Angiotensin-converting enzyme (ACE) inhibition in type 2, diabetic patients – interaction with ACE insertion/deletion polymorphism

Angiotensin-converting enzyme (ACE) insertion(I)/deletion (D) polymorphism may modify the effect of inhibition of the renin–angiotensin–aldosterone system (RAAS) on survival and cardiorenal outcomes in type 2, diabetes. A consecutive cohort of 2089 Chinese type 2 diabetic patients with mean (±standard deviation) age of 59.7±13.1 years were genotyped for this polymorphism by polymerase chain reaction method and were followed prospectively for a median period of 44.6 (interquartile range: 23.7, 57.5) months. Clinical outcomes, including all-cause mortality, cardiovascular and renal end points, were examined. The frequency for I allele was 67.1 and 32.9% for D allele, with observed genotype frequencies of 45.8, 42.6, and 11.6% for 3, DI and DD, respectively. ACE DD polymorphism was an independent predictor for renal end point with hazard ratio (HR) (95% confidence interval) of 1.72 (1.16, 2.56), but not for cardiovascular end point or mortality. After controlling for confounding factors, including ACE I/D genotype, the usage of RAAS inhibitors was associated with reduced risk of mortality (HR 0.34 (0.23, 0.50)) and renal end point (HR 0.55 (0.40, 0.75)). On subgroup analysis, the beneficial effects on survival (II vs DI vs DD: HR 0.29 (0.16, 0.51) vs 0.25 (0.14, 0.46) vs 1.33 (0.41, 4.31)) and renoprotection (II vs DI vs DD: 0.52 (0.30, 0.90) vs 0.43 (0.25, 0.72) vs 0.95 (0.43, 2.12)) were most evident in II and DI carriers. In conclusion, inhibition of RAAS was associated with reduced risk of mortality and occurrence of renal end point in Chinese type 2 diabetic patients. These benefits were most evident among II and DI carriers

Familial young-onset diabetes, pre-diabetes and cardiovascular disease are associated with genetic variants of DACH1 in Chinese

10.1371/journal.pone.0084770PLoS ONE91e8477

Then and now: Use of 16S rDNA gene sequencing for bacterial identification and discovery of novel bacteria in clinical microbiology laboratories

In the last decade, as a result of the widespread use of PCR and DNA sequencing, 16S rDNA sequencing has played a pivotal role in the accurate identification of bacterial isolates and the discovery of novel bacteria in clinical microbiology laboratories. For bacterial identification, 16S rDNA sequencing is particularly important in the case of bacteria with unusual phenotypic profiles, rare bacteria, slow-growing bacteria, uncultivable bacteria and culture-negative infections. Not only has it provided insights into aetiologies of infectious disease, but it also helps clinicians in choosing antibiotics and in determining the duration of treatment and infection control procedures. With the use of 16S rDNA sequencing, 215 novel bacterial species, 29 of which belong to novel genera, have been discovered from human specimens in the past 7.years of the 21st century (2001-2007). One hundred of the 215 novel species, 15 belonging to novel genera, have been found in four or more subjects. The largest number of novel species discovered were of the genera Mycobacterium (n = 12) and Nocardia (n = 6). The oral cavity/dental-related specimens (n = 19) and the gastrointestinal tract (n = 26) were the most important sites for discovery and/or reservoirs of novel species. Among the 100 novel species, Streptococcus sinensis, Laribacter hongkongensis, Clostridium hathewayi and Borrelia spielmanii have been most thoroughly characterized, with the reservoirs and routes of transmission documented, and S. sinensis, L. hongkongensis and C. hathewayi have been found globally. One of the greatest hurdles in putting 16S rDNA sequencing into routine use in clinical microbiology laboratories is automation of the technology. The only step that can be automated at the moment is input of the 16S rDNA sequence of the bacterial isolate for identification into one of the software packages that will generate the result of the identity of the isolate on the basis of its sequence database. However, studies on the accuracy of the software packages have given highly varied results, and interpretation of results remains difficult for most technicians, and even for clinical microbiologists. To fully utilize 16S rDNA sequencing in clinical microbiology, better guidelines are needed for interpretation of the identification results, and additional/supplementary methods are necessary for bacterial species that cannot be identified confidently by 16S rDNA sequencing alone. © 2008 The Authors Journal compilation © 2008 European Society of Clinical Microbiology and Infectious Diseases.link_to_subscribed_fulltex