April 23, 2024

Blood Based Biomarker: A Promising Avenue for Non-Invasive Disease Diagnosis and Monitoring

Blood is one of the most accessible biofluids in the human body. It carries biological molecules that are released by organs, tissues and cells. These molecules can provide a wealth of information about physiological and pathological processes occurring inside the body. Researchers have long sought to identify and characterize biomarkers in the blood that can serve as signatures of disease. Successful identification of such biomarkers has the potential to transform medicine by enabling simple, inexpensive and non-invasive methods for disease diagnosis, prognosis, screening and monitoring treatment response.

What is blood based biomarker?

Blood Based Biomarkers refer to molecules found in blood samples that can act as indicators of normal biological or pathological processes, or as indicators of exposure to environmental agents including chemicals, drugs, or infectious agents. Some key types of blood biomarkers include:

Proteins: Blood contains thousands of different proteins secreted by tissues and organs. Certain disease states cause abnormal increases or decreases in specific protein biomarkers. For example, increased levels of cancer antigen 125 (CA125) in blood can indicate ovarian cancer.

Nucleic acids: Circulating cell-free DNA, RNA and microRNAs in blood plasma/serum have shown promise as biomarkers. Aberrant levels or mutations can provide clues about diseases.

Metabolites: Small molecule metabolites reflect changes in metabolism and can serve as biomarkers. Elevated lactate levels point to anaerobic metabolism in diseases like cancer.

Cells: Changes in the number or characteristics of different blood cell types like lymphocytes or neutrophils can act as biomarkers.

Exosomes: These nano-sized vesicles secreted by cells transport cargo that mirrors their cell of origin. Exosomal biomarkers show promise for liquid biopsies.

Immune complexes: Complexes of antigens and antibodies found in bloodstream can act as biomarkers of infection, autoimmunity or cancer.

Technological Advances Driving Progress in Blood Biomarker Discovery

Major technological developments over the past couple of decades are fueling the exploration of blood biomarkers. Advanced technologies with high sensitivity and throughput capabilities like microfluidics, next generation sequencing and mass spectrometry have enabled comprehensive molecular profiling of blood samples. This has accelerated the discovery process from hypotheses generated through traditional research to an era of hypothesis-free approaches. Some key technologies impacting the field include:

Mass spectrometry: Techniques like targeted and untargeted MS combined with statistical learning are employed for multi-omics profiling of proteins, metabolites and other molecules in blood to detect novel patterns.

Next generation sequencing: NGS allows for detailed characterization and quantification of nucleic acid biomarkers like cell-free DNA, RNA transcripts andmicroRNAs with high sensitivity.

Microarrays: Protein, nucleic acid and metabolite microarrays enable rapid screening and validation of large biomarker candidate panels.

Microfluidics: Lab-on-a-chip platforms automate sample preparation, separation, reaction and detection in an integrated microfluidic set-up, requiring just milliliters of sample.

Recent Advances in Blood Biomarkers for Major Disease Categories

Oncology biomarkers: Blood-based biomarkers have shown great potential for cancer screening, diagnosis, prognosis, treatment monitoring and relapse detection. Examples include CEA for colorectal cancer, PSA for prostate cancer, CA125 for ovarian cancer and CtDNA for various cancers.

Neurological disease biomarkers: Biomarkers for conditions like Alzheimer’s (amyloid-β, tau), multiple sclerosis (oligoclonal bands), Parkinson’s disease (alpha-synuclein) and traumatic brain injury are areas of active investigation.

Cardiovascular disease biomarkers: Traditional biomarkers like cardiac troponins, CK-MB, myoglobin, BNP/NT-proBNP aid in diagnosis of ACS, heart failure. Emerging biomarkers provide insights into inflammation, plaque vulnerability, aneurysms.

Infectious disease biomarkers: Pathogen-derived and host response biomarkers can serve as surrogate markers for pathogen detection, gauge disease severity and treatment response for sepsis, viral infections like HIV, hepatitis, influenza etc.

Metabolic disease biomarkers: Studies on biomarkers for obesity, diabetes, non-alcoholic fatty liver disease etc. focus on metabolites, adipokines and markers reflecting insulin resistance, beta cell function, liver health.

Psychiatric disorders: Research areas include autoantibodies as potential schizophrenia and autism biomarkers; cytokines as depression biomarkers; BDNF as biomarker for mood disorders and schizophrenia.

Autoimmune disease biomarkers: Biomarkers for rheumatoid arthritis include autoantibodies, acute phase proteins; multiple sclerosis – oligoclonal bands; systemic lupus erythematosus – anti-dsDNA antibodies etc.

Future Prospects and Challenges

Development of non-invasive, multiplex assays for simultaneous measurement of biomarker panels will vastly improve disease diagnostics and management over individual markers. Integration of multi-omic approaches holds promise to unravel novel disease pathways and actionable biomarker combinations. Advancements in exosome biology may uncover new opportunities in liquid biopsy applications. Standardization of pre-analytical variables and assay protocols will be important to translate biomarkers into clinical usage. Combining biomarkers with clinical and imaging data utilizing artificial intelligence promises more accurate disease modeling. Overall, blood based biomarker have high clinical potential but challenges of validation and regulatory approval remain. With continued progress, they may transform healthcare through personalized and predictive medicine approaches.

1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it