Abstract
To determine the relationship between basal metabolic rate (BMR) and multiple sclerosis (MS) susceptibility, we analyzed genome-wide association study (GWAS) summary statistics data from the International Multiple Sclerosis Genetics Consortium on a total of 115,803 participants of European descent, including 47,429 patients with MS and 68,374 controls. We selected 378 independent genetic variants strongly associated with BMR in a GWAS involving 454,874 participants as instrumental variables to examine a potential causal relationship between BMR and MS. A genetically predicted higher BMR was associated with a greater risk of MS (odds ratio [OR]: 1.283 per one standard deviation increase in BMR, 95% confidence interval [CI]: 1.108–1.486, P = 0.001). Moreover, we used the lasso method to eliminate heterogeneity (Q statistic = 384.58, P = 0.370). There was no pleiotropy in our study and no bias was found in the sensitivity analysis using the leave-one-out test. We provide novel evidence that a higher BMR is an independent causal risk factor in the development of MS. Further work is warranted to elucidate the potential mechanisms.
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The data that support the findings of this study are available from the corresponding author on reasonable request.
References
Anthanont P, Jensen MD (2016) Does basal metabolic rate predict weight gain? Am J Clin Nutr 104:959–963. https://doi.org/10.3945/ajcn.116.134965
Ascherio A, Munger KL, Lunemann JD (2012) The initiation and prevention of multiple sclerosis. Nat Rev Neurol 8:602–612. https://doi.org/10.1038/nrneurol.2012.198
Ascherio A et al (2014) Vitamin D as an early predictor of multiple sclerosis activity and progression. JAMA Neurol 71:306–314. https://doi.org/10.1001/jamaneurol.2013.5993
Avaria-Llautureo J, Hernandez CE, Rodriguez-Serrano E, Venditti C (2019) The decoupled nature of basal metabolic rate and body temperature in endotherm evolution. Nature 572:651–654. https://doi.org/10.1038/s41586-019-1476-9
Blakemore SJ, Burnett S, Dahl RE (2010) The role of puberty in the developing adolescent brain. Hum Brain Mapp 31:926–933. https://doi.org/10.1002/hbm.21052
Bowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40:304–314. https://doi.org/10.1002/gepi.21965
Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan N, Thompson J (2017) A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med 36:1783–1802. https://doi.org/10.1002/sim.7221
Brion MJ, Shakhbazov K, Visscher PM (2013) Calculating statistical power in Mendelian randomization studies. Int J Epidemiol 42:1497–1501. https://doi.org/10.1093/ije/dyt179
Davies NM, Holmes MV, Davey Smith G (2018) Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ 362:k601. https://doi.org/10.1136/bmj.k601
Demerens C et al (1996) Induction of myelination in the central nervous system by electrical activity. Proc Natl Acad Sci U S A 93:9887–9892. https://doi.org/10.1073/pnas.93.18.9887
Drabsch T, Holzapfel C, Stecher L, Petzold J, Skurk T, Hauner H (2018) Associations between C-reactive protein, insulin sensitivity, and resting metabolic rate in adults: a mediator analysis. Front Endocrinol (Lausanne) 9:556. https://doi.org/10.3389/fendo.2018.00556
Eisenberg DT, Kuzawa CW, Hayes MG (2010) Worldwide allele frequencies of the human apolipoprotein E gene: climate, local adaptations, and evolutionary history. Am J Phys Anthropol 143:100–111. https://doi.org/10.1002/ajpa.21298
Elsworth B, Lyon M, Alexander T (2020) The MRC IEU OpenGWAS data infrastructure. bioRxiv. https://doi.org/10.1101/2020.08.10.244293
Emdin CA, Khera AV, Kathiresan S (2017) Mendelian randomization. JAMA 318:1925–1926. https://doi.org/10.1001/jama.2017.17219
Ference BA et al (2016) Variation in PCSK9 and HMGCR and risk of cardiovascular disease and diabetes. N Engl J Med 375:2144–2153. https://doi.org/10.1056/NEJMoa1604304
Froehle AW (2008) Climate variables as predictors of basal metabolic rate: new equations. Am J Hum Biol 20:510–529. https://doi.org/10.1002/ajhb.20769
Hemani G et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. Elife 7. https://doi.org/10.7554/eLife.34408
Hemani G, Haycock P, Zheng J, Gaunt T, Elsworth B (2020) TwoSampleMR: Two Sample MR functions and interface to MR Base database. R package version 0.4.26
Henry CJ (2005) Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr 8:1133–1152. https://doi.org/10.1079/phn2005801
International Multiple Sclerosis Genetics, C. et al (2011) Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476:214–219. https://doi.org/10.1038/nature10251
International Multiple Sclerosis Genetics, C (2019) Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365. https://doi.org/10.1126/science.aav7188
Jacobs BM, Noyce AJ, Giovannoni G, Dobson R (2020) BMI and low vitamin D are causal factors for multiple sclerosis: A Mendelian randomization study. Neurol Neuroimmunol Neuroinflamm 7. https://doi.org/10.1212/NXI.0000000000000662
Jeffery KJ, Rovelli C (2020) Transitions in brain evolution: space, time and entropy. Trends Neurosci 43:467–474. https://doi.org/10.1016/j.tins.2020.04.008
