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Journal about Dietary Fiber and Metabolic Syndrome: A Meta-Analysis and Review of Related Mechanisms
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  nutrients Review Dietary Fiber and Metabolic Syndrome:A Meta-Analysis and Review of Related Mechanisms  Jia-Ping Chen  ID  , Guo-Chong Chen, Xiao-Ping Wang, Liqiang Qin * and Yanjie Bai *    ID Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, 199 Ren’ai Road,Dushu Lake Higher Education District, Suzhou 215123, China; primajiaping@126.com (J.-P.C.);lsguorong@126.com (G.-C.C.); wxplm@suda.edu.cn (X.-P.W.) *  Correspondence: qinliqiang@suda.edu.cn (L.Q.); baiyanjie@suda.edu.cn (Y.B.);Tel.: +86-1391-5543-146 (L.Q.); +86-1377-1782-625 (Y.B.)Received: 13 November 2017; Accepted: 21 December 2017; Published: 26 December 2017 Abstract:  (1) Background: Dietary fiber intake may provide beneficial effects on the components of metabolic syndrome (MetS); however, observational studies reported inconsistent results for the relationship between dietary fiber intake and MetS risk. We conducted a meta-analysis to quantifyprevious observational studies and a narrative review to summarize mechanisms involved in thepotential relationship. (2) Methods: The literature was searched on PubMed and Web of Science until 28 November 2017. A random-effects model was used to calculate the summary risk estimates. Eleven cross-sectional studies and three cohort studies were included in the meta-analysis. Results from the srcinal studies were reported as odds ratios (ORs) or relative ratios (RRs) of the MetSassociated with different levels of dietary fiber intake, and the ORs/RRs comparing the highestwith lowest categories of the intake were pooled. (3) Results: For the cross-sectional studies, the pooled OR was  0.70 (95% confidence  interval (CI): 0.61–0.82) with evidence of high heterogeneity ( I  2 = 74.4%,  p  < 0.001)  and publication bias (  p  for Egger’s test < 0.001). After removing four studies,results remained significant (OR = 0.67, 95% CI: 0.58–0.78) and the heterogeneity was largelyreduced ( I  2 = 32.4%,  p  = 0.181). For the cohort studies, the pooled RR was 0.86  (95% CI: 0.70–1.06) .(4) Conclusion: Although the meta-analysis suggests an inverse association between dietary fiberintake and risk of MetS, and the association was supported by a wide range of mechanism studies,the findings are limited by insufficient cohort data. More prospective studies are needed to further verify the association between dietary fiber intake and the risk of MetS. Keywords:  dietary fiber; metabolic syndrome; meta-analysis; mechanisms 1. Introduction Metabolic syndrome (MetS) is a cluster of symptoms that increases the risks for various chronicdisease including cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) [ 1 , 2 ]. The main features of MetS include abdominal obesity, high blood pressure, hyperglycemia/insulin resistance,and dyslipidemia [ 3 , 4 ]. The most commonly used criteria for diagnosis of MetS are the NationalCholesterol Education Program Adult Treatment Panel III (NCEP ATP-III) and the InternationalDiabetes Federation (IDF) [ 5 , 6 ], both of which include fasting plasma glucose, blood pressure, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and body fat (waist circumference). MetS has become a global public health issue. Its prevalence has been estimated to vary between 20–27% in adults from developing countries [ 7 – 10 ], and even higher in developed nations [ 11 – 13 ]. According to the National Health and Nutrition Examination Survey in the U.S., the overall prevalence of MetS increased from 32.9% in 2003–2004 to 34.7% in 2011–2012 [ 11 ]. These estimates illustrate theneed to control and prevent MetS. Dietary and lifestyle modifications are among the most promising Nutrients  2018 ,  10 , 24; doi:10.3390/nu10010024 www.mdpi.com/journal/nutrients  Nutrients  2018 ,  10 , 24 2 of 17 andeconomicallyefficientapproachesinreducingawiderangeofnon-communicablechronicdiseases, including MetS [14]. Dietary fibers, as defined by the American Association of Cereal Chemists International, are