Polygenic risk scores (PRS) have emerged as promising tools for stratifying inherited disease risk, yet their translation into clinical practice is constrained by a critical and frequently unmet requirement: demonstration that scores derived in one cohort retain discriminatory value when applied independently in a different population. For suspected familial hypercholesterolemia (FH), a substantial proportion of patients that meet clinical criteria still test negative for a monogenic cause. These patients are presumed to carry polygenic LDL-C burden, yet PRS validation is still scarce. Here, we evaluated a published hypercholesterolemia PRS (PGS000936) spanning 5,386 genome-wide loci. We tested the score in 117 monogenic-negative hypercholesterolemia cases and 496 controls from the German general population genotyped on the Illumina Global Screening Array v3.0, with imputation to approximately 11 million variants. The score was applied without retraining, using externally derived {beta}-coefficients. The PRS clearly distinguished cases from controls (p < 2x10-16), with a mean PRS of 0.97 in cases versus 0.52 in controls. Decile analysis revealed a seven-fold increase in odds of hypercholesterolemia in the top 10% of the distribution (OR 7.55; 95% CI 4.36-13.07; p < 0.0001). In 94 cases for which clinical data was available, higher PRS was associated with significantly higher LDL-C levels before treatment. Importantly, the PRS distribution in the German control cohort was shifted relative to that of the control cohort in the initial study, suggesting that population-matched calibration is required for accurate odds ratio estimation and proper interpretation of PRS values. These findings show that the published hypercholesterolemia PRS (PGS000936) can effectively stratify risk and identify individuals with a high genetic burden in a real-world German clinical setting, supporting its clinical utility when appropriately calibrated for the local population.