Associations between explorative dietary patterns and serum lipid levels and their interactions with ApoA5 and ApoE haplotype in patients with recently diagnosed type 2 diabetes
© The Author(s) 2016
Received: 29 June 2016
Accepted: 20 September 2016
Published: 27 September 2016
In patients with type 2 diabetes (T2D), responsiveness of serum lipid concentrations to dietary patterns may vary by genotype. The aims of the present study were to identify explorative dietary patterns and to examine their independent associations with serum lipid levels and interactions with apolipoprotein (Apo)A5 and ApoE variants among patients recently diagnosed with T2D.
Within a cross-sectional analysis, participants of the German Diabetes Study (n = 348) with mean T2D duration of 6 months were investigated for fasting serum lipid levels, ApoA5 and ApoE genotypes; food consumption frequencies were assessed by a food propensity questionnaire. Dietary patterns were derived using principal component analysis (PCA) and reduced rank regression (RRR), which extracts patterns explaining variation in serum lipid concentrations.
PCA yielded interpretable dietary patterns which were, however, not related to serum lipid levels. Relevance of the RRR patterns varied by genotype: a preferred consumption of fruit gum, fruit juice, and potato dumpling, whilst avoiding fruits and vegetables independently associated with higher triglyceride levels among ApoA5*2. Patients in the highest compared to the lowest tertile of pattern adherence had 99 % higher triglycerides. Lower consumption frequencies of butter, cream cake, French fries, or high-percentage alcoholic beverages were independently related to lower LDL-cholesterol among ApoE2 carriers, with those in the highest compared to the lowest tertile of pattern adherence having 40 % lower LDL-cholesterol (both Pinteraction < 0.05).
Our explorative data analyses suggest that associations of dietary patterns with triglycerides and LDL-cholesterol differ by ApoA5 and ApoE haplotype in recently diagnosed T2D.
Trial registration Clinicaltrials.gov: NCT01055093. Date of registration: January 22, 2010 (retrospectively registered). Date of enrolment of first participant to the trial: September 2005
Patients with type 2 diabetes (T2D) have a high risk for developing cardiovascular disease (CVD) . Lifestyle modification including nutrition intervention, weight loss, and increased physical activity may allow some T2D patients to reduce their individual CVD risk by improving serum lipid profiles . Current dietary recommendations for individuals who would benefit from lowering their LDL-cholesterol, irrespective of whether they are suffering from diabetes, focus on a dietary pattern that emphasizes intake of vegetables, fruits, whole-grains, legumes, and nuts, that includes low-fat dairy products and seafood, and limits intake of red meats, sweets, and sugar sweetened beverages . The most common pattern of dyslipidemia in patients with T2D is characterized by elevated triglycerides and reduced HDL-cholesterol levels . However, evidence on dietary interventions which effectively treat this combination of disturbed serum lipids in patients with T2D, especially in those who are recently diagnosed, is uncertain.
The response of serum lipids to diet shows large interindividual variation , which might be caused by genes whose products affect lipoprotein metabolism (e.g. apolipoprotein (Apo)E and ApoA5) [4, 5]. In the general population of European ancestry, three ApoE (i.e. ApoE2, ApoE3, ApoE4) and ApoA5 (i.e. ApoA5*1, ApoA5*2, ApoA5*3) gene polymorphisms were identified [6, 7]. ApoE2 is associated with reduced and ApoE4 with increased LDL-cholesterol levels and CVD risk when compared to ApoE3 . Increased CVD risk due to elevated serum triglyceride concentrations is also present in ApoA5*2 and ApoA5*3 compared to ApoA5*1 [4, 7, 9]. However, little is known, whether this also applies to T2D patients.
In terms of potential gene-diet interactions, the majority of studies have explored interactions with single nutrients, especially dietary fat, fatty acids, and cholesterol rather than considering the effect of dietary patterns. Furthermore, evidence for individuals with T2D is scarce [10, 11]. Generally, people do not consume isolated nutrients or foods, but complex combinations of foods which may act interactively or synergistically . Explorative dietary patterns analyses, i.e. principal component analysis (PCA) and reduced rank regression (RRR), and examination of interactions between these dietary patterns, and ApoA5 and ApoE variants on serum lipids will therefore provide insights into diet-disease relations. PCA extracts dietary patterns which explain maximal variation in food intake , whereas RRR dietary patterns explain maximal variation in serum lipid concentrations .
