Background: Previous research suggests associations between energy balance, eating frequency, macronutrient content, and macronutrient distribution with body composition. In particular, energy balance and protein intake have been conventionally evaluated in 24-hr time blocks, consistent with dietary recommendations and general public understanding. However, there is a potential benefit to investigating energy balance and protein intake in smaller increments of time to account for dynamic changes that occur within-day.Objective: The purpose of this study was to evaluate protein intake/distribution relative to energy balance fluctuations during the day and body composition in collegiate female basketball players.Methods: Subjects provided information on dietary intake and expenditure. Body composition was assessed by multi-current bioelectrical impedance. Energy balance (EB) and related protein distribution variables were determined with a Computerized Time-Line Energy Analysis procedure. Data were analyzed for associations between energy balance, protein intake and distribution, and body composition. Data are displayed as either traditional 24-hr EB and total protein intake or dynamic protein variables in relation to real-time EB (ingestion within ± 400 kcal EB or > 0 kcal EB).Results: There was no relationship between net 24-hr energy balance and percentage body fat. A statistically significant positive relationship was observed between total protein intake and body fat mass (R = .597; p = .031). No relationship was observed between protein distribution variables (g in ± 400 kcal EB, g in > 0 kcal EB) and percentage body fat. Protein eating occurrences (>10g, ± 400 kcal EB) was inversely correlated with BMI (R = -.650; p = .016). Subjects with the greatest energy deficits presented with lower lean body mass (R= -.736; p = .004).Conclusion: These data suggest that within-day protein distribution relative to energy balance are associated with BMI, but not with percentage body fat. Those with the highest protein intake had the highest body fat mass, with no correlation between protein intake and total energy intake detected. In this group, no association between 24hr intake net values or within-day intake values were found to be related to body fat percentage. However, the greatest energy balance deficit during the day was strongly inversely associated with lean body mass, indicative of potentially deleterious effects of energy restriction.

Diet and lifestyle choices play a vital role in the overall health of an individual. There are many types of diets with varying instructions on what kind and how much of a food, or food group, should be eaten. This cross sectional study focused on possible health benefits of a vegetarian diet in regards to the body composition of non-vegetarian and vegetarian women. Total meat, poultry, seafood, and fish (MPSF) intake were separated into three categories to compare low-to-very-low, moderate, and high intake. Anthropometric measurements collected included waist to hip ratio (WHR), weight, height, and percentage of body fat. No significant differences were found between the vegetarian and non-vegetarian categories BMI, body fat percentage, or WHR. Conclusions found by previous research were not supported by the results of this research. Factors including geographic location and socioeconomic status could impact the availability of healthy food for both vegetarians and non-vegetarians.

The purpose of this investigation was to expand the evaluation of Normal Weight Obesity (NWO) and its association with insulin resistance using a nationally representative sample of U.S. adults. A cross-sectional study including 5,983 subjects was conducted. Body fat percentage was assessed using dual energy X-ray absorptiometry (DXA). Expanded Normal Weight Obesity (eNWO) categories (pairings of BMI and body fat percentage classifications) were determined by standard cut-points for BMI and the gender specific median for body fat percentage. Homeostatic Model Assessment-Insulin Resistance (HOMA-IR) levels were used to index insulin resistance. Mean ± SE values were as follows: BMI: 27.9 ± 0.2 (women) and 27.8 ± 0.1 (men); body fat percentage: 40.5 ± 0.2 (women) and 27.8 ± 0.2 (men); HOMA-IR: 2.04 ± 0.05 (women) 2.47 ± 0.09 (men). HOMA-IR differed systematically and in a dose-response fashion across all levels of the eNWO categories (F = 291.3, P < 0.0001). As BMI levels increased, HOMA-IR increased significantly and within each BMI category, higher levels of body fat were associated significantly with higher levels of HOMA-IR. Both high BMI and high body fat percentage are strongly related to insulin resistance. In this study, insulin resistance increased incrementally according to BMI levels primarily and body fat levels secondarily. Consequently, due to the costs associated with precisely measuring body fat, and the accuracy of using BMI independently, we recommend that BMI be used in its standard form to predict insulin resistance and not be supplemented with an estimate of body fat.

