For this investigation, we analysed samples from a randomized controlled trial assessing the impact of calorie restriction and exercise on endocrine and metabolic markers (Koehler et al., 2016) as well as behavioural adaptations (Martin et al., 2021). In order to test the independent and combined effects of caloric restriction (CR) and exercise (EX), the study was conducted using a four-way crossover design during which participants underwent two 4-day conditions of CR and two 4-day control conditions in energy balance (CON) (Figure 1). During one CR and one CON condition, participants conducted aerobic exercise (CR+EX; CON+EX). During the other CR and CON conditions, no exercise was conducted (CR-EX; CON-EX). To match energy balance within CR and CON conditions, dietary energy intake was adjusted for the energy expended during exercise. The order of the experimental conditions was randomly assigned. After each condition, participants underwent wash out periods of ad libitum food intake. Wash-out periods were set to at least 4 days following CON and at least 10 days following CR conditions. The study was approved by the ethical review board of the German Sport University Cologne and was conducted in accordance with the Declaration of Helsinki.
Participants were recruited from the university community in accordance with the following inclusion criteria: male, 18-30 years of age, ≥3 h/week aerobic exercise, normal (body-mass index: 19-25 kg/m2; body fat percentage below 15%) and stable body weight (±3 kg during the past 6 months). Exclusion criteria were: smoking, infectious disease within the past 4 weeks, intake of medication that could influence the study outcomes, cardiovascular disease or orthopaedic impairment interfering with conducting exercise, diabetes or history of a clinical eating disorder. All participants provided written informed consent prior to participation in the study.
Body Weight and Body Composition
At baseline and the beginning and end of each condition, body weight and body composition were assessed with a bioimpedance scale (Tanita BC 418 MA, Tanita, Amsterdam, The Netherlands). All measurements were carried out in the morning with participants in a fasted (≥10 hours) and well-hydrated state (Koehler et al., 2016).
Caloric restriction was operationally achieved by reducing energy availability, defined as dietary energy intake minus exercise energy expenditure, to 15 kcal/kg FFM. Energy balance was assumed at an energy availability of 40 kcal/kg FFM (Loucks & Thuma, 2003). Detailed meal plans were provided to ensure energy intake was in accordance with the respective conditions. Macronutrient distribution was set to recommendations of the German Nutrition Association (50-55% carbohydrates, 30-35% fat, 10-15% protein (Deutsche Gesellschaft für Ernährung, 2012). During all conditions, participants weighed all foods consumed as well as leftovers with a food scale and reported their intake daily. Analysis of food logs occurred on a daily basis (EBIS pro version 7.0, University of Hohenheim, Stuttgart, Germany, 2005) and meal plans were adjusted if reported and prescribed intake differed by ≥50 kcal.
During exercise conditions, participants performed supervised exercise on the bicycle ergometer at 60% of their VO2peak until an exercise energy expenditure of 15 kcal/kg FFM was achieved. Outside of the intervention, participants were instructed to abstain from any additional exercise, which was monitored with an activity tracker (SenseWear Pro3 armband, Bodymedia, Pittsburgh, USA).
Stable Nitrogen Isotope Ratio Analysis and Calculations
For analysis of δ15Nurea, participants were asked to collect three urine samples per day. Samples were collected in the morning in a fasted state as well as in the afternoon and at bedtime. Participants were instructed to record the time of sample collection and the total urine volume. A 50-mL aliquot of each sample was stored in the participants’ refrigerators until delivery to the laboratory, which occurred within 24-48 hours. Urinary urea was isolated using the xanthydrol method (Hülsemann et al., 2017), and δ15N of urea was assessed using elemental analysis-isotope ratio mass spectrometry. Isotope ratios are expressed relative to atmospheric nitrogen (AIR). The elemental analyser (Eurovektor EA 3000, Hekatech, Wegberg, Germany) was coupled to a Delta C continuous-flow isotope ratio mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). The working standard gas (N2, purity 5.0, Linde, Munich, Germany) and the working standard (creatine-monohydrate, AlzChem, Trostberg, Germany) were scale calibrated using IAEA-N-1 (+0.4 ‰) and IAEA-N-2 (+20.3 ‰) for δ15N values (IAEA, Vienna, Austria). All measurements were carried out in triplicates and the standard deviation for triplicate measure of the working standard was ±0.2‰. Instrument stability and analytical performance were checked by regular analysis of the working standard and zero blanks. For the present analysis, data from the three daily samples was aggregated into a daily average of δ15Nurea, and δ15Nurea was further averaged across each of the 4-day study condition. The trophic shift (Δ15N) was calculated as the difference between δ15Nurea and dietary nitrogen isotope ratio (δ15Ndiet), which was obtained from food logs as previously described (Huelsemann et al., 2013).
Statistical analyses were performed with R (version 4.1.1). If not stated otherwise, data are reported as mean ± standard error of the mean (SEM). Normality of the data was assessed with Shapiro-Wilk-test. Linear mixed model analyses were used to identify differences in outcomes over time and between study conditions, using the subject identifier to account for repeated measures design. If the linear models revealed a trend for time or condition effects (p<0.1) post-hoc analyses were performed. Based on normality of the data, one-sided paired t-tests were conducted. Significance was set at p≤0.05. In case of multiple comparisons, p-values were adjusted for multiple testing using the Holm-correction. Effect sizes were calculated from the difference of means and standard deviation of those differences, with d=0.2 considered as small, d=0.5 as medium and d>0.8 as large effect (Cohen, 1992).
In order to evaluate to the impact of potential confounders on Δ15N is, multiple regression analyses were performed using Δ15N as dependent and protein intake (g), calorie restriction (kcal/kg FFM) and an interaction term as independent variables. Subject identifiers were included in all models and adjusted R2 was interpreted. The standardized coefficient (β) was obtained from multiple linear regression with Z-transformed variables.