As LLM-generated content proliferates online, texts are increasingly subject to repeated processing and translation by models, making it critical to understand how such iterative reprocessing reshapes language. Prior work has shown that this degrades factual content and reduces diversity, but the fine-grained linguistic shifts underlying these effects remain unexplored. We track changes in epistemic markers, grammatical voice, degree adverbs, and nominalisation density across 12 iterations of round-trip translation applied to 600 BBC News articles, varying intermediate language, translation model, and chain topology across 17 experimental configurations. We find a consistent epistemic shift: evidential and factive markers increase while hedges decline, potentially causing tentative claims to read as more certain. Concurrently, texts undergo register-level formalisation—informal degree adverbs give way to formal alternatives, active-voice density drops, by-phrase passives attrite disproportionately, and nominalisation density rises. We also record clear model-specific patterns for certain settings. These shifts erode the markers of source, register, and agency, offering a fine-grained account of the factual degradation reported in previous studies.