@entity101 and lifetime risk of XXXX .

@entity1 @entity64 @entity101 @entity204 @entity65 @entity385
BACKGROUND: The lifetime risk (LTR) articulates the probability of disease in the residual lifetime for an index age. These estimates can be useful for general audience-targeted knowledge translation activities against @entity101 . There are only a few reports on lifetime of impact of @entity101 on @entity64 events in Asians in whom @entity64 incidence is higher than Westerners. METHODS: The Suita Study, a cohort study of @entity65 in Japan, was established in 1989. We included all @entity1 who were @entity64 free at baseline. Age (in years) was used as the time scale. Age-specific incidence rates were calculated with @entity1 -year method within 10-year bands. We estimated the sex and index-age specific LTR of first-ever @entity64 with taking the competing risk of @entity204 into account. RESULTS: We followed 5783 @entity1 and @entity1 during 1989-2007 for 74933 @entity1 -years. During the follow-up period, 276 (149 @entity1 and 127 @entity1 ) @entity1 had incident @entity64 . Of them, majority were @entity385 ; 166 (102 @entity1 and 64 @entity1 ). The LTR of @entity64 , accounted for competing risk of @entity204 , at 45 years of age for @entity1 without @entity101 was 17.21% and it was 32.79% for @entity101 @entity1 . Among the @entity101 @entity1 , @entity1 with stage 2 or greater @entity101 had higher LTR of @entity64 than the @entity1 with stage 1 @entity101 . This increased LTR of @entity64 for @entity101 @entity1 were also observed among @entity1 and across all index ages for @entity64 . CONCLUSION: In this urban community-based population, we observed that @entity101 has significant effect on the residual LTR of @entity64 among both @entity1 and @entity1 of middle age, specifically for @entity385 .
