The minimum methionine requirement in the presence of excess dietary cysteine has not been determined in older adults. This study aimed to determine the minimum methionine requirement in healthy older adults using the indicator amino acid oxidation (IAAO) method. Fifteen healthy adults ≥ 60 years of age received seven methionine intakes (0 to 20 mg/kg/d) plus excess dietary cysteine (40 mg/kg/d). Oxidation of the indicator, L-[1-¹³C]phenylalanine (F¹³CO₂), was used to estimate the mean minimum methionine requirement using a change-point mixed-effect model. There was no statistical difference between male and female requirement estimates, so the data were pooled to generate a mean of 5.1 mg/kg/d (R_m² = 0.46, R_c² = 0.77; p < 0.01; 95% CI: 3.67, 6.53 mg/kg/d). This is the first study to estimate the minimum methionine requirement in healthy older adults, which is the same between the sexes and as our lab’s previous estimate in young adults. The findings are relevant considering current recommendations for increased consumption of plant foods, which will help to establish the appropriate balance of methionine and cysteine intake required to satisfy the sulphur amino acid requirements of older adults.

A model specification test is a statistical procedure used to assess whether a given statistical model accurately represents the underlying data-generating process. The smoothing-based nonparametric specification test is widely used due to its efficiency against “singular” local alternatives. However, large modern datasets create various computational problems when implementing the nonparametric specification test. The divide-and-conquer algorithm is highly effective for handling large datasets, as it can break down a large dataset into more manageable datasets. By applying divide-and-conquer, the nonparametric specification test can handle the computational problems induced by the massive size of the modern datasets, leading to improved scalability and efficiency and reduced processing time. However, the selection of smoothing parameters for optimal power of the distributed algorithm is an important problem. The rate of the smoothing parameter that ensures rate optimality of the test in the context of testing the specification of a nonlinear parametric regression function is studied in the literature. In this paper, we verified the uniqueness of the rate of the smoothing parameter that ensures the rate optimality of divide-and-conquer-based tests. By employing a penalty method to select the smoothing parameter, we obtain a test with an asymptotic normal null distribution and adaptiveness properties. The performance of this test is further illustrated through numerical simulations.

Composite likelihood usually ignores dependencies among response components, while variational approximation to likelihood ignores dependencies among parameter components. We derive a Gaussian variational approximation to the composite log-likelihood function for Poisson and Gamma regression models with crossed random effects. We show consistency and asymptotic normality of the estimates derived from this approximation and support this theory with some simulation studies. The approach is computationally much faster than a Gaussian variational approximation to the full log-likelihood function.