Multimorbidity—the co-occurrence of multiple diseases—is associated to poor prognosis, but the scarce knowledge of its development over time hampers the effectiveness of clinical interventions. Here we identify multimorbidity clusters, trace their evolution in older adults, and detect the clinical trajectories and mortality of single individuals as they move among clusters over 12 years. By means of a fuzzy c-means cluster algorithm, we group 2931 people ≥60 years in five clinically meaningful multimorbidity clusters (52%). The remaining 48% are part of an unspecific cluster (i.e. none of the diseases are overrepresented), which greatly fuels other clusters at follow-ups. Clusters contribute differentially to the longitudinal development of other clusters and to mortality. We report that multimorbidity clusters and their trajectories may help identifying homogeneous groups of people with similar needs and prognosis, and assisting clinicians and health care systems in the personalization of clinical interventions and preventive strategies.

多种疾病（多种疾病的并发）与不良预后相关，但随着时间的推移，对这种疾病发展的了解不足，阻碍了临床干预的有效性。在这里，我们确定了多发病率簇，追踪了它们在老年人中的进化，并检测了单人在12年间在簇中移动时的临床轨迹和死亡率。通过模糊c均值聚类算法，我们将5个具有临床意义的多发病率聚类（52％）的2931名≥60岁的人分组。其余的48％是非特异性簇的一部分（即，没有一种疾病被过度代表），这在后续行动中极大地推动了其他簇的生长。集群对其他集群的纵向发展和死亡率的贡献不同。