0.39 0.98 (0.96, 1.01) 0.19
Triglycerides, SD 0.99 (0.96, 1.02) 0.52 1.00 (0.97, 1.02) 0.74
Fasting glucose, mmol/l 0.99 (0.97, 1.02) 0.62 1.00 (0.98, 1.02) 0.92

SNP: single-nucleotide polymorphisms; OR: odds ratio; SD: standard deviation; LDL: low density lipoprotein; HDL: high density lipoprotein;

1-SD equals to 4.5 kg/m2 for BMI, 38.7 mg/dL for LDL-cholesterol, 15.5 mg/dL for HDL-cholesterol, and 90.7 mg/dL for triglycerides.

Table 3.

Causal effect of handgrip strength (kg) on cardiovascular risk factors, diabetes and coronary artery disease.

Odds ratio 95% confidence interval p-value
Coronary artery disease 0.94 0.91 to 0.99 0.01
Myocardial infarction 0.93 0.89 to 0.98 0.003
Type 2 diabetes 0.99 0.96 to 1.02 0.52
Beta 95% confidence interval p-value
Body mass index, SD 0.0003 −0.01 to 0.02 0.97
LDL-cholesterol, SD −0.005 −0.01 to 0.001 0.11
HDL-cholesterol, SD −0.002 −0.03 to 0.02 0.90
Triglycerides, SD −0.007 −0.03 to 0.01 0.49
Fasting glucose, mmol/l −0.003 −0.01 to 0.0006 0.09

1-SD equals to 4.77 kg/m2 for BMI, 38.7 mg/dL for LDL-cholesterol, 15.5 mg/dL for HDL-cholesterol, and 90.7 mg/dL for triglycerides.

Discussion

The current Mendelian randomization analysis using the most updated GWAS results on handgrip found that greater grip strength was significantly associated with lower risks of coronary artery disease and myocardial infarction. Moreover, greater handgrip tends to be associated with more-favorable cardiovascular disease biomarkers, including LDL-cholesterol and triglycerides, although the result was not statistically significant in the MR analysis. The MR analysis did not support a causal effect of handgrip on body mass index or HDL-cholesterol.

No randomized controlled trial (RCT) specifically on handgrip was found. One recent RCT on resistance training showed that higher-volume resistance training improved muscle strengths and also reduced LDL-cholesterol17. However, as levels of some inflammation markers such as interleukin-1 and interleukin-6 were also reduced during the resistance training, whether the beneficial effect on cardiometabolic health was due to the improvement in muscular strength or the reduction in inflammation was unclear17. A previous review of physiologic research also suggested functional and metabolic benefits of muscle strength, including potential causal pathway18. Moreover, results of the current MR analysis are in line with earlier observational studies showing that greater grip strength in adulthood was associated with lower risks of cardiovascular mortality, irrespective of sex and age groups19, 20. However, in traditional observational studies, the beneficial effects could be confounded by general physical fitness, which was associated closely with both muscle strength and the risk of cardiovascular disease21. Individuals with lower muscular strength may also be less healthy overall than those with higher grip strength, while those with higher grip strength may also have more leisure time physical activity and tend to participate resistance training22. Thus, as a method that complementary to and analogous with RCTs, Mendelian randomization may be an appropriate study design to assess whether muscle strength can directly affect risk of cardiovascular diseases. Already, regarding the long-held candidates of CAD biomarkers, such as LDL-cholesterol23, HDL-cholesterol24, blood pressure25 and glycosylated hemoglobin A1c26, MR has been suggested to be an useful approach to infer causality27.

To date, no Mendelian randomization on handgrip was found. The current study is the first Mendelian randomization study providing causal evidence in terms of a protective effect of handgrip strength on the CAD/MI risk. The strengths of this study include the very large sample size and the use of genetic variants to avoid some of the key limitations of traditional multivariable regression approaches. Mendelian randomization study using a small number of genetic variants in specific gene regions as instrumental variable will provide close parallels to a RCT28. As in two-sample MR, data on phenotype and outcomes can be obtained from different individuals, genetic associations with the phenotype and outcomes can be estimated on large consortia, thus it greatly increases power compared with Mendelian randomization analysis in one sample15. Moreover, compared with MR within one sample which is more likely to subject to weak instrumental bias due to potential correlation between genetic variants and confounders, two-sample MR may avoid statistical overfitting but tends to provide conservative estimation29.

