High prevalence of ACE DD genotype among north Indian end stage renal disease patients

BACKGROUND: The Renin-Angiotensin system (RAS) is a key regulator of both blood pressure and kidney functions and their interaction. In such a situation, genetic variability in the genes of different components of RAS is likely to contribute for its heterogeneous association in the renal disease patients. Angiotensin converting enzyme-1 (ACE-1) is an important component of RAS which determines the vasoactive peptide Angiotensin-II. METHODS: In the present study, we have investigated 127 ESRD patients and 150 normal healthy controls from north India to deduce the association between ACE gene polymorphism and ESRD. The inclusion criteria for patients included a constantly elevated serum creatinine level above normal range (ranging from 3.4 to 15.8) and further the patients were recommended for renal transplantation. A total of 150 normal healthy controls were also genotyped for ACE I/D polymorphism. The criterion of defining control sample as normal was totally based on the absence of any kidney disease determined from the serum creatinin level. Genotyping of ACE I/D were assayed by polymerase chain reaction (PCR) based DNA amplification using specific flanking primers Based on the method described elsewhere. RESULTS: The difference of DD and II genotypes was found highly significant among the two groups (p = 0.025; OR = 3.524; 95%CI = 1.54-8.07). The combined genotype DD v/s ID+II comparison validated that DD genotype is a high risk genotype for ESRD (p = 0.001; OR = 5.74; 95%CI limit = 3.4-8.5). However, no correlation was obtained for different biochemical parameters of lipid profile and renal function among DD and non DD genotype. Interestingly, ~87% of the DD ESRD patients were found hypertensive in comparison to the 65% patients of non DD genotype CONCLUSION: Based on these observations we conclude that ACE DD genotype implicate a strong possible role in the hypertensive state and in renal damage among north Indians. The study will help in predetermining the timing, type and doses of anti-hypertensive therapy for ESRD patients

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability