Risk Factor Studies

1 Genetic Risk Factors

Evidence supports a genetic contribution to ME/CFS susceptibility, with the genetic architecture increasingly well-characterised by large-scale studies:

  • Family studies: First-degree relatives of ME/CFS patients have a higher risk of developing the condition (Dibble, McGrath, and Ponting 2020). Family clustering is consistently reported, though shared environmental exposures cannot be excluded
  • Twin studies: Monozygotic twins show higher concordance than dizygotic twins, with heritability estimates of 30–50% (Dibble, McGrath, and Ponting 2020). This suggests a moderate genetic contribution with substantial environmental influence
  • Genome-wide studies: DecodeME (n>15,000) and the Maccallini 2026 meta-GWAS (19,470 cases, 699,111 controls) have identified replicated brain-enriched genetic architecture with glutamatergic synapse genes as the most specific signal (DecodeME Consortium, Ponting, et al. 2025) (Maccallini 2026). SNP heritability is 9.5%. No significant genetic correlation with autoimmune diseases. See Section Omics Studies and Chapter Genetic and Epigenetic Factors.

2 Environmental Risk Factors

Environmental triggers interact with genetic susceptibility to precipitate ME/CFS:

  • Infectious triggers: Viral infection is the most common precipitant, identified in 60–80% of cases. Epstein-Barr virus, enteroviruses, HHV-6, and SARS-CoV-2 are the most frequently implicated pathogens. The Dubbo Infection Outcomes Study documented ME/CFS-like illness in 11% of patients following acute EBV, Ross River virus, or Coxiella burnetii infection, with severity of acute illness predicting chronic sequelae
  • Toxic exposures: Organophosphate pesticides, solvents, mold/mycotoxin exposure, and heavy metals have been reported as triggers in case series. Systematic epidemiological evidence is limited
  • Physical trauma: Surgery, childbirth, and physical injury occasionally precede ME/CFS onset, possibly through immune activation or HPA axis disruption
  • Psychological stress: Severe or prolonged stress is reported as a precipitant in some cases, though its role is controversial and must be distinguished from the psychogenic model that attributes ME/CFS to stress itself
  • Vaccination: Rare cases of ME/CFS onset following vaccination have been reported. Population-level studies do not show elevated risk, and the benefit of vaccination (particularly for infection prevention in existing ME/CFS patients) far outweighs any theoretical risk

3 Demographic Risk Factors

Demographic factors associated with ME/CFS risk include:

  • Sex: Female predominance (3–4:1) is robust across studies (Jason and Mirin 2018) (Lim et al. 2020). Potential explanations include hormonal influences on immune function, higher autoimmune disease susceptibility in females, and possible diagnostic bias (males may be less likely to seek care or receive the diagnosis). Critically, ME/CFS onset and severity in women is modulated by reproductive life events: menarche, menstrual cycle phase, pregnancy, the postpartum period, and perimenopause each represent vulnerability windows for onset or relapse (Thomas et al. 2022). Reproductive hormones—particularly estrogen and progesterone—interact with the HPA axis, immune function, and autonomic regulation in ways that may explain this pattern. Women with ME/CFS also show distinctive gynecological histories: earlier menopause (mean age 37.6 vs. 48.6 years in controls), higher rates of excessive menstrual bleeding, pelvic pain, and hysterectomy (Boneva et al. 2015) (Boneva, Lin, and Unger 2011)
  • Age: Bimodal onset-age distribution with peaks at approximately 16 and 37 years (McGrath et al. 2026). Early onset is associated with higher severity (OR 2.15), greater infectious trigger rates (especially infectious mononucleosis), and more familial clustering (OR 1.43). Pediatric onset may carry distinct biological features rather than simply earlier presentation of the same disease (Rowe 2019)
  • Socioeconomic status: Community-based studies show no association with higher socioeconomic status. The historical perception of ME/CFS as a “yuppie flu” reflected clinic-based sampling bias (wealthier patients had better access to specialists who could diagnose the condition). However, the disease produces substantial downward socioeconomic drift: 18.3% employment rate in ME/CFS patients on benefit vs. 83.8% in the general population (Bowden et al. 2026), and this employment gap drives downstream material deprivation including food insecurity (see Section Quality of Life and Disability Studies)

References

Boneva, Roumiana S, Jin-Mann Lin, and Elizabeth R Unger. 2011. “Gynecological History in Chronic Fatigue Syndrome: A Population-Based Case-Control Study.” Journal of Women’s Health 20 (1): 21–28. https://doi.org/10.1089/jwh.2009.1900.
Boneva, Roumiana S, Jin-Mann Lin, Florian Wieser, Urs M Nater, Beate Ditzen, Rebecca N Taylor, and Elizabeth R Unger. 2015. “Early Menopause and Other Gynecologic Risk Indicators for Chronic Fatigue Syndrome in Women.” Menopause 22 (8): 826–34. https://doi.org/10.1097/GME.0000000000000411.
Bowden, Nicholas, Keith McLeod, Francesca Anns, Leanne Catchpole, Fiona Charlton, Barry Taylor, Rosamund Vallings, Hien Vu, and Warren Tate. 2026. “Health, Labour Market, and Social Service Outcomes for People with Myalgic Encephalomyelitis / Chronic Fatigue Syndrome on a Health or Disability Related Benefit: An Aotearoa | New Zealand Nationwide Cross-Sectional Study Using the Integrated Data Infrastructure.” BMC Public Health 26 (1): 1834. https://doi.org/10.1186/s12889-026-27499-7.
DecodeME Consortium, Chris P Ponting, et al. 2025. “Initial Findings from the DecodeME Genome-Wide Association Study of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” medRxiv. https://doi.org/10.1101/2025.08.06.25333109v1.
Dibble, Joshua J, Simon J McGrath, and Chris P Ponting. 2020. “Genetic Risk Factors of ME/CFS: A Critical Review.” Human Molecular Genetics 29 (R1): R117–24. https://doi.org/10.1093/hmg/ddaa169.
Jason, Leonard A, and Arthur A Mirin. 2018. “Estimating Prevalence, Demographics, and Costs of ME/CFS Using Large Scale Medical Claims Data and Machine Learning.” Frontiers in Pediatrics 6: 412. https://doi.org/10.3389/fped.2018.00412.
Lim, Eun-Jin, Young-Chul Ahn, Eun-Su Jang, Si-Woo Lee, Soo-Hyung Lee, and Chang-Gue Son. 2020. “Systematic Review and Meta-Analysis of the Prevalence of Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME).” Journal of Translational Medicine 18 (1): 100. https://doi.org/10.1186/s12967-020-02269-0.
Maccallini, P. 2026. “Biological Insights from Genome-Wide Association Studies and Whole Genome Sequencing of Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome.” Research Square [Preprint], June. https://doi.org/10.21203/rs.3.rs-9702020/v1.
McGrath, Simon J., Charlie B. Hillier, Joshua J. Dibble, Trude Schei, Arild Angelsen, and Audrey A. Ryback. 2026. “Incidence Age Is Bimodal for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome, with Higher Severity Burden for Early Onset Disease.” Oxford Open Immunology 7 (1): iqag007. https://doi.org/10.1093/oxfimm/iqag007.
Rowe, Katharine S. 2019. “Long Term Follow up of Young People with Chronic Fatigue Syndrome Attending a Pediatric Outpatient Service.” Frontiers in Pediatrics 7: 21. https://doi.org/10.3389/fped.2019.00021.
Thomas, Natalie, Caroline Gurvich, Katherine Huang, Paul R Gooley, and Christopher W Armstrong. 2022. “The Underlying Sex Differences in Neuroendocrine Adaptations Relevant to Myalgic Encephalomyelitis Chronic Fatigue Syndrome.” Frontiers in Neuroendocrinology 66: 100995. https://doi.org/10.1016/j.yfrne.2022.100995.