Skip to main content Accessibility help
×
Home
Hostname: page-component-78dcdb465f-vddjc Total loading time: 1.081 Render date: 2021-04-20T01:23:01.094Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false, "newCitedByModal": true }

Association of fish and n-3 fatty acid intake with the risk of type 2 diabetes: a meta-analysis of prospective studies

Published online by Cambridge University Press:  04 July 2012

Yunping Zhou
Affiliation:
Department of Epidemiology and Health Statistics, Shandong University, Jinan 250012, Shandong, People's Republic of China
Changwei Tian
Affiliation:
Department of Epidemiology and Health Statistics, Shandong University, Jinan 250012, Shandong, People's Republic of China
Chongqi Jia
Affiliation:
Department of Epidemiology and Health Statistics, Shandong University, Jinan 250012, Shandong, People's Republic of China
Corresponding
E-mail address:
Rights & Permissions[Opens in a new window]

Abstract

Results from observational studies on the association of fish and n-3 fatty acid consumption with type 2 diabetes mellitus (T2DM) risk are conflicting. Hence, a meta-analysis was performed to investigate this association from cohort studies. A comprehensive search was then conducted to identify cohort studies on the association of fish and/or n-3 fatty acid intake with T2DM risk. In the highest v. lowest categorical analyses, the fixed or random-effect model was selected based on the homogeneity test among studies. Linear and non-linear dose–response relationships were also assessed by univariate and bivariate random-effect meta-regression with restricted maximum likelihood estimation. In the highest v. lowest categorical analyses, the pooled relative risk (RR) of T2DM for intake of fish and n-3 fatty acid was 1·146 (95 % CI 0·975, 1·346) and 1·076 (95 % CI 0·955, 1·213), respectively. In the linear dose–response relationship, the pooled RR for an increment of one time (about 105 g)/week of fish intake (four times/month) and of 0·1 g/d of n-3 fatty acid intake was 1·042 (95 % CI 1·026, 1·058) and 1·057 (95 % CI 1·042, 1·073), respectively. The significant non-linear dose–response associations of fish and n-3 fatty acid intake with T2DM risk were not observed. The present evidence from observational studies suggests that the intake of both fish and n-3 fatty acids might be weakly positively associated with the T2DM risk. Further studies are needed to confirm these results.

Type
Systematic Review with Meta-analysis
Copyright
Copyright © The Authors 2012

Diet is widely believed to play an important role in the development of type 2 diabetes mellitus (T2DM)(Reference Carter, Gray and Troughton1, Reference Knowler, Barrett-Connor and Fowler2). Among dietary components, fish, an ideal source of n-3 PUFA, has been documented to be associated with T2DM risk, by experimental research and observational studies. Experimental research suggested that n-3 fatty acids could lower glucose utilisation and increase glucagon-stimulated C-peptide(Reference Mostad, Bjerve and Bjorgaas3) or hepatic gluconeogenesis(Reference Woodman, Mori and Burke4) with increasing uptake and oxidation of NEFA in the liver(Reference Puhakainen, Ahola and Yki-Jarvinen5). Therefore, fish intake and n-3 fatty acid consumption may increase T2DM risk by increasing circulating concentrations of glucose(Reference Kaushik, Mozaffarian and Spiegelman6). Vessby et al. (Reference Vessby, Karlstrom and Boberg7) also reported that fasting glucose increased significantly after consumption of fish. Besides, n-3 fatty acids may cause oxidative stress and subsequent increase in pro-inflammatory products known to promote T2DM(Reference Osterud and Elvevoll8). Moreover, recent studies have suggested that environmental contaminants such as dioxins(Reference Lee, Lee and Song9) and methyl mercury, found in fish, might raise T2DM risk(Reference Mozaffarian and Rimm10). Furthermore, mouse models showed that elevated blood mercury levels may interrupt insulin signalling pathways, and decrease plasma insulin and elevate blood glucose levels(Reference Chen, Huang and Tsai11). A cross-sectional study also suggested that serum concentrations of persistent organic pollutants were strongly associated with diabetes prevalence(Reference Lee, Lee and Song9). However, an ecological study reported that populations with a high consumption of fish and marine animals have a lower prevalence of T2DM than do other populations(Reference Nkondjock and Receveur12), and n-3 fatty acid supplementation may increase insulin sensitivity(Reference Fedor and Kelley13) in animal models. Besides, cross-sectional studies showed inverse(Reference Panagiotakos, Zeimbekis and Boutziouka14, Reference Ruidavets, Bongard and Dallongeville15), no(Reference Harding, Day and Khaw16, Reference Adler, Boyko and Schraer17), and positive(Reference Bjerregaard, Pedersen and Mulvad18) associations between fish consumption and glycaemic status. Prospective studies reported that fish intake is either positively(Reference Kaushik, Mozaffarian and Spiegelman6, Reference Djousse, Gaziano and Buring19) or not associated(Reference Schulze, Manson and Willett20) with T2DM risk.

