 The study presents a computational method to detect early warning signals of critical transitions during the progression of complex diseases using a single sample. The method proposes a novel index called single sample Kullbach-Liebler divergence, SKLD, to explore and quantify disturbances on the background caused by a case sample. The pre-disease state is signaled by significant changes in SKLD. The algorithm was applied to six real datasets and validated its effectiveness and accuracy. The method identifies critical states or tipping points at a single sample level and provides SKLD signaling markers for practical application, making it of great potential in personalized pre-disease diagnosis. This article was authored by Jiayu and Zhong, Rui Liu, and Pei Chen. We are article.tv, links in the description below.