Acute allograft rejection remains a major complication in kidney transplantation, highlighting the need for accurate and early diagnostic tools to enable prompt treatment. Standard diagnostic methods, such as serum creatinine monitoring, lack sufficient sensitivity and specificity. As a result, subclinical rejection may go undetected, or unnecessary biopsies may be performed, posing additional risks. Previously, a novel, non-invasive urine test based on metabolomic profiling was developed to detect renal allograft rejection. However, the early post-transplant period remains particularly challenging, as reliable biomarker-based detection in this critical window is not yet well established. This study, conducted within the UMBRELLA project, analyzed 682 urine samples from 109 kidney transplant recipients. The test utilized a specific urinary metabolite constellation based on alanine, citrate, lactate, and urea. A total of 29 clinical and transplant-related parameters, including donor and recipient characteristics, ischemia times, and donor type, were evaluated for their effect on the test’s ability to detect biopsy-confirmed rejection within the first 14 days after transplantation. Univariate analysis identified 10 significant confounding factors, including lower residual urine volume before transplantation, reduced eGFR, use of induction therapy, longer warm and cold ischemia times, deceased donor status, younger recipient age, and certain HLA mismatches. Multivariate analysis confirmed the relevance of living donation. Subgroup analysis revealed the highest diagnostic accuracy in recipients of living donor kidneys, with an AUC of 0.720 (95% CI, 0.62–0.82), followed by recipients with a short warm ischemia time (<30 min), who achieved an AUC of 0.702 (95% CI, 0.61–0.79). Clinical complications often coincided with abnormal metabolite test results. In conclusion, this study underscores the importance of considering donor type and ischemia times when interpreting urinary metabolite constellations for rejection monitoring in the early post-transplant period. The findings suggest a distinct metabolic profile in recipients of deceased donor kidneys within the first 2 weeks after transplantation. Understanding these influencing factors may enhance the accuracy of non-invasive rejection detection and support timely clinical interventions to improve patient outcomes.