Researchers have developed a computer program that analyzes molecules in blood plasma to search for biomarkers that identify individuals who are at risk of becoming overweight and developing obesity-related diseases.
The results are described in the journal Frontiers in Bioengineering and Biotechnology.
“The test is 90% accurate in detecting whether the subject will gain weight without any kind of intervention,” the author said. “It also shows whether there’s a risk of developing diseases such as diabetes, high blood pressure and dyslipidemia [ abnormally elevated levels of fat in the blood ]. It’s an important tool because health professionals can use it to recommend lifestyle changes before a problem materializes.”
The test consists of a mass spectrometer analysis to detect all the metabolites present in the patient’s blood and produce a profile of the various metabolic processes at work in the organism. The data obtained by mass spectrometry are processed by the new software.
“The program screens the blood sample for five metabolites that function as biomarkers with the potential to predict weight gain,” the author said. “When one of these biomarkers is present in the sample, the patient will tend to develop diabetes if he or she becomes obese.”
The software files are open-source and can be downloaded free of charge from the internet. According to the author, any health service with access to a mass spectrometer can apply the methodology.
The methodology developed combines metabolomics (the analysis of all the metabolites in a biological sample) with machine learning, a subdiscipline of artificial intelligence. The researchers used data obtained from the analysis of blood samples supplied by 180 people to “teach” the program to recognize a pattern associated with weight gain.
Half the volunteers included in the study were within the body mass index (BMI) range deemed healthy, while the rest were overweight to varying degrees or obese.
“Anthropometric measurements [ weight, height and body mass ] were taken for all participants, who also completed a questionnaire on family history of chronic disease, as well as age and gender,” the author said. “We used some of the patients to train the software and the others to validate it by comparing its results with their own anthropometrics and health history. The random forest machine learning algorithm was used for the training part.”
The researchers found that 18 metabolites can serve as biomarkers of metabolic processes relating to fat accumulation, five of which have the potential to predict weight gain.
“Prostaglandin B2 and carboxy-leukotriene B4 are metabolites of arachidonic acid [ a fatty acid in the omega-6 family ] known to participate in inflammatory processes, in the recruiting of cells to the site of inflammation, and in the production of reactive oxygen species [ an excess of which impairs cell functioning ],” another author said. “Two other molecules we identified were argininosuccinate and dihydrobiopterin, both of which are involved in the nitric oxide cycle and can be considered markers of free radical production.”
According to the author, the combination of these biomarkers suggests that feedback from the inflammatory cascade occurs in overweight individuals. “This finding matches those of several studies that describe low-grade chronic inflammation as one of the active deleterious processes in the overweight condition,” the author added.
The fifth biomarker found to be a potential predictor of weight gain was carboxy-methyl-propyl-furanpropanoic acid (CMPF), a metabolite associated with dysfunction of the insulin-producing cells in the pancreas and the development of diabetes. “Considering there were diabetics in the study group, this biomarker could be the link between weight gain and diabetes,” the author said.
The author said the computer program can also be used by health professionals to assess the effectiveness of a treatment prescribed to reduce a patient’s percentage of body fat.
“Even before the subject loses weight, it’s possible to know whether the intervention is working well. If the metabolic processes that lead to fat accumulation are interrupted, the 18 metabolites we identified will tend to disappear from blood plasma,” the author added.