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Original Investigation |

Fiber and Magnesium Intake and Incidence of Type 2 Diabetes:  A Prospective Study and Meta-analysis FREE

Matthias B. Schulze, DrPH; Mandy Schulz, DrPH; Christin Heidemann, DrPH; Anja Schienkiewitz, MPH; Kurt Hoffmann, PhD; Heiner Boeing, PhD
[+] Author Affiliations

Author Affiliations: Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal.


Arch Intern Med. 2007;167(9):956-965. doi:10.1001/archinte.167.9.956.
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Published online

Background  Prospective studies on fiber and magnesium intake and risk of type 2 diabetes mellitus were inconsistent. We examined associations between fiber and magnesium intake and risk of type 2 diabetes and summarized existing prospective studies by meta-analysis.

Methods  We conducted a prospective cohort study of 9702 men and 15 365 women aged 35 to 65 years who were observed for incident diabetes from 1994 to 2005. Dietary intake of fiber and magnesium were measured with a validated food-frequency questionnaire. We estimated the relative risk (RR) by means of Cox proportional hazards analysis. We searched PubMed through May 2006 for prospective cohort studies of fiber and magnesium intake and risk of type 2 diabetes. We identified 9 cohort studies of fiber and 8 studies of magnesium intake and calculated summary RRs by means of a random-effects model.

Results  During 176 117 person-years of follow-up, we observed 844 incident cases of type 2 diabetes in the European Prospective Investigation Into Cancer and Nutrition–Potsdam. Higher cereal fiber intake was inversely associated with diabetes risk (RR for extreme quintiles, 0.72 [95% confidence interval [CI], 0.56-0.93]), while fruit fiber (0.89 [95% CI, 0.70-1.13]) and vegetable fiber (0.93 [95% CI, 0.74-1.17]) were not significantly associated. Meta-analyses showed a reduced diabetes risk with higher cereal fiber intake (RR for extreme categories, 0.67 [95% CI, 0.62-0.72]), but no significant associations for fruit (0.96 [95% CI, 0.88-1.04]) and vegetable fiber (1.04 [95% CI, 0.94-1.15]). Magnesium intake was not related to diabetes risk in the European Prospective Investigation Into Cancer and Nutrition–Potsdam (RR for extreme quintiles, 0.99 [95% CI, 0.78-1.26]); however, meta-analysis showed a significant inverse association (RR for extreme categories, 0.77 [95% CI, 0.72-0.84]).

Conclusion  Higher cereal fiber and magnesium intakes may decrease diabetes risk.

Figures in this Article

The predicted increase in diabetes mellitus prevalence from 171 million individuals diagnosed as having diabetes worldwide in 2000 to 370 million by the year 20301 and the alarming projections in terms of associated morbidities and mortality,2 as well as health care costs,3 emphasize the need for preventive action. Current guidelines for the prevention of type 2 diabetes by the American Diabetes Association4 and the European Association for the Study of Diabetes5 include goals for total dietary fiber intake. It has been suggested that the benefits of increased fiber intake result principally from the greater consumption of soluble forms6 due to effects on gastric emptying, macronutrient absorption, and reduced postprandial glucose responses.79 Insoluble fiber may reduce diabetes risk by the production of short-chain fatty acids in the colon and their effect on hepatic insulin sensitivity.10,11 However, although prospective studies have observed reduced diabetes risk with high cereal fiber and whole grain consumption,1220 these findings have not been confirmed by all studies,16,21 and beneficial effects of fruit and vegetable fiber remain unclear so far. In addition, while magnesium deficiency is plausibly linked to diabetes,22 the absence of clinical trials and the inconsistency among prospective studies regarding the role of magnesium in diabetes prevention14,21,2325 preclude definitive recommendations at present.5

The aims of this study were to evaluate the association between total, cereal, fruit, and vegetable fiber, as well as soluble and insoluble fiber and magnesium intake, and risk of type 2 diabetes in a large prospective cohort study of men and women and to summarize the existing evidence from prospective studies by meta-analysis.

