Blood-Based Biomarkers in the Diagnosis and Risk Stratification of Pancreatic Cysts

The use of blood-based biomarkers for the assessment of pancreatic cystic lesions is a rapidly growing field with incredible potential. CA 19-9 remains the only blood-based marker in common use, while many novel biomarkers are in early stages of development and validation. We highlight current work in the fields of proteomics, metabolomics, cell-free DNA/circulating tumor DNA, extracellular vesicles, and microRNA among others, as well as barriers to development and future directions in the work of blood-based biomarkers for pancreatic cystic lesions.

The updated development of blood-based biomarkers for Huntington's disease

Huntington's disease is a progressive neurodegenerative disease caused by mutation of the huntingtin (HTT) gene. The identification of mutation carriers before symptom onset provides an opportunity to intervene in the early stage of the disease course. Optimal biomarkers are of great value to reflect neuropathological and clinical progression and are sensitive to potential disease-modifying treatments. Blood-based biomarkers have the merits of minimal invasiveness, low cost, easy accessibility and safety. In this review, we summarized the updated development of blood-based biomarkers for HD from six aspects, including neuronal injuries, oxidative stress, endocrine functions, immune reactions, metabolism and differentially expressed miRNAs. The blood-based biomarkers presented and discussed in this review were close to clinical applicability and might facilitate clinical design as surrogate endpoints. Exploration and validation of robust blood-based biomarkers require further standard and systemic study design in the future.

Combining Select Blood-Based Biomarkers with Neuropsychological Assessment to Detect Mild Cognitive Impairment among Mexican Americans

BACKGROUND

Recent work has supported use of blood-based biomarkers in detection of amnestic mild cognitive impairment (MCI). Inclusion of neuropsychological measures has shown promise in enhancing utility of biomarkers to detect disease.

OBJECTIVE

The present study sought to develop cognitive-biomarker profiles for detection of MCI.

METHODS

Data were analyzed on 463 participants (normal control n = 378; MCI n = 85) from HABLE. Random forest analyses determined proteomic profile of MCI. Separate linear regression analyses determined variance accounted for by select biomarkers per neuropsychological measure. When neuropsychological measure with the least shared variance was identified, it was then combined with select biomarkers to create a biomarker-cognitive profile.

RESULTS

The biomarker-cognitive profile was 90% accurate in detecting MCI. Among amnestic MCI cases, the detection accuracy of the biomarker-cognitive profile was 92% and increased to 94% with demographic variables.

CONCLUSION

The biomarker-cognitive profile for MCI was highly accurate in its detection with use of only five biomarkers.