@article {8989, title = {What Cut-Point in Gait Speed Best Discriminates Community-Dwelling Older Adults With Mobility Complaints From Those Without? A Pooled Analysis From the Sarcopenia Definitions and Outcomes Consortium.}, journal = {J Gerontol A Biol Sci Med Sci}, volume = {76}, year = {2021}, month = {2021 09 13}, pages = {e321-e327}, abstract = {

BACKGROUND: Cut-points to define slow walking speed have largely been derived from expert opinion.

METHODS: Study participants (13 589 men and 5043 women aged >=65years) had walking speed (m/s) measured over 4-6 m (mean {\textpm} SD: 1.20 {\textpm} 0.27 m/s in men and 0.94 {\textpm} 0.24 m/s in women.) Mobility limitation was defined as any self-reported difficulty with walking approximately 1/4 mile (prevalence: 12.6\% men, 26.4\% women). Sex-stratified classification and regression tree (CART) models with 10-fold cross-validation identified walking speed cut-points that optimally discriminated those who reported mobility limitation from those who did not.

RESULTS: Among 5043 women, CART analysis identified 2 cut-points, classifying 4144 (82.2\%) with walking speed >=0.75 m/s, which we labeled as "fast"; 478 (9.5\%) as "intermediate" (walking speed >=0.62 m/s but <0.75 m/s); and 421 (8.3\%) as "slow" (walking speed <0.62 m/s). Among 13 589 men, CART analysis identified 3 cut-points, classifying 10 001 (73.6\%) with walking speed >=1.00 m/s ("very fast"); 2901 (21.3\%) as "fast" (walking speed >=0.74 m/s but <1.00 m/s); 497 (3.7\%) as "intermediate" (walking speed >=0.57 m/s but <0.74 m/s); and 190 (1.4\%) as "slow" (walking speed <0.57 m/s). Prevalence of self-reported mobility limitation was lowest in the "fast" or "very fast" (11\% for men and 19\% for women) and highest in the "slow" (60.5\% in men and 71.0\% in women). Rounding the 2 slower cut-points to 0.60 m/s and 0.75 m/s reclassified very few participants.

CONCLUSIONS: Cut-points in walking speed of approximately 0.60 m/s and 0.75 m/s discriminate those with self-reported mobility limitation from those without.

}, keywords = {Aged, Female, Gait, Humans, Independent Living, Male, Mobility Limitation, Sarcopenia, Walking, Walking Speed}, issn = {1758-535X}, doi = {10.1093/gerona/glab183}, author = {Cawthon, Peggy M and Patel, Sheena M and Kritchevsky, Stephen B and Newman, Anne B and Santanasto, Adam and Kiel, Douglas P and Travison, Thomas G and Lane, Nancy and Cummings, Steven R and Orwoll, Eric S and Duchowny, Kate A and Kwok, Timothy and Hirani, Vasant and Schousboe, John and Karlsson, Magnus K and Mellstr{\"o}m, Dan and Ohlsson, Claes and Ljunggren, Osten and Xue, Qian-Li and Shardell, Michelle and Jordan, Joanne M and Pencina, Karol M and Fielding, Roger A and Magaziner, Jay and Correa-de-Araujo, Rosaly and Bhasin, Shalender and Manini, Todd M} } @article {9804, title = {Sociodemographic, lifestyle, and medical factors associated with calculated free testosterone concentrations in men: individual participant data meta-analyses.}, journal = {Eur J Endocrinol}, volume = {191}, year = {2024}, month = {2024 Oct 29}, pages = {523-534}, abstract = {

OBJECTIVE: Sociodemographic, lifestyle, and medical variables influence total testosterone (T) and sex hormone-binding globulin (SHBG) concentrations. The relationship between these factors and "free" T remains unclear. We examined 21 sociodemographic, lifestyle, and medical predictors influencing calculated free T (cFT) in community-dwelling men across ages.

DESIGN: This is a cross-sectional analysis in 20 631 participants in the Androgens in Men Study.

METHODS: Individual participant data (IPD) were provided by 9 cohorts. Total T was determined using mass spectrometry, SHBG using immunoassays, and cFT using the Vermeulen formula. Associations were analyzed using 2-stage random effects IPD meta-analyses.

RESULTS: Cohort median ages ranged from 40 to 76 years and median cFT concentrations from 174.3 to 422.8 pmol/L. In men aged 17-99 years, there was a linear inverse association of cFT with age (-57.2 pmol/L [95\% confidence interval, -69.4, -44.9] per 1 SD increase in age). Calculated free T increased with increasing baseline body mass index (BMI) among men with BMI < 23.6 kg/m2, but decreased among men with BMI > 23.6 kg/m2 (-24.7 pmol/L [-29.1, -20.3] per 1 SD increase in the 25.4-29.6 kg/m2 BMI range). Calculated free T was lower in younger men, who were married or in a de facto relationship (-18.4 pmol/L [-27.6, -9.3]) and in men who formerly smoked (-5.7 pmol/L [-8.9, -2.6]), were in poor general health (-14.0 pmol/L [-20.1, -7.8]), and had diabetes (-19.6 pmol/L [-23.0, -16.3]), cardiovascular disease (-5.8 pmol/L [-8.3, -3.2]), or cancer (-19.2 pmol/L [-24.4, -14.1]).

CONCLUSIONS: Calculated free T was most prominently associated with age and BMI. The linear, inverse association with age, nonlinear association with BMI, and presence of diabetes, cancer, and sociodemographic factors should be considered when interpreting cFT values.

}, keywords = {Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Body Mass Index, Cohort Studies, Cross-Sectional Studies, Humans, Life Style, Male, Middle Aged, Sex Hormone-Binding Globulin, Sociodemographic Factors, Socioeconomic Factors, Testosterone, Young Adult}, issn = {1479-683X}, doi = {10.1093/ejendo/lvae133}, author = {Narinx, Nick and Marriott, Ross J and Murray, Kevin and Adams, Robert J and Ballantyne, Christie M and Bauer, Douglas C and Bhasin, Shalender and Biggs, Mary L and Cawthon, Peggy M and Couper, David J and Dobs, Adrian S and Flicker, Leon and Hankey, Graeme J and Hannemann, Anke and Wilkening, Robin and Martin, Sean A and Matsumoto, Alvin M and Ohlsson, Claes and O{\textquoteright}Neill, Terence W and Orwoll, Eric S and Shores, Molly M and Steveling, Antje and Travison, Thomas G and Wittert, Gary A and Wu, Frederick C W and Antonio, Leen and Vanderschueren, Dirk and Yeap, Bu B} }