STUDY POPULATIONS

The European Prospective Investigation Into Cancer and Nutrition (EPIC)–Potsdam study is part of the multicenter prospective cohort study EPIC.26,27 In Potsdam, Germany, 27 548 subjects, 16 644 women aged mainly 35 to 65 years and 10 904 men aged mainly 40 to 65 years, from the general population were recruited between 1994 and 1998.28 The baseline examination included anthropometric measurements and a self-administered validated food-frequency questionnaire, as well as a personal interview including questions on prevalent diseases and a questionnaire on sociodemographic and lifestyle characteristics. Follow-up questionnaires have been administered every 2 to 3 years to identify incident cases of diabetes mellitus. Response rates for follow-up rounds 1, 2, and 3 exceed 90%. We also considered questionnaires within the ongoing fourth follow-up round sent out until January 31, 2005, of which 96.2% were returned by August 31, 2005.

The prevalence of diabetes mellitus at baseline was evaluated by a physician using information on self-reported medical diagnoses, medication records, and dieting behavior. Uncertainties regarding a proper diagnosis were clarified with the participant or treating physician. After exclusion of participants with prevalent diabetes at baseline or with self-reported diabetes during follow-up but without physician confirmation (n = 1567), with missing follow-up time (n = 589), with missing diet and confounder information at baseline (n = 226), or with implausible energy intake (<800 or >6000 kcal/d; n = 99), 9702 men and 15 365 women remained for analyses.

Informed consent was obtained from all participants of the study, and approval was given by the Ethical Committee of the State of Brandenburg, Germany.

DIETARY ASSESSMENT

All participants were asked to complete a semiquantitative food-frequency questionnaire. This questionnaire assessed the average frequency of intake and the portion size of 148 foods consumed during the 12 months before examination. Frequency of intake was measured using 10 categories, ranging from “never” to “5 times per day or more.” Portion sizes were estimated with the use of photographs of standard portion sizes. Information on frequency of intake and portion size was used to calculate the amount of each food in grams consumed on average per day. Intakes of fiber and magnesium were estimated on the basis of the German Food Code and Nutrient Data Base,29 version II.3, and adjusted for total energy intake by the residual method.30 The validity and reproducibility of the food-frequency questionnaire have been described previously.3133 Briefly, the corrected correlation coefficients between food-frequency questionnaire and twelve 24-h dietary recalls for energy-adjusted intake of total fiber was 0.66.31

ASCERTAINMENT OF TYPE 2 DIABETES

Potentially incident cases of diabetes were identified via self-reports of a diabetes diagnosis, diabetes-relevant medication, or dietary treatment owing to diabetes. All potentially incident cases were verified by questionnaires mailed to the diagnosing physician asking about the date and type of diagnosis, diagnostic tests, and treatment of diabetes. Only cases with a physician diagnosis of type 2 diabetes mellitus (International Statistical Classification of Diseases, 10th Revision, E11) and a diagnosis date after the baseline examination were considered as confirmed incident cases of type 2 diabetes.

ASSESSMENT OF LIFESTYLE EXPOSURES

Information on educational attainment, smoking, occupational activity level, and leisure-time physical activity were assessed with a self-administered questionnaire and a personal interview. We considered sports activities and biking as leisure-time activities, both calculated as the average time spent per week during the 12 months before baseline recruitment. Anthropometric measurement procedures followed standard protocols under strict quality control.34,35

STATISTICAL ANALYSES

We estimated the relative risk (RR) for each quintile of energy-adjusted fiber and magnesium intake compared with the lowest quintile by means of Cox proportional hazards analysis stratified by age. Age was used as the primary time-dependent variable in all models, with entry time defined as the subject's age at recruitment and exit time as the date of diagnosis of diabetes (International Statistical Classification of Diseases, 10th Revision, E10, E11, E13, and E14), death, or return of the last follow-up questionnaire. We used information on covariates obtained from the baseline examination in multivariate analyses, including sex, body mass index, waist circumference, educational achievement (no vocational training or in training, vocational training, technical school, technical college, or university degree), occupational activity (light, moderate, or heavy), sports activity (0, 0.1-4.0, or >4.0 h/wk), cycling (0, 0.1-2.4, 2.5-4.9, or ≥5 h/wk), smoking (never, past, current <20 cigarettes per day, or current ≥20 cigarettes per day), total energy intake, alcohol intake, carbohydrate intake, and the ratios of polyunsaturated and monounsaturated fatty acids to saturated fatty acids in the diet. The significance of linear trends across quintiles of fiber and magnesium intake was tested by assigning each participant the median value for the quintile and modeling this value as a continuous variable.36 Because risk estimates were similar for men and women, we pooled both sexes in our analyses. These statistical analyses were performed with SAS statistical software, release 9.1 (SAS Institute Inc, Cary, NC).

META-ANALYSES

We conducted separate meta-analyses for different fiber sources and magnesium by means of Review Manager, version 4.2 (Copenhagen, Denmark, Nordic Cochrane Centre, The Cochrane Collaboration, 2003). Studies were identified by searching PubMed through May 2006 for the terms magnesium, fiber or fibre, and diabetes and by reviewing references of published studies and reviews. Studies were included if they were prospective cohort studies on type 2 diabetes. For studies reporting RRs for continuous intake, estimates for categories were obtained from study authors. For all meta-analyses, data were extracted by 2 investigators from multivariate-adjusted models. Summary RRs were random effects estimates, which allow each of the studies in the meta-analysis to estimate a different effect size.

Baseline characteristics by quintiles of total fiber intake are shown in Table 1. Participants with higher fiber intake were more likely to be women, engaged more frequently in sports activities and cycling, and were less likely to smoke. With regard to dietary characteristics, higher fiber consumption was related to higher intake of carbohydrates and magnesium and lower alcohol intake and a more favorable fatty acid profile.

Table Graphic Jump LocationTable 1. Baseline Characteristics by Quintiles of Carbohydrate and Fiber Intake, EPIC-Potsdam Study*

During 176 117 person-years of follow-up (mean, 7.0 years), we observed 844 incident cases of type 2 diabetes mellitus (491 men and 353 women). In all, 31.2% of cases were treated by diet only, 24.4% by oral agents only, 35.8% by diet and oral agents, and the remaining 8.6% by insulin alone or in combination with diet and/or oral agents. After adjustment for age, sex, education, activity, anthropometric measures, and other individual characteristics, neither magnesium nor total fiber intake was associated with risk of developing type 2 diabetes (Table 2). Intake of soluble fiber was inversely associated with diabetes risk in a multivariate model (RR for extreme quintiles, 0.79; 95% confidence interval [CI], 0.63-0.98); however, this association was no longer significant after adjustment for dietary confounders and attenuated with additional adjustment for insoluble fiber intake (RR, 0.83; 95% CI, 0.57-1.22). Insoluble fiber intake was not significantly associated with diabetes risk after adjustment for confounders.

Table Graphic Jump LocationTable 2. Relative Risk of Type 2 Diabetes Mellitus by Quintiles of Magnesium and Total, Soluble, and Insoluble Fiber Intake

We further evaluated different sources of dietary fiber. Cereal fiber was correlated with consumption of whole-grain bread (r = 0.71) and muesli (r = 0.34) in EPIC-Potsdam. In addition, cereal fiber was highly correlated with intake of soluble (r = 0.82) and insoluble (r = 0.72) fiber, while these associations were moderate for fruit fiber (r = 0.43 and 0.55, respectively) and vegetable fiber (r = 0.33 and 0.47, respectively). Cereal fiber intake was inversely associated with diabetes risk (Table 3). The RR for extreme quintiles was 0.73 (95% CI, 0.57-0.94) after adjustment for lifestyle and dietary confounders. This association remained similar after adjustment for other fiber sources. In contrast, neither fruit nor vegetable fiber was significantly related to diabetes risk. Similar to the finding for cereal fiber, higher whole-grain bread intake was inversely associated with diabetes risk. The RR for extreme quintiles (≥80.2 vs <4.4 g/d) was 0.78 (95% CI, 0.62-0.97), after adjustment for age, sex, lifestyle confounders, anthropometry, and dietary fat quality. This association remained essentially unchanged with additional adjustment for the intake of carbohydrates and magnesium (RR, 0.78; 95% CI, 0.61-0.99).