Prospective cohort studies are assumed to provide better evidence than case–control studies, since they are not biased by recall of past dietary habits after T2DM has been diagnosed. Therefore, we decided to focus this meta-analysis on results from prospective cohort studies to: (1) assess the effects and evaluate the dose–response relationship between fish and n-3 fatty acid consumption with T2DM risk; (2) evaluate the potential heterogeneity among studies; and (3) explore the potential publication bias.

Methods

Search strategy

A comprehensive search was performed for relevant articles published between January 1990 and July 2011 using the following databases: (1) PubMed; (2) Web of Science (ISI); (3) China Biology Medical literature database (CBM); (4) Database of Chinese Scientific and Technical Periodicals (VIP) and (5) China National Knowledge Infrastructure (CNKI). Search terms included ‘fish’, ‘ω-3 fatty acid’, ‘n-3 fatty acid’ and ‘diabetes’. Moreover, we identified studies not captured by our database by reviewing reference lists from retrieved articles to search for further relevant articles.

Eligibility criteria

Each identified study was independently reviewed by two investigators to determine whether an individual study was eligible for inclusion in this meta-analysis. The inclusion criteria were as follows: (1) cohort study; (2) the exposure of interest was the frequency of fish intake or n-3 fatty acid consumption; (3) the outcome of interest was T2DM and (4) multivariate adjusted relative risk (RR) estimates or hazard ratios with 95 % CI relating to each category of fish or n-3 fatty acid consumption. If there was disagreement between the two investigators about eligibility of the article, it was resolved by consensus with a third reviewer.

Data extraction

The following data were collected from all studies: the first author's name, year of publication, country where the study was performed, sex, participant age at baseline, sample size, duration of follow-up, number of cases, methods for measurement and range of exposure, variables adjusted for in the analysis, as well as multivariate adjusted RR and 95 % CI for the highest v. lowest categories of fish and n-3 fatty acid intake or for each category of fish or n-3 fatty acid. For studies that reported results from various covariate analyses, we abstracted the estimates based on the model that included the most potential confounders. For fish consumption, measurement of fish intake varied among studies (grams, servings or times consumed per d, week, or month), and we used times/month as a standard measure of fish intake using the following equivalence: 105 g/time(Reference He, Song and Daviglus21). As the levels of fish consumption were often given by a range, the value of exposure was assigned as the midpoints of the ranges of the reported categories of fish intake. When the lowest category was open-ended, we set the lower boundary to 0. When the highest category was open-ended, we assumed the values as 1·2 times the lower bound(Reference Berlin, Longnecker and Greenland22). For n-3 fatty acid intake, we used g/d as a standard measure, and the median value of each category was extracted as reported in the original studies. If results were reported for both total fish and the type of fish (lean and fatty), as in one study(Reference van Woudenbergh, van Ballegooijen and Kuijsten23), we used the results for total fish in the main analysis. Of the relevant studies, one(Reference Patel, Sharp and Luben24) was excluded because it had only two levels of fish intake. The study quality was assessed using the nine-star Newcastle–Ottawa Scale(Reference Wells, Shea and O'connell25).

Statistical analysis

A pooled measure was calculated as the inverse variance-weighted mean of the natural logarithm of multivariate adjusted RR with 95 % CI for the highest v. lowest levels to assess the association of fish and n-3 fatty acid intake with T2DM risk. The Q test and I 2 of Higgins & Thompson(Reference Higgins and Thompson26) were used to assess heterogeneity among studies. I 2 describes the proportion of total variation attributable to between-study heterogeneity as opposed to random error or chance. In the presence of substantial heterogeneity (I 2>50 %)(Reference Higgins, Thompson and Deeks27), the DerSimonian and Laird random-effect model was adopted as the pooling method; otherwise, the fixed-effect model was used as the pooling method. Meta-regression with restricted maximum likelihood (REML) estimation was performed to assess the potentially important covariate exerting substantial impact on between-study heterogeneity. The ‘leave one out’ sensitivity analysis(Reference Patsopoulos, Evangelou and Ioannidis28) was carried out using I 2>50 % as the criterion to evaluate the key studies with substantial impact on between-study heterogeneity. Publication bias was estimated using Egger's regression asymmetry test(Reference Egger, Davey Smith and Schneider29). An analysis of influence was conducted(Reference Tobias30), which describes how robust the pooled estimator is to the removal of individual studies. An individual study is suspected of excessive influence, if the point estimate of its omitted analysis lies outside the 95 % CI of the combined analysis.

