Novel fluid biomarkers for mild cognitive impairment: A systematic review and meta-analysis

Neurofilament Light Chain as a Biomarker of Global Cognition in Individuals With Possible Vascular Mild Cognitive Impairment

BACKGROUND

Neurofilament Light Chain (NfL) is a biomarker of axonal injury elevated in mild cognitive impairment (MCI) and Alzheimer's disease dementia. Blood NfL also inversely correlates with cognitive performance in those conditions. However, few studies have assessed NfL as a biomarker of global cognition in individuals demonstrating mild cognitive deficits who are at risk for vascular-related cognitive decline.

OBJECTIVE

To assess the relationship between blood NfL and global cognition in individuals with possible vascular MCI (vMCI) throughout cardiac rehabilitation (CR). Additionally, NfL levels were compared to age/sex-matched cognitively unimpaired (CU) controls.

METHOD

Participants with coronary artery disease (vMCI or CU) were recruited at entry to a 24-week CR program. Global cognition was measured using the Montreal Cognitive Assessment (MoCA) and plasma NfL level (pg/ml) was quantified using a highly sensitive enzyme-linked immunosorbent assay.

RESULTS

Higher plasma NfL was correlated with worse MoCA scores at baseline (β = -.352, P = .029) in 43 individuals with vMCI after adjusting for age, sex, and education. An increase in NfL was associated with worse global cognition (b[SE] = -4.81[2.06], P = .023) over time, however baseline NfL did not predict a decline in global cognition. NfL levels did not differ between the vMCI (n = 39) and CU (n = 39) groups (F(1, 76) = 1.37, P = .245).

CONCLUSION

Plasma NfL correlates with global cognition at baseline in individuals with vMCI, and is associated with decline in global cognition during CR. Our findings increase understanding of NfL and neurobiological mechanisms associated with cognitive decline in vMCI.

Analysis of Speech Features in Alzheimer's Disease with Machine Learning: A Case-Control Study

BACKGROUND

Changes in the speech and language of patients with Alzheimer's disease (AD) have been reported. Using machine learning to characterize these irregularities may contribute to the early, non-invasive diagnosis of AD.

METHODS

We conducted cognitive function assessments, including the Mini-Mental State Examination, with 83 patients with AD and 75 healthy elderly participants, and recorded pre- and post-assessment conversations to evaluate participants' speech. We analyzed the characteristics of the spectrum, intensity, fundamental frequency, and minute temporal variation (∆) of the intensity and fundamental frequency of the speech and compared them between patients with AD and healthy participants. Additionally, we evaluated the performance of the speech features that differed between the two groups as single explanatory variables.

RESULTS

We found significant differences in almost all elements of the speech spectrum between the two groups. Regarding the intensity, we found significant differences in all the factors except for the standard deviation between the two groups. In the performance evaluation, the areas under the curve revealed by logistic regression analysis were higher for the center of gravity (0.908 ± 0.036), mean skewness (0.904 ± 0.023), kurtosis (0.932 ± 0.023), and standard deviation (0.977 ± 0.012) of the spectra.

CONCLUSIONS

This study used machine learning to reveal speech features of patients diagnosed with AD in comparison with healthy elderly people. Significant differences were found between the two groups in all components of the spectrum, paving the way for early non-invasive diagnosis of AD in the future.

CSF and blood glial fibrillary acidic protein for the diagnosis of Alzheimer's disease: A systematic review and meta-analysis

Recently included in the 2024 new revised diagnostic criteria of Alzheimer's disease (AD), glial fibrillary acidic protein (GFAP) has garnered significant attention. A systematic review and meta-analysis were performed to comprehensively evaluate the diagnostic, differential diagnostic, and prospective diagnostic performance of GFAP in cerebrospinal fluid (CSF) and blood for AD continuum. A literature search using common electronic databases, important websites and historical search way was performed from inception to the beginning of March 2023. The inclusion criteria was studies evaluating the diagnostic accuracy of GFAP in CSF and/or blood for the AD continuum patients, utilizing PET scans, CSF biomarkers and/or clinical criteria. The systematic review and meta-analysis were conducted referring to the Cochrane Handbook. In total, 34 articles were eventually included in the meta-analysis, 29 of which were published within the past three years. Blood GFAP exhibited good diagnostic accuracy across various AD continuum patients, and the summary area under curve for distinguishing PET positive and negative individuals, CSF biomarkers defined positive and negative individuals, clinically diagnosed AD and cognitive unimpaired controls, AD and/or mild cognitive impairment and other neurological diseases, and prospective cases and controls was 0.85[0.81-0.88], 0.77[0.73-0.81], 0.92[0.90-0.94], 0.80[0.77-0.84], and 0.79[0.75-0.82], respectively. Only several studies were recognized to evaluate the diagnostic accuracy of CSF GFAP, which was not as good as that of blood GFAP (paired mixed data: AUC = 0.86 vs. AUC = 0.77), but its accuracy remarkably increased to AUC = 0.91 when combined with other factors like sex, age, and ApoE genotype. In summary, GFAP, particularly in blood, shown good diagnostic, differential diagnostic, and prospective diagnostic accuracy for AD continuum patients, with improved accuracy when used alongside other basic indexes.

