Protocol for a systematic review and meta-analysis: to investigate the association of adherence to plant-based diets with cardiovascular disease risk

List of abbreviations

CVD: Cardiovascular disease

DASH: Dietary Approaches to Stop Hypertension

HR: Hazard ratio

ISI: Institute for Scientific Information

NHLBI: National Heart, Lung, and Blood Institute

OR: Odds ratio

PBD(s): Plant-based diet(s)

PEO: Population, Exposure, and Outcome

PRISMA: Preferred Reporting Items for Systematic reviews and Meta-Analysis

PRISMA-P: Preferred Reporting Items for Systematic reviews and Meta-Analysis protocols

RCTs: Randomized controlled trials

RR: Relative risk

WHO: World Health Organization

95% CIs: 95% Confidence intervals

Introduction

In the literature, there are widespread inconsistencies with regards to how plant-based diets (PBDs) have been described. This is due to the scarcity of data on how to define a PBD. PBDs are known for emphasising the consumption of foods derived from plants, such as fruits, vegetables, whole grains, legumes, nuts, and seeds. Ostfeld defined a PBD as a diet solely made up of naturally derived plant foods, which does not include any animal foods (e.g. resembling a vegan diet), not exempting eggs or dairy products.¹ Other well-known healthy dietary patterns that predominantly contain plant foods are the Mediterranean and Dietary Approaches to Stop Hypertension (DASH) diets. The Mediterranean diet encourages the consumption of healthy plant foods namely vegetables, fruits, whole grains, and nuts, with olive oil as primary source of added fat. Moreover, the Mediterranean diet limits the intake of red and processed meat, poultry, and dairy products and is low in sugar.² The DASH diet also promotes the consumption of plant foods that are primarily characteristic of a PBD, such as fruits, vegetables, whole grains, nuts, and seeds. Additionally, animal foods are also included in the DASH diet in minimal amounts of red meat, fish, poultry and low-fat dairy products.²

A dietary pattern is the amount, variety, combination of foods and beverages in a diet and the frequency with which they are usually consumed.^3,4 Dietary pattern analysis can be predefined (a priori) or data driven (a posteriori); these are two different approaches used to assess dietary patterns. An a posteriori analysis identifies similarities and assesses the variance (i.e. variables or the foods consumed) within a group (i.e. the observations in a data set or rather the study population). Principal component analysis, cluster analysis and factor analysis are among the multivariable statistical techniques utilised to obtain a posteriori scores. In comparison, a priori analysis assigns a score to each nutrient or food, based on existing nutrition knowledge that has a strong health-related focus, and the recommendations relative to a specific population’s dietary guidelines.^5–7 Different scoring systems can be used to construct a priori indices, which can subsequently measure adherence to a dietary pattern.⁸

Dietary indices are based on an individual’s reported intake of either nutrients, foods or the combination of them. Certain foods and nutrients are beneficial for health and consumption thereof should be encouraged to achieve nutritional adequacy, while others are detrimental for health and should be limited and consumed in moderation.⁹ Dietary indices may include only certain nutrients, foods or food groups in their construction and therefore often do not reflect total dietary intake. Nutrient intake data should preferably be based on country-specific food composition tables, if available. Often energy-adjusted intakes of nutrients or foods are used when constructing dietary indices. Each of the dietary components (e.g. nutrients or foods) included in the dietary index are scored either as absolute using predefined cut-off values or relative based on the distribution of intake (e.g. quintiles).

Predefined cut-off values¹⁰ are based on dietary recommendations and are indicative of healthy intakes. Relative scoring depends on the distribution of intake within a specific population; whereby higher intakes are scored higher for the adequacy components and lower for the moderation components. However, relative scoring may not necessarily reflect optimum intakes. The scores of the individual components are summed to obtain the final dietary index score, which can be used as either a continuous variable¹¹ or to group individuals in absolute or relative categories. The total score of a dietary index may range depending on the number of food items, grouping of dietary components, and type of scoring system that is used.^6,7 An example of relative scoring is illustrated in the approach by Satjia et al. In their study they created different versions of a PBD index. Food groups were classified as healthy plant foods, less healthy plant foods, and animal foods. These food groups were ranked by quintiles of consumption and graded with positive or reverse scores, depending on which PBD index was calculated.¹²

Different dietary indices might rank an individual’s PBD adherence differently and create inconsistences, which may consequently influence the diet–disease associations across published studies.¹³ Evidence-based literature has shown that there are diverse associations reported between PBDs and cardiovascular disease (CVD) and CVD risk. A PBD¹ has been encouraged as a lifestyle intervention in two case reports on CVD.^14,15 These patients with angina refused surgery, but opted to join a Cardiac Wellness Program that recommended adopting a PBD for the reversal of CVD and/or the prevention of CVD risk.^1,14,15 Another study by Lara and colleagues¹⁶ reported on the association between a plant-based dietary pattern amongst other dietary patterns and the incidence of CVD hospitalisation due to heart failure. A high adherence to the plant-based dietary pattern was statistically significantly associated with a decreased likelihood of incident heart failure. However, several studies have focussed on the association of PBDs with CVD risk, specifically the reduced risk of developing hypertension and type 2 diabetes.^13,17–19

Rationale

Nutrition-based epidemiological studies are crucial to generate findings on the association between a PBD and health outcomes. Evidence of these associations may be inconsistent amongst studies that are conducted across different regions and countries. The latter may be due to studies utilising different dietary pattern analysis and scoring systems to construct plant-based diet indices for measuring adherence to a PBD. Furthermore, the extent of such discrepancies between the definitions of a PBD and measures of adherence as they apply globally; to the study of the associations between adherence to a PBD and CVD risk, warrants further investigation. Thus, considering these variations, it could prove valuable to investigate to what extent different methods may influence study findings. This is important prior to conducting studies in under-researched populations such as Africa, where there is a scarcity of resources and paucity of data on the association of PBDs with CVD risk. Therefore, this review aims to address this gap in the literature on PBD studies.

