@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} } @article {9917, title = {Association Between Cigarette Smoking~and Subclinical Markers of Cardiovascular Harm.}, journal = {J Am Coll Cardiol}, volume = {85}, year = {2025}, month = {2025 Mar 18}, pages = {1018-1034}, abstract = {

BACKGROUND: Cigarette smoking is a strong risk factor for cardiovascular harm.

OBJECTIVES: The study sought to explore the detailed relationships between smoking intensity, pack-years, and~time since cessation with inflammation, thrombosis, and subclinical atherosclerosis markers of cardiovascular harm.

METHODS: We included 182,364 participants (mean age 58.2 years, 69.0\% female) from 22 cohorts of the Cross Cohort Collaboration with self-reported smoking status, including smoking intensity and/or pack-years, and concurrent subclinical marker measurements. Markers were categorized into 3 domains: inflammation (high-sensitivity C-reactive protein, interleukin-6, glycoprotein acetylation), thrombosis (fibrinogen, D-dimer), and subclinical atherosclerosis (coronary artery calcium, carotid intima-media thickness, carotid plaque, and ankle-brachial index). Utilizing multivariate regression models and restricted cubic splines, we assessed associations of smoking status, intensity, pack-years, time since cessation, and subclinical markers.

RESULTS: A total of 15.3\% of participants currently smoke (mean 16.7 cigarettes/day, mean 30.0 pack-years), and 34.6\% of participants formerly smoked (median 19.0 years since quitting, mean 22.4 pack-years). Participants with a history of smoking showed higher levels of all subclinical markers compared with those who have never smoked, with stronger associations observed in those currently smoke. Among participants who currently smoke, smoking intensity showed a clear dose-response relationship with all markers, except for D-dimer, specifically with incremental 1\% to 9\% higher levels of subclinical markers per 10 cigarettes. After 20 cigarettes, the patterns appeared to plateau for blood markers, while they continued to increase for atherosclerosis markers. Among those who have ever smoked, robust dose-response relationships were observed for pack-years with all subclinical markers, with incremental 1\% to 9\% higher levels per 10 pack-years. The dose-response effects persisted after 20 pack-years for all markers, though with a milder slope. Among participants who smoked formerly, there were substantially lower levels of biomarkers with longer time since quitting, and most markers were not different compared with those who have never smoked by 30 years, except for the coronary artery calcium score, which remained 19\% higher even beyond quitting after 30 years.

CONCLUSIONS: Smoking-relevant parameters show strong and dose-response relationships across 3 domains of subclinical markers of cardiovascular harm. The sensitivity of the tested subclinical markers to small increments in cigarette exposure suggests potential value in the regulation of new and existing tobacco products.

}, keywords = {Aged, Atherosclerosis, Biomarkers, Cardiovascular Diseases, Carotid Intima-Media Thickness, Cigarette Smoking, Cohort Studies, Female, Humans, Male, Middle Aged, Risk Factors}, issn = {1558-3597}, doi = {10.1016/j.jacc.2024.12.032}, author = {Yao, Zhiqi and Tasdighi, Erfan and Dardari, Zeina A and Jha, Kunal K and Osuji, Ngozi and Rajan, Tanuja and Boakye, Ellen and Rodriguez, Carlos J and Matsushita, Kunihiro and Simonsick, Eleanor M and Lima, Jo{\~a}o A C and Widome, Rachel and Cohen, Debbie L and Appel, Lawrence J and Khera, Amit and Hall, Michael E and Judd, Suzanne E and Cole, Shelley A and Vasan, Ramachandran S and Benjamin, Emelia J and Lotufo, Paulo A and Bensenor, Isabela M and El Khoudary, Samar R and Barinas-Mitchell, Emma and Janssen, Imke and Psaty, Bruce M and Eaton, Charles B and Lamonte, Michael J and Cawthon, Peggy M and Orwoll, Eric S and Irvin, Marguerite R and Bhatnagar, Aruni and DeFilippis, Andrew P and El-Shahaway, Omar and Blaha, Michael J} }