In the dose–response analysis about the relationship between fish and n-3 fatty acid intake and T2DM risk, the between-study heterogeneity was taken into account. The method proposed by Greenland & Longnecker(Reference Greenland and Longnecker31) and Orsini et al. (Reference Orsini, Bellocco and Greenland32) was used to calculate the study-specific slopes (linear trend) and their standard errors from the correlated natural logarithm of RR and their CI across categories of fish and n-3 fatty acid intake, and then the univariate random-effect meta-regression with REML estimation was performed to synthesise the study-specific slopes. The non-linear dose–response association of fish and n-3 fatty acid intake with T2DM risk was assessed by bivariate random-effect meta-regression with REML estimation(Reference White33) used to pool the study-specific two trend components generated by generalised least squares(Reference Greenland and Longnecker31, Reference Orsini, Bellocco and Greenland32) based on the restricted cubic spline model(Reference Wells, Shea and O'connell25, Reference Bagnardi, Zambon and Quatto34) with three knot values at percentiles of 10, 50 and 90 % in the dose distribution. The potential non-linearity was tested on the coefficient of the second spline(Reference Wells, Shea and O'connell25). The adequacy of the bivariate random-effects model with respect to the linear one is evaluated by comparing the Akaike's information criteria between the two models. The results for both linear and non-linear models were reported. All statistical analyses were performed with STATA version 11.2 (Stata Corporation). All reported probabilities (P values) were two-sided, with P < 0·05 considered statistically significant.

Results

Study characteristics

Overall, ten publications with thirteen cohort studies(Reference Kaushik, Mozaffarian and Spiegelman6, Reference Djousse, Gaziano and Buring19, Reference van Woudenbergh, van Ballegooijen and Kuijsten23, Reference van Dam, Willett and Rimm35Reference Villegas, Xiang and Elasy41) were identified in the analysis for the association of fish and n-3 fatty acid consumption with risk of T2DM (Fig. 1). Of the ten articles, one study(Reference Kaushik, Mozaffarian and Spiegelman6) included three independent cohorts, and another one(Reference Villegas, Xiang and Elasy41) reported two independent cohorts; seven of the publications were conducted in the USA(Reference Kaushik, Mozaffarian and Spiegelman6, Reference Djousse, Gaziano and Buring19, Reference van Dam, Willett and Rimm35Reference Krishnan, Coogan and Boggs38, Reference Djousse, Biggs and Lemaitre40), one in the Netherlands(Reference van Woudenbergh, van Ballegooijen and Kuijsten23) and two in Asia(Reference Brostow, Odegaard and Koh39, Reference Villegas, Xiang and Elasy41). General characteristics in the published articles included in this meta-analysis are shown in Tables 1 and 2. Data on dietary assessment were collected by using FFQ (seven articles(Reference Djousse, Gaziano and Buring19, Reference van Woudenbergh, van Ballegooijen and Kuijsten23, Reference van Dam, Willett and Rimm35, Reference Meyer, Kushi and Jacobs37, Reference Krishnan, Coogan and Boggs38, Reference Djousse, Biggs and Lemaitre40, Reference Villegas, Xiang and Elasy41)) and semiquantitative FFQ (SFFQ) (three articles(Reference Kaushik, Mozaffarian and Spiegelman6, Reference Song, Manson and Buring36, Reference Brostow, Odegaard and Koh39)). The range of follow-up period was from 4 to 15 years. All studies included met quality criteria ranging from 6 to 7 stars. For studies on n-3 fatty acids, four articles(Reference Kaushik, Mozaffarian and Spiegelman6, Reference van Dam, Willett and Rimm35, Reference Meyer, Kushi and Jacobs37, Reference Villegas, Xiang and Elasy41) reported long-chain n-3 fatty acids and three articles(Reference Djousse, Gaziano and Buring19, Reference Song, Manson and Buring36, Reference Brostow, Odegaard and Koh39) reported n-3 fatty acids. Most studies provided risk estimates that were adjusted for smoking, alcohol consumption, physical activity (or exercise) and age.

Fig. 1 Selection of studies for inclusion in meta-analysis.

Table 1 Characteristics of prospective studies on fish intake and type 2 diabetes

(Relative risks (RR) and 95 % confidence intervals)

F, female; M, male; Q, quintile; SFFQ, semiquantitative FFQ.

Table 2 Characteristics of prospective studies on n-3 fatty acid intake and type 2 diabetes

(Relative risks (RR) and 95 % confidence intervals)

F, female; Q, quintile; M, male; SFFQ, semiquantitative FFQ.

* Long-chain n-3 fatty acids.

n-3 Fatty acids.

