BackgroundIn response to the health risks posed by asbestos exposure, some countries have imposed strict regulations and adopted bans, whereas other countries have intervened less and continue to use varying quantities of asbestos.ObjectivesThis study was designed to assess, on a global scale, national experiences of recent mortality from pleural mesothelioma, historical trends in asbestos use, adoption of bans, and their possible interrelationships.MethodsFor 31 countries with available data, we analyzed recent pleural mesothelioma (International Classification of Diseases, 10th Revision) mortality rates (MRs) using age-adjusted period MRs (deaths/million/year) from 1996 to 2005. We calculated annual percent changes (APCs) in age-adjusted MRs to characterize trends during the period. We characterized historical patterns of asbestos use by per capita asbestos use (kilograms per capita/year) and the status of national bans.ResultsPeriod MRs increased with statistical significance in five countries, with marginal significance in two countries, and were equivocal in 24 countries (five countries in Northern and Western Europe recorded negative APC values). Countries adopting asbestos bans reduced use rates about twice as fast as those not adopting bans. Turning points in use preceded bans. Change in asbestos use during 1970–1985 was a significant predictor of APC in mortality for pleural mesothelioma, with an adjusted R2 value of 0.47 (p < 0.0001).ConclusionsThe observed disparities in global mesothelioma trends likely relate to country-to-country disparities in asbestos use trends.

Objective The impact of the clustering of metabolic factors on chronic kidney disease (CKD) in non-obese individuals remains unclear. Methods We conducted a follow-up study of 23,894 Japanese adults (age, 18-69 years) who continuously received annual health examinations between 2000 and 2011. Obesity, high blood pressure, high triglycerides, low high-density lipoprotein (HDL) cholesterol and high fasting blood sugar were defined as metabolic factors, and CKD was defined as renal dysfunction (estimated glomerular filtration rate: <60 mL/min/1.73 m 2 ) or proteinuria (dipstick test: ! 1+). The association between the clustering of metabolic factors and CKD was assessed based on the presence or absence of obesity using a Cox proportional hazard model. Results Of 2,867 subjects with ! 3 metabolic factors, 650 (22.7%) were non-obese. These individuals were older and had higher metabolic risks than their obese counterparts at baseline. Among the entire cohort of 23,894 subjects, 1,764 developed renal dysfunction and 904 developed proteinuria during an average followup period of 7.8 years. The cumulative incidence of renal dysfunction was higher (22.1% vs. 16.1%), whereas that of proteinuria was lower (10.5% vs. 14.4%), among the non-obese subjects with ! 3 metabolic factors than the obese subjects with ! 3 metabolic factors after 11 years. The adjusted relative risk (RR) (95% confidence interval) of renal dysfunction was 1.54 (1.34-1.77) and 1.67 (1.35-2.07) for the obese and nonobese subjects with ! 3 metabolic factors, respectively. Conclusion Non-obese subjects with ! 3 metabolic factors, who are missed based on the essential criterion of obesity for metabolic syndrome, may have an equal or slightly higher risk of renal dysfunction than obese subjects with ! 3 metabolic factors.

In Japanese men, although the risk of CVD death before dialysis initiation can never be ignored, CKD patients aged <60 years with a GFR of <30 mL/min/1.73 m are more likely to undergo dialysis prior to death.

Background: The combined associations of body mass index (BMI) levels and metabolic dysfunction with medical and dental care utilizations is unclear. Methods: A 4-year follow-up study was performed in 16,386 Japanese male employees (mean age 48.2 [standard deviation, 11.0] years) without a history of cardiovascular disease (CVD), cancer, or renal failure. They were classified into eight phenotypes based on four BMI levels (underweight, <18.5; normal weight, 18.5-24.9; overweight, 25.0-29.9; and obese, ≥30.0 kg=m 2) and the presence or absence of ≥2 of 4 metabolic abnormalities: high blood pressure, high triglycerides, low highdensity-lipoprotein cholesterol, and high blood sugar. Based on their health insurance claims data, we compared medical and dental care days and costs among the eight different BMI=metabolic phenotypes during 2010-2013. Results: The combinations of BMI levels and metabolic status were significantly associated with the adjusted mean and median medical outpatient days and costs and the median dental outpatient days and costs. The obese=unhealthy subjects had the highest medical outpatient days and costs, and the underweight=unhealthy subjects had the highest dental outpatient days and costs. The underweight=unhealthy subjects also had the highest medical inpatient days and hospitalization rates of CVD, and had higher medical costs compared with the obese=healthy subjects. The differences in median medical costs between healthy and unhealthy phenotypes were larger year by year across all BMI levels. Conclusions: Identification of obesity phenotypes using both BMI levels (including the underweight level) and metabolic status may more precisely predict healthcare days and costs compared with either BMI or metabolic status alone.