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Nutritional Epidemiology

Dietary Patterns Throughout Adult Life Are Associated with Body Mass Index, Waist Circumference, Blood Pressure, and Red Cell Folate^1,2

³ MRC Human Nutrition Research, Elsie Widdowson Laboratory, Cambridge CB1 9NL, UK and ⁴ MRC National Survey of Health and Development, University College and Royal Free Medical School, London WC1E 6BT, UK

^* To whom correspondence should be addressed. E-mail: sarah.mcnaughton{at}deakin.edu.au.

	ABSTRACT

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
Dietary patterns are important in the prevention of chronic disease; however, there are few studies that include repeat measures of dietary patterns. The objective of this study was to assess the relations between dietary patterns during adult life (at ages 36, 43, and 53 y) and risk factors for chronic disease at age 53 y. Participants of a longitudinal study of health completed a 5-d food diary at 3 occasions during adult life (n = 1265). Factor analysis was used to identify dietary patterns and a pattern score was calculated from the consumption of the food items in each dietary pattern. Means and 95% CI for dietary pattern scores were calculated for each risk factor category using random effects models adjusted for socio-demographic and health-related behaviors. In women, the fruit, vegetables, and dairy pattern was inversely associated with BMI (P < 0.004), waist circumference (P = 0.0007), blood pressure (P = 0.02), and was positively associated with red cell folate (P < 0.03). The ethnic foods and alcohol pattern was also inversely associated with blood pressure (P = 0.008), whereas the meat, potatoes and sweet foods pattern was positively associated with glycated hemoglobin (P = 0.01). In men, a mixed pattern was inversely associated with waist circumference (P = 0.02) and blood pressure (P = 0.01), whereas there were no significant associations with the ethnic foods and alcohol pattern. Specific dietary patterns throughout adult life were associated with chronic disease risk factors.

	Introduction

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

Increasingly, dietary patterns or overall diet are thought to be important determinants of chronic disease. Research from the Health Professionals Follow-Up Study and the Nurse Health Study cohorts suggest that dietary patterns associated with high consumption of fruit, vegetables, whole-grains, legumes, and poultry (known as the "prudent" pattern) have been associated with a lower risk of coronary heart disease (2,3). A dietary pattern, known as the "Western" pattern, and characterized by high consumption of refined cereals, processed and red meats, desserts, and high- fat dairy products, was associated with an increased risk of coronary heart disease, colon cancer, and Type II diabetes (2,4–6). These dietary patterns, and similar ones in other populations, are associated with obesity and chronic disease risk factors (2,7–11).

Dietary patterns vary according to age, sex, ethnicity, and culture (12–14). Most research on dietary patterns has been conducted in the United States and little work has been conducted on dietary patterns within the United Kingdom (15,16). Results from other populations have identified different dietary patterns from those in the United States (17,18). These differences in the types of foods consumed and the combinations of food types may have important effects on the prevention of chronic disease (19). In addition, many existing studies investigating the relation between dietary patterns and chronic disease have tended to use only one assessment of diet in adult life (9,20,21), and recent research has suggested that repeated measures during adult life are important (22). The objective of this study was to assess the relation between dietary patterns at multiple time points during adult life and risk factors for chronic disease.

	Methods

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The Medical Research Council (MRC) National Survey of Health and Development (NSHD, also known as the 1946 British Birth Cohort) is a longitudinal study conducted on a social class stratified, random sample of 5362 singleton births in England, Scotland, or Wales during the 1st wk of March, 1946. Medical, social, educational, and other information has been collected throughout the life course (23). The data analyzed here relates to dietary records collected in 1982, 1989, and 1999 when subjects were aged 36, 43 and 53 y, respectively (24–26). Risk factors for this group were measured in 1999 at age 53 y. The population interviewed at the age of 53 y is considered still representative of 53 y–olds in the native born population (23). Ethical approval was provided by the North Thames Multicentre Research Ethics Committee.

Subjects were classified according to their social class and region of residence. Four regions of residence were defined: 1) Scotland, 2) the North (north, northwest, Yorkshire), 3) Central, Southwest and Wales (midlands, north midlands, east, south, southwest, Wales), 4) London and Southeast. Categories of occupational social class in 1999 were defined as nonmanual (managerial, professional, skilled professional ancillaries, and service providers) and manual (skilled, nonskilled, and agricultural workers) (27). The highest educational qualification achieved by 26 y of age was classified as: 1) advanced secondary education (A levels or equivalent, usually attained at 18 y) and higher education (degree level or equivalent), 2) ordinary secondary qualifications (O levels or equivalent, usually attained at 16 y), and 3) less than secondary qualifications (28).

