Advances in personalized medicine have revealed many new biomarkers for this disease. These biomarkers can be used to diagnose and monitor diseases, predict patient response to certain treatments, and identify people who are likely to be healthy now but at risk of developing the disease in the future.
Biomarker type:
Diagnosis: Predict the presence of a disease or condition.
Prognosis: Identifies specific individuals who are more likely to benefit from treatment.
Prognosis: Determines the likelihood of recurrence or progression of the disease in a patient who already has the disease.
Vulnerability: Determines how likely a person is to contract a disease.
Thanks to the performance and cost-effectiveness of the latest Next Generation Sequencing (NGS) technologies, gene biomarkers have recently been incorporated into clinical diagnostics. However, the DNA code does not always reflect the actual physiological state. Outside the cell nucleus, a biological "soup" of molecules is constantly being created and broken down. Identifying and measuring these molecules can give a more accurate picture of our health or disease state.
Lipids, a class of biomolecules including fatty acids, vitamins, monoglycerides, and phospholipids, play a central role in functions such as cellular signaling and energy storage. Their levels fluctuate in response to stimuli such as food intake, exercise, and illness. Therefore, research is exploring lipids (the accumulation of lipids present in a cell, tissue, or organism at a given point in time) as a potential source of non-invasive biomarkers, since lipids can be extracted from blood samples.
The problem of modern medicine is that many patients go to the doctor after the first symptoms of the disease appear. Therefore, the diagnosis is often made after the disease has progressed. The goal of personalized medicine is to monitor your health in order to prevent disease.
A new study published in the journal PLOS Biology led by Professor Chris Lauber can use lipid profiles to predict the risk of developing type 2 diabetes (DM2) and cardiovascular disease (CVD) years before the onset of the disease. Lauber is part of the Lipotype team, a company that conducts geological analysis of firearms using state-of-the-art mass spectrometry (MS).
Currently, risk assessment for type 2 diabetes and CVD includes the use of a patient's history and measurement of high-density and low-density cholesterol. Lauber and colleagues suggested that there could be hundreds of different lipids in the blood that contribute to the risk of disease. In their study, they analyzed blood data from over 4,000 healthy middle-aged and Swedish women who participated in the follow-up study from 1991 to 1994 and 2015.
From baseline blood assessments obtained in the 90s, 184 lipids were analyzed via MS, and machine learning generated risk scores for T2D and CVD for the 23-years of subsequent follow up. These risk scores were then used to stratify patients into 6 categories ranging from low to high disease risk. A lipidomics risk score resulted in a 168% increased incidence rate in the high-risk group for T2D, and a 77% decreased incidence rate in the lowest risk group. For CVD, lipidomics predicted an 84% increase in incidence rate in the high-risk group, and 53% decrease in the lowest risk group.
In contrast, the longitudinal case study data showed that, by 2015, 13.8% of the study participants had developed T2D, while 22% had developed CVD. “Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence,” the authors write in the paper.
Technology Networks recently interviewed Lauber to discuss the study and its wider implications for biomarker discovery in more detail.
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