the “edible parts of plants or analogous carbohydrates that are resistant to digestion and absorption in the humansmallintestinewithcompleteorpartialfermentationinthelargeintestine”[ 15 ]. Increasingtotaldietary fiber has been shown to reduce body fat [ 16 ], improve glycemic response [ 17 ], as well as reduce bloodpressure[ 18 ],TGandlow-densitylipoproteincholesterol(LDL-C)[ 19 , 20 ]. However,thereported relationship between dietary fiber intake and MetS risk has not been consistent [ 21 ]. Therefore, a meta-analysis was performed to summarize published observational studies on the relationship of  dietary fiber intake and the risk of MetS. We also reviewed multiple potential mechanisms involved in this possible relation. 2. Methods 2.1. Literature Search The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed in the report of this meta-analysis [ 22 ]. Literature search was first conducted onthe PubMed and Web of Science databases on 13 January 2017. We conducted the comprehensiveliterature search on 28 November 2017 by using the following search terms as free texts: (fiber OR fibre OR food component) and (metabolic syndrome OR insulin resistance syndrome) and (cohort OR prospective OR follow-up OR incidence OR cross-sectional OR case-control). Besides, the MeSH terms of “dietary fiber”, “food”, “metabolic syndrome”, “insulin resistance”, “cohort studies”, “longitudinalstudies”, “incidence”, “case-control studies”, “cross-sectional studies” were also searched in PubMed. ResultsfromWebofSciencewererefinedbyexcludingthosereportsthatpublishedaseditorials,letters, meetings, books or reviews. Details of the search strategies are provided in Appendix A (Table A1). Additional references from retrieved full publications were also carefully reviewed. 2.2. Study Selection Studies were selected based on the following criteria: (1) The study design was cross-sectional, case-control, or cohort; (2) the exposure of interest was dietary fiber intake; (3) the outcome of interest was MetS; (4) age of the participants was  ≥ 18 years; and (5) odds ratio (OR) or relative risk (RR) with corresponding 95% confidence interval (CI) for the highest versus the lowest dietary fiber intake related to MetS were available. Studies in which all participants were adolescents or patients withcancer were excluded. When one population was reported in several publications, the publications with smaller sample size were excluded to avoid data duplication. 2.3. Data Extraction The following data were extracted from each study: Name of the first author, publication year,study location, length of follow-up (for cohort studies), type of fiber, sex and age of participants,number of participants, comparison of dietary fiber intake, methods of MetS diagnosis and dietary assessment, variables adjusted for in the analysis, and the OR/RR of MetS and corresponding 95% CIs for each category of dietary fiber intake. The most fully adjusted OR/RR was chosen when several estimates for the same exposure were reported with different levels of adjustments. 2.4. Quality Assessment The methodological quality of the included cross-sectional studies was assessed using an 11-item checklistthatwasrecommendedbyAgencyforHealthcareResearchandQuality(AHRQ).Articlequality wasassessedasfollows: Lowquality=0–3;moderatequality=4–7;and highquality=8–11[23] . ThequalityoftheincludedcohortstudieswasassessedusingtheNewcastle-OttawaScale(NOS),whichwasrecognized  Nutrients  2018 ,  10 , 24 3 of 17 as a good study quality assessment tool for cohort studies. Rating criteria for the NOS were as follows: Lowquality=0–5;mediumquality=6–7;andhighquality=8–9[24]. 2.5. Statistical Analysis The statistical analysis was performed with Stata/MP version 14.1 (StataCorp, College Station,TX, USA). A random-effects model was applied to combine risk estimates of MetS for the highestcompared with the lowest category of fiber intake. Heterogeneity test was performed using the  I  2 and Q statistics. The  I  2 statistic describes the percentage of variation across studies that is due to heterogeneity rather than chance [ 25 ].  