METHODS AND RESULTS: This was a retrospective longitudinal study of HF patients aged ≥18 years hospitalized at a tertiary healthcare center between January 1, 2009 and December 31, 2013 in Ghana. Patients were eligible if they were discharged from first admission for HF (index admission) and followed up to time of all-cause, cardiovascular, and HF mortality or end of study. Multivariable time-dependent Cox model and inverse-probability-of-treatment weighting of marginal structural model were used to estimate associations between statin treatment and outcomes. Adjusted hazard ratios were also estimated for lipophilic and hydrophilic statin compared with no statin use. The study included 1488 patients (mean age 60.3±14.2 years) with 9306 person-years of observation. Using the time-dependent Cox model, the 5-year adjusted hazard ratios with 95% CI for statin treatment on all-cause, cardiovascular, and HF mortality were 0.68 (0.55-0.83), 0.67 (0.54-0.82), and 0.63 (0.51-0.79), respectively. Use of inverse-probability-of-treatment weighting resulted in estimates of 0.79 (0.65-0.96), 0.77 (0.63-0.96), and 0.77 (0.61-0.95) for statin treatment on all-cause, cardiovascular, and HF mortality, respectively, compared with no statin use.

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.