Effect of Basal Metabolic Rate on Cancer: A Mendelian Randomization Study

Jack C M Ng, C Mary Schooling, Jack C M Ng, C Mary Schooling

Abstract

Background: Basal metabolic rate is associated with cancer, but these observations are open to confounding. Limited evidence from Mendelian randomization studies exists, with inconclusive results. Moreover, whether basal metabolic rate has a similar role in cancer for men and women independent of insulin-like growth factor 1 increasing cancer risk has not been investigated. Methods: We conducted a two-sample Mendelian randomization study using summary data from the UK Biobank to estimate the causal effect of basal metabolic rate on cancer. Overall and sex-specific analysis and multiple sensitivity analyses were performed including multivariable Mendelian randomization to control for insulin-like growth factor 1. Results: We obtained 782 genetic variants strongly (p-value < 5 × 10-8) and independently (r 2 < 0.01) predicting basal metabolic rate. Genetically predicted higher basal metabolic rate was associated with an increase in cancer risk overall (odds ratio, 1.06; 95% confidence interval, 1.02-1.10) with similar estimates by sex (odds ratio for men, 1.07; 95% confidence interval, 1.002-1.14; odds ratio for women, 1.06; 95% confidence interval, 0.995-1.12). Sensitivity analyses including adjustment for insulin-like growth factor 1 showed directionally consistent results. Conclusion: Higher basal metabolic rate might increase cancer risk. Basal metabolic rate as a potential modifiable target of cancer prevention warrants further study.

Keywords: Mendelian randomization; basal metabolic rate; cancer; evolutionary biology; metabolism.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Ng and Schooling.

References

    1. Aktipis C. A., Nesse R. M. (2013). Evolutionary foundations for cancer biology. Evol. Appl. 6 144–159. 10.1111/eva.12034
    1. An J. H., Yoon J. H., Suk M. H., Shin Y. A. (2016). Up-regulation of lipolysis genes and increased production of AMP-activated protein kinase protein in the skeletal muscle of rats after resistance training. J. Exerc. Rehabil. 12 163–170. 10.12965/jer.1632578.289
    1. Anonymous. (2021). Online Sample Size and Power Calculator for Mendelian Randomization with a Binary Outcome. Available online at: (accessed February 11, 2021).
    1. Boddy A. M., Kokko H., Breden F., Wilkinson G. S., Aktipis C. A. (2015). Cancer susceptibility and reproductive trade-offs: a model of the evolution of cancer defences. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370:20140220. 10.1098/rstb.2014.0220
    1. Bowden J., Davey Smith G., Haycock P. C., Burgess S. (2016a). Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40 304–314. 10.1002/gepi.21965
    1. Bowden J., Del Greco M. F., Minelli C., Davey Smith G., Sheehan N. A., Thompson J. R. (2016b). Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int. J. Epidemiol. 45 1961–1974. 10.1093/ije/dyw220
    1. Burgess S., Thompson S. G. (2015). Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am. J. Epidemiol. 181 251–260. 10.1093/aje/kwu283
    1. Burgess S., Thompson S. G. (2017). Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 32 377–389. 10.1007/s10654-017-0255-x
    1. Burgess S., Davies N. M., Thompson S. G. (2016). Bias due to participant overlap in two-sample Mendelian randomization. Genet. Epidemiol. 40 597–608. 10.1002/gepi.21998
    1. Burgess S., Scott R. A., Timpson N. J., Davey Smith G., Thompson S. G., Consortium E.-I. (2015). Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur. J. Epidemiol. 30 543–552. 10.1007/s10654-015-0011-z
    1. Cancer Research UK. (2021a). Cancer Mortality by Age. Available online at: (accessed February 11, 2021).
    1. Cancer Research UK. (2021b). Prostate Cancer Statistics. Available online at: (accessed February 11, 2021).
    1. Cornish A. J., Law P. J., Timofeeva M., Palin K., Farrington S. M., Palles C., et al. (2020). Modifiable pathways for colorectal cancer: a mendelian randomisation analysis. Lancet Gastroenterol. Hepatol. 5 55–62. 10.1016/S2468-1253(19)30294-8
    1. Del Greco M. F., Foco L., Pichler I., Eller P., Eller K., Benyamin B., et al. (2017). Serum iron level and kidney function: a Mendelian randomization study. Nephrol. Dial. Transplant. 32 273–278. 10.1093/ndt/gfw215
    1. Fieller E. C. (1954). Some problems in interval estimation. J. R. Stat. Soc. Series B Stat. Methodol. 16 175–185. 10.1111/j.2517-6161.1954.tb00159.x
    1. Freeman G., Cowling B. J., Schooling C. M. (2013). Power and sample size calculations for Mendelian randomization studies using one genetic instrument. Int. J. Epidemiol. 42 1157–1163. 10.1093/ije/dyt110
    1. Hemani G., Zheng J., Elsworth B., Wade K. H., Haberland V., Baird D., et al. (2018). The MR-Base platform supports systematic causal inference across the human phenome. eLife. 7:e34408. 10.7554/eLife.34408
    1. Jagim A. R., Camic C. L., Askow A., Luedke J., Erickson J., Kerksick C. M., et al. (2019). Sex differences in resting metabolic rate among athletes. J. Strength Cond. Res. 33 3008–3014. 10.1519/JSC.0000000000002813
    1. Kazemi A., Speakman J. R., Soltani S., Djafarian K. (2020). Effect of calorie restriction or protein intake on circulating levels of insulin like growth factor I in humans: a systematic review and meta-analysis. Clin. Nutr. 39 1705–1716. 10.1016/j.clnu.2019.07.030
    1. Kliemann N., Murphy N., Viallon V., Freisling H., Tsilidis K. K., Rinaldi S., et al. (2020). Predicted basal metabolic rate and cancer risk in the European Prospective Investigation into Cancer and Nutrition. Int. J. Cancer 147 648–661. 10.1002/ijc.32753
    1. Lampe J. W. (2020). Diet and Cancer Prevention Research: From Mechanism to Implementation. J. Cancer Prev. 25 65–69. 10.15430/JCP.2020.25.2.65
