Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Genetics and Epigenetics

Increased BMI and late-life mobility dysfunction; overlap of genetic effects in brain regions

International Journal of Obesity volume 47, pages 358–364 (2023)Cite this article

Subjects

Abstract

Background

How obesity earlier in life impacts upon mobility dysfunctions in late life is not well understood. Pernicious effects of excess weight on the musculoskeletal system and mobility dysfunctions are well-recognized. However, increasingly more data support the link of obesity to overall motor defects that are regulated in the brain.

Objectives

To assess the causal relationship between body mass index (BMI) at midlife and performance of the Timed Up-and-Go test (TUG) in late life among a population-based longitudinal cohort of Chinese adults living in Singapore.

Methods

We evaluated genetic predispositions for BMI in 8342 participants who were followed up from measurement of BMI at average 53 years, to TUG test (as a functional mobility measure) 20 years later.

Results

A robust 75.83% of genetically determined BMI effects on late-life TUG scores were mediated through midlife BMI (Pindirect-effect = 9.24 × 10−21). Utilizing Mendelian randomization, we demonstrated a causal effect between BMI and functional mobility in late life (βIVW = 0.180, PIVW = 0.001). Secondary gene enrichment evaluations highlighted down-regulation of genes at BMI risk loci that were correlated with poorer functional mobility in the substantia nigra and amygdala regions as compared to all other tissues. These genes also exhibit differential expression patterns during human brain development.

Conclusions

We report a causal effect of obesity on mobility dysfunction. Our findings highlight potential neuronal dysfunctions in regulating predispositions on the causal pathway from obesity to mobility dysfunction.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Linear relationship between weighted genetic risk score for body mass index (BMI) (wGRS, instrument variable), midlife BMI (exposure), and timed up-and-go (TUG in late life, outcome).
Fig. 2: Mediation analysis to quantify effects of genetic variant on timed up-and-go (TUG) in late life through midlife BMI (significant indirect effect, P = 9.24 × 10−21) and not through midlife BMI (non-significant direct effect, P = 0.489).
Fig. 3: Top tissue enrichments observed with gene expression data in tissues from GTEx database (both side).

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

References

  1. NCD Risk Factor Collaboration (NCD-RisC).Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet. 2017;390:2627–42.

  2. Health effects of overweight and obesity in 195 countries. N Engl J Med. 2017;377:1495–7.

  3. Fontaine KR, Barofsky I. Obesity and health-related quality of life. Obes Rev. 2001;2:173–82.

    Article  CAS  PubMed  Google Scholar 

  4. Ferraro KF, Su YP, Gretebeck RJ, Black DR, Badylak SF. Body mass index and disability in adulthood: a 20-year panel study. Am J Public Health. 2002;92:834–40.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Launer LJ, Harris T, Rumpel C, Madans J. Body mass index, weight change, and risk of mobility disability in middle-aged and older women. The epidemiologic follow-up study of NHANES I. JAMA. 1994;271:1093–8.

    Article  CAS  PubMed  Google Scholar 

  6. Stenholm S, Rantanen T, Heliövaara M, Koskinen S. The mediating role of C-reactive protein and handgrip strength between obesity and walking limitation. J Am Geriatr Soc. 2008;56:462–9.

    Article  PubMed  Google Scholar 

  7. Tay J, Goss AM, Locher JL, Ard JD, Gower BA. Physical function and strength in relation to inflammation in older adults with obesity and increased cardiometabolic risk. J Nutr Health Aging. 2019;23:949–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wearing SC, Hennig EM, Byrne NM, Steele JR, Hills AP. Musculoskeletal disorders associated with obesity: a biomechanical perspective. Obes Rev. 2006;7:239–50.

    Article  CAS  PubMed  Google Scholar 

  9. Nantel J, Mathieu ME, Prince F. Physical activity and obesity: biomechanical and physiological key concepts. J Obes. 2011;2011:650230.

    Article  PubMed  Google Scholar 

  10. Barros WMA, da Silva KG, Silva RKP, Souza A, da Silva ABJ, Silva MRM, et al. Effects of overweight/obesity on motor performance in children: a systematic review. Front Endocrinol. 2021;12:759165.

    Article  Google Scholar 

  11. Battaglia G, Giustino V, Tabacchi G, Lanza M, Schena F, Biino V, et al. Interrelationship between age, gender, and weight status on motor coordination in italian children and early adolescents aged 6-13 years old. Front Pediatr. 2021;9:738294.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Farooqi S, O’Rahilly S. Genetics of obesity in humans. Endocr Rev. 2006;27:710–18.

    Article  CAS  PubMed  Google Scholar 

  14. Ndiaye FK, Huyvaert M, Ortalli A, Canouil M, Lecoeur C, Verbanck M, et al. The expression of genes in top obesity-associated loci is enriched in insula and substantia nigra brain regions involved in addiction and reward. Int J Obes. 2020;44:539–43.

