Metabolomic and lipidomic signatures in autosomal dominant and late-onset Alzheimer's disease brains

Brenna C Novotny; Maria Victoria Fernandez; Ciyang Wang; John P Budde; Kristy Bergmann; Abdallah M Eteleeb; Joseph Bradley; Carol Webster; Curtis Ebl; Joanne Norton; Jen Gentsch; Umber Dube; Fengxian Wang; John C Morris; Randall J Bateman; Richard J Perrin; Eric McDade; Chengjie Xiong; Jasmeer Chhatwal; Dominantly Inherited Alzheimer Network (DIAN) Study Group; Alzheimer's Disease Neuroimaging Initiative; Alzheimer's Disease Metabolomics Consortium (ADMC); Alison Goate; Martin Farlow; Peter Schofield; Helena Chui; Celeste M Karch; Carlos Cruchaga; Bruno A Benitez; Oscar Harari

doi:10.1002/alz.12800

Metabolomic and lipidomic signatures in autosomal dominant and late-onset Alzheimer's disease brains

Alzheimers Dement. 2023 May;19(5):1785-1799. doi: 10.1002/alz.12800. Epub 2022 Oct 17.

Authors

Brenna C Novotny^{1

2

3}, Maria Victoria Fernandez^{1

2

3}, Ciyang Wang^{1

2

3

4}, John P Budde^{1

2

3}, Kristy Bergmann^{1

2

3}, Abdallah M Eteleeb^{1

2

3}, Joseph Bradley⁴, Carol Webster^{1

2

3}, Curtis Ebl^{1

2

3}, Joanne Norton^{1

2

3}, Jen Gentsch^{1

2

3}, Umber Dube¹, Fengxian Wang^{1

2

3}, John C Morris^{2

5

6}, Randall J Bateman^{2

5

6}, Richard J Perrin^{2

5

6

7}, Eric McDade⁶, Chengjie Xiong⁵, Jasmeer Chhatwal⁸; Dominantly Inherited Alzheimer Network (DIAN) Study Group; Alzheimer's Disease Neuroimaging Initiative⁹; Alzheimer's Disease Metabolomics Consortium (ADMC)¹⁰; Alison Goate¹¹, Martin Farlow¹², Peter Schofield¹³, Helena Chui¹⁴, Celeste M Karch^{1

2

3}, Carlos Cruchaga^{1

2

3

5}, Bruno A Benitez¹⁵, Oscar Harari^{1

2

3

5}

Affiliations

¹ Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA.
² Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, Missouri, USA.
³ NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, Missouri, USA.
⁴ Division of Biology & Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri, USA.
⁵ The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, USA.
⁶ Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA.
⁷ Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA.
⁸ Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
⁹ Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
¹⁰ Data used in the preparation of this article were generated by the Alzheimer's Disease Metabolomics Consortium (ADMC). As such, the investigators within the ADMC provided data but did not participate in the analysis or writing of this report. A complete listing of ADMC investigators can be found at: https://sites.duke.edu/adnimetab/team/.
¹¹ Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹² Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
¹³ Neuroscience Research Australia, Randwick, Sydney, NSW, Australia.
¹⁴ Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
¹⁵ Department of Neurology, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.

Abstract

Introduction: The identification of multiple genetic risk factors for Alzheimer's disease (AD) suggests that many pathways contribute to AD onset and progression. However, the metabolomic and lipidomic profiles in carriers of distinct genetic risk factors are not fully understood. The metabolome can provide a direct image of dysregulated pathways in the brain.

Methods: We interrogated metabolomic signatures in the AD brain, including carriers of pathogenic variants in APP, PSEN1, and PSEN2 (autosomal dominant AD; ADAD), APOE ɛ4, and TREM2 risk variant carriers, and sporadic AD (sAD).

Results: We identified 133 unique and shared metabolites associated with ADAD, TREM2, and sAD. We identified a signature of 16 metabolites significantly altered between groups and associated with AD duration.

Discussion: AD genetic variants show distinct metabolic perturbations. Investigation of these metabolites may provide greater insight into the etiology of AD and its impact on clinical presentation.

Highlights: APP/PSEN1/PSEN2 and TREM2 variant carriers show distinct metabolic changes. A total of 133 metabolites were differentially abundant in AD genetic groups. β-citrylglutamate is differentially abundant in autosomal dominant, TREM2, and sporadic AD. A 16-metabolite profile shows differences between Alzheimer's disease (AD) genetic groups. The identified metabolic profile is associated with duration of disease.

Keywords: APOE; APP; PSEN1; PSEN2; TREM2; autosomal dominant Alzheimer's disease; lipidomics; metabolomics; β-citrylglutamate.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Alzheimer Disease* / genetics
Alzheimer Disease* / pathology
Amyloid beta-Protein Precursor / genetics
Brain / pathology
Heterozygote
Humans
Lipidomics
Mutation
Presenilin-1 / genetics

Substances

Amyloid beta-Protein Precursor
Presenilin-1

Abstract

Publication types

MeSH terms

Substances

Grants and funding