Jeffrey Metter

We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, Pinter= 2.6 x 10-8). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDARADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 x 10-8), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight= 9.1 x 10-8), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10-4). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.

PURPOSE: The purpose of this study was to compare age and gender effects of strength training (ST) on resting metabolic rate (RMR), energy expenditure of physical activity (EEPA), and body composition. METHODS: RMR and EEPA were measured before and after 24 wk of ST in 10 young men (20-30 yr), 9 young women (20-30 yr), 11 older men (65-75 yr), and 10 older women (65-75 yr). RESULTS: When all subjects were pooled together, absolute RMR significantly increased by 7% (5928 +/- 1225 vs 6328 +/- 1336 kJ.d-1, P < 0.001). Furthermore, ST increased absolute RMR by 7% in both young (6302 +/- 1458 vs 6719 +/- 1617 kJ x d(-1), P < 0.01) and older (5614 +/- 916 vs 5999 +/- 973 kJ x d(-1), P < 0.05) subjects, with no significant interaction between the two age groups. In contrast, there was a significant gender x time interaction (P < 0.05) for absolute RMR with men increasing RMR by 9% (6645 +/- 1073 vs 7237 +/- 1150 kJ x d(-1), P < 0.001), whereas women showed no significant increase (5170 +/- 884 vs 5366 +/- 692 kJ x d(-1), P = 0.108). When RMR was adjusted for fat-free mass (FFM) using ANCOVA, with all subjects pooled together, there was still a significant increase in RMR with ST. Additionally, there was still a gender effect (P < 0.05) and no significant age effect (P = NS), with only the men still showing a significant elevation in RMR. Moreover, EEPA and TEE estimated with a Tritrac accelerometer and TEE estimated by the Stanford Seven-Day Physical Activity Recall Questionnaire did not change in response to ST for any group. CONCLUSIONS: In conclusion, changes in absolute and relative RMR in response to ST are influenced by gender but not age. In contrast to what has been suggested previously, changes in body composition in response to ST are not due to changes in physical activity outside of training.

BACKGROUND AND PURPOSE: The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time window for intervention. The goal of this study was to develop a supervised learning method to recognize acute cerebral ischemia (ACI) and differentiate that from stroke mimics in an emergency setting. METHODS: Consecutive patients presenting to the emergency department with stroke-like symptoms, within 4.5 hours of symptoms onset, in 2 tertiary care stroke centers were randomized for inclusion in the model. We developed an artificial neural network (ANN) model. The learning algorithm was based on backpropagation. To validate the model, we used a 10-fold cross-validation method. RESULTS: A total of 260 patients (equal number of stroke mimics and ACIs) were enrolled for the development and validation of our ANN model. Our analysis indicated that the average sensitivity and specificity of ANN for the diagnosis of ACI based on the 10-fold cross-validation analysis was 80.0% (95% confidence interval, 71.8-86.3) and 86.2% (95% confidence interval, 78.7-91.4), respectively. The median precision of ANN for the diagnosis of ACI was 92% (95% confidence interval, 88.7-95.3). CONCLUSIONS: Our results show that ANN can be an effective tool for the recognition of ACI and differentiation of ACI from stroke mimics at the initial examination.

A comparison of salivary flow rates was made between three groups of female individuals according to their menopausal status. The three groups consisted of healthy, dentate, nonmedicated women (with the exception of the use of estrogen) from the Baltimore Longitudinal Study of Aging. One group consisted of premenopausal women (n = 51), their mean age was 39 years. Another group (n = 26) was perimenopausal with a mean age of 48 years. A third group (n = 76) was postmenopausal with a mean age of 69 years. The groups were evaluated for unstimulated (UPAR) and stimulated parotid gland flow rates (SPAR), unstimulated (USUB) and stimulated submandibular/sublingual gland flow rates (SSUB), and stimulated whole-saliva flow rates (SWHOLE). The parotid flow rates were determined using a Carlson-Crittenden cup, while the submandibular/sublingual flow rates were determined using the National Institute of Dental Research collector. A 2% citrate solution was used for stimulation in glandular collections. Chewing a 1-cm3 cube of paraffin was used to stimulate whole saliva. The results showed no significant differences in UPAR, SPAR, and SWHOLE between the three groups. However, the premenopausal women had higher USUB than the postmenopausal group. The premenopausal women also had higher SSUB than perimenopausal and postmenopausal groups. There were no differences in salivary flow rates between those taking estrogen and those that were not medicated.