Objective- To investigate the extent to which changes in body fat percentage (BF%) and weight (BW) relate to changes in C-reactive protein (CRP) in women, while statistically controlling for possible confounders, such as age, initial body weight, and menopause status. Methods and Results- A cohort of 150 free-living subjects was followed prospectively over a 2½-year period. BF% was measured using dual energy X-ray absorptiometry (DEXA), while BW was determined with a calibrated, electronic scale. There was no significant relationship between changes in BF% and CRP, regardless of age, initial BW, and menopause status. However, changes in BW were predictive of changes in CRP (F=7.75, p=0.006, R2=0.05). The association remained significant after adjusting for differences in baseline age, initial BW, and menopause status (F=9.17, p=0.003, R2=0.08). Conclusions- Changes in BF% are not predictive of changes in CRP. However, in agreement with other studies, variations in BW are predictive of changes in CRP. Evidently, changes in CRP are more a function of changes in BW than changes in BF% in middle-aged women. If a causal relationship is assumed, then weight gain over time is likely to increase risk of elevated CRP levels and possibly cardiovascular disease.

The Body Mass Index (BMI) is a tool used broadly by public health agencies to assess weight in populations. However, when differentiating between fat mass and fat free mass the formula (BMI = weight in kilograms/height in meters2) is not applicable. Research suggests that evaluating body fat percentage and adipose tissue deposition may provide a nuanced indication of overall health, making it more accurate on an individual basis. This study evaluated four methods (Body Mass Index, waist circumference, A Body Shape Index, and Waist to Stature Index) that assess body composition and their ability to predict body fat percentage in female collegiate athletes and overweight/obese females. The study also investigated if the CUN‐BAE formula could calculate body fat percentage accurately in comparison to air displacement plethysmography in both populations. The study found that the universality of these algorithms is uncertain in diverse populations and that the predictive power of anthropometric‐based formulas is inconsistent when considering body fat percentage.

Purpose: To assess the dietary intake and body composition of recreational and competitive female athletes, for the purpose of analyzing the relationships between macronutrient intake and body composition.. The main aim was to determine the relationship between caloric intake, carbohydrate (CHO) intake and protein intake with body fat percentage in active females. Methods: Using an IRB approved protocol, 44 volunteer female recreational and competitive athletes 18 years of age or older were recruited. Interviews were conducted to gather information on within day energy balance by assessing the time and amount of foods/beverages consumed, and the duration and intensity (using a Rating of Perceived Exertion scale) of activity performed on the day of assessment. All analyses were performed using Nutritiming™ (Calorie and Pulse Technologies, Atlanta, GA) to assess energy surpluses, energy deficits, and end of day energy balance. Information on date of birth, race/ethnicity, menstrual status, sleep and wake times, and prior diagnoses of metabolic disease and/or eating disorders were collected at the time of the interview. Height was assessed using a standard stadiometer. Weight and body composition were assessed via Bioelectrical Impedance Analysis (BIA) using InBody 230 (BioSpace Co. USA). The BIA assessment was performed to determine body fat percentage, Basal Metabolic Rate (BMR), Body Mass Index (BMI), segmental body composition, and fat and lean mass in kilograms. Nutrient data were derived using an interviewer-led, 24-hour recall. Results: CHO intake/kg total mass was significantly and inversely correlated with body fat percentage and BMI, (p=0.018 and p=0.001 respectively). Protein intake/kg total mass was also inversely and significantly correlated with body fat percentage (p=0.006). Fat intake was not significantly associated with BMI, body fat percent, or lean mass in kilograms. Total energy intake/kg total mass was inversely associated with BMI (p=0.001), with fat mass (p=0.001), and with body fat percentage, (p=0.001). CHO intake/kg total mass was positively associated with the total number of hours spent in an anabolic (i.e., EB>0) state (p=0.001), and was inversely associated with the total number of hours spent in a catabolic (i.e., EB < 0) state (p=0.001). CHO intake/kg total mass was the only substrate to be significantly correlated with the number of hours spent ± 400 kcal EB over a 24 hour period (p=0.001). Z-scores were created to establish categories of body composition and energy balance values. Utilizing Chi-Square tests, it was determined that more hours spent in an energy surplus (> 400kcal) was associated with higher body fat percent (p=0.042). Conclusions: CHO restriction, whether done intentionally or as a function of an energy restrictive intake, was commonly observed in this subject pool. Of the females surveyed, 79% did not meet their daily energy needs and, on average, consumed 49% of the recommended daily intake of CHO established for active people. The findings that subjects with lower CHO intakes had higher body fat levels, and that CHO was associated with improved maintenance of energy balance, which was also associated with lower body fat percent, suggest that physically active women should not restrict CHO to achieve a desired body composition. It was also observed that end-of-day energy balance was not associated with either energy substrate consumption or body composition.

