Type 2 diabetes has been associated with cognitive decline, but its metabolic mechanism remains unclear. In the present study, we attempted to investigate brain region-specific metabolic changes in db/db mice with cognitive decline and explore the potential metabolic mechanism linking type 2 diabetes and cognitive decline. We analyzed the metabolic changes in seven brain regions of two types of mice (wild-type mice and db/db mice with cognitive decline) using a H NMR-based metabolomic approach. Then, a mixed-model analysis was used to evaluate the effects of mice type, brain region, and their interaction on metabolic changes. Compared with the wild-type mice, the db/db mice with cognitive decline had significant increases in lactate, glutamine (Gln) and taurine as well as significant decreases in alanine, aspartate, choline, succinate, γ-Aminobutyric acid (GABA), glutamate (Glu), glycine, N-acetylaspartate, inosine monophosphate, adenosine monophosphate, adenosine diphosphate, and nicotinamide adenine dinucleotide. Brain region-specific metabolic differences were also observed between these two mouse types. In addition, we found significant interaction effects of mice type and brain region on creatine/phosphocreatine, lactate, aspartate, GABA, N-acetylaspartate and taurine. Based on metabolic pathway analysis, the present study suggests that cognitive decline in db/db mice might be linked to a series of brain region-specific metabolic changes, involving an increase in anaerobic glycolysis, a decrease in tricarboxylic acid (TCA) and Gln-Glu/GABA cycles as well as a disturbance in lactate-alanine shuttle and membrane metabolism.
AimTo develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk.MethodsA total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality.ResultsThe DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality.ConclusionsOur findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions.
BackgroundElucidation of metabolic profiles during diabetes progression helps understand the pathogenesis of diabetes mellitus. In this study, urine metabonomics was used to identify time-related metabolic changes that occur during the development of diabetes mellitus and characterize the biochemical process of diabetes on a systemic, metabolic level.Methodology/Principal FindingsUrine samples were collected from diabetic rats and age-matched controls at different time points: 1, 5, 10, and 15 weeks after diabetes modeling. 1H nuclear magnetic resonance (1H NMR) spectra of the urine samples were obtained and analyzed by multivariate data analysis and quantitative statistical analysis. The metabolic patterns of diabetic groups are separated from the controls at each time point, suggesting that the metabolic profiles of diabetic rats were markedly different from the controls. Moreover, the samples from the diabetic 1-wk group are closely associated, whereas those of the diabetic 15-wk group are scattered, suggesting that the presence of various of complications contributes significantly to the pathogenesis of diabetes. Quantitative analysis indicated that urinary metabolites related to energy metabolism, tricarboxylic acid (TCA) cycle, and methylamine metabolism are involved in the evolution of diabetes.Conclusions/SignificanceThe results highlighted that the numbers of metabolic changes were related to diabetes progression, and the perturbed metabolites represent potential metabolic biomarkers and provide clues that can elucidate the mechanisms underlying the generation and development of diabetes as well as its complication.
Diabetes mellitus (DM) can result in cognitive dysfunction, but its potential metabolic mechanisms remain unclear. In the present study, we analyzed the metabolite profiling in eight different brain regions of the normal rats and the streptozotocin (STZ)-induced diabetic rats accompanied by cognitive dysfunction using a H NMR-based metabolomic approach. A mixed linear model analysis was performed to assess the effects of DM, brain region and their interaction on metabolic changes. We found that different brain regions in rats displayed significant metabolic differences. In addition, the hippocampus was more susceptible to DM compared with other brain regions in rats. More interestingly, significant interaction effects of DM and brain region were observed on alanine, creatine/creatine-phosphate, lactate, succinate, aspartate, glutamate, glutamine, γ-aminobutyric acid, glycine, choline, N-acetylaspartate, myo-inositol and taurine. Based on metabolic pathway analysis, we speculate that cognitive dysfunction in the STZ-induced diabetic rats may be associated with brain region-specific metabolic alterations involving energy metabolism, neurotransmitters, membrane metabolism and osmoregulation.
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