Daphne L. van der A

Dietary fiber and subsequent changes in body weight and waist circumference in European men and women

BACKGROUND Dietary fiber may play a role in obesity prevention. Until now, the role that fiber from different sources plays in weight change had rarely been studied. OBJECTIVE Our aim was to investigate the association of total dietary fiber, cereal fiber, and fruit and vegetable fiber with changes in weight and waist circumference. DESIGN We conducted a prospective cohort study with 89,432 European participants, aged 20-78 y, who were free of cancer, cardiovascular disease, and diabetes at baseline and who were followed for an average of 6.5 y. Dietary information was collected by using validated country-specific food-frequency questionnaires. Multiple linear regression analysis was performed in each center studied, and estimates were combined by using random-effects meta-analyses. Adjustments were made for follow-up duration, other dietary variables, and baseline anthropometric, demographic, and lifestyle factors. RESULTS Total fiber was inversely associated with subsequent weight and waist circumference change. For a 10-g/d higher total fiber intake, the pooled estimate was -39 g/y (95% CI: -71, -7 g/y) for weight change and -0.08 cm/y (95% CI: -0.11, -0.05 cm/y) for waist circumference change. A 10-g/d higher fiber intake from cereals was associated with -77 g/y (95% CI: -127, -26 g/y) weight change and -0.10 cm/y (95% CI: -0.18, -0.02 cm/y) waist circumference change. Fruit and vegetable fiber was not associated with weight change but had a similar association with waist circumference change when compared with intake of total dietary fiber and cereal fiber. CONCLUSION Our finding may support a beneficial role of higher intake of dietary fiber, especially cereal fiber, in prevention of body-weight and waist circumference gain.

Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study

Summary Background Conflicting evidence exists regarding the association between saturated fatty acids (SFAs) and type 2 diabetes. In this longitudinal case-cohort study, we aimed to investigate the prospective associations between objectively measured individual plasma phospholipid SFAs and incident type 2 diabetes in EPIC-InterAct participants. Methods The EPIC-InterAct case-cohort study includes 12 403 people with incident type 2 diabetes and a representative subcohort of 16 154 individuals who were selected from a cohort of 340 234 European participants with 3·99 million person-years of follow-up (the EPIC study). Incident type 2 diabetes was ascertained until Dec 31, 2007, by a review of several sources of evidence. Gas chromatography was used to measure the distribution of fatty acids in plasma phospholipids (mol%); samples from people with type 2 diabetes and subcohort participants were processed in a random order by centre, and laboratory staff were masked to participant characteristics. We estimated country-specific hazard ratios (HRs) for associations per SD of each SFA with incident type 2 diabetes using Prentice-weighted Cox regression, which is weighted for case-cohort sampling, and pooled our findings using random-effects meta-analysis. Findings SFAs accounted for 46% of total plasma phospholipid fatty acids. In adjusted analyses, different individual SFAs were associated with incident type 2 diabetes in opposing directions. Even-chain SFAs that were measured (14:0 [myristic acid], 16:0 [palmitic acid], and 18:0 [stearic acid]) were positively associated with incident type 2 diabetes (HR [95% CI] per SD difference: myristic acid 1·15 [95% CI 1·09–1·22], palmitic acid 1·26 [1·15–1·37], and stearic acid 1·06 [1·00–1·13]). By contrast, measured odd-chain SFAs (15:0 [pentadecanoic acid] and 17:0 [heptadecanoic acid]) were inversely associated with incident type 2 diabetes (HR [95% CI] per 1 SD difference: 0·79 [0·73–0·85] for pentadecanoic acid and 0·67 [0·63–0·71] for heptadecanoic acid), as were measured longer-chain SFAs (20:0 [arachidic acid], 22:0 [behenic acid], 23:0 [tricosanoic acid], and 24:0 [lignoceric acid]), with HRs ranging from 0·72 to 0·81 (95% CIs ranging between 0·61 and 0·92). Our findings were robust to a range of sensitivity analyses. Interpretation Different individual plasma phospholipid SFAs were associated with incident type 2 diabetes in opposite directions, which suggests that SFAs are not homogeneous in their effects. Our findings emphasise the importance of the recognition of subtypes of these fatty acids. An improved understanding of differences in sources of individual SFAs from dietary intake versus endogenous metabolism is needed. Funding EU FP6 programme, Medical Research Council Epidemiology Unit, Medical Research Council Human Nutrition Research, and Cambridge Lipidomics Biomarker Research Initiative.

