High coffee consumption, brain volume and risk of dementia and stroke

ABSTRACT

Background

Coffee is a highly popular beverage worldwide, containing caffeine which is a central nervous system stimulant.

Objectives

We examined whether habitual coffee consumption is associated with differences in brain volumes or the odds of dementia or stroke.

Methods

We conducted prospective analyses of habitual coffee consumption on 398,646 UK Biobank participants (age 37–73 years), including 17,702 participants with MRI information. We examined the associations with brain volume using covariate adjusted linear regression, and with odds of dementia (4,333 incident cases) and stroke (6,181 incident cases) using logistic regression.

Results

There were inverse linear associations between habitual coffee consumption and total brain (fully adjusted β per cup −1.42, 95% CI −1.89, −0.94), grey matter (β −0.91, 95% CI −1.20, −0.62), white matter (β −0.51, 95% CI −0.83, −0.19) and hippocampal volumes (β −0.01, 95% CI −0.02, −0.003), but no evidence to support an association with white matter hyperintensity (WMH) volume (β −0.01, 95% CI −0.07, 0.05). The association between coffee consumption and dementia was non-linear (P_{non-linearity}= 0.0001), with evidence for higher odds for non-coffee and decaffeinated coffee drinkers and those drinking >6 cups/day, compared to light coffee drinkers. After full covariate adjustment, consumption of >6 cups/day was associated with 53% higher odds of dementia compared to consumption of 1–2 cups/day (fully adjusted OR 1.53, 95% CI 1.28, 1.83), with less evidence for an association with stroke (OR 1.17, 95% CI 1.00, 1.37, p= 0.055).

Conclusion

High coffee consumption was associated with smaller total brain volumes and increased odds of dementia.

KEYWORDS:

• Coffee
• brain volume
• dementia
• stroke
• volumetric MRI
• UK Biobank
• prospective cohort

Data availability statement

All data will be available to approved users of the UK Biobank upon application.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

The author’s responsibilities were as follows – KP analysed data and prepared the first draft; AM, AZ advised on analyses, analysed the data, and drafted the manuscript; EH conceptualised, funded, and supervised the study and drafted the manuscript; JTO and DL along with all other authors interpreted the results, revised the paper and approved the final manuscript.

Additional information

Funding

This work was supported by the National Health and Medical Research Council under Grants GNT1157281 and GNT1123603.

Notes on contributors

Kitty Pham

Kitty Pham is a current PhD candidate with the Australian Centre for Precision Health, Clinical & Health Sciences, University of South Australia. Her background is in medical radiation. She is currently registered as a Medical Radiation Practitioner (Radiation Therapist). Her PhD research project investigates the impacts of serum lipids and dietary fats on brain health outcomes. She is interested in MRI neuroimaging indicators for dementia disease risk and the application of large population cohort data to clinical settings.

Anwar Mulugeta

Anwar Mulugeta is a Research Associate at Nutritional and Genetic Epidemiology Research Group and Steering committee member at the Australian Centre for Precision Health. He has training in Genetic Epidemiology, Medical Pharmacology and Pharmacy. He is currently affiliated with Department of Pharmacology and Clinical Pharmacy, Addis Ababa University (AAU), Ethiopia. He previously worked as Clinical Trial Coordinator for AAU and Massachusetts General Hospital collaborative project. He focuses on understanding the contributions of genetics, environment and the interplay between them on disease risk including cognitive impairment, dementia, depression, and other health outcomes. He applies various genetic and bioinformatic approaches including mendelian randomizations, phenome-wide associations, genome-wide associations, gene-environment interactions, and machine learning.

Ang Zhou

Ang Zhou has training in medical sciences and biostatistics. He is working as a research associate in the nutritional and genetic epidemiology group at the Australian Centre for Precision Health. Ang applies methodologies in genetic epidemiology to understand causal associations between modifiable lifestyle factors and health outcomes related to cognitive and cardiometabolic functions. His research also involves understanding gene-environment interplay on health outcomes.

John T. O’Brien

John T O’Brien is a Professor at the Department of Psychiatry at the University of Cambridge. His research interests include the role of biomarkers, especially MRI, SPECT and PET imaging, in the differential and early diagnosis of dementia, including identifying those ‘at risk’ of future cognitive decline and developing markers of onset and progression of disease. He is interested in late-life depression, especially the role of vascular and inflammatory factors in precipitating and perpetuating depression in late-life, and the ability of vascular interventions to improve or prevent depressive and cognitive symptoms. Professor O’Brien is also the NIHR Clinical Research Network National Specialty Lead for Dementia and a member of several clinical guideline groups which undertake a number of multimodal MR, ligand PET and MEG studies, longitudinal clinical and e-record studies, and a number of clinical studies in dementia, including trials of pharmacological and non-pharmacological management.

David J. Llewellyn

David J Llewellyn is a Professor at the University of Exeter Medical School and a Fellow at the Alan Turing Institute. He also holds an honorary contract with Devon Partnership NHS Trust. He has advanced training in epidemiology and data science. His research aims to enhance the timely detection of dementia, with a focus on developing strategies for primary and secondary prevention. He uses a combination of evidence synthesis, data science and machine learning to develop new translational insights to identify more effective interventions and enhance the diagnostic pathway for dementia. He is an expert on the evaluation of cognitive function and dementia and is a member of the scientific advisory boards of the English Longitudinal Study of Ageing and the UCL Centre for Longitudinal Studies. He sits on Alzheimer’s Research UK’s Grant Review Board and their Clinical Policy Advisory Panel. David is also the Exeter Institute for Data Science and Artificial Intelligence Clinical Theme Lead and the Turing Exeter University Clinical Lead. He sits on the Steering Committee and leads the Clinical Advisory Group of the Early Detection of Neurodegeneration Initiative and is Director of the DEMON Network.

Elina Hyppönen

Elina Hyppönen is the Director of the Australian Centre for Precision Health and a Professor in Nutritional and Genetic Epidemiology at the University of South Australia. She is also Senior Principal Research Fellow at the South Australian Health and Medical Research Instiute, Honorary Professor in Epidemiology at University College London, and Adjunct Professor at Tampere University. Professor Hyppönen has an interdisciplinary academic background, with academic qualifications in epidemiology, medical statistics, nutrition and public health. She leads the Nutritional and Genetic Epidemiology group which has a focus on using genetic tools to inform on dietary and lifestyle guidelines for optimal health. She has a long-term research interest in life-course and intergenerational epidemiology, and an extensive track record in gene and risk factor discovery. Her current interests are related to implementing phenome wide analyses and systems epidemiology approaches to establish effective strategies for disease prediction and prevention.