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

Plasma circulating microRNAs associated with obesity, body fat distribution, and fat mass: the Rotterdam Study

Abstract

Background

MicroRNAs (miRNAs) represent a class of small non-coding RNAs that regulate gene expression post-transcriptionally and are implicated in the pathogenesis of different diseases. Limited studies have investigated the association of circulating miRNAs with obesity and body fat distribution and their link to obesity-related diseases using population-based data.

Methods

We conducted a genome-wide profile of circulating miRNAs in plasma, collected between 2002 and 2005, in 1208 participants from the population-based Rotterdam Study cohort. Obesity and body fat distribution were measured as body mass index (BMI), waist-to-hip ratio (WHR), android-fat to gynoid-fat ratio (AGR), and fat mass index (FMI) measured by anthropometrics and Dual X-ray Absorptiometry. Multivariable linear regression models were used to assess the association of 591 miRNAs well-expressed in plasma with these traits adjusted for potential covariates. We further sought for the association of identified miRNAs with cardiovascular and metabolic diseases in the Rotterdam study and previous publications.

Results

Plasma levels of 65 miRNAs were associated with BMI, 40 miRNAs with WHR, 65 miRNAs with FMI, and 15 miRNAs with AGR surpassing the Bonferroni-corrected P < 8.46 × 10−5. Of these, 12 miRNAs were significantly associated with all traits, while four miRNAs were associated only with WHR, three miRNAs only with FMI, and miR-378i was associated only with AGR. The most significant association among the overlapping miRNAs was with miR-193a-5p, which was shown to be associated with type 2 diabetes and hepatic steatosis in the Rotterdam Study. Moreover, five of the obesity-associated miRNAs and two of the body fat distribution miRNAs have been correlated previously to cardiovascular disease.

Conclusions

This study indicates that plasma levels of several miRNAs are associated with obesity and body fat distribution which could help to better understand the underlying mechanisms and may have the biomarker potential for obesity-related diseases.

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

Access options

Buy this article

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

Fig. 1: Flowchart of the study participants.
Fig. 2: UpSet plots showing the intersections of miRNAs associated with four obesity-related traits.
Fig. 3: Comparison between plasma levels of 12 identified miRNAs in three BMI groups.
Fig. 4: Volcano plots showing the association of miRNAs with AGR (A) and FMI (B) after adjusting for BMI.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available in the supplementary material of this article. Correspondence and additional data requests should be addressed to MG.

References

  1. Csige I, Ujvárosy D, Szabó Z, Lőrincz I, Paragh G, Harangi M, et al. The impact of obesity on the cardiovascular system. J Diabetes Res. 2018;2018:3407306.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Ghaben AL, Scherer PE. Adipogenesis and metabolic health. Nat Rev Mol Cell Biol. 2019;20:242–58.

    Article  CAS  PubMed  Google Scholar 

  3. Bovet P, Chiolero A, Gedeon J. Health effects of overweight and obesity in 195 Countries. N Engl J Med. 2017;377:1495–6.

    Article  PubMed  Google Scholar 

  4. Lin X, Li H. Obesity: epidemiology, pathophysiology, and therapeutics. Front Endocrinol (Lausanne). 2021;12:706978.

    Article  Google Scholar 

  5. Lovejoy JC. The menopause and obesity. Prim Care. 2003;30:317–25.

    Article  PubMed  Google Scholar 

  6. Christakis NA, Fowler JH. The spread of obesity in a large social network over 32 years. N Engl J Med. 2007;357:370–9.

    Article  CAS  PubMed  Google Scholar 

  7. Wardle J, Haase AM, Steptoe A, Nillapun M, Jonwutiwes K, Bellisie F. Gender differences in food choice: the contribution of health beliefs and dieting. Ann Behav Med. 2004;27:107–16.

    Article  PubMed  Google Scholar 

  8. Yang W, He Kelly TJ. Genetic epidemiology of obesity. Epidemiol Rev. 2007;29:49–61.

    Article  PubMed  Google Scholar 

  9. Després J-P, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444:881–7.

    Article  PubMed  Google Scholar 

  10. Ortega FB, Lavie CJ, Blair SN. Obesity and cardiovascular disease. Circ Res. 2016;118:1752–70.

    Article  CAS  PubMed  Google Scholar 

  11. Jensen MD. Role of body fat distribution and the metabolic complications of obesity. J Clin Endocrinol Metab. 2008;93:s57–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sigulem DM, Devincenzi MU, Lessa AC. Diagnosis of child and adolescent nutritional status. J Pediatr. 2000;76:S275–84.