Fish

High v. low analysis

Overall, six publications with nine cohort studies(Reference Kaushik, Mozaffarian and Spiegelman6, Reference Djousse, Gaziano and Buring19, Reference van Woudenbergh, van Ballegooijen and Kuijsten23, Reference Krishnan, Coogan and Boggs38, Reference Djousse, Biggs and Lemaitre40, Reference Villegas, Xiang and Elasy41) including 367 757 subjects were included in the analysis on the association of fish intake with T2DM risk. The pooled RR was 1·146 (95 % CI 0·975, 1·346) with substantial between-study heterogeneity (P heterogeneity < 0·001, I 2 = 79·0 %) (Fig. 2).

Fig. 2 Forest plot of relative risk (RR) of high v. low analysis for fish intake with type 2 diabetes mellitus risk. ⋄ Denotes the pooled RR. ♦ Indicates the RR in each study, with the square sizes inversely proportional to the standard error of the RR. Horizontal lines represent the 95 % CI. * One study with different cohorts. ES, effect size; I − V, fixed effects model; D+L, random effects model. (A colour version of this figure can be found online at http://www.journals.cambridge.org/bjn).

Sources of heterogeneity and sensitivity analysis

To explore the heterogeneity, we performed meta-regression for covariate, and sensitivity analysis for individual results. However, the univariate meta-regression analysis, with the covariates publication year, sex (male, female, both sexes), sample size, methods of dietary assessment (FFQ, SFFQ), duration of follow-up, and study quality, showed that no covariate had a significant impact on between-study heterogeneity. In the sensitivity analysis, two studies conducted by Djousse et al. (Reference Djousse, Gaziano and Buring19) and Villegas et al. (Reference Villegas, Xiang and Elasy41) for the Shanghai Women's Health Study were found to be the key contributors to the between-study heterogeneity. After excluding these two studies, no substantial between-study heterogeneity was observed among the seven cohorts left (P heterogeneity = 0·198, I 2 = 30·1 %) and the pooled RR was 1·157 (95 % CI 1·051, 1·274).

No significant influence and publication bias were observed before and after the sensitivity analysis.

Dose–response meta-analysis

Overall, three publications with five cohort studies(Reference Kaushik, Mozaffarian and Spiegelman6, Reference van Woudenbergh, van Ballegooijen and Kuijsten23, Reference Djousse, Biggs and Lemaitre40) were available to evaluate the dose–response association of fish intake with T2DM risk. For the linear trend analysis, the pooled RR for an increment of one time (about 105 g)/week of fish intake (four times/month) was 1·042 (95 % CI 1·026, 1·058), with no between-study heterogeneity (P heterogeneity = 0·421, I 2 = 0·00 %). For the non-linear trend analysis, the overall association was significant (P overall association < 0·001), with an increase of fish intake generally associated with higher T2DM risk, but the non-linearity was not significant (P non-linearity = 0·150) (Fig. 3).

Fig. 3 Non-linear dose–response relationship between fish intake and type 2 diabetes mellitus risk assessed by restricted cubic spline model with three knots. Relative risk (RR, ). , 95 % CI.

n-3 Fatty acids

High v. low analysis

Overall, seven publications with ten cohort studies(Reference Kaushik, Mozaffarian and Spiegelman6, Reference Djousse, Gaziano and Buring19, Reference van Dam, Willett and Rimm35Reference Meyer, Kushi and Jacobs37, Reference Brostow, Odegaard and Koh39, Reference Villegas, Xiang and Elasy41) involving 506 665 subjects were included in the analysis on the association of n-3 fatty acid intake with T2DM risk. The pooled RR was 1·076 (95 % CI 0·955, 1·213), with substantial between-study heterogeneity (P heterogeneity < 0·001, I 2 = 84·8 %) (Fig. 4).

Fig. 4 Forest plot of relative risk (RR) of high v. low analysis for n-3 fatty acid intake with type 2 diabetes mellitus risk. ⋄ Denotes the pooled RR. ♦ Indicates the RR in each study, with the square sizes inversely proportional to the standard error of the RR. Horizontal lines represent the 95 % CI. * One study with different cohorts. ES, effect size; I − V, fixed effects model; D+L, random effects model. (A colour version of this figure can be found online at http://www.journals.cambridge.org/bjn).

Sources of heterogeneity and sensitivity analysis

To explore the heterogeneity, we performed meta-regression for covariate, and sensitivity analysis for individual results. However, the univariate meta-regression analysis, with the covariates publication year, sex (male, female, both sexes), sample size, methods of dietary assessment (FFQ, SFFQ), duration of follow-up, type of n-3 fatty acid (n-3 fatty acids, long-chain n-3 fatty acids), and study quality, showed that no covariate had a significant impact on between-study heterogeneity. In the sensitivity analysis, three studies conducted by Djousse et al. (Reference Djousse, Gaziano and Buring19), Brostow et al. (Reference Brostow, Odegaard and Koh39) and Villegas et al. (Reference Villegas, Xiang and Elasy41) for the Shanghai Women's Health Study were found to be the key contributors to the between-study heterogeneity. After excluding these three studies, no substantial between-study heterogeneity was observed among the seven cohorts left (P heterogeneity = 0·108, I 2 = 42·5 %) and the pooled RR was 1·155 (95 % CI 1·094, 1·220).

