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Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbrücke, Germany;
* Department of Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke University, Magdeburg, Germany; and
Department of Internal Medicine and
** Institute of Clinical Chemistry, University Hospital, Hamburg-Eppendorf, Germany
2To whom correspondence should be addressed. E-mail: Weikert{at}mail.dife.de.
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ABSTRACT |
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 TOP ABSTRACT SUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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KEY WORDS: • coronary heart disease • nutrition • folate • homocysteine • vitamin B-12
Moderately elevated homocysteine (Hcy)3 concentrations are associated with an increased risk of cardiovascular disease (1–3). Among the factors known to influence the metabolism of homocysteine are several nutrients, including folate, vitamin B-12, and vitamin B-6. These vitamins serve as cofactors in the enzymatic pathways of homocysteine metabolism (4). Increasing evidence suggests that higher plasma concentrations of folate and vitamin B-12 are inversely related to Hcy concentration and are associated with a reduced risk of coronary heart disease (CHD) (2). Thus, the benefits of folate and vitamin B-12 with respect to CHD risk may largely be explained by their homocysteine-lowering effects (5). The identification of dietary components that potentially affect the blood levels of these 3 biomarkers may provide further insight into the relation between diet and the risk of CHD.
One way of investigating this relation is to focus on dietary patterns. Dietary patterns represent a broad picture of food and nutrient composition and may be more predictive of disease risk than individual food and nutrient consumption (6). The traditional exploratory methods widely applied to derive dietary patterns in epidemiologic studies of CHD, such as principal component analysis and factor analysis, aim to identify patterns that explain a high percentage of the variation in food consumption. However, these patterns are not necessarily associated with CHD risk (7). Our group introduced the reduced rank regression (RRR) statistical method to nutritional sciences. In contrast to other exploratory methods, this method enables identification of dietary patterns associated with plasma levels of biomarkers (8–10) that are closely related to the disease of interest. Thus, RRR allows for prior information about a specific pathway and thereby provides a more direct approach to putative associations between diet and disease.
Using RRR, we aimed to identify a dietary pattern that relates to plasma levels of folate, vitamin B-12, and homocysteine. In a second step, we investigated the predictive ability of this pattern for CHD risk in 2 independent German study populations.
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SUBJECTS AND METHODS |
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Exclusion criteria for cases and controls were as follows: known diagnosis of cancer, acute consumptive or severe chronic disease, or previous CHD diagnosis. Individual age-matched controls were randomly selected from the same city district by use of the population registry; 2 controls/patient were invited to participate in the study via postal mail. The participation rate of eligible controls was 67%. The final control group comprised 255 women. All women gave written informed consent and the Ethics Committee of the University of Hamburg approved the study protocol.
The European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study is part of the large-scale pan-European prospective cohort study EPIC and includes 27,548 individuals aged 35–65 y who were recruited from the general population between 1994 and 1998 (11). All participants gave written informed consent and the Ethics Committee of the Federal State of Brandenburg gave approval for all study procedures. Information about changes in lifestyle and incident diseases is assessed biennially by a self-administered questionnaire (12). All potential cases of incident MI were identified by self-reports or death certificates. Case subjects were defined as participants who developed MI [ICD-10 I21.0-I21.9 (13)]. All incident cases of nonfatal or fatal MI were verified by patients’ medical records or death certificate according to WHO MONICA criteria (14).
EPIC-Potsdam participants with missing follow-up status, dietary intake, or covariate data (n = 241), as well as subjects with prevalent MI at baseline (n = 512), were excluded, leaving 26,795 participants (10,396 men and 16,360 women) for analyses. Among these, we identified 157 cases of MI (115 nonfatal and 42 fatal) occurring between baseline and April 30 2004. Mean follow-up was 4.6 y.
Data collection. In both studies, dietary intake information was collected by a validated self-administered scanner-readable FFQ that included questions on frequency and portion size of 148 food items eaten during the previous year (15). In the CORA study, diet information was collected within 3 d of admission in cases and on the day of visit to the study center in controls. In the EPIC study, the FFQ was sent to all participants by mail and later collected on the occasion of baseline examination at the study center.
