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Saliva samples as a tool to study the effect of meal timing on metabolic and inflammatory biomarkers
(2020)
Meal timing affects metabolic regulation in humans. Most studies use blood samples fortheir investigations. Saliva, although easily available and non-invasive, seems to be rarely used forchrononutritional studies. In this pilot study, we tested if saliva samples could be used to studythe effect of timing of carbohydrate and fat intake on metabolic rhythms. In this cross-over trial, 29 nonobese men were randomized to two isocaloric 4-week diets: (1) carbohydrate-rich meals until13:30 and high-fat meals between 16:30 and 22:00 or (2) the inverse order of meals. Stimulated salivasamples were collected every 4 h for 24 h at the end of each intervention, and levels of hormones andinflammatory biomarkers were assessed in saliva and blood. Cortisol, melatonin, resistin, adiponectin, interleukin-6 and MCP-1 demonstrated distinct diurnal variations, mirroring daytime reports inblood and showing significant correlations with blood levels. The rhythm patterns were similar forboth diets, indicating that timing of carbohydrate and fat intake has a minimal effect on metabolicand inflammatory biomarkers in saliva. Our study revealed that saliva is a promising tool for thenon-invasive assessment of metabolic rhythms in chrononutritional studies, but standardisation of sample collection is needed in out-of-lab studies.
Background
High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods.
Methods
We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort.
Results
We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis.
Conclusions
We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods.