Blood-based biomarkers of Alzheimer’s disease: Challenging but feasible

Laboratory of Personality & Cognition, Intramural Research Program, National Institue on Aging, NIH, USA.
Biomarkers in Medicine (Impact Factor: 2.65). 02/2010; 4(1):65-79. DOI: 10.2217/bmm.09.84
Source: PubMed


Blood-based biomarkers present a considerable challenge: technically, as blood is a complex tissue and conceptually, as blood lacks direct contact with brain. Nonetheless, increasing evidence suggests that there is a blood protein signature, and possibly a transcript signature, that might act to increase confidence in diagnosis, be used to predict progression in either disease or prodromal states, and that may also be used to monitor disease progression. Evidence for this optimism comes partly from candidate protein studies, including those suggesting that amyloid-beta measures might have value in prediction and those studies of inflammatory markers that consistently show change in Alzheimer's disease, and partly from true proteomics studies that are beginning to identify markers in blood that replicate across studies and populations.

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Available from: Simon Lovestone, May 19, 2015
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    • "After our primary analysis with the a priori selected 10 metabolites that failed to replicate their original results, we examined the entire metabolomics data acquired in both BLSA and AGES-RS samples using machine learning approaches. By using rigorous partitioning of samples into training/test sets in two independent samples from the BLSA and AGES-RS studies, we guarded against overfitting of data which is a common cause of inflated classification accuracies while using high-dimensional data sets such as metabolomics and proteomics on relatively small sample sizes[26]. In both the BLSA and AGES-RS samples, the analyses did not reveal a high level of discrimination between groups based on serum metabolite concentrations. "
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    ABSTRACT: Recently, quantitative metabolomics identified a panel of 10 plasma lipids that were highly predictive of conversion to Alzheimer's disease (AD) in cognitively normal older individuals (n = 28, area under the curve [AUC] = 0.92, sensitivity/specificity of 90%/90%). We failed to replicate these findings in a substantially larger study from two independent cohorts—the Baltimore Longitudinal Study of Aging ([BLSA], n = 93, AUC = 0.642, sensitivity/specificity of 51.6%/65.7%) and the Age, Gene/Environment Susceptibility-Reykjavik Study ([AGES-RS], n = 100, AUC = 0.395, sensitivity/specificity of 47.0%/36.0%). In analyses applying machine learning methods to all 187 metabolite concentrations assayed, we find a modest signal in the BLSA with distinct metabolites associated with the preclinical and symptomatic stages of AD, whereas the same methods gave poor classification accuracies in the AGES-RS samples. We believe that ours is the largest blood biomarker study of preclinical AD to date. These findings underscore the importance of large-scale independent validation of index findings from biomarker studies with relatively small sample sizes.
    Full-text · Article · Jan 2016 · Alzheimer's and Dementia
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    • "To date, there has been no reported simple and inexpensive procedure available to confirm the early diagnosis of MCI and AD. Easily accessible tissues such as buccal cells need to be considered for initial population-based pre-screening tests which could be performed at much lower cost before confirmation via brain PET imaging and/or cerebrospinal fluid diagnostics of amyloid and tau-proteins [37] [38] [39]. "
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    ABSTRACT: Mild cognitive impairment (MCI) may reflect early stages of neurodegenerative disorders such as Alzheimer's disease (AD). Our hypothesis was that cytokeratin 14 (CK14) expression could be used with blood-based biomarkers such as homocysteine, vitamin B12, and folate to identify individuals with MCI or AD from the Australian Imaging, Biomarkers and Lifestyle (AIBL) flagship study of aging. Buccal cells from 54 individuals were analyzed by a newly developed method that is rapid, automated, and quantitative for buccal cell CK14 expression levels. CK14 was negatively correlated with plasma Mg2 + and LDL, while positively correlated with vitamin B12, red cell hematocrit/volume, and basophils in the MCI group and positively correlated with insulin and vitamin B12 in the AD group. The combined biomarker panel (CK14 expression, plasma vitamin B12, and homocysteine) was significantly lower in the MCI (p = 0.003) and AD (p = 0.0001) groups compared with controls. Receiver-operating characteristic curves yielded area under the curve (AUC) values of 0.829 for the MCI (p = 0.002) group and 0.856 for the AD (p = 0.0003) group. These complex associations of multiple related parameters highlight the differences between the MCI and AD cohorts and possibly an underlying metabolic pathology associated with the development of early memory impairment. The changes in buccal cell CK14 expression observed in this pilot study supports previous results suggesting the peripheral biomarkers and metabolic changes are not restricted to brain pathology alone in MCI and AD and could prove useful as a potential biomarker in identifying individuals with an increased risk of developing MCI and eventually AD.
    Full-text · Article · Sep 2015 · Journal of Alzheimer's disease: JAD
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    • "Blood is an interesting potential source of biomarkers because it allows repeated measurements, is easily obtained, and is highly suitable for high-throughput measurements [29]. Putative endometriosis biomarkers are mostly glycoproteins, growth or adhesion factors, hormones, or proteins related to immunology or angiogenesis [17] [23] [30]. "
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    ABSTRACT: Endometriosis is histologically characterized by the displacement of endometrial tissue to extrauterine locations including the pelvic peritoneum, ovaries, and bowel. An important cause of infertility and pelvic pain, the individual and global socioeconomic burden of endometriosis is significant. Laparoscopy remains the gold standard for the diagnosis of the condition. However, the invasive nature of surgery, coupled with the lack of a laboratory biomarker for the disease, results in a mean latency of 7-11 years from onset of symptoms to definitive diagnosis. Unfortunately, the delay in diagnosis may have significant consequences in terms of disease progression. The discovery of a sufficiently sensitive and specific biomarker for the nonsurgical detection of endometriosis promises earlier diagnosis and prevention of deleterious sequelae and represents a clear research priority. In this review, we describe and discuss the current status of biomarkers of endometriosis in plasma, urine, and endometrium.
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