Several assumptions or methodologic considerations bear discussion. First, both genetic variants used for genetically determined handgrip were strongly related to handgrip. No obvious reason exists for the existence of confounders of the association between the genetic variants and the outcomes considered here, for example by population stratification, because the underlying studies relate to relatively ethnically homogeneous populations of mainly European ancestry. Second, the genetic variants used are not known to be associated with other phenotypes that might influence coronary artery disease and or risk factors, thus making biases from direct associations of SNPs with the outcomes, i.e., “pleiotropy” or violation of the “exclusion-restriction” assumption, unlikely. Moreover, we found no evidence of horizontal pleiotropy, i.e. that the genetic variants used to predict handgrip had effects on coronary artery disease or its risk factors independent of effects via handgrip. Third, given the use of summarized data in two samples, handgrip was not measured in the sample with the outcome. However, two-sample instrumental variable analysis is less vulnerable to more robust to chance associations than analysis of a single sample30.

Our study also has several limitations. First, due to the use of aggregated genome-wide data, whether the effect of handgrip on coronary artery disease varies by sex or age cannot be examined, although truly causal effect is expected to be consistent. Previous cohort studies of grip strength and cardiovascular mortality showed mixed results on effect modification by sex, with one suggesting that grip strength was more predictive of mortality in men31, others found that the association was consistent in men and wome3, 32. Such discrepancies suggest that the association could be due to residual confounding. Second, while no obvious pleiotropy was reported for the 2 genetic variants used, the possibility of residual pleiotropy cannot be fully ruled out. Third, canalization may also influence the results. However, as canalization reflects compensatory mechanisms, it tends to bias the gene-exposure association towards the null5. The use of multiple genetic variants as instrumental variables may, to some extent, compensate this influence. Fourth, while the two selected SNPs were not in linkage disequilibrium with each other, it still possible that they are in linkage disequilibrium with SNPs that influence unknown risk factors for coronary artery disease. Fifth, some cohorts in the CHARGE consortium are also included in the CARDIoGRAMplusC4D consortium. Therefore the possibility of sample overlapping cannot be fully ruled out. This may have introduced bias in the results; however, given the sample size employed, this effect would likely be small since the CHARGE comprised <5% of the overall CARDIoGRAMplusC4D consortium33. Finally, given the small number of genetic variants employed in this study, further Mendelian randomization analyses using more SNPs identified from updated GWAS are warranted to replicate the current study.

In conclusion, this study provides evidence supporting a causal role for higher grip strength in lowering coronary artery disease risk. The findings offer a further rationale for physical activity or resistance training to maintain muscular strength in older age.

Acknowledgements

All of the data provided in the paper is publicly available. Investigators who have made their genome-wide data available to scientist may not necessarily agree with comments made in this paper and the authors take full responsibility for the contents of this paper. LX receives financial support from University of Hong Kong/China Medical Board Grant (CMB 2015/16 First Round). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author Contributions

L.X. has substantial contributions to conception and design; both L.X. and Y.T.H. contribute to acquisition and interpretation of data, writing the article and final approval of the version to be published.

Competing Interests

The authors declare that they have no competing interests.

