Hyperglycemia is associated with relatively lower lean body mass in older adults.

BACKGROUND/OBJECTIVES: Older adults with known diabetes are vulnerable to accelerated loss of lean body mass. However, the relationship of hyperglycemia per se with lean body mass is not fully understood. We sought to examine the independent relationship of hyperglycemia with relative lean body mass in older persons without a reported history of diabetes. DESIGN: Cross-sectional nationally representative survey. SETTING: United States. PARTICIPANTS: We studied U.S. adults >50 years without known diabetes (n=5434) in the National Health and Nutrition Examination Survey (1999-2004). MEASUREMENTS: In linear regression models, we studied the relationship of measured HbA1c (<5.0%, 5.0-5.4%, 5.5-5.9%, 6.0-6.4%, ≥6.5%) with percent lean body mass, measured by dual-energy x-ray absorptiometry, after accounting for potential confounders. RESULTS: Among older U.S. men and women, progressively higher HbA1c was associated with relatively lower total, appendicular, and trunk percent lean mass, independent of demographics and height (all p<0.05). Accounting for physical activity, C-reactive protein, and diabetes-related comorbidities (heart disease, peripheral arterial disease, arthritis, neuropathy, hip fracture, amputation, cancer, pulmonary disease), undiagnosed diabetes (i.e. HbA1c ≥6.5%) versus reference (<5.0%) in both men and women was associated with lower total (-3.5±0.8% and -2.9±0.8%), appendicular (-1.8±0.5% and -1.2±0.4%), and trunk percent lean mass (-1.2±0.4% and -1.3±0.5%), respectively (all p<0.05). Persons at increased risk for diabetes (i.e. HbA1c 6.0-6.4%) also had significant decrements at these sites versus reference. CONCLUSIONS: Hyperglycemia is associated with relatively lower lean mass in a nationally representative population of older adults without history of diabetes. Future longitudinal studies are needed to investigate the relationship of hyperglycemia with the accelerated decline of skeletal muscle mass in older persons.

Bayesian modeling of age-specific survival in nesting studies under Dirichlet priors.

There has been much work done in nest survival analysis using the maximum likelihood (ML) method. The ML method suffers from the instability of numerical calculations when models having a large number of unknown parameters are used. A Bayesian approach of model fitting is developed to estimate age-specific survival rates for nesting studies using a large class of prior distributions. The computation is done by Gibbs sampling. Some latent variables are introduced to simplify the full conditional distributions. The method is illustrated using both a real and a simulated data set. Results indicate that Bayesian analysis provides stable and accurate estimates of nest survival rates.