Table Graphic Jump LocationTable 3. Relative Risk of Type 2 Diabetes Mellitus by Quintiles of Cereal, Fruit, and Vegetable Fiber Intake

We conducted separate meta-analyses for different fiber sources and magnesium. In addition to our study, we were able to identify 8 previous publications on cereal, vegetable, and/or fruit fiber intake1218,21 and 5 on magnesium intake.14,21,2325 The same study population was investigated with regard to cereal fiber consumption in 2 publications,12,15 and we used data from the most recent publication only, with longer follow-up and a larger number of cases.15 Publications from the Atherosclerosis Risk in Communities Study16,23 reported separate associations for black and white participants, which we treated as separate cohorts. One publication involved 2 different cohorts.24 This resulted in a total of 9 cohort studies on fiber intake and 8 on magnesium intake (Table 4). Three publications did not report RRs for categories of fiber intake,15,16,21 but these data were provided on request. Most studies reported RRs for quintiles of intake; only 1 study for cereal fiber,17 which accounted for 1.5% of cases, and 2 studies for magnesium,23 accounting for 11.3% of cases, reported RRs for quartiles. The Figure shows the results of the cohort studies for the highest category of fiber and magnesium intake compared with the lowest category. The RR of type 2 diabetes for all cohort studies combined was 0.67 (95% CI, 0.62-0.72) for cereal fiber, 0.96 (95% CI, 0.88-1.04) for fruit fiber, 1.04 (95% CI, 0.94-1.15) for vegetable fiber, and 0.77 (95% CI, 0.72-0.84) for magnesium. The P values for heterogeneity in results were .04, .50, .59, and .04, respectively. A test for heterogeneity in the meta-analysis on magnesium intake was nonsignificant after exclusion of the 2 Atherosclerosis Risk in Communities study cohorts23 for which RRs were reported comparing quartiles (combined RR after exclusion, 0.76; 95% CI, 0.70-0.82; P for heterogeneity = .08). A test for heterogeneity was borderline significant for cereal fiber after exclusion of the study by Montonen et al,17 for which RRs were reported comparing quartiles (combined RR after exclusion, 0.68; 95% CI, 0.62-0.73; P for heterogeneity = .05). Exclusion of the study contributing the largest number of cases to the meta-analysis on cereal fiber15 did not appreciably change the result (combined RR after exclusion, 0.72; 95% CI, 0.65-0.79); however, a test for heterogeneity was no longer significant (P = .14).

Table Graphic Jump LocationTable 4. Cohort Studies of Fiber and Magnesium Intake and Risk for Type 2 Diabetes Mellitus
Place holder to copy figure label and caption
Figure.

Relative risks (RRs) for the association between cereal fiber (A), fruit fiber (B), vegetable fiber (C), and magnesium (D) consumption and risk of type 2 diabetes mellitus for individual cohort studies and all cohort studies combined. CI indicates confidence interval; NA, not available. In a 1997 publication (Salmerón et al12) from the cohort studied by Hu et al,15 the median intake of energy-adjusted cereal fiber intake was 7.5 g/d in the highest and 2.0 g/d in the lowest quintile.

Graphic Jump Location

We found that higher cereal fiber intake was associated with a reduced risk of type 2 diabetes mellitus independent of age, sex, and lifestyle risk factors. Intake of fruit or vegetable fiber and magnesium were not significantly associated with diabetes risk. Meta-analyses of cohort studies showed an inverse association for cereal fiber and magnesium, but no beneficial effect of other fiber sources.

It has been suggested that the benefits of increased fiber intake result principally from the greater consumption of soluble forms.6 Consumption of soluble fiber has effects on gastric emptying and macronutrient absorption and reduces postprandial glucose responses.79 This contradicts the observation from previous prospective studies that insoluble fiber, but not soluble fiber, is inversely related to diabetes risk.14,17 In our study, there was no major difference between both fiber forms in terms of RRs for quintiles, although the intake of soluble fiber was substantially lower than that of insoluble fiber, similar to earlier studies.14,17 A possible explanation is therefore that the quantity of soluble fiber consumed in an average diet is insufficient to have a distinguishable effect on glycemic control.37 The mechanisms by which insoluble fiber may lead to a reduced diabetes risk are largely unknown.37,38 A potential mechanism relates to the production of short-chain fatty acids in the colon and their effect on hepatic insulin sensitivity.10,11 Eating insoluble fiber–enriched white bread for 3 days significantly improved whole-body insulin sensitivity in overweight and obese women compared with regular white bread.39 However, studies on wheat bran–rich insoluble fiber among patients with type 2 diabetes or impaired glucose tolerance are conflicting.4042

