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- Shi, Liu, et al.
(författare)
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Discovery and validation of plasma proteomic biomarkers relating to brain amyloid burden by SOMAscan assay.
- 2019
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Ingår i: Alzheimer's & dementia : the journal of the Alzheimer's Association. - : Wiley. - 1552-5279 .- 1552-5260. ; 15:11, s. 1478-1488
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Tidskriftsartikel (refereegranskat)abstract
- Plasma proteins have been widely studied as candidate biomarkers to predict brain amyloid deposition to increase recruitment efficiency in secondary prevention clinical trials for Alzheimer's disease. Most such biomarker studies are targeted to specific proteins or are biased toward high abundant proteins.4001 plasma proteins were measured in two groups of participants (discovery group=516, replication group=365) selected from the European Medical Information Framework for Alzheimer's disease Multimodal Biomarker Discovery study, all of whom had measures of amyloid.A panel of proteins (n=44), along with age and apolipoprotein E (APOE) ε4, predicted brain amyloid deposition with good performance in both the discovery group (area under the curve=0.78) and the replication group (area under the curve=0.68). Furthermore, a causal relationship between amyloid and tau was confirmed by Mendelian randomization.The results suggest that high-dimensional plasma protein testing could be a useful and reproducible approach for measuring brain amyloid deposition.
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| 2. |
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| 3. |
- Jekel, Katrin, et al.
(författare)
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Mild cognitive impairment and deficits in instrumental activities of daily living: a systematic review.
- 2015
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Ingår i: Alzheimer's Research & Therapy. - : Springer Science and Business Media LLC. - 1758-9193. ; 7:1
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Forskningsöversikt (refereegranskat)abstract
- Introduction: There is a growing body of evidence that subtle deficits in instrumental activities of daily living (IADL) may be present in mild cognitive impairment (MCI). However, it is not clear if there are IADL domains that are consistently affected across patients with MCI. In this systematic review, therefore, we aimed to summarize research results regarding the performance of MCI patients in specific IADL (sub)domains compared with persons who are cognitively normal and/or patients with dementia. Methods: The databases PsycINFO, PubMed and Web of Science were searched for relevant literature in December 2013. Publications from 1999 onward were considered for inclusion. Altogether, 497 articles were retrieved. Reference lists of selected articles were searched for potentially relevant articles. After screening the abstracts of these 497 articles, 37 articles were included in this review. Results: In 35 studies, IADL deficits (such as problems with medication intake, telephone use, keeping appointments, finding things at home and using everyday technology) were documented in patients with MCI. Financial capacity in patients with MCI was affected in the majority of studies. Effect sizes for group differences between patients with MCI and healthy controls were predominantly moderate to large. Performance-based instruments showed slight advantages (in terms of effect sizes) in detecting group differences in IADL functioning between patients with MCI, patients with Alzheimer’s disease and healthy controls. Conclusion: IADL requiring higher neuropsychological functioning seem to be most severely affected in patients with MCI. A reliable identification of such deficits is necessary, as patients with MCI with IADL deficits seem to have a higher risk of converting to dementia than patients with MCI without IADL deficits. The use of assessment tools specifically designed and validated for patients with MCI is therefore strongly recommended. Furthermore, the development of performance-based assessment instruments should be intensified, as they allow a valid and reliable assessment of subtle IADL deficits in MCI, even if a proxy is not available. Another important point to consider when designing new scales is the inclusion of technology-associated IADL. Novel instruments for clinical practice should be time-efficient and easy to administer.
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| 4. |
- Kim, Min, et al.
(författare)
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Primary fatty amides in plasma associated with brain amyloid burden, hippocampal volume, and memory in the European Medical Information Framework for Alzheimer's Disease biomarker discovery cohort
- 2019
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Ingår i: Alzheimer's & Dementia. - : Elsevier. - 1552-5260 .- 1552-5279. ; 15:6, s. 817-827
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Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTION: A critical and as-yet unmet need in Alzheimer's disease (AD) is the discovery of peripheral small molecule biomarkers. Given that brain pathology precedes clinical symptom onset, we set out to test whether metabolites in blood associated with pathology as indexed by cerebrospinal fluid (CSF) AD biomarkers.METHODS: This study analyzed 593 plasma samples selected from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, of individuals who were cognitively healthy (n = 242), had mild cognitive impairment (n = 236), or had AD-type dementia (n = 115). Logistic regressions were carried out between plasma metabolites (n = 883) and CSF markers, magnetic resonance imaging, cognition, and clinical diagnosis.RESULTS: Eight metabolites were associated with amyloid β and one with t-tau in CSF, these were primary fatty acid amides (PFAMs), lipokines, and amino acids. From these, PFAMs, glutamate, and aspartate also associated with hippocampal volume and memory.DISCUSSION: PFAMs have been found increased and associated with amyloid β burden in CSF and clinical measures.
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| 5. |
- Reijs, Babette L R, et al.
(författare)
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Association Between Later Life Lifestyle Factors and Alzheimer's Disease Biomarkers in Non-Demented Individuals : A Longitudinal Descriptive Cohort Study
- 2017
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Ingår i: Journal of Alzheimer's Disease. - : IOS Press. - 1387-2877 .- 1875-8908. ; 60:4, s. 1387-1395
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Lifestyle factors have been associated with the risk of dementia, but the association with Alzheimer's disease (AD) remains unclear.OBJECTIVE: To examine the association between later life lifestyle factors and AD biomarkers (i.e., amyloid-β 1-42 (Aβ42) and tau in cerebrospinal fluid (CSF), and hippocampal volume) in individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). In addition, to examine the effect of later life lifestyle factors on developing AD-type dementia in individuals with MCI.METHODS: We selected individuals with SCD (n = 111) and MCI (n = 353) from the DESCRIPA and Kuopio Longitudinal MCI studies. CSF Aβ42 and tau concentrations were assessed with ELISA assay and hippocampal volume with multi-atlas segmentation. Lifestyle was assessed by clinical interview at baseline for: social activity, physical activity, cognitive activity, smoking, alcohol consumption, and sleep. We performed logistic and Cox regression analyses adjusted for study site, age, gender, education, and diagnosis. Prediction for AD-type dementia was performed in individuals with MCI only.RESULTS: Later life lifestyle factors were not associated with AD biomarkers or with conversion to AD-type dementia. AD biomarkers were strongly associated with conversion to AD-type dementia, but these relations were not modulated by lifestyle factors. Apolipoprotein E (APOE) genotype did not influence the results.CONCLUSIONS: Later life lifestyle factors had no impact on key AD biomarkers in individuals with SCD and MCI or on conversion to AD-type dementia in MCI.
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| 6. |
- Stamate, Daniel, et al.
(författare)
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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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Ingår i: Alzheimer’s & Dementia. - : John Wiley & Sons. - 2352-8737. ; 5:C, s. 933-938
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Tidskriftsartikel (refereegranskat)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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