From open radical hysterectomy to robot-assisted laparoscopic radical hysterectomy for early stage cervical cancer: aspects of a single institution learning curve

We analysed the introduction of the robot-assisted laparoscopic radical hysterectomy in patients with early-stage cervical cancer with respect to patient benefits and surgeon-related aspects of a surgical learning curve. A retrospective review of the first 14 robot-assisted laparoscopic radical hysterectomies and the last 14 open radical hysterectomies in a similar clinical setting with the same surgical team was conducted. Patients were candidates for a laparoscopic sentinel node procedure, pelvic lymph node dissection and open radical hysterectomy (RH) before August 2006 and were candidates for a laparoscopic sentinel node procedure, pelvic lymph node dissection and robot-assisted laparoscopic radical hysterectomy (RALRH) after August 2006. Overall, blood loss in the open cases was significantly more compared with the robot cases. Median hospital stay after RALRH was 5 days less than after RH. The median theatre time in the learning period for the robot procedure was reduced from 9 h to less that 4 h and compared well to the 3 h and 45 min for an open procedure. Three complications occurred in the open group and one in the robot group. RALRH is feasible and of benefit to the patient with early stage cervical cancer by a reduction of blood loss and reduced hospital stay. Introduction of this new technique requires a learning curve of less than 15 cases that will reduce the operating time to a level comparable to open surgery

Glucose challenge increases circulating progenitor cells in Asian Indian male subjects with normal glucose tolerance which is compromised in subjects with pre-diabetes: A pilot study

<p>Abstract</p> <p>Background</p> <p>Haematopoietic stem cells undergo mobilization from bone marrow to blood in response to physiological stimuli such as ischemia and tissue injury. The aim of study was to determine the kinetics of circulating CD34⁺and CD133⁺CD34⁺progenitor cells in response to 75 g glucose load in subjects with normal and impaired glucose metabolism.</p> <p>Methods</p> <p>Asian Indian male subjects (n = 50) with no prior history of glucose imbalance were subjected to 2 hour oral glucose tolerance test (OGTT). 24 subjects had normal glucose tolerance (NGT), 17 subjects had impaired glucose tolerance (IGT) and 9 had impaired fasting glucose (IFG). The IGT and IFG subjects were grouped together as pre-diabetes group (n = 26). Progenitor cell counts in peripheral circulation at fasting and 2 hour post glucose challenge were measured using direct two-color flow cytometry.</p> <p>Results</p> <p>The pre-diabetes group was more insulin resistant (p < 0.0001) as measured by homeostasis assessment model (HOMA-IR) compared to NGT group. A 2.5-fold increase in CD34⁺cells (p = 0.003) and CD133⁺CD34⁺(p = 0.019) cells was seen 2 hours post glucose challenge in the NGT group. This increase for both the cell types was attenuated in subjects with IGT. CD34⁺cell counts in response to glucose challenge inversely correlated with neutrophil counts (ρ = -0.330, p = 0.019), while post load counts of CD133⁺CD34⁺cells inversely correlated with serum creatinine (ρ = -0.312, p = 0.023).</p> <p>Conclusion</p> <p>There is a 2.5-fold increase in the circulating levels of haematopoietic stem cells in response to glucose challenge in healthy Asian Indian male subjects which is attenuated in subjects with pre-diabetes.</p

Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels. RESEARCH DESIGN AND METHODS We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c. CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c

Proximal correlates of metabolic phenotypes during ‘at-risk' and ‘case' stages of the metabolic disease continuum