    Article  CAS  Google Scholar 

  15. Podsiadlo D, Richardson S. The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39:142–8.

    Article  CAS  PubMed  Google Scholar 

  16. Benavent-Caballer V, Sendín-Magdalena A, Lisón JF, Rosado-Calatayud P, Amer-Cuenca JJ, Salvador-Coloma P, et al. Physical factors underlying the Timed “Up and Go” test in older adults. Geriatr Nurs. 2016;37:122–7.

    Article  PubMed  Google Scholar 

  17. McGough EL, Kelly VE, Logsdon RG, McCurry SM, Cochrane BB, Engel JM, et al. Associations between physical performance and executive function in older adults with mild cognitive impairment: gait speed and the timed “up & go” test. Phys Ther. 2011;91:1198–207.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hankin JH, Stram DO, Arakawa K, Park S, Low SH, Lee HP, et al. Singapore Chinese Health Study: development, validation, and calibration of the quantitative food frequency questionnaire. Nutr Cancer. 2001;39:187–95.

    Article  CAS  PubMed  Google Scholar 

  19. Dorajoo R, Chang X, Gurung RL, Li Z, Wang L, Wang R, et al. Loci for human leukocyte telomere length in the Singaporean Chinese population and trans-ethnic genetic studies. Nat Commun. 2019;10:2491.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Chang X, Gurung RL, Wang L, Jin A, Li Z, Wang R, et al. Low frequency variants associated with leukocyte telomere length in the Singapore Chinese population. Commun Biol. 2021;4:1–9.

    Article  Google Scholar 

  21. Chang X, Dorajoo R, Sun Y, Han Y, Wang L, Khor C-C, et al. Gene-diet interaction effects on BMI levels in the Singapore Chinese population. Nutr J. 2018;17:31.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Censin JC, Peters SAE, Bovijn J, Ferreira T, Pulit SL, Mägi R, et al. Causal relationships between obesity and the leading causes of death in women and men. PLoS Genet. 2019;15:e1008405.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sobel ME. Direct and indirect effects in linear structural equation models. Soc Methods Res. 1987;16:155–76.

    Article  Google Scholar 

  25. Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017;36:1783–802.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Greco MF, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34:2926–40.

    Article  Google Scholar 

  27. Bowden J, Spiller W, Del Greco MF, Sheehan N, Thompson J, Minelli C, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol. 2018;47:1264–78.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.

    Article  PubMed  PubMed Central  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;40:304–14.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. 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. 2016;45:1961–74.

    PubMed  PubMed Central  Google Scholar 

  31. Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8:1826.

    Article  PubMed  PubMed Central  Google Scholar 

  32. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369:1318-30.

  33. Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, et al. Spatio-temporal transcriptome of the human brain. Nature. 2011;478:483–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yoo JE, Jang W, Shin DW, Jeong SM, Jung HW, Youn J, et al. Timed up and go test and the risk of parkinson’s disease: a nation-wide retrospective cohort study. Mov Disord. 2020;35:1263–7. PubMed PMID: 32293759. Epub 2020/04/16. eng

    Article  PubMed  Google Scholar 

  35. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377–89.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Loos RJF, Yeo GSH. The genetics of obesity: from discovery to biology. Nat Rev Genet. 2022;23:120–33.

    Article  CAS  PubMed  Google Scholar 

  37. Hebebrand J, Albayrak Ö, Adan R, Antel J, Dieguez C, de Jong J, et al. “Eating addiction”, rather than “food addiction”, better captures addictive-like eating behavior. Neurosci Biobehav Rev. 2014;47:295–306.

    Article  PubMed  Google Scholar 

  38. de Heredia FP, Gómez-Martínez S, Marcos A. Obesity, inflammation and the immune system. Proc Nutr Soc. 2012;71:332–8.

    Article  PubMed  Google Scholar 

  39. Hamani C, Saint-Cyr JA, Fraser J, Kaplitt M, Lozano AM. The subthalamic nucleus in the context of movement disorders. Brain. 2004;127:4–20.

    Article  PubMed  Google Scholar 

  40. Begg DP, Woods SC. Hedonic and homeostatic overlap following fat ingestion. Cell Metab. 2013;18:459–60.

    Article  CAS  PubMed  Google Scholar 

  41. Braak H, Braak E, Yilmazer D, de Vos RA, Jansen EN, Bohl J, et al. Amygdala pathology in Parkinson’s disease. Acta Neuropathol. 1994;88:493–500.

    Article  CAS  PubMed  Google Scholar 

  42. Huang P, Xuan M, Gu Q, Yu X, Xu X, Luo W, et al. Abnormal amygdala function in Parkinson’s disease patients and its relationship to depression. J Affect Disord. 2015;183:263–8.

    Article  PubMed  Google Scholar 

  43. Hu X, Song X, Yuan Y, Li E, Liu J, Liu W, et al. Abnormal functional connectivity of the amygdala is associated with depression in Parkinson’s disease. Mov Disord. 2015;30:238–44.

