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"Disease Biomarkers"
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Loneliness and biomarkers of brain pathology in people with subjective cognitive decline
Loneliness is a neuropsychiatric symptom that has been associated with cognitive impairment and dementia. We aimed to investigate whether depressive symptomatology and biomarkers of Alzheimer’s disease (AD) and cerebrovascular disease (CVD) are associated with loneliness. Secondly, we aimed to investigate whether loneliness, depressive symptomatology, and biomarkers of AD and CVD are associated with subjective cognitive decline (SCD). We included 215 cognitively unimpaired participants (70 y/o) with cerebrospinal fluid biomarkers, magnetic resonance imaging, and questionnaires for loneliness, depressive symptomatology, and SCD. For aim 1, our findings showed that CVD and depressive symptomatology were the most relevant measures to discriminate people with loneliness. For aim 2, a random forest classification model showed that loneliness contributed to discriminate individuals with SCD, but logistic regression showed that its partial predictive effect was non-significant when depressive symptomatology and AD biomarkers were included in the models. We conclude that loneliness is associated with SCD, CVD, and depressive symptomatology. Given the complex interplay between loneliness, depressive symptomatology, and SCD, more research is needed to fully clarify the unique role of each neuropsychiatric symptom in relation to biomarkers of brain pathology.
Journal Article
Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup
The National Institute on Aging and the Alzheimer's Association convened three separate work groups in 2011 and single work groups in 2012 and 2018 to create recommendations for the diagnosis and characterization of Alzheimer's disease (AD). The present document updates the 2018 research framework in response to several recent developments. Defining diseases biologically, rather than based on syndromic presentation, has long been standard in many areas of medicine (e.g., oncology), and is becoming a unifying concept common to all neurodegenerative diseases, not just AD. The present document is consistent with this principle. Our intent is to present objective criteria for diagnosis and staging AD, incorporating recent advances in biomarkers, to serve as a bridge between research and clinical care. These criteria are not intended to provide step‐by‐step clinical practice guidelines for clinical workflow or specific treatment protocols, but rather serve as general principles to inform diagnosis and staging of AD that reflect current science.
Highlights
We define Alzheimer's disease (AD) to be a biological process that begins with the appearance of AD neuropathologic change (ADNPC) while people are asymptomatic. Progression of the neuropathologic burden leads to the later appearance and progression of clinical symptoms.
Early‐changing Core 1 biomarkers (amyloid positron emission tomography [PET], approved cerebrospinal fluid biomarkers, and accurate plasma biomarkers [especially phosphorylated tau 217]) map onto either the amyloid beta or AD tauopathy pathway; however, these reflect the presence of ADNPC more generally (i.e., both neuritic plaques and tangles).
An abnormal Core 1 biomarker result is sufficient to establish a diagnosis of AD and to inform clinical decision making throughout the disease continuum.
Later‐changing Core 2 biomarkers (biofluid and tau PET) can provide prognostic information, and when abnormal, will increase confidence that AD is contributing to symptoms.
An integrated biological and clinical staging scheme is described that accommodates the fact that common copathologies, cognitive reserve, and resistance may modify relationships between clinical and biological AD stages.
Journal Article
Preanalytical sample handling recommendations for Alzheimer's disease plasma biomarkers
We examined the influence of common preanalytical factors on the measurement of Alzheimer's disease–specific biomarkers in human plasma.
Amyloid β peptides (Aβ[1-40], Aβ[1-42]) and total Tau plasma concentrations were quantified using fully automated Roche Elecsys assays.
Aβ(1-40), Aβ(1-42), and total Tau plasma concentrations were not affected by up to three freeze/thaw cycles, up to five tube transfers, the collection tube material, or the size; circadian rhythm had a minor effect. All three biomarkers were influenced by the anticoagulant used, particularly total Tau. Aβ concentrations began decreasing 1 hour after blood draw/before centrifugation and decreased by up to 5% and 10% at 2 and 6 hours, respectively. For separated plasma, time to measurement influenced Aβ levels by up to 7% after 6 hours and 10% after 24 hours.
Our findings provide guidance for standardizing blood sample collection, handling, and storage to ensure reliable analysis of Alzheimer's disease plasma biomarkers in routine practice and clinical trials.
•Blood-based Alzheimer's disease biomarkers were measured with fully automated and highly precise Roche Elecsys assays.•Preanalytical sample handling affected measured concentrations of biomarkers.•Time between sample collection and centrifugation, and between centrifugation and measurement, impacted the measured concentrations of Aβ peptides.•Type of anticoagulant substantially affected measured levels of total Tau.•Recommendations for standardized sample collection and processing are provided.
Journal Article
Short-term variability of Alzheimer’s disease plasma biomarkers in a mixed memory clinic cohort
Background
For clinical implementation of Alzheimer’s disease (AD) blood-based biomarkers (BBMs), knowledge of short-term variability, is crucial to ensure safe and correct biomarker interpretation, i.e., to capture changes or treatment effects that lie beyond that of expected short-term variability and considered clinically relevant. In this study we investigated short-term intra- and inter-individual variability of AD biomarkers in the intended use population, memory clinic patients.
