#学点健康小知识# 【#反式脂肪酸会加速大脑衰老#[吃惊]】反式脂肪酸对大脑有直接毒性，而大脑对于 反式脂肪酸的毒性作用很敏感。大脑皮层的海马区是主管学习、记忆的部位，反式脂肪酸进入人体内 后，可以在海马区的神经细胞里沉积，破坏神经细胞的抗氧化系统，造成神经细胞发生氧化损伤，甚至 让部分神经细胞凋亡，从而引发海马区结构的变化。海马区发生异常后，必然会影响我们的学习和记忆 能力，让我们的大脑发生衰老。（央视网） ...

Core Insights - The risk of developing Alzheimer's disease significantly increases when the brain is in a state of "extreme aging," with a probability of illness being 12 times higher over the next decade compared to those with a "younger brain" [1] - The mortality risk for individuals with "extremely aged" brains is 182% higher within 15 years, while those with the "youngest brains" experience a 40% reduction in mortality risk [1] - The brain plays a crucial role as a "gatekeeper" in determining health and longevity [1]

Core Viewpoint - The research published in PNAS reveals that brain aging follows a nonlinear process rather than a linear one, with insulin resistance being a key factor influencing this aging process [1][6][12]. Group 1: Nonlinear Aging Process - The study analyzed fMRI data from over 19,300 participants, showing that brain network instability changes with age in a nonlinear (S-shaped) manner [6][7]. - The S-shaped model indicates a slow change in early life, followed by accelerated changes approaching middle age (around 43.7 years), and then a plateau phase [9][11]. - Insulin resistance is identified as a driving mechanism for the trajectory of brain aging, affecting glucose metabolism and neuronal energy supply [12][17]. Group 2: Role of Ketone Bodies - Ketone bodies, such as D-β-hydroxybutyrate (D-βHB), can bypass insulin resistance and provide an alternative energy source for neurons, making them a potential intervention for brain aging [19][18]. - A study involving 101 healthy adults demonstrated that D-βHB significantly stabilizes brain networks, especially in the 40-59 age group, where the effect is 84.62% greater than in the 20-39 age group [24][26]. - The effectiveness of D-βHB diminishes in the 60-79 age group, indicating that middle age (40-59 years) is a critical intervention period for brain health [25][26].

Core Viewpoint - The research published in PNAS reveals that brain aging follows a nonlinear process rather than a linear one, with insulin resistance being a key factor influencing this aging process [1][6][12]. Group 1: Nonlinear Aging Process - The study analyzed fMRI data from over 19,300 participants, demonstrating that brain network instability changes with age in a nonlinear (S-shaped) manner [6][7]. - The S-shaped model indicates a slow change in early life, followed by accelerated changes approaching middle age (around 43.7 years), and then a plateau phase [9][11]. - Insulin resistance is identified as a driving mechanism for the trajectory of brain aging, affecting glucose metabolism and neuronal energy supply [12][17]. Group 2: Role of Ketone Bodies - Ketone bodies, such as D-β-hydroxybutyrate (D-βHB), can bypass insulin resistance and provide an alternative energy source for neurons, making them a potential intervention for combating brain aging [19][18]. - A study involving 101 healthy adults showed that D-βHB significantly stabilizes brain networks, particularly in the 40-59 age group, where the effect is 84.62% greater than in the 20-39 age group [24][26]. - The effectiveness of D-βHB diminishes in the 60-79 age group, indicating that middle age (40-59 years) is a critical intervention period for brain health [25][26].

Core Viewpoint - The article discusses a significant study identifying 64 genes that influence brain aging speed and suggests 13 potential anti-aging drugs, utilizing AI models to analyze brain scans and genetic data [1][3]. Research Overview - The study is noted as the largest attempt to determine genetic factors affecting organ aging, with implications for developing new brain anti-aging drugs [3]. - The research aims to identify factors leading to brain aging and explore potential solutions [5]. Methodology - The study uses Brain Age Gap (BAG) as a marker for brain aging, defined as the difference between predicted brain age and actual age [6]. - Data from 29,097 healthy participants in the UK Biobank was used to train seven AI models for brain age estimation [8]. - Validation was conducted using data from 3,227 healthy and 6,637 brain disease subjects, employing various assessment metrics [9][10]. Genetic Analysis - A Genome-Wide Association Study (GWAS) was performed on 31,520 healthy participants to identify genetic variations associated with BAG [11][12]. - The study explored the causal relationship between BAG and 18 brain diseases, finding a significant impact on intelligence [13][14]. Drug Discovery - The research identified 64 druggable genes linked to biological pathways related to brain aging, suggesting that targeting these genes could help combat aging or related diseases [14][15]. - A drug repurposing analysis revealed 466 potential anti-aging drugs, with 29 showing promise in delaying brain aging [17][18]. - Among these, 20 drugs, including Dasatinib and Diclofenac, have been previously noted for their anti-aging potential, with 13 currently undergoing clinical trials [19][20].