Decision tree supports the interpretation of CSF biomarkers in Alzheimer's disease

CSF biomarkers for early-onset Alzheimer's disease in Chinese population from PUMCH dementia cohort

Introduction

Alzheimer's disease (AD) is one of the highly concerned degenerative disorders in recent decades. Though vast amount of researches has been done in various aspects, early-onset subtype, however, needs more investigation in diagnosis for its atypical manifestations and progression process. Fundamental CSF biomarkers of early-onset AD are explored in PUMCH dementia cohort to depict its laboratory characteristics.

Materials and methods

A total of 125 individuals (age of onset <65 years old) from PUMCH dementia cohort were recruited consecutively and classified into AD, non-AD dementia, and control groups. Levels of amyloid-β 42 (Aβ42), total tau (t-tau) and phosphorylated tau (p-tau) were measured using ELISA INNOTEST (Fujirebio, Ghent, Belgium). Students' t-test or non-parametric test are used to evaluate the differences between groups. Area under curve (AUC) of receiver operating characteristic (ROC) curve was introduced to prove the diagnostic powers of corresponding markers. Logistic regression is used to establish diagnostic model to combine several markers together to promote the diagnostic power.

Results

The average of all three biomarkers and two calculated ratios (t-tau/Aβ42, p-tau/Aβ42) were statistically different in the AD group compared with the other two groups (Ps < 0.01). From our data, we were able to provide cutoff values (Aβ42 < 570.9 pg/mL; p-tau > 56.49 pg/mL; t-tau > 241.6 pg/mL; t-tau/Aβ42 > 0.529; p-tau/Aβ42 > 0.0846) with acceptable diagnostic accuracy compared to other studies. Using a combination of biomarkers and logistic regression (area under curve 0.951), we were able to further improve diagnostic efficacy.

Discussion

Our study supports the diagnostic usefulness of biomarkers and defined cutoff values to diagnose early-onset AD. We showed that the ratios of t-tau/Aβ42 and p-tau/Aβ42 are more sensitive than relying on Aβ42 levels alone, and that we can further improve diagnostic accuracy by combining biomarkers.

Cerebrospinal Fluid α-Calcitonin Gene-Related Peptide: A Comparison between Alzheimer's Disease and Multiple Sclerosis

Alzheimer's disease (AD) and Multiple Sclerosis (MS) represent an emerging health problem on a global scale, as they are responsible for a significant contribution to the burden of disability in Western countries. Limited numbers of cerebrospinal fluid (CSF) diagnostic markers are available for each disease (amyloid and tau deposition markers for AD and oligoclonal bands for MS) representing mostly state markers that provide few, if any, clues about the severity of the clinical phenotype. α-CGRP is a neuropeptide implied in nociception, vasodilation, synaptic plasticity and immune functions. This neuropeptide is expressed in encephalic regions connected to memory, attention, autonomic and behavioral functions and is also expressed by spinal motor neurons. The present work confronted α-CGRP levels between 19 AD, 27 MS and 17 control subjects using an ELISA/EIA assay. We measured higher CSF α-CGRP contents in control subjects with respect to AD, as shown in previous studies, as well as in MS patients in comparison to AD. The control subjects and MS patients did not significantly differ between each other. We did not observe a relationship between CSF protein content, albumin quotient and α-CGRP. We also describe, retrospectively, an association between higher CSF CGRP content and higher MRI overall lesion count in MS and between lower α-CGRP and worse attention and visuo-perceptual skills in AD. We speculate that α-CGRP could be differentially involved in both disabling diseases.

Proposed clinical indicators for efficient screening and testing for COVID-19 infection using Classification and Regression Trees (CART) analysis

The introduction and rapid transmission of SARS-CoV-2 in the United States resulted in methods to assess, mitigate, and contain the resulting COVID-19 disease derived from limited knowledge. Screening for testing has been based on symptoms typically observed in inpatients, yet outpatient symptoms may differ. Classification and regression trees recursive partitioning created a decision tree classifying participants into laboratory-confirmed cases and non-cases. Demographic and symptom data from patients ages 18-87 years enrolled from March 29-June 8, 2020 were included. Presence or absence of SARS-CoV-2 was the target variable. Of 832 tested, 77 (9.3%) tested positive. Cases significantly more often reported diarrhea (12 percentage points (PP)), fever (15 PP), nausea/vomiting (9 PP), loss of taste/smell (52 PP), and contact with a COVID-19 case (54 PP), but less frequently reported sore throat (-27 PP). The 4-terminal node optimal tree had sensitivity of 69%, specificity of 78%, positive predictive value of 20%, negative predictive value of 97%, and AUC of 76%. Among those referred for testing, negative responses to two questions could classify about half (49%) of tested persons with low risk for SARS-CoV-2 and would save limited testing resources. Outpatient symptoms of COVID-19 appear to be broader than the inpatient syndrome.Initial supplies of anticipated COVID-19 vaccines may be limited and administration of first such available vaccines may need to be prioritized for essential workers, the most vulnerable, or those likely to have a robust response to vaccine. Another priority group could be those not previously infected. Those who screen out of testing may be less likely to have been infected by SARS-CoV-2 virus thus may be prioritized for vaccination when supplies are limited.

