Dataset Information

Interpreting Biomarker Results in Individual Patients With Mild Cognitive Impairment in the Alzheimer's Biomarkers in Daily Practice (ABIDE) Project.

ABSTRACT:

Importance

Biomarkers do not determine conversion to Alzheimer disease (AD) perfectly, and criteria do not specify how to take patient characteristics into account. Consequently, biomarker use may be challenging for clinicians, especially in patients with mild cognitive impairment (MCI).

Objective

To construct biomarker-based prognostic models that enable determination of future AD dementia in patients with MCI.

Design, setting, and participants

This study is part of the Alzheimer's Biomarkers in Daily Practice (ABIDE) project. A total of 525 patients with MCI from the Amsterdam Dementia Cohort (longitudinal cohort, tertiary referral center) were studied. All patients had their baseline visit to a memory clinic from September 1, 1997, through August 31, 2014. Prognostic models were constructed by Cox proportional hazards regression with patient characteristics (age, sex, and Mini-Mental State Examination [MMSE] score), magnetic resonance imaging (MRI) biomarkers (hippocampal volume, normalized whole-brain volume), cerebrospinal fluid (CSF) biomarkers (amyloid-?1-42, tau), and combined biomarkers. Data were analyzed from November 1, 2015, to October 1, 2016.

Main outcomes and measures

Clinical end points were AD dementia and any type of dementia after 1 and 3 years.

Results

Of the 525 patients, 210 (40.0%) were female, and the mean (SD) age was 67.3?(8.4) years. On the basis of age, sex, and MMSE score only, the 3-year progression risk to AD dementia ranged from 26% (95% CI, 19%-34%) in younger men with MMSE scores of 29 to 76% (95% CI, 65%-84%) in older women with MMSE scores of 24 (1-year risk: 6% [95% CI, 4%-9%] to 24% [95% CI, 18%-32%]). Three- and 1-year progression risks were 86% (95% CI, 71%-95%) and 27% (95% CI, 17%-41%) when MRI results were abnormal, 82% (95% CI, 73%-89%) and 26% (95% CI, 20%-33%) when CSF test results were abnormal, and 89% (95% CI, 79%-95%) and 26% (95% CI, 18%-36%) when the results of both tests were abnormal. Conversely, 3- and 1-year progression risks were 18% (95% CI, 13%-27%) and 3% (95% CI, 2%-5%) after normal MRI results, 6% (95% CI, 3%-9%) and 1% (95% CI, 0.5%-2%) after normal CSF test results, and 4% (95% CI, 2%-7%) and 0.5% (95% CI, 0.2%-1%) after combined normal MRI and CSF test results. The prognostic value of models determining any type of dementia were in the same order of magnitude although somewhat lower. External validation in Alzheimer's Disease Neuroimaging Initiative 2 showed that our models were highly robust.

Conclusions and relevance

This study provides biomarker-based prognostic models that may help determine AD dementia and any type of dementia in patients with MCI at the individual level. This finding supports clinical decision making and application of biomarkers in daily practice.

SUBMITTER: van Maurik IS

PROVIDER: S-EPMC5822193 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Publications

Interpreting Biomarker Results in Individual Patients With Mild Cognitive Impairment in the Alzheimer's Biomarkers in Daily Practice (ABIDE) Project.

van Maurik Ingrid S IS Zwan Marissa D MD Tijms Betty M BM Bouwman Femke H FH Teunissen Charlotte E CE Scheltens Philip P Wattjes Mike P MP Barkhof Frederik F Berkhof Johannes J van der Flier Wiesje M WM

JAMA neurology 20171201 12

<h4>Importance</h4>Biomarkers do not determine conversion to Alzheimer disease (AD) perfectly, and criteria do not specify how to take patient characteristics into account. Consequently, biomarker use may be challenging for clinicians, especially in patients with mild cognitive impairment (MCI).<h4>Objective</h4>To construct biomarker-based prognostic models that enable determination of future AD dementia in patients with MCI.<h4>Design, setting, and participants</h4>This study is part of the Al ...[more]