Objectives

This protocol is for a systematic review of studies on the association of PBD with CVD risk in order to:

Review questions

Across studies of the association of PBD with CVD risk, the review will seek to address the following questions:

Protocol

Search strategy

A comprehensive literature search will be conducted to identify eligible studies without any restriction to country. A search strategy will be applied in all electronic databases and adapted accordingly. The search terms will utilise the following key words as free texts and/or medical subject headings to find relevant studies: plant-based diet OR plant-based OR adherence to a plant-based diet OR healthy dietary pattern AND dietary pattern analysis OR a priori OR a posteriori OR dietary indices AND cardiovascular disease OR heart disease OR ischaemic/ischemic chest pain OR myocardial infarction OR heart attack OR coronary artery disease OR congestive heart failure OR cardiac arrest OR stroke OR cerebrovascular disease OR sudden death OR sudden cardiac death OR hypertension OR high blood pressure OR diabetes OR dysglycaemia OR dysglycemia OR dyslipidaemia OR dyslipidemia OR hyperglycaemia OR hyperglycemia OR prediabetes OR impaired glucose tolerance OR impaired fasting glycaemia/glycemia OR obesity OR overweight OR metabolic syndrome OR cardiovascular risk score OR cardiovascular risk model.

Study records

Selection process

The Rayyan application will be utilised during the selection process. Two reviewers will independently screen the identified titles and abstracts. The inclusion and exclusion criteria will serve as guide for the reviewers to select eligible records and studies. After completing the titles and abstracts screening, the two reviewers will discuss any discrepancies and reach a consensus to include or exclude the record. Subsequently, the full text of all eligible records will be retrieved. Full text will be reviewed independently by the two reviewers and checked by a third reviewer for consensus. The Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) flow diagram will be utilised to summarise the study selection process. Exclusion reasons will be provided and documented for all the full text reviewed studies, which do not meet the criteria of the review.

Data extraction

Two reviewers will independently extract data from all eligible studies, using the data collection form for RCTs and non-RCTs from the Cochrane Collaboration²¹ that will be adapted if necessary. Disagreements will be discussed with a third reviewer and resolved by consensus. Data items will be captured in Microsoft Excel (RRID:SCR_016137) spreadsheets; Google Sheets (RRID:SCR_017679) is an open-access alternative. The following general data items will be extracted from each study: the first author’s name, year of publication, geographical region, country, sample size and study design. The PEO strategy will also be utilised to extract study-specific data. For example, data related to the baseline characteristics of cohort study participants, and the duration of follow-up will be extracted. Study-specific data will include the demographics of study participants such as age, sex and ethnicity, the dietary exposure and how it was defined, how adherence was measured including the dietary assessment method and reference period that was used, how the food intake was quantified such as the level of intake (e.g., tertiles of consumption), which dietary pattern analysis approach was applied to construct the dietary index, also which health outcome was assessed and how the outcomes were ascertained. Moreover, we will extract data on which covariates were adjusted for in the regression models of the included studies, and report on the measurement of potential confounders.

Risk of bias assessment

The risk of bias will be assessed independently by two reviewers. Each study will be appraised using the National Heart, Lung, and Blood Institute (NHLBI) Quality Assessment Tool for Observational studies²² to rate the methodological quality. The NHLBI tools will be utilised to assess the risk of bias for all observational study types namely cross-sectional, cohort and case-control studies. The quality scores of the included studies will be calculated based on 14 criteria for cross-sectional and cohort studies and 12 criteria for case-control studies. This risk of bias assessment tool was selected because it was developed by a working group assessing cardiovascular risk which is the outcome of interest for our systematic review. Moreover, the NHLBI tools were developed using a rigorous approach and are accompanied by a user-friendly guide that assists the reviewer(s) to answer/interpret the criteria that are included in each tool. All the included studies will be categorised as having a methodological quality that is good (score > 11), fair (score 6-9), or poor (score < 6).²³

Conclusion

This systematic review will aim to highlight the inconsistencies in defining a PBD and the need for a universal definition. It will summarise which methods are commonly used to construct dietary indices that measure adherence to a PBD. Furthermore, it will investigate the association of adherence to a PBD with CVD risk from a global perspective. This may be important to improve the global acceptance of these study findings to inform policymakers and practitioners.

Data availability

Author contributions

TL and APK conceived and designed the protocol. TL drafted the manuscript. APK, AEZ, MF, SD and RTE critically revised the manuscript for methodological and clinical content. All authors approved the final version of the manuscript.