Foods were classified into 49 food groups based on nutrient profiles or culinary usage (16). Usual nutrient intake was estimated from the consumed food items using the German Food Code BLS II.2 (17). The FFQ included the question, "Have you taken vitamin supplements regularly during the past year?" Participants who answered yes to that question were regarded as users of vitamin supplements. Among the participants in the CORA study, 47 cases (23.5%) and 94 controls (36.8%) were identified as users of vitamin supplements, whereas only 17% of the EPIC-Potsdam participants (17.8% of cases) regularly used vitamin supplements.
Information about sociodemographic characteristics and suspected risk factors for CHD were obtained by questionnaires and computer-assisted person-to-person interviews in both the CORA and EPIC-Potsdam studies at the time of recruitment (18). For the CORA study, smoking status categories were defined as nonsmoker, former smoker who stopped 
2 y ago, former smoker who stopped >2 y ago, current smoker with <20 cigarettes/d and, current smoker with 
20 cigarettes/d (19). In the EPIC-Potsdam study, smoking status categories were defined as nonsmoker, former smoker, and current cigarette smoker.
Blood lipids were measured in the CORA study only. Dyslipidemia was established in subjects with LDL cholesterol concentrations > 3.37 mmol/L (130 mg/dL), HDL cholesterol concentrations < 1.04 mmol/L (40 mg/dL), and triglyceride concentrations > 2.26 mmol/L (200 mg/dL), or in those who reported use of cholesterol-lowering medication. In the EPIC-Potsdam study, a history of dyslipidemia was based on self-reports of a diagnosis or of taking cholesterol-lowering medication.
Blood pressure was measured similarly in both studies, as described elsewhere (20), and hypertension was defined as having systolic blood pressure 140 mm Hg or diastolic blood pressure 90 mm Hg or taking antihypertensive medication. History of diabetes was based on self-reports of a diagnosis or of taking antidiabetic medication. Body height and weight were measured and BMI [weight (kg)/height2 (m2)] was calculated. Physical activity level was calculated from the self-reported duration and intensity of physical activity (including, e.g., walking, bicycling, sports, gardening), taking into account the metabolic equivalents (21). Educational attainment was defined as a dichotomized variable: less than high school education vs. high school education or university degree.
In the CORA study, status of hormone replacement therapy was defined as premenopausal nonuse and postmenopausal nonuse, past use, or current use of hormone replacement therapy.
Fasting blood samples were collected from all participants of the CORA study. Among subjects who experienced an acute MI, these samples were taken as soon as possible, at least within 24 h. EDTA tubes were placed on ice within 10 min and centrifuged at 4°C for 10 min at 3000 rpm. Within 45 min of blood collection, plasma samples were frozen in liquid nitrogen and stored at –80°C. Folate analysis was conducted at the Institute of Clinical Chemistry, University Hospital of Hamburg-Eppendorf. Folate was determined by a competitive immunoassay on an IMMULITE2000 Analyser (DPC Biermann). Analyses of vitamin B-12 and Hcy were performed at the Department of Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke University, Magdeburg. Hcy was determined by a fluorescence polarization immunoassay on an Abbott IMx analyzer (Abbott). Vitamin B-12 was determined using an electrochemiluminescence immunoassay on a Roche Elecsys immunoassay analyzer (Roche Diagnostics). The between-run CV values were 6.15, 3, and 6% for folate, Hcy, and vitamin B-12, respectively.
Statistical analysis. To derive a dietary pattern predictive for risk of CHD, the RRR statistical method implemented in the PLS procedure of SAS (SAS Institute) was applied to the case-control study population. The application of this method in dietary pattern analyses is described in detail elsewhere (8). Briefly, RRR is applied to food groups, with biochemical markers for CHD chosen as response variables. In this way, linear functions of food groups can be derived by maximizing the proportion of explained biomarker variation. This approach ensures that differences in RRR pattern score (dietary pattern score) among study participants are related to differences in CHD biomarker concentrations. It is important to derive RRR patterns in the pooled data of cases and controls to guarantee that the observed variation in biomarker concentrations reflects in part the distinct biomarker profiles of cases and controls. We used the intake data of 49 food groups as predictors and plasma concentrations of Hcy, folate, and vitamin B-12 as response variables. Because the number of extracted RRR pattern scores cannot be higher than the number of selected responses, 3 scores were obtained.