    Article  Google Scholar 

  13. Neovius M, Linné Y, Barkeling B, Rössner S. Discrepancies between classification systems of childhood obesity. Obes Rev. 2004;5:105–14.

    Article  CAS  PubMed  Google Scholar 

  14. Oreopoulos A, Kalantar-Zadeh K, Sharma AM, Fonarow GC. The obesity paradox in the elderly: potential mechanisms and clinical implications. Clin Geriatr Med. 2009;25:643–59.

    Article  PubMed  Google Scholar 

  15. Liu DJ, Peloso GM, Yu H, Butterworth AS, Wang X, Mahajan A, et al. Exome-wide association study of plasma lipids in> 300,000 individuals. Nate Genet. 2017;49:1758–66.

    Article  CAS  Google Scholar 

  16. Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ, et al. Genetic association of waist-to-hip ratio with cardiometabolic traits, type 2 diabetes, and coronary heart disease. JAMA. 2017;317:626–34.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lotta LA, Gulati P, Day FR, Payne F, Ongen H, Van De Bunt M, et al. Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance. Nat Genet. 2017;49:17–26.

    Article  CAS  PubMed  Google Scholar 

  18. Cao Q, Yu S, Xiong W, Li Y, Li H, Li J, et al. Waist-hip ratio as a predictor of myocardial infarction risk: a systematic review and meta-analysis. Medicine (Baltimore). 2018;97:e11639.

    Article  Google Scholar 

  19. Otsuka M, Kishikawa T, Yoshikawa T, Yamagami M, Ohno M, Takata A, et al. MicroRNAs and liver disease. J Hum Genet. 2017;62:75–80.

    Article  CAS  PubMed  Google Scholar 

  20. Schueller F, Roy S, Vucur M, Trautwein C, Luedde T, Roderburg C. The role of miRNAs in the pathophysiology of liver diseases and toxicity. Int J Mol Sci. 2018;19:261.

    Article  PubMed Central  Google Scholar 

  21. Pirola CJ, Gianotti TF, Castaño GO, Mallardi P, San Martino J, Ledesma MMGL, et al. Circulating microRNA signature in non-alcoholic fatty liver disease: from serum non-coding RNAs to liver histology and disease pathogenesis. Gut. 2015;64:800–12.

    Article  CAS  PubMed  Google Scholar 

  22. Condrat CE, Thompson DC, Barbu MG, Bugnar OL, Boboc A, Cretoiu D, et al. miRNAs as biomarkers in disease: latest findings regarding their role in diagnosis and prognosis. Cells. 2020;9:276.

    Article  CAS  PubMed Central  Google Scholar 

  23. Landrier J-F, Derghal A, Mounien L. MicroRNAs in obesity and related metabolic disorders. Cells. 2019;8:859.

    Article  CAS  PubMed Central  Google Scholar 

  24. Hilton C, Neville MJ, Wittemans LBL, Todorcevic M, Pinnick KE, Pulit SL, et al. MicroRNA-196a links human body fat distribution to adipose tissue extracellular matrix composition. EBioMedicine. 2019;44:467–75.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Ikram MA, Brusselle G, Ghanbari M, Goedegebure A, Ikram MK, Kavousi M, et al. Objectives, design and main findings until 2020 from the Rotterdam Study. Eur J Epidemiol. 2020;35:483–517.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Alferink LJM, Trajanoska K, Erler NS, Schoufour JD, de Knegt RJ, Ikram MA, et al. Nonalcoholic fatty liver disease in the Rotterdam study: about muscle mass, sarcopenia, fat mass, and fat distribution. J Bone Miner Rese. 2019;341254–63.

    Article  CAS  Google Scholar 

  27. Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Circulation. 2009;120:1640–5.

    Article  CAS  PubMed  Google Scholar 

  28. Voortman T, Kiefte-de Jong JC, Ikram MA, Stricker BH, van Rooij FJA, Lahousse L, et al. Adherence to the 2015 Dutch dietary guidelines and risk of non-communicable diseases and mortality in the Rotterdam Study. Eur J Epidemiol. 2017;32:993–1005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. World Health Organization & International Diabetes Federation. Definition and diagnosis of diabetes mellitus and intermediate hyperglycaemia: report of a WHO/IDF consultation. World Health Organization; 2006. https://apps.who.int/iris/handle/10665/43588.

  30. Santosa S, Jensen MD. Why are we shaped differently, and why does it matter?. Am J Physiol Endocrinol Metab. 2008;295:E531–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Eminaga S, Christodoulou DC, Vigneault F, Church GM, Seidman JG. Quantification of microRNA expression with next-generation sequencing. Curr Protoc Mol Biol. 2013;103:1–4.