Thus, this study aimed to identify explorative dietary patterns derived by PCA and RRR in patients recently diagnosed with T2D, to examine the relevance of these dietary patterns for serum lipid concentrations, and to investigate whether associations of dietary patterns with serum lipid concentrations differ between ApoE and ApoA5 haplotypes.
With respect to variables included in the present analysis, anthropometric measurements, laboratory parameters, and questionnaires on food consumption frequencies and SES had been collected and measured consecutively during the baseline examination day of each patient. Genomic DNA was extracted from whole blood samples and stored at −80 °C. Specifically for this analysis, ApoA5 and ApoE genotyping and PCA and RRR dietary pattern extraction was newly conducted.
Food consumption frequencies
Habitual food consumption frequencies during the last 4 weeks to 3 months before the examination were assessed using a qualitative food propensity questionnaire (FPQ) [16, 17]. Foods and food groups being typically consumed in Germany were included. The 85 food items of the FPQ cover the following food groups: meat/meat products, fish/seafood, eggs/egg-based dishes, milk/dairy products, sweets, bread/pastry/cereals, nuts/seeds, salty snacks, fats, soup/stew, sauces/condiments, fruits, vegetables/legumes, potatoes/potato dishes, non-alcoholic beverages, and alcoholic beverages. The frequency of intake was assessed in six categories: i.e. never/very seldom; 1–3 times per month; 1–2 times per week; 3–6 times per week; 1 time per day; >1 time per day [16, 17].
Empirically derived dietary patterns were extracted using PCA and RRR. PCA reduces the number of observed variables (i.e. food groups) into a smaller number of principal components (i.e. dietary patterns), which explain maximal predictor variation (i.e. food intake) [12, 13, 18]. RRR determines linear functions of predictors (i.e. dietary patterns) by maximizing the explained variance in response variables (i.e. serum concentrations of triglycerides, HDL-, LDL-cholesterol) . Of note, RRR but not PCA patterns predict health-related outcomes . Before PCA and RRR analyses, food consumption frequencies from the FPQ were transformed to sex-specific standard normal scores (mean = 0, SD = 1).
PCA analyses were conducted with the PROC FACTOR procedure in SAS using the standardized consumption frequencies. Three factors were retained based on the following criteria: eigenvalue-one criterion, scree test, and interpretability of the derived dietary patterns (e.g. if at least three food groups loaded high on each factor). Factors were rotated by an orthogonal transformation and food groups with absolute factor loadings ≥0.4 were considered as contributing to a pattern . Individual factor scores were calculated according to the approach of simplified pattern, which reduces population dependency of the dietary patterns [18, 19].
RRR analyses were performed using the RRR option in the SAS procedure PLS . Transformed standardized consumption frequencies were used as predictor variables. Response variables were serum concentrations of triglycerides, HDL-, and LDL-cholesterol. Triglycerides were log-transformed to improve normality. Three factors were extracted as the number of factors always equals the number of response variables. According to previous research, all food groups with absolute factor loadings ≥0.2 were included [13, 20]. Individual RRR factor scores were again calculated according to the simplified pattern approach [19, 20].
Genomic DNA was extracted from whole blood using the blood extraction kit (Qiagen, Hilden, Germany). ApoE and ApoA5 genotyping was conducted using real-time polymerase chain reaction based allelic discrimination according to manufacturers’ recommendations with probe based genotyping assays for the SNPs rs429258, rs7412, rs662799, and rs3135506 (Life Technologies, Darmstadt, Germany). The genotype concordance of >99.9 % was determined using TaqMan Genotyper software v.1.3 (Life Technologies). Resequencing of 1 % of randomly chosen individuals was conducted for data validation and quality management which confirmed the genotyping results to 100 %. ApoE genotypes were classified into three haplotypes, i.e. ApoE2 (E2/E2, E2/E3; rs429258: TT, rs7412: TT/CT), ApoE3 (E3/E3; rs429258: TT, rs7412: CC), ApoE4 (E3/E4, E4/E4; rs429258: CT/CC, rs7412: CC). ApoA5 haplotypes were combined as ApoA5*1 (rs662799: AA, rs3135506: GG), ApoA5*2 (rs662799: AG/GG, rs3135506: GG/CG), and ApoA5*3 (rs662799: AA, rs3135506: CG) as previously reported [6, 7, 21, 22]. Patients with the ApoE2/4 (rs429258: CT, rs7412: CT) (n = 7) and rare other ApoA5 combinations, i.e. ApoA5*amb (n = 5) genotype, were excluded from the analyses due to small sample sizes (Fig. 1).