Methods
In a consecutive sample of memory clinic patients (AD
n
= 27, non-AD
n
= 20), blood samples were collected on three separate days within a period of 36 days and analysed for plasma Aβ40, Aβ42, p-tau181, p-tau217, p-tau231, T-tau, neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). We measured intra- and inter-individual variability and explored if the variability could be affected by confounding factors. Secondly, we established the minimum change required to detect a difference between two given blood samples that exceeds intra-individual variability and analytical variation (reference change value, RCV). Finally, we tested if classification accuracy varied across the three visits.
Results
Intra-individual variability ranged from ~ 3% (Aβ42/40) to ~ 12% (T-tau). Inter-individual variability ranged from ~ 7% (Aβ40) to ~ 39% (NfL). Adjusting the models for time, eGFR, Hba1c, and BMI did not affect the variation. RCV was lowest for Aβ42/Aβ40 (- ~ 15%/ + ~ 17%) and highest in p-tau181 (- ~ 30/ + ~ 42%). No variation in classification accuracies was found across visits.
Conclusion
We found low intra-individual variability, robust to various factors, appropriate to capture individual changes in AD BBMs, while moderate inter-individual variability may give rise to caution in diagnostic contexts. High RCVs may pose challenges for AD BBMs with low fold changes and consequently, short-term variability is important to take into consideration when, e.g., estimating intervention effect and longitudinal changes of AD BBM levels.
Trial registration
Clinicaltrials.gov (NCT05175664), date of registration 2021–12-01.
Journal Article
Brain connectivity and metacognition in persons with subjective cognitive decline (COSCODE): rationale and study design
Background
Subjective cognitive decline (SCD) is the subjective perception of a decline in memory and/or other cognitive functions in the absence of objective evidence. Some SCD individuals however may suffer from very early stages of neurodegenerative diseases (such as Alzheimer’s disease, AD), minor psychiatric conditions, neurological, and/or somatic comorbidities. Even if a theoretical framework has been established, the etiology of SCD remains far from elucidated. Clinical observations recently lead to the hypothesis that individuals with incipient AD may have overestimated metacognitive judgements of their own cognitive performance, while those with psychiatric disorders typically present underestimated metacognitive judgements. Moreover, brain connectivity changes are known correlates of AD and psychiatric conditions and might be used as biomarkers to discriminate SCD individuals of different etiologies. The aim of the COSCODE study is to identify metacognition, connectivity, behavioral, and biomarker profiles associated with different etiologies of SCD. Here we present its rationale and study design.
Methods
COSCODE is an observational, longitudinal (4 years), prospective clinical cohort study involving 120 SCD, and 80 control study participants (40 individuals with no cognitive impairment, and 40 living with mild cognitive impairment - MCI, or dementia due to AD), all of which will undergo diffusion magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) as well as behavioral and biomarker assessments at baseline and after 1 and 2 years. Both hypothesis-driven and data-driven cluster analysis approaches will be used to identify SCD sub-types based on metacognition, connectivity, behavioral, and biomarker features.
Conclusion
COSCODE will allow defining and interpreting the constellation of signs and symptoms associated with different etiologies of SCD, paving the way to the development of cost-effective risk assessment and prevention protocols.
Journal Article
MicroRNAs as disease progression biomarkers and therapeutic targets in experimental autoimmune encephalomyelitis model of multiple sclerosis
Multiple sclerosis is an autoimmune neurodegenerative disease of the central nervous system characterized by pronounced inflammatory infiltrates entering the brain, spinal cord and optic nerve leading to demyelination. Focal demyelination is associated with relapsing-remitting multiple sclerosis, while progressive forms of the disease show axonal degeneration and neuronal loss. The tests currently used in the clinical diagnosis and management of multiple sclerosis have limitations due to specificity and sensitivity. MicroRNAs (miRNAs) are dysregulated in many diseases and disorders including demyelinating and neuroinflammatory diseases. A review of recent studies with the experimental autoimmune encephalomyelitis animal model (mostly female mice 6-12 weeks of age) has confirmed miRNAs as biomarkers of experimental autoimmune encephalomyelitis disease and importantly at the pre-onset (asymptomatic) stage when assessed in blood plasma and urine exosomes, and spinal cord tissue. The expression of certain miRNAs was also dysregulated at the onset and peak of disease in blood plasma and urine exosomes, brain and spinal cord tissue, and at the post-peak (chronic) stage of experimental autoimmune encephalomyelitis disease in spinal cord tissue. Therapies using miRNA mimics or inhibitors were found to delay the induction and alleviate the severity of experimental autoimmune encephalomyelitis disease. Interestingly, experimental autoimmune encephalomyelitis disease severity was reduced by overexpression of miR-146a, miR-23b, miR-497, miR-26a, and miR-20b, or by suppression of miR-182, miR-181c, miR-223, miR-155, and miR-873. Further studies are warranted on determining more fully miRNA profiles in blood plasma and urine exosomes of experimental autoimmune encephalomyelitis animals since they could serve as biomarkers of asymptomatic multiple sclerosis and disease course. Additionally, studies should be performed with male mice of a similar age, and with aged male and female mice.
