Impact of Menstrual Phases on Stress Markers: A Pilot Study

PURPOSE: Previous research has shown that different phases of the menstrual cycle may impact biometrics such as markers of stress and inflammation [e.g., cortisol (CORT), interleukin-6] as well as body composition. However, there is scarce literature regarding markers of stress and oxidative stress such as salivary a-amylase (sAA), immunoglobin-A (SIgA) and uric acid (UA), in relation to the four different menstrual phases. The purpose of this study was to examine the impact of menstrual phases on sAA, CORT, UA and SIgA. METHODS: 21 pre-menopausal women with regular menstrual cycles (n=9) oral contraceptive users (OC) and (n=12) non-oral contraceptive users (non-OC) recorded baseline cycle dates using the Flo Period Tracker appä. Participants began experimental testing after recording baseline dates, consisting of four total sessions with one session occurring during the 1) menses, 2) late follicular, 3) ovulatory and 4) late luteal phase. Salivary markers: CORT, sAA, UA, and SIgA, along with diastolic and systolic blood pressure (BP), total body water (TBW) and body fat percentage (BF%) were recorded during each phase. BF% and TBW were determined via InBody bioelectric-impedance analyzerä. 500uL of saliva was collected, with samples immediately frozen at -80°C until analysis. Saliva samples were centrifuged at 4°C for a duration of 15 minutes at 1500g prior to analysis and duplicated for CORT, sAA, UA and SIgA concentrations. Statistical procedures were conducted via SAS v 9.4 (Cary, NC). One way repeated measures analysis of variance was used to evaluate outcome measures as well as changes in salivary markers and body composition measurements across different menstrual cycle phases. Fisher’s Least Significant Difference test was used to compare means in the instance of a significant main effect (p \u3c 0.05). Partial eta squared (hp2) was run to determine effect size. RESULTS: sAA concentrations were significantly lower during the follicular phase compared menstruation phase (p = 0.006, ηp2 = 0.14). The main effect for SIgA approached significance (p = 0.05). There were no changes in CORT, UA, BF%, TBW or diastolic and systolic blood pressure. CONCLUSION: These findings suggest the menstrual cycle influences sAA concentrations in both OC users and non-OC users. More research needs to be conducted with a larger sample size in order to determine significance of SIgA in relation to menstrual phases

Predicting Resting Metabolic Rate in Healthy Adults using Body Composition and Circumference Measurements

Measurement of resting metabolic rate (RMR) is an important factor for weight management. Previous research has reported several variables to estimate RMR such as body size, percent fat (%BF), age, and sex; however, little is known regarding the effect of circumference measures in estimating RMR. PURPOSE: The purpose of this study was to develop a model to estimate RMR using waist circumference (WC), an easily obtainable measure, and cross-validate it to previously published models. METHODS:Subjects were 140 adult men and women, ages 18-65 years. RMR was measured through indirect calorimetry, %BF was measured through air displacement plethysmography, and fat mass and fat-free mass were determined from %BF and weight. Other variables collected were: weight, height, age, sex, ethnicity, body mass index, WC, hip circumference, waist-to-hip ratio, waist-to-height ratio, and %BF estimated from bioelectrical impedance analysis. Subjects were randomly divided into derivation and cross-validation samples. A multiple regression model was developed to determine the most accurate estimation of RMR in the derivation sample. The cross-validation sample was used to confirm the accuracy of the model and to compare the accuracy to published models. RESULTS:The best predictors for estimating RMR were body weight, r = 0.70, p= 0.031, age, r = -0.30, p= 0.012, and sex, r = 0.51, p= 0.018. Other factors failed to account for significant variation in the model. The derived equation for estimating RMR is: RMR (kcal/day) = 843.11 + 8.77(weight) – 4.23(age) + 228.54(sex, M = 1, F = 0), R2= 0.68, SEE = 173 kcal/day. Cross-validation statistics were: R2= 0.54, p £0.05, SEE = 199 kcal/day, and total error = 198 kcal/day. In published models, R2ranged from 0.47 to 0.57, SEE ranged from 192 to 213 kcal/day, and total error ranged from 212 to 1311 kcal/day. CONCLUSIONS:Cross-validation to published models for estimating RMR were similar to those of the derived model; however, the total error in the derived equation was lower than any of the previously published models. Several published models considerably overestimate RMR compared to the current model. The results of this study suggest that RMR can be reasonably estimated with easily obtainable measures which allow for estimation and implementation of RMR for weight management in clinical practice