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.
OBJECTIVES: This study aimed to collect real-world cost and HRQOL data, and investigate their associations with multiple disease-severity indicators among AD patients in Thailand.
METHODS: We recruited AD patients aged ≥60 years accompanied by their caregivers at a university-affiliated tertiary hospital. A one-time structured interview was conducted to collect disease-severity indicators, HRQOL, and caregiving information using standardized tools. The hospital's database was used to retrieve healthcare resource utilization occurred over 6 months preceding the interview date. Costs were annualized and stratified based on cognitive status. Generalized linear models were employed to evaluate determinants of costs and HRQOL.
RESULTS: Among 148 community-dwelling patients, average annual total societal costs of AD care were $8014 (95% confidence interval [CI]: $7295-$8844) per patient. Total costs of patients with severe stage ($9860; 95% CI: $8785-$11 328) were almost twice as high as those of mild stage ($5524; 95% CI: $4649-$6593). The major cost driver was direct medical costs, particularly those incurred by AD prescriptions. Functional status was the strongest determinant for both total costs and patient's HRQOL (P value
DISCUSSION: Several treatment options are available for different stages of prostate cancer. Hormone therapy known as androgen deprivation therapy (ADT) is the first line treatment used to treat advanced prostate cancer. Chemical castration by gonadotropin-releasing hormone agonists suppresses lutenizing hormone production, which in turn inhibits the production of testosterone and dihydrotestosterone. This will prevent the growth of prostate cancer cells. However, ADT causes deleterious effects on bone health because the androgens are essential in preserving optimal bone health in men.
CONCLUSION: Various observational studies showed that long-term ADT for advanced or metastatic prostate cancer was associated with decreased bone mineral density, as well as altered body composition that might affect bone health. Considering the potential impact of osteoporotic fracture, interventions to mitigate these skeletal adverse effects should be considered by physicians when initiating ADT on their patients.
METHODS: Forty-eight hospital departments were recruited via open call and stratified by country. Departments were assigned to the operational program (intervention) or usual routine (control group). Data for analyses included 36 of these departments and their 5285 patients (median 147 per department; range 29-201), 2529 staff members (70; 10-393), 1750 medical records (50; 50-50), and standards compliance assessments. Follow-up was measured after 1 year. The outcomes were health status, service delivery, and standards compliance.
RESULTS: No health differences between groups were found, but the intervention group had higher identification of lifestyle risk (81% versus 60%, p health effects, the bias, and the limitations should be considered in implementation efforts and further studies.
TRIAL REGISTRATION: ClinicalTrials.gov : NCT01563575. Registered 27 March 2012. https://clinicaltrials.gov/ct2/show/NCT01563575.
Methods: We adopted a comparative cross-sectional study on pre-clinical medical students who appeared in two different admission tests. The stress, anxiety, and depression levels of students were measured by the depression, anxiety, stress scale (DASS-21), and their burnout level was measured by the Copenhagen Burnout Inventory.
Results: The stress, anxiety, and depression scores between MMI and PI were not significantly different (p-value > 0.05). The personal, work and client burnout scores between MMI and PI were not significantly different (p-value > 0.05). The prevalence of stress (MMI = 39%, PI = 36.9%), anxiety (MMI = 78%, PI = 67.4%), depression (MMI = 41%, PI = 36.2%) and burnout (MMI = 29%, PI = 31.9%) between MMI and PI cohorts was not significantly different (p-value > 0.05). These results showed similar levels of stress, anxiety, depression, and burnout in students at the end of the pre-clinical phase.
Conclusions: This study showed similar psychological health status of the pre-clinical students who were enrolled by two different admission tests. The prevalence of stress, anxiety, burnout, and depression among the pre-clinical medical students was comparable to the global prevalence. The results indicate that medical schools can consider implementing either MMI or PI to recruit suitable candidates for medical training.