Dietary Intake of Total, Animal, and Vegetable Protein and Risk of Type 2 Diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-NL Study

OBJECTIVE Dietary recommendations are focused mainly on relative dietary fat and carbohydrate content in relation to diabetes risk. Meanwhile, high-protein diets may contribute to disturbance of glucose metabolism, but evidence from prospective studies is scarce. We examined the association among dietary total, vegetable, and animal protein intake and diabetes incidence and whether consuming 5 energy % from protein at the expense of 5 energy % from either carbohydrates or fat was associated with diabetes risk. RESEARCH DESIGN AND METHODS A prospective cohort study was conducted among 38,094 participants of the European Prospective Investigation into Cancer and Nutrition (EPIC)-NL study. Dietary protein intake was measured with a validated food frequency questionnaire. Incident diabetes was verified against medical records. RESULTS During 10 years of follow-up, 918 incident cases of diabetes were documented. Diabetes risk increased with higher total protein (hazard ratio 2.15 [95% CI 1.77–2.60] highest vs. lowest quartile) and animal protein (2.18 [1.80–2.63]) intake. Adjustment for confounders did not materially change these results. Further adjustment for adiposity measures attenuated the associations. Vegetable protein was not related to diabetes. Consuming 5 energy % from total or animal protein at the expense of 5 energy % from carbohydrates or fat increased diabetes risk. CONCLUSIONS Diets high in animal protein are associated with an increased diabetes risk. Our findings also suggest a similar association for total protein itself instead of only animal sources. Consumption of energy from protein at the expense of energy from either carbohydrates or fat may similarly increase diabetes risk. This finding indicates that accounting for protein content in dietary recommendations for diabetes prevention may be useful.

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies.In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted.Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases.

Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study

Objective To identify existing prediction models for the risk of development of type 2 diabetes and to externally validate them in a large independent cohort. Data sources Systematic search of English, German, and Dutch literature in PubMed until February 2011 to identify prediction models for diabetes. Design Performance of the models was assessed in terms of discrimination (C statistic) and calibration (calibration plots and Hosmer-Lemeshow test).The validation study was a prospective cohort study, with a case cohort study in a random subcohort. Setting Models were applied to the Dutch cohort of the European Prospective Investigation into Cancer and Nutrition cohort study (EPIC-NL). Participants 38 379 people aged 20-70 with no diabetes at baseline, 2506 of whom made up the random subcohort. Outcome measure Incident type 2 diabetes. Results The review identified 16 studies containing 25 prediction models. We considered 12 models as basic because they were based on variables that can be assessed non-invasively and 13 models as extended because they additionally included conventional biomarkers such as glucose concentration. During a median follow-up of 10.2 years there were 924 cases in the full EPIC-NL cohort and 79 in the random subcohort. The C statistic for the basic models ranged from 0.74 (95% confidence interval 0.73 to 0.75) to 0.84 (0.82 to 0.85) for risk at 7.5 years. For prediction models including biomarkers the C statistic ranged from 0.81 (0.80 to 0.83) to 0.93 (0.92 to 0.94). Most prediction models overestimated the observed risk of diabetes, particularly at higher observed risks. After adjustment for differences in incidence of diabetes, calibration improved considerably. Conclusions Most basic prediction models can identify people at high risk of developing diabetes in a time frame of five to 10 years. Models including biomarkers classified cases slightly better than basic ones. Most models overestimated the actual risk of diabetes. Existing prediction models therefore perform well to identify those at high risk, but cannot sufficiently quantify actual risk of future diabetes.

