Young-onset dementia (YOD) – typically defined as dementia with symptom onset before age 65 – is receiving increasing global attention. This growing visibility may give the impression that YOD is rising sharply, but current evidence suggests a more nuanced picture. The most defensible interpretation is that improved diagnosis, biomarker advances and strengthened surveillance systems are driving much of the apparent increase, while shifts in midlife risk exposures may also be contributing to genuine growth in some regions.¹^,³

Why rising reports of YOD may not reflect true incidence

Improved case-finding and earlier referral

Across international health systems, clinicians are becoming more attuned to the non‑amnestic presentations commonly seen in younger adults, including behavioural changes, language difficulties and visuospatial impairment. Historically, these symptoms were more likely to be attributed to stress, depression or other functional causes. Newer evidence shows that increased awareness, earlier referral pathways and broader use of neuroimaging have collectively increased the likelihood that cognitive symptoms in younger adults are investigated and recognised as early neurodegenerative disease.²

Biomarker advances are transforming diagnostic confidence

A major development of the past five years is the increasing maturity of blood-based biomarkers for Alzheimer’s disease and related dementias. Plasma phosphorylated tau (ptau) isoforms, neurofilament light (NfL) and glial fibrillary acidic protein (GFAP) are demonstrating improved diagnostic performance in research and specialist practice.³^,⁴ Although these biomarkers are not yet uniformly integrated into routine clinical pathways globally, their expanding use enables earlier and more scalable biological characterisation – which increases detected cases without necessarily indicating a true increase in underlying incidence.³^,⁴

Better surveillance and more consistent datasets

Many countries are investing in improved dementia surveillance, linking data across primary care, hospitals and aged-care sectors. These improvements strengthen case capture and reduce undercounting, meaning that increases in recorded YOD cases may in part reflect more complete data rather than changing disease dynamics.⁵

Why YOD may be genuinely increasing

Global burden analyses show sustained growth

Recent analyses using Global Burden of Disease (GBD) data demonstrate substantial increases in the number of dementia cases occurring in adults under 70 years worldwide.¹ Although these increases reflect population growth, demographic change and longer lifespans, the data underscore that YOD remains a significant and growing global health concern with substantial social and economic impacts.¹

Midlife risk profiles may be shifting

Contrary to earlier assumptions that young-onset dementia is predominantly genetic, contemporary research shows a diverse range of risk factors. A large UK Biobank cohort study identified associations between incident YOD and cardiometabolic disease (including stroke, diabetes and heart disease), depression, socioeconomic disadvantage, social isolation, inflammatory biomarkers (such as C-reactive protein), vitamin D deficiency, hearing impairment and alcohol use disorder.² These findings expand our understanding of risk and highlight that modifiable exposures during midlife may play an important role in determining earlier vulnerability.

Alcohol-related risk as a critical contributor

The relationship between alcohol and cognitive decline is complex, but the same UK Biobank analysis found a clear association between alcohol use disorder and higher YOD risk.² Given global patterns of harmful alcohol consumption in some populations, this represents an important and potentially modifiable contributor to early cognitive decline.

A fresh angle: what brain ageing research is revealing

One of the most innovative developments in recent neuroscience is the use of biological brain age modelling to understand early neurodegenerative vulnerability. Brain-age models, powered by neuroimaging and machine learning, attempt to estimate the biological age of the brain relative to chronological age. A higher brain age gap may indicate accelerated ageing processes that precede clinical symptoms.⁶^,⁷

Multimodal MRI studies show that incorporating both structural and functional imaging improves brain age prediction accuracy, suggesting that ageing trajectories differ meaningfully between individuals.⁶ Complementary research using diffusion MRI demonstrates that microstructural measures may reveal subtler early changes that conventional imaging misses, potentially offering earlier insights into neurodegenerative processes.⁸

Although brain-age models are not yet clinical diagnostic tools, they illustrate the future direction of dementia science: earlier detection, risk stratification and personalised monitoring enabled by quantitative biomarkers.⁷^,⁸

What this means for clinicians and informed consumers

Current evidence suggests that rising recognition and true changes in risk both contribute to the observed increase in young-onset dementia. For clinical practice, several implications emerge:

• Recognition is improving, driven by heightened awareness of atypical presentations and expanding biomarker capabilities.³^,⁴
• Risk is multifactorial, extending beyond genetics to include cardiometabolic, psychosocial, inflammatory and lifestyle-related factors.²
• The global burden is increasing, underscoring the need for early assessment and robust care pathways, especially for younger adults experiencing progressive cognitive or behavioural change.¹
• Clinicians should maintain vigilance when younger adults present with symptoms that are persistent, progressive or functionally impairing, using structured assessment, collateral history, substance and medication review, mental health evaluation and timely referral pathways.

This article provides general educational information only. It does not offer medical advice or make therapeutic claims. All clinical decisions should be based on individual assessment and current practice guidelines.

References

1. Li Z, Yang Y, Liu Y, et al. Global burden of dementia in younger people: an analysis of data from the 2021 Global Burden of Disease Study. eClinicalMedicine. 2024;77:102868. https://doi.org/10.1016/j.eclinm.2024.102868
2. Hendriks S, Ranson JM, Peetoom K, et al. Risk factors for young-onset dementia in the UK Biobank. JAMA Neurol. 2024;81(2):134–142. https://jamanetwork.com/journals/jamaneurology/fullarticle/2813439
3. Hansson O, Blennow K, Zetterberg H, Dage JL. Blood biomarkers for Alzheimer’s disease in clinical practice and trials. Nature Aging. 2023. https://www.nature.com/articles/s43587-023-00403-3
4. Dark HE, Duggan MR, Walker KA. Plasma biomarkers for Alzheimer’s and related dementias: a review and outlook for clinical neuropsychology. Arch Clin Neuropsychol. 2024;39(3):313–324. https://doi.org/10.1093/arclin/acae019
5. Australian Government Department of Health and Aged Care. Updated Dementia in Australia report and National Dementia Data Improvement Plan 2023–2033 released. 2023. https://www.health.gov.au/news/updated-dementia-in-australia-report-and-national-dementia-data-improvement-plan-2023-2033-released
6. Guan S, Jiang R, Meng C, Biswal B. Brain age prediction across the human lifespan using multimodal MRI data. GeroScience. 2024. https://link.springer.com/article/10.1007/s11357-023-00924-0
7. Azzam M, Xu Z, Liu R, et al. A review of artificial intelligence-based brain age estimation and its applications for related diseases. Briefings in Functional Genomics. 2024. https://doi.org/10.1093/bfgp/elae042
8. Gao C, Kim ME, Ramadass K, et al. Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease. Imaging Neuroscience. 2025;3:imag_a_00552. https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00552/128745