Ghrelin

Data are expressed as means ± SEM. The incremental areas under the curve (iAUC) for blood glucose, serum insulin, subjective satiety and in vitro rate of starch hydrolysis as well as the negative area under the curve for glucose (neg AUC), were calculated using the trapezoid model. The glycaemic and insulinaemic indexes (GI and II) were calculated from the 120 min incremental post-prandial area for blood glucose and serum insulin by using WWB as a reference (GI and II = 100). In addition, the course of glycaemia was analyzed by calculation a glycaemic profile (GP); The time (min) during which the blood glucose was above fasting concentration was divided with the incremental peak value (mM) of blood glucose for each subject and test meal (Graph Pad Prism, version 4.03, Graph Pad Software, San Diego, CA, USA). In the cases where the blood glucose concentration remained above fasting for the entire 180 min, the duration value was set to 180 min. A GP index was calculated from the GP by using WWB as a reference (GP Index = 100). HI was calculated from the 180 min incremental area for starch hydrolysis in vitro by using WWB as a reference. Relative changes (%) from fasting concentration to the nadir and to the concentration at 180 min after commencing breakfast were calculated for plasma ghrelin.
The data were analyzed with a general linear model (ANOVA) followed by Tukey's multiple comparison test (MINITAB, release 14.13, Minitab Inc, State College, PA). In the cases of unevenly distributed residuals (tested with Anderson-Darling and considered unevenly distributed when p < 0.05), Box Cox transformation were performed on the data prior to the ANOVA.
Significant difference between the products at different time points where evaluated using a mixed model (PROC MIXED in SAS release 8.01, SAS Institute Inc, Cary, NC) with repeated measures and an autoregressive covariance structure. When significant interactions between treatment and time were found, Tukey's multiple comparison test were performed for each time point (MINITAB, release 14.13, Minitab Inc).
Correlation analysis was conducted to evaluate the relation among dependent measures with the use of Spearman's partial coefficients controlling for subjects (two-tailed test), (SPSS software, version 16.0; SPSS Inc, Chicago, IL, USA).
Due to problems drawing capillary blood samples from one subject, the blood glucose statistics was analyzed with n = 11. One subject failed to ingest the WGRP meal according to instructions and data from this product for that subject was therefore excluded from the statistical analysis.

All analyses were cross-sectional and performed using SAS/STAT 8.2. Leptin, ghrelin, and adiponectin were square-root transformed and insulin log transformed based on the distribution of residuals from the multivariate regression models. We evaluated the relationship of age, sex, BMI, and time of storage of blood sample on hormones using multiple regression. Partial correlations adjusted for age, sex, and BMI were calculated for hormones and QUICKI, with and without control of other potential confounders. The relationships between hormones and sleep were evaluated using multiple linear regression after control for potential confounders including age, sex, BMI, SDB, and morningness tendencies (as measured using the Horne-Ostberg questionnaire, an indirect surrogate of earlier rising time). In all analyses involving insulin, glucose, and QUICKI (but not leptin, ghrelin, and adiponectin), participants with diabetes (self-reported diagnosis, or currently taking insulin or diabetic medications, or with glucose >300 mg/dl) were excluded. Participants with SDB were not removed from the analyses shown. When controlling for AHI in models, participants who used continuous positive airway pressure or who had inadequate sleep were excluded. Because controlling for AHI did not significantly change the sleep-hormone regression coefficients, these analyses are not shown. The relationship of BMI with average nightly sleep was evaluated using a quadratic fit. This was examined using multiple visits (n = 1,828) from 1,040 participants with sleep diary data available. Mixed modeling techniques were used to account for within-subject correlation for participants with multiple visits. SAS procedure mixed was used for modeling and hypothesis testing using robust standard errors and a compound symmetric within-subject correlation structure. All reported p values are two-sided. For illustrative purposes, changes in leptin, ghrelin, and BMI for different sleep amounts were calculated at the average values and sex distribution of the relevant sample.

The studied group consisted of 60 patients (group termed urolithiasis patients—UP), which: (1) reported to the urology office and were qualified for the treatment due to the need for extracorporeal shockwave lithotripsy—(ESWL); (2) showed hyperuricemia (in men and post-menopausal women above 7 mg/dL (416.4 µmol/L), in premenopausal women above 6 mg/dL (356.9 µmol/L)) and did not take before any medications lowering UA; (3) had abnormal blood pressure values (>130/85 mmHg) or received antihypertensive treatment, and (4) showed an abnormal waist circumference > 94 cm in male(M), > 80 cm in female(F).
After the initial examination in the form of determining the level of serum glucose, high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) and performing morphology, samples were selected, leaving only those patients who showed at least 3 out of 5 features of MetS ((1) incorrect waist size—for Caucasian, Middle East, and Mediterranean population: 94 cm (M), 80 cm (F); (2) TG = 150 mg/dL or appropriate therapy; (3) High-density lipoprotein cholesterol (HDL-C) 40 mg/dL (M), 50 mg/dL (F), or appropriate therapy; (4) blood pressure: 130/85 mmHg or appropriate therapy, and (5) fasting blood glucose 100 mg/dL or using appropriate therapy) and did not show features of other acute or chronic inflammatory conditions. Finally, 57 patients aged 27–60 met the qualification criteria and were included in this study.
The control group (CG) comprised 30 healthy volunteers who reported to the laboratory for their check-ups. The conditions for participation in the study were age (18–60 years) and the absence of acute and chronic inflammation, pregnancy, and diagnosed diseases. After the initial examination in the form of determining the levels of glucose, HDL-C, and TG and performing morphology, samples were selected, leaving those patients who did not show signs of MetS and did not show signs of other acute or chronic inflammatory conditions. Finally, 29 patients aged 22–60 were qualified for the study.
The structure of the study group is shown in Figure 2.
After qualification, all eligible participants had their UA, ghrelin, leptin, IL-6, TNF-α, and CRP serum levels measured. Next, in both studied groups, urinalysis assessments were made. The full research model is presented in Figure 3.