No significant influence and publication bias were observed before and after the sensitivity analysis.

Dose–response meta-analysis

Overall, four studies with six cohorts(Reference Kaushik, Mozaffarian and Spiegelman6, Reference Djousse, Gaziano and Buring19, Reference Song, Manson and Buring36, Reference Brostow, Odegaard and Koh39) were available to evaluate the dose–response association of n-3 fatty acid intake with T2DM risk. For the linear trend analysis, the pooled RR for an increment of 0·1 g/d of n-3 fatty acid intake was 1·030 (95 % CI 1·002, 1·058), with substantial between-study heterogeneity (P heterogeneity < 0·001, I 2 = 92·1 %). There was not much evidence for an overall association (P overall association = 0·076) with an increase of n-3 fatty acid intake with an almost slight increase of T2DM risk, and the non-linearity was also not significant (P non-linearity = 0·084).

Overall, three studies conducted by Kaushik et al. (Reference Kaushik, Mozaffarian and Spiegelman6) for the Health Professionals Follow-up Study, Song et al. (Reference Song, Manson and Buring36) and Brostow et al. (Reference Brostow, Odegaard and Koh39) were the key contributors to the between-study heterogeneity assessed by the ‘leave one out’ sensitivity analysis(Reference Patsopoulos, Evangelou and Ioannidis28). After excluding these three studies, no between-study heterogeneity was observed among the three cohorts left (P heterogeneity = 0·46, I 2 = 0·0 %), and the linear trend of pooled RR for an increment of 0·1 g/d of n-3 fatty acid intake was 1·057 (95 % CI 1·042, 1·073). Moreover, after excluding these three studies, the overall association in the non-linear dose–response model was significant (P non-linear model < 0·001), with an increase of n-3 fatty acid intake generally associated with higher T2DM risk, but the non-linearity was not significant (P non-linearity = 0·105) (Fig. 5).

Fig. 5 Non-linear dose–response relationship between n-3 fatty acid intake and type 2 diabetes mellitus risk assessed by restricted cubic spline model with three knots. Relative risk (RR, ). , 95 % CI.

Discussion

In this meta-analysis, a weakly positive association of fish and n-3 fatty acid intake with T2DM risk was found. For high v. low intake analysis, an increased but not significant T2DM risk was found before sensitivity analysis, and the increased T2DM risk was significant after sensitivity analysis. For dose–response analyses, the linear dose–response analyses reported a significantly positive association before and after sensitivity analysis. Considering the fact that categories of fish and n-3 fatty acid intake differed between studies, which might complicate the interpretation of the pooled results across study populations with different categories, a dose–response meta-analysis could provide a more robust method to combine results from individual studies and would better quantify the relationship between fish and n-3 fatty acid and T2DM risk than does the ‘high v. low intake’ analysis.

Between-study heterogeneity is common in meta-analysis, and our meta-analysis also showed significant between-study heterogeneity in the analyses of both fish and n-3 fatty acid intake. Although most studies in this meta-analysis used multivariate regression to adjusted confounders, other indeterminate characteristics that vary among studies, such as design quality, characteristics of the sample, non-comparable measures of fish and n-3 fatty acid intake, variation of the unmeasured covariate, diagnosis criteria of diabetes, etc. could be the causes of between-study heterogeneity. Hence, we used meta-regression and ‘leave one out’ sensitivity analysis(Reference Patsopoulos, Evangelou and Ioannidis28), which aims to reduce between-study heterogeneity and explore the potential important causes of between-study heterogeneity for both covariate and studies. However, our meta-analysis did not identify any of the aforementioned covariates as being an important contributor to between-study heterogeneity. Moreover, T2DM has a complex aetiology and pathophysiology generated by the combined effects of genes and environmental factors. Although the aforementioned covariates were not found to be important sources of disease–effect heterogeneity across the studies in this meta-analysis, other genetic background and other environmental variables as well as their possible interaction also deserve to be considered as potential contributors to this disease–effect unconformity. In this respect, the lack of relevant study-level covariate in the reported articles precluded our more robust assessment of sources of this heterogeneity. Whatever the reason, disease–effect inhomogeneity will finally influence the pooled-effect estimate. Thus, we performed the ‘leave one out’ sensitivity analysis(Reference Patsopoulos, Evangelou and Ioannidis28) using I 2>50 % as the criterion to exclude the key studies that had substantial impact on between-study heterogeneity; and the results suggested that higher intake of fish and n-3 fatty acids might weakly increase the T2DM risk.