Background: Factors that contribute to body fat and adiposity include energy consumption, macronutrient intake, and physical activity. Alcohol not only contributes to total energy consumed but also influences metabolic pathways that may alter fat oxidation and storage. Alcohol provides 7.1 kilocalories per gram (kcal/g) and makes up 6-10% of the daily caloric intake of adults in the United States. Cross-sectional studies have shown that increased alcohol intake is associated with higher body mass index (BMI), especially in men. Other studies suggest that there is a “U” shaped association whereby non-drinkers and heavy drinkers have a higher BMI and waist-to-hip ratio (WHR) then low to moderate drinkers. While many previous studies evaluate alcohol based on the average consumption (g/day), there is increasing evidence that it is the pattern of alcohol consumption (ie. frequency) that influences body composition. The purpose of this study is to evaluate the effect of the frequency of wine, beer, and liquor consumption on body fat percent (BF%) and WHR in a population of university faculty and staff. Methods: The Center for Health Discovery and Well Being (CHDWB) cohort trial is being conducted at Emory University in Atlanta, GA. Recruitment of faculty and staff for the study began in 2007. Demographic, reported dietary intake including wine, beer, and liquor consumption, and anthropometric data including weight, height, BF%, and waist circumference are collected at baseline and annually thereafter. We used linear regression models to determine the effect of frequency and quantity of wine, beer, and liquor consumption on BF% while controlling for age and the effects of the other types of alcohol. We applied the Kruskal-Wallis test to determine if the median BF% and waist-hip ratio (WHR) was significantly different for those that reported at different five different frequencies (several times a year to 5-7 days a week). Results: Baseline visits have been conducted on 700 participants. Their median age was 51 years (66% female). Median weight was 76.9 kg (range, 65.3 - 90.5 kg) and mean BMI was 27.9 + 6.4 kg/m2. A significant negative relationship was observed between frequency of beer consumption and BF% in women (p Conclusions: The frequency of wine intake consumed by university employees and staff independently predicted BF% and BMI. Greater frequency of wine consumption was associated with lower BF%.

OBJECTIVE: The main objective of the present study was to assess the association between varying amounts of strength training and insulin resistance. Another goal was to assess the influence of several potential confounding variables on the strength training and insulin resistance relationship. Lastly, the role of waist circumference, fat free mass, and body fat percentage on the association between strength training and insulin resistance was assessed. METHODS: This cross-sectional study included 6561 randomly selected men and women in the US. Data were collected using the precise protocol established by NHANES. HOMA-IR was used as the outcome variable. Both time spent strength training and frequency of strength training bouts were used as exposure variables. RESULTS: There was not a statistically significant relationship between strength training and insulin resistance in women. However, after controlling for 10 potential confounding variables, men who reported no strength training had significantly higher levels of HOMA-IR compared to men who reported moderate or high levels of strength training (F = 9.87, P < 0.0001). Odds ratios were also assessed, and 10 potential confounding variables were controlled. Men reporting no strength training had 2.42 times the odds of having insulin resistance compared to men reporting moderate levels of strength training (95% CI: 1.19 to 4.93). Similarly, men reporting no strength training had 2.50 times the odds of having insulin resistance compared to men reporting high levels of strength training (95% CI: 1.25 to 5.00). CONCLUSION: There was a strong relationship between strength training and insulin resistance in US men, but not in US women. Differences in waist circumference, fat free mass, and body fat percentage, as well as demographic and lifestyle measures, do not appear to mediate the relationship.

No description available.

The postpartum period represents a high-risk period for body weight retention and obesity. Several studies have investigated the role of aerobic exercise on postpartum weight retention and other body composition outcomes; however, there has been little attention given to resistance training in postpartum women. Thus, the purpose of this four-month randomized study was to determine the effectiveness of resistance training on strength, body composition, return to pre-pregnancy weight, and bone mineral density (BMD) in postpartum women. Sixty postpartum women were randomly assigned to either a resistance training group or a comparison group. The resistance training group participated in a progressive resistance training program twice weekly for four months. The comparison group participated in a flexibility program twice weekly for four months. Strength changes were assessed for the upper body (bench press), lower body (leg press), and the core (abdominal curl-ups). Body composition, including BMD, was measured by dual energy x-ray absorptiometry. Over the four-month study, the resistance training group demonstrated a 36.7% increase in bench press, a 31.1% increase in leg press, and a 222.6% increase in abdominal curl-ups (p < 0.05). The flexibility group improved by 7.7% for bench press, 6.6% for leg press, and by 43.0% for abdominal curl-ups (p < 0.05). Group*period interactions were significant for the leg press, bench press, and abdominal curl-ups (p < 0.05). Both groups decreased in body weight, body fat percentage, and fat tissue (p < 0.05). Neither group significantly changed in lean tissue, whole body BMD, and hip BMD (p > 0.05). Group*period interactions were not significant for any body composition outcome (p > 0.05). These results suggest that a twice weekly resistance training program is superior to flexibility training to increase strength; however, resistance training may not be enough to influence body composition to a greater extent than flexibility training in postpartum women. More research is warranted.