Impact of review method on the conclusions of clinical reviews: A systematic review on dietary interventions in depression as a case in point

Florian Thomas-Odenthal; Patricio Molero; Willem van der Does; Marc Molendijk

doi:10.1371/journal.pone.0238131

. 2020 Sep 16;15(9):e0238131. doi: 10.1371/journal.pone.0238131

Impact of review method on the conclusions of clinical reviews: A systematic review on dietary interventions in depression as a case in point

Florian Thomas-Odenthal ^1,^*,^#, Patricio Molero ^2,^#, Willem van der Does ^1,^3,^4,^5,^#, Marc Molendijk ^1,^5,^*,^#

Editor: Lisa Susan Wieland⁶

PMCID: PMC7494108 PMID: 32936801

Abstract

Background

The recommendations of experts who write review articles are a critical determinant of the adaptation of new treatments by clinicians. Several types of reviews exist (narrative, systematic, meta-analytic), and some of these are more vulnerable to researcher bias than others. Recently, the interest in nutritional interventions in psychiatry has increased and many experts, who are often active researchers on this topic, have come to strong conclusions about the benefits of a healthy diet on depression. In a young and active field of study, we aimed to investigate whether the strength of an author’s conclusion is associated with the type of review article they wrote.

Methods

Systematic searches were performed in PubMed, Web of Science, Cochrane Database of Systematic Reviews, and Google Scholar for narrative reviews and systematic reviews with and without meta-analyses on the effects of diet on depression (final search date: May 30^th, 2020). Conclusions were extracted from the abstract and discussion section and rated as strong, moderate, or weak by independent raters who were blind to study type. A benchmark on legitimate conclusion strength was based on a GRADE assessment of the highest level of evidence. This systematic review was registered with PROSPERO, number CRD42020141372.

Findings

24 narrative reviews, 12 systematic reviews, and 14 meta-analyses were included. In the abstract, 33% of narrative reviews and 8% of systematic reviews came to strong conclusions, whereas no meta-analysis did. Narrative reviews were 8.94 (95% CI: 2.17, 36.84) times more likely to report stronger conclusions in the abstract than systematic reviews with and without meta-analyses. These findings were similar for conclusions in the discussion section. Narrative reviews used 45.6% fewer input studies and were more likely to be written by authors with potential conflicts of interest. A study limitation is the subjective nature of the conclusion classification system despite high inter-rater agreements and its confirmation outside of the review team.

Conclusions

We have shown that narrative reviews come to stronger conclusions about the benefits of a healthy diet on depression despite inconclusive evidence. This finding empirically underscores the importance of a systematic method for summarizing the evidence of a field of study. Journal editors may want to reconsider publishing narrative reviews before meta-analytic reviews are available.

Introduction

New treatments do not always deliver on their promise [1]. If a new treatment that looks promising is widely adopted, the long-term effects are often less than expected from the initial evaluations [2]. This process usually takes many years, sometimes decades to complete, and it affects pharmacotherapy as well as psychotherapy [3]. Recent years have seen a marked increase of research on the effects of diet on depression. The broader field of nutrition and psychopathology has been labeled ‘nutritional psychiatry’ [4]. To date, a sizable amount of research exists that investigates the effects of a diet on the treatment and prevention of depression [5,6].

Treatment recommendations and guidelines should optimally rely on the evidence from multiple randomized controlled trials (RCTs), particularly RCTs that are large and well-conducted [7]. Meta-analyses and systematic reviews of RCT data provide the highest level of evidence as they synthesize and evaluate the available evidence with high standards of conduct and reporting before coming to an informed decision [8,9]. An advantage of meta-analyses over systematic reviews is that they statistically pool the available evidence and assess publication bias and between-study heterogeneity [10]. To date, one systematic review and one meta-analysis exist on the effects of dietary interventions on depressive symptoms as assessed through RCTs. The systematic review [11] presented mixed results with almost half of the studies showing a null-effect. The meta-analysis [12] showed a small positive effect of dietary interventions on depressive symptoms, but this effect is difficult to interpret because of the presence of publication bias and heterogeneity among study outcomes.

The RCTs included in the aforementioned systematic review [11] and meta-analysis [12] are typically of short duration (e.g., 8–12 weeks), and have problems with statistical power, blinding, expectation bias, and attrition [13–15]. In the event proper evidence from RCTs is absent, well-conducted prospective cohort studies may serve to provide the best available evidence [9,16,17]. The available meta-analyses of cohort studies report statistical associations between a healthy diet–in particular, the Mediterranean diet–and the incidence of depression over time [5,18]. However, reversed causation, undetected biases, and residual confounding may underlie such relationships [14,15,18,19]. Hence, these study limitations preclude strong conclusions.

Notwithstanding the limited evidence, many authors of narrative reviews come to firm conclusions about the effects of a diet on depression (e.g.: “Studies have shown that diet and nutrition play a significant role in the prevention and clinical treatment of depression” (page 10) [20]). On the contrary, authors of systematic reviews seem to come to less firm conclusions (e.g., "The results of this meta-analysis suggest that healthy pattern may decrease the risk of depression, whereas western-style may increase the risk of depression. However, more randomized controlled trails and cohort studies are urgently required to confirm this finding" (page 373) [6]). So, the clinical implementation of diet to treat or prevent depression may depend on which conclusions and recommendations are adopted by clinicians [2].