Fruit and vegetable intakes and subsequent changes in body weight in European populations: results from the project on Diet, Obesity, and Genes (DiOGenes)

BACKGROUND High fruit and vegetable intakes may limit weight gain, particularly in susceptible persons, such as those who stop smoking. OBJECTIVE The objective was to assess the association of fruit and vegetable intake with subsequent weight change in a large-scale prospective study. DESIGN The data used were from 89,432 men and women from 5 countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC). The association between fruit and vegetable intake and weight change after a mean follow-up of 6.5 y was assessed by linear regression. Polytomous logistic regression was used to evaluate whether fruit and vegetable intake relates to weight gain, weight loss, or both. RESULTS Per 100-g intake of fruit and vegetables, weight change was -14 g/y (95% CI: -19, -9 g/y). In those who stopped smoking during follow-up, this value was -37 g/y (95% CI: -58, -15 g/y; P for interaction < 0.0001). When weight gain and loss were analyzed separately per 100-g intake of fruit and vegetables in a combined model, the odds ratios (95% CIs) were 0.97 (0.95, 0.98) for weight gain > or =0.5 and <1 kg/y, 0.94 (0.92, 0.96) for weight gain > or =1 kg/y, and 0.97 (0.95, 0.99) for weight loss > or =0.5 kg/y. In those who stopped smoking during follow-up, the odds ratios (95% CIs) were 0.93 (0.88, 0.99), 0.87 (0.81, 0.92), and 0.97 (0.88, 1.07), respectively (P for interaction < 0.0001). CONCLUSIONS Fruit and vegetable intake relates significantly, albeit weakly inversely, to weight change. For persons who stop smoking, high fruit and vegetable intakes may be recommended to reduce the risk of weight gain.

Trend in obesity prevalence in European adult cohort populations during follow-up since 1996 and their predictions to 2015.

Objective To investigate trends in obesity prevalence in recent years and to predict the obesity prevalence in 2015 in European populations. Methods Data of 97 942 participants from seven cohorts involved in the European Prospective Investigation into Cancer and Nutrition (EPIC) study participating in the Diogenes project (named as “Diogenes cohort” in the following) with weight measurements at baseline and follow-up were used to predict future obesity prevalence with logistic linear and non-linear (leveling off) regression models. In addition, linear and leveling off models were fitted to the EPIC-Potsdam dataset with five weight measures during the observation period to find out which of these two models might provide the more realistic prediction. Results During a mean follow-up period of 6 years, the obesity prevalence in the Diogenes cohort increased from 13% to 17%. The linear prediction model predicted an overall obesity prevalence of about 30% in 2015, whereas the leveling off model predicted a prevalence of about 20%. In the EPIC-Potsdam cohort, the shape of obesity trend favors a leveling off model among men (R2 = 0.98), and a linear model among women (R2 = 0.99). Conclusion Our data show an increase in obesity prevalence since the 1990ies, and predictions by 2015 suggests a sizeable further increase in European populations. However, the estimates from the leveling off model were considerably lower.

Association Between Low-Density Lipoprotein Cholesterol–Lowering Genetic Variants and Risk of Type 2 Diabetes: A Meta-analysis

Importance Low-density lipoprotein cholesterol (LDL-C)-lowering alleles in or near NPC1L1 or HMGCR, encoding the respective molecular targets of ezetimibe and statins, have previously been used as proxies to study the efficacy of these lipid-lowering drugs. Alleles near HMGCR are associated with a higher risk of type 2 diabetes, similar to the increased incidence of new-onset diabetes associated with statin treatment in randomized clinical trials. It is unknown whether alleles near NPC1L1 are associated with the risk of type 2 diabetes. Objective To investigate whether LDL-C-lowering alleles in or near NPC1L1 and other genes encoding current or prospective molecular targets of lipid-lowering therapy (ie, HMGCR, PCSK9, ABCG5/G8, LDLR) are associated with the risk of type 2 diabetes. Design, Setting, and Participants The associations with type 2 diabetes and coronary artery disease of LDL-C-lowering genetic variants were investigated in meta-analyses of genetic association studies. Meta-analyses included 50 775 individuals with type 2 diabetes and 270 269 controls and 60 801 individuals with coronary artery disease and 123 504 controls. Data collection took place in Europe and the United States between 1991 and 2016. Exposures Low-density lipoprotein cholesterol-lowering alleles in or near NPC1L1, HMGCR, PCSK9, ABCG5/G8, and LDLR. Main Outcomes and Measures Odds ratios (ORs) for type 2 diabetes and coronary artery disease. Results Low-density lipoprotein cholesterol-lowering genetic variants at NPC1L1 were inversely associated with coronary artery disease (OR for a genetically predicted 1-mmol/L [38.7-mg/dL] reduction in LDL-C of 0.61 [95% CI, 0.42-0.88]; P = .008) and directly associated with type 2 diabetes (OR for a genetically predicted 1-mmol/L reduction in LDL-C of 2.42 [95% CI, 1.70-3.43]; P < .001). For PCSK9 genetic variants, the OR for type 2 diabetes per 1-mmol/L genetically predicted reduction in LDL-C was 1.19 (95% CI, 1.02-1.38; P = .03). For a given reduction in LDL-C, genetic variants were associated with a similar reduction in coronary artery disease risk (I2 = 0% for heterogeneity in genetic associations; P = .93). However, associations with type 2 diabetes were heterogeneous (I2 = 77.2%; P = .002), indicating gene-specific associations with metabolic risk of LDL-C-lowering alleles. Conclusions and Relevance In this meta-analysis, exposure to LDL-C-lowering genetic variants in or near NPC1L1 and other genes was associated with a higher risk of type 2 diabetes. These data provide insights into potential adverse effects of LDL-C-lowering therapy.

