Is there an association between coronary atherosclerosis and carcinoma of the prostate in men aged 50 years and older? An autopsy and coroner based post-mortem study.

BACKGROUND: Atherosclerotic disease is the most common cause of death in the United States and prostate cancer has the highest incidence among males in the United States. Reports have indicated that atherosclerosis and cancers my share common pathoetiologic and pathogenetic cascades. If atherosclerosis and cancers have common pathoetiologic and pathogenetic cascades, both diseases will co-occur and patients may represent a potential target group for cancer screening interventions. MATERIALS AND METHODS: Prostates and coronary vessels were examined from 37 deceased men, aged 50 years and older, who died unexpectedly and suddenly from traumatic causes. Tissue sections of the entire prostate were examined for benign and malignant lesions. Analysis of Variance was used to compare mean coronary artery atherosclerosis scores among groups of men with diagnosis of adenocarcinoma, intraepithelial neoplasm, benign hyperplasia and normal prostate glands. RESULTS: Twelve prostates (32.5%) showed adenocarcinoma of the prostate, four with Gleason score 7 and eight with Gleason score 6. After adjustment for age and race, there remained no statistical difference between prostate pathology groups and atherosclerosis score (F = 0.72; P = 0.55). CONCLUSIONS: To our knowledge, ours is the first study to use direct pathological examination of tissues for definitive identification of atherosclerosis and prostate cancer. In our case series, the occurrence and progression of coronary atherosclerotic disease and cancer of the prostate were not associated.

Pregnancy outcomes in women with gestational diabetes treated with metformin or insulin: a case-control study.

AIMS: To compare maternal and neonatal outcomes in women with gestational diabetes mellitus (GDM) treated with either metformin or insulin. METHODS: One hundred and twenty-seven women with GDM not adequately controlled by dietary measures received metformin 500 mg twice daily initially. The dose was titrated to achieve target blood glucose values. Pregnancy outcomes in the 100 women who remained exclusively on metformin were compared with 100 women with GDM treated with insulin matched for age, weight and ethnicity. RESULTS: There were no significant differences in baseline maternal risk factors. Women treated with insulin had significantly greater mean (sem) weight gain from enrolment to term (2.72 +/- 0.4 vs. 0.94 +/- 0.3 kg; P < 0.001). There was no difference between the metformin and insulin groups, respectively, comparing gestational hypertension (6 vs. 7%, P = 0.9), pre-eclampsia (9 vs. 2%, P = 0.06) induction of labour (26 vs. 24%, P = 0.87) or rate of Caesarean section (48 vs. 52%, P = 0.67). No perinatal loss occurred in either group. Neonatal morbidity was improved in the metformin group; prematurity (0 vs. 10%, P < 0.01), neonatal jaundice (8 vs. 30%, P < 0.01) and admission to neonatal unit (6 vs. 19%, P < 0.01). The incidence of macrosomia (birthweight centile > 90) was not significantly different [metformin (14%) vs. insulin (25%); P = 0.07]. CONCLUSIONS: Women with GDM treated with metformin and with similar baseline risk factors for adverse pregnancy outcomes had less weight gain and improved neonatal outcomes compared with those treated with insulin. Diabet. Med. 26, 798-802 (2009).

Visceral fat mass as a novel risk factor for predicting gestational diabetes in obese pregnant women.

OBJECTIVE: To develop a model to predict gestational diabetes mellitus incorporating classical and a novel risk factor, visceral fat mass. METHODS: Three hundred two obese non-diabetic pregnant women underwent body composition analysis at booking by bioimpedance analysis. Of this cohort, 72 (24%) developed gestational diabetes mellitus. Principal component analysis was initially performed to identify possible clustering of the gestational diabetes mellitus and non-GDM groups. A machine learning algorithm was then applied to develop a GDM predictive model utilising random forest and decision tree modelling. RESULTS: The predictive model was trained on 227 samples and validated using an independent testing subset of 75 samples where the model achieved a validation prediction accuracy of 77.53%. According to the decision tree developed, visceral fat mass emerged as the most important variable in determining the risk of gestational diabetes mellitus. CONCLUSIONS: We present a model incorporating visceral fat mass, which is a novel risk factor in predicting gestational diabetes mellitus in obese pregnant women.