Background: Changes in body composition in men and women occur with age, but these changes are affected by numerous covariate factors. Objective: The study examined patterns of change in body composition and determined the effects of long-term patterns of change in physical activity in older men and women and in menopausal status and estrogen use in women. Design: Serial measures of height, weight, body mass index (BMI), total body fat (BF), percentage BF, and fat-free mass (FFM) from underwater weighing of 102 men and 108 women enrolled in the Fels Longitudinal Study were analyzed. Physical activity levels and menopausal status were included as covariates. Results: There were significant age-related decreases in FFM and height and increases in total BF, percentage BF, weight, and BMI. Physical activity was associated with decreases in total BF, percentage BF, weight, and BMI in men and were associated with increases in FFM and decreases in total BF and percentage BF in women. Postmenopausal women had significantly higher total BF and percentage BF than did pre-and perimenopausal women. The longer the time since menopause the greater were the increases in weight, BMI, total BF, and percentage BF; however, estrogen use attenuated these increases. Conclusions: Low FFM can be improved by increased physical activity. The effects of an intervention program on body composition can be masked if only body weight or BMI is measured. The effects of physical activity were more profound in postmenopausal than in premenopausal women, and estrogen use had beneficial effects on body composition.Am J Clin Nutr 1999;70:405-11.
The findings from these mixed longitudinal data indicate that TBW volume, on average, maintains a reasonable degree of stability in men and women through a large portion of adulthood. These TBW data are recommended as current reference data for healthy adults.
: TBW in these healthy adults is relatively stable through a large portion of adulthood. There are significant race and sex differences in TBW. These accurate and precise equations for TBW provide a useful tool for the clinical prediction of TBW in renal disease for white and black adults. These are the first TBW prediction equations that are specific for blacks.
Knowing whether a protein can be processed and the resulting peptides presented by major histocompatibility complex (MHC) is highly important for immunotherapy design. MHC ligands can be predicted by in silico peptide-MHC class-I binding prediction algorithms. However, prediction performance differs considerably, depending on the selected algorithm, MHC class-I type, and peptide length. We evaluated the prediction performance of 13 algorithms based on binding affinity data of 8-to 11-mer peptides derived from the HPV16 E6 and E7 proteins to the most prevalent human leukocyte antigen (HLA) types. Peptides from high to low predicted binding likelihood were synthesized, and their HLA binding was experimentally verified by in vitro competitive binding assays. Based on the actual binding capacity of the peptides, the performance of prediction algorithms was analyzed by calculating receiver operating characteristics (ROC) and the area under the curve (A ROC). No algorithm outperformed others, but different algorithms predicted best for particular HLA types and peptide lengths. The sensitivity, specificity, and accuracy of decision thresholds were calculated. Commonly used decision thresholds yielded only 40% sensitivity. To increase sensitivity, optimal thresholds were calculated, validated, and compared. In order to make maximal use of prediction algorithms available online, we developed MHCcombine, a web application that allows simultaneous querying and output combination of up to 13 prediction algorithms. Taken together, we provide here an evaluation of peptide-MHC class-I binding prediction tools and recommendations to increase prediction sensitivity to extend the number of potential epitopes applicable as targets for immunotherapy.
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