| 11. |
- Stamate, Daniel, et al.
(author)
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A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood : Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort
- 2019
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In: Alzheimer’s & Dementia. - : John Wiley & Sons. - 2352-8737. ; 5:C, s. 933-938
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Journal article (peer-reviewed)abstract
- IntroductionMachine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers.MethodsThis study analyzed samples from 242 cognitively normal (CN) people and 115 with AD‐type dementia utilizing plasma metabolites (n = 883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV).ResultsOn the test data, DL produced the AUC of 0.85 (0.80–0.89), XGBoost produced 0.88 (0.86–0.89) and RF produced 0.85 (0.83–0.87). By comparison, CSF measures of amyloid, p‐tau and t‐tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively.DiscussionThis study showed that plasma metabolites have the potential to match the AUC of well‐established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders.
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| 12. |
- Westwood, Sarah, et al.
(author)
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The influence of insulin resistance on cerebrospinal fluid and plasma biomarkers of Alzheimer's pathology.
- 2017
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In: Alzheimer's research & therapy. - : Springer Science and Business Media LLC. - 1758-9193. ; 9:1
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Journal article (peer-reviewed)abstract
- Insulin resistance (IR) has previously been associated with an increased risk of developing Alzheimer's disease (AD), although the relationship between IR and AD is not yet clear. Here, we examined the influence of IR on AD using plasma and cerebrospinal fluid (CSF) biomarkers related to IR and AD in cognitively healthy men. We also aimedto characterise the shared protein signatures between IR and AD.Fifty-eight cognitively healthy men, 28 IR and 30 non-IR (age and APOE ε4 matched), were drawn from the Metabolic Syndrome in Men study in Kuopio, Finland. CSF AD biomarkers (amyloid β-peptide (Aβ), total tau and tau phosphorylated at the Thr181 epitope) were examined with respect to IR. Targeted proteomics using ELISA and Luminex xMAP assays were performed to assess the influence of IR on previously identified CSF and plasma protein biomarker candidates of AD pathology. Furthermore, CSF and plasma SOMAscan was performed to discover proteins that associate with IR and CSF AD biomarkers.CSF AD biomarkers did not differ between IR and non-IR groups, although plasma insulin correlated with CSF Aβ/tau across the whole cohort. In total, 200 CSF and 487 plasma proteins were differentially expressed between IR and non-IR subjects, and significantly enriched pathways, many of which have been previously implicated in AD, were identified. CSF and plasma proteins significantly associated with CSF AD biomarkers were also discovered, and those sensitive to both IR and AD were identified.These data indicate that IR is not directly related to the level of CSF AD pathology in cognitively healthy men. Proteins that associated with both AD and IR are potential markers indicative of shared pathology.
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| 13. |
- Xu, Jin, et al.
(author)
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Sex-Specific Metabolic Pathways Were Associated with Alzheimer's Disease (AD) Endophenotypes in the European Medical Information Framework for AD Multimodal Biomarker Discovery Cohort
- 2021
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In: Biomedicines. - : MDPI. - 2227-9059. ; 9:11
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Journal article (peer-reviewed)abstract
- BACKGROUND: physiological differences between males and females could contribute to the development of Alzheimer's Disease (AD). Here, we examined metabolic pathways that may lead to precision medicine initiatives.METHODS: We explored whether sex modifies the association of 540 plasma metabolites with AD endophenotypes including diagnosis, cerebrospinal fluid (CSF) biomarkers, brain imaging, and cognition using regression analyses for 695 participants (377 females), followed by sex-specific pathway overrepresentation analyses, APOE ε4 stratification and assessment of metabolites' discriminatory performance in AD.RESULTS: In females with AD, vanillylmandelate (tyrosine pathway) was increased and tryptophan betaine (tryptophan pathway) was decreased. The inclusion of these two metabolites (area under curve (AUC) = 0.83, standard error (SE) = 0.029) to a baseline model (covariates + CSF biomarkers, AUC = 0.92, SE = 0.019) resulted in a significantly higher AUC of 0.96 (SE = 0.012). Kynurenate was decreased in males with AD (AUC = 0.679, SE = 0.046).CONCLUSIONS: metabolic sex-specific differences were reported, covering neurotransmission and inflammation pathways with AD endophenotypes. Two metabolites, in pathways related to dopamine and serotonin, were associated to females, paving the way to personalised treatment.
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