    Article  Google Scholar 

  32. Szelenberger R, Kacprzak M, Saluk-Bijak J, Zielinska M, Bijak M. Plasma MicroRNA as a novel diagnostic. Clin Chim Acta. 2019;499:98–107.

    Article  CAS  PubMed  Google Scholar 

  33. Kunej T, Skok DJ, Zorc M, Ogrinc A, Michal JJ, Kovac M, et al. Obesity gene atlas in mammals. J Genom. 2013;1:45.

    Article  Google Scholar 

  34. Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, et al. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2009;37:D98–104.

    Article  CAS  PubMed  Google Scholar 

  35. Dumortier O, Hinault C, Van Obberghen E. MicroRNAs and metabolism crosstalk in energy homeostasis. Cell Metab. 2013;18:312–24.

    Article  CAS  PubMed  Google Scholar 

  36. Ghanbari M, Sedaghat S, De Looper HWJ, Hofman A, Erkeland SJ, Franco OH, et al. The association of common polymorphisms in mi R‐196a2 with waist to hip ratio and mi R‐1908 with serum lipid and glucose. Obesity. 2015;23:495–503.

    Article  CAS  PubMed  Google Scholar 

  37. Cheng M, Mei B, Zhou Q, Zhang M, Huang H, Han L, et al. Computational analyses of obesity associated loci generated by genome-wide association studies. PLoS ONE. 2018;13:e0199987.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Ortega FJ, Mercader JM, Catalan V, Moreno-Navarrete JM, Pueyo N, Sabater M, et al. Targeting the circulating microRNA signature of obesity. Clin Chem. 2013;59:781–92.

    Article  CAS  PubMed  Google Scholar 

  39. Youssef EM, Elfiky AM, Abu-Shahba N, Elhefnawi MM. Expression profiling and analysis of some miRNAs in subcutaneous white adipose tissue during development of obesity. Genes Nutr. 2020;15:1–14.

    Article  Google Scholar 

  40. Zhang X, Mens MMJ, Abozaid YJ, Bos D, Darwish Murad S, de Knegt RJ, et al. Circulatory microRNAs as potential biomarkers for fatty liver disease: the Rotterdam study. Aliment Pharmacol Ther. 2021;53:432–42.

    CAS  PubMed  Google Scholar 

  41. Mens MMJ, Mustafa R, Ahmadizar F, Ikram MA, Evangelou M, Kavousi M, et al. MiR-139-5p is a causal biomarker for type 2 diabetes; Results from genome-wide microRNA profiling and Mendelian randomization analysis in a population-based study. 2021. https://www.medrxiv.org/content/10.1101/2021.05.13.21257090v1.full.

  42. Ravanidis S, Grundler F, de Toledo FW, Dimitriou E, Tekos F, Skaperda Z, et al. Fasting-mediated metabolic and toxicity reprogramming impacts circulating microRNA levels in humans. Food Chem Toxicol. 2021;152:112187.

    Article  CAS  PubMed  Google Scholar 

  43. Lin Y-Y, Chou C-F, Giovarelli M, Briata P, Gherzi R, Chen C-Y. KSRP and MicroRNA 145 are negative regulators of lipolysis in white adipose tissue. Mol Cell Biol. 2014;34:2339–49.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Kirby TJ, Walton RG, Finlin B, Zhu B, Unal R, Rasouli N, et al. Integrative mRNA-microRNA analyses reveal novel interactions related to insulin sensitivity in human adipose tissue. Physiol Genom. 2016;48:145–53.

    Article  CAS  Google Scholar 

  45. Viesti A, Collares R, Salgado W Jr, Pretti da Cunha Tirapelli D, dos Santos JS. The expression of LEP, LEPR, IGF1 and IL10 in obesity and the relationship with microRNAs. PLoS ONE. 2014;9:e93512.

    Article  Google Scholar 

  46. Pascut D, Tamini S, Bresolin S, Giraudi P, Basso G, Minocci A, et al. Differences in circulating microRNA signature in Prader–Willi syndrome and non-syndromic obesity. Endocr Connect. 2018;7:1262–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Liu X, He Y, Feng Z, Sheng J, Dong A, Zhang M, et al. miR-345-5p regulates adipogenesis via targeting VEGF-B. Aging. 2020;12:17114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zheng S, Guo S, Sun G, Shi Y, Wei Z, Tang Y, et al. Gain of metabolic benefit with ablation of miR-149-3p from subcutaneous adipose tissue in diet-induced obese mice. Mol Ther Nucleic Acids. 2019;18:194–203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Xu L, Jiang L, Gu K, Liu Z, Xu X. Regulation of MicroRNA-378 expression in mature human adipose tissue cells by adiponectin, free fatty acids and dexamethasone. Trop J Pharm Res. 2018;17:29–34.