    Article  PubMed  Google Scholar 

  44. Sun X, Kroemer NB, Veldhuizen MG, Babbs AE, de Araujo IE, Gitelman DR, et al. Basolateral amygdala response to food cues in the absence of hunger is associated with weight gain susceptibility. J Neurosci. 2015;35:7964–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Yan L, Li L, Lei J. Long noncoding RNA small nucleolar RNA host gene 12/microRNA-138-5p/nuclear factor I/B regulates neuronal apoptosis, inflammatory response, and oxidative stress in Parkinson’s disease. Bioengineered. 2021;12:12867–79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Maes T, Mascaró C, Ortega A, Lunardi S, Ciceri F, Somervaille TC, et al. KDM1 histone lysine demethylases as targets for treatments of oncological and neurodegenerative disease. Epigenomics. 2015;7:609–26.

    Article  CAS  PubMed  Google Scholar 

  47. Shamsuzzama, Kumar L, Haque R, Nazir A. Role of MicroRNA Let-7 in modulating multifactorial aspect of neurodegenerative diseases: an overview. Mol Neurobiol. 2016;53:2787–93.

    Article  CAS  PubMed  Google Scholar 

  48. Brahmachari S, Karuppagounder SS, Ge P, Lee S, Dawson VL, Dawson TM, et al. c-Abl and Parkinson’s disease: mechanisms and therapeutic potential. J Parkinsons Dis. 2017;7:589–601.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kolk SM, Gunput RA, Tran TS, van den Heuvel DM, Prasad AA, Hellemons AJ, et al. Semaphorin 3F is a bifunctional guidance cue for dopaminergic axons and controls their fasciculation, channeling, rostral growth, and intracortical targeting. J Neurosci. 2009;29:12542–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kusy S, Funkelstein L, Bourgais D, Drabkin H, Rougon G, Roche J, et al. Redundant functions but temporal and regional regulation of two alternatively spliced isoforms of Semaphorin 3F in the nervous system. Mol Cell Neurosci. 2003;24:409–18.

    Article  CAS  PubMed  Google Scholar 

  51. Dorajoo R, Ong RT, Sim X, Wang L, Liu W, Tai ES, et al. The contribution of recently identified adult BMI risk loci to paediatric obesity in a Singaporean Chinese childhood dataset. Pediatr Obes. 2017;12:e46–e50.

    Article  CAS  PubMed  Google Scholar 

  52. Dorajoo R, Blakemore AI, Sim X, Ong RT, Ng DP, Seielstad M, et al. Replication of 13 obesity loci among Singaporean Chinese, Malay and Asian-Indian populations. Int J Obes. 2012;36:159–63.

    Article  CAS  Google Scholar 

Download references

Funding

The Singapore Chinese Health Study was supported by grants from the National Medical Research Council, Singapore (NMRC/CIRG/1456/2016), National Institutes of Health (R01 CA144034 and UM1 CA182876) and National Research Foundation, Singapore (Project Number 370062002). W-PK is supported by the National Medical Research Council, Singapore (MOH-CSASI19nov-0001). C-CK was supported by National Research Foundation Singapore (NRF-NRFI2018-01).

Author information

Authors and Affiliations

  1. Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore

    Xuling Chang, Fang Lin Ng & Chew-Kiat Heng

  2. Khoo Teck Puat – National University Children’s Medical Institute, National University Health System, Singapore, 119074, Singapore

    Xuling Chang, Fang Lin Ng & Chew-Kiat Heng

  3. Department of Infectious Diseases, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Melbourne, 3000, VIC, Australia

    Xuling Chang

  4. Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, 119077, Singapore

    Kevin Yiqiang Chua

  5. Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, 138672, Singapore

    Ling Wang, Jianjun Liu, Chiea-Chuen Khor & Rajkumar Dorajoo

  6. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore

    Jianjun Liu

  7. Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15232, USA

    Jian-Min Yuan

  8. Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA

    Jian-Min Yuan

  9. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, 169856, Singapore

    Chiea-Chuen Khor

  10. Health Systems and Services Research, Duke-NUS Medical School Singapore, Singapore, 169857, Singapore

    Rajkumar Dorajoo

  11. Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117545, Singapore

    Woon-Puay Koh

  12. Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, 117609, Singapore

    Woon-Puay Koh

Authors
  1. Xuling Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kevin Yiqiang Chua
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fang Lin Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ling Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianjun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jian-Min Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chiea-Chuen Khor
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chew-Kiat Heng
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Rajkumar Dorajoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Woon-Puay Koh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RD, C-KH, and W-PK, contributed to the study design. W-PK, and J-MY contributed to the sample recruitment and data processing. C-CK, and W-PK generated genotyping data. RD, XC, LW, JL, and C-CK performed genotype quality controls, imputation and genotyping analyses. RD, XC, KYC, and FLN performed statistical analyses. RD, and XC performed bioinformatics analyses. RD, C-KH, and XC verified underlying data. RD, KYC, and XC, drafted the manuscript. All authors critically reviewed the manuscript.

Corresponding authors

Correspondence to Chew-Kiat Heng or Rajkumar Dorajoo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, X., Chua, K.Y., Ng, F.L. et al. Increased BMI and late-life mobility dysfunction; overlap of genetic effects in brain regions. Int J Obes 47, 358–364 (2023). https://doi.org/10.1038/s41366-023-01275-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-023-01275-8

Search

Advanced search

Quick links