Extent: 11p.OBJECTIVE: To examine the social and behavioural correlates of metabolic phenotypes during ‘at-risk’ and ‘case’ stages of the metabolic disease continuum. DESIGN: Cross-sectional study of a random population sample. PARTICIPANTS: A total of 718 community-dwelling adults (57% female), aged 18--92 years from a regional South Australian city. MEASUREMENTS: Total body fat and lean mass and abdominal fat mass were assessed by dual energy x-ray absorptiometry. Fasting venous blood was collected in the morning for assessment of glycated haemoglobin, plasma glucose, serum triglycerides, cholesterol lipoproteins and insulin. Seated blood pressure (BP) was measured. Physical activity and smoking, alcohol and diet (96-item food frequency), sleep duration and frequency of sleep disordered breathing (SDB) symptoms, and family history of cardiometabolic disease, education, lifetime occupation and household income were assessed by questionnaire. Current medications were determined by clinical inventory. RESULTS: 36.5% were pharmacologically managed for a metabolic risk factor or had known diabetes (‘cases’), otherwise were classified as the ‘at-risk’ population. In both ‘at-risk’ and ‘cases’, four major metabolic phenotypes were identified using principal components analysis that explained over 77% of the metabolic variance between people: fat mass/insulinemia (FMI); BP; lipidaemia/lean mass (LLM) and glycaemia (GLY). The BP phenotype was uncorrelated with other phenotypes in ‘cases’, whereas all phenotypes were inter-correlated in the ‘at-risk’. Over and above other socioeconomic and behavioural factors, medications were the dominant correlates of all phenotypes in ‘cases’ and SDB symptom frequency was most strongly associated with FMI, LLM and GLY phenotypes in the ‘at-risk’. CONCLUSION: Previous research has shown FMI, LLM and GLY phenotypes to be most strongly predictive of diabetes development. Reducing SDB symptom frequency and optimising the duration of sleep may be important concomitant interventions to standard diabetes risk reduction interventions. Prospective studies are required to examine this hypothesis.MT Haren, G Misan, JF Grant, JD Buckley, PRC Howe, AW Taylor, J Newbury and RA McDermot

Proximal correlates of metabolic phenotypes during ‘at-risk' and ‘case' stages of the metabolic disease continuum

Extent: 11p.OBJECTIVE: To examine the social and behavioural correlates of metabolic phenotypes during ‘at-risk’ and ‘case’ stages of the metabolic disease continuum. DESIGN: Cross-sectional study of a random population sample. PARTICIPANTS: A total of 718 community-dwelling adults (57% female), aged 18--92 years from a regional South Australian city. MEASUREMENTS: Total body fat and lean mass and abdominal fat mass were assessed by dual energy x-ray absorptiometry. Fasting venous blood was collected in the morning for assessment of glycated haemoglobin, plasma glucose, serum triglycerides, cholesterol lipoproteins and insulin. Seated blood pressure (BP) was measured. Physical activity and smoking, alcohol and diet (96-item food frequency), sleep duration and frequency of sleep disordered breathing (SDB) symptoms, and family history of cardiometabolic disease, education, lifetime occupation and household income were assessed by questionnaire. Current medications were determined by clinical inventory. RESULTS: 36.5% were pharmacologically managed for a metabolic risk factor or had known diabetes (‘cases’), otherwise were classified as the ‘at-risk’ population. In both ‘at-risk’ and ‘cases’, four major metabolic phenotypes were identified using principal components analysis that explained over 77% of the metabolic variance between people: fat mass/insulinemia (FMI); BP; lipidaemia/lean mass (LLM) and glycaemia (GLY). The BP phenotype was uncorrelated with other phenotypes in ‘cases’, whereas all phenotypes were inter-correlated in the ‘at-risk’. Over and above other socioeconomic and behavioural factors, medications were the dominant correlates of all phenotypes in ‘cases’ and SDB symptom frequency was most strongly associated with FMI, LLM and GLY phenotypes in the ‘at-risk’. CONCLUSION: Previous research has shown FMI, LLM and GLY phenotypes to be most strongly predictive of diabetes development. Reducing SDB symptom frequency and optimising the duration of sleep may be important concomitant interventions to standard diabetes risk reduction interventions. Prospective studies are required to examine this hypothesis.MT Haren, G Misan, JF Grant, JD Buckley, PRC Howe, AW Taylor, J Newbury and RA McDermot