Footnotes

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Wander PL, et al. Greater hand-grip strength predicts a lower risk of developing type 2 diabetes over 10 years in leaner Japanese Americans. Diabetes Res Clin Pract. 2011;92:261–264. doi: 10.1016/j.diabres.2011.01.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Kawamoto R, et al. Handgrip strength is associated with metabolic syndrome among middle-aged and elderly community-dwelling persons. Clin Exp Hypertens. 2016;38:245–251. doi: 10.3109/10641963.2015.1081232. [DOI] [PubMed] [Google Scholar]
  • 3.Lopez-Jaramillo P, et al. Association of handgrip strength to cardiovascular mortality in pre-diabetic and diabetic patients: a subanalysis of the ORIGIN trial. Int J Cardiol. 2014;174:458–461. doi: 10.1016/j.ijcard.2014.04.013. [DOI] [PubMed] [Google Scholar]
  • 4.Rantanen T, et al. Midlife hand grip strength as a predictor of old age disability. JAMA. 1999;281:558–560. doi: 10.1001/jama.281.6.558. [DOI] [PubMed] [Google Scholar]
  • 5.Smith GD, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1–22. doi: 10.1093/ije/dyg070. [DOI] [PubMed] [Google Scholar]
  • 6.Haycock, P. C. et al. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. Am J Clin Nutr., doi:10.3945/ajcn.115.118216 (2016). [DOI] [PMC free article] [PubMed]
  • 7.Matteini, A. M. et al. GWAS analysis of handgrip and lower body strength in older adults in the CHARGE consortium. Aging Cell, doi:10.1111/acel.12468 (2016). [DOI] [PMC free article] [PubMed]
  • 8.Schunkert H, et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nature genetics. 2011;43:333–338. doi: 10.1038/ng.784. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Nikpay M, et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47:1121–1130. doi: 10.1038/ng.3396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Morris AP, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44:981–990. doi: 10.1038/ng.2383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Mahajan A, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nature genetics. 2014;46:234–244. doi: 10.1038/ng.2897. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Locke AE, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206. doi: 10.1038/nature14177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Global Lipids Genetics, C et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–1283. doi: 10.1038/ng.2797. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Dupuis J, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nature genetics. 2010;42:105–116. doi: 10.1038/ng.520. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Burgess S, et al. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. European journal of epidemiology. 2015;30:543–552. doi: 10.1007/s10654-015-0011-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Palmer TM, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res. 2012;21:223–242. doi: 10.1177/0962280210394459. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Nunes PR, et al. Effect of resistance training on muscular strength and indicators of abdominal adiposity, metabolic risk, and inflammation in postmenopausal women: controlled and randomized clinical trial of efficacy of training volume. Age (Dordrecht, Netherlands) 2016;38:40. doi: 10.1007/s11357-016-9901-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Wolfe RR. The underappreciated role of muscle in health and disease. American Journal of Clinical Nutrition. 2006;84:475–482. doi: 10.1093/ajcn/84.3.475. [DOI] [PubMed] [Google Scholar]
  • 19.Celis-Morales CA, et al. The association between physical activity and risk of mortality is modulated by grip strength and cardiorespiratory fitness: evidence from 498 135 UK-Biobank participants. Eur Heart J. 2017;38:116–122. doi: 10.1093/eurheartj/ehw249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Strand, B. H. et al. The association of grip strength from midlife onwards with all-cause and cause-specific mortality over 17 years of follow-up in the Tromso Study. Journal of epidemiology and community health, doi:10.1136/jech-2015-206776 (2016). [DOI] [PMC free article] [PubMed]
  • 21.O’Hartaigh B, et al. Usefulness of physical fitness and the metabolic syndrome to predict vascular disease risk in older Chinese (from the Guangzhou Biobank Cohort Study-Cardiovascular Disease Subcohort [GBCS-CVD]) Am J Cardiol. 2011;108:845–850. doi: 10.1016/j.amjcard.2011.05.010. [DOI] [PubMed] [Google Scholar]
  • 22.Ruiz JR, et al. Association between muscular strength and mortality in men: prospective cohort study. BMJ. 2008;337:a439–a439. doi: 10.1136/bmj.a439. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Colantonio LD, et al. Association of Serum Lipids and Coronary Heart Disease in Contemporary Observational Studies. Circulation. 2016;133:256–264. doi: 10.1161/CIRCULATIONAHA.115.011646. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Toth PP, et al. Should low high-density lipoprotein cholesterol (HDL-C) be treated? Best Pract Res Clin Endocrinol Metab. 2014;28:353–368. doi: 10.1016/j.beem.2013.11.002. [DOI] [PubMed] [Google Scholar]
  • 25.Banach M, et al. Lipids, blood pressure and kidney update 2015. Lipids Health Dis. 2015;14:167. doi: 10.1186/s12944-015-0169-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Xu L, Chan WM, Hui YF, Lam TH. Association between HbA1c and cardiovascular disease mortality in older Hong Kong Chinese with diabetes. Diabet Med. 2012;29:393–398. doi: 10.1111/j.1464-5491.2011.03456.x. [DOI] [PubMed] [Google Scholar]
  • 27.Jansen H, Samani NJ, Schunkert H. Mendelian randomization studies in coronary artery disease. Eur Heart J. 2014;35:1917–1924. doi: 10.1093/eurheartj/ehu208. [DOI] [PubMed] [Google Scholar]
  • 28.Hingorani A, Humphries S. Nature’s randomised trials. Lancet. 2005;366:1906–1908. doi: 10.1016/S0140-6736(05)67767-7. [DOI] [PubMed] [Google Scholar]
  • 29.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;40:304–314. doi: 10.1002/gepi.21965. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Taylor AE, et al. Mendelian randomization in health research: using appropriate genetic variants and avoiding biased estimates. Econ Hum Biol. 2014;13:99–106. doi: 10.1016/j.ehb.2013.12.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Gale CR, Martyn CN, Cooper C, Sayer AA. Grip strength, body composition, and mortality. Int J Epidemiol. 2007;36:228–235. doi: 10.1093/ije/dyl224. [DOI] [PubMed] [Google Scholar]
  • 32.Artero EG, et al. Effects of muscular strength on cardiovascular risk factors and prognosis. J Cardiopulm Rehabil Prev. 2012;32:351–358. doi: 10.1097/HCR.0b013e3182642688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Psaty BM, et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet. 2009;2:73–80. doi: 10.1161/CIRCGENETICS.108.829747. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES