Biomarkers used in the medical field, are a part of a relatively new clinical toolset categorized by their clinical applications. The four main classes are molecular, physiologic, histologic and radiographic biomarkers. All four types of biomarkers have a clinical role in narrowing or guiding treatment decisions and follow a sub-categorization of being either predictive, prognostic, or diagnostic.
Predictive Predictive molecular, cellular, or imaging biomarkers that pass validation can serve as a method of predicting clinical outcomes. Predictive biomarkers are used to help optimize ideal treatments, and often indicate the likelihood of benefiting from a specific therapy. For example, in metastatic
colorectal cancer, predictive biomarkers can serve as a way of evaluating and improving patient survival rates and in the individual case by case scenario, they can serve as a way of sparing patients from needless toxicity that arises from cancer treatment plans. Common examples of predictive biomarkers are genes such as
estrogen receptor,
progesterone receptor and
HER2/neu in
breast cancer, the
Philadelphia chromosome in
chronic myelogenous leukemia,
c-KIT mutations in
gastrointestinal stromal tumors and
epidermal growth factor receptor mutations in
non-small-cell lung cancer.
Diagnostic , a number of different
cardiac biomarkers can be measured to determine exactly when an attack occurred and how severe it was. Diagnostic biomarkers that meet a burden of proof can serve a role in narrowing down diagnosis. This can lead to diagnosis that are significantly more specific to individual patients. A biomarker can be a traceable substance that is introduced into an organism as a means to examine organ function or other aspects of health. It can also be a substance whose detection indicates a particular disease state, for example, the presence of an
antibody may indicate an
infection. An example is the
traumatic brain injury (TBI) blood-based biomarker test consisted of measuring the levels of neuronal
Ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and
Glial fibrillary acidic protein (GFAP) to aid in the diagnosis of the presence of cranial lesion(s) among moderate to mild TBI patients that is(are) otherwise only diagnosable with the use of a
CT scan of the head. Another example is KRAS, an
oncogene that encodes a
GTPase involved in several
signal transduction pathways. Biomarkers for precision oncology are typically utilized in the molecular diagnostics of
chronic myeloid leukemia,
colon,
breast, and
lung cancer, and in
melanoma.
Digital Digital biomarkers are a novel emerging field of biomarkers, mostly collected by smart
biosensors. So far, digital biomarkers have been focusing on monitoring vital parameters such as
accelerometer data and
heartrate but also
speech. Novel non-invasive, molecular digital biomarkers are increasingly available recorded by e.g. on-skin sweat analysis (
internet-enabled Sudorology), which can be seen as next-generation digital biomarkers. Collecting and tracking digital biomarkers have become more easily available with the advancement of smartphones and
wearables. In
Parkinson's disease (PD), for example, finger tapping a mobile phone via counting apps have been used as a method of (self-)evaluating
bradykinesia and effectiveness of medication. Digital biomarkers are currently being used in conjugation with
artificial intelligence (A.I.) in order to recognize symptoms for
mild cognitive impairment (MCI). One major current use of digital biomarkers involves keeping track of regular brain activity. Specific neural indicators can be measured by devices to evaluate patients for any neuro abnormalities. The data collected can determine the patients disease probability or condition. While patients carryout everyday tasks (IADL), computers are using machine learning to observe and detect any deviation from normal behavior. These markers are used as signs or indicators of cognitive decline. == Nutrition and diet assessment ==