Dietary restriction of individual amino acids stimulates unique molecular responses in mouse liver.

Dietary protein and essential amino acid (EAA) restriction promotes favorable metabolic reprogramming, ultimately resulting in improvements to both health and lifespan. However, as individual EAAs have distinct catabolites and engage diverse downstream signaling pathways, it remains unclear to what extent shared or AA-specific molecular mechanisms promote diet-associated phenotypes. Here, we investigated the physiological and molecular effects of restricting either dietary methionine, leucine, or isoleucine (Met-R, Leu-R, and Ile-R) for 3 weeks in C57BL/6J male mice. While all 3 AA-depleted diets promoted fat and lean mass loss and slightly improved glucose tolerance, the molecular responses were more diverse; while hepatic metabolites altered by Met-R and Leu-R were highly similar, Ile-R led to dramatic changes in metabolites, including a 3-fold reduction in the oncometabolite 2-hydroxyglutarate. Pathways regulated in an EAA-specific manner included glycolysis, the pentose phosphate pathway (PPP), nucleotide metabolism, the TCA cycle and amino acid metabolism. Transcriptiome analysis and global profiling of histone post-translational modifications (PTMs) revealed different patterns of responses to each diet, although Met-R and Leu-R again shared similar transcriptional responses. While the pattern of global histone PTMs were largely unique for each dietary intervention, Met-R and Ile-R had similar changes in histone-3 methylation/acetylation PTMs at lysine-9. Few similarities were observed between the physiological or molecular responses to EAA restriction and treatment with rapamycin, an inhibitor of the mTORC1 AA-responsive protein kinase, indicating the response to EAA restriction may be largely independent of mTORC1. Together, these results demonstrate that dietary restriction of individual EAAs has unique, EAA-specific effects on the hepatic metabolome, epigenome, and transcriptome, and suggests that the specific EAAs present in dietary protein may play a key role at regulating health at the molecular level.

Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes.

Purpose: To gain an understanding of the heterogeneous group of type 2 diabetes (T2D) patients, we aimed to identify patients with the homogenous long-term HbA1c trajectories and to predict the trajectory membership for each patient using explainable machine learning methods and different clinical-, treatment-, and socio-economic-related predictors. Patients and Methods: Electronic health records data covering primary and specialized healthcare on 9631 patients having T2D diagnosis were extracted from the North Karelia region, Finland. Six-year HbA1c trajectories were examined with growth mixture models. Linear discriminant analysis and neural networks were applied to predict the trajectory membership individually. Results: Three HbA1c trajectories were distinguished over six years: "stable, adequate" (86.5%), "improving, but inadequate" (7.3%), and "fluctuating, inadequate" (6.2%) glycemic control. Prior glucose levels, duration of T2D, use of insulin only, use of insulin together with some oral antidiabetic medications, and use of only metformin were the most important predictors for the long-term treatment balance. The prediction model had a balanced accuracy of 85% and a receiving operating characteristic area under the curve of 91%, indicating high performance. Moreover, the results based on SHAP (Shapley additive explanations) values show that it is possible to explain the outcomes of machine learning methods at the population and individual levels. Conclusion: Heterogeneity in long-term glycemic control can be predicted with confidence by utilizing information from previous HbA1c levels, fasting plasma glucose, duration of T2D, and use of antidiabetic medications. In future, the expected development of HbA1c could be predicted based on the patient's unique risk factors offering a practical tool for clinicians to support treatment planning.

Impact of Navier's slip and chemical reaction on the hydromagnetic hybrid nanofluid flow and mass transfer due to porous stretching sheet.

Hybrid nanofluids (HNFs) comprise combinations of different nanoparticles suspended in base fluid. Applications of such nanofluids are rising in the areas of energy and biomedical engineering including smart (functional) coatings. Motivated by these developments, the present article examines theoretically the magnetohydrodynamic coating boundary layer flow of HNFs from a stretching sheet under the transverse magnetic field in porous media with chemically reactive nanoparticles. Darcy's law is deployed. Momentum slips of both first and second order are included as is solutal slip. The transformed boundary value problem is solved analytically. Closed form solutions for velocity are derived in terms of exponential functions and for the concentration field in terms of incomplete Gamma functions by the application of the Laplace transformation technique. The influence of selected parameters e.g. suction/injection, magnetic field and slips on velocity and concentration distributions are visualized graphically. Concentration magnitudes are elevated with stronger magnetic field whereas they are suppressed with greater wall solutal slip. Magnetic field suppresses velocity and increases the thickness of the hydrodynamic boundary layer. The flow is accelerated with reduction in inverse Darcy number and stronger suction direct to reduce in skin friction. The concentration magnitudes are boosted with magnetic field whereas they are depleted with increasing solutal slip. The analysis provides a good foundation for further investigations using numerical methods.

The MHD Newtonian hybrid nanofluid flow and mass transfer analysis due to super-linear stretching sheet embedded in porous medium.

The steady magnetohydrodynamics (MHD) incompressible hybrid nanofluid flow and mass transfer due to porous stretching surface with quadratic velocity is investigated in the presence of mass transpiration and chemical reaction. The basic laminar boundary layer equations for momentum and mass transfer, which are non-linear partial differential equations, are converted into non-linear ordinary differential equations by means of similarity transformation. The mass equation in the presence of chemical reaction is a differential equation with variable coefficients, which is transformed to a confluent hypergeometric differential equation. The mass transfer is analyzed for two different boundary conditions of concentration field that are prescribed surface concentration (PSC) and prescribed mass flux (PMF). The asymptotic solution of concentration filed for large Schmidt number is analyzed using Wentzel-Kramer-Brillouin (WKB) method. The parameters influence the flow are suction/injection, superlinear stretching parameter, porosity, magnetic parameter, hybrid nanofluid terms, Brinkman ratio and the effect of these are analysed using graphs.