Biospecimen handling and laboratory analyses of fasting blood glucose, fasting C-peptide, fasting insulin, and HbA1c were described in detail elsewhere [14, 15]. Serum concentrations of triglycerides, HDL-, and LDL-cholesterol were measured on a Hitachi 912 analyzer (Roche Diagnostics, Mannheim, Germany) and a cobas c311 (Roche Diagnostics, Mannheim, Germany) . To allow comparability of these methods, serum lipid levels used for analysis were adjusted for the laboratory method. Homeostasis Model Assessment (HOMA) for insulin resistance (HOMA-IR) and beta-cell function (HOMA-B) were calculated as described before . As HOMA-IR and HOMA-B are calculated using fasting insulin concentration and as participants applied their last insulin dose the evening before the examination day, patients treated with intermediate- or long-acting insulin (n = 21) were excluded from HOMA analyses.
Standardized questionnaires were used to assess parameters of SES. As single dimensions of the SES, highest school-leaving qualification, current employment status, and current/former employment position were considered [24, 25].
SAS (version 9.4; SAS Institute, Cary, NC) procedures were used for data analyses.
Regression models with dietary patterns
PCA and RRR factors were used as independent predictors in multiple linear regression models with serum lipid levels (triglycerides, HDL-, LDL-cholesterol) as dependent variables. Dietary patterns were additionally related to further dependent variables, i.e. BMI, WHR, fasting blood glucose, fasting C-peptide, HbA1c, HOMA-IR, and HOMA-B, to explore their overall interpretability. Adjusted means of the dependent variables were calculated by tertiles of the PCA and RRR dietary patterns to obtain intuitive values for presentation and to better illustrate the effect sizes . Triglycerides, fasting C-peptide, HOMA-IR, and HOMA-B were log-transformed prior to analysis to improve normality and back transformed (yielding geometric means and their corresponding 95 % confidence interval) for presentation in tables and figures. Multiple linear regression analyses with continuous pattern scores as independent variables were used to calculate P-values for a linear trend.
The basic model (model 1) presents unadjusted data. For the adjusted model (model 2), the following covariates were considered as potentially confounding the association of dietary patterns with serum lipid levels and parameters of metabolic control: age, sex, BMI, diabetes duration, type of glucose-lowering medication [diet/oral glucose-lowering medication/insulin + oral glucose-lowering medication/insulin], lipid-lowering medication [yes/no]. For model 3, parameters of SES, i.e. current employment status, highest school-leaving qualification, and current/former employment position, were additionally considered. Variables were initially tested separately and only included in the model if they modified regression coefficients of the pattern scores in the unadjusted models (>10 %), improved the coefficient of determination (>5 %), or significantly predicted the dependent variable. To ensure comparability between models of the same dependent variable, we included all confounders which met the above mentioned criteria in any of the models to investigate the association of dietary patterns with this respective dependent variable.
Interaction effects between dietary patterns and genotype on serum lipid concentrations
Interactions of the RRR dietary patterns and haplotypes of ApoA5 and ApoE on serum lipid concentrations were tested using multiple linear regression analysis. Models were adjusted for the respective confounders of model 3 as described above.
Because of the large number of analyses and the problem of multiple testing, Bonferroni correction was applied individually for each set of analyses using P < 0.05/m as significance level, with m indicating the number of dependent variables to be analyzed: associations of dietary patterns with primary outcome variables (m = 3: triglycerides, HDL-, LDL-cholesterol) and associations of dietary patterns with secondary outcome variables (m = 8: BMI, WHR, waist circumference, fasting blood glucose, fasting C-peptide, HOMA-IR, HOMA-B, HbA1c). For interaction effects of dietary patterns and genotype on serum lipid levels, haplotype-specific P-values for associations between continuous pattern scores and serum lipids were only tested if interactions were significant. For these analyses, Bonferroni correction was thus applied for the number of haplotypes (m = 3: ApoA5*1, ApoA5*2, ApoA5*3 and ApoE2, ApoE3, ApoE4, respectively). P < 0.05/m was considered statistically significant.
