Subtypes of Gestational Diabetes Mellitus Are Differentially Associated With Newborn and Childhood Metabolic Outcomes.

Objective: Subtypes of gestational diabetes mellitus (GDM) based on insulin sensitivity and secretion have been described. We addressed the hypothesis that GDM subtypes are differentially associated with newborn and child anthropometric and glycemic outcomes.

Research Design And Methods: Newborn and child (age 11-14 years) outcomes were examined in 7,970 and 4,160 mother-offspring dyads, respectively, who participated in the Hyperglycemia and Adverse Pregnancy Outcome Study (HAPO) and Follow-Up Study.

Biomarkers.

Background: Alzheimer's disease and type 2 diabetes mellitus rank among the top ten leading global causes of death. The association between diabetes and Alzheimer's is linked to chronic low-grade inflammation, hyperinsulinemia, and the interplay between peripheral and central insulin resistance, influencing insulin signalling. We evaluated the association between diabetes and Alzheimer's-related neuropathology in cognitively unimpaired older adults with diabetes.

Biomarkers.

Background: Obesity is a prevalent comorbid condition that doubles the risk of Alzheimer's disease. The proposed mechanisms underlying neuronal damage involve chronic low-grade inflammation, the production of reactive oxidative species, and altered brain glucose metabolism. In Alzheimer's disease, amyloid is thought to influence tau deposition and glucose metabolism.

Public Health.

Background: International collaboration is crucial to the future of research in dementia with Lewy bodies (DLB). Although it is hoped that a single global DLB cohort can be created through combination of data from the many longitudinal studies conducted internationally, a high likelihood of phenotypic heterogeneity may prohibit harmonisation and analysis of datasets. Our primary objective is to determine whether significant heterogeneity is observed in the sociodemographic and clinical characteristics of people with DLB globally.

Artificial intelligence and food flavor: How AI models are shaping the future and revolutionary technologies for flavor food development.

The food flavor science, traditionally reliant on experimental methods, is now entering a promising era with the help of artificial intelligence (AI). By integrating existing technologies with AI, researchers can explore and develop new flavor substances in a digital environment, saving time and resources. More and more research will use AI and big data to enhance product flavor, improve product quality, meet consumer needs, and drive the industry toward a smarter and more sustainable future.