Salivary inflammatory biomarkers are predictive of mild cognitive impairment and Alzheimer's disease in a feasibility study

Alzheimer's disease (AD) is an insidious disease. Its distinctive pathology forms over a considerable length of time without symptoms. There is a need to detect this disease, before even subtle changes occur in cognition. Hallmark AD biomarkers, tau and amyloid-β, have shown promising results in CSF and blood. However, detecting early changes in these biomarkers and others will involve screening a wide group of healthy, asymptomatic individuals. Saliva is a feasible alternative. Sample collection is economical, non-invasive and saliva is an abundant source of proteins including tau and amyloid-β. This work sought to extend an earlier promising untargeted mass spectrometry study in saliva from individuals with mild cognitive impairment (MCI) or AD with age- and gender-matched cognitively normal from the South Australian Neurodegenerative Disease cohort. Five proteins, with key roles in inflammation, were chosen from this study and measured by ELISA from individuals with AD (n = 16), MCI (n = 15) and cognitively normal (n = 29). The concentrations of Cystatin-C, Interleukin-1 receptor antagonist, Stratifin, Matrix metalloproteinase 9 and Haptoglobin proteins had altered abundance in saliva from AD and MCI, consistent with the earlier study. Receiver operating characteristic analysis showed that combinations of these proteins demonstrated excellent diagnostic accuracy for distinguishing both MCI (area under curve = 0.97) and AD (area under curve = 0.97) from cognitively normal. These results provide evidence for saliva being a valuable source of biomarkers for early detection of cognitive impairment in individuals on the AD continuum and potentially other neurodegenerative diseases.

Multi-Omics, an Integrated Approach to Identify Novel Blood Biomarkers of Alzheimer's Disease

The metabolomic and proteomic basis of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD/MCI pathogenesis is unclear. This study compared the metabolomic and proteomic signature of plasma from cognitively normal (CN) and dementia patients diagnosed with MCI or AD, to identify specific cellular pathways and new biomarkers altered with the progression of the disease. We analysed 80 plasma samples from individuals with MCI or AD, as well as age- and gender-matched CN individuals, by utilising mass spectrometry methods and data analyses that included combined pathway analysis and model predictions. Several proteins clearly identified AD from the MCI and CN groups and included plasma actins, mannan-binding lectin serine protease 1, serum amyloid A2, fibronectin and extracellular matrix protein 1 and Keratin 9. The integrated pathway analysis showed various metabolic pathways were affected in AD, such as the arginine, alanine, aspartate, glutamate and pyruvate metabolism pathways. Therefore, our multi-omics approach identified novel plasma biomarkers for the MCI and AD groups, identified changes in metabolic processes, and may form the basis of a biomarker panel for stratifying dementia participants in future clinical trials.

Salivaomics as a Potential Tool for Predicting Alzheimer's Disease During the Early Stages of Neurodegeneration

BACKGROUND

The metabolomic and proteomic basis of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is poorly understood and the relationships between systemic abnormalities in metabolism and AD/AMCI pathogenesis are unclear.

OBJECTIVE

The aim of the study was to compare the metabolomic and proteomic signature of saliva from cognitively normal and patients diagnosed with MCI or AD, to identify specific cellular pathways altered with the progression of the disease.

METHODS

We analyzed 80 saliva samples from individuals with MCI or AD as well as age- and gender-matched healthy controls. Saliva proteomic and metabolomic analyses were conducted utilizing mass spectrometry methods and data combined using pathway analysis.

RESULTS

We found significant alterations in multiple cellular pathways, demonstrating that at the omics level, disease progression impacts numerous cellular processes. Multivariate statistics using SIMCA showed that partial least squares-data analysis could be used to provide separation of the three groups.

CONCLUSION

This study found significant changes in metabolites and proteins from multiple cellular pathways in saliva. These changes were associated with AD, demonstrating that this approach might prove useful to identify new biomarkers based upon integration of multi-omics parameters.

Can the simple clinical score usefully predict the mortality risk and length of stay for a recently admitted patient?

OBJECTIVES

The aim of the present study was to determine whether an aggregate simple clinical score (SCS) has a role in predicting the imminent mortality and in-hospital length of stay (LOS) of newly admitted, acutely unwell General Medical in-patients.

METHODS

Data were collected prospectively from adult patients admitted through an Acute Medical Unit between February and August 2013. Using logistic regression analysis before and after adjustment for age, the SCS was assessed for its association with LOS and mortality, including 30-day mortality, just for those patients for full resuscitation. Changes in sensitivity and specificity after adding SCS to age as a predictor, as well as the change in the net reclassification index, were determined using the predicted probabilities from the logistic regression models.

RESULTS

The SCS was superior to age in predicting mortality of any patient within 30 days. It did not assist in predicting 30-day mortality for those patients who were for full resuscitation. The ability of the SCS to predict long stay (> 72h) remained relatively low (64%) and was inferior to published rates achieved by bedside clinician assessment (74%-82%).

CONCLUSION

There was no useful prospective role for the SCS in predicting LOS and mortality of in-patients newly admitted to a General Medicine service.