METHODS: All English-language medical literature published from inception till October 2014 which met the inclusion criteria were reviewed and analyzed.
RESULTS: A total of nine papers were included, reviewed and analyzed. The total sample size was 4276 patients. All studies used either of the two DPP4 inhibitors - Vildagliptin or Sitagliptin, vs sulphonylurea or meglitinides. Patients receiving DPP4 inhibitors were less likely to develop symptomatic hypoglycemia (risk ratio 0.46; 95% CI, 0.30-0.70), confirmed hypoglycemia (risk ratio 0.36; 95% CI, 0.21-0.64) and severe hypoglycemia (risk ratio 0.22; 95% CI, 0.10-0.53) compared with patients on sulphonylureas. There was no statistically significant difference in HbA1C changes comparing Vildagliptin and sulphonylurea.
CONCLUSION: DPP4 inhibitor is a safer alternative to sulphonylurea in Muslim patients with type 2 diabetes mellitus who fast during the month of Ramadan as it is associated with lower risk of symptomatic, confirmed and severe hypoglycemia, with efficacy comparable to sulphonylurea.
METHODS AND FINDINGS: The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed.
CONCLUSIONS: These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.
METHODS: Retrospective data on serum calcium and infusion rates was collected from 2011-2015. The relationship between peak calcium efflux (PER) and time was determined using a scatterplot and linear regression. A comparison between regimens was made based on treatment efficacy (hypocalcaemia duration, total infusion amount and time) and calcium excursions (outside target range, peak and trough calcium) using bar charts and an unpaired t-test.
RESULTS: Fifty-one and 34 patients on the original and new regimens respectively were included. Mean PER was lower (2.16 vs 2.56 mmol/h; P = 0.03) and occurred earlier (17.6 vs 23.2 h; P = 0.13) for the new regimen. Both scatterplot and regression showed a large correlation between PER and time (R-square 0.64, SE 1.53, P risk of rebound hypercalcemia.
METHODS: A nested-case control study was conducted within the prospective EPIC cohort (>520,000 participants, 10 European countries). After a mean 7.5 mean years of follow-up, 121 hepatocellular carcinoma (HCC), 34 intrahepatic bile duct (IHBC) and 131 gallbladder and biliary tract (GBTC) cases were identified and matched to 2 controls each. Circulating biomarkers were measured in serum taken at recruitment into the cohort, prior to cancer diagnosis. Multivariable adjusted conditional logistic regression was used to calculate odds ratios and 95% confidence intervals (OR; 95%CI).
RESULTS: In multivariable models, 1SD increase of each log-transformed biomarker was positively associated with HCC risk (OR(GGT)=4.23, 95%CI:2.72-6.59; OR(ALP)=3.43, 95%CI:2.31-5.10;OR(AST)=3.00, 95%CI:2.04-4.42; OR(ALT)=2.69, 95%CI:1.89-3.84; OR(Bilirubin)=2.25, 95%CI:1.58-3.20). Each liver enzyme (OR(GGT)=4.98; 95%CI:1.75-14.17; OR(AST)=3.10, 95%CI:1.04-9.30; OR(ALT)=2.86, 95%CI:1.26-6.48, OR(ALP)=2.31, 95%CI:1.10-4.86) but not bilirubin (OR(Bilirubin)=1.46,95%CI:0.85-2.51) showed a significant association with IHBC. Only ALP was significantly associated with GBTC risk (OR(ALP)=1.59, 95%CI:1.20-2.09).
CONCLUSION: This study shows positive associations between circulating liver biomarkers in sera collected prior to cancer diagnoses and the risks of developing HCC or IHBC, but not GBTC.
METHODS: We used data from an ongoing individual participant meta-analysis involving 17 population-based cohorts worldwide. We selected 60,211 participants without cardiovascular disease at baseline with available data on ethnicity (White, Black, Asian or Hispanic). We generated a multivariable linear regression model containing risk factors and ethnicity predicting mean common carotid intima-media thickness (CIMT) and a multivariable Cox regression model predicting myocardial infarction or stroke. For each risk factor we assessed how the association with the preclinical and clinical measures of cardiovascular atherosclerotic disease was affected by ethnicity.
RESULTS: Ethnicity appeared to significantly modify the associations between risk factors and CIMT and cardiovascular events. The association between age and CIMT was weaker in Blacks and Hispanics. Systolic blood pressure associated more strongly with CIMT in Asians. HDL cholesterol and smoking associated less with CIMT in Blacks. Furthermore, the association of age and total cholesterol levels with the occurrence of cardiovascular events differed between Blacks and Whites.
CONCLUSION: The magnitude of associations between risk factors and the presence of atherosclerotic disease differs between race/ethnic groups. These subtle, yet significant differences provide insight in the etiology of cardiovascular disease among race/ethnic groups. These insights aid the race/ethnic-specific implementation of primary prevention.