Intake of dietary fats and fatty acids and the incidence of type 2 diabetes: A systematic review and dose-response meta-analysis of prospective observational studies.

BACKGROUND: The role of fat quantity and quality in type 2 diabetes (T2D) prevention is controversial. Thus, this systematic review and meta-analysis aimed to investigate the associations between intake of dietary fat and fatty acids and T2D, and to evaluate the certainty of evidence. METHODS AND
FINDINGS: We systematically searched PubMed and Web of Science through 28 October 2019 for prospective observational studies in adults on the associations between intake of dietary fat and fatty acids and T2D incidence. The systematic literature search and data extraction were conducted independently by 2 researchers. We conducted linear and nonlinear random effects dose-response meta-analyses, calculated summary relative risks (SRRs) with their corresponding 95% confidence intervals (95% CIs), and assessed the certainty of evidence. In total, 15,070 publications were identified in the literature search after the removal of duplicates. Out of the 180 articles screened in full text, 23 studies (19 cohorts) met our inclusion criteria, with 11 studies (6 cohorts) conducted in the US, 7 studies (7 cohorts) in Europe, 4 studies (5 cohorts) in Asia, and 1 study (1 cohort) in Australia. We mainly observed no or weak linear associations between dietary fats and fatty acids and T2D incidence. In nonlinear dose-response meta-analyses, the protective association for vegetable fat and T2D was steeper at lower levels up to 13 g/d (SRR [95% CI]: 0.81 [0.76; 0.88], pnonlinearity = 0.012, n = 5 studies) than at higher levels. Saturated fatty acids showed an apparent protective association above intakes around 17 g/d with T2D (SRR [95% CI]: 0.95 [0.90; 1.00], pnonlinearity = 0.028, n = 11). There was a nonsignificant association of a decrease in T2D incidence for polyunsaturated fatty acid intakes up to 5 g/d (SRR [95% CI]: 0.96 [0.91; 1.01], pnonlinearity = 0.023, n = 8), and for alpha-linolenic acid consumption up to 560 mg/d (SRR [95% CI]: 0.95 [0.90; 1.00], pnonlinearity = 0.014, n = 11), after which the curve rose slightly, remaining close to no association. The association for long-chain omega-3 fatty acids and T2D was approximately linear for intakes up to 270 mg/d (SRR [95% CI]: 1.10 [1.06; 1.15], pnonlinearity < 0.001, n = 16), with a flattening curve thereafter. Certainty of evidence was very low to moderate. Limitations of the study are the high unexplained inconsistency between studies, the measurement of intake of dietary fats and fatty acids via self-report on a food group level, which is likely to lead to measurement errors, and the possible influence of unmeasured confounders on the findings.
CONCLUSIONS: There was no association between total fat intake and the incidence of T2D. However, for specific fats and fatty acids, dose-response curves provided insights for significant associations with T2D. In particular, a high intake of vegetable fat was inversely associated with T2D incidence. Thus, a diet including vegetable fat rather than animal fat might be beneficial regarding T2D prevention.

Introduction

Diabetes mellitus is a global health burden with a worldwide prevalence of 9% [1]. Diabetes is characterized by a chronic state of hyperglycemia [2,3]. Type 2 diabetes is the most common type of diabetes (T2D) and accounts for approximately 90% of all cases [1]. In T2D, beta-cell mass and function are lost progressively based on an initial state of insulin resistance [2-4]. T2D increases the risk for diabetes-related complications (e.g., coronary heart disease, stroke, diabetic nephropathy) [5], comorbidities (e.g., depression) [6], and premature death [1,7], and thus leads to higher healthcare costs [1,8]. Apart from unmodifiable risk factors, such as age and family history of diabetes [1,2], several lifestyle-related factors, including smoking, overweight and (abdominal) obesity, and physical activity affect the onset of T2D [9]. Furthermore, diet is a key modifiable factor in the prevention of T2D [10-12]. In this context, the role of dietary fats and fatty acids in T2D prevention is debated [13]. Dietary fats include a wide range of fatty acids, with different chemical structures and biological functions, that play an important role in metabolic pathways influencing the risk of T2D [14]. Current dietary guidelines on the prevention of T2D recommend a diet low in total fat and animal fat, and high in vegetable fat [11,12]. Additionally, higher intakes of monounsaturated fatty acids [12,15], polyunsaturated fatty acids [12,15], and omega-3 fatty acids [12], as well as lower intakes of saturated fatty acids [11] and trans-fatty acids [12], are recommended. While results of meta-analyses have indicated a protective association of vegetable fat intake with T2D incidence, the intake of single types of fatty acids, such as saturated fatty acids, monounsaturated fatty acids, and polyunsaturated fatty acids, was not associated with incidence of T2D [16]. However, these meta-analyses summarized prospective cohort studies published up to the year 2014 [17-20], and new prospective cohort studies examining the associations between dietary fat and fatty acid intake have recently been published [21-27]. Moreover, dose–response relationships have not yet been examined for the majority of these associations. Thus, an updated systematic review and dose–response meta-analysis are necessary. Additionally, a certainty of evidence assessment for these updated meta-analyses is warranted.

