Dataset Information

Body mass index and survival after diagnosis of invasive breast cancer: a study based on the Japanese National Clinical Database-Breast Cancer Registry.

ABSTRACT: Few studies have reported the association between body mass index (BMI) and outcome among Asian breast cancer patients. We analyzed data for 20,090 female invasive breast cancer patients who had been followed-up for a median period of 6.7 years entered in the National Clinical Database-Breast Cancer Registry between 2004 and 2006. We used mainly the WHO criteria for BMI (kg/m(2) ) categories; <18.5 (underweight), ?18.5-<21.8 (reference), ?21.8-<25, ?25-<30 (overweight), and ?30 (obese). We divided normal weight patients into two subgroups because this category includes many patients compared to others. The timing of BMI measurement was not specified. The Cox proportional hazards model and cubic spline regression were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Smoking, alcohol, and physical activity were not controlled. A total of 1418 all-cause, 937 breast cancer-specific deaths, and 2433 recurrences were observed. Obesity was associated with an increased risk of all-cause (HR: 1.46; 95% CI: 1.16-1.83) and breast cancer-specific death (HR: 1.47; 95% CI: 1.11-1.93) for all patients, and with all-cause (HR: 1.47; 95% CI: 1.13-1.92) and breast cancer-specific death (HR: 1.58; 95% CI: 1.13-2.20) for postmenopausal patients. Being underweight was associated with an increased risk of all-cause death for all (HR: 1.41; 95% CI: 1.16-1.71) and for postmenopausal patients (HR: 1.45; 95% CI: 1.15-1.84). With regard to subtype and menopausal status, obesity was associated with an increased risk of breast cancer-specific death for all cases of luminal B tumor (HR: 2.59; 95% CI: 1.51-4.43; Pheterogeneity of Luminal B vs. Triple negative = 0.016) and for postmenopausal patients with luminal B tumor (HR: 3.24; 95% CI: 1.71-6.17). Being obese or underweight is associated with a higher risk of death among female breast cancer patients in Japan.

SUBMITTER: Kawai M

PROVIDER: S-EPMC4924391 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Body mass index and survival after diagnosis of invasive breast cancer: a study based on the Japanese National Clinical Database-Breast Cancer Registry.

Kawai Masaaki M Tomotaki Ai A Miyata Hiroaki H Iwamoto Takayuki T Niikura Naoki N Anan Keisei K Hayashi Naoki N Aogi Kenjiro K Ishida Takanori T Masuoka Hideji H Iijima Kotaro K Masuda Shinobu S Tsugawa Koichiro K Kinoshita Takayuki T Nakamura Seigo S Tokuda Yutaka Y

Cancer medicine 20160229 6

Few studies have reported the association between body mass index (BMI) and outcome among Asian breast cancer patients. We analyzed data for 20,090 female invasive breast cancer patients who had been followed-up for a median period of 6.7 years entered in the National Clinical Database-Breast Cancer Registry between 2004 and 2006. We used mainly the WHO criteria for BMI (kg/m(2) ) categories; <18.5 (underweight), ≥18.5-<21.8 (reference), ≥21.8-<25, ≥25-<30 (overweight), and ≥30 (obese). We divid ...[more]

PMID: 26923549

Similar Datasets

Project description:BackgroundBy identifying individuals at high risk for non-alcoholic fatty liver disease (NAFLD), interventional programs could be targeted more effectively. Some studies have demonstrated that triglyceride glucose-body mass index (TyG-BMI) showed an independent positive association with NAFLD. However, research on its diagnostic value in patients with suspected NAFLD is limited. In this study, we aimed to evaluate whether TyG-BMI was accurate in detecting NAFLD in the general Japanese population.MethodsA cross-sectional study of 14,280 individuals who underwent a comprehensive health examination was conducted. Standard protocols were followed to collect anthropometric measurements, lab data, and ultrasonography features. All participants were randomly stratified into the development group (n = 7118) and validation group (n = 7162). The TyG-BMI was calculated. Following this, the diagnostic value of the TyG-BMI was evaluated based on the area under the receiver-operating characteristic curve (AUROC). Two cutoff points were selected and used to rule out or rule in the NALFD, and the specificity, sensitivity, negative predictive value, and positive predictive value were explored, respectively. In order to verify the stability of the results, external verification was performed.ResultsThere were 1272 and 1243 NAFLD participants in the development and validation groups, respectively. The area under the ROC curve (AUC) of TyG-BMI was 0.888 (95% CI 0.876-0.896) and 0.884 (95% CI 0.875-0.894) for the training and validation group, respectively. Using the low TyG-BMI (182.2) cutoff, NAFLD could be excluded with high accuracy (negative predictive value: 96.9% in estimation and 96.9% in validation). The presence of NAFLD could effectively be determined by applying the high cutoff of TyG-BMI (224.0), as the positive predictive value of the estimation and validation groups is 70.7% and 70.1%, respectively. As a result of applying this model, 9996 (70%) of the 14,280 participants would not have undergone ultrasonography, with an accurate prediction of 9308 (93.1%). AUC was 0.874 for external validation using 183,730 Chinese non-obese participants. TyG-BMI was demonstrated to be an excellent diagnostic tool by both internal and external validation.ConclusionsIn conclusion, the present study developed and validated a simple, non-invasive, and cost-effective tool to accurately separate participants with and without NAFLD in the Japanese population, rendering ultrasonography for identifying NAFLD unnecessary in a substantial proportion of people.

Dataset Information

Body mass index and survival after diagnosis of invasive breast cancer: a study based on the Japanese National Clinical Database-Breast Cancer Registry.

Publications

Body mass index and survival after diagnosis of invasive breast cancer: a study based on the Japanese National Clinical Database-Breast Cancer Registry.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets