Circulatory Metabolite Ratios as Indicators of Lifestyle Risk Factors Based on a Greek NAFLD Case-Control Study.
Charalambos FotakisAthina I AmanatidouMaria KafyraVasiliki AndreouIoanna Panagiota KalafatiMaria ZervouGeorgios V DedoussisPublished in: Nutrients (2024)
An ensemble of confounding factors, such as an unhealthy diet, obesity, physical inactivity, and smoking, have been linked to a lifestyle that increases one's susceptibility to chronic diseases and early mortality. The circulatory metabolome may provide a rational means of pinpointing the advent of metabolite variations that reflect an adherence to a lifestyle and are associated with the occurrence of chronic diseases. Data related to four major modifiable lifestyle factors, including adherence to the Mediterranean diet (estimated on MedDietScore), body mass index (BMI), smoking, and physical activity level (PAL), were used to create the lifestyle risk score (LS). The LS was further categorized into four groups, where a higher score group indicates a less healthy lifestyle. Drawing on this, we analyzed 223 NMR serum spectra, 89 MASLD patients and 134 controls; these were coupled to chemometrics to identify "key" features and understand the biological processes involved in specific lifestyles. The unsupervised analysis verified that lifestyle was the factor influencing the samples' differentiation, while the supervised analysis highlighted metabolic signatures. Τhe metabolic ratios of alanine/formic acid and leucine/formic acid, with AUROC > 0.8, may constitute discriminant indexes of lifestyle. On these grounds, this research contributed to understanding the impact of lifestyle on the circulatory metabolome and highlighted "prudent lifestyle" biomarkers.
Keyphrases
- physical activity
- weight loss
- metabolic syndrome
- body mass index
- cardiovascular disease
- risk factors
- mental health
- type diabetes
- machine learning
- insulin resistance
- extracorporeal membrane oxygenation
- risk assessment
- high resolution
- dna methylation
- weight gain
- gene expression
- cardiovascular events
- genome wide
- ejection fraction
- depressive symptoms
- smoking cessation
- deep learning
- mass spectrometry
- convolutional neural network