Therefore, our first aim was to examine the associations between dietary intakes of total fat, animal fat, vegetable fat, and various types of fatty acids (saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids [including omega-6 and omega-3 fatty acids], and trans-fatty acids) and T2D incidence in an updated systematic review and dose–response meta-analysis of prospective observational studies in an adult population. Second, we aimed to evaluate the certainty of evidence for these associations.

Methods

Our protocol was prospectively registered at PROSPERO (CRD42019128664). We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [28] (see S1 PRISMA Checklist).

Study search and selection

PubMed, Web of Science, and reference lists of relevant publications were systematically searched from their starting dates to 28 October 2019 applying no restrictions or filters. The following search terms were used in combination: (fat OR fats OR fatty OR “fish oil” OR “fish oils”) AND diabetes AND (“observational study” OR prospective OR cohort OR cohorts OR longitudinal OR “case-control” OR retrospective OR “follow-up”).

The literature search and study selection were conducted by 3 investigators independently (MN, NI, and JBe). Disagreements were solved via discussion until consensus was reached. Studies were included if they met the following criteria: (1) prospective observational studies (cohort studies, nested case–control studies, case–cohort studies, follow-up of randomized controlled trials [RCTs]), (2) main focus on adults (≥18 years), (3) reported on associations between intake of total fat, animal fat, vegetable fat, or types of fatty acids (e.g., saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids) and incidence of T2D, and (4) provided effect estimates, reported as hazard ratios, relative risks (RRs), or odds ratios, with corresponding 95% confidence intervals (CIs).

Studies including children, adolescents, pregnant women, individuals with diabetes at baseline, or specific patient groups (e.g., patients after myocardial infarction), as well as animal studies and studies investigating fatty acids measured as biomarkers in plasma/serum, were excluded.

Data extraction

Data extraction was conducted by one author (MN) and double-checked by a second author (JBa). The following characteristics were extracted from each study: last name of the first author, year of publication, the country where the study was conducted, the cohort name (if any), duration of follow-up, characteristics of the cohort at baseline (age, sex), total number of participants, number of cases of T2D, outcome assessment (self-report of diabetes with or without objective medical details, use of diabetes medication, blood test, medical records), exposure (total fat, animal fat, vegetable fat, types of fatty acids), exposure assessment (questionnaire with or without validation, interviews), fat or fatty acid intake per category, person-years and number of cases per category, and maximally adjusted risk estimates expressed as hazard ratios, RRs, or odds ratios with corresponding 95% CIs and adjustment factors. If important data were missing, we contacted the authors of the original studies for more information.

Risk of bias assessment

Risk of bias assessment for each study was conducted by 2 investigators (MN and LS) independently, using the Cochrane Risk of bias in Non-randomized Studies of Interventions (ROBINS-I) tool [29]. The tool includes 7 domains of bias due to (1) confounding, (2) selection of participants, (3) exposure assessment, (4) misclassification of exposure during follow-up, (5) missing data, (6) measurement of the outcome, and (7) selective reporting of the results. The detailed description of each potential risk of bias domain is provided in S1 Table. Discrepancies were resolved by consensus or the consultation of a third reviewer (SS).

Certainty of evidence assessment

Additionally, we evaluated the certainty of evidence for each association using the updated Grading of Recommendations Assessment, Development and Evaluations (GRADE) [30] system, which integrates the application of ROBINS-I. In contrast to the previous version [31], observational studies also start at a high certainty of evidence level [30]. However, a lack of randomization leads to a downgrading by 2 levels (to low), unless the study design reduces confounding and selection bias, as evaluated by ROBINS-I. Additionally, indications of inconsistency, indirectness, imprecision, and publication bias can lead to downgrading, while large effects and a dose–response gradient can lead to upgrading [30,31]. High and moderate certainty of evidence mean that it is very likely or probable that the true effect lies close to the estimated effect. Our confidence in the result is limited or weak if the certainty of evidence is rated as low or very low, respectively [31].