Elevated circulating levels of GFAP associated with reduced volumes in hippocampal subregions linked to mild cognitive impairment among community-dwelling elderly individuals

Objective

Cerebrospinal fluid biomarkers are challenging to use for diagnosing mild cognitive impairment (MCI) in large populations, and there is an urgent need for new blood biomarkers. The aim of this study is to investigate whether astrocyte activation is correlated with hippocampal atrophy, and to assess the potential of glial fibrillary acidic protein (GFAP) as a biomarker for diagnosing MCI among community-dwelling older individuals.

Methods

This cross-sectional study included 107 older adults. The levels of GFAP in serum were measured, and the volumetric assessment of gray matter within hippocampal subregions was conducted using Voxel-Based Morphometry (VBM). The relationship between hippocampal subregion volume and blood biomarkers were analyzed using partial correlation. The effectiveness of blood biomarkers in differentiating MCI was assessed using a receiver operating characteristic (ROC) curve.

Results

We found that serum GFAP levels were significantly elevated in the MCI group compared to the cognitively normal (CN) group. Additionally, individuals with MCI exhibited a reduction gray matter volume in specific hippocampal subregions. Notably, the right dentate gyrus (DG) and right cornu ammonis (CA) subregions were found to be effective for distinguishing MCI patients from CN individuals. Serum levels of GFAP demonstrate a sensitivity of 65.9% and a specificity of 75.6% in differentiating patients with MCI from CN individuals.

Conclusion

Specific atrophy within hippocampal subregions has been observed in the brains of community-dwelling elderly individuals. Elevated levels of circulating GFAP may serve as a sensitive peripheral biomarker indicative of hippocampal-specific cognitive alterations in patients with MCI.

Peripheral GFAP and NfL as early biomarkers for dementia: longitudinal insights from the UK Biobank

BACKGROUND

Peripheral glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) are sensitive markers of neuroinflammation and neuronal damage. Previous studies with highly selected participants have shown that peripheral GFAP and NfL levels are elevated in the pre-clinical phase of Alzheimer's disease (AD) and dementia. However, the predictive value of GFAP and NfL for dementia requires more evidence from population-based cohorts.

METHODS

This was a prospective cohort study to evaluate UK Biobank participants enrolled from 2006 to 2010 using plasma GFAP and NfL measurements measured by Olink Target Platform and prospectively followed up for dementia diagnosis. Primary outcome was the risk of clinical diagnosed dementia. Secondary outcomes were cognition. Linear regression was used to assess the associations between peripheral GFAP and NfL with cognition. Cox proportional hazard models with cross-validations were used to estimate associations between elevated GFAP and NfL with risk of dementia. All models were adjusted for covariates.

RESULTS

A subsample of 48,542 participants in the UK Biobank with peripheral GFAP and NfL measurements were evaluated. With an average follow-up of 13.18 ± 2.42 years, 1312 new all-cause dementia cases were identified. Peripheral GFAP and NfL increased up to 15 years before dementia diagnosis was made. After strictly adjusting for confounders, increment in NfL was found to be associated with decreased numeric memory and prolonged reaction time. A greater annualized rate of change in GFAP was significantly associated with faster global cognitive decline. Elevation of GFAP (hazard ratio (HR) ranges from 2.25 to 3.15) and NfL (HR ranges from 1.98 to 4.23) increased the risk for several types of dementia. GFAP and NfL significantly improved the predictive values for dementia using previous models (area under the curve (AUC) ranges from 0.80 to 0.89, C-index ranges from 0.86 to 0.91). The AD genetic risk score and number of APOE*E4 alleles strongly correlated with GFAP and NfL levels.

CONCLUSIONS

These results suggest that peripheral GFAP and NfL are potential biomarkers for the early diagnosis of dementia. In addition, anti-inflammatory therapies in the initial stages of dementia may have potential benefits.

Circulating biomarkers of inflammaging and Alzheimer's disease to track age-related trajectories of dementia: Can we develop a clinically relevant composite combination?