Subjects provided information on behaviors, such as alcohol consumption, physical activity, and smoking, during an interview with trained health nurses in 1999. Alcohol consumption was categorized as none, special occasions only, and more frequently. Physical activity was based on response to the question: Do you regularly participate in vigorous leisure activity? (yes/no). Smoking status was categorized as nonsmokers, past smokers, and current smokers.

    Risk factor measurement. A physical examination was conducted including anthropometric and blood pressure measurements, and a nonfasting blood sample was collected in 1999 at age 53 y (23,29,30). Waist circumference, height, and weight were measured with a standardized protocol, and BMI was calculated (kg/m²), with "at risk" defined as a waist circumference >102 cm in men and >88 cm in women (31) and the standard cut-offs used for BMI (32). Blood pressure was measured twice, with the survey member seated and after 5 min of rest, using an Omron HEM-705 automated digital oscillometric sphygmomanometer (Omron); the 2nd blood pressure reading was used for this analysis (33). Subjects were defined as at risk with respect to hypertension if their systolic blood pressure was >140 mm Hg and their diastolic blood pressure was >90 mm Hg, or if the subject was taking medication for hypertension (34).

A nonfasting blood sample was also collected. Plasma total cholesterol and HDL was analyzed by enzymatic CHOD-PAP using standardized procedures using a Bayer DAX-72 analyzer (29). HDL cholesterol is measured after precipitation of all other lipid fractions using manganese/phosphotungstic acid. LDL cholesterol was calculated using the Friedwald formula. Standard cut-offs for determining subjects at risk with respect to lipids were used (total cholesterol, >5.0 mmol/L; HDL, <1.0 mmol/L; LDL cholesterol, >3.0 mmol/L) (35). Red cell folate was measured by a standardized magnetically enhanced enzyme immunoassay using specific folate antibody on an Abbott Axsym analyzer. Glycated hemoglobin (HbA₁C) percentage was measured using ion-exchange chromatographic separation using HPLC on a Tosoh A1c-2.2 Analyzer. Red cell folate and HbA₁C were categorized into thirds. There were too few subjects with a HbA₁C level of >7% to use this classification (men, 0.5%; women, 2.0%) and no subjects included in this subset of NSHD participants (n = 1265) were self-reported diabetics.

    Dietary assessment. Methods used to assess the dietary intake of participants have been described in detail previously (26). Briefly, dietary intake was assessed at each time point using a 5-d food diary in which participants were asked record information over consecutive days on their food and beverage intake in household measures, including brand names of food products, food preparation methods, and any recipes used (25). Food and beverage intake from the food diary were coded using a specially designed data-entry program (25) and analyzed using in-house nutrient analysis programs at MRC Human Nutrition Research, Cambridge (24). At all 3 ages, the diaries were completed between spring and autumn (April to September 1982, June to October 1989, and May to November 1999). All foods and beverages consumed in 1982, 1989, or 1999 were allocated to 1 of 126 individual food groups. Because the distribution of the consumption of foods and beverage items was highly skewed, a binary variable for each food group was created and respondents were categorized as consumers or nonconsumers. Subjects were also asked to record any dietary supplements that they consumed each day and supplement users were defined as those subjects who reported taking at least 1 supplement (including vitamins, minerals, and bioactive compounds) during the 5-d food diary (36).

    Statistical analysis. Dietary patterns were assessed for men and women separately using Mplus factor analysis for binary variables [Mplus statistical package (37)]. Exploratory factor analysis of food intake data collected at age 53 y was conducted to obtain food and beverage items that loaded highly (factor loading of 0.25) on a particular factor. The number of factors determined was based on their interpretability, the number of items with high loadings, and root mean square errors of <0.05. Cross-sectional analysis at age 36 and 43 y was also conducted and cross-checked against those determined at age 53 for consistency. Three dietary patterns in women and 2 dietary patterns in men, consistent across the 3 time points were identified (labeled) as distinct dietary patterns (38).

We used the simplified dietary pattern variable score developed by Schulze et al. (39), which is less population-dependent and is the sum of unweighted standardized food variables that load high on a specific pattern of interest (that is, each food consumed contributed 1 point). A simplified score is particularly useful in longitudinal studies where weightings or factor loadings would certainly change. For each dietary pattern at age 53 y, we derived a simplified score from the consumption of the r food items in that dietary pattern. That is, we summed the number of the consumed items (X1 + X2 + ...+ Xr, r <126). Consumption of items with negative loadings were subtracted from the score (39). These were used to obtain the respondents' dietary pattern scores for the 3 time points. These same equations were used to obtain the respondents' dietary pattern scores at ages 36 and 43 y. Therefore, each subject has a score for each dietary pattern with higher scores reflecting consumption of more food items associated with that dietary pattern.

Chronic disease risk factors measured in 1999 at age 53 y were categorized into risk categories according to established criteria (described above). The longitudinal analysis was conducted using the 3 repeat measures of dietary pattern scores of each pattern modeled simultaneously as variables in a mixed model, with random slopes and random intercepts. This enables one to describe the trend in dietary pattern (or dietary pattern trajectories) over time while taking account of the correlation that exists between successive measures of dietary data (40). This model was used to investigate the associations between the dietary pattern trajectories and each risk factor, with time entered as a covariate (Model 1).Potential confounding factors, including socio-demographic factors (age, social class, education, and region) and lifestyle behaviors (smoking, physical activity, alcohol, and supplement use) were investigated and, where associated with the dietary pattern and the risk factor, were included in the full multivariate model (Model 2). We have used this statistical approach to determine whether subjects with adverse chronic disease risk in later life have had different dietary patterns over adult life compared with those subjects at lower chronic disease risk.

Values presented are means and 95% CI. Associations between socio-demographic factors and dietary patterns were investigated using chi-square analysis, after tertiles of dietary pattern score were calculated and used as cut-off points for grouping into thirds. Differences with P-values < 0.05 were considered significant.

	Results

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
Factor analysis revealed 3 dietary patterns for women and were labeled ethnic foods and alcohol; meat, potatoes, and sweet foods; and fruit, vegetables, and dairy. Two dietary patterns were apparent in men and were labeled ethnic foods and alcohol; and mixed. Full details of the food and beverage items loading on each pattern and the associated factor loadings can be found in Supplemental Table 1. Detailed descriptions of the dietary patterns have been published elsewhere (38). Briefly, stability was assessed according to agreement between tertile categories over the 3 time points. The ethnic foods and alcohol pattern; the fruit, vegetables and dairy pattern; and the mixed pattern showed fair to moderate agreement ( = 0.28–0.44), whereas there was poor agreement for the meat, potatoes and sweet foods pattern (38). Characteristics of the study population with respect to important socio-demographic and lifestyle behaviors across thirds of dietary pattern scores at age 53 are presented in Tables 1 and 2. Among women, social class, education, physical activity, and alcohol consumption showed significant associations with the ethnic foods and alcohol pattern and the fruit, vegetables, and dairy pattern. Region of residence was associated with the meat, potatoes, and sweet foods pattern only, smoking status was associated with the fruit, vegetables, and dairy pattern, and supplement use was associated with all 3 dietary patterns. Among men, associations were found between social class, education, smoking, and physical activity and both dietary patterns, whereas supplement use and alcohol consumption were only associated with the ethnic foods and alcohol pattern.

	Discussion

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
In this study of longitudinal dietary patterns, certain dietary patterns were associated with healthier risk-factor profiles. Among women, a dietary pattern characterized by intakes of fruits, vegetables, and low fat dairy was inversely associated with BMI, waist circumference, and blood pressure and was positively associated with red cell folate. In men, we identified a mixed pattern that was also inversely associated with waist circumference and blood pressure. The ethnic foods and alcohol pattern was inversely associated with blood pressure in women, but not in men. In women, we also identified a meat, potatoes, and sweet foods pattern that was positively associated with HbA₁C. There were no significant associations between any of the dietary patterns and total, HDL, or LDL cholesterol in either men or women.

Comparison with other work is not straightforward due to data-driven approach to dietary pattern analysis with differences in the dietary assessment methods, the number and type of food groupings, and the statistical analysis techniques (41). Despite these variations in methodology, substantial similarities were identified in a range of population groups, and our results are consistent with the findings of other studies (41,42). Similar to our results, Newby et al. (7,8), using both factor analysis and cluster analysis, identified a consistent dietary pattern characterized by reduced-fat dairy products, cereals, and fruit that was significantly inversely associated with an annual change in BMI among women and waist circumference in both men and women. Similar inverse associations between dietary patterns characterized by whole-grain cereals, fruits, and vegetables with BMI and weight gain have been reported elsewhere (2,43).

In line with our results in women, van Dam et al. (9) identified a dietary pattern among Dutch adults labeled as cosmopolitan (rice, chicken fish, vegetables, and wine) that was inversely associated with blood pressure. Although we also found a relation between the fruit, vegetables, and dairy pattern in women and the mixed pattern in men that was inversely associated with blood pressure, van Dam et al. (9) did not identify corresponding dietary patterns among their population. In our women, the fruit, vegetables, and dairy pattern, which was inversely associated with blood pressure, may be similar to the combination diet in the DASH study (44).