Machine learning-based decision tree classifier for the diagnosis of progressive supranuclear palsy and corticobasal degeneration

Aims

This study aimed to clarify the different topographical distribution of tau pathology between progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) and establish a machine learning‐based decision tree classifier.

Methods

Paraffin‐embedded sections of the temporal cortex, motor cortex, caudate nucleus, globus pallidus, subthalamic nucleus, substantia nigra, red nucleus, and midbrain tectum from 1020 PSP and 199 CBD cases were assessed by phospho‐tau immunohistochemistry. The severity of tau lesions (i.e., neurofibrillary tangle, coiled body, tufted astrocyte or astrocytic plaque, and tau threads) was semi‐quantitatively scored in each region. Hierarchical cluster analysis was performed using tau pathology scores. A decision tree classifier was made with tau pathology scores using 914 cases. Cross‐validation was done using 305 cases. An additional ten cases were used for a validation study.

Results

Cluster analysis displayed two distinct clusters; the first cluster included only CBD, and the other cluster included all PSP and six CBD cases. We built a decision tree, which used only seven decision nodes. The scores of tau threads in the caudate nucleus were the most decisive factor for predicting CBD. In a cross‐validation, 302 out of 305 cases were correctly diagnosed. In the pilot validation study, three investigators made a correct diagnosis in all cases using the decision tree.

Conclusion

Regardless of the morphology of astrocytic tau lesions, semi‐quantitative tau pathology scores in select brain regions are sufficient to distinguish PSP and CBD. The decision tree simplifies neuropathologic differential diagnosis of PSP and CBD.

Adenosine and Metabotropic Glutamate Receptors Are Present in Blood Serum and Exosomes from SAMP8 Mice: Modulation by Aging and Resveratrol

Adenosine (ARs) and metabotropic glutamate receptors (mGluRs) are G-protein coupled receptors (GPCRs) that are modulated in the brain of SAMP8 mice, an animal model of Alzheimer's disease (AD). In the present work, it is shown the presence of ARs and mGluRs in blood serum and derived exosomes from SAMP8 mice as well as its possible modulation by aging and resveratrol (RSV) consumption. In blood serum, adenosine A₁ and A_2A receptors remained unaltered from 5 to 7 months of age. However, an age-related decrease in adenosine level was observed, while 5'-Nucleotidase activity was not modulated. Regarding the glutamatergic system, it was observed a decrease in mGluR₅ density and glutamate levels in older mice. In addition, dietary RSV supplementation caused an age-dependent modulation in both adenosinergic and glutamatergic systems. These GPCRs were also found in blood serum-derived exosomes, which might suggest that these receptors could be released into circulation via exosomes. Interestingly, changes elicited by age and RSV supplementation on mGluR₅ density, and adenosine and glutamate levels were similar to that detected in whole-brain. Therefore, we might suggest that the quantification of these receptors, and their corresponding endogenous ligands, in blood serum could have predictive value for early diagnosis in combination with other distinctive hallmarks of AD.

The discriminative capacity of CSF β-amyloid 42 and Tau in neurodegenerative diseases in the Chinese population

INTRODUCTION

In the past few years, the β-amyloid 42 peptide and tau protein in cerebrospinal fluid (CSF) have become primary diagnostic biomarkers in differentiating Alzheimer's disease (AD) and cognitive normal controls. As we know, several neurodegenerative diseases have been reported to overlap with AD in neuropathology and clinical symptoms. To examine the discriminative utility of these biomarkers in AD and other neurodegenerative diseases, we measured them in a cohort of Chinese population.