Our meta-analysis showed strong evidence that high cereal fiber intake reduces the risk of developing type 2 diabetes. Whole-grain and bran products from wheat and corn are the major source of cereal fiber in US cohorts43 and typically contain insoluble fiber. In our cohort, as well as in a Finnish cohort,17 rye bread plays an important role in cereal fiber consumption with a higher proportion of soluble fiber. Rye bread has also been shown to result in a lower postprandial insulin response compared with wheat bread independent of its fiber content.44 Cereal fiber was highly correlated with whole-grain bread intake in EPIC-Potsdam, and it is likely that cereal fiber is a marker for whole-grain foods, which have also been shown to relate to a lower diabetes risk in prospective studies.14,17,19,20 Components in whole grains that may be protective in addition to fiber include resistant starch, oligosaccharides, trace minerals like magnesium, phenolic compounds, and phytoestrogens.45,46 Although this might explain in part the beneficial effect, cereal fiber was associated with diabetes risk independent of magnesium in EPIC-Potsdam and a previous cohort.18 Hypomagnesemia has been associated with a reduction of tyrosine kinase activity at the insulin receptor level, which may result in the impairment of insulin action and development of insulin resistance22 and is a common feature of patients with type 2 diabetes.47 Our meta-analysis showed a significant inverse association between magnesium intake and risk of type 2 diabetes. Still, magnesium intake was not related to diabetes risk in EPIC-Potsdam, and there was suggestion of heterogeneity across studies.

All potential cases in our study were verified through the treating physician. However, we considered only clinically apparent type 2 diabetes and did not screen our study population for diabetes at baseline; thus, it is possible that prevalent but undiagnosed cases of diabetes remained in our analyses. The glycemic index, a measure of carbohydrate quality in terms of quantified glucose response to foods, is not available in the German Food Code and Nutrient Data Base version II.3, and we were not able to address whether the association between fiber and magnesium intake and diabetes risk is independent or may be modified by the glycemic index. The potential of residual confounding applies similarly to our study and the cohort studies included in our meta-analysis. Thus, it is possible that other factors may explain the inverse associations between cereal fiber and magnesium intake and diabetes risk or that they may mask a potential association with fruit or vegetable fiber. It is possible that participants in our study changed their dietary intake after the baseline measurement. The lack of repeated measurement of diet may have led to an underestimation of association.48

With the exception of the study by Montonen et al,17 all studies included in our meta-analyses estimated dietary intake on the basis of semiquantitative food-frequency questionnaires. Food-frequency questionnaires work well in ranking individuals with regard to their intake, but they are not suitable tools for quantifying dietary intake in absolute terms.49 In addition, the intake of fiber and magnesium has been energy-adjusted by the residuals method30 in our study and several other cohort studies1216,18,25 which results in an underestimation of variance of intake. This, in addition to differences in questionnaire design, limits the comparability of questionnaire data across studies and their interpretation in quantitative terms. This also hampers the application of meta-analysis techniques more suitable for studies that report separate RRs for several risk classes.5052

Healthier eating was encouraged in all previous randomized prevention studies,5356 but the frequent consumption of whole-grain foods was specifically mentioned only in the Finnish diabetes prevention study.54 Owing to changes in several components of the diet, activity patterns, and a targeted weight reduction, it is not possible to determine to what extent the increased intake of whole-grain foods might have contributed to the reduced risk of diabetes observed in these studies. For example, in the Finnish diabetes prevention study, about twice the number in the intervention group (25%) compared with the control group (12%) met the goal of fiber intake (≥15 g/1000 kcal), although the proportion was considerably lower compared with weight reduction (43% vs 13%), fat intake (47% vs 26%), and exercise (86% vs 71%).54 Increased fiber intake was not significantly associated with diabetes risk in this study independent of reductions in total fat intake.57 At present, scientific evidence therefore relies largely on prospective observational studies. This evidence should be supported by studies clarifying the mechanisms by which cereal fiber may be linked to reduced diabetes risk.

