METHODS: Urine and urethral swab samples were collected from the primary public sexual health clinic in Singapore and tested for C. trachomatis (CT) or N. gonorrhoeae (NG) infection and for the presence of M. genitalium. Antibiotic resistance in M. genitalium strains detected was determined by screening for genomic mutations associated with macrolide and fluroquinolone resistance.
RESULTS: We report the results of a study into M. genitalium prevalence at the national sexual health clinic in Singapore. M. genitalium was heavily associated with CT infection (8.1% of cases), but present in only of 2.4% in CT negative cases and not independently linked to NG infection. Furthermore, we found high rates of resistance mutations to both macrolides (25%) and fluoroquinolones (37.5%) with a majority of resistant strains being dual-resistant. Resistance mutations were only found in strains from patients with CT co-infection.
CONCLUSIONS: Our results support targeted screening of CT positive patients for M. genitalium as a cost-effective strategy to reduce the incidence of M. genitalium in the absence of comprehensive routine screening. The high rate of dual resistance also highlights the need to ensure the availability of alternative antibiotics for the treatment of multi-drug resistant M. genitalium isolates.
RECENT FINDINGS: There has been a growing appreciation for an independent link between NAFLD and CVD, culminating in a scientific statement by the American Heart Association in 2022. More recently, studies have begun to identify biomarkers of the three NAFLD phases as potent predictors of cardiovascular risk. Despite the body of evidence supporting a connection between hepatic biomarkers and CVD, more research is certainly needed, as some studies find no significant relationship. If this relationship continues to be robust and readily reproducible, NAFLD and its biomarkers may have an exciting role in the future of cardiovascular risk prediction, possibly as risk-enhancing factors or as components of novel cardiovascular risk prediction models.