Standardized mycobacterial interspersed repetitive-unit-variable-number tandem repeat (MIRU-VNTR) typing based on 15 and 24 loci recently has been proposed for Mycobacterium tuberculosis genotyping. So far, this optimized system has been assessed in a single, 1-year population-based study performed in Germany (M. C. Oelemann, R. Diel, V. Vatin, W. Haas, S. Rusch-Gerdes, C. Locht, S. Niemann, and P. Supply, J. Clin. Microbiol. 45:691-697, 2007). Here, we evaluated these optimized formats in a much larger population-based study conducted during 39 months in the Brussels capital region of Belgium. Isolates from 807 patients were genotyped. The resolution power, cluster, and lineage identification by the standardized MIRU-VNTR sets were compared to those obtained using standardized IS6110-restriction fragment length polymorphism (RFLP), spoligotyping, and a previous 12-MIRU-VNTR-locus set. On a subset representing 77% of the cases during a 16-month period, a high concordance was observed between unique isolates or strain clusters as defined by standardized MIRU-VNTR and IS6110-RFLP (i.e., more than five IS6110 bands). When extended to the entire population-based collection, the discriminatory subset of 15 loci decreased the strain-clustering rate by almost twofold compared to that of the old 12-locus set. The addition of the nine ancillary MIRU-VNTR loci and/or spoligotyping only slightly further decreased this strain-clustering rate. Familial, social, and/or geographic proximity links were found in 48% of the clusters identified, and well-known risk factors for tuberculosis transmission were identified. Finally, an excellent correspondence was determined between our MIRU-VNTR-spoligotyping strain identifications and external reference strain lineages included in the MIRU-VNTRplus database and identified by, e.g., large sequence polymorphisms. Our results reinforce the proposal of standardized MIRU-VNTR typing as a new reference genotyping method for the epidemiological and phylogenetic screening of M. tuberculosis strains
