Unique dietary patterns and chronic disease risk profiles of adult men: the Framingham nutrition studies

OBJECTIVE: To identify the dietary patterns of adult men and examine their relationships with nutrient intake and chronic disease risk over long-term follow-up. DESIGN/SUBJECTS: Baseline 145-item food frequency questionnaires from 1,666 Framingham Offspring-Spouse cohort men were used to identify comprehensive dietary patterns. Independent 3-day dietary records at baseline and 8 years later provided estimates of subjects' nutrient intake by dietary pattern. Chronic disease risk factor status was compared at baseline and 16-year follow-up across all male dietary patterns. STATISTICAL ANALYSES: Cluster analysis was applied to food frequency data to identify non-overlapping male dietary patterns. Analysis of covariance and logistic regression were used to compare nutrient intake, summary nutritional risk scores, and chronic disease risk status at baseline and follow-up by male dietary pattern. RESULTS: Five distinct and comprehensive dietary patterns of Framingham Offspring-Spouse men were identified and ordered according to overall nutritional risk: Transition to Heart Healthy, Higher Starch, Average Male, Lower Variety, and Empty Calories. Nutritional risk was high and varied by dietary pattern; key nutrient contrasts were stable over 8-year follow-up. Chronic disease risk also varied by dietary pattern and specific subgroup differences persisted over 16 years, notably rates of overweight/obesity and smoking. CONCLUSIONS: Quantitative cluster analysis applied to food frequency questionnaire data identified five distinct, comprehensive, and stable dietary patterns of adult Framingham Offspring-Spouse cohort men. The close associations between the dietary patterns, nutritional risk, and chronic disease profiles of men emphasize the importance of targeted preventive nutrition interventions to promote health in the male population.     

Comparison of cluster and principal component analysis techniques to derive dietary patterns in Irish adults

The aims of the present study were to examine and compare dietary patterns in adults using cluster and factor analyses and to examine the format of the dietary variables on the pattern solutions (i.e. expressed as grams/day (g/d) of each food group or as the percentage contribution to total energy intake). Food intake data were derived from the North/South Ireland Food Consumption Survey 1997-9, which was a randomised cross-sectional study of 7 d recorded food and nutrient intakes of a representative sample of 1379 Irish adults aged 18-64 years. Cluster analysis was performed using the k-means algorithm and principal component analysis (PCA) was used to extract dietary factors. Food data were reduced to thirty-three food groups. For cluster analysis, the most suitable format of the food-group variable was found to be the percentage contribution to energy intake, which produced six clusters: 'Traditional Irish'; 'Continental'; 'Unhealthy foods'; 'Light-meal foods & low-fat milk'; 'Healthy foods'; 'Wholemeal bread & desserts'. For PCA, food groups in the format of g/d were found to be the most suitable format, and this revealed four dietary patterns: 'Unhealthy foods & high alcohol'; 'Traditional Irish'; 'Healthy foods'; 'Sweet convenience foods & low alcohol'. In summary, cluster and PCA identified similar dietary patterns when presented with the same dataset. However, the two dietary pattern methods required a different format of the food-group variable, and the most appropriate format of the input variable should be considered in future studies.     

The internal validity of a dietary pattern analysis. The Framingham Nutrition Studies

STUDY OBJECTIVES: To examine the internal validity of a dietary pattern analysis and its ability to discriminate clusters of people with similar dietary patterns using independently assessed nutrient intakes and heart disease risk factors. DESIGN AND PARTICIPANTS: Population based study characterising dietary patterns using cluster analysis applied to data from the semiquantitative Framingham food frequency questionnaire collected from 1942 women ages 18-76 years, between 1984-88. Setting: Framingham, Massachusetts. MAIN RESULTS: Of 1942 women included in the cluster analysis, 1828 (94%) were assigned to one of the five dietary pattern clusters: Heart Healthy, Light Eating, Wine and Moderate Eating, High Fat, and Empty Calorie. Dietary patterns differed substantially in terms of individual nutrient intakes, overall dietary risk, heart disease risk factors, and predicted heart disease risk. Women in the Heart Healthy cluster had the most nutrient dense eating pattern, the lowest level of dietary risk, more favourable risk factor levels, and the lowest probability of developing heart disease. Those in the Empty Calorie cluster had a less nutritious dietary pattern, the greatest level of dietary risk, a heavier burden of heart disease risk factors, and a relatively higher probability of developing heart disease. Cluster reproducibility using discriminant analysis showed that 80% of the sample was correctly classified. The cluster technique was highly sensitive and specific (75% to 100%). CONCLUSIONS: These findings support the internal validity of a dietary pattern analysis for characterising dietary exposures in epidemiological research. The authors encourage other researchers to explore this technique when investigating relations between nutrition, health, and disease.     