Journal Article
Association between telomere length in peripheral blood leukocytes and risk of ischemic stroke in a Han Chinese population: a linear and non-linear Mendelian randomization analysis
Background
Many contradictory conclusions pertaining to the telomere length in peripheral leukocyte chromosomes as a potential biomarker for ischemic stroke (IS) risk have been reported by the various observational studies in previous years. This study aims to investigate whether the leukocyte telomere length is associated with an increased IS risk or not, based on the Mendelian randomization (MR) approach.
Methods
Based on the NHGRI-EBI GWAS Catalog database, the Chinese online genetic database as well as the previous published studies, twelve single nucleotide polymorphisms (SNPs) with minor allele frequency ≥ 0.05 were selected and the leukocyte telomere length was measured in 431 first-ever IS patients and 304 healthy controls (quantitative polymerase chain reaction). To explore linear and non-linear effect of telomere length on the IS risk, we preformed the linear MR analysis (the inverse-variance weighted method, the maximum likelihood method, and the mode-based estimation method), and the non-linear MR analysis (semiparametric method with three tests for non-linearity, including the quadratic test, Cochran’s
Q
test, and the fractional polynomial test).
Results
Two verified SNPs (rs11125529 and rs412658) were chosen as instrumental variables. In linear MR analysis, the adjusted odds ratios and 95% confidence intervals of IS for genetically predicted telomere lengths, based on the two SNPs, were 1.312 (0.979 to 1.759), 1.326 (0.932 to 1.888) and 1.226 (0.844 to 1.781) for the inverse-variance weighted method, the maximum likelihood method, and the mode-based estimation method, respectively. Three tests for nonlinearity failed to reject the null exactly, indicating that the relationship between telomere length and IS risk is unlikely to be non-linear.
Conclusion
This MR study based on individual data does not provide strong evidence for a positive linear or non-linear effect of telomere length on the IS risk.
Journal Article
Molecularly Imprinted Polymers for the Determination of Cancer Biomarkers
Biomarkers can provide critical information about cancer and many other diseases; therefore, developing analytical systems for recognising biomarkers is an essential direction in bioanalytical chemistry. Recently molecularly imprinted polymers (MIPs) have been applied in analytical systems to determine biomarkers. This article aims to an overview of MIPs used for the detection of cancer biomarkers, namely: prostate cancer (PSA), breast cancer (CA15-3, HER-2), epithelial ovarian cancer (CA-125), hepatocellular carcinoma (AFP), and small molecule cancer biomarkers (5-HIAA and neopterin). These cancer biomarkers may be found in tumours, blood, urine, faeces, or other body fluids or tissues. The determination of low concentrations of biomarkers in these complex matrices is technically challenging. The overviewed studies used MIP-based biosensors to assess natural or artificial samples such as blood, serum, plasma, or urine. Molecular imprinting technology and MIP-based sensor creation principles are outlined. Analytical signal determination methods and the nature and chemical structure of the imprinted polymers are discussed. Based on the reviewed biosensors, the results are compared, and the most suitable materials for each biomarker are discussed.
Journal Article
Identification of Receptor Tyrosine Kinase, Discoidin Domain Receptor 1 (DDR1), as a Potential Biomarker for Serous Ovarian Cancer
Ovarian cancer, one of the most common gynecological malignancies, has an aggressive phenotype. It is necessary to develop novel and more effective treatment strategies against advanced disease. Protein tyrosine kinases (PTKs) play an important role in the signal transduction pathways involved in tumorigenesis, and represent potential targets for anticancer therapies. In this study, we performed cDNA subtraction following polymerase chain reaction (PCR) using degenerate oligonucleotide primers to identify specifically overexpressed PTKs in ovarian cancer. Three PTKs, janus kinase 1, insulin-like growth factor 1 receptor, and discoidin domain receptor 1 (DDR1), were identified and only DDR1 was overexpressed in all ovarian cancer tissues examined for the validation by quantitative real-time PCR. The DDR1 protein was expressed in 63% (42/67) of serous ovarian cancer tissue, whereas it was undetectable in normal ovarian surface epithelium. DDR1 was expressed significantly more frequently in high-grade (79%) and advanced stage (77%) tumors compared to low-grade (50%) and early stage (43%) tumors. The expression of the DDR1 protein significantly correlated with poor disease-free survival. Although its functional role and clinical utility remain to be examined in future studies, our results suggest that the expression of DDR1 may serve as both a potential biomarker and a molecular target for advanced ovarian cancer.
Journal Article