Statistical analysis

We calculated summary RRs (SRRs) using a random effects model, taking both within- and between-study variability into account [32]. The average of the natural logarithm of the RRs was estimated, and the RR from each study was weighted using the method of moments by DerSimonian and Laird [33]. We conducted linear dose–response meta-analyses using the method by Greenland and Longnecker [34]. We computed study-specific slopes (linear trends) and 95% CIs based on the natural logarithm of the RRs and 95% CIs across categories of each exposure (total fat, animal fat, vegetable fat, various types of fatty acids). For this analysis, the number of cases and person-years per category and the exposure values with RRs and corresponding 95% CIs of at least 3 categories were needed. If not reported, the distribution of cases and person-years was estimated, using information on the total number of cases and the number of total participants plus the follow-up period as previously described elsewhere [35]. If a study reported the exposure categories as ranges, the midpoint between the lower and the upper limit was calculated for each category. For open categories, a similar range to the adjacent category was assumed. If the dietary fat or fatty acid dose per category was not reported in grams per day but as percent of total energy intake, we converted energy percent into grams per day. We calculated the calories of the dietary fat/fatty acid by multiplying energy percent by the mean energy intake in the cohort. In order to estimate grams per day, we divided the calories of this dietary fat/fatty acid by 9.1 kcal, which is the amount of calories provided by 1 gram of fat intake. If mean total energy intake of the cohort was not reported in the publication [36,37], information from another publication of the same cohort was used [38-41]. The doses for the linear dose–response meta-analyses were chosen as previously described [18,42]. Nonlinear dose–response meta-analyses were conducted using a restricted cubic spline model as described by Orsini et al. [43], with 3 knots at the 10th, 50th, and 90th percentile of frequency of each exposure. We used a likelihood ratio to test for nonlinearity, checked goodness of fit (χ2) for the nonlinear model compared to the linear model, and interpreted the curve based on visual inspection of the graph.

To assess potential heterogeneity, we conducted subgroup analyses stratified by sex, geographic location, duration of follow-up, number of cases, exposure assessment, outcome assessment, quality score, and adjustment for confounding factors, and applied meta-regression analysis [44]. Furthermore, we conducted sensitivity analyses omitting 1 study at a time to investigate the influence of each study on the results.

We calculated I2 and τ2 as measures of the inconsistency and between-study variability of the risk estimates, respectively, and computed 95% prediction intervals (95% PIs), which show the range in which the underlying true effect of future studies will lie with 95% certainty [45,46].

Publication bias and small study effects were assessed using funnel plots and Egger’s test [47,48] if at least 10 studies were available, as recommended by Cochrane [49]. Potential publication bias was indicated by asymmetry of the funnel plot and a p-value of <0.1 for Egger’s test [48].

All statistical analyses were conducted using STATA version 14.1.

Results

In total, 23 studies (19 cohorts) met our inclusion criteria (S1 Fig). Excluded studies with respective exclusion reasons are displayed in S2 Table. The characteristics of the included studies are summarized in S3 Table. Eleven studies (6 cohorts) were conducted in the US [25-27,37,50-56], 7 studies (7 cohorts) in Europe [21-23,36,57-59], 4 studies (5 cohorts) in Asia [24,60-62], and 1 study (1 cohort) in Australia [63]. All studies used validated food frequency questionnaires for the exposure assessment, except for 2 studies that used 3- or 4-day food records [57,59]. Four studies validated the dietary intakes of fatty acids against biomarkers measured in adipose tissue [37,52,64] and erythrocyte membranes [36] and reported weak to moderate correlations (Spearman correlation coefficients between ≤0.19 and 0.51) [36,37,52,64]. All studies adjusted for age, sex, smoking, education, and total energy intake, except for 2 studies that did not adjust for education [51] or for education, smoking, and total energy intake [57].

Twenty studies were judged as being at moderate risk of bias, and 3 studies as being at serious risk of bias, due to insufficient adjustment of relevant confounders, as described above (S4 Table). Generally, risk of bias due to confounding and exposure assessment could never be low, because of the possibility of residual confounding in observational studies and measurement error in the dietary assessment.

Fig 1 summarizes the results of the linear dose–response meta-analyses for each type of fat and fatty acid regarding T2D incidence. Forest plots of all meta-analyses for each exposure are displayed in S2 and S3 Figs. In these analyses, we mainly observed no or weak associations between dietary fat and fatty acid intake and T2D incidence.

Fig 1

Summary relative risks (SRRs) with 95% confidence intervals (95% CIs) for the associations of total fat, animal fat, vegetable fat, and different fatty acids with incidence of type 2 diabetes in linear dose–response meta-analyses.