Alzheimer's disease (AD) is a rapidly growing global concern due to a consistent rise of the prevalence of dementia which is mainly caused by the aging population worldwide. An early diagnosis of AD remains important as interventions are plausibly more effective when started at the earliest stages. Recent developments in clinical research have focused on the use of blood-based biomarkers for improve diagnosis/prognosis of neurodegenerative diseases, particularly AD. Unlike invasive cerebrospinal fluid tests, circulating biomarkers are less invasive and will become increasingly cheaper and simple to use in larger number of patients with mild symptoms or at risk of dementia. In addition to AD-specific markers, there is growing interest in biomarkers of inflammaging/neuro-inflammaging, an age-related chronic low-grade inflammatory condition increasingly recognized as one of the main risk factor for almost all age-related diseases, including AD. Several inflammatory markers have been associated with cognitive performance and AD development and progression. The presence of senescent cells, a key driver of inflammaging, has also been linked to AD pathogenesis, and senolytic therapy is emerging as a potential treatment strategy. Here, we describe blood-based biomarkers clinically relevant for AD diagnosis/prognosis and biomarkers of inflammaging associated with AD. Through a systematic review approach, we propose that a combination of circulating neurodegeneration and inflammatory biomarkers may contribute to improving early diagnosis and prognosis, as well as providing valuable insights into the trajectory of cognitive decline and dementia in the aging population.

Blood levels of glial fibrillary acidic protein for predicting clinical progression to Alzheimer's disease in adults without dementia: a systematic review and meta-analysis protocol

BACKGROUND

There is urgent clinical need to identify reliable prognostic biomarkers that predict the progression of dementia symptoms in individuals with early-phase Alzheimer's disease (AD) especially given the research on and predicted applications of amyloid-beta (Aβ)-directed immunotherapies to remove Aβ from the brain. Cross-sectional studies have reported higher levels of cerebrospinal fluid and blood glial fibrillary acidic protein (GFAP) in individuals with AD-associated dementia than in cognitively unimpaired individuals. Further, recent longitudinal studies have assessed the prognostic potential of baseline blood GFAP levels as a predictor of future cognitive decline in cognitively unimpaired individuals and in those with mild cognitive impairment (MCI) due to AD. In this systematic review and meta-analysis, we propose analyzing longitudinal studies on blood GFAP levels to predict future cognitive decline.

METHODS

This study will include prospective and retrospective cohort studies that assessed blood GFAP levels as a prognostic factor and any prediction models that incorporated blood GFAP levels in cognitively unimpaired individuals or those with MCI. The primary outcome will be conversion to MCI or AD in cognitively unimpaired individuals or conversion to AD in individuals with MCI. Articles from PubMed and Embase will be extracted up to December 31, 2023, without language restrictions. An independent dual screening of abstracts and potentially eligible full-text reports will be conducted. Data will be dual-extracted using the CHeck list for critical appraisal, data extraction for systematic Reviews of prediction Modeling Studies (CHARMS)-prognostic factor, and CHARMS checklists, and we will dual-rate the risk of bias and applicability using the Quality In Prognosis Studies and Prediction Study Risk-of-Bias Assessment tools. We will qualitatively synthesize the study data, participants, index biomarkers, predictive model characteristics, and clinical outcomes. If appropriate, random-effects meta-analyses will be performed to obtain summary estimates. Finally, we will assess the body of evidence using the Grading of Recommendation, Assessment, Development, and Evaluation Approach.

DISCUSSION

This systematic review and meta-analysis will comprehensively evaluate and synthesize existing evidence on blood GFAP levels for prognosticating presymptomatic individuals and those with MCI to help advance risk-stratified treatment strategies for early-phase AD.

TRIAL REGISTRATION

PROSPERO CRD42023481200.

Enhancing Alzheimer's Disease Diagnosis and Care by Focusing on Plasma Biomarkers for Identifying Mild Cognitive Impairment

Biomarkers that accurately identify mild cognitive impairment (MCI) are of greater importance for Alzheimer's disease (AD) management and treatment. On the other hand, blood-based biomarkers are not only more practical but also less invasive than the common cerebrospinal fluid biomarkers. In their report in the Journal of Alzheimer's Disease, Wang and collaborators identified 67 upregulated and 220 downregulated long noncoding RNAs (lncRNAs). They further demonstrated that 4 of these lncRNAs could discriminate MCI from cognitively healthy individuals. Apart from their significance as potential biomarkers for MCI diagnosis, these lncRNAs can offer additional information on the cellular mechanisms of AD pathology.

Biomarker Methodologies: A NIMH Perspective

Biomarkers are critically important in the development of drugs, biologics, medical devices, and psychosocial interventions for psychiatric disorders. As the lead federal agency charged with setting and supporting the national agenda for mental health research, the National Institute of Mental Health (NIMH) funds a broad portfolio of basic, translational, and clinical research focused on identifying, developing, and validating biomarkers for serious mental illnesses and neurodevelopmental conditions. In psychiatric research over the past 10 years, there has been an intensive effort to identify biomarkers as potential tools to improve treatment options for individuals with mental health concerns and increase success in the development of novel interventions. This chapter highlights examples of biomarker technologies that have been utilized to advance understanding of the pathophysiology of psychiatric disorders and the development of novel therapeutics.