Subsequently, the 3 dietary pattern scores were used as predictors for CHD in crude and adjusted conditional logistic regression models in the CORA study. Trends in CHD risk across pattern scores were assessed in logistic models by categorizing the scores in quintiles and using the quintile number as an independent variable.
For each individual food group, the explained proportion of variation of the dietary pattern score was expressed as the product of the corresponding standardized score parameter and the Pearson correlation coefficient x 100. In this way, the food groups that contributed most to the respective dietary pattern were identified.
For the derived RRR pattern, risks for CHD were estimated in the CORA study (odds ratio) using conditional logistic regression models. To cross-validate the results from the CORA study in a prospective cohort study, risk estimates were calculated for the same pattern in the EPIC-Potsdam study (hazard ratio) by Cox proportional hazard regression. We furthermore simplified the pattern score calculation by retaining only those food groups that each explained >5% of the score variation and setting their score parameters equal to 1 (22). This approach allowed us to concentrate on the most important food groups that can be assumed to be closely related to Hcy metabolism and may thus be reproducible in further studies. To further explore whether the food groups alone are related to CHD, we also estimated the relative risks (RRs) for CHD associated with the selected food groups in crude and adjusted models.
Major characteristics of the study populations are expressed as means ± SD or percentages. RRs are presented as point estimates and their 95% CI. All P-values are 2-tailed, and values < 0.05 are considered significant. All statistical analyses were performed using SAS software, version 8.02 (SAS Institute).
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RESULTS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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50%, whereas the mean Hcy concentration decreased by nearly a third.
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DISCUSSION |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Increasing evidence suggests a positive association between Hcy concentrations and the risk of CHD (2), and modifications of dietary patterns have a decisive effect on Hcy concentrations (23). Moreover, there is an inverse association between Hcy and plasma concentrations of folate and vitamin B-12, underlining an important role for diet in homocysteine metabolism (24–27). Data from the German Food Code (17) suggest that mushrooms, fresh fruit, cruciferous vegetables, nuts, and whole-grain bread contain considerable amounts of folate, which could explain the protective effect of these food groups for CHD (28). The increased consumption of vegetables and citrus fruit, both good sources of folate, improved folate status and decreased Hcy concentrations in a controlled dietary trial (29).
In contrast, food groups that are the most relevant sources of vitamin B-12 such as fish and meat did not explain relevant proportions of biomarker concentrations and were therefore not components of the identified pattern in our study. Furthermore, published reports concerning the relation between vitamin B-12 intake and Hcy concentration are inconsistent (25,30–33). Consistent with our pattern of food groups, other studies have linked intake of whole grains, fruit, and vegetables with reduced risk of CHD (34–37). Moreover, some components of our simplified food pattern are also components of "prudent" patterns, which were protective for CHD in both the Nurses’ Health Study (38) and the Health Professionals Follow-up Study (39). Moreover, french fries, which are nutritionally similar to the food group of fried potatoes in the present study, had a negative factor loading in the prudent pattern in the Nurses’ Health Study (38), whereas they were one of the most important foods characterizing the "Western" pattern that increased CHD risk (38,39). The prudent pattern itself was positively associated with plasma folate and inversely associated with Hcy concentration in a subsample of the Health Professionals Follow-up Study (40), which again is in line with our findings. An inverse association between intake of nuts and risk of CHD was demonstrated consistently in several prospective studies (34).