Statistical power considerations
Power and sample size analyses were conducted with the PROC POWER procedure for multiple linear regression in SAS . A sample size of n = 348 ensures that an association between serum concentrations of triglycerides, HDL-cholesterol, and LDL-cholesterol and explorative dietary patterns can be detected with a power of 80 % if the corresponding partial correlation adjusted for up to eight potential confounders is greater than or equal to 0.15.
Characteristics of type 2 diabetes patients
N (% male)
348 (64 %)
Waist circumference (cm)
Duration since diagnosis of diabetes (months)
Glucose-lowering medication (diet/oral glucose-lowering medication/insulin + oral glucose-lowering medication/insulin)
164 (47 %)/157 (45 %)/17 (5 %)/10 (3 %)
Fasting blood glucose (mg/dl)
Fasting C-peptide (ng/ml)
2.9 (2.2; 4.0)
3.8 (2.5; 5.7)
76.1 (50.1; 120.8)
Lipid-lowering medication (yes/no)
73 (21 %)/275 (79 %)
126 (91; 185)
ApoA5 haplotype (ApoA5*1/ApoA5*2/ApoA5*3/ApoA5*amb)
251 (72 %)/45 (13 %)/47 (14 %)/5 (1 %)
ApoE haplotype (ApoE2-expressing/ApoE3-expressing/ApoE4-expressing/ApoE2/4)
56 (16 %)/205 (59 %)/80 (23 %)/7 (2 %)
Current employment status (employed/unemployed)
227 (65 %)/121 (35 %)
Highest school-leaving qualification (“Hauptschule”/”Realschule”/Polytechnic Secondary School/”Fachhochschulreife”/”Abitur”/no school-leaving certificate/others)
126 (36 %)/80 (23 %)/5 (1 %)/40 (11 %)/85 (24 %)/6 (2 %)/6 (2 %)
Current/former employment position [laborer/employee/official/self-employed/supporting family member/no further details/still in education (unpaid)/others]
69 (20 %)/222 (64 %)/14 (4 %)/26 (7 %)/1 (0.3 %)/9 (3 %)/1 (0.3 %)/2 (0.6 %)/4 (1 %)
Included food groups and explained variance in the explorative dietary patterns
Included food groups
Explained variance in food intake (%)
PCA pattern 1
Cookie filled (chocolate or cream)
Ham high-fat (with visible fat)
Sausages high-fat (e.g. salami, pork, blood, or liver sausage)
PCA pattern 2
PCA pattern 3
Cheese high-fat (e.g. Gouda, Edam, or Tilsiter cheese, cream cheese)
Cheese low-fat (e.g. Harz, Limburger, or Mainz cheese, reduced-fat cheese)
Cottage cheese low-fat (<10 % fat)
Dairy low-fat (milk, yoghurt, kefir, sour milk ≤1.5 % fat)
Explained variance in response variables (%)
RRR pattern 1
Fruit juice unsweetened
RRR pattern 2
Fruit, herbal tea
RRR pattern 3
High-percentage alcoholic beverages (e.g. schnapps, cognac, whiskey)
Associations between the dietary patterns derived by principal component analysis with serum lipid levels
PCA pattern 1
PCA pattern 2
PCA pattern 3
Mean (95 % CI)
Mean (95 % CI)
Mean (95 % CI)
P trend *
Mean (95 % CI)
Mean (95 % CI)
Mean (95 % CI)
P trend *
Mean (95 % CI)
Mean (95 % CI)
Mean (95 % CI)
P trend *
127 (115; 140)
124 (113; 137)
143 (129; 158)
133 (120; 147)
136 (123; 150)
125 (113; 138)
136 (123; 151)
123 (112; 136)
135 (122; 149)
115 (99; 132)
111 (97; 128)
127 (110; 147)
120 (103; 138)
121 (105; 140)
112 (98; 129)
121 (105; 140)
109 (94; 126)
119 (103; 137)
87 (65; 115)
84 (64; 111)
96 (72; 127)
87 (65; 116)
91 (69; 120)
85 (64; 112)
92 (70; 122)
83 (63; 110)
88 (67; 117)
47.9 (45.7; 50.1)
50.4 (48.2; 52.6)
45.5 (43.3; 47.7)
47.1 (44.8; 49.3)
47.8 (45.6; 50.1)
48.9 (46.7; 51.1)
47.2 (45.0; 49.5)
48.5 (46.2; 50.7)
48.1 (45.9; 50.3)
49.9 (46.9; 52.9)
52.7 (49.7; 55.7)
49.0 (45.9; 52.0)
49.8 (46.7; 52.9)
50.1 (47.0; 53.2)
51.5 (48.6; 54.4)
50.0 (47.0; 52.9)
51.3 (48.2; 54.4)
50.8 (47.8; 53.8)
52.1 (46.1; 58.1)
55.4 (49.6; 61.3)
51.2 (45.1; 57.2)
53.0 (46.9; 59.1)
52.9 (46.9; 58.9)
54.3 (48.3; 60.3)
53.4 (47.3; 59.4)
53.4 (47.4; 59.4)
53.7 (47.6; 59.8)
120 (113; 127)
135 (128; 142)
125 (118; 132)
127 (121; 134)
128 (121; 135)
125 (118; 131)
132 (125; 139)
124 (117; 131)
124 (118; 131)
109 (101; 118)
123 (115; 132)
117 (107; 126)
117 (107; 126)
117 (108; 126)
116 (107; 124)
121 (112; 130)
113 (103; 122)
114 (106; 123)
108 (90; 126)
122 (105; 140)
115 (97; 133)
117 (98; 135)
116 (98; 134)
118 (100; 136)
122 (105; 140)
113 (95; 131)
114 (96; 132)
Additionally, adherence to RRR pattern 1 was directly and independently related to fasting blood glucose, fasting C-peptide, and HOMA-IR, whereas independent inverse associations were observed for RRR pattern 2 with fasting C-peptide and HOMA-IR (Additional file 1: Table S3).
This study provides evidence for a role of ApoA5 and ApoE genotypes in responsiveness of serum lipid levels to RRR derived dietary patterns in patients with recently diagnosed T2D. Preferred consumption of fruit gum, fruit juice, and potato dumpling, whilst avoiding fruits and vegetables (RRR pattern 1) appeared to be particularly detrimental for serum triglyceride levels of ApoA5*2 carriers. ApoE2 carriers with a closer adherence to the dietary pattern characterized by low consumption frequencies of butter, cream cake, French fries, and high-percentage alcoholic beverages (RRR pattern 3) showed lower LDL-cholesterol levels.
The allele distribution of ApoA5 and ApoE in our cohort was similar to that reported for other populations of European ancestry [7, 22]. Dietary patterns derived by PCA only accounted for 3–7 % of the total variance in food consumption frequencies which is, however, comparable to previous findings from a cohort of healthy individuals and a population-based sample including patients with diabetes [27, 28]. PCA patterns were interpretable, but did not independently relate to serum lipid levels. A closer adherence to PCA pattern 1 was nonetheless associated with poorer glucose homeostasis, i.e. higher fasting C-peptide concentrations and insulin resistance. Our results are in accordance with previous studies in people without diabetes, where dietary patterns characterized by high intake of refined foods, red meat, full-fat dairy, sweets, and snacks were adversely associated with glucose homeostasis, but not with body composition [29, 30]. The absence of associations between the PCA dietary patterns and serum lipids in our study indicates that adverse food choices, as reflected by our PCA pattern 1, may be detrimental for glucose homeostasis rather than for serum lipid concentrations.