    1. Larsson S. C., Carter P., Vithayathil M., Kar S., Mason A. M., Burgess S. (2020). Insulin-like growth factor-1 and site-specific cancers: a Mendelian randomization study. Cancer Med. 9 6836–6842. 10.1002/cam4.3345
    1. Li W., Saud S. M., Young M. R., Chen G., Hua B. (2015). Targeting Ampk for cancer prevention and treatment. Oncotarget 6 7365–7378. 10.18632/oncotarget.3629
    1. Lloyd-Jones L. R., Robinson M. R., Yang J., Visscher P. M. (2018). Transformation of summary statistics from linear mixed model association on all-or-none traits to odds ratio. Genetics 208 1397–1408. 10.1534/genetics.117.300360
    1. Lugo D., Pulido A. L., Mihos C. G., Issa O., Cusnir M., Horvath S. A., et al. (2019). The effects of physical activity on cancer prevention, treatment and prognosis: a review of the literature. Complement Ther. Med. 44 9–13. 10.1016/j.ctim.2019.03.013
    1. Maciak S., Michalak P. (2015). Cell size and cancer: a new solution to Peto’s paradox? Evol. Appl. 8 2–8. 10.1111/eva.12228
    1. MacKenzie-Shalders K., Kelly J. T., So D., Coffey V. G., Byrne N. M. (2020). The effect of exercise interventions on resting metabolic rate: a systematic review and meta-analysis. J. Sports Sci. 38 1635–1649. 10.1080/02640414.2020.1754716
    1. Meynet O., Ricci J. E. (2014). Caloric restriction and cancer: molecular mechanisms and clinical implications. Trends Mol. Med. 20 419–427. 10.1016/j.molmed.2014.05.001
    1. Mole P. A. (1990). Impact of energy intake and exercise on resting metabolic rate. Sports Med. 10 72–87. 10.2165/00007256-199010020-00002
    1. Mullur R., Liu Y. Y., Brent G. A. (2014). Thyroid hormone regulation of metabolism. Physiol. Rev. 94 355–382. 10.1152/physrev.00030.2013
    1. Murphy N., Carreras-Torres R., Song M., Chan A. T., Martin R. M., Papadimitriou N., et al. (2020a). Circulating levels of Insulin-Like Growth Factor 1 and Insulin-Like Growth Factor Binding Protein 3 associate with risk of colorectal cancer based on serologic and Mendelian randomization analyses. Gastroenterology 158 1300–1312.e20. 10.1053/j.gastro.2019.12.020
    1. Murphy N., Knuppel A., Papadimitriou N., Martin R. M., Tsilidis K. K., Smith-Byrne K., et al. (2020b). Insulin-like growth factor-1, insulin-like growth factor-binding protein-3, and breast cancer risk: observational and Mendelian randomization analyses with approximately 430 000 women. Ann. Oncol. 31 641–649. 10.1016/j.annonc.2020.01.066
    1. Neale Lab. (2018). Ukbb Gwas Imputed V3 - File Manifest Release 20180731. Available online at: (accessed February 11, 2021).
    1. Nguyen T. Y., Batterham M. J., Edwards C. (2016). Comparison of resting energy expenditure between cancer subjects and healthy controls: a meta-analysis. Nutr. Cancer 68 374–387. 10.1080/01635581.2016.1153667
    1. Palmer T. M., Sterne J. A., Harbord R. M., Lawlor D. A., Sheehan N. A., Meng S., et al. (2011). Instrumental variable estimation of causal risk ratios and causal odds ratios in Mendelian randomization analyses. Am. J. Epidemiol. 173 1392–1403. 10.1093/aje/kwr026
    1. Papadimitriou N., Dimou N., Tsilidis K. K., Banbury B., Martin R. M., Lewis S. J., et al. (2020). Physical activity and risks of breast and colorectal cancer: a Mendelian randomisation analysis. Nat. Commun. 11:597. 10.1038/s41467-020-14389-8
    1. Paternoster R., Brame R., Mazerolle P., Piquero A. (1998). Using the correct statistical test for the equality of regression coefficients. Criminology 36 859–866.
    1. Rees J. M. B., Wood A. M., Burgess S. (2017). Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy. Stat. Med. 36 4705–4718. 10.1002/sim.7492
    1. Ruggiero C., Metter E. J., Melenovsky V., Cherubini A., Najjar S. S., Ble A., et al. (2008). High basal metabolic rate is a risk factor for mortality: the Baltimore Longitudinal Study of Aging. J. Gerontol. A Biol. Sci. Med. Sci. 63 698–706. 10.1093/gerona/63.7.698
    1. Sanchez Lopez de Nava A., Raja A. (2020). Physiology, Metabolism. Available online at: (accessed February 11, 2021).
    1. Sanderson E., Davey Smith G., Windmeijer F., Bowden J. (2019). An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int. J. Epidemiol. 48 713–727. 10.1093/ije/dyy262
    1. Saunders C. N., Cornish A. J., Kinnersley B., Law P. J., Claus E. B., Il’yasova D., et al. (2020). Lack of association between modifiable exposures and glioma risk: a Mendelian randomization analysis. Neuro Oncol. 22 207–215. 10.1093/neuonc/noz209
    1. Schooling C. M. (2017). Tachykinin neurokinin 3 receptor antagonists: a new treatment for cardiovascular disease? Lancet. 390 709–711. 10.1016/S0140-6736(16)31648-8
    1. Schooling C. M., Lopez P. M., Yang Z., Zhao J. V., Au Yeung S. L., Huang J. V. (2021). Use of multivariable mendelian randomization to address biases due to competing risk before recruitment. Front. Genet. 11:610852. 10.3389/fgene.2020.610852
    1. Schumacher F. R., Al Olama A. A., Berndt S. I., Benlloch S., Ahmed M., Saunders E. J., et al. (2018). Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat. Genet. 50 928–936. 10.1038/s41588-018-0142-8
    1. Slob E. A. W., Burgess S. (2020). A comparison of robust Mendelian randomization methods using summary data. Genet. Epidemiol. 44 313–329. 10.1002/gepi.22295
    1. Smith G. D., Ebrahim S. (2003). ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32 1–22. 10.1093/ije/dyg070
    1. Soultoukis G. A., Partridge L. (2016). Dietary protein, metabolism, and aging. Annu. Rev. Biochem. 85 5–34. 10.1146/annurev-biochem-060815-014422
    1. Sudlow C., Gallacher J., Allen N., Beral V., Burton P., Danesh J., et al. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12:e1001779. 10.1371/journal.pmed.1001779
    1. Swanson E. M., Dantzer B. (2014). Insulin-like growth factor-1 is associated with life-history variation across mammalia. Proc. Biol. Sci. 281:20132458. 10.1098/rspb.2013.2458
    1. UK Biobank. (2020). Ukb : Data-Field 21022. Available online at: (accessed February 11, 2021).
    1. Verbanck M., Chen C. Y., Neale B., Do R. (2018). Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50 693–698. 10.1038/s41588-018-0099-7
    1. Vigneri R., Sciacca L., Vigneri P. (2020). Rethinking the relationship between insulin and cancer. Trends Endocrinol. Metab. 31 551–560. 10.1016/j.tem.2020.05.004
    1. Wald A. (1940). The fitting of straight lines if both variables are subject to error. Ann. Math. Statist. 11 284–300. 10.1214/aoms/1177731868
    1. Watts E. L., Fensom G. K., Smith Byrne K., Perez-Cornago A., Allen N. E., Knuppel A., et al. (2021). Circulating insulin-like growth factor-i. total and free testosterone concentrations and prostate cancer risk in 200 000 men in UK Biobank. Int. J. Cancer. 148 2274–2288. 10.1002/ijc.33416
    1. Wells J. C. K., Nesse R. M., Sear R., Johnstone R. A., Stearns S. C. (2017). Evolutionary public health: introducing the concept. Lancet 390 500–509. 10.1016/S0140-6736(17)30572-X
    1. Werner H., Laron Z. (2020). Role of the Gh-Igf1 system in progression of cancer. Mol. Cell. Endocrinol. 518:111003. 10.1016/j.mce.2020.111003
    1. World Health Organization. (2018). Cancer. Available online at: (accessed April 13, 2020).
    1. Yavorska O. O., Burgess S. (2017). Mendelianrandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 46 1734–1739. 10.1093/ije/dyx034
    1. Yuan S., Kar S., Vithayathil M., Carter P., Mason A. M., Burgess S., et al. (2020). Causal associations of thyroid function and dysfunction with overall, breast and thyroid cancer: a two-sample Mendelian randomization study. Int. J. Cancer 147 1895–1903. 10.1002/ijc.32988
    1. Zhang H., Ahearn T. U., Lecarpentier J., Barnes D., Beesley J., Qi G., et al. (2020). Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses. Nat. Genet. 52 572–581. 10.1038/s41588-020-0609-2
    1. Zheng J., Baird D., Borges M. C., Bowden J., Hemani G., Haycock P., et al. (2017). Recent developments in Mendelian randomization studies. Curr. Epidemiol. Rep. 4 330–345. 10.1007/s40471-017-0128-6

Source: PubMed

Sponsorzy i współpracownicy

Warunki medyczne

Interwencje lekowe

3
Subskrybuj