However, we detected nonlinear associations for specific fats and fatty acids (Figs 2–5). We observed a steep significant association with a decrease in T2D incidence up to a 13-g/d intake of vegetable fat (SRR [95% CI]: 0.81 [0.76; 0.88], pnonlinearity = 0.012; goodness of fit: χ2nonlinear = 47.4 versus χ2linear = 37.1), after which the curve almost reached a plateau (Fig 2C). Regarding saturated fatty acids, the curve declined after a dose of 8 g/d (SRR [95% CI]: 1.02 [0.97; 1.07]), with an apparent association with a decrease in T2D incidence for intakes around 17 g/d (SRR [95% CI]: 0.95 [0.90; 1.00], pnonlinearity = 0.028; goodness of fit: χ2nonlinear = 39.7 versus χ2linear = 15.2) (Fig 3A). For polyunsaturated fatty acids, doses up to 5 g/d were nonsignificantly associated with reduced T2D incidence (SRR [95% CI]: 0.96 [0.91; 1.01], pnonlinearity = 0.023; goodness of fit: χ2nonlinear = 42.1 versus χ2linear = 29.6), after which the curve rose slightly, remaining close to no association (Fig 3C). We observed a steep significant association with a rise in T2D incidence up to an intake of 270 mg of long-chain omega-3 fatty acids (SRR [95% CI]: 1.10 [1.06; 1.15], pnonlinearity = <0.001; goodness of fit: χ2nonlinear = 105.7 versus χ2linear = 70.9), with a more modest association with increased T2D incidence thereafter (Fig 5C). The curves for eicosapentaenoic acid and docosahexaenoic acid showed an inverse U-shape, with a steep, but nonsignificant, association with a rise in T2D incidence up to intakes of 110 mg/d and 200 mg/d, respectively (Fig 5D and 5E). Regarding alpha-linoleic acid, we observed a flat J-shaped relation, with an apparent association with a decrease in T2D incidence up to an alpha-linolenic acid intake of 560 mg/d (SRR [95% CI]: 0.95 [0.90; 1.00], pnonlinearity = 0.014; goodness of fit: χ2nonlinear = 54.6 versus χ2linear = 29.0), after which the curve moderately rose, remaining close to no association (Fig 5B).

Fig 2

Nonlinear dose–response meta-analyses for the associations between dietary fats and incidence of type 2 diabetes.

(A) Total fat. (B) Animal fat. (C) Vegetable fat.

Fig 5

Nonlinear dose–response meta-analyses for the associations between specific fatty acids and incidence of type 2 diabetes.

(A) Linoleic acid. (B) Alpha-linolenic acid. (C) Long-chain omega-3 fatty acids. (D) Eicosapentaenoic acid. (E) Docosahexaenoic acid.

Fig 3

Nonlinear dose–response meta-analyses for the associations between types of fatty acids and incidence of type 2 diabetes.

(A) Saturated fatty acids. (B) Monounsaturated fatty acids. (C) Polyunsaturated fatty acids. (D) Trans-fatty acids.

Nonlinear dose–response meta-analyses for the associations between omega-6 and omega-3 fatty acids and incidence of type 2 diabetes.

(A) Omega-6 fatty acids. (B) Omega-3 fatty acids. (C) Omega-6:omega-3 ratio.

Certainty of evidence

No association was rated as having a high certainty of evidence. We found moderate, low, and very low certainty of evidence for 5, 6, and 4 associations, respectively (Figs 1 and S5). This judgment was mainly driven by concerns regarding risk of bias due to the possibility of residual confounding, inconsistency, and indirectness.

Subgroup and sensitivity analysis

S6 Table and S4 and S5 Figs display the results of the subgroup and sensitivity analyses, respectively. Most of the results were robust in both analyses. However, important geographical differences were observed regarding long-chain omega-3 fatty acids. The association was attenuated in European studies but was stronger in US populations. Contrary to the main analysis, an inverse association between long-chain omega-3 fatty acids and T2D incidence was observed in Asian populations (S6 Table). These differences were also apparent in nonlinear dose–response meta-analyses stratified by geographic location (S6 Fig).

In sensitivity analyses, based on the stepwise omission of 1 study at a time, the exclusion of the PREDIMED study [23] led to a reduced and more precise estimate for vegetable fat (S4 Fig), while the exclusion of the Nurses’ Health Study [37] led to an association with reduced T2D incidence for trans-fatty acids (S5 Fig).

Small study effects and publication bias

Ten or more studies were available for saturated fatty acids, monounsaturated fatty acids, long-chain omega-3 fatty acids, and alpha-linolenic acid. There was no indication for small study effects according to the funnel plots or the Egger’s test for these associations (S7 Fig). However, for long-chain omega-3 fatty acids and alpha-linolenic acid, the funnel plots indicated between-study variability due to values outside of the 95% confidence limits.

Discussion

In this systematic review and dose–response meta-analysis, we observed an association with decreased T2D incidence for higher intake of vegetable fat, especially of plant-based alpha-linolenic acid, and for total polyunsaturated fatty acids in lower doses. Animal-based long-chain omega-3 fatty acids were associated with increased T2D incidence; however, geographic differences were observed. A harmful association for saturated fatty acids was not confirmed. Our findings add to the body of evidence that these associations are not linear. Most studies were of moderate risk of bias, and the certainty of evidence was very low to moderate.

