Real-time systems require the formal guarantee of timing constraints, not only for the individual tasks but also for the end-to-end latency of data flows. The data flow among multiple tasks, e.g., from sensors to actuators, is described by a cause-effect chain, independent from the priority order of the tasks. In this paper, we provide an end-to-end timing-analysis for cause-effect chains on asynchronized distributed systems with periodic task activations, considering the maximum reaction time (i.e., the duration of data processing) and the maximum data age (i.e., the worst-case data freshness). We first provide an analysis of the end-to-end latency on one local electronic control unit (ECU) that has to consider only the jobs in a bounded time interval. We extend our analysis to globally asynchronized systems by exploiting a compositional property to combine the local results. Throughout synthesized data based on an automotive benchmark as well as on randomized parameters, we show that our analytical results improve the state-of-the-art.