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      WHO guidelines for a healthy diet and mortality from cardiovascular disease in European and American elderly: the CHANCES project 1 2

      research-article
      3 , 4 , 3 , 3 , 3 , 5 , 6 , 7 , 8 , 7 , 8 , 7 , 8 , 7 , 9 , 10 , 10 , 11 , 11 , 12 , 11 , 5 , 5 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20–23 , 3 , 3 , *
      The American Journal of Clinical Nutrition
      American Society for Nutrition
      aging, CHANCES, cardiovascular disease, cohort, meta-analysis

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          Abstract

          Background: Cardiovascular disease (CVD) represents a leading cause of mortality worldwide, especially in the elderly. Lowering the number of CVD deaths requires preventive strategies targeted on the elderly.

          Objective: The objective was to generate evidence on the association between WHO dietary recommendations and mortality from CVD, coronary artery disease (CAD), and stroke in the elderly aged ≥60 y.

          Design: We analyzed data from 10 prospective cohort studies from Europe and the United States comprising a total sample of 281,874 men and women free from chronic diseases at baseline. Components of the Healthy Diet Indicator (HDI) included saturated fatty acids, polyunsaturated fatty acids, mono- and disaccharides, protein, cholesterol, dietary fiber, and fruit and vegetables. Cohort-specific HRs adjusted for sex, education, smoking, physical activity, and energy and alcohol intakes were pooled by using a random-effects model.

          Results: During 3,322,768 person-years of follow-up, 12,492 people died of CVD. An increase of 10 HDI points (complete adherence to an additional WHO guideline) was, on average, not associated with CVD mortality (HR: 0.94; 95% CI: 0.86, 1.03), CAD mortality (HR: 0.99; 95% CI: 0.85, 1.14), or stroke mortality (HR: 0.95; 95% CI: 0.88, 1.03). However, after stratification of the data by geographic region, adherence to the HDI was associated with reduced CVD mortality in the southern European cohorts (HR: 0.87; 95% CI: 0.79, 0.96; I 2 = 0%) and in the US cohort (HR: 0.85; 95% CI: 0.83, 0.87; I 2 = not applicable).

          Conclusion: Overall, greater adherence to the WHO dietary guidelines was not significantly associated with CVD mortality, but the results varied across regions. Clear inverse associations were observed in elderly populations in southern Europe and the United States.

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          Most cited references73

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          Diet, nutrition and the prevention of chronic diseases.

          Shifting dietary patterns, a decline in energy expenditure associated with a sedentary lifestyle, an ageing population--together with tobacco use and alcohol consumption--are major risk factors for noncommunicable diseases and pose an increasing challenge to public health. This report of a Joint WHO/FAO Expert Consultation reviews the evidence on the effects of diet and nutrition on chronic diseases and makes recommendations for public health policies and strategies that encompass societal, behavioural and ecological dimensions. Although the primary aim of the Consultation was to set targets related to diet and nutrition, the importance of physical activity was also emphasized. The Consultation considered diet in the context of the macro-economic implications of public health recommendations on agriculture and the global supply and demand for fresh and processed foodstuffs. In setting out ways to decrease the burden of chronic diseases such as obesity, type 2 diabetes, cardiovascular diseases (including hypertension and stroke), cancer, dental diseases and osteoporosis, this report proposes that nutrition should be placed at the forefront of public health policies and programmes. This report will be of interest to policy-makers and public health professionals alike, in a wide range of disciplines including nutrition, general medicine and gerontology. It shows how, at the population level, diet and exercise throughout the life course can reduce the threat of a global epidemic of chronic diseases.
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            How should meta-regression analyses be undertaken and interpreted?

            Appropriate methods for meta-regression applied to a set of clinical trials, and the limitations and pitfalls in interpretation, are insufficiently recognized. Here we summarize recent research focusing on these issues, and consider three published examples of meta-regression in the light of this work. One principal methodological issue is that meta-regression should be weighted to take account of both within-trial variances of treatment effects and the residual between-trial heterogeneity (that is, heterogeneity not explained by the covariates in the regression). This corresponds to random effects meta-regression. The associations derived from meta-regressions are observational, and have a weaker interpretation than the causal relationships derived from randomized comparisons. This applies particularly when averages of patient characteristics in each trial are used as covariates in the regression. Data dredging is the main pitfall in reaching reliable conclusions from meta-regression. It can only be avoided by prespecification of covariates that will be investigated as potential sources of heterogeneity. However, in practice this is not always easy to achieve. The examples considered in this paper show the tension between the scientific rationale for using meta-regression and the difficult interpretative problems to which such analyses are prone. Copyright 2002 John Wiley & Sons, Ltd.
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              Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements.

              Previous cohort studies of fat intake and risk of coronary heart disease (CHD) have been inconsistent, probably due in part to methodological differences and various limitations, including inadequate dietary assessment and incomplete adjustment for total energy intake. The authors analyzed repeated assessment of diet from the Nurses' Health Study to examine the associations between intakes of four major types of fat (saturated, monounsaturated, polyunsaturated, and trans fats) and risk of CHD during 14 years of follow-up (1980-1994) by using alternative methods for energy adjustment. In particular, the authors compared four risk models for energy adjustment: the standard multivariate model, the energy-partition model, the nutrient residual model, and the multivariate nutrient density model. Within each model, the authors compared four different approaches for analyzing repeated dietary measurements: baseline diet only, the most recent diet, and two different algorithms for calculating cumulative average diets. The substantive results were consistent across all models; that is, higher intakes of saturated and trans fats were associated with increased risk of CHD, while higher intakes of monounsaturated and polyunsaturated fats were associated with reduced risk. When nutrients were considered as continuous variables, the four energy-adjustment methods yielded similar associationS. However, the interpretation of the relative risks differed across models. In addition, within each model, the methods using the cumulative averages in general yielded stronger associations than did those using either only baseline diet or the most recent diet. When the nutrients were categorized according to quintiles, the residual and the nutrient density models, which gave similar results, yielded statistically more significant tests for linear trend than did the standard and the partition models.
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                Author and article information

                Journal
                Am J Clin Nutr
                Am. J. Clin. Nutr
                ajcn
                The American Journal of Clinical Nutrition
                American Society for Nutrition
                0002-9165
                1938-3207
                October 2015
                9 September 2015
                9 September 2015
                : 102
                : 4
                : 745-756
                Affiliations
                [3 ]Division of Human Nutrition, Wageningen University, Wageningen, Netherlands;
                [4 ]Centre of Clinical Epidemiology, Institute for Medical Informatics, Biometry and Epidemiology, University Hospital, University Duisburg-Essen, Essen, Germany;
                [5 ]Department of Epidemiology, Erasmus MC, University Medical Centre, Rotterdam, Netherlands;
                [6 ]Global Public Health, Leiden University College, the Hague, Netherlands;
                [7 ]Department of Hygiene, Epidemiology and Medical Statistics, University of Athens, Medical School, Athens, Greece;
                [8 ]Hellenic Health Foundation, Athens, Greece;
                [9 ]The Tisch Cancer Institute, Mount Sinai School of Medicine, New York, NY;
                [10 ]Department of Epidemiology and Public Health, University College London, London, United Kingdom;
                [11 ]UKCRC Centre of Excellence for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast, United Kingdom;
                [12 ]Unit of Nutrition, Environment and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology, Barcelona, Spain;
                [13 ]Department of Epidemiology, Julius Centre, Utrecht, Netherlands;
                [14 ]Department of Epidemiology and Population Studies, Jagiellonian University, Krakow, Poland;
                [15 ]Institute of Internal and Preventive Medicine, Siberian Branch of the Russian Academy of Medical Sciences, Novosibirsk, Russia;
                [16 ]Novosibirsk State Medical University, Novosibirsk, Russia;
                [17 ]National Institute of Public Health, Prague, Czech Republic;
                [18 ]Department of Public Health and Clinical Medicine, Nutritional Research, Umeå University, Umeå, Sweden;
                [19 ]Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD;
                [20 ]Department for Determinants of Chronic Diseases, National Institute for Public Health and the Environment, Bilthoven, Netherlands;
                [21 ]Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, Netherlands;
                [22 ]Department of Epidemiology and Biostatistics, The School of Public Health, Imperial College London, London, United Kingdom; and
                [23 ]Department of Social & Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
                Author notes
                *To whom correspondence should be addressed: E-mail: edith.feskens@ 123456wur.nl .
                [1]

                The included cohorts were financially supported by regional governments of Andalucia, Asturias, Basque Country, Murcia, and Navarra, The Spanish Ministry of Health (ISCIII RETICC RD06/0020/0091), and the Catalan Institute of Oncology (EPIC-Spain); regional government of Västerbotten, Sweden (EPIC-Sweden); Dutch Ministry of Public Health, Welfare and Sports, Netherlands Cancer Registry, LK Research Funds, Dutch Prevention Funds, Dutch ZON, World Cancer Research Fund, Statistics Netherlands (EPIC-Netherlands); Hellenic Health Foundation, Stavros Niarchos Foundation (EPIC-Elderly Greece); Intramural Research Program of the NIH, National Cancer Institute (NIH-AARP), the Wellcome Trust “Determinants of Cardiovascular Diseases in Eastern Europe: A multi-centre cohort study” (064947/Z/01/Z), MacArthur Foundation “Health and Social Upheaval (a research network)” and National Institute on Aging “Health disparities and aging in societies in transition (the HAPIEE study)” (grant no. 1R01 AG23522). The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. HEALTH –F3-2010-242244. The Rotterdam Study is supported by the Erasmus Medical Center and Erasmus University Rotterdam; The Netherlands Organization for Scientific Research; The Netherlands Organization for Health Research and Development; the Research Institute for Diseases in the Elderly; The Netherlands Genomics Initiative; the Ministry of Education, Culture and Science; the Ministry of Health, Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. SENECA was a Concerted Action within the EURONUT programme of the European Union. This is an open access article distributed under the CC-BY license ( http://creativecommons.org/licenses/by/3.0/).

                [2]

                Supplemental Tables 1 and 2 are available from the “Supplemental data” link in the online posting of the article and from the same link in the online table of contents at http://ajcn.nutrition.org.

                Article
                095117
                10.3945/ajcn.114.095117
                4588736
                26354545
                55dcf7f8-bebf-4a92-b8b5-8c0f282fbb2c

                This is an open access article distributed under the CC-BY license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 3 July 2014
                : 23 July 2015
                Page count
                Pages: 12
                Categories
                Cardiovascular Disease Risk

                Nutrition & Dietetics
                aging,chances,cardiovascular disease,cohort,meta-analysis
                Nutrition & Dietetics
                aging, chances, cardiovascular disease, cohort, meta-analysis

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