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      Relation between Accumulated Air Pollution Exposure and Sub-Clinical Cardiovascular Disease in 33,723 Danish 60–74-Year-Old Males from the Background Population (AIR-CARD): A Method Article

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          Cardiovascular disease is one of the main causes of death and disability in the Western world, and there is increasing evidence that air pollution is a risk factor for developing sub-clinical cardiovascular diseases. Previous studies have shown a correlation between cardiovascular disease and short-term exposure to elevated air pollution levels. However, the literature on the impact of long-term effect of air pollution is limited. We have a unique opportunity to evaluate this correlation. The DEHM/UBM/AirGIS model system calculates air pollution in a high temporal and spatial resolution and traces air pollution retrospectively to year 1979. The model calculates accumulated exposure using annual exposure from PM<sub>2.5</sub> in relation to home and work addresses and takes into account working hours and holidays. We link the results from this model system to a population-based cardiovascular screening cohort of 33,723 individuals in the age of 60–74 to assess the contribution of the specific accumulated air pollution to the presence of sub-clinical arteriosclerosis in the coronary vessels, abdominal aortic aneurysms, and peripheral arterial disease. This correlation will be further analyzed in relation to specific air pollutants. This study will introduce more precise data for a longer period of time and incorporate participant’s home and work addresses.

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          Most cited references 48

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          Alternative projections of mortality and disability by cause 1990–2020: Global Burden of Disease Study

          The Lancet, 349(9064), 1498-1504
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            Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study.

            Long-term exposure to particulate matter air pollution has been associated with increased cardiopulmonary mortality in the USA. We aimed to assess the relation between traffic-related air pollution and mortality in participants of the Netherlands Cohort study on Diet and Cancer (NLCS), an ongoing study. We investigated a random sample of 5000 people from the full cohort of the NLCS study (age 55-69 years) from 1986 to 1994. Long-term exposure to traffic-related air pollutants (black smoke and nitrogen dioxide) was estimated for the 1986 home address. Exposure was characterised with the measured regional and urban background concentration and an indicator variable for living near major roads. The association between exposure to air pollution and (cause specific) mortality was assessed with Cox's proportional hazards models, with adjustment for potential confounders. 489 (11%) of 4492 people with data died during the follow-up period. Cardiopulmonary mortality was associated with living near a major road (relative risk 1.95, 95% CI 1.09-3.52) and, less consistently, with the estimated ambient background concentration (1.34, 0.68-2.64). The relative risk for living near a major road was 1.41 (0.94-2.12) for total deaths. Non-cardiopulmonary, non-lung cancer deaths were unrelated to air pollution (1.03, 0.54-1.96 for living near a major road). Long-term exposure to traffic-related air pollution may shorten life expectancy.
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              Long-term prognosis associated with coronary calcification: observations from a registry of 25,253 patients.

              The purpose of this study was to develop risk-adjusted multivariable models that include risk factors and coronary artery calcium (CAC) scores measured with electron-beam tomography in asymptomatic patients for the prediction of all-cause mortality. Several smaller studies have documented the efficacy of CAC testing for assessment of cardiovascular risk. Larger studies with longer follow-up will lend strength to the hypothesis that CAC testing will improve outcomes, cost-effectiveness, and safety of primary prevention efforts. We used an observational outcome study of a cohort of 25,253 consecutive, asymptomatic individuals referred by their primary physician for CAC scanning to assess cardiovascular risk. Multivariable Cox proportional hazards models were developed to predict all-cause mortality. Risk-adjusted models incorporated traditional risk factors for coronary disease and CAC scores. The frequency of CAC scores was 44%, 14%, 20%, 13%, 6%, and 4% for scores of 0, 1 to 10, 11 to 100, 101 to 400, 401 to 1,000, and >1,000, respectively. During a mean follow-up of 6.8 +/- 3 years, the death rate was 2% (510 deaths). The CAC was an independent predictor of mortality in a multivariable model controlling for age, gender, ethnicity, and cardiac risk factors (model chi-square = 2,017, p 1,000, respectively (p 1,000 (p < 0.0001). This large observational data series shows that CAC provides independent incremental information in addition to traditional risk factors in the prediction of all-cause mortality.

                Author and article information

                S. Karger AG
                January 2021
                25 November 2020
                : 146
                : 1
                : 19-26
                aCardiovascular Research Unit, Odense University Hospital – Svendborg, Svendborg, Denmark
                bDepartment of Clinical Epidemiology, Odense University Hospital, Odense, Denmark
                cDepartment of Cardiology, Sygehus Lillebælt, Lillebaelt, Denmark
                dDepartment of Environmental Science, Faculty of Technical Sciences, Aarhus University, Aarhus, Denmark
                eDepartment of Cardiology, Odense University Hospital, Odense, Denmark
                fDepartment of Cardiothoracic and Vascular Surgery T, Odense University Hospital, Odense, Denmark
                Author notes
                *Jess Lambrechtsen, Cardiovascular Research Unit, Odense University Hospital – Svendborg, Baagøes Allé 31, DK–5700 Svendborg (Denmark), Jess.Lambrechtsen@rsyd.dk
                511128 Cardiology 2021;146:19–26
                © 2020 S. Karger AG, Basel

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                Page count
                Figures: 2, Pages: 8
                CAD and AMI: Clinical Trial Design


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