We aim to investigate whether research methods that are more sensitive to researcher bias, like narrative reviews, are more likely to overstate the benefits of a treatment–in this case, a healthy diet for depression–than research methods that are less sensitive to researcher bias, like systematic reviews with and without meta-analyses. We hypothesize that narrative reviews report more positive conclusions and recommendations about the benefits of a healthy diet on depression than systematic reviews with and without meta-analyses because narrative reviews lack the systematic method for searching and evaluating the evidence. We also hypothesize that systematic reviews without meta-analyses report more positive conclusions and recommendations than systematic reviews with meta-analyses because systematic reviews without meta-analyses do not evaluate the evidence statistically. In case different review types indeed come to different conclusions, we will explore the number of input papers, various indicators of impact, and potential conflicts of interest as possible explanations for the existence of this relationship.

Method

As a guideline for conducting and reporting this systematic review, we followed the PRISMA [21] statement (see S1 Checklist). A protocol for this review is registered at PROSPERO, number CRD42020141372 (date of registration: April 28^th, 2020).

Search strategy

We systemically searched for meta-analyses, systematic reviews, and narrative reviews in the electronic medical databases PubMed (1964–2020), Web of Science (1974–2020), and Cochrane Database of Systematic Reviews (2005–2019) as well as the preregistration platforms PROSPERO and OSF (a free, open source web application) and the preprint servers OSF and BioRxiv from inception to May 30^th, 2020. We also performed a non-systematic search in Google Scholar to identify articles that were not captured by our main search. We used the following search terms: (diet OR food) AND ("depressive*" OR depression OR "mental health" OR "mental disorder*") AND ("systematic review" OR meta-analysis OR review). We also screened the reference lists of the included articles for eligible articles. The complete search strategy is presented in S1 Text.

Inclusion criteria

We retained peer-reviewed narrative/literature reviews, perspectives/(expert) opinions, systematic reviews, and meta-analyses on the potential effects of dietary patterns (e.g., healthy or unhealthy diet) or food groups (e.g., fruits, vegetables, or fish) on depression [22,23] and/or depressive symptoms (as measured by self-report depression symptom scales). Articles must have been written in English, German, Dutch, Spanish, or French to be included.

Study selection

Two members of the review team (FT-O and MM) independently screened titles and abstracts of each study for eligibility. In a next round of selection, the full-text of articles was assessed for eligibility. Disagreement about the selection was resolved through consensus.

Data extraction

FT-O and MM independently extracted the data using a prior designed extraction form. The extraction form was pilot-tested and refined accordingly. From each article, we extracted data on: a) publication date; b) number of studies included; b) whether it was a narrative review, systematic review, or meta-analysis; c) conclusions and recommendations; d) the effect sizes from meta-analyses (i.e., odds ratios, hazard ratios, relative risks, and their respective 95% confidence intervals); e) the funding sources; f) the number of input papers; g) indicators of impact (i.e., Altmetric score, impact factor of journal, number of citations); h) whether studies were written by authors with financial conflicts of interest, that is, whether authors reported to have received funding by food industry companies (e.g., Woolworth, Nestle, Taki Maki) or meat or dairy research or marketing companies (e.g., Meat and Livestock Australia); and i) whether studies were written by authors with allegiance bias. We operationalized allegiance bias as being a member of the International Society for Nutritional Psychiatry Research (ISNPR) because this society has recently published very strong conclusions about the potential role of diets on the treatment and prevention of depression [24].

From the systematic reviews with and without meta-analyses, we further extracted data on a) participant characteristics (e.g., country, total number of participants), b) method for assessing dietary patterns or food groups (e.g., diet quality scores or indexes), c) method for assessing dietary intake (e.g., food frequency questionnaires or 24-h dietary recall, food record), d) comparators (e.g., different diets or relative use), e) type of outcome (e.g., diagnosis or depressive symptoms), f) study design, and g) length of follow-up (for systematic reviews and meta-analyses of RCTs and longitudinal studies).

Conclusion and recommendation classification

Conclusions were defined as an overall summary or interpretation of the main findings, and recommendations as an endorsement to treat or prevent depression through a diet. Conclusions and recommendations were extracted from both the abstract and discussion section. For recording the conclusions and recommendations and to reduce bias in this, we adapted the method by Antman et al [2]. One author collected all the conclusions and recommendations, while another author classified the conclusions and recommendations blind to study type into suitable categories (in this case: strong, moderate, or weak). After this, the first author categorized all the conclusions and recommendations accordingly using this classification system.

We identified strong conclusions through keywords, like “compelling support” or “key modifiable targets” and when authors claimed the existence of a causal relationship between diet and depression. An example of a strong conclusion would be “diet and nutrition are central determinants of mental health”. Weak conclusions do not mention the existence of a causal relationship but, at a maximum, an association. These conclusions often come together with contrastive statements, like “however, further research is needed to establish this relationship”. Keywords for weak conclusions are “suggest” or “may/might be”. A moderate conclusion is a mix of strong and weak keywords, or weak keywords in combination with causal statements, like “diet appears to confer some protection” or “diet may prevent”. A strong conclusion together with a contrastive statement also resembles a moderate conclusion. Inter-rater reliability was assessed over random selections of conclusions within the author team as well as two neutral investigators blind to study type to further reduce potential bias. Disagreement was discussed and resolved through consensus.

Quality assessment

We assessed the methodological quality of the included systematic reviews and meta-analyses with the AMSTAR II [25] tool. Based on each item of the quality assessment, we assessed an overall inter-rater agreement. Furthermore, we graded the certainty of the evidence in this field using the GRADE [26] approach. The certainty of the evidence reflects the extent to which we are confident that the estimate of the effect is correct–in this case, the effect of a diet on depression–and can be classified as very low, low, moderate, and high [7]. We did this to obtain a benchmark for the strength of conclusions regarding the association between diet and depressive symptoms as well as the prevention and treatment of depression through a healthy diet. The GRADE assessment was based on a meta-analysis on results derived from RCTs–the highest level of evidence. An evidence profile was generated [27].

Statistical analyses

Agreement regarding the classification of conclusions was assessed through Cohen's Kappa (κ) and rank-correlation coefficients calculated over random samples of ten randomly chosen conclusions ordered from the weakest to the strongest conclusion. Ordinal regression analyses were run to test whether narrative reviews reported stronger conclusions than systematic reviews or meta-analyses. In a similar manner, associations between study type or strength of conclusions and methodological quality were assessed. In post-hoc analyses, the potential associations between study types or strength of conclusions and the number of input studies, number of citations, journal impact factor, Altmetric scores, ISNPR membership, and food industry funding were explored. To assess potential differences regarding the number of input studies and indicators of impact among the study types, independent t-tests were performed, or Mann-Whitney U exact tests in case the normality assumption was violated. Ordinal regression analyses were also conducted to assess putative associations between potential financial or non-financial conflicts of interest and study types or strength of conclusions. The significance level was set at an α level of 0.05, one- or two-tailed, depending on whether we tested a hypothesis or not. Odds ratios (ORs) and their respective 95% confidence intervals (CIs) were used as the measure for effect size. Analyses were performed in SPSS version 25 [28].

Role of the funding source

There was no funding source for this study. The corresponding authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Results

Our initial search for meta-analyses, systematic reviews, and narrative reviews yielded 1,868 records after duplicates were removed. After reading the titles and abstracts, we excluded 1,787 records. Another 31 records were excluded after applying the inclusion and exclusion criteria (see S1 Table for the reasons for exclusion per study). Hence, we included a total number of 50 records, among which were 14 meta-analyses, 12 systematic reviews, and 24 narrative reviews/expert opinions (see Fig 1 for a flow-chart).

All systematic reviews and meta-analyses assessed the effects of a diet on depression in the healthy adult population [5,6,11,18,29–48] except for two systematic reviews that assessed children and adolescents [49,50]. The average number of participants included per systematic review with and without meta-analyses was 128,271. In 84% of the cases, the study was performed in a Western country. The reviews mostly included prospective cohort, cross-sectional, or case-control studies, whereas five reviews investigated both observational studies and RCT [31,38–41], and one reviewed RCTs alone [11]. Most reviews included studies investigated the impact of diets, such as Mediterranean, healthy diets, or unhealthy diets. Four studies investigated the effects of fish consumption [29,33,45,46] and another four studies the effects of fruit and vegetable intake [40,42,47,48]. Food intake of participants was measured with food-frequency questionnaires (FFQs), 24-h dietary recalls, diet history questionnaires, or other standardized or non-standardized food intake questionnaires. Depression outcome was measured with standardized self-report depression scales, such as the Beck Depression Inventory or Center for Epidemiological Studies–Depression, formal diagnoses, or antidepressant medication intake. All systematic reviews and meta-analyses found positive effects of healthy diets or food groups on depression in observational studies, or negative effects of unhealthy diets or food groups. The only included systematic review of RCTs concluded that only half of the included RCTs showed significant effects for healthy diets on depression in the treatment, relative to the control group, while the other half reported null effects [11]. A full overview of the basic characteristics and results of the included meta-analyses and systematic reviews can be found in S2 Table.

We categorized the conclusions and recommendations into “strong”, “moderate”, and “weak” (see S3 Table) with high inter-rater agreement (average κ = 0.67, SE = 0.10, P < 0.0001; from four rater pairs on ten different randomly chosen papers [κ‘s per rater pair were 0.55, 0.63, 0.69, and 0.70]). Strong and significant average rank-correlation coefficients calculated over random samples of ten randomly chosen conclusions, ordered from the weakest to the strongest conclusion by four reviewers, further validated the reliability of our conclusion categorization (Kendall’s Tau = 0.72, P < 0.0001 [Tau’s per rater pair were 0.69, 0.78, 0.81, and 0.82]). Note that in all instances, conclusions were assessed blind to the articles from which they were derived. Furthermore, in case there was a discrepancy between assessors, these always involved differences between neighboring classifications (e.g., weak vs. moderate and never weak vs. strong).

Tables 1 and 2 show the conclusion classification by study type of the abstract and discussion section, respectively. In the abstract, narrative reviews were 8.94 (95% CI: 2.17, 36.84) times more likely to report stronger conclusions than meta-analyses and systematic reviews, and systematic reviews were 3.43 (95% CI: 0.66 17.85) times more likely to report stronger conclusions than meta-analyses (P = 0.001, see Table 1). In the discussion, narrative reviews were 3.01 (95% CI: 0.95, 9.58) times more likely to report stronger conclusions than meta-analyses and systematic reviews, and systematic reviews were 2.06 (95% CI: 0.39, 10.83) times more likely to report stronger conclusions than meta-analyses (P = 0.048, see Table 2). After removing expert opinion papers (n = 3) from the analysis, these associations remained the same. Similarly, the associations did not change when Rahe et al [36] was treated as a systematic review instead of a meta-analysis as this study only assessed heterogeneity statistically and showed individual but not pooled effect estimates. Sensitivity analyses also showed that these patterns of results were not due to a particular study (data not shown).

Table 1. Strength of conclusions (abstract) per study type.

	Meta-analyses	Systematic Reviews	Narrative Reviews
Strong	0 (0%)	1 (8.3%)	8 (33.3%)
Moderate	7 (50%)	8 (66.7%)	11 (45.8%)
Weak	7 (50%)	3 (25%)	2 (8.3%)
None	0 (0%)	0 (0%)	3 (12.5%)

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Percentages are shown in parentheses.

Table 2. Strength of conclusions (discussion) per study type.

	Meta-analyses	Systematic Reviews	Narrative Reviews
Strong	0 (0%)	1 (8.3%)	7 (29.2%)
Moderate	9 (64.3%)	8 (66.7%)	12 (50%)
Weak	5 (35.7%)	3 (25%)	5 (20.8%)

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Percentages are shown in parentheses.

Narrative reviews also appeared to report stronger recommendations in both the abstracts and discussion sections but these associations were not statistically significant (OR_abstract = 1.67, 95% CI: 0.29, 9.69, P = 0.569, see Table 3; OR_discussion = 4.53, 95% CI: 0.98, 20.96, P = 0.053, Table 4). After including the reviews into these analyses that provided no recommendations, these association became significant (OR_abstract = 3.28, 95% CI: 1.28, 8.42, P = 0.014; OR_discussion = 3.78, 95% CI: 1.72, 8.32, P = 0.001).

Table 3. Strength of recommendations (abstract) per study type.

	Meta-analyses	Systematic Reviews	Narrative Reviews
Strong	1 (7.1%)	0 (0%)	7 (29.2%)
Moderate	0 (0%)	2 (25%)	4 (16.7%)
Weak	0 (0%)	0 (0%)	0 (0%)
None	13 (92.9%)	9 (75%)	13 (54.2%)

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35 out of 50 studies reported no recommendations in the abstract. Percentages are shown in parentheses.

Table 4. Strength of recommendations (discussion) per study type.

	Meta-analyses	Systematic Reviews	Narrative Reviews
Strong	1 (7.1%)	2 (16.7%)	14 (58.3%)
Moderate	1 (7.1%)	4 (33.3%)	2 (8.3%)
Weak	0 (0%)	0 (0%)	0 (0%)
None	12 (85.7%)	6 (50%)	8 (33.3%)

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26 out of 50 studies reported no recommendations in the discussion. Percentages are shown in parentheses.

Inter-rater agreement regarding the AMSTAR assessment of the methodological quality of the included meta-analyses and systematic reviews was high (κ = 0.91, SE = 0.02 [~83% agreement], P < 0.0001; see S4 Table for details). Overall, most systematic reviews and meta-analyses were of critically low quality, except for two meta-analyses that were of low quality and one meta-analysis of moderate quality. Associations between methodological quality and conclusions and recommendations were not calculated due to a lack of variance in the quality among the study types. A more lenient quality assessment, for instance, by excluding items assessing whether studies were preregistered (item 2), whether they had language restrictions (item 4), or whether they included a list of excluded, but potentially relevant studies (item 7) did not increase variation in methodological quality to such an extent that analyses with study types were feasible.

ISNPR members were more likely to have written narrative reviews (OR = 5.10, 95% CI: 1.37, 19.05, P = 0.015) and to have reported stronger conclusions (OR_abstract = 4.50, 95% CI: 1.22, 16.55, P = 0.024; OR_discussion = 4.03, 95% CI: 1.10, 14.71, P = 0.035) and recommendations (OR_abstract = 18.00, 95% CI: 1.27, 255.74, P = 0.033) relative to non-members (see S5 Table). ISNPR membership was also associated with a larger likelihood of having potential financial interests (P < 0.01, see Table N in S5 Table). Furthermore, narrative reviews used 45.6% fewer primary input papers (mean = 11.08, standard deviation (SD) = 6.98) than systematic reviews and meta-analyses (mean = 20.35, SD = 12.18; P = 0.002). No statistically significant associations existed between industry funding and study types (OR = 2.43, 95% CI: 0.54, 10.87, P = 0.246) or strength of conclusions (OR_abstract = 3.01, 95% CI: 0.65, 13.95, P = 0.158; OR_discussion = 3.60, 95% CI: 0.74, 17.48, P = 0.112). There were no significant differences in number of citations, Altmetric scores, and journal impact factors as a function of study type.

All associations remained similar after controlling for publication year, number of input papers, or journal impact factor.