In the explanation of our present results, the limitations in our meta-analysis should be taken into consideration. First, measurement errors in the assessment of dietary intake are known to bias effect estimates, particularly when using FFQ to assess n-3 fatty acid consumption, although most of the studies included in our meta-analysis used a validated FFQ. Random measurement error in dietary exposures most frequently attenuates risk estimates(Reference Beaton42). We cannot exclude the possibility that measurement errors and lack of accurate data on categories of exposure might have resulted in attenuated associations and that such attenuation might explain, in part, why the associations we observed are weak. Second, most of the included studies did not assess extensive details about the specific subcategories of fish and n-3 fatty acid consumed. EPA and DHA are present mainly in fatty fish, which may indicate that it is also important to pay attention to the type of fish consumption instead of total fish intake alone. In our present study, only two publications by van Woudenbergh et al. (Reference van Woudenbergh, van Ballegooijen and Kuijsten23) and Villegas et al. (Reference Villegas, Xiang and Elasy41) reported results for both total fish and the type of fish (lean and fatty fish or freshwater and saltwater fish). As for n-3 fatty acids, only one study conducted by Djousse et al. (Reference Djousse, Gaziano and Buring19) reported results for both total marine n-3 fatty acids and three types of n-3 fatty acids (α-linolenic acid, EPA and DHA). Therefore, we cannot perform our meta-analysis for the subtype of fish or n-3 fatty acid to assess the potential effects. Third, considering the small number of studies included in our meta-analysis for both high v. low intake and linear and non-linear dose–response analyses, the validity of our publication bias test might be questioned.

Several suggestions should be considered in further studies. First, the data on n-3 fatty acids from these studies are derived from FFQ, which is very useful for ranking within populations, but have provided narrow ranges of estimated intake that may be questionable as biologically relevant(Reference Brostow, Odegaard and Koh39). Thus, in contrast to estimates from FFQ, the measurement of plasma phospholipid or cholesteryl ester fatty acids may provide an objective measure of exposure. Second, further cohort studies are warranted to estimate the specific type of fish and n-3 fatty acids, because only three studies(Reference Djousse, Gaziano and Buring19, Reference van Woudenbergh, van Ballegooijen and Kuijsten23, Reference Villegas, Xiang and Elasy41) in our meta-analysis assessed the subtype of fish and n-3 fatty acid consumed. Third, most of the studies included were conducted in Americans, only one in Dutch and two in Chinese; considering the underlying disease–effect unconformity across different geographical locations, more studies deserve to be conducted in other populations. Fourth, the meta-analysis of observational studies presented particular challenges because of inherent biases and variations in study design; and hence, more research and different approaches such as randomised feeding or supplementation studies are warranted to investigate which, if any, specific type of n-3 fatty acid is involved in T2DM aetiology.

In summary, this meta-analysis suggested that higher fish and n-3 fatty acid consumption might be associated with a weak increase of T2DM risk. Since the potential biases and confounders could not be ruled out completely in this meta-analysis, further studies are warranted to confirm these results.

Acknowledgements

The authors' contributions to the present study were as follows: Y. Z. conceived the study design; Y. Z. and C. T. searched and selected the articles, and extracted, analysed and interpreted the data; Y. Z. and C. J. drafted the manuscript. All authors read and approved the final version of the manuscript. The authors have no conflicts of interest to declare. This study was not funded.

