Blood biomarkers for estimating energy intake in Japanese male collegiate athletes: a pilot study | BMC Sports sciences, medicine and rehabilitation
Study design and participants
This observational cross-sectional study was approved by the Juntendo University Ethics Committee (approval no.: 29-82, date: September 11, 2017) and the Wayo Women’s University Ethics Committee for Biological and Epidemiological Studies directed at humans (approval no.: 1851 , date: April 5, 2019) and was conducted in accordance with the Declaration of Helsinki. All participants were informed about the benefits and risks of participating in the study before obtaining written informed consent. All participants signed an institutionally approved informed consent form.
The minimum sample size was initially calculated at 25 participants, with an α level of 0.05 (two-sided), a power of 0.90, an effect size (f2) of 0.5 (large) [8]and 2–11 independent variables included in the multiple regression analysis [9]. Twenty-eight male athletes from the sports club of Juntendo University Faculty of Sports and Health Sciences participated in this study from July to August 2019. (a) Male students, (b) those who joined the club to improve competitiveness, and (c) those without a current serious clinical condition were included in the study. The athletes took part in athletics (throwing and decathlon), handball and basketball competitions. Participants were recruited through club announcements. The screening procedure included assessment of exercise history and self-reported medical history. In addition, height, weight, and body composition were assessed using dual-energy X-ray absorptiometry [DXA]), EI based on BMR, and blood components based on blood tests after a certain period of fasting. A two-week nutritional study was conducted. Two participants whose blood samples showed abnormal values were excluded. Finally, data from 26 participants were analyzed.
Anthropometry and DXA
Total body mass was measured to the nearest 0.1 kg on a medical scale (HBF-212, TANITA Inc., Tokyo, Japan), while height was measured to the nearest 0.1 cm using a stadiometer (YG-200 , YAGAMI Inc., Nagoya, Japan). Body composition (body fat percentage and lean mass [FFM]) was measured by a trained radiologist using a Hologic QDR 4500 DXA scanner (Hologic, Inc., Bedford, MA). The intra- and inter-instrument reliability of the DXA method has been reported in previous studies [10, 11].
The equipment was calibrated daily according to the manufacturer’s instructions. All scans were analyzed using Hologic QDR version 12.1 software (Hologic, Inc.). Based on the results of the DXA analyses, the head area was excluded and the FFM and body fat mass were determined. To determine the technician’s error in using the software to estimate body composition, the technician analyzed ten full-body scans twice using the same method. Based on the results of the measurement, the following were technical errors (absolute and relative errors): FFM (0.067 kg and 0.11%) and fat mass (0.070 kg and 0.76%).
Three-day nutrition report
Trained registered dietitians provided participants with written and verbal instructions on how to complete the 3-day nutrition record (DR) [12]. The analysis of nutritional intake was carried out by a certified sports dietitian, regardless of the participants’ sports club, and the results were not shared with the team leaders. The participants were asked to report their nutritional intake honestly.
As part of the DR, participants were asked to record the meal, its location, and all food and drinks consumed (except water) for three consecutive days. To maximize feasibility, intake was recorded for 3 days, 2 days with training and 1 day without training, but not on days with special events (e.g. birthdays or championship match days). The DR form included meal time, meal location, name of the dish, ingredients in the dish, and total amount of food consumed. The participants were asked to keep track of the food and drinks they consumed from the time they got up to the time they went to bed, including supplements and drinks.
In addition, detailed information was also recorded on associations (presence or absence of oil, etc.), dairy products (skimmed milk, etc.) and intake amounts. Participants were asked to record as much information as possible, including portion size consumed and details of any leftovers, using household measurements (e.g. cups, pieces, tablespoons and weight). At the same time, participants took photos of all food and drinks next to scale cards (length: 9 cm; width: 5.5 cm, with 1 cm graduations) using their smartphone cameras. For purchased food items, additional photos were requested with the product name and food label. The photos were sent immediately to the email addresses provided.
Based on the DRs and meal photos, a registered dietitian (certified sports dietitian) analyzed energy and nutrient intake using nutritional analysis software (Calorie Make, version 1.0.10 and Nutrition Navigation, version 5.3.0; Toyo System Science Co., Ltd., Kanagawa, Japan).
Determination of basal metabolism
The FFM measured with DXA was considered as lean body mass (LBM). BMR was estimated from the calculated LBM, using the following formula from the Japan Institute of Sports Sciences (JISS): 28.5 kcal/kg LBM [13]. The EI/BMR ratio was set as the outcome variable.
Blood samples
Fasting blood samples were collected from the antecubital vein without stasis. The analyzed parameters included a total of 36 items related to the following: serum protein, amino acid and nitrogen compound, iron metabolism, serum enzyme, glucose metabolism, serum lipids, blood cells, pituitary hormone, thyroid hormone, adrenocortical hormone, gonadal hormones. hormone and other bioactivities. All tests were performed in a commercial laboratory (SRL Inc., Tokyo, Japan).
static analysis
SPSS Statistics version 26.0 (IBM Corp., Armonk, NY) was used for all statistical analyses. Descriptive statistics were calculated for each variable and indicated as the mean (standard deviation). [SD]) and the median values (first and third quartiles, Q1 and Q3). The normality of the data distribution was verified using the Shapiro-Wilk test. Non-normally distributed variables were then log-transformed and used in subsequent analyses.
Among the 36 selected blood biomarkers, we confirmed the pairwise correlation and excluded when the Pearson correlation coefficient between two independent variables was >0.6. As a result, 18 independent variables were pre-selected and dietary EI/BMR variables were included in the multiple regression analysis (the stepwise) analysis as predictive variables to obtain the best model for predicting EI/BMR based on the selected biomarkers.
Before multiple regression, multicollinearity was assessed for each independent variable. Multicollinearity was evaluated using the variance inflation factor (VIF), which was defined as the inverse of tolerance. A VIF of > 5.0 indicates multicollinearity between two variables in a regression model [14]. The degree of agreement between the estimation formula and the nutritional assessment results was confirmed by performing a Bland-Altman analysis [15]. a Pvalue of <0.05 was considered significant. The data for regression analysis met the assumptions of homoscedasticity, independence, normality, and linearity.