Johnstone AM, Murison SD, Duncan JS, Rance KA, Speakman JR (2005) Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. Am J Clin Nutr 82:941–948. https://doi.org/10.1093/ajcn/82.5.941
Jones DS, Podolsky SH (2015) The history and fate of the gold standard. Lancet 385:1502–1503. https://doi.org/10.1016/S0140-6736(15)60742-5
Lawlor DA, Davey Smith G, Kundu D, Bruckdorfer KR, Ebrahim S (2004) Those confounded vitamins: what can we learn from the differences between observational versus randomised trial evidence? Lancet 363:1724–1727. https://doi.org/10.1016/S0140-6736(04)16260-0
Licht-Mayer S et al (2020) Enhanced axonal response of mitochondria to demyelination offers neuroprotection: implications for multiple sclerosis. Acta Neuropathol 140:143–167. https://doi.org/10.1007/s00401-020-02179-x
Lubetzki C, Zalc B, Williams A, Stadelmann C, Stankoff B (2020) Remyelination in multiple sclerosis: from basic science to clinical translation. Lancet Neurol 19:678–688. https://doi.org/10.1016/S1474-4422(20)30140-X
Mokry LE et al (2015) Vitamin D and risk of multiple sclerosis: a mendelian randomization study. PLoS Med 12:e1001866. https://doi.org/10.1371/journal.pmed.1001866
Mokry LE, Ross S, Timpson NJ, Sawcer S, Davey Smith G, Richards JB (2016) Obesity and multiple sclerosis: a mendelian randomization study. PLoS Med 13:e1002053. https://doi.org/10.1371/journal.pmed.1002053
Munger KL et al (2013) Childhood body mass index and multiple sclerosis risk: a long-term cohort study. Multiple Sclerosis (Houndmills, Basingstoke, England) 19:1323–1329. https://doi.org/10.1177/1352458513483889
Norin T, Metcalfe NB (2019) Ecological and evolutionary consequences of metabolic rate plasticity in response to environmental change. Philos Trans R Soc Lond B Biol Sci 374:20180180. https://doi.org/10.1098/rstb.2018.0180
Olsson T, Barcellos LF, Alfredsson L (2017) Interactions between genetic, lifestyle and environmental risk factors for multiple sclerosis. Nat Rev Neurol 13:25–36. https://doi.org/10.1038/nrneurol.2016.187
Patsopoulos NA, De Jager PL (2020) Genetic and gene expression signatures in multiple sclerosis. Multiple Sclerosis (Houndmills, Basingstoke, England) 26:576–581. https://doi.org/10.1177/1352458519898332
Reich DS, Lucchinetti CF, Calabresi PA (2018) Multiple sclerosis. N Engl J Med 378:169–180. https://doi.org/10.1056/NEJMra1401483
Rone MB et al (2016) Oligodendrogliopathy in multiple sclerosis: low glycolytic metabolic rate promotes oligodendrocyte survival. J Neurosci 36:4698–4707. https://doi.org/10.1523/JNEUROSCI.4077-15.2016
Schrödinger E (1944) What is life? The physical Aspect of the Living Cell. Cambridge University Press, Cambridge
Team, R. C. (2019) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. In. https://www.R-project.org/
Trapp BD, Stys PK (2009) Virtual hypoxia and chronic necrosis of demyelinated axons in multiple sclerosis. Lancet Neurol 8:280–291. https://doi.org/10.1016/S1474-4422(09)70043-2
Walton C et al (2020) Rising prevalence of multiple sclerosis worldwide: insights from the Atlas of MS, third edition. Mult Scler (Houndmills, Basingstoke, England) 26:1816–1821. https://doi.org/10.1177/1352458520970841
Wesnes K et al (2015) Body size and the risk of multiple sclerosis in Norway and Italy: the EnvIMS study. Mult Scler (Houndmills, Basingstoke, England) 21:388–395. https://doi.org/10.1177/1352458514546785
Wesnes K et al (2018) Physical activity is associated with a decreased multiple sclerosis risk: the EnvIMS study. Mult Scler (Houndmills, Basingstoke, England) 24:150–157. https://doi.org/10.1177/1352458517694088
Acknowledgements
We gratefully thank the IMSGC, MRC-IEU, and GIANT for access to their summary statistics data.
Funding
This work was supported by grants from the National Natural Science Foundation of China (81771300, 81971140), Natural Science Foundation of Guangdong Province (2017A030313853), and Guangzhou Science and Technology Plan Project (201904010444).
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Chunxin Liu, Yaxin Lu and Jingjing Chen contributed equally to the study.
Conception and design: Chunxin Liu and Wei Qiu.
Analysis and interpretation: Chunxin Liu, Yaxin Lu and Jingjing Chen.
Data collection: Chunxin Liu, Yaxin Lu and Jingjing Chen.
Critically revised the manuscript: Zifeng Liu and Yiqiang Zhan.
Obtained funding: Wei Qiu.
Overall responsibility: Zifeng Liu and Yiqiang Zhan.
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The data sources used in this study (IMSGC and MRC-IEU) obtained informed consent from all participants. Separate institutional review board approval was not required for this study.
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Chunxin Liu, Yaxin Lu and Jingjing Chen share first authorship.
Yiqiang Zhan and Zifeng Liu share senior authorship.
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Liu, C., Lu, Y., Chen, J. et al. Basal metabolic rate and risk of multiple sclerosis: a Mendelian randomization study. Metab Brain Dis 37, 1855–1861 (2022). https://doi.org/10.1007/s11011-022-00973-y
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DOI: https://doi.org/10.1007/s11011-022-00973-y