Maintaining good cognitive function at older age is important, but our knowledge of patterns and predictors of cognitive aging is still limited. We used Bayesian model-based clustering to group 5064 participants of the Long Life Family Study (ages 49–110 years) into clusters characterized by distinct trajectories of cognitive change in the domains of episodic memory, attention, processing speed, and verbal fluency. For each domain, we identified 4 or 5 large clusters with representative patterns of change ranging from rapid decline to exceptionally slow change. We annotated the clusters by their correlation with genetic and molecular biomarkers, non-genetic risk factors, medical history, and other markers of aging to discover correlates of cognitive changes and neuroprotection. The annotation analysis discovered both predictors of multi-domain cognitive change such as gait speed and predictors of domain-specific cognitive change such as IL6 and NTproBNP that correlate only with change of processing speed or APOE genotypes that correlate only with change of processing speed and logical memory. These patterns also suggest that cognitive decline starts at young age and that maintaining good physical function correlates with slower cognitive decline. To better understand the agreement of cognitive changes across multiple domains, we summarized the results of the cluster analysis into a score of cognitive function change. This score showed that extreme patterns of change affecting multiple cognitive domains simultaneously are rare in this study and that specific signatures of biomarkers of inflammation and metabolic disease predict severity of cognitive changes. The substantial heterogeneity of change patterns within and between cognitive domains and the net of correlations between patterns of cognitive aging and other aging traits emphasizes the importance of measuring a wide range of cognitive functions and the need for studying cognitive aging in concert with other aging traits.

在老年时保持良好的认知功能很重要，但我们对认知衰老的模式和预测因素的了解仍然有限。我们使用基于贝叶斯模型的聚类将长寿家庭研究的 5064 名参与者（年龄 49-110 岁）分组，这些聚类的特征是情景记忆、注意力、处理速度和语言流畅性领域的认知变化的不同轨迹。对于每个领域，我们确定了 4 或 5 个具有代表性变化模式的大型集群，范围从快速下降到异常缓慢的变化。我们通过它们与遗传和分子生​​物标志物、非遗传风险因素、病史和其他衰老标志物的相关性来注释这些簇，以发现认知变化和神经保护的相关性。注释分析发现了多域认知变化（例如步态速度）的预测因子和特定域认知变化（例如 IL6 和 NTproBNP）的预测因子（仅与处理速度的变化相关）或APOE基因型（仅与处理速度和逻辑的变化相关）记忆。这些模式还表明，认知能力下降从年轻时就开始了，保持良好的身体机能与减缓认知能力下降相关。为了更好地理解跨多个领域的认知变化的一致性，我们将聚类分析的结果总结为认知功能变化的分数。该分数表明，在本研究中，同时影响多个认知领域的极端变化模式很少见，并且炎症和代谢疾病生物标志物的特定特征可以预测认知变化的严重程度。 认知领域内部和之间的变化模式的显着异质性以及认知衰老模式与其他衰老特征之间的相关性网络强调了测量广泛的认知功能的重要性以及将认知衰老与其他衰老特征结合起来研究的必要性。