Comparison of plasma and neuroimaging biomarkers to predict cognitive decline in non-demented memory clinic patients

Augusto J Mendes; Federica Ribaldi; Aurelien Lathuiliere; Nicholas J Ashton; Henrik Zetterberg; Marc Abramowicz; Max Scheffler; Frédéric Assal; Valentina Garibotto; Kaj Blennow; Giovanni B Frisoni

doi:10.1186/s13195-024-01478-9

Comparison of plasma and neuroimaging biomarkers to predict cognitive decline in non-demented memory clinic patients

Alzheimers Res Ther. 2024 May 16;16(1):110. doi: 10.1186/s13195-024-01478-9.

Authors

Augusto J Mendes^{1

2}, Federica Ribaldi^{3

4}, Aurelien Lathuiliere^{3

4}, Nicholas J Ashton^{5

6

7

8}, Henrik Zetterberg^{5

9

10

11

12

13}, Marc Abramowicz¹⁴, Max Scheffler¹⁵, Frédéric Assal¹⁶, Valentina Garibotto^{17

18

19}, Kaj Blennow²⁰, Giovanni B Frisoni^{3

4}

Affiliations

¹ Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland. augusto.martinsmendes@unige.ch.
² Geneva Memory Center, Department of Rehabilitation and Geriatrics,, Geneva University Hospitals, Geneva, Switzerland. augusto.martinsmendes@unige.ch.
³ Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland.
⁴ Geneva Memory Center, Department of Rehabilitation and Geriatrics,, Geneva University Hospitals, Geneva, Switzerland.
⁵ Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.
⁶ King's College London, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK.
⁷ NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK.
⁸ Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway.
⁹ Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.
¹⁰ Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK.
¹¹ UK Dementia Research Institute at UCL, London, UK.
¹² Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China.
¹³ Wisconsin Alzheimer?s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
¹⁴ Genetic Medicine, Diagnostics Dept, University Hospitals and University of Geneva, Geneva, Switzerland.
¹⁵ Division of Radiology, Geneva University Hospitals, Geneva, Switzerland.
¹⁶ Division of Neurology, Department of Clinical Neurosciences, Faculty of Medicine, Geneva University Hospitals, University of Geneva, Geneva, Switzerland.
¹⁷ Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland.
¹⁸ Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland.
¹⁹ CIBM Center for Biomedical Imaging, Geneva, Switzerland.
²⁰ Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden.

Abstract

Background: Plasma biomarkers of Alzheimer's disease (AD) pathology, neurodegeneration, and neuroinflammation are ideally suited for secondary prevention programs in self-sufficient persons at-risk of dementia. Plasma biomarkers have been shown to be highly correlated with traditional imaging biomarkers. However, their comparative predictive value versus traditional AD biomarkers is still unclear in cognitively unimpaired (CU) subjects and with mild cognitive impairment (MCI).

Methods: Plasma (Aβ42/40, p-tau181, p-tau231, NfL, and GFAP) and neuroimaging (hippocampal volume, centiloid of amyloid-PET, and tau-SUVR of tau-PET) biomarkers were assessed at baseline in 218 non-demented subjects (CU = 140; MCI = 78) from the Geneva Memory Center. Global cognition (MMSE) was evaluated at baseline and at follow-ups up to 5.7 years. We used linear mixed-effects models and Cox proportional-hazards regression to assess the association between biomarkers and cognitive decline. Lastly, sample size calculations using the linear mixed-effects models were performed on subjects positive for amyloid-PET combined with tau-PET and plasma biomarker positivity.

Results: Cognitive decline was significantly predicted in MCI by baseline plasma NfL (β=-0.55), GFAP (β=-0.36), hippocampal volume (β = 0.44), centiloid (β=-0.38), and tau-SUVR (β=-0.66) (all p < 0.05). Subgroup analysis with amyloid-positive MCI participants also showed that only NfL and GFAP were the only significant predictors of cognitive decline among plasma biomarkers. Overall, NfL and tau-SUVR showed the highest prognostic values (hazard ratios of 7.3 and 5.9). Lastly, we demonstrated that adding NfL to the inclusion criteria could reduce the sample sizes of future AD clinical trials by up to one-fourth in subjects with amyloid-PET positivity or by half in subjects with amyloid-PET and tau-PET positivity.

Conclusions: Plasma NfL and GFAP predict cognitive decline in a similar manner to traditional imaging techniques in amyloid-positive MCI patients. Hence, even though they are non-specific biomarkers of AD, both can be implemented in memory clinic workups as important prognostic biomarkers. Likewise, future clinical trials might employ plasma biomarkers as additional inclusion criteria to stratify patients at higher risk of cognitive decline to reduce sample sizes and enhance effectiveness.

Publication types

Comparative Study

MeSH terms

Aged
Amyloid beta-Peptides* / blood
Biomarkers* / blood
Cognitive Dysfunction* / blood
Cognitive Dysfunction* / diagnostic imaging
Female
Glial Fibrillary Acidic Protein / blood
Hippocampus / diagnostic imaging
Hippocampus / pathology
Humans
Male
Middle Aged
Neurofilament Proteins / blood
Neuroimaging / methods
Peptide Fragments / blood
Positron-Emission Tomography*
tau Proteins* / blood

Substances

GFAP protein, human
neurofilament protein L