Our results are consistent with findings from previous meta-analyses investigating the associations of high versus low fat and fatty acid intakes with T2D incidence [17,19,20,65,66] and do not support guidelines recommending increased intake of monounsaturated fatty acids [15], total omega-3 fatty acids [12], or long-chain omega-3 fatty acids [15], or lower intake of saturated fatty acids [11] and trans-fatty acids [12], for T2D prevention. In line with previous results of high versus low intake meta-analysis [17], our nonlinear dose–response meta-analysis indicated a significant association of decreased T2D incidence with increasing vegetable fat intake. A recent meta-analysis of RCTs [65] found increased T2D incidence with higher omega-6 fatty acid intake and an inverse association for alpha-linolenic acid intake [65]. However, these associations were very imprecisely estimated, based on only 2 trials, and the control groups of the trials varied, including comparisons with mixed fat intake or low doses of the same fatty acid [65]. We observed geographic differences regarding long-chain omega-3 fatty acids, which were also found by Wallin and colleagues [67] and in biomarker studies [62,68]. Previous high versus low intake meta-analyses observed an inverse, though nonsignificant, association between alpha-linolenic acid intake and T2D incidence [17,18]. In our nonlinear dose–response meta-analysis, an apparent association with decreased T2D incidence was observed for an intake of up to 560 mg alpha-linolenic acid per day. However, this estimate was also imprecise. Regarding trans-fatty acids, our null findings confirm the results of 1 high versus low intake meta-analysis [17], but not another [20]. Differences might be explained by different compositions of trans-fatty acids because neither meta-analysis differentiated between industrial and ruminant trans-fatty acids. However, biomarker studies showed that ruminant trans-fatty acids were associated with decreased T2D incidence [20], while industrial trans-fatty acids increased T2D incidence [69]. However, our results are supported by findings of previously conducted RCTs suggesting no effect on glucose metabolism when comparing diets high in total trans-fatty acids to diets low in total trans-fatty acids [70].

In general, individuals following an unhealthy diet (e.g., high animal fat intake through meat consumption) are also likely to have an unhealthy lifestyle (e.g., higher rates of obesity and smoking and lower levels of physical activity) [71,72]. Despite the adjustment for body mass index (BMI), smoking, and physical activity, residual confounding is possible. However, recent evidence does not support the long-held belief that high fat diets lead to obesity, and thus, T2D. In contrast, apart from being an energy source, fatty acids also have important bioactive properties [14]. Moreover, it is likely that any association of fat or fatty acid intake with T2D incidence depends on the overall dietary pattern and the food source [14,73]. For example, olive oil is associated with decreased T2D incidence [74], while the health effects of other vegetable fats, such as palm oil and coconut oil, are debated [14]. Vegetable fats also include plant-derived polyunsaturated fatty acids. In a meta-analysis of RCTs, higher intakes of plant-based polyunsaturated fatty acids showed beneficial effects on insulin resistance (HOMA-IR) and fasting insulin levels compared to higher intakes of carbohydrate or saturated fatty acids [75]. Additionally, there is indication that lower levels of plant-derived alpha-linolenic acid are associated with higher pro-inflammatory markers [76] and therefore influence inflammatory processes playing an important role in the development of T2D [77,78]. In contrast, depending on geographic location, higher intakes of animal-based long-chain omega-3 fatty acids were associated with increased T2D incidence. In this context, differences in food preparation between countries might play a role [14]. In addition, investigations into the influence of genetic susceptibility on the association between fat and fatty acid intake and T2D incidence have not yielded consistent insight [14,79-81]. Therefore, further research examining the role of genetic susceptibility is warranted [14]. The different food sources and structures of fatty acids play an important role as well. Saturated fatty acids are contained in meat products, including red and processed meat, which are associated with increased T2D incidence [16]. Additionally, short-chain, even-chain saturated fatty acids increase T2D incidence [82,83]. However, saturated fatty acids are also contained in dairy products, as well as in low concentrations in peanuts and canola oil, which are sources of odd-chain and very-long-chain saturated fatty acids, respectively, which decreased T2D incidence in biomarker studies [82-84]. Dairy products are also a source of ruminant trans-fatty acids, which are produced by bacterial metabolism of polyunsaturated fatty acids in the stomach of ruminants [85] and which were associated with decreased T2D incidence [20]. In contrast, industrial trans-fatty acids from processed food products have been shown to be associated with increased T2D incidence [69]. And although all studies included in our meta-analyses adjusted for additional dietary factors, such as other fatty acids, none of these studies investigated the food sources, for example animal versus plant products, in their analyses. Additionally, the nutrient composition of the whole diet plays a role. For example, substitution studies indicate that an isocaloric replacement of carbohydrates with saturated fatty acids is associated with decreased T2D incidence [79]. However, replacing saturated fatty acids or carbohydrates with polyunsaturated fatty acids lowered fasting glucose levels and glycated hemoglobin (HbA1C) and improved insulin resistance (HOMA-IR), but did not affect fasting glucose or postprandial glucose and insulin levels [86].