Genetic influences on the insulin response of the beta cell to different secretagogues

Aims/hypothesis: The aim of the present study was to estimate the heritability of the beta cell insulin response to glucose and to glucose combined with glucagon-like peptide-1 (GLP-1) or with GLP-1 plus arginine. Methods: This was a twin-family study that included 54 families from the Netherlands Twin Register. The participants were healthy twin pairs and their siblings of the same sex, aged 20 to 50 years. Insulin response of the beta cell was assessed by a modified hyperglycaemic clamp with additional GLP-1 and arginine. Insulin sensitivity index (ISI) was assessed by the euglycaemic-hyperinsulinaemic clamp. Multivariate structural equation modelling was used to obtain heritabilities and the genetic factors underlying individual differences in BMI, ISI and secretory responses of the beta cell. Results: The heritability of insulin levels in response to glucose was 52% and 77% for the first and second phase, respectively, 53% in response to glucose+GLP-1 and 80% in response to an additional arginine bolus. Insulin responses to the administration of glucose, glucose+GLP-1 and glucose+GLP-1+arginine were highly correlated (0.62<r<0.79). Heritability of BMI and ISI was 74% and 60% respectively. The genetic factors that influenced BMI and ISI explained about half of the heritability of insulin levels in response to the three secretagogues. The other half was due to genetic factors specific to the beta cell. Conclusions/interpretation: In healthy adults, genetic factors explain most of the individual differences in the secretory capacity of the beta cell. These genetic influences are partly independent from the genes that influence BMI and ISI. © 2009 Springer-Verlag

Determinants of urinary albumin excretion within the normal range in patients with type 2 diabetes: the Randomised Olmesartan and Diabetes Microalbuminuria Prevention (ROADMAP) study

In contrast to microalbuminuric type 2 diabetic patients, the factors correlated with urinary albumin excretion are less well known in normoalbuminuric patients. This may be important because even within the normoalbuminuric range, higher rates of albuminuria are known to be associated with higher renal and cardiovascular risk. At the time of screening for the Randomised Olmesartan and Diabetes Microalbuminuria Prevention (ROADMAP) Study, the urinary albumin/creatinine ratio (UACR) was 0.44 mg/mmol in 4,449 type 2 diabetic patients. The independent correlates of UACR were analysed. Independent correlates of UACR during baseline were (in descending order): night-time systolic BP (r (s) = 0.19); HbA(1c) (r (s) = 0.18); mean 24 h systolic BP (r (s) = 0.16); fasting blood glucose (r (s) = 0.16); night-time diastolic BP (r (s) = 0.12); office systolic BP, sitting (r (s) = 0.11), standing (r (s) = 0.10); estimated GFR (r (s) = 0.10); heart rate, sitting (r (s) = 0.10); haemoglobin (r (s) = -0.10); triacylglycerol (r (s) = 0.09); and uric acid (r (s) = -0.08; all p a parts per thousand currency signaEuro parts per thousand 0.001). Significantly higher albumin excretion rates were found for the following categorical variables: higher waist circumference (more marked in men); presence of the metabolic syndrome; smoking (difference more marked in males); female sex; antihypertensive treatment; use of amlodipine; insulin treatment; family history of diabetes; and family history of cardiovascular disease (more marked in women). Although observational correlations do not prove causality, in normoalbuminuric type 2 diabetic patients the albumin excretion rate is correlated with many factors that are potentially susceptible to intervention. ClinicalTrials.gov ID no.: NCT00185159 This study was sponsored by Daichii-Sankyo.Nephrolog

ABO Blood Group and the Risk of Hepatocellular Carcinoma: A Case-Control Study in Patients with Chronic Hepatitis B