I  2 < 25.0% was considered as little or no heterogeneity, 25–50% was considered as moderate heterogeneity, and >50% suggested high heterogeneity, respectively. For the Q statistic,  p  < 0.1  was considered statistically significant [ 26 ]. Franco et al. separately reported results for soluble and insoluble fiber [ 27 ]. The results were combined with the inverse variance weight, and the pooled OR was used in the meta-analysis. Cabello-Saavedra et al. [ 28 ] reported results  based on both NCEP ATP-III and IDF for MetS diagnostic criteria. For this study, we analyzedthe results based on the ATP-III in the primary meta-analysis and used IDF-based estimate in a sensitivity analysis. Both OR values from ATP-III and IDF were used in subgroup analysis stratified  by MetS assessment. OR values from the study of Fujii et al. [ 29 ] and de Oliveira et al. [ 30 ] were notincluded in the subgroup analysis because the study used neither ATP-III nor IDF criteria. Resultsfor men and women in the study of   Kouki et al. [31]  were treated as two samples. In the studies of  Fujii et al. [29] and Kouki et al. [31] ,ORsand95%CIsofMetSwerereportedforcontinuousfiberintake (e.g., for 1 g/1000 kcal  increase). We converted the estimates corresponding to the reported mean fiber intake in the studies, and used the converted estimates in the high vs. low analysis so that individual studies were assigned with statistically reasonable weight. Subgroup and sensitivity analyses wereconducted to investigate potential sources of heterogeneity. Publication bias was investigated by funnel plots and Egger’s test [32]. 3. Results 3.1. Study Characteristics The process of study selection is shown in Figure 1. Fifty-four publications were identified for the full-text review. Twenty-three reports were excluded due to the absence of reported RR or OR valuesand eleven were excluded because study outcome was not reported on MetS. We further excluded twocohort studies and one cross-sectional study because the participants overlapped with the participantsin other studies with larger sample sizes [ 33 – 35 ]. Other publications were excluded because fiber was analyzedasacontinuousvariable[ 36 ],allparticipantswereadolescent[ 37 ]orcolorectalcancerpatientswho werelikelytohavecompletelychangedtheirdietsduetocancerdiagnosis[ 38 ]. Finally,fourteenstudieswere included in the meta-analysis, eleven of which were cross-sectional studies and three were cohort studies. Thequalityscoresoftheselectedcross-sectionalandcohortstudiesarepresentedinTables1and2, respectively. Eightcross-sectionalstudiesandtwocohortstudieswereofhighquality,threecross-sectional studiesandonecohortstudywereofmoderatequality. Detailedcharacteristicsofthecross-sectionalstudiesarepresentedinTable1. Thesecross-sectional studies were carried out in Brazil ( N   = 3), Spain ( N   = 2), Finland ( N   = 1), Italy ( N   = 1), the U.S.  ( N   = 2) ,  Japan ( N   = 1) and Iran ( N   = 1). Sample size varied from 175 to 10,473, totaling 26,403 subjects.All cross-sectional studies included both men and women. Eight studies did not specify the type of dietary fiber [ 28 , 30 , 31 , 39 – 43 ], one reported specified fiber types and total fiber [ 29 ], Franco et al. [ 27 ] reported on soluble and insoluble fiber and Steemburgo et al. [ 44 ] reported on soluble fiber and total dietary fiber. Fujii et al. reported total fiber intake as well as fiber from different botanical sources [ 29 ]. Criteria for diagnosing MetS were different among the studies. Six studies used the NCEP ATP-IIIcriteria [ 27 , 31 , 40 – 43 ], two studies used the IDF criteria [ 29 , 44 ], one used both [ 28 ], one used the modified NCEP ATP-III criteria [ 30 ] and one study used harmonized definition [ 29 ]. Methods for diet  Nutrients  2018 ,  10 , 24 4 of 17 assessment were also different. Two studies used 24 h dietary recalls [ 30 , 43 ], three studies used 3- or 4-day food records [ 31 , 39 , 44 ], five studies used food frequency questionnaires (FFQ) [ 27 , 28 , 40 , 41 ] and one study used a brief diet history questionnaire [29]. Figure 1.  Flow chart of study selection.
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