In conclusion, the evidence from our study and previous studies, summarized by means of meta-analysis, strongly supports that higher cereal fiber and magnesium intake may decrease diabetes risk. Whole-grain foods are therefore important in diabetes prevention.

Correspondence: Matthias B. Schulze, DrPH, Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany (mschulze@dife.de).

Accepted for Publication: December 20, 2006.

Author Contributions: Dr Schulze had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Schulze. Acquisition of data: Schulze, Schulz, and Boeing. Analysis and interpretation of data: Schulze, Schulz, Heidemann, Schienkiewitz, Hoffmann, and Boeing. Drafting of the manuscript: Schulze. Critical revision of the manuscript for important intellectual content: Schulz, Heidemann, Schienkiewitz, Hoffmann, and Boeing. Statistical analysis: Schulze, Schulz, Heidemann, Schienkiewitz, and Hoffmann. Obtained funding: Boeing. Study supervision: Boeing.

Financial Disclosure: None reported.

Funding/Support: The recruitment phase of the EPIC-Potsdam Study was supported by grant 01 EA 9401 from the Federal Ministry of Science, Germany, and grant SOC 95201408 05F02 from the European Union. The follow-up of the EPIC-Potsdam Study was supported by grant 70-2488-Ha I from German Cancer Aid and grant SOC 98200769 05F02 from the European Community. Dr Schulze is supported by grant FP6-2005-513946 from the European Union.

Role of the Sponsor: The funding organizations had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; and in the preparation, review, or approval of the manuscript.

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Cohen  MLeong  VWSalmon  EMartin  FI Role of guar and dietary fibre in the management of diabetes mellitus. Med J Aust 1980;159- 61
PubMed
Jenkins  DJKendall  CWAugustin  LS  et al.  Effect of wheat bran on glycemic control and risk factors for cardiovascular disease in type 2 diabetes. Diabetes Care 2002;251522- 1528
PubMed
Liu  S Intake of refined carbohydrates and whole grain foods in relation to risk of type 2 diabetes mellitus and coronary heart disease. J Am Coll Nutr 2002;21298- 306
PubMed
Juntunen  KSLaaksonen  DEAutio  K  et al.  Structural differences between rye and wheat breads but not total fiber content may explain the lower postprandial insulin response to rye bread. Am J Clin Nutr 2003;78957- 964
PubMed
Slavin  JLMartini  MCJacobs  DR  JrMarquart  L Plausible mechanisms for the protectiveness of whole grains. Am J Clin Nutr 1999;70 ((suppl)) 459S- 463S
PubMed
Slavin  JL Whole grains and human health. Nutr Res Rev 2004;1799- 110doi:10-1079/NRR200374
Tosiello  L Hypomagnesemia and diabetes mellitus: a review of clinical implications. Arch Intern Med 1996;1561143- 1148
PubMed
Hu  FBStampfer  MJRimm  E  et al.  Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements. Am J Epidemiol 1999;149531- 540
PubMed
Willett  WLenart  E Food frequency methods. Willett  WNutritional Epidemiology. New York, NY Oxford University Press1998;74- 100
Greenland  SLongnecker  MP Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. Am J Epidemiol 1992;1351301- 1309
PubMed
Shi  JQCopas  JB Meta-analysis for trend estimation. Stat Med 2004;233- 19
PubMed
Hartemink  NBoshuizen  HCNagelkerke  NJJacobs  MAvan Houwelingen  HC Combining risk estimates from observational studies with different exposure cutpoints: a meta-analysis on body mass index and diabetes type 2. Am J Epidemiol 2006;1631042- 1052
PubMed
Pan  XRLi  GWHu  YH  et al.  Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: the Da Qing IGT and Diabetes Study. Diabetes Care 1997;20537- 544
PubMed
Tuomilehto  JLindstrom  JEriksson  JG  et al. Finnish Diabetes Prevention Study Group, Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001;3441343- 1350
PubMed
Knowler  WCBarrett-Connor  EFowler  SE  et al. Diabetes Prevention Program Research Group, Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346393- 403
PubMed
Ramachandran  ASnehalatha  CMary  SMukesh  BBhaskar  ADVijay  V The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia 2006;49289- 297
PubMed
Lindström  JPeltonen  MEriksson  JG  et al.  High-fibre, low-fat diet predicts long-term weight loss and decreased type 2 diabetes risk: the Finnish Diabetes Prevention Study. Diabetologia 2006;49912- 920
PubMed

Figures

Place holder to copy figure label and caption
Figure.