References

1Carter, P, Gray, LJ, Troughton, J, et al. (2010) Fruit and vegetable intake and incidence of type 2 diabetes mellitus: systematic review and meta-analysis. BMJ 341, c4229.CrossRefGoogle ScholarPubMed
2Knowler, WC, Barrett-Connor, E, Fowler, SE, et al. (2002) Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346, 393403.Google ScholarPubMed
3Mostad, IL, Bjerve, KS, Bjorgaas, MR, et al. (2006) Effects of n-3 fatty acids in subjects with type 2 diabetes: reduction of insulin sensitivity and time-dependent alteration from carbohydrate to fat oxidation. Am J Clin Nutr 84, 540550.CrossRefGoogle ScholarPubMed
4Woodman, RJ, Mori, TA, Burke, V, et al. (2002) Effects of purified eicosapentaenoic and docosahexaenoic acids on glycemic control, blood pressure, and serum lipids in type 2 diabetic patients with treated hypertension. Am J Clin Nutr 76, 10071015.CrossRefGoogle ScholarPubMed
5Puhakainen, I, Ahola, I & Yki-Jarvinen, H (1995) Dietary supplementation with n-3 fatty acids increases gluconeogenesis from glycerol but not hepatic glucose production in patients with non-insulin-dependent diabetes mellitus. Am J Clin Nutr 61, 121126.CrossRefGoogle Scholar
6Kaushik, M, Mozaffarian, D, Spiegelman, D, et al. (2009) Long-chain omega-3 fatty acids, fish intake, and the risk of type 2 diabetes mellitus. Am J Clin Nutr 90, 613620.CrossRefGoogle ScholarPubMed
7Vessby, B, Karlstrom, B, Boberg, M, et al. (1992) Polyunsaturated fatty acids may impair blood glucose control in type 2 diabetic patients. Diabet Med 9, 126133.CrossRefGoogle ScholarPubMed
8Osterud, B & Elvevoll, EO (2011) Dietary omega-3 fatty acids and risk of type 2 diabetes: lack of antioxidants? Am J Clin Nutr 94, 617618.CrossRefGoogle ScholarPubMed
9Lee, DH, Lee, IK, Song, K, et al. (2006) A strong dose–response relation between serum concentrations of persistent organic pollutants and diabetes: results from the National Health and Examination Survey 1999–2002. Diabetes Care 29, 16381644.CrossRefGoogle ScholarPubMed
10Mozaffarian, D & Rimm, EB (2006) Fish intake, contaminants, and human health: evaluating the risks and the benefits. JAMA 296, 18851899.CrossRefGoogle ScholarPubMed
11Chen, YW, Huang, CF, Tsai, KS, et al. (2006) The role of phosphoinositide 3-kinase/Akt signaling in low-dose mercury-induced mouse pancreatic beta-cell dysfunction in vitro and in vivo. Diabetes 55, 16141624.CrossRefGoogle ScholarPubMed
12Nkondjock, A & Receveur, O (2003) Fish-seafood consumption, obesity, and risk of type 2 diabetes: an ecological study. Diabetes Metab 29, 635642.CrossRefGoogle Scholar
13Fedor, D & Kelley, DS (2009) Prevention of insulin resistance by n-3 polyunsaturated fatty acids. Curr Opin Clin Nutr Metab Care 12, 138146.CrossRefGoogle ScholarPubMed
14Panagiotakos, DB, Zeimbekis, A, Boutziouka, V, et al. (2007) Long-term fish intake is associated with better lipid profile, arterial blood pressure, and blood glucose levels in elderly people from Mediterranean islands (MEDIS epidemiological study). Med Sci Monit 13, CR307CR312.Google Scholar
15Ruidavets, JB, Bongard, V, Dallongeville, J, et al. (2007) High consumptions of grain, fish, dairy products and combinations of these are associated with a low prevalence of metabolic syndrome. J Epidemiol Community Health 61, 810817.CrossRefGoogle ScholarPubMed
16Harding, AH, Day, NE, Khaw, KT, et al. (2004) Habitual fish consumption and glycated haemoglobin: the EPIC-Norfolk study. Eur J Clin Nutr 58, 277284.CrossRefGoogle ScholarPubMed
17Adler, AI, Boyko, EJ, Schraer, CD, et al. (1994) Lower prevalence of impaired glucose tolerance and diabetes associated with daily seal oil or salmon consumption among Alaska Natives. Diabetes Care 17, 14981501.CrossRefGoogle ScholarPubMed
18Bjerregaard, P, Pedersen, HS & Mulvad, G (2000) The associations of a marine diet with plasma lipids, blood glucose, blood pressure and obesity among the inuit in Greenland. Eur J Clin Nutr 54, 732737.CrossRefGoogle ScholarPubMed
19Djousse, L, Gaziano, JM, Buring, JE, et al. (2011) Dietary omega-3 fatty acids and fish consumption and risk of type 2 diabetes. Am J Clin Nutr 93, 143150.CrossRefGoogle ScholarPubMed
20Schulze, MB, Manson, JE, Willett, WC, et al. (2003) Processed meat intake and incidence of type 2 diabetes in younger and middle-aged women. Diabetologia 46, 14651473.CrossRefGoogle ScholarPubMed
21He, K, Song, Y, Daviglus, ML, et al. (2004) Accumulated evidence on fish consumption and coronary heart disease mortality: a meta-analysis of cohort studies. Circulation 109, 27052711.CrossRefGoogle ScholarPubMed
22Berlin, JA, Longnecker, MP & Greenland, S (1993) Meta-analysis of epidemiologic dose–response data. Epidemiology 4, 218228.CrossRefGoogle ScholarPubMed
