Dietary carbohydrate, low-carbohydrate diet score, and the risk of type 2 diabetes: A protocol for a systematic review and dose-response meta-analysis of cohort studies
Creators: name: Jayedi, Ahmad
name: Hosseini, Fateme
name: Khan, Tauseef
name: Shab-bidar, Sakineh
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Medicine and Health Sciences

description: Dietary carbohydrate, low-carbohydrate diet score, and the risk of type 2 diabetes: A protocol for a systematic review and dose-response meta-analysis of cohort studies Fateme Hosseini, Ahmad Jayedi, Tauseef Ahmad Khan, Sakineh Shab-Bidar Background Existing evidence suggests a link between quality of dietary carbohydrate and the risk of developing type 2 diabetes. A number of meta-analyses of observational studies suggested evidence of a positive association between dietary glycemic index and load and the risk of type 2 diabetes 1-4. Three previous meta-analyses of prospective cohort studies indicated no association between amount of dietary carbohydrate and the risk of developing type 2 diabetes 3,5,6. However, prospective cohort studies in Asian countries have published new findings 7-9. Consumption of dietary carbohydrate in Asian countries is substantially higher than in Western countries. However, potential regional difference in the association between dietary carbohydrate and the risk of developing type 2 diabetes has not been investigated. A recent publication from the PURE study in 21 countries across the world indicated that higher rice consumption was associated with a greater risk of developing type 2 diabetes with the strongest association in South Asia, a modest, nonsignificant association in other regions 10. White rice consumption is substantially more common in Asian countries and this may partly explain the potential regional difference in the association of rice consumption with diabetes. We therefore aimed to perform an updated systematic review and dose-response meta-analysis of prospective cohort studies of the association of dietary carbohydrate, low-carbohydrate diet score and the risk of developing type 2 diabetes, and potential confounding by geographical region. Review question Is there an association between dietary carbohydrate, low-carbohydrate diet score and incidence of type 2 diabetes in the general population? Searches The systematic literature search will be conducted to November 2020, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement 11. The systematic search will be conducted by using pre-defined search terms in PubMed, Scopus, and Web of Siences. Titles and abstracts will be screened according to the pre-defined inclusion and exclusion criteria to identify eligible studies. Full texts will be retrieved and independently assessed for eligibility by two review authors. Any disagreements will be resolved by consensus. Reference lists of all potential relevant articles and reviews will be screened to find further potential relevant studies. The systematic search will be restricted to articles published in English. Search terms: (Carbohydrate OR Carbohydrates OR “Glycemic index” OR “Glycemic index” OR "Diet, Carbohydrate-Restricted" OR "Carbohydrate-Restricted Diet" OR "Low-Carbohydrate Diets" OR "Low-Carbohydrate Diet" OR "Diet, High-Protein Low-Carbohydrate" OR "High Protein Low Carbohydrate Diet" OR "Atkins Diet" OR "High-Protein Carbohydrate-Restricted Diet" OR "South Beach Diet" OR “Diet, Ketogenic” OR "Ketogenic Diet" OR "Modified Atkins diet" OR Ketosis) AND ("Diabetes Mellitus" OR "Diabetic patients" OR DM OR "Diabetes Mellitus, Type 2" OR "Type 2 Diabetes " OR "Diabetes Mellitus, Noninsulin-Dependent" OR "Diabetes Mellitus, Ketosis-Resistant" OR "Non-Insulin-Dependent Diabetes Mellitus" OR "NIDDM" OR "Diabetes Mellitus" OR T2DM OR T2D) AND ("population-based" OR prospective OR "case control” OR longitudinal OR follow-up OR cohort OR retrospective OR "Longitudinal Studies" OR "Prospective Studies” OR "Case-Control Studies" OR "Cohort Studies" OR "Retrospective Studies"). Types of study to be included Prospective observational studies. Study Selection Inclusion and exclusion criteria were defined according to the PICOS (population, intervention/exposure, comparator, outcome, and study design) framework. Published prospective cohort studies will be considered eligible for inclusion in the meta-analysis if they had the following criteria: 1) prospective observational studies that were conducted in the general population aged 18 years or older; 2) reported dietary carbohydrate consumption, as either g/d or percent calorie, and low-carbohydrate diet score as exposure 3) considered type 2 diabetes as the outcomes of interest; 4) provided estimates of relative risk, hazard ratio (HR) or rate ratio with corresponding 95% confidence intervals (CIs) for ≥2 quantitative categories of dietary carbohydrate or low-carbohydrate diet score; and 5) reported the number of cases and noncases and person-years in each category of a dietary exposures. Studies reporting continuous estimation from the associations will also be eligible. Review studies, interventional studies, and studies conducted in diseased populations will be excluded. Two independent investigators (FH and AJ) will carry out an initial screening of all titles and abstracts from retrieved papers to identify potential eligible studies that should be included in the analysis. If the same dataset had been published in more than one publication, we will include the one with greater participants. Condition or domain being studied The following outcome will be investigated: Type 2 diabetes Participants/population Inclusion: General population aged 18 years and older. Exclusion: Diseases population, patients with type 1 diabetes, children, adolescent, and pregnant or breastfeeding women Intervention(s), exposure(s) Carbohydrate intake (g/d or % calorie), low-carbohydrate diet score (unit) Comparator(s)/control Low intake of carbohydrate, low-carbohydrate diet score, or dose-response. Main outcome(s) Type 2 diabetes Additional outcome(s) None Data