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The purpose of this study was to determine the effect of hydration status on the change in sweat sodium (Na+), chloride (Cl−), and potassium (K+) concentrations during exercise‐heat stress. Fifteen subjects (Six female, nine male; 29 ± 9 y; 71 ± 14 kg) completed 90 min of cycling (81% HRmax) in the heat (~33°C, 42% rh) with fluid replacement to maintain euhydration (EUH) or without fluid to dehydrate to 2.4 ± 0.4% body mass loss (DEH). Sweat was collected from the forehead (FH), right scapula (SCAP), and left (LVFA) and right (RVFA) ventral forearms using the absorbent pad technique at the beginning (0–30 min) and end of exercise (60–90 min). Sweat was analyzed for Na+, Cl−, and K+ concentrations using ion chromatography. Data are reported as mean ± SD or median ± IQR. There were no differences (Paired t‐tests or Wilcoxon signed‐rank tests) between EUH and DEH in the change in sweat Na+ (FH: 24.3 ± 21.5 vs. 30.8 ± 22.4 mmol/L; SCAP: 9.7 ± 6.2 vs. 9.6 ± 8.2 mmol/L; LVFA: 7.5 ± 6.0 vs. 5.6 ± 5.9 mmol/L; RVFA: 8.2 ± 8.6 vs. 7.8 ± 5.2 mmol/L), sweat Cl−, or sweat K+ at any site (p = 0.07–0.99). The change in sweat electrolyte concentrations during 90 min of exercise in the heat was not significantly influenced by mild dehydration in recreational to moderately‐trained male and female athletes. [ABSTRACT FROM AUTHOR]

Purpose: To quantify total sweat electrolyte losses at two relative exercise intensities and determine the effect of workload on the relation between regional (REG) and whole body (WB) sweat electrolyte concentrations.Methods: Eleven recreational athletes (7 men, 4 women; 71.5 ± 8.4 kg) completed two randomized trials cycling (30 °C, 44% rh) for 90 min at 45% (LOW) and 65% (MOD) of VO2max in a plastic isolation chamber to determine WB sweat [Na+] and [Cl-] using the washdown technique. REG sweat [Na+] and [Cl-] were measured at 11 REG sites using absorbent patches. Total sweat electrolyte losses were the product of WB sweat loss (WBSL) and WB sweat electrolyte concentrations.Results: WBSL (0.86 ± 0.15 vs. 1.27 ± 0.24 L), WB sweat [Na+] (32.6 ± 14.3 vs. 52.7 ± 14.6 mmol/L), WB sweat [Cl-] (29.8 ± 13.6 vs. 52.5 ± 15.6 mmol/L), total sweat Na+ loss (659 ± 340 vs. 1565 ± 590 mg), and total sweat Cl- loss (931 ± 494 vs. 2378 ± 853 mg) increased significantly (p < 0.05) from LOW to MOD. REG sweat [Na+] and [Cl-] increased from LOW to MOD at all sites except thigh and calf. Intensity had a significant effect on the regression model predicting WB from REG at the ventral wrist, lower back, thigh, and calf for sweat [Na+] and [Cl-].Conclusion: Total sweat Na+ and Cl- losses increased by ~ 150% with increased exercise intensity. Regression equations can be used to predict WB sweat [Na+] and [Cl-] from some REG sites (e.g., dorsal forearm) irrespective of intensity (between 45 and 65% VO2max), but other sites (especially ventral wrist, lower back, thigh, and calf) require separate prediction equations accounting for workload. [ABSTRACT FROM AUTHOR]

Purpose: To quantify total sweat electrolyte losses at two relative exercise intensities and determine the effect of workload on the relation between regional (REG) and whole body (WB) sweat electrolyte concentrations.Methods: Eleven recreational athletes (7 men, 4 women; 71.5 ± 8.4 kg) completed two randomized trials cycling (30 °C, 44% rh) for 90 min at 45% (LOW) and 65% (MOD) of VO2max in a plastic isolation chamber to determine WB sweat [Na+] and [Cl-] using the washdown technique. REG sweat [Na+] and [Cl-] were measured at 11 REG sites using absorbent patches. Total sweat electrolyte losses were the product of WB sweat loss (WBSL) and WB sweat electrolyte concentrations.Results: WBSL (0.86 ± 0.15 vs. 1.27 ± 0.24 L), WB sweat [Na+] (32.6 ± 14.3 vs. 52.7 ± 14.6 mmol/L), WB sweat [Cl-] (29.8 ± 13.6 vs. 52.5 ± 15.6 mmol/L), total sweat Na+ loss (659 ± 340 vs. 1565 ± 590 mg), and total sweat Cl- loss (931 ± 494 vs. 2378 ± 853 mg) increased significantly (p < 0.05) from LOW to MOD. REG sweat [Na+] and [Cl-] increased from LOW to MOD at all sites except thigh and calf. Intensity had a significant effect on the regression model predicting WB from REG at the ventral wrist, lower back, thigh, and calf for sweat [Na+] and [Cl-].Conclusion: Total sweat Na+ and Cl- losses increased by ~ 150% with increased exercise intensity. Regression equations can be used to predict WB sweat [Na+] and [Cl-] from some REG sites (e.g., dorsal forearm) irrespective of intensity (between 45 and 65% VO2max), but other sites (especially ventral wrist, lower back, thigh, and calf) require separate prediction equations accounting for workload. [ABSTRACT FROM AUTHOR]

Several letters to the editor are presented in response to the article "Association of weight status with mortality in adults with incident diabetes," by M.R. Carnethon and colleagues in the previous issue and a response from the author of the article is also presented.

Background: Diabetes (DM) is more common among adults with lower socioeconomic status (SES). However, it is not known whether the socioeconomic disparities in DM are obscured by the overall rising prevalence of DM. Our objective was to test whether disparities in diabetes by SES have changed between 1999 and 2010. Methods: Participants from National Health and Nutrition Examination Surveys aged > 20 years with data available to determine DM at each examination were included (n= 11,952). DM was determined as fasting glucose =126 mg/dL or self-report of DM medication use. Educational attainment was used to represent SES and categorized into 4 groups. Predictive marginal probabilities from logistic regression models were used to calculate the age-standardized prevalence of DM by educational category across 3 survey periods (1999-2002, 2003-2006, 2007- 2010). We tested whether the relationship of SES with DM changed over time using interaction terms between education and survey period. Results: The age-standardized prevalence of diabetes was 8.5% (95% CI: 7.3-9.7) in 1999-2002, 9.6% (95% CI: 8.4-10.7) in 2003-2006 and 11.3% (95% CI: 10.0-12.5) in 2007-2010. In each survey period, DM prevalence was highest among adults with the least education (p<0.01) and that relationship did not change over time (p=0.50 for education*year). When we stratified by race/ethnicity, non-Hispanic whites and blacks had the same socioeconomic patterning over time as the total population (Figure). However, education was not associated with DM in Mexican Americans, and this did not change over time. Conclusion: Socioeconomic disparities in DM persist over time; however, the inverse gradient in DM prevalence is not present in Mexican Americans. [ABSTRACT FROM AUTHOR]