    Article  CAS  Google Scholar 

  50. Carrer M, Liu N, Grueter CE, Williams AH, Frisard MI, Hulver MW, et al. Control of mitochondrial metabolism and systemic energy homeostasis by microRNAs 378 and 378. Proc Nat Acad Sci USA. 2012;109:15330–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Gerin I, Bommer GT, McCoin CS, Sousa KM, Krishnan V, MacDougald OA. Roles for miRNA-378/378* in adipocyte gene expression and lipogenesis. Am J Physiol Endocrinol Metab. 2010;299:E198–206.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Choi WH, Ahn J, Um MY, Jung CH, Jung SE, Ha TY. Circulating microRNA expression profiling in young obese Korean women. Nutr Res Pract. 2020;14:412–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yang Z, Wei Z, Wu X, Yang H. Screening of exosomal miRNAs derived from subcutaneous and visceral adipose tissues: determination of targets for the treatment of obesity and associated metabolic disorders. Mol Med Rep. 2018;18:3314–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. The Lancet. The link between cancer and obesity. Lancet. 2017;390:1716.

    Article  Google Scholar 

  55. De Pergola G, Silvestris F. Obesity as a major risk factor for cancer. J Obes. 2013;2013:291546.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Hopkins BD, Goncalves MD, Cantley LC. Obesity and cancer mechanisms: cancer metabolism. J Clin Oncol. 2016;34:4277.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Tao Z, Zheng S, He X, Sun J, He C, Zhang L. Hsa_circ_0037897 may be a risk factor for essential hypertension via hsa-miR-145-5p. Clin Exp Hypertens. 2021;43:281–6.

    Article  CAS  PubMed  Google Scholar 

  58. Zhou Q, Li D, Zheng H, He Z, Qian F, Wu X, et al. A novel lncRNA‐miRNA‐mRNA competing endogenous RNA regulatory network in lung adenocarcinoma and kidney renal papillary cell carcinoma. Thoracic Cancer. 2021;12:2526–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Sonoda T, Matsuzaki J, Yamamoto Y, Sakurai T, Aoki Y, Takizawa S, et al. Serum microRNA-based risk prediction for stroke. Stroke. 2019;50:1510–8.

    Article  CAS  PubMed  Google Scholar 

  60. Gholikhani-Darbroud R, Khaki-Khatibi F, Mansouri F, Hajahmadipoorrafsanjani M, Ghojazadeh M. Decreased circulatory microRNA-4478 as a specific biomarker for diagnosing non-ST-segment elevation myocardial infarction (NSTEMI) and its association with soluble leptin receptor. Bratislavske Lekarske Listy. 2017;118:684–90.

    CAS  PubMed  Google Scholar 

  61. Zhang H, Hao J, Sun X, Zhang Y, Wei Q. Circulating pro-angiogenic micro-ribonucleic acid in patients with coronary heart disease. Interact Cardiovasc Thorac Surg. 2018;27:336–42.

    PubMed  Google Scholar 

  62. Chen L, Bai J, Liu J, Lu H, Zheng K. A four-microRNA panel in peripheral blood identified as an early biomarker to diagnose acute myocardial infarction. Front Physiol. 2021;12:669590.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge the contribution of participants, staff, general practitioners, and pharmacists in the Rotterdam Study. This manuscript is part of the Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy (SOPHIA) project. SOPHIA has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 875534. This Joint Undertaking support from the European Union’s Horizon 2020 research and innovation program and EFPIA and T1D Exchange, JDRF, and Obesity Action Coalition.

Funding

The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. MiRNA expression analyses by HTG EdgeSeq WTA was funded by Johnson & Johnson. The project was partly supported by the Erasmus MC Fellowship (EMCF20213) grant of MG. The mentioned funders had no role in the design and conduct of the study, nor in the decision to submit the manuscript for publication.

Author information

Authors and Affiliations

Authors

Contributions

MG and MAI were responsible for designing the research; YA conducted the analysis; XZ and MM helped in the miRNAs analysis; MG, FA, TV, ML, MK, and FR provided consultation regarding the T2D, lifestyle factors and obesity-related traits data in the Rotterdam study; All authors have critically reviewed and approved the final manuscript.

Corresponding author

Correspondence to Mohsen Ghanbari.

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 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

Abozaid, Y.J., Zhang, X., Mens, M.M.J. et al. Plasma circulating microRNAs associated with obesity, body fat distribution, and fat mass: the Rotterdam Study. Int J Obes 46, 2137–2144 (2022). https://doi.org/10.1038/s41366-022-01227-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-022-01227-8

Search

Quick links