PMID: 29049480

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Project description:<h4><strong>INTRODUCTION </strong>Alzheimer's disease is a prevalent disease with a heavy global burden and is suggested to be a metabolic disease in the brain in recent years. The metabolome is considered to be the most promising phenotype which reflects changes in genetic, transcript, and protein profiles as well as environmental effects. Aiming to obtain a comprehensive understanding and convenient diagnosis of MCI and AD from another perspective, researchers are working on AD metabolomics. Urine is more convenient which could reflect the change of disease at an earlier stage. Thus, we conducted a cross-sectional study to investigate novel diagnostic panels.</h4><p><strong>METHODS </strong>We first enrolled participants from China-Japan Friendship Hospital from April 2022 to November 2022, collected urine samples and conducted an LC-MS/MS analysis. In parallel, clinical data were collected and clinical examinations were performed. After statistical and bioinformatics analyzes, significant risk factors and differential urinary metabolites were determined. We attempt to investigate diagnostic panels based on machine learning including LASSO and SVM.</p><p><strong>RESULTS </strong>57 AD patients, 43 MCI patients and 62 CN subjects were enrolled. A total of 2,140 metabolites were identified among which 125 significantly differed between the AD and CN groups, including 46 upregulated ones and 79 downregulated ones. In parallel, there were 93 significant differential metabolites between the MCI and CN groups, including 23 upregulated ones and 70 downregulated ones. AD diagnostic panel (30 metabolites+ age + APOE) achieved an AUC of 0.9575 in the test set while MCI diagnostic panel (45 metabolites+ age + APOE) achieved an AUC of 0.7333 in the test set. Atropine, S-Methyl-L-cysteine-S-oxide, D-Mannose 6-phosphate (M6P), Spiculisporic Acid, N-Acetyl-L-methionine, 13,14-dihydro-15-keto-tetranor Prostaglandin D2, Pyridoxal 5'-Phosphate (PLP) and 17(S)-HpDHA were considered valuable for both AD and MCI diagnosis and defined as hub metabolites. Besides, diagnostic metabolites were weakly correlated with cognitive functions.</p><p><strong>DISCUSSION </strong>In conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN. Atropine, M6P and PLP were evidence-based hub metabolites in AD.</p>

Project description:BackgroundMalnutrition is common in patients with Alzheimer's disease (AD) dementia and mild cognitive impairment (MCI) and is associated with institutionalization and increased mortality. Malnutrition is the result of a negative energy balance, which could be due to reduced dietary intake and/or higher energy expenditure. To study underlying mechanisms for malnutrition, we investigated dietary intake and resting energy expenditure (REE) of patients with AD dementia, MCI, and controls. In addition, we studied associations of global cognition (Mini-Mental State Examination (MMSE)) and AD biomarkers with dietary intake and REE.MethodsWe included 219 participants from the NUDAD project, 71 patients with AD dementia (age 68 ± 8 years, 58% female, MMSE 24 ± 3), 52 with MCI (67 ± 8 years, 42% female, MMSE 26 ± 2), and 96 controls (62 ± 7 years, 52% female, MMSE 28 ± 2). We used a 238-item food frequency questionnaire to assess dietary intake (energy, protein, carbohydrate, and fat). In a subgroup of 92 participants (30 patients with AD dementia, 22 with MCI, and 40 controls) we measured REE with indirect calorimetry. Between-group differences in dietary intake and REE were tested with ANOVAs. In the total sample, linear regression analyses were used to explore potential associations of MMSE score and AD biomarkers with dietary intake and REE. All analyses were adjusted for age, sex, education, and body mass index or fat-free mass.ResultsPatients with AD dementia and MCI did not differ from controls in total energy intake (1991 ± 71 and 2172 ± 80 vs 2022 ± 61 kcal/day, p > 0.05) nor in protein, carbohydrate, or fat intake. Patients with AD dementia and MCI had a higher REE than controls (1704 ± 41 and 1754 ± 47 vs 1569 ± 34 kcal/day, p < 0.05). We did not find any association of MMSE score or AD biomarkers with dietary intake or REE.ConclusionsWe found a higher REE, despite similar energy intake in patients with AD and MCI compared to controls. These findings suggest that elevated metabolism rather than reduced energy intake explains malnutrition in AD. These results could be useful to optimize dietary advice for patients with AD dementia and MCI.

Dataset Information

Interpreting Biomarker Results in Individual Patients With Mild Cognitive Impairment in the Alzheimer's Biomarkers in Daily Practice (ABIDE) Project.

Importance

Objective

Design, setting, and participants

Main outcomes and measures

Results

Conclusions and relevance

Publications

Interpreting Biomarker Results in Individual Patients With Mild Cognitive Impairment in the Alzheimer's Biomarkers in Daily Practice (ABIDE) Project.

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