RRR patterns represent a combination of food intakes that affects concentrations of the biomarkers chosen as response variables rather than foods and beverages that are often consumed together, which may impede their interpretability . Nonetheless, higher adherence to dietary patterns similar to RRR pattern 1, which associated with higher triglyceride and LDL-cholesterol levels in our cohort of patients with recent-onset T2D, were found to associate with increased CVD risk in the Whitehall II, MONICA/KORA, and EPIC study [32–34]. Our RRR pattern 2, characterized by high consumption frequencies of coffee and boiled potatoes, but few margarine or egg noodles, showed some similarities with two previously described ‘traditional’ patterns: A ‘traditional’ pattern characterized by high intake of potatoes, meat, vegetables and legumes, margarine and other fats and low intake of pasta, rice, and tea, which associated with higher triglyceride and LDL-cholesterol levels among a random sample of the general population in Northern Germany ; and a ‘traditional’ pattern characterized by high intake of potatoes, coffee, eggs, vegetables, and legumes and low intake of sweets and fast food was related to higher CVD risk in participants of the EPIC-Netherlands cohort . We, in contrast, observed beneficial associations with triglycerides and HDL-cholesterol for the RRR 2 pattern, which may result in beneficial cardio-vascular effects. The deviating findings may be attributable to the processed and red meat, which contributed to both ‘traditional’ patterns —and may have entailed a higher legume and vegetable consumption—but was not part of our dietary pattern. Of note, current evidence for the role of red and processed meat on CVD risk is inconclusive , which might be partly due to residual confounding in observational studies . Recent findings suggest robust associations between processed meats and CVD risk, but small or no risk increases for unprocessed red meats [36, 37]. Consumption of red and processed meats were not related to mortality . The associations we observed for lower consumption frequencies of butter and processed high-fat foods (i.e. cream cake, French fries), and high-percentage alcoholic beverages as part of RRR pattern 3 with higher HDL-cholesterol levels are in line with previous observations with respect to the inverse association between high-fat foods (i.e. meat, margarine, other fats) and processed foods (i.e. fried potatoes, burgers, sausages) and HDL-cholesterol [28, 31, 33]. Concerning the association between high-percentage alcoholic beverages and HDL-cholesterol, existing results from dietary patterns suggested a direct relationship [31, 39, 40] rather than an inverse association as seen in our cohort. Thus, a possible direct association between alcoholic beverages and HDL-cholesterol may be obscured by the other foods of RRR pattern 3 in our study.
Of note, only a few studies have examined genetic variations for ApoA5 in patients with T2D and to the best of our knowledge, no study has described interactions by ApoA5 and ApoE haplotype with empirically derived dietary patterns and serum lipid levels. Thus, our findings of associations between dietary patterns and serum lipids being specific for haplotypes extend the current literature of studies, which have confirmed haplotype-specific effects of single nutrients or foods on serum lipid levels among ApoA5 and ApoE carriers. In an intervention study with newly diagnosed T2D patients and participants with impaired glucose tolerance, carriers of the rs662799 minor allele, a marker to define ApoA5*2 haplotype , showed greater increases of triglyceride levels with a high carbohydrate diet (65 % energy from carbohydrates) rich in refined grains compared to major allele carriers . This finding is in line with our observation of an association between RRR 1 pattern and triglycerides among ApoA5*2 carriers. Dietary fat intake has also been reported to modify the effect of ApoA5 on serum triglyceride concentrations among young individuals without diabetes [43, 44]. However, in our study among patients with T2D, associations between triglyceride levels and RRR 3 pattern (low consumption frequencies of butter and processed high-fat foods) did not differ by ApoA5 haplotype. Lowest LDL-cholesterol levels were observed for ApoE2 compared to other ApoE haplotypes , which was still present after a four-week diet rich in saturated or mono-unsaturated fatty acids in young healthy individuals . Our results extend these findings as we additionally observed lower LDL-cholesterol concentrations with a closer adherence to a RRR pattern 3 among ApoE2 carriers.
ApoE facilitates the transport and distribution of cholesterol , whereas ApoA5 plays an important role in plasma triglyceride homeostasis, probably by activating lipoprotein lipase induced hydrolysis of triglycerides [7, 46, 47]. The proposed mechanisms for the occurrence of lower LDL-cholesterol concentrations among ApoE2 and higher triglyceride levels among ApoA5*2 carriers compared to the respective other Apo variants are as follows: changes in the nucleotide bases result in alterations of the amino acid sequence, which influence functionality of the Apo protein [47, 48]. Concerning ApoA5*2, a reduced protein activity may result in elevated triglyceride levels . ApoE2 might be characterized by lower intestinal cholesterol absorption and weaker LDL-receptor binding compared to ApoE3 and ApoE4. The reduced LDL-receptor affinity triggers an up-regulation of the LDL-receptor, which in combination results in an increased LDL removal in ApoE2 carriers . Dietary factors may further amplify these mechanisms .
Strengths and limitations
Strengths of our study are the in-depth metabolic phenotyping of each patient. Also, as dietary patterns consider interactive and synergistic effects between nutrients and foods, findings from interactions between dietary patterns and serum lipids by haplotypes might provide insights beyond those of single foods or nutrients . Limitations of our study are, first, the probability of selection bias due to the higher interest of health-conscious people in clinical studies, which is reflected by good glucometabolic control. Second, although food frequency questionnaires or FPQs are widely used to assess diet-disease associations in cohort studies, this dietary assessment method suffers from considerable limitations in estimating dietary intake (e.g. reporting bias such as underreporting) [50, 51]. Also, assessment of dietary intake in the present study only covered consumption frequencies and no portion sizes. However, it was previously shown that variance in food intake is mainly explained by consumption frequencies rather than portion sizes . Third, dietary patterns might be part of specific lifestyles . However, due to incomplete data on further lifestyle factors (e.g. physical activity, smoking), associations could not be adjusted for these potential confounders. General limitations of pattern analyses, i.e. subjective decisions on the choice of the number of factors extracted, the approach of rotation, and pre-grouping of the food items , also need to be considered. By calculating patterns according to the simplified approach , we tried to reduce population dependency and to increase reproducibility in different populations .
In conclusion, in patients with recently diagnosed T2D, using RRR analysis, we identified dietary patterns, which are independently associated with serum lipid levels and modified by ApoA5 and ApoE haplotype. Our explorative data analyses suggest that a closer adherence to a dietary pattern characterized by frequent consumption of fruit gum, fruit juice, and potato dumpling and lower frequencies of fruits and vegetables associated with higher triglyceride levels mainly among ApoA5*2 carriers, while lower consumption frequencies of butter, cream cake, French fries, or high-percentage alcoholic beverages related to lower LDL-cholesterol levels among ApoE2 carriers. Thus, despite glucose- and lipid-lowering therapies and the higher awareness of the importance of nutrition in patients with recently diagnosed T2D, a genotype-specific association between dietary patterns and serum lipid concentrations seem to persist.
AEB, KSW, and BK conceived the project and formulated the research questions; KSW was responsible for data management, carried out the statistical analyses and drafted the manuscript; BK and JK performed analysis of genetic data; KS supervised and gave input on the performance of the statistical analyses; PS, JS, KM, and MR contributed to discussion and reviewed/edited the manuscript; AEB and MR supervised the project. All authors critically reviewed the manuscript. MR is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript.
We appreciate the voluntary contribution of all study participants. We also thank the staff of the Clinical Research Center of DDZ for excellent technical assistance and taking care of the patients. We further thank Sylvia Jacob and Ulrike Nitzgen, German Diabetes Center Düsseldorf, for technical assistance.
The GDS Group consists of M. Roden (speaker), A.E. Buyken, J. Eckel, G. Geerling, H., Al-Hasani, C. Herder, A. Icks, J. Kotzka, O. Kuss, E. Lammert, J. Lundbom, D. Markgraf, K. Müssig, W. Rathmann, J. Szendroedi, D. Ziegler and their co-workers who are responsible for the design and performance of the GDS.
The authors declare that they have no competing interests.
Availability of data and materials
Due to restrictions imposed by the ethics committee of Heinrich-Heine-University Düsseldorf regarding patient consent, data are available upon request. Requests for data may be sent to the last author Prof. Dr. Michael Roden (email@example.com).
Ethics and consent to participate
The German Diabetes Study is performed according to the Declaration of Helsinki and approved by the ethics committee of Heinrich-Heine-University Düsseldorf (study reference numbers: 2478 and 4508).
The German Diabetes Study was initiated and is performed by the German Diabetes Center (DDZ), which is funded by the German Federal Ministry of Health and the Ministry of Innovation, Science, Research and Technology of the state North Rhine-Westphalia. This study was supported in part by a grant from the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e. V.) and by a grant of the Helmholtz Alliance Imaging and Curing Environmental Metabolic Diseases (ICEMED).
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