BACKGROUND: Studies have observed an association between the ABO blood group and risk of certain malignancies. However, no studies of the association with hepatocellular carcinoma (HCC) risk are available. We conducted this hospital-based case-control study to examine the association with HCC in patients with chronic hepatitis B (CHB). METHODS: From January 2004 to December 2008, a total of 6275 consecutive eligible patients with chronic hepatitis B virus (HBV) infection were recruited. 1105 of them were patients with HBV-related HCC and 5,170 patients were CHB without HCC. Multivariate logistic regression models were used to investigate the association between the ABO blood group and HCC risk. RESULTS: Compared with subjects with blood type O, the adjusted odds ratio (AOR) for the association of those with blood type A and HCC risk was 1.39 [95% confidence interval (CI), 1.05-1.83] after adjusting for age, sex, type 2 diabetes, cirrhosis, hepatitis B e antigen, and HBV DNA. The associations were only statistically significant [AOR (95%CI) = 1.56(1.14-2.13)] for men, for being hepatitis B e antigen positive [AOR (95%CI) = 4.92(2.83-8.57)], for those with cirrhosis [AOR (95%CI), 1.57(1.12-2.20)], and for those with HBV DNA≤10(5)copies/mL [AOR (95%CI), 1.58(1.04-2.42)]. Stratified analysis by sex indicated that compared with those with blood type O, those with blood type B also had a significantly high risk of HCC among men, whereas, those with blood type AB or B had a low risk of HCC among women. CONCLUSIONS: The ABO blood type was associated with the risk of HCC in Chinese patients with CHB. The association was gender-related

Computerized clinical decision support systems for drug prescribing and management: A decision-maker-researcher partnership systematic review

<p>Abstract</p> <p>Background</p> <p>Computerized clinical decision support systems (CCDSSs) for drug therapy management are designed to promote safe and effective medication use. Evidence documenting the effectiveness of CCDSSs for improving drug therapy is necessary for informed adoption decisions. The objective of this review was to systematically review randomized controlled trials assessing the effects of CCDSSs for drug therapy management on process of care and patient outcomes. We also sought to identify system and study characteristics that predicted benefit.</p> <p>Methods</p> <p>We conducted a decision-maker-researcher partnership systematic review. We updated our earlier reviews (1998, 2005) by searching MEDLINE, EMBASE, EBM Reviews, Inspec, and other databases, and consulting reference lists through January 2010. Authors of 82% of included studies confirmed or supplemented extracted data. We included only randomized controlled trials that evaluated the effect on process of care or patient outcomes of a CCDSS for drug therapy management compared to care provided without a CCDSS. A study was considered to have a positive effect (<it>i.e.</it>, CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive.</p> <p>Results</p> <p>Sixty-five studies met our inclusion criteria, including 41 new studies since our previous review. Methodological quality was generally high and unchanged with time. CCDSSs improved process of care performance in 37 of the 59 studies assessing this type of outcome (64%, 57% of all studies). Twenty-nine trials assessed patient outcomes, of which six trials (21%, 9% of all trials) reported improvements.</p> <p>Conclusions</p> <p>CCDSSs inconsistently improved process of care measures and seldomly improved patient outcomes. Lack of clear patient benefit and lack of data on harms and costs preclude a recommendation to adopt CCDSSs for drug therapy management.</p

Smoking-by-genotype interaction in type 2 diabetes risk and fasting glucose.

Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p<1x10-7 (adjusted for multiple hypothesis-testing with the IBC array). Two SNPs had significant joint effects in the overall model and significant main effects only in one smoking stratum: rs140637 (FBN1) in AA individuals had a significant main effect only among smokers, and rs1444261 (closest gene C2orf63) in EA individuals had a significant main effect only among nonsmokers. Three additional SNPs were identified as having potential interaction by exhibiting a significant main effects only in smokers: rs1801232 (CUBN) in AA individuals, rs12243326 (TCF7L2) in EA individuals, and rs4132670 (TCF7L2) in EA individuals. No SNP met significance for potential interaction with smoking on baseline FG. The identification of these loci provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D