Cardiometabolic risk factors and brain age: a meta-analysis to quantify brain structural differences related to diabetes, hypertension, and obesity.

IF 3.3 2区 医学 Q2 NEUROSCIENCES
Journal of Psychiatry & Neuroscience Pub Date : 2025-03-11 Print Date: 2025-03-01 DOI:10.1503/jpn.240105
Maya Selitser, Lorielle M F Dietze, Sean R McWhinney, Tomas Hajek
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引用次数: 0

Abstract

Background: Cardiometabolic risk factors - including diabetes, hypertension, and obesity - have long been linked with adverse health outcomes such as strokes, but more subtle brain changes in regional brain volumes and cortical thickness associated with these risk factors are less understood. Computer models can now be used to estimate brain age based on structural magnetic resonance imaging data, and subtle brain changes related to cardiometabolic risk factors may manifest as an older-appearing brain in prediction models; thus, we sought to investigate the relationship between cardiometabolic risk factors and machine learning-predicted brain age.

Methods: We performed a systematic search of PubMed and Scopus. We used the brain age gap, which represents the difference between one's predicted and chronological age, as an index of brain structural integrity. We calculated the Cohen d statistic for mean differences in the brain age gap of people with and without diabetes, hypertension, or obesity and performed random effects meta-analyses.

Results: We identified 185 studies, of which 14 met inclusion criteria. Among the 3 cardiometabolic risk factors, diabetes had the highest effect size (12 study samples; d = 0.275, 95% confidence interval [CI] 0.198-0.352; n = 47 436), followed by hypertension (10 study samples; d = 0.113, 95% CI 0.063-0.162; n = 45 102) and obesity (5 study samples; d = 0.112, 95% CI 0.037-0.187; n = 15 678). These effects remained significant in sensitivity analyses that included only studies that controlled for confounding effects of the other cardiometabolic risk factors.

Limitations: Our study tested effect sizes of only categorically defined cardiometabolic risk factors and is limited by inconsistencies in diabetes classification, a smaller pooled sample in the obesity analysis, and limited age range reporting.

Conclusion: Our findings show that each of the cardiometabolic risk factors uniquely contributes to brain structure, as captured by brain age. The effect size for diabetes was more than 2 times greater than the independent effects of hypertension and obesity. We therefore highlight diabetes as a primary target for the prevention of brain structural changes that may lead to cognitive decline and dementia.

心脏代谢危险因素和脑年龄:量化与糖尿病、高血压和肥胖相关的脑结构差异的荟萃分析
背景:心脏代谢危险因素——包括糖尿病、高血压和肥胖——长期以来一直被认为与中风等不良健康结果有关,但与这些危险因素相关的区域脑容量和皮质厚度等更细微的大脑变化却鲜为人知。计算机模型现在可以用来估计基于结构磁共振成像数据的大脑年龄,与心脏代谢危险因素相关的细微大脑变化可能在预测模型中表现为看起来更老的大脑;因此,我们试图研究心脏代谢危险因素与机器学习预测的大脑年龄之间的关系。方法:系统检索PubMed和Scopus数据库。我们使用大脑年龄差距,它代表了一个人的预测年龄和实际年龄之间的差异,作为大脑结构完整性的指标。我们计算了患有和不患有糖尿病、高血压或肥胖的人脑年龄差距的平均差异的科恩统计量,并进行了随机效应荟萃分析。结果:我们确定了185项研究,其中14项符合纳入标准。在3个心脏代谢危险因素中,糖尿病的效应量最高(12个研究样本;d = 0.275, 95%可信区间[CI] 0.198 ~ 0.352;N = 47 436),其次是高血压(10个研究样本;d = 0.113, 95% CI 0.063 ~ 0.162;N = 45 102)和肥胖(5个研究样本;d = 0.112, 95% CI 0.037 ~ 0.187;N = 15 678)。这些影响在敏感性分析中仍然显著,仅包括控制其他心脏代谢危险因素混杂影响的研究。局限性:我们的研究仅测试了分类定义的心脏代谢危险因素的效应大小,并且受到糖尿病分类不一致、肥胖分析中较小的汇总样本和有限的年龄范围报告的限制。结论:我们的研究结果表明,每一种心脏代谢风险因素都对大脑结构有独特的影响,正如大脑年龄所捕捉到的那样。糖尿病的效应量比高血压和肥胖的独立效应大2倍以上。因此,我们强调糖尿病是预防可能导致认知能力下降和痴呆的大脑结构变化的主要目标。
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来源期刊
CiteScore
6.80
自引率
2.30%
发文量
51
审稿时长
2 months
期刊介绍: The Journal of Psychiatry & Neuroscience publishes papers at the intersection of psychiatry and neuroscience that advance our understanding of the neural mechanisms involved in the etiology and treatment of psychiatric disorders. This includes studies on patients with psychiatric disorders, healthy humans, and experimental animals as well as studies in vitro. Original research articles, including clinical trials with a mechanistic component, and review papers will be considered.
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