23van Woudenbergh, GJ, van Ballegooijen, AJ, Kuijsten, A, et al. (2009) Eating fish and risk of type 2 diabetes: a population-based, prospective follow-up study. Diabetes Care 32, 20212026.CrossRefGoogle ScholarPubMed
24Patel, PS, Sharp, SJ, Luben, RN, et al. (2009) Association between type of dietary fish and seafood intake and the risk of incident type 2 diabetes: the European Prospective Investigation of Cancer (EPIC)-Norfolk cohort study. Diabetes Care 32, 18571863.CrossRefGoogle ScholarPubMed
25Wells, G, Shea, B, O'connell, D, et al. (2000) The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa: Department of Epidemiology and Community Medicine.http://www.ohri.ca/programs/clinical_epidemiology/nos_manual.pdf.Google Scholar
26Higgins, JP & Thompson, SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21, 15391558.CrossRefGoogle ScholarPubMed
27Higgins, JP, Thompson, SG, Deeks, JJ, et al. (2003) Measuring inconsistency in meta-analyses. BMJ 327, 557560.CrossRefGoogle ScholarPubMed
28Patsopoulos, NA, Evangelou, E & Ioannidis, JP (2008) Sensitivity of between-study heterogeneity in meta-analysis: proposed metrics and empirical evaluation. Int J Epidemiol 37, 11481157.CrossRefGoogle ScholarPubMed
29Egger, M, Davey Smith, G, Schneider, M, et al. (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315, 629634.CrossRefGoogle ScholarPubMed
30Tobias, A (1999) Assessing the influence of a single study in the meta-analysis estimate. Stata Tech Bull 47, 1517.Google Scholar
31Greenland, S & Longnecker, MP (1992) Methods for trend estimation from summarized dose–response data, with applications to meta-analysis. Am J Epidemiol 135, 13011309.CrossRefGoogle ScholarPubMed
32Orsini, N, Bellocco, R & Greenland, S (2006) Generalized least squares for trend estimation of summarized dose–response data. Stata J 6, 4057.Google Scholar
33White, IR (2009) Multivariate random-effects meta-analysis. Stata J 9, 4056.Google Scholar
34Bagnardi, V, Zambon, A, Quatto, P, et al. (2004) Flexible meta-regression functions for modeling aggregate dose–response data, with an application to alcohol and mortality. Am J Epidemiol 159, 10771086.CrossRefGoogle ScholarPubMed
35van Dam, RM, Willett, WC, Rimm, EB, et al. (2002) Dietary fat and meat intake in relation to risk of type 2 diabetes in men. Diabetes Care 25, 417424.CrossRefGoogle ScholarPubMed
36Song, Y, Manson, JE, Buring, JE, et al. (2004) A prospective study of red meat consumption and type 2 diabetes in middle-aged and elderly women: the Women's Health Study. Diabetes Care 27, 21082115.CrossRefGoogle ScholarPubMed
37Meyer, KA, Kushi, LH, Jacobs, DR Jr, et al. (2001) Dietary fat and incidence of type 2 diabetes in older Iowa women. Diabetes Care 24, 15281535.CrossRefGoogle ScholarPubMed
38Krishnan, S, Coogan, PF, Boggs, DA, et al. (2010) Consumption of restaurant foods and incidence of type 2 diabetes in African American women. Am J Clin Nutr 91, 465471.CrossRefGoogle ScholarPubMed
39Brostow, DP, Odegaard, AO, Koh, WP, et al. (2011) Omega-3 fatty acids and incident type 2 diabetes: the Singapore Chinese Health Study. Am J Clin Nutr 94, 520526.CrossRefGoogle ScholarPubMed
40Djousse, L, Biggs, ML, Lemaitre, RN, et al. (2011) Plasma omega-3 fatty acids and incident diabetes in older adults. Am J Clin Nutr 94, 527533.CrossRefGoogle ScholarPubMed
41Villegas, R, Xiang, YB, Elasy, T, et al. (2011) Fish, shellfish, and long-chain n-3 fatty acid consumption and risk of incident type 2 diabetes in middle-aged Chinese men and women. Am J Clin Nutr 94, 543551.CrossRefGoogle ScholarPubMed
42Beaton, GH (1994) Approaches to analysis of dietary data: relationship between planned analyses and choice of methodology. Am J Clin Nutr 59, 253S261S.CrossRefGoogle ScholarPubMed

Altmetric attention score

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 207
Total number of PDF views: 483 *
View data table for this chart

* Views captured on Cambridge Core between September 2016 - 20th April 2021. This data will be updated every 24 hours.

You have Access

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Association of fish and n-3 fatty acid intake with the risk of type 2 diabetes: a meta-analysis of prospective studies
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Association of fish and n-3 fatty acid intake with the risk of type 2 diabetes: a meta-analysis of prospective studies
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Association of fish and n-3 fatty acid intake with the risk of type 2 diabetes: a meta-analysis of prospective studies
Available formats
×
×

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *