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Aims/hypothesis Polycystic ovary syndrome (PCOS) is a risk factor of type 2 diabetes. Screening for impaired glucose metabolism (IGM) with an OGTT has been recommended, but this is relatively time-consuming and inconvenient. Thus, a strategy that could minimise the need for an OGTT would be beneficial. Materials and methods Consecutive PCOS patients (n=118) with fasting glucose < 6.1 mmol/l were included in the study. Parameters derived from medical history, clinical examination and fasting blood samples were assessed by decision tree modelling for their ability to discriminate women with IGM (2-h OGTT value >= 7.8 mmol/l) from those with NGT. Results According to the OGTT results, 93 PCOS women had NGT and 25 had IGM. The best decision tree consisted of HOMA-IR, the proinsulin:insulin ratio, proinsulin, 17-OH progesterone and the ratio of luteinising hormone:follicle-stimulating hormone. This tree identified 69 women with NGT. The remaining 49 women included all women with IGM (100% sensitivity, 74% specificity to detect IGM). Pruning this tree to three levels still identified 53 women with NGT (100% sensitivity, 57% specificity to detect IGM). Restricting the data matrix used for tree modelling to medical history and clinical parameters produced a tree using BMI, waist circumference and WHR. Pruning this tree to two levels separated 27 women with NGT (100% sensitivity, 29% specificity to detect IGM). The validity of both trees was tested by a leave-10%-out cross-validation. Conclusions/interpretation Decision trees are useful tools for separating PCOS women with NGT from those with IGM. They can be used for stratifying the metabolic screening of PCOS women, whereby the number of OGTTs can be markedly reduced.
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.
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.