Real-time systems require the formal guarantee of timing-constraints, not only for the individual tasks but also for the data-propagation paths. A cause-effect chain describes the data flow among multiple tasks, e.g., from sensors to actuators, 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 (duration of data processing) and the maximum data age (ivorst-case data freshness). On one local electronic control unit (ECU), we present how to compute the exact local (ivorst-case) end-to-end latencies when the execution time of the periodic tasks is fixed. We further extend our analysis to globally asynchronized systems by combining 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 for periodic task activations.