extraction (selection and coding) Two independent researchers (FH and AJ) will record the following characteristics from the identified studies: first author’s name, date of publication, country, age range, sex, study participants, number of cases, duration of follow-up, method of assessment of dietary intake, and list of variables that were entered into the multivariable model as potential confounders. Risk of bias (quality) assessment Quality of original studies included in the meta-analysis will be evaluated using a 9-point Newcastle–Ottawa Scale 12. Accordingly, studies with 1–3, 4–6, and 7–9 points will be rated poor, fair, and high quality, respectively. Two independent investigators (FH and AJ) will perform the quality assessment to examine the possible risk of bias associated with each of the included studies. The certainty of the evidence will be assessed by using the GRADE tool 13. This tool grades the evidence as high, moderate, low, or very low quality. Observational studies such as prospective cohort studies start as low-quality evidence that can be downgraded or upgraded on the basis of pre-specified criteria. The criteria used to downgrade the evidence include study limitations, inconsistency, indirectness, imprecision, and publication bias. The criteria used to upgrade the quality of the evidence include a large magnitude of association, a dose–response gradient, and attenuation by plausible confounding. Disagreements will be solved by consulting the principal investigator (SS-B). Strategy for data synthesis We will perform a random-effect meta-analysis to estimate the HR and its 95%CI as the effect size in the present meta-analysis. For studies that reported the effect size as relative risks, we will consider them as equal to HR 14. Three types of analyses will be carried out in the present meta-analysis. First, we will perform a pairwise meta-analysis to combine the HRs for the highest compared with the lowest category dietary carbohydrate and low-carbohydrate diet score in each primary study. For studies with sex-specific effect sizes, we will combine sex-specific estimates using a fixed-effects model and will use the combined effect size for the analyses. The Cochran Q and I2 statistics will be used to test for heterogeneity 15. Our main subgroup analysis will be based on geographical region. We will also perform a series of subgroup analyses to detect potential sources of heterogeneity, based on gender, follow-up duration, number of participants and adjustments for main confounders including energy (yes or no), body mass index (yes or no), smoking (yes or no), alcohol drinking (yes or no) and physical activity (yes or no). Publication bias will be examined by visual inspection of funnel plots when at least 10 primary studies were available 16. Formal statistical assessment of funnel plot asymmetry will also be done with Egger’s regression asymmetry test 17 and Begg’s test 18. A trim and fill method will be used to detect the effect of probable missing studies on the overall effect size 19. We will also conduct a sensitivity analysis, in which each prospective cohort study will be excluded in turn to examine the influence of that study on the overall estimate. Second, we will perform a random-effects dose-response meta-analysis to estimate the HR of diabetes for a 10% increment in daily calorie intake form carbohydrate and a 10-unit increase in low-carbohydrat diet score using the method presented by Greenland and colleagues 20,21. In this method, distribution of cases and personyears and the reported effect estimates across categories of dietary carbohydrate and low-carbohydrate diet score will be required. We will consider the median of each category. For studies that reported dietary carbohydrate and low-carbohydrate diet score as a range, we will estimate the midpoint in each category by calculating the midpoint of the lower and upper bounds. When the highest and lowest categories were open-ended, we will assume the same interval as those of the adjacent category. For studies that reported dietary carbohydrate as g/d, we will convert g/d to percent calorie from carbohydrate by using the average daily energy intake of the study participants. Finally, we will perform a one-stage linear mixed effects meta-analysis to clarify the shape of the dose-response associations 22. This method estimates the study specific slope lines and combines them to obtain an overall average slope in a single stage, and allows to include studies with two categories of exposures in dose-response analysis. We will include all studies in the main analysis. However, due to substantial difference in carbohydrate consumption in Asian and Western countries, we will perform nonlinear dose-response analyses in Asian and Western countries. Statistical analyses will be conducted using STATA version 15.0. A two-tailed P value of less than 0.05 will be considered significant. Analysis of subgroups or subsets We will separately test the associations in Asian and Western countries. Type and method of review Meta-analysis of prospective cohort studies Anticipated or actual start date 20 November 2020 Anticipated completion date 20 December 2020 Funding sources/sponsors None. Conflicts of interest None known Language English Country Iran References: 1. Barclay AW, Petocz P, McMillan-Price J, et al. Glycemic index, glycemic load, and chronic disease risk—a meta-analysis of observational studies. The American journal of clinical nutrition. 2008;87(3):627-637. 2. Bhupathiraju SN, Tobias DK, Malik VS, et al. Glycemic index, glycemic load, and risk of type 2 diabetes: results from 3 large US cohorts and an updated meta-analysis. The American journal of clinical nutrition. 2014;100(1):218-232. 3. Greenwood DC, Threapleton DE, Evans CE, et al. Glycemic index, glycemic load, carbohydrates, and type 2 diabetes: systematic review and dose–response meta-analysis of prospective studies. Diabetes care. 2013;36(12):4166-4171. 4. Livesey G, Taylor R, Livesey HF, et al. 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