To our knowledge, this is the first study indicating that mushrooms as components of diet may have a role in reducing the risk for CHD. When analyzed as a single food group, however, mushrooms were not associated with reduced risk for CHD. Thus, the effect may occur only when mushrooms are consumed in combination with other food groups, but overall data are too limited to draw final conclusions, and our findings require confirmation by others. Generally, there is a paucity of epidemiologic and experimental studies addressing the biologic relevance of mushrooms for human diseases (41). In earlier studies, mushrooms were not analyzed as a separate food group, but rather were included in the group of vegetables (38,39). The significance of olive oil consumption for CHD risk is controversial (42,43). Data from a case-control study in Spain support an inverse association of olive oil intake with CHD risk (44), whereas results from other studies do not (43,45). Olive oil is traditional to the Mediterranean-type diet that was shown to be related to reduced risk for death due to CHD in a prospective study in Greece, although the mild inverse association of olive oil intake with mortality was not significant in that study (46). Although we identified olive oil as a component of the CHD protective pattern, intake was strikingly low in our study populations. This raises the question of whether olive oil may simply constitute a surrogate marker for healthier behavior. It can be assumed that people who consume more olive oil than others in our populations also apparently consume more folate and vitamin B-12 in their overall diets. However, it seems plausible that olive oil has a role in the prevention of CHD because it contains high levels of monounsaturated fatty acids and phenolic components (42,47,48). Moderate alcohol consumption, especially of red wine, has repeatedly been associated with lower CHD risk (49). Indeed, wine contributes to the described dietary pattern, which is in line with another study observing an inverse relation between homocysteine levels and alcohol intake (50). However, such an association, which would suggest a new potential mechanism by which wine consumption may lower CHD risk, is still debatable (51). Moreover, wine does not contain noteworthy amounts of folate or vitamin B-12, and its effect may be explained by the fact that the inverse association between folate intake and Hcy concentrations is intensified by alcohol consumption (52). However, it should be kept in mind that the identified foods may simply constitute indicators of an eating style and/or surrogate markers of other unknown dietary components.
The main strength of the study is the similarity of findings in 2 independently compiled German study populations with 2 different study designs. The direct use of the same food pattern was facilitated by the utilization of identical FFQs in the 2 studies. In the statistical analysis, we controlled for known CHD risk factors, consistent with former studies (53). However, due to measurement errors, incomplete information, and biased instruments common in observational studies, we cannot exclude residual confounding. We expect that intake of food presumed to be healthy is overestimated, and variation in biomarkers explained by food intake is underestimated. Thus, estimates of risk for CHD may be attenuated. In this context, it is also noteworthy that detailed information about the amounts of folate and vitamin B-12 consumed through supplements is missing. To explore the effect of supplement intake on CHD risk, we carried out a sensitivity analysis excluding vitamin supplement users, but results did not change appreciably. Further limitations are due to the fact that the pattern was derived from the CORA study, which is a retrospective case-control study (9). Recruitment of controls from the general population is prone to selection bias. Indeed, only 67% of the potential controls participated in the CORA study, suggesting that the proportion of health-conscious individuals was higher than in the normal population.
The potential advantages of RRR over traditional dimension-reduction techniques such as principal component analysis and factor analysis were described in detail elsewhere (8). On the other hand, it is important to note that there are several disadvantages to choosing specific biomarkers as responses in RRR. Specific biomarkers are intermediate variables that may be important for some but not all individuals. Because the method focuses on variables related to a specific pathway from diet to disease, other putative pathways cannot be considered. Thus, foods that have an influence on CHD but are not associated with this particular pathway may be excluded. Accordingly, we identified a different dietary pattern related to CHD risk, when using a different set of biomarkers in RRR (9).
Like other dietary pattern methods, such as principal component analysis and factor analysis, the RRR method produces linear functions of all food groups, which should be simplified using this new food pattern approach (22) to be more easily applied among other study populations and to concentrate on a few key food groups. This approach was successfully applied, demonstrating that a specific food pattern consisting of only 8 food groups was similarly associated with CHD risk in 2 different German studies. Because the described associations were present in German study populations, generalization appears to be limited. Different patterns may arise from a similar analytic approach in other geographic regions or cultures, because it can be assumed that dietary sources of the nutrients of interest vary considerably among different populations.
In conclusion, the RRR method is a helpful tool with which to derive dietary patterns related to the risk of CHD. We demonstrated that a combined high intake of whole-grain bread, fresh fruit, olive oil, wine, mushrooms, cruciferous vegetables, and nuts, but a low intake of fried potatoes affects biomarkers of homocysteine metabolism and is associated with decreased risk for CHD in 2 independent German study populations. These findings suggest that the biomarker profile of folate, Hcy, and vitamin B-12 may be involved in putative pathways connecting diet and the development of coronary artery disease.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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3 Abbreviations used: CHD, coronary heart disease; CORA, Coronary Risk Factors for Atherosclerosis in Women; EPIC, European Prospective Investigation into Cancer and Nutrition; Hcy, homocysteine; MI, myocardial infarction; RR, relative risk; RRR, reduced rank regression.
Manuscript received 2 February 2005. Initial review completed 23 February 2005. Revision accepted 20 May 2005.
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LITERATURE CITED |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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