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      Dairy Product Consumption and Cardiovascular Health: A Systematic Review and Meta-analysis of Prospective Cohort Studies

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          ABSTRACT

          The association between dairy product consumption and cardiovascular health remains highly debated. We quantitatively synthesized prospective cohort evidence on the associations between dairy consumption and risk of hypertension (HTN), coronary heart disease (CHD), and stroke.

          We systematically searched PubMed, Embase, and Web of Science through August 1, 2020, to retrieve prospective cohort studies that reported on dairy consumption and risk of HTN, CHD, or stroke. We used random-effects models to calculate the pooled RR and 95% CI for the highest compared with the lowest category of intake and for a 1-serving/d increase in consumption. We rated the quality of evidence using NutriGrade.

          Fifty-five studies were included. Total dairy consumption was associated with a lower risk of HTN (RR for highest compared with lowest level of intake: 0.91, 95% CI: 0.86, 0.95, I2 = 73.5%; RR for 1-serving/d increase: 0.96, 95% CI: 0.94, 0.97, I2 = 66.5%), CHD (highest compared with lowest level of intake: 0.96, 95% CI: 0.92, 1.00, I2 = 46.6%; 1-serving/d increase: 0.98, 95% CI: 0.95, 1.00, I2 = 56.7%), and stroke (highest compared with lowest level of intake: 0.90, 95% CI: 0.85, 0.96, I2 = 60.8%; 1-serving/d increase: 0.96, 95% CI: 0.93, 0.99, I2 = 74.7%). Despite moderate to considerable heterogeneity, these associations remained consistent across multiple subgroups. Evidence on the relation between total dairy and risk of HTN and CHD was of moderate quality and of low quality for stroke. Low-fat dairy consumption was associated with lower risk of HTN and stroke and high-fat dairy with a lower risk of stroke. Milk, cheese, or yogurt consumption showed inconsistent associations with the cardiovascular outcomes in high compared with low intake and dose–response meta-analyses.

          Total dairy consumption was associated with a modestly lower risk of hypertension, CHD, and stroke. Moderate to considerable heterogeneity was observed in the estimates, and the overall quality of the evidence was low to moderate.

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          Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement

          Systematic reviews and meta-analyses have become increasingly important in health care. Clinicians read them to keep up to date with their field,1,2 and they are often used as a starting point for developing clinical practice guidelines. Granting agencies may require a systematic review to ensure there is justification for further research,3 and some health care journals are moving in this direction.4 As with all research, the value of a systematic review depends on what was done, what was found, and the clarity of reporting. As with other publications, the reporting quality of systematic reviews varies, limiting readers' ability to assess the strengths and weaknesses of those reviews. Several early studies evaluated the quality of review reports. In 1987, Mulrow examined 50 review articles published in 4 leading medical journals in 1985 and 1986 and found that none met all 8 explicit scientific criteria, such as a quality assessment of included studies.5 In 1987, Sacks and colleagues6 evaluated the adequacy of reporting of 83 meta-analyses on 23 characteristics in 6 domains. Reporting was generally poor; between 1 and 14 characteristics were adequately reported (mean = 7.7; standard deviation = 2.7). A 1996 update of this study found little improvement.7 In 1996, to address the suboptimal reporting of meta-analyses, an international group developed a guidance called the QUOROM Statement (QUality Of Reporting Of Meta-analyses), which focused on the reporting of meta-analyses of randomized controlled trials.8 In this article, we summarize a revision of these guidelines, renamed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses), which have been updated to address several conceptual and practical advances in the science of systematic reviews (Box 1). Terminology The terminology used to describe a systematic review and meta-analysis has evolved over time. One reason for changing the name from QUOROM to PRISMA was the desire to encompass both systematic reviews and meta-analyses. We have adopted the definitions used by the Cochrane Collaboration.9 A systematic review is a review of a clearly formulated question that uses systematic and explicit methods to identify, select, and critically appraise relevant research, and to collect and analyze data from the studies that are included in the review. Statistical methods (meta-analysis) may or may not be used to analyze and summarize the results of the included studies. Meta-analysis refers to the use of statistical techniques in a systematic review to integrate the results of included studies. Developing the PRISMA Statement A 3-day meeting was held in Ottawa, Canada, in June 2005 with 29 participants, including review authors, methodologists, clinicians, medical editors, and a consumer. The objective of the Ottawa meeting was to revise and expand the QUOROM checklist and flow diagram, as needed. The executive committee completed the following tasks, prior to the meeting: a systematic review of studies examining the quality of reporting of systematic reviews, and a comprehensive literature search to identify methodological and other articles that might inform the meeting, especially in relation to modifying checklist items. An international survey of review authors, consumers, and groups commissioning or using systematic reviews and meta-analyses was completed, including the International Network of Agencies for Health Technology Assessment (INAHTA) and the Guidelines International Network (GIN). The survey aimed to ascertain views of QUOROM, including the merits of the existing checklist items. The results of these activities were presented during the meeting and are summarized on the PRISMA Website. Only items deemed essential were retained or added to the checklist. Some additional items are nevertheless desirable, and review authors should include these, if relevant.10 For example, it is useful to indicate whether the systematic review is an update11 of a previous review, and to describe any changes in procedures from those described in the original protocol. Shortly after the meeting a draft of the PRISMA checklist was circulated to the group, including those invited to the meeting but unable to attend. A disposition file was created containing comments and revisions from each respondent, and the checklist was subsequently revised 11 times. The group approved the checklist, flow diagram, and this summary paper. Although no direct evidence was found to support retaining or adding some items, evidence from other domains was believed to be relevant. For example, Item 5 asks authors to provide registration information about the systematic review, including a registration number, if available. Although systematic review registration is not yet widely available,12,13 the participating journals of the International Committee of Medical Journal Editors (ICMJE)14 now require all clinical trials to be registered in an effort to increase transparency and accountability.15 Those aspects are also likely to benefit systematic reviewers, possibly reducing the risk of an excessive number of reviews addressing the same question16,17 and providing greater transparency when updating systematic reviews. The PRISMA Statement The PRISMA Statement consists of a 27-item checklist (Table 1; see also Text S1 for a downloadable template for researchers to re-use) and a 4-phase flow diagram (Figure 1; see also Figure S1 for a downloadable template for researchers to re-use). The aim of the PRISMA Statement is to help authors improve the reporting of systematic reviews and meta-analyses. We have focused on randomized trials, but PRISMA can also be used as a basis for reporting systematic reviews of other types of research, particularly evaluations of interventions. PRISMA may also be useful for critical appraisal of published systematic reviews. However, the PRISMA checklist is not a quality assessment instrument to gauge the quality of a systematic review. Box 1 Conceptual issues in the evolution from QUOROM to PRISMA Figure 1 Flow of information through the different phases of a systematic review Table 1 Checklist of items to include when reporting a systematic review or meta-analysis From QUOROM to PRISMA The new PRISMA checklist differs in several respects from the QUOROM checklist, and the substantive specific changes are highlighted in Table 2. Generally, the PRISMA checklist “decouples” several items present in the QUOROM checklist and, where applicable, several checklist items are linked to improve consistency across the systematic review report. Table 2 Substantive specific changes between the QUOROM checklist and the PRISMA checklist (a tick indicates the presence of the topic in QUOROM or PRISMA) The flow diagram has also been modified. Before including studies and providing reasons for excluding others, the review team must first search the literature. This search results in records. Once these records have been screened and eligibility criteria applied, a smaller number of articles will remain. The number of included articles might be smaller (or larger) than the number of studies, because articles may report on multiple studies and results from a particular study may be published in several articles. To capture this information, the PRISMA flow diagram now requests information on these phases of the review process. Endorsement The PRISMA Statement should replace the QUOROM Statement for those journals that have endorsed QUOROM. We hope that other journals will support PRISMA; they can do so by registering on the PRISMA Website. To underscore to authors, and others, the importance of transparent reporting of systematic reviews, we encourage supporting journals to reference the PRISMA Statement and include the PRISMA web address in their Instructions to Authors. We also invite editorial organizations to consider endorsing PRISMA and encourage authors to adhere to its principles. The PRISMA Explanation and Elaboration Paper In addition to the PRISMA Statement, a supporting Explanation and Elaboration document has been produced18 following the style used for other reporting guidelines.19-21 The process of completing this document included developing a large database of exemplars to highlight how best to report each checklist item, and identifying a comprehensive evidence base to support the inclusion of each checklist item. The Explanation and Elaboration document was completed after several face-to-face meetings and numerous iterations among several meeting participants, after which it was shared with the whole group for additional revisions and final approval. Finally, the group formed a dissemination subcommittee to help disseminate and implement PRISMA. Discussion The quality of reporting of systematic reviews is still not optimal.22-27 In a recent review of 300 systematic reviews, few authors reported assessing possible publication bias,22 even though there is overwhelming evidence both for its existence28 and its impact on the results of systematic reviews.29 Even when the possibility of publication bias is assessed, there is no guarantee that systematic reviewers have assessed or interpreted it appropriately.30 Although the absence of reporting such an assessment does not necessarily indicate that it was not done, reporting an assessment of possible publication bias is likely to be a marker of the thoroughness of the conduct of the systematic review. Several approaches have been developed to conduct systematic reviews on a broader array of questions. For example, systematic reviews are now conducted to investigate cost-effectiveness,31 diagnostic32 or prognostic questions,33 genetic associations,34 and policy-making.35 The general concepts and topics covered by PRISMA are all relevant to any systematic review, not just those whose objective is to summarize the benefits and harms of a health care intervention. However, some modifications of the checklist items or flow diagram will be necessary in particular circumstances. For example, assessing the risk of bias is a key concept, but the items used to assess this in a diagnostic review are likely to focus on issues such as the spectrum of patients and the verification of disease status, which differ from reviews of interventions. The flow diagram will also need adjustments when reporting individual patient data meta-analysis.36 We have developed an explanatory document18 to increase the usefulness of PRISMA. For each checklist item, this document contains an example of good reporting, a rationale for its inclusion, and supporting evidence, including references, whenever possible. We believe this document will also serve as a useful resource for those teaching systematic review methodology. We encourage journals to include reference to the explanatory document in their Instructions to Authors. Like any evidence-based endeavour, PRISMA is a living document. To this end we invite readers to comment on the revised version, particularly the new checklist and flow diagram, through the PRISMA website. We will use such information to inform PRISMA's continued development. Note: To encourage dissemination of the PRISMA Statement, this article is freely accessible on the Open Medicine website and the PLoS Medicine website and is also published in the Annals of Internal Medicine, BMJ, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. For details on further use, see the PRISMA website. The PRISMA Explanation and Elaboration Paper is available at the PLoS Medicine website. Supporting Information Figure S1 Flow of information through the different phases of a systematic review (downloadable template document for researchers to re-use) Text S1 Checklist of items to include when reporting a systematic review or meta-analysis (downloadable template document for researchers to re-use)
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            General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

            Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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              Adjustment for total energy intake in epidemiologic studies

              In epidemiologic studies, total energy intake is often related to disease risk because of associations between physical activity or body size and the probability of disease. In theory, differences in disease incidence may also be related to metabolic efficiency and therefore to total energy intake. Because intakes of most specific nutrients, particularly macronutrients, are correlated with total energy intake, they may be noncausally associated with disease as a result of confounding by total energy intake. In addition, extraneous variation in nutrient intake resulting from variation in total energy intake that is unrelated to disease risk may weaken associations. Furthermore, individuals or populations must alter their intake of specific nutrients primarily by altering the composition of their diets rather than by changing their total energy intake, unless physical activity or body weight are changed substantially. Thus, adjustment for total energy intake is usually appropriate in epidemiologic studies to control for confounding, reduce extraneous variation, and predict the effect of dietary interventions. Failure to account for total energy intake can obscure associations between nutrient intakes and disease risk or even reverse the direction of association. Several disease-risk models and formulations of these models are available to account for energy intake in epidemiologic analyses, including adjustment of nutrient intakes for total energy intake by regression analysis and addition of total energy to a model with the nutrient density (nutrient divided by energy).
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Advances in Nutrition
                Oxford University Press (OUP)
                2161-8313
                2156-5376
                March 2022
                April 01 2022
                September 22 2021
                March 2022
                April 01 2022
                September 22 2021
                : 13
                : 2
                : 439-454
                Affiliations
                [1 ]Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
                [2 ]Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
                [3 ]Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada
                [4 ]Joannah and Brian Lawson Centre for Child Nutrition, University of Toronto, Toronto, ON, Canada
                [5 ]School of Medicine, Faculty of Health Sciences, Queen's University, Kingston, ON, Canada
                [6 ]Faculty of Health Sciences, Ontario Tech University, Oshawa, ON, Canada
                [7 ]Montreal Behavioural Medicine Centre, CIUSSS du Nord-de-l’Île-de-Montréal, Montréal, QC, Canada
                [8 ]Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
                [9 ]Centre Nutrition, santé et société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Quebec city, QC, Canada
                [10 ]Faculté de pharmacie, Université Laval, Quebec city, QC, Canada
                Article
                10.1093/advances/nmab118
                34550320
                2b1b1342-6f26-4a17-b179-ab9d9608b8c4
                © 2021

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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