Relative risks (RRs) for the association between cereal fiber (A), fruit fiber (B), vegetable fiber (C), and magnesium (D) consumption and risk of type 2 diabetes mellitus for individual cohort studies and all cohort studies combined. CI indicates confidence interval; NA, not available. In a 1997 publication (Salmerón et al12) from the cohort studied by Hu et al,15 the median intake of energy-adjusted cereal fiber intake was 7.5 g/d in the highest and 2.0 g/d in the lowest quintile.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Baseline Characteristics by Quintiles of Carbohydrate and Fiber Intake, EPIC-Potsdam Study*
Table Graphic Jump LocationTable 2. Relative Risk of Type 2 Diabetes Mellitus by Quintiles of Magnesium and Total, Soluble, and Insoluble Fiber Intake
Table Graphic Jump LocationTable 3. Relative Risk of Type 2 Diabetes Mellitus by Quintiles of Cereal, Fruit, and Vegetable Fiber Intake
Table Graphic Jump LocationTable 4. Cohort Studies of Fiber and Magnesium Intake and Risk for Type 2 Diabetes Mellitus

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PubMed
Cohen  MLeong  VWSalmon  EMartin  FI Role of guar and dietary fibre in the management of diabetes mellitus. Med J Aust 1980;159- 61
PubMed
Jenkins  DJKendall  CWAugustin  LS  et al.  Effect of wheat bran on glycemic control and risk factors for cardiovascular disease in type 2 diabetes. Diabetes Care 2002;251522- 1528
PubMed
Liu  S Intake of refined carbohydrates and whole grain foods in relation to risk of type 2 diabetes mellitus and coronary heart disease. J Am Coll Nutr 2002;21298- 306
PubMed
Juntunen  KSLaaksonen  DEAutio  K  et al.  Structural differences between rye and wheat breads but not total fiber content may explain the lower postprandial insulin response to rye bread. Am J Clin Nutr 2003;78957- 964
PubMed
Slavin  JLMartini  MCJacobs  DR  JrMarquart  L Plausible mechanisms for the protectiveness of whole grains. Am J Clin Nutr 1999;70 ((suppl)) 459S- 463S
PubMed
Slavin  JL Whole grains and human health. Nutr Res Rev 2004;1799- 110doi:10-1079/NRR200374
Tosiello  L Hypomagnesemia and diabetes mellitus: a review of clinical implications. Arch Intern Med 1996;1561143- 1148
PubMed
Hu  FBStampfer  MJRimm  E  et al.  Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements. Am J Epidemiol 1999;149531- 540
PubMed
Willett  WLenart  E Food frequency methods. Willett  WNutritional Epidemiology. New York, NY Oxford University Press1998;74- 100
Greenland  SLongnecker  MP Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. Am J Epidemiol 1992;1351301- 1309
PubMed
Shi  JQCopas  JB Meta-analysis for trend estimation. Stat Med 2004;233- 19
PubMed
Hartemink  NBoshuizen  HCNagelkerke  NJJacobs  MAvan Houwelingen  HC Combining risk estimates from observational studies with different exposure cutpoints: a meta-analysis on body mass index and diabetes type 2. Am J Epidemiol 2006;1631042- 1052
PubMed
Pan  XRLi  GWHu  YH  et al.  Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: the Da Qing IGT and Diabetes Study. Diabetes Care 1997;20537- 544
PubMed
Tuomilehto  JLindstrom  JEriksson  JG  et al. Finnish Diabetes Prevention Study Group, Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001;3441343- 1350
PubMed
Knowler  WCBarrett-Connor  EFowler  SE  et al. Diabetes Prevention Program Research Group, Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346393- 403
PubMed
Ramachandran  ASnehalatha  CMary  SMukesh  BBhaskar  ADVijay  V The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia 2006;49289- 297
PubMed
Lindström  JPeltonen  MEriksson  JG  et al.  High-fibre, low-fat diet predicts long-term weight loss and decreased type 2 diabetes risk: the Finnish Diabetes Prevention Study. Diabetologia 2006;49912- 920
PubMed

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