Strengths and limitations

To our knowledge, this is the first dose–response meta-analysis that provides a comprehensive overview of all associations between dietary fat and fatty acid intake and T2D incidence, including extensive subgroup and sensitivity analyses. Additionally, we assessed the risk of bias of each included study and evaluated the certainty of evidence for each association using validated tools. Because we only included prospective studies, risk of recall and selection bias was reduced.

However, our study also has a number of limitations. For half of the exposures, only 5 or fewer studies were available for the meta-analyses. Therefore, subgroup analyses of these associations were only based on a few studies or were not possible at all. Moreover, publication bias could only be assessed for saturated fatty acids, monounsaturated fatty acids, long-chain omega-3 fatty acids, and alpha-linolenic acid. Additionally, most of the observed high inconsistency between the studies remained unexplained, leading to lower certainty of evidence. This might be due to different fatty acid compositions of the fatty acid classes (e.g., differences between the studies regarding the proportions of even-chain and odd-chain saturated fatty acids in total saturated fatty acids). The applied conventional classification into groups of fat (e.g., vegetable fat) and classes of fatty acids (e.g., saturated fatty acids) might conceal differences regarding bioactive properties of different fatty acids within each group and class [14]. Investigating finer strata of these classes in biomarker studies might provide further insights. Additionally, since dietary fat intake was assessed via self-reports, measurement errors are likely. Moreover, in food frequency questionnaires, only the main food sources for fatty acids are included, and they are assessed on a food group level, which might lead to difficulties in quantifying fat and fatty acid intake. Only 4 of the included studies validated the dietary intakes of fatty acids measured via food frequency questionnaires against biomarkers, and these studies reported weak to moderate correlations. Biomarker studies might therefore add a more objective and reliable measure, especially for omega-6 and omega-3 fatty acids [14]. Furthermore, most studies provided no information on the main food sources contributing to fat and fatty acid intake. However, the food sources play a major role, especially for the interpretation of the results regarding saturated fatty acids, monounsaturated fatty acids, and possibly trans-fatty acids [14]. Such uncertainties contributed to the downgrading regarding the certainty of evidence. Therefore, future studies should also investigate the role of different food sources in relation to the association of fats and fatty acids with T2D incidence. Moreover, we observed geographic differences in the association of T2D incidence with long-chain omega-3 fatty acids. Reasons for these differences are not yet clear, and more research regarding the possible mediating role of genetic susceptibility is warranted. Lastly, since we included observational studies, residual confounding cannot be ruled out.

Conclusions

In our linear dose–response meta-analyses, we mainly observed no or weak associations between intake of dietary fats and fatty acids and T2D incidence. However, in nonlinear dose–response meta-analyses, we observed a significant association of decreased T2D incidence with higher intakes of vegetable fat, as well as a non-significant decrease in T2D incidence for polyunsaturated fatty acids and alpha-linolenic acid in lower doses. Long-chain omega-3 fatty acids were associated with a significant decrease in incidence of T2D in Asian populations, and with a significant increase in incidence of T2D in US populations. A harmful association for saturated fatty acids was not confirmed. However, our results are limited by very low to moderate certainty of evidence. To strengthen the evidence, future studies should focus on the association between the fatty acid composition of the diet and T2D. In addition, further research is needed to investigate the role of different food sources regarding the association between fatty acid intake and T2D incidence.

PRISMA checklist.

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Flow chart of literature search.

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Linear dose–response meta-analyses of the associations between total fat, animal fat, and vegetable fat and incidence of type 2 diabetes.

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Linear dose–response meta-analyses of the associations between specific fatty acids and incidence of type 2 diabetes.

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Sensitivity analyses for total fat, animal fat, and vegetable fat.

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Sensitivity analyses for specific fatty acids.

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Nonlinear dose–response meta-analyses for the association between long-chain omega-3 fatty acids and incidence of type 2 diabetes stratified by geographic region.

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Funnel plots.

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Description and decision criteria for each domain in ROBINS-I.

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List of excluded studies.

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Study characteristics of the included studies.

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ROBINS-I judgment for each domain and overall.

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GRADE judgment for each domain and overall.

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Linear dose–response meta-analyses by subgroups.

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6 Apr 2020

Dear Dr Schlesinger,

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7 May 2020

Dear Dr. Schlesinger,

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In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

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We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Emma Veitch, PhD

PLOS Medicine

On behalf of Clare Stone, PhD, Acting Chief Editor,

PLOS Medicine

plosmedicine.org

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Requests from the editors:

*Please structure your abstract using the PLOS Medicine headings (Background, Methods and Findings, Conclusions; Methods and Findings sections combined into one single subsection). Please also make sure the abstract subsections are written in complete sentences, not sentence fragments/bullet type points. The methodological detail included currently in the abstract is at an appropriate level though so would recommend this level of detail is kept.

*At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

*If possible, please update the in-text callouts to references to numbered callouts in square brackets (eg [1, 2]), if you've used referencing software this should be fairly simple - many thanks.