GRADE assessment of meta-analysis of RCTs

The GRADE approach was applied to obtain a benchmark for the strength of conclusions regarding the association between diet and depressive symptoms. Initially, we wanted to apply the GRADE assessment on Firth et al [12] as they present the only meta-analysis of studies reporting on the effects of dietary interventions on depressive symptoms derived by means of RCTs. However, we noted two crucial errors in the article that, when corrected, would lead to a substantially different result. An erratum for this study has already been published [51]; however, we think the meta-analysis has not been corrected to a sufficient degree. We, therefore, decided to rerun this meta-analysis following the exact same approach of Firth et al [12] to obtain corrected results as well as the input material for the GRADE assessment: One notable difference relative to Firth et al [12] is that we have applied an extended final search date (August 3^rd, 2019 versus December 3^rd, 2018). Another difference is that we did not pool all the data in one analysis. Instead, we analyzed the data separately to answer the following questions: 1) can a healthy diet prevent depression? 2) can a healthy diet treat depression? and 3) is a healthy diet associated with a reduction in depressive symptoms over time? This was done to reduce between-study heterogeneity and because of the GRADE requirement that questions are formulated regarding specific populations, interventions, comparators, and outcomes.

The meta-analyses revealed no evidence for the hypotheses that a diet can treat or prevent depression. A small statistically significant benefit of a healthy diet on depressive symptoms was found in association studies that did not specifically aim to prevent or treat depression (see Table 5). Yet, substantial and significant between-study heterogeneity (I² = 49%) was observed. Cumulative meta-analysis also showed that in 2000, 2005, 2010, and 2015 there was no ground to formulate strong conclusions regarding an effect of a diet on depression. For further information on this meta-analysis, we refer to S2 Text.

Table 5. GRADE summary-findings table based on a newly performed meta-analysis of RCTs.

Outcome	Effect size (95% CI)	k	N intervention / control	Certainty of evidence
Prevention	g = 0.06 (-0.10, 0.22)	2	512 / 513	Low (⨁⨁◯◯) ^a
Treatment	g = -0.27 (-0.66, 0.13)	4	115 / 101	Very low (⨁◯◯◯) ^b
Association	g = -0.14 (-0.24, -0.04)	15	18,622 / 26,877	Very low (⨁◯◯◯) ^c

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Abbreviations. CI, confidence interval; g, Hedges' g; N, number of participants.

^a Downgraded once for serious risk of bias, once for imprecision.

^b Downgraded twice for very serious risk of bias, once for imprecision.

^c Downgraded twice for very serious risk of bias, once for inconsistency.

A full overview of the GRADE evaluation and the reasons for up- or downgrading the evidence can be found in S1 Appendix. In sum, GRADE indicated very low to low certainty-evidence for the proposed associations between diet and depression that are under study here (see Table 5). These findings, thus, indicate that strong conclusions about the potential effects of diet on depression are not warranted.

Discussion

This systematic review reports substantial discrepancies in the strength of conclusions reported over study types. Narrative reviews were more likely to report stronger conclusions and recommendations regarding the benefits of a healthy diet on depression relative to systematic reviews and meta-analyses, whereas systematic reviews were slightly more likely to report stronger conclusions and recommendations than meta-analyses. In fact, no single meta-analysis came to a strong conclusion regarding the supposed effect of diet on depression. In line with this was the result of a GRADE evaluation of the highest level of evidence, which dictated that the certainty of the evidence is low regarding the prevention, and very low regarding the treatment of depression through a healthy diet as well as the association between diet and depressive symptoms over time. An AMSTAR assessment revealed that the methodological quality of meta-analyses and systematic reviews was mainly critically low. Hence, we can conclude that a substantial part of narrative reviews, and a minor part of systematic reviews, overstate the benefits of a healthy diet on depression.

Although we can only speculate on explanations underlying the biased conclusion formulation, we did find some informative correlates of it. Narrative reviews used 45.6% fewer input studies. This finding could indicate that authors selectively cited easily accessible or mainly positive input material [52]. Selective citation practices could be justified if authors who came to strong conclusions would cite the scientifically strongest articles only. Yet, this seems unlikely since our GRADE assessment showed that no such strong articles exist because of the presence of serious risk of bias in the study designs and executions. Additionally, our meta-analyses of RCTs indicated that there is no evidence for the notion that a diet can treat depression or prevent it from occurring. Financial and non-financial conflicts of interest may also affect narrative reviews more than other types of reviews. ISNPR members wrote 30% of all papers, 45.8% of narrative reviews, and only two meta-analyses [5,12] on this topic. One of these meta-analyses [5] was included in this systematic review, while the other [12] was excluded because of crucial errors favoring the authors' study hypotheses. As in all areas of health research, many factors may underlie the formulation of biased conclusions, ranging from a drive to make a positive contribution to personal experiences to financial interests [53–56].

Food industry-funded studies may also more likely to report stronger conclusions than non-funded studies. The effect size of this association was large (OR = 3.60), but it did not reach statistical significance. Authors tend to underreport their financial conflicts of interest [57], but we do not know if this is also the case in nutritional mental health research. Yet, a recent debate about a new dietary recommendation on red meat consumption disclosed that financial conflicts of interest may indeed play a role also in the nutritional field [58].

Authors of narrative reviews tend to report stronger conclusions in the abstracts than in the discussion sections among the study types. Word limits of abstracts may force authors to generate more generic conclusions [59]. As most readers may only read the title and abstract, we think the abstract should already convey the right message to begin with. When scientific experts conclude that “diet and nutrition are central determinants of mental health” and that “nutrition is a crucial factor in the high prevalence and incidence of mental disorders” (page 271) [24], the data that underlie these conclusions should be convincing. This is not yet the case. As a consequence, the general media have disseminated grossly overstated conclusions [60,61]. A healthy diet has few downsides, but patients may inaccurately believe that they are themselves responsible for their depression (e.g., “My bad dietary habits made me depressed”).

A strength of this systematic review is that we used a transparent and quantitative method for identifying study limitations and their potential sources of bias. We applied a systematic method for searching and extracting relevant data with high inter-rater agreements. We used rigorous, scientific, and standardized instruments to assess the certainty of the evidence (GRADE) and the methodological quality (AMSTAR) to gauge which conclusions are most likely correct. Furthermore, we obtained the highest level of evidence regarding the effects of diet on depression and reduced between-study heterogeneity by pooling, through a meta-analysis, results from RCTs based on their populations, interventions, comparators, and outcomes (i.e., prevention, treatment, association), which increases the validity of our findings [62].

A limitation of our work is the subjective nature of our conclusion classification system, although this was done with high inter-rater agreements, which was confirmed outside of the review team. Secondly, the GRADE approach may be less applicable for lifestyle interventions as the evidence from large and well-conducted RCTs is often absent; more lenient rules for appraising the evidence have been suggested (e.g., HEALM [63]). Thirdly, the present research is limited to the field of diet and depression but similar inferences may apply to different exposures, like nutraceuticals, and different outcomes, like cardiovascular health (our GRADE evaluation of other patient-relevant health outcomes already indicated low to very low certainty-evidence also in these fields of study; see S1 Appendix). Lastly, there may be sub-samples in the population for whom diet may directly affect mood (e.g., people with celiac disease) [64]. However, this was hardly, if ever, acknowledged in the generic conclusions that we encountered and was, therefore, also not investigated here. Future research should investigate all this and invest in large-scale and long-term randomized controlled dietary intervention and prevention trials.

Our work indicates that conclusions derived from systematic reviews with meta-analyses should be favored over those derived from less rigorous scientific methods, like narrative reviews. Awareness of this should be high on the agenda of journal editors and reviewers to reconsider publishing narrative reviews before meta-analytic reviews are available. Our work may also encourage researchers to use systematic reviews instead of narrative reviews to protect themselves against their own biases. The preregistration and open access of such work may further reduce these researcher biases.

Supporting information

S1 Checklist. PRISMA checklist.

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S1 Text. Search strategy.

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S2 Text. Details on our meta-analysis of RCTs.

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S1 Table. Excluded studies with reason.

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S2 Table. Basic characteristics and results of assessed systematic reviews.

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S3 Table. Reported conclusions and recommendations of included systematic reviews with classified strength.

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S4 Table. AMSTAR II scoring.

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S5 Table. Association between study types and strength of conclusion, number of input papers, indicators of impact, and potential conflicts of interest.

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S1 Appendix. GRADE evaluation of three patient-relevant healthcare questions.

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Acknowledgments

We thank our colleagues from the Universities of Leiden and Navarra for the discussions about the topic of this manuscript on formal and informal occasions. We also want to thank Anouk Mentink, Cristina Vidal Adroher, Liv Caro Henrich, Marta Santos Burguete, and Mirjam Christina Reidick for their help in classifying conclusions and/or proof-reading of earlier versions of his manuscript.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The authors received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0238131.r001

Decision Letter 0

Lisa Susan Wieland

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

19 May 2020

PONE-D-20-10335

Diets against depression: strong conclusions, weak evidence. A systematic review

PLOS ONE

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Without relevance to this work, P. Molero reports to have received research grants from the Ministry of Education (Spain), the Government of Navarra (Spain), the Spanish Foundation of Psychiatry and Mental Health and AstraZeneca; he is a clinical consultant for MedAvanteProPhase and has received lecture honoraria from or has been a consultant for AB-Biotics, Janssen, Novumed, Roland Berger, and Scienta. The other authors declare no competing interests.'

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Reviewer #1: This is a very interesting manuscript. I have some suggestions that may help improve some aspects of the manuscript:

- General: some sentences need revision in order to be correct (e.g. past /present tenses; and some words have missing letters).

- One of the main aspects which need to be clarified in the abstract and in the main manuscript is the exact objective (see comment below).

- Abstract: I suggest to be more precise in the objective of the work presented and revise which aspects are important to be mentioned in the abstract (and which are not so important). I think that the abstract needs more structure to present the main focus of the manuscript.

- Introduction: Authors should be sure that all statements are referenced (e.g. first 4 sentences of the introduction need to be references).

- If I understood correctly, the objective of the present review is to investigate the difference between the conclusions given in the different type of reviews (narrative reviews, systematic reviews (with/and without meta-analysis)) regarding diet and depression. In this sense, I would suggest authors to rephrase the introduction, in order to backup their objective. For example, they could mention which kind of literature is found for diet and depression; give an overview (and definition) of narrative review vs. systematic review (with/without meta-analysis); and how conclusion of each type of review could influence practice.

- Line 52-55: Recommendations and guidelines DO NOT HAVE TO rely on RCTs. It is correct, that the best evidence usually stems from RCTs, because they can investigate causality (not because standards of conduct are high: there are RCTs with low quality standards, and also non-RCTs with high quality standards). Also, there are some research questions which cannot be answered with RCTs (which is often the case in nutritional topics), thus guidelines have to be done with non RCTs. Suggest authors to revise some literature about evidence and study designs and rephrase this section.

- Recommend to revise the following terms used: systematic review and meta-analysis. They are not strictly different type of reviews. A systematic review can include a meta-analysis, but does not necessary have to include one. My recommendation would be to use: narrative reviews, systematic reviews without meta-analysis, and systematic reviews with meta-analysis.

- Method: Suggest to include the date of registration in PROSPERO (also in the Supl. material where the registration form is presented)

- Method / Search strategy: please explain what OSF means.

- Why did authors restrict your search from January 2019 until September 2019?

- Did authors use only free-text search or also controlled vocabulary?

- In the PROSPERO registration form, authors list more databases that were searched. Which databases were included in the final version of the review?

- Suggest to include the whole search strategy as Supl. material, since the search strategy presented seems incomplete (also when opening the link provided in the PROSPERO form, the search strategy is incomplete).

- Method / Inclusion criteria: which depressive symptoms were considered eligible?

- Method / Data extraction: how did authors address missing data?

- Method / Conclusion and recommendation classification: how did authors define the conclusion, in cases where the study reported different statements between the conclusion in the abstract section and in the main manuscript? Also, one study could report on various conclusions: how was this handled (where all conclusions extracted and rated)?

- Lines 149-154: I did not understand what this paragraph is describing about the method of GRADE? To what outcomes did authors assess GRADE? Did they do this to all included reviews or only to the identified meta-analyses?

- Method / Statistical analysis: did authors assess association with correlation coefficient (Spearman, Pearson)? Please specify.

- Results: Suggest to start the results section more general. How many references were identified in the databases, how many screened, how many included, etc. Before presenting any results it is important to give the reader an overview about the studies/publications included, their general characteristics, etc.

- Authors start the results describing their GRADE assessment of one meta-analysis. I do not understand how this should be understood contextualizing their objective? I think authors should first give results addressing their objective (i.e. how many narrative reviews, systematic reviews, etc. were found and what did they conclude).

- Lines 196-200: which outcomes were assessed with GRADE? The manuscript is lacking an overview about which publications were taken into consideration for the GRADE approach (this is also missing in the methods section).

- Line 202: I think this is how the whole results section should start with.

- Line 205: the study of Firth et al was excluded, but their data was used to re-run meta-analysis (described at the beginning of the results section)? This is confusing.

- Suggest to re-order the results section and provide all necessary information for the reader in order to understand the results. Supplementary material is vast and includes all results, but I suggest to depict the most important results also in the text.

- The conclusion sections is well written and has all elements needed in the conclusion section. As an option, authors could include some further limitations about the design of their investigation (e.g. search strategy)?

Reviewer #2: Methods

Search strategy

1. although the authors state they used PRISMA to report their study, the description of the search strategy is not compliant with that guideline. The authors don’t report the coverage of the databases – the period of time covered in the database when it is searched (not the period of time during which the searches were run by the investigators). More importantly, it seems unlikely the search strategy reported is the actual search run in each database (PRISMA recommends reporting of at least one search strategy “…such that it could be repeated”), because it lacks database-specific syntax or indication of use of field codes.

2. If this is the actual search strategy, it is not systematic or comprehensive:

a) The lack of field codes suggest that, at best, the authors conducted a basic search in PubMed, while not taking advantage of the extensive controlled vocabulary (Medical Subject Headings) that should be used to ensure thorough searching.

b) The search terms used do not appear to fully reflect “diet” as conceptualized by the authors. That is, the authors wanted to include reviews that examined the impact of consumption of food groups such as fruits, vegs, or fish on depressive symptoms. Yet the search string contains no terms about food groups.

c) At the same time, several search terms seem inappropriate for the topic of depression: anxiety, psychosis.

3. The protocol for this study indicates that Cochrane Library and Google Scholar would also be searched, but this does not appear to be the case? Please clarify if these were completed and if so, why the results are not included here. If not done, please explain why.

Inclusion criteria

Expert opinion articles do not seem to me to be in the same category of document as reviews or meta analyses, so it’s not clear why they should have been included in this study. Given their nature and typically brief format, it is expected that such documents would be selective in the studies cited and perhaps adamant in the conclusions drawn, so lumping them in with narrative reviews (which is done in the Results) might lead to bias.

Conclusion and Recommendation Classification

If conclusions and recommendations were provided in both the abstract and discussion section of a review, the authors extracted only the information in the abstract. I’d like to see a rationale for doing this. Wouldn’t it be more appropriate to extract from both sections, and to look at the impact of section on the outcomes the authors are interested in?

Results

The entire first paragraph here (lines 174-187) seems to be about the meta analysis the authors used to create what I will call a “gold standard” for the true or real effect of diet on depressive symptoms. I think the manuscript would flow better if this paragraph were in the Methods section.

Association between study type and strength of conclusions

1. I’m concerned about the data as reported here and in the flow chart (Fig 1).

a) minor detail: OSF and PROSPERO are not bibliographic databases, so the # of records found by those searches should be included in the box used to indicate “additional articles identified through other means”.

b) The initial search results, across all databases and resources, found a total of 1696 records. The flow chart indicates that, after duplicate removal, 1696 records remained. So there were no duplicates? I work in an environment where colleagues are collaborating on multiple SRs per year, and I have never heard of a systematic review where the searches produced no duplicates. That oddity needs to be explained

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Reviewer #1: Yes: Daniela Küllenberg de Gaudry

Reviewer #2: No

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PLoS One. 2020 Sep 16;15(9):e0238131. doi: 10.1371/journal.pone.0238131.r002

Author response to Decision Letter 0

3 Jul 2020

We thank Lisa Susan Wieland, the academic editor of PLOS ONE, for the opportunity to submit a revised version of our manuscript. We also thank the reviewers for their valuable feedback. We incorporated the feedback and replied to each comment by the reviewers individually in the "response to reviewers" document. Furthermore, we clarified the objectives by focusing more on the association between conclusion strength and review types instead on the researcher biases underlying such conclusions. We made this clearer specifically in the abstract and introduction. We also altered the presentation of the results by first presenting the results of our systematic review while replacing the section on the GRADE approach to the end. We think this way the reading flow of the paper has improved without losing relevant elements. Moreover, we improved our search strategy based on the reviewers’ comments and conducted an entirely new search. We found 9 additional papers and this time we extracted conclusions from the abstract as well as the discussion. Based on this data, we performed separate analyses on the associations between conclusions of abstracts and discussion sections with review types with similar results. Lastly, we adapted our manuscript to the style requirements of the journal, altered the title, and updated our competing interest section by including data availability statements all in accordance with PLOS ONE guidelines. The competing interest statement itself has not altered. By incorporating the feedback, we think the paper has become much more valuable and thereby interesting for the readership of PLOS ONE. We hope the editors and reviewers agree.

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(51.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0238131.r003

Decision Letter 1

Lisa Susan Wieland

11 Aug 2020

Impact of review method on the conclusions of clinical reviews: A systematic review on dietary interventions in depression as a case in point.

PONE-D-20-10335R1

Dear Dr. Thomas-Odenthal,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Lisa Susan Wieland

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

There is one additional item that I would like you to consider amending, however it is optional. The plain language equivalent of low certainty evidence in GRADE should use the term 'may' to reflect the low level of certainty. Therefore, in S.9 Appendix when translating low certainty evidence, it is better to say ‘The evidence suggests that a healthy diet may not prevent depressive symptoms.’ instead of ‘The evidence suggests that a healthy diet does not prevent depressive symptoms.’ Consider changing this and the other plain language statements for low quality evidence.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: I Don't Know

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: The authors have done a good job of responding to my comments. I have one minor suggestion: they indicate they searches "Cochrane reviews". I believe they are referring to the Cochrane Database of Systematic Reviews. if so, that name should replace "cochrane reviews" throughout the manuscript.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0238131.r004

Acceptance letter

Lisa Susan Wieland

17 Aug 2020

PONE-D-20-10335R1

Impact of review method on the conclusions of clinical reviews: A systematic review on dietary interventions in depression as a case in point.

Dear Dr. Thomas-Odenthal:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Lisa Susan Wieland

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. PRISMA checklist.

(DOC)

Click here for additional data file.^{(64.5KB, doc)}

S1 Text. Search strategy.

(DOCX)

Click here for additional data file.^{(19.4KB, docx)}

S2 Text. Details on our meta-analysis of RCTs.

(DOCX)

Click here for additional data file.^{(425.6KB, docx)}

S1 Table. Excluded studies with reason.

(DOCX)

Click here for additional data file.^{(50KB, docx)}

S2 Table. Basic characteristics and results of assessed systematic reviews.

(DOCX)

Click here for additional data file.^{(132KB, docx)}

S3 Table. Reported conclusions and recommendations of included systematic reviews with classified strength.

(DOCX)

Click here for additional data file.^{(126KB, docx)}

S4 Table. AMSTAR II scoring.

(DOCX)

Click here for additional data file.^{(56.8KB, docx)}

S5 Table. Association between study types and strength of conclusion, number of input papers, indicators of impact, and potential conflicts of interest.

(DOCX)

Click here for additional data file.^{(100.9KB, docx)}

S1 Appendix. GRADE evaluation of three patient-relevant healthcare questions.

(DOCX)

Click here for additional data file.^{(51.4KB, docx)}

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file.^{(51.4KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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Impact of review method on the conclusions of clinical reviews: A systematic review on dietary interventions in depression as a case in point

Florian Thomas-Odenthal

Patricio Molero

Willem van der Does

Marc Molendijk

Roles

Abstract

Background

Methods

Findings

Conclusions

Introduction

Method

Search strategy

Inclusion criteria

Study selection

Data extraction

Conclusion and recommendation classification

Quality assessment

Statistical analyses

Role of the funding source

Results

Fig 1. Flowchart of study selection.

Table 1. Strength of conclusions (abstract) per study type.

Table 2. Strength of conclusions (discussion) per study type.

Table 3. Strength of recommendations (abstract) per study type.

Table 4. Strength of recommendations (discussion) per study type.

GRADE assessment of meta-analysis of RCTs

Table 5. GRADE summary-findings table based on a newly performed meta-analysis of RCTs.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Lisa Susan Wieland

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Lisa Susan Wieland

Roles

Acceptance letter

Lisa Susan Wieland

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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