Carbohydrate quantity and quality and risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition–Netherlands (EPIC-NL) study

BACKGROUND Carbohydrate quantity and quality may play an important role in the development of type 2 diabetes. OBJECTIVE We investigated the associations of dietary glycemic load (GL), glycemic index (GI), carbohydrate, and fiber intake with the incidence of type 2 diabetes. DESIGN A prospective cohort study was conducted in 37,846 participants of the EPIC-NL (European Prospective Investigation into Cancer and Nutrition-Netherlands) study, aged 21-70 y at baseline and free of diabetes. Dietary intake was assessed with the use of a validated food-frequency questionnaire. Incident diabetes cases were mainly self-reported and verified against general practitioner records. RESULTS During a mean follow-up of 10 y, 915 incident diabetes cases were documented. Dietary GL was associated with an increased diabetes risk after adjustment for age, sex, established diabetes risk factors, and dietary factors [hazard ratio (HR) per SD increase: 1.27; 95% CI: 1.11, 1.44; P < 0.001] [corrected]. GI tended to increase diabetes risk (HR: 1.08; 95% CI: 1.00, 1.17; P = 0.05). Dietary fiber was inversely associated with diabetes risk (HR: 0.92; 95% CI: 0.85, 0.99; P < 0.05), whereas carbohydrate intake was associated with increased diabetes risk (HR: 1.15; 95% CI: 1.01, 1.32; P < 0.05). Of the carbohydrate subtypes, only starch was related to increased diabetes risk [HR: 1.25 (1.07, 1.46), P < 0.05]. All associations became slightly stronger after exclusion of energy misreporters. CONCLUSIONS Diets high in GL, GI, and starch and low in fiber were associated with an increased diabetes risk. Both carbohydrate quantity and quality seem to be important factors in diabetes prevention. Energy misreporting contributed to a slight attenuation of associations.

Common Genetic Variants Highlight the Role of Insulin Resistance and Body Fat Distribution in Type 2 Diabetes, Independent of Obesity

We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterize their association with intermediate phenotypes, and to investigate their role in type 2 diabetes (T2D) risk among normal-weight, overweight, and obese individuals. We investigated the association of genetic scores with euglycemic-hyperinsulinemic clamp– and oral glucose tolerance test–based measures of insulin resistance and secretion and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight, and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (β in SDs per allele [95% CI], −0.03 [−0.04, −0.01]; P = 0.004). This score was associated with lower BMI (−0.01 [−0.01, −0.0]; P = 0.02) and gluteofemoral fat mass (−0.03 [−0.05, −0.02; P = 1.4 × 10−6) and with higher alanine transaminase (0.02 [0.01, 0.03]; P = 0.002) and γ-glutamyl transferase (0.02 [0.01, 0.03]; P = 0.001). While the secretion score had a stronger association with T2D in leaner individuals (Pinteraction = 0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI or waist strata (Pinteraction > 0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.