METHODS

We measured CSF Aβ₄₂, t-tau and p-tau₁₈₁ by ELISA tests and calculated the ratios of t-tau/Aβ₄₂ and p-tau₁₈₁/Aβ₄₂ in 240 Chinese Han patients with AD (n = 82), frontotemporal dementia (FTD, n = 20), Huntington's disease (HD, n = 27), multiple system atrophy (MSA, n = 24), spinocerebellar ataxia type-3 (SCA3, n = 27), amyotrophic lateral sclerosis (ALS, n = 36) and controls (n = 24).

RESULTS

As expected, all biomarkers showed high discriminative capacity between AD and non-AD groups (p < .05) except for the elevated CSF t-tau in FTD (p > .05). Comparing with the controls, tau related biomarkers significantly elevated in the FTD (p < .001) and MSA (p < .05) groups. Surprisingly, comparing with controls, we found that CSF Aβ₄₂ increased remarkably in the SCA3 (p < .05), HD and ALS groups (p < .001), achieving a high specificity, respectively.

CONCLUSION

To our best knowledge, this is the first comprehensive study in the Han Chinese population that confirmed the discriminative utility of CSF Aβ₄₂ and tau biomarkers between AD and other neurodegenerative diseases.

Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches

Aβ-amyloid deposition is a key feature of Alzheimer’s disease, but Consortium to Establish a Registry for Alzheimer's Disease (CERAD) assessment, based on neuritic plaque density, shows a limited relationships to dementia. Thal phase is based on a neuroanatomical hierarchy of Aβ-deposition, and in combination with Braak neurofibrillary tangle staging also allows derivation of primary age-related tauopathy (PART). We sought to determine whether Thal Aβ phase predicts dementia better than CERAD in a population-representative cohort (n = 186) derived from the Cognitive Function and Ageing Study (CFAS). Cerebral amyloid angiopathy (CAA) was quantitied as the number of neuroanatomical areas involved and cases meeting criteria for PART were defined to determine if they are a distinct pathological group within the ageing population. Agreement with the Thal scheme was excellent. In univariate analysis Thal phase performed less well as a predictor of dementia than CERAD, Braak or CAA. Logistic regression, decision tree and linear discriminant analysis were performed for multivariable analysis, with similar results. Thal phase did not provide a better explanation of dementia than CERAD, and there was no additional benefit to including more than one assessment of Aβ in the model. Number of areas involved by CAA was highly correlated with assessment based on a severity score (p < 0.001). The presence of capillary involvement (CAA type I) was associated with higher Thal phase and Braak stage (p < 0.001). CAA was not associated with microinfarcts (p = 0.1). Cases satisfying pathological criteria for PART were present at a frequency of 10.2% but were not older and did not have a higher likelihood of dementia than a comparison group of individuals with similar Braak stage but with more Aβ. They also did not have higher hippocampal-tau stage, although PART was weakly associated with increased presence of thorn-shaped astrocytes (p = 0.048), suggesting common age-related mechanisms. Thal phase is highly applicable in a population-representative setting and allows definition of pathological subgroups, such as PART. Thal phase, plaque density, and extent and type of CAA measure different aspects of Aβ pathology, but addition of more than one Aβ measure does not improve dementia prediction, probably because these variables are highly correlated. Machine learning predictions reveal the importance of combining neuropathological measurements for the assessment of dementia.

Clinical Profiles of Selected Biomarkers Identifying Infection and Cancer Patients: A Gorzów Hospital Example

Introduction

Many pathobiological processes that manifest in a patient's organs could be associated with biomarker levels that are detectable in different human systems. However, biomarkers that promote early disease diagnosis should not be tested only in personalized medicine but also in large-scale diagnostic evaluations of patients, such as for medical management.

Objective

We aimed to create an easy algorithmic risk assessment tool that is based on obtainable "everyday" biomarkers, identifying infection and cancer patients.

Patients

We obtained the study data from the electronic medical records of 517 patients (186 infection and 331 cancer episodes) hospitalized at Gorzów Hospital, Poland, over a one and a half-year period from the 1^st of January 2017 to the 30^th of June 2018.

Methods and Results

A set of consecutive statistical methods (cluster analysis, ANOVA, and ROC analysis) was used to predict infection and cancer. For in-hospital diagnosis, our approach showed independent clusters of patients by age, sex, MPV, and disease fractions. From the set of available "everyday" biomarkers, we established the most likely bioindicators for infection and cancer together with their classification cutoffs.

Conclusions

Despite infection and cancer being very different diseases in their clinical characteristics, it seems possible to discriminate them using "everyday" biomarkers and popular statistical methods. The estimated cutoffs for the specified biomarkers can be used to allocate patients to appropriate risk groups for stratification purposes (medical management or epidemiological administration).