*Currently the study reporting has used ROBINS-I and GRADE to evaluate study quality, which is good, but it doesn't seem that a reporting tool (such as PRISMA) was used for the entirety of the systematic review (in general). This point was raised by reviewers, it would be good to address this by using the PRISMA tool to update any details of methodological/findings reporting within the manuscript, and then provide the completed PRISMA checklist as a supporting information file. When completing the checklist, please use section and paragraph numbers, rather than page numbers.

*Minor typos/grammatical points - eg, Page 19 (Discussion), "recent evidence does not support the long-time believe that high fat diets" --> long-term belief

*One reviewer has asked authors to change use of the first person ("we") in the paper - the editors would disagree, and although this can be considered a bit colloquial in scientific manuscripts, we prefer this type of direct writing.

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Comments from the reviewers:

Reviewer #1: I confine my remarks to statistical aspects of this paper.

Mostly, everything was find but I have a couple issues to resolve before I can recommend publication.

Line 120-123 were unclear to me. What exactly was done?

While I like the use of nonlinear models, I don't think a p value (line 125) is the right way to judge. Instead, look at the differences in the models and make a judgement as to whether the complexity of splines is made up for by improvements in fit.

For the forest plot, I'd like to see more of the area devoted to the graph. In the first column, one line is much longer than all the others, it could be wrapped. Then the second column (dose) could be merged with the first. I[m not sure all of the next three columns are needed. Some might be put in footnotes.

Peter Flom

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Reviewer #2: General:

This is a well conducted systematic review of associations between fat quality and incidence of T2D. Clearly written and presented. Methods appear robust. Bias and limitations of these observational studies is made clear. Certainly of evidence is determined and presented.

Specific:

Abstract - please include details of total number of articles retrieved using key words and total number included in the review.

Introduction. Line 70. I do not understand the statement used to justify conduct of this 2nd systematic review, following rapidly on the back of the author's previous 2019 BMJ umbrella review: 'Because new prospective cohort studies examining the associations between dietary fat and fatty acid intake have recently been published (10-16)'. The BMJ analysis was conducted using trials published up to August 2018. Yet the additional references quoted (refs 10-16) as updating that review were published between 2015 - 2019 (Dow et al. BJN. 2016;Ericson et al. AJCN. 2015; Guasch-Ferre et al. AJCN. 2017:Ha et al. Diab Res Clin Pract. 2019; Ma et al. AJCN. 2015 etc). This is not correct, and requires clarification. Clearly this review is differentiated and warranted, but the justification provided appears confused.

I suggest that some trial methodology information is provided in the Abstract. The findings of this review are arguably unexpected. It is important that readers have ready access to limitations (or strengths) of the 1ry cohort data. Eg. Line 295-7: dietary fat was assessed via self-report, FFQs were the main assessment tool (90% of studies reported), food group level used for assessment.

Line 72-3. Also, it is stated by the current authors that certainty of evidence …. has not been evaluated'; yes contrary to this their earlier BMJ analysis states that a quality evaluation was conducted 'The quality of evidence was evaluated by using a modified version of NutriGrade'. This also requires clarification.

Methods. The methods are valid, and the analysis looks to be well conducted. The protocol was prospectively registered on PROSPERO, as required. Risk of bias was also correctly handled using Cochrane tools. However, some questions still arise

Was the data extracted and checked by 2 independent reviewers?

Results. Line 143. States that 23 studies met the inclusion criteria. However only 22 studies (11+6+4+1) are shown as included, lines 145-7. Why is there a discrepancy?

Also, data on number of European cohorts is missing. Total cohorts = 19? Only 11 are reported in the expanded text.

Statistical analyses. I would recommend including brief description of methods from references #24,25,26, for the linear dose-response metanalyses.

Figures 2 and 3. I would recommend higher quality figures for publication. Also ensure that the y axis is consistent for all figures within a single multi-panel plot, eg. Fig 3, PUFA plot.

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Reviewer #3: The manuscript entitled: "Intake of dietary fats, fatty acids and the incidence of type 2 diabetes: a systematic review and dose-response meta-analysis of prospective observational studies" by Neuenschwander et al., describes the impact of various fats and fatty acids in intake and the relationship with diabetes incidence which is an important issue. Yet, the current manuscript confirms results from previous meta-analyses. However, if authors added some supplementary analysis from subgroups or secondary outcomes (such as diabetes risk factors), the manuscript could be more novel. In addition, the English style/grammar of the manuscripts needs to be adjusted. Please see specific comments below.

General:

There are a few typos and grammar mistakes which should be revised. Please avoid using "we" in the manuscript.

Please add the definition for each abbreviation that you write for the first time and use abbreviations after first in the text. For example, type 2 diabetes (T2D).