Among US adults, Kerver et al. (45) identified a Western pattern rich in red and processed meats, eggs, high fat dairy, potatoes, refined grains, and sweets that is similar to the meat, potatoes, and sweet foods pattern in the women of the current study. As found in our meat, potatoes and sweet foods pattern, their Western pattern was positively associated with HbA₁C. Similarly, van Dam et al. (9) identified a similar pattern labeled as traditional (high in red meat and potatoes, low in fruit and low-fat dairy) that was positively associated with plasma glucose. Mizoue et al. (11) identified a high fruit, vegetable, and dairy pattern that was inversely associated with impaired glucose metabolism.

In the current study, none of the dietary patterns showed significant associations with blood lipids. Similar to our results, the Western pattern identified by Kerver et al. (45) and the prudent and Western patterns identified in the Nurse's Health Study were not associated with blood lipids (10), although previous analysis of this cohort found associations between the prudent and Western dietary patterns and risk of coronary heart disease (3). Similarly, in the Lyon Diet Heart Study, subjects following a Mediterranean type diet showed a 50–70% reduction in risk of recurrent heart disease, despite the intervention group and control group maintaining similar risk factor profiles with respect to lipids, blood pressure, and body weight (46,47). Therefore, previous research has indicated that reduction in cardiovascular disease risk may be achieved through pathways other than blood lipids. However, it is possible that our lack of associations was due to the use of nonfasting blood samples.

The use of consumption vs. nonconsumption of foods in this analysis was necessary due to highly skewed distributions of the consumption of the food and beverage items. This was primarily due to the retention of a relatively large number of individual food items compared with the smaller number of very broad food groups used in most studies and resulted in a focus on the types of food consumed. This is a possible disadvantage of this analysis because it represents foregoing information on quantities of food consumed in favor of more detailed information on the types of foods consumed. However, with this approach, we identified differences between the food patterns that would not have emerged with broader food groups. For example, in women, the meat, potatoes, and sweet foods pattern did contain some vegetables (carrots and peas) that were different from the vegetables consumed in the fruit, vegetables, and dairy pattern and the ethnic foods and alcohol pattern (broccoli and spinach, respectively), and these differences may have contributed to the relation observed with red cell folate. Of note, McCann et al. (48) compared results using 36 broad food groups and 168 (mostly) single food items in a dietary pattern analysis using factor analysis. They found that, although the number and type of dietary patterns did not change, the relation between dietary patterns and endometrial cancer risk was substantially attenuated when using broad groups, which suggests that greater detail in food groupings is required.

Seasonal variation within each survey period has been minimized given that, at all 3 ages, the diaries were completed over a large portion of the year, but always excluded winter (April to September 1982, June to October 1989, and May to November 1999), when shortages of fresh fruits and vegetables would be most apparent. However, seasonal variation in food supplies may have been greatest in 1982, insofar as subsequent growth in the importation of produce from tropical and subtropical countries has provided a year-around supply of a wide range of fruit and vegetables (26).

Although we acknowledge the difficulties associated with measuring dietary intake and the potential for measurement error, food diary methods generally provide good estimates of usual intake (49). In this study, subjects were not aware of their risk categories when they recorded dietary intake, and the measurements of diet were collected prospectively; therefore, we would expect the potential for reporting bias to be reduced and that any measurement error present would not be associated with later health status. Nondifferential measurement error tends to result in attenuation of the observed relations rather than a strengthening of relations (50,51).

In this study, socio-demographic factors (age, social class, education, and region) and lifestyle behaviors (smoking, physical activity, alcohol and supplement use) are investigated as potential confounders. However, it is possible that other confounders were not measured in this study. In addition, as the confounders may be measured with error, it is possible that there was residual confounding.

The strength of this study is that it incorporates multiple measures of dietary intake over an adult life. Many epidemiological studies assume relatively stable behavioral patterns, and fluctuations in behavior are generally not taken into account when predicting effects of behavioral patterns on health. It is also noted that research at the intraindividual level is limited, and studies tend to focus on time-related differences between cohorts (52).

This study adds to the existing literature by identifying longitudinal dietary patterns during adult life and showing that specific dietary patterns are associated with chronic disease risk factors. Among women, a fruit, vegetables, and dairy dietary pattern was inversely associated with BMI, waist circumference, and blood pressure and was significantly positively associated with red cell folate, whereas among men, the mixed pattern was also inversely associated with waist circumference and blood pressure. An ethnic foods and alcohol pattern was inversely associated with blood pressure in women, but not in men; and in women, a meat, potatoes, and sweet foods pattern was positively associated with HbA₁C.

	FOOTNOTES

 
¹ This work was supported by funding from the Medical Research Council (U.K.).

² Supplemental Table 1 is available with the online posting of this paper at jn.nutrition.org.

Manuscript received 18 July 2006. Initial review completed 21 August 2006. Revision accepted 30 October 2006.

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