Title:

Intake of dietary fats, fatty acids and the incidence of type 2 diabetes should be modified to Intake of dietary fats and fatty acids with the incidence of type 2 diabetes. Because fatty acids could also mean in serum/plasma, it is better to specify that it is intake.

Abstract

Line 33: associations between dietary fat, fatty acids and T2D= please rewrite to association between dietary fat and fatty acid intake with T2D.

Conclusion: Especially…. Please re-write the sentence not starting by especially, perhaps "Specifically"

Introduction:

Please define diabetes in the first paragraph and then type 2 diabetes briefly. Perhaps adding the risk factors.

Line 59: What other problems, please be specific.

Line 67-68: Please give general fat (total, vegetable and animal fat) recommendations for individuals with T2D. The study is about dietary fats more information about any single fat in introduction gives more insight to the readers.

Lines 67-68: Based on recent studies all trans fatty acids may not be harmful. Please explain briefly about industrial and ruminant trans fatty acids. Did the researchers consider the different types of trans-fats in their analyses?

Line 68: What fatty acids? Do you mean trans fats and saturated fats? Please be specific.

Line 74-79: Please write in 2 sentences.

Method:

Line 89: It would be better if you add a PRISMA for your paper, it makes easier for the readers to understand study search and selection.

Result and discussion:

Providing more categorized information in discussion section based on diabetes criteria's (secondary outcomes) would be more interesting results for paper. For example, authors could explain effects of different dietary fats on fasting glucose/insulin, insulin resistance (HOMA-IR), HbA1c, etc... instead of only diabetes incidence (main outcome which is the most clinically relevant). This would add novelty to the meta-analysis.

The discussion does not support the different incidence of type 2 diabetes between Asia and the U.S population which is described in conclusion. Please discuss more in depth these results.

Line 236: Did you consider the different types of trans fat in your analysis? Did the other paper consider the different transfat?

Limitations;

Regarding the different geographic location of reviewed papers, mediatory role of ethnic andgenetic could be considered as a limitation.

Did any of the studies use biomarkers of fat intake to validate their FFQ or dietary recalls?

Conclusion:

Line 311: between dietary fats, fatty acids and type 2 diabetes incidence= please rewrite.

Line 317: too long sentence with many AND- please rewrite.

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Any attachments provided with reviews can be seen via the following link:

[LINK]

30 Jul 2020

Dear Dr. Schlesinger,

Thank you very much for re-submitting your manuscript "Intake of dietary fats and fatty acids with the incidence of type 2 diabetes: a systematic review and dose-response meta-analysis of prospective observational studies" (PMEDICINE-D-20-00813R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by original reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Aug 06 2020 11:59PM.

Sincerely,

Clare Stone, PhD

Managing Editor

PLOS Medicine

plosmedicine.org

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Requests from Editors:

Title: please amend, needs to be "... and the incidence ..."

Please include p values in the abstract and throughout for quantifiable data and where 95% Cis are given.

Line 41 and some of the display items, comma rather than apostrophe in the publication number, please

Line 46, we have "trend" which is often a trigger word for "non-significant association"; it looks like this is indeed a significant trend, however (please include the p value, I think at line 254)

- relatedly, at line 50 the "incidence decreased" refers to an association for alpha linolenic acid which is not obviously significant to my eye. Please include p values and state if significant or not.

Abstract- "low to moderate” appears twice, please remove one of the mentions.

- in the abstract, the limitations sentence can be reorganized. The sentence on limitations should be explicit, startin with ‘limitations of this study are…..’ and please remove “Thus, the certainty of evidence is low to moderate” as this is too vague to be of value.

Line 41 “In total, 15’070” should be “15, 070”

Line 57 - “Our results suggest weak associations” – there is either an association or not depending on p values. Please be clear. Weak association implies not and if so, don’t state there is one, please.

Line 60- saying that plant fats prevent T2D would be over reaching. Please rethink some rerding, e.g. to substitution of plant for animal fats in diets

- to go back to p values, at line 264 we have "doses ... reduced T2D incidence" and then the estimate/95% CI look non-significant -please provide p value. Please do not make statements if they can’t be supported.

Line 384, "... to our knowledge the first"

- reference 73 looks odd, also two or three others – please use referencing according to Vancouver style.

PRISMA attachment – please replace page numbers with sections and paragraphs as these change during formatting.

Comments from Reviewers:

Reviewer #2: The authors have revised the manuscript in accordance with recommended changes; and answered questions previously raised.

Any attachments provided with reviews can be seen via the following link:

[LINK]

10 Nov 2020

Dear Dr. Schlesinger,

On behalf of my colleagues and the academic editor, Dr. Sanjay Basu, I am delighted to inform you that your manuscript entitled "Intake of dietary fats and fatty acids and the incidence of type 2 diabetes: a systematic review and dose-response meta-analysis of prospective observational studies" (PMEDICINE-D-20-00813R3) has been accepted for publication in PLOS Medicine.

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Richard Turner

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