Dietary patterns and nutrient intakes of 7-year-old children taking part in an atherosclerosis prevention project in Finland

OBJECTIVE: To evaluate the dietary patterns of 7-year-old children participating in an atherosclerosis prevention project and the relationship of those dietary patterns to nutrient intakes and serum cholesterol values. DESIGN: In the randomized, prospective Special Turku Coronary Risk Factor Intervention Project (STRIP) 1,062 children were randomly assigned to an intervention group (n=540; low-saturated fat, low-cholesterol diet) or to a control group (n=522; unrestricted diet) at 7 months of age. SUBJECTS/SETTINGS: The intervention families received, at 6-month intervals, individualized counseling that focused on the known environmental atherosclerosis risk factors and aimed at reducing children's saturated fat and cholesterol intake. Nutrition counseling was targeted at the child but, because of the young age of the children, was given to the parents. When children were 7 years old, food and nutrient intakes of 307 intervention and 323 control children were studied using 4-day food records. STATISTICAL ANALYSES PERFORMED: K-means cluster analysis was used to classify children into 4 groups on the basis of similarity of food intake. Differences in nutrient intakes and serum lipid concentrations between children in the 4 food intake clusters were evaluated using Tukey's multiple comparison test. RESULTS: Intervention children dominated the bread, skim milk, and margarine cluster and the cereals, rice, and pasta cluster whereas the 1.5%-fat milk and butter cluster included mainly control children. Saturated fat intake was nearest to the recommendations, that is 11.7% and 11.9% of energy, in the bread, skim milk, and margarine cluster and the cereals, rice, and pasta cluster, respectively. Children in the bread, skim milk, and margarine cluster had 20% to 27% higher fiber intakes (P<.001) whereas children in the sugar and sweets cluster had markedly higher sugar intakes than children in other clusters (P<.001). Serum cholesterol concentrations were lower in those clusters with high dietary ratios of polyunsaturated to saturated fat. CONCLUSION: Detailed and repeated dietary counseling of parents, starting when children are aged 7 months, that aims at decreasing children's exposure to known nutrition risk factors for coronary heart disease modifies children's food patterns and nutrient intakes toward expected values.     

An exploratory study of dietary intake patterns among adults diagnosed with cardiovascular risk factors

Abstract

The objective of the present exploratory study was to assess compliance with the nutrient recommendations among a convenience sample of adults diagnosed with cardiovascular risk factors in northern Greece and evaluate their dietary intake patterns. Ninety-two people participated in this cross-sectional study. Dietary assessment was carried out using a semi-quantitative food frequency questionnaire. Principal components analysis (PCA) and hierarchical cluster analysis (HCA) were adopted to obtain dietary patterns and classify individuals with similar dietary behaviour. HCA, performed on the factorial scores obtained from PCA, revealed a 4-group interpretable and statistically significant clustering of participants. For all clusters, the mean daily intake for saturated fatty acids was more than 10% of total calories, while the mean sodium intake was above 1500?mg; additionally, a relatively low mean Mediterranean diet score was recorded. Dietary interventions should be considered to raise awareness and expand knowledge on the nutritional and functional benefits of heart-healthy foods.     

Clustering of dietary variables and other lifestyle factors (Dutch Nutritional Surveillance System).

STUDY OBJECTIVE--The aim was to investigate whether dietary factors cluster in a favourable or unfavourable way and to characterise the groups identified by lifestyle and sociodemographic variables. DESIGN AND SETTING--This cross sectional study was based on data of the 1987-1988 Dutch national food consumption survey (DNFCS), obtained from a panel by a stratified probability sample of the non-institutionalised Dutch population. PARTICIPANTS--3781 adults (1802 males and 1979 females) of the DNFCS, aged 19 to 85 years, were studied. MEASUREMENTS AND MAIN RESULTS--To estimate dietary intake two day food records were used. Lifestyle factors were collected by structured questionnaire and sociodemographic variables were available from panel information. Cluster analysis was used to classify subjects into groups based on similarities in dietary variables. Subsequently, these groups were characterised by sociodemographic and lifestyle factors as well as by the consumption of food groups. Eight clusters were found. In comparison with the guidelines, the dietary quality in four clusters was poor. The cluster with the poorest dietary intake (high intake of fat, cholesterol, and alcohol; low intake of dietary fibre) showed on average a high consumption of animal products (except milk), fats and oils, snacks, and alcoholic beverages, and a low consumption of fruit, potatoes, vegetables, and sugar rich products. Smoking, body mass index, dietary regimen on own initiative, hours of sleep, gender, age, socioeconomic status, and day of the week were found to discriminate among the clusters. CONCLUSIONS--Cluster analysis resulted in substantial differences in mean nutrient intake and seems useful for dietary risk group identification. Undesirable lifestyle habits were interrelated in some clusters, but an exclusive lifestyle for health risk has not been found.     

Comparative strategies for using cluster analysis to assess dietary patterns

OBJECTIVES: To characterize dietary patterns using two different cluster analysis strategies. DESIGN: In this cross-sectional study, diet information was assessed by five 24-hour recalls collected over 10 months. All foods were classified into 24 food subgroups. Demographic, health, and anthropometric data were collected via home visit. SUBJECTS: One hundred seventy-nine community-dwelling adults, aged 66 to 87 years, in rural Pennsylvania. STATISTICAL ANALYSIS: Cluster analysis was performed. RESULTS: The methods differed in the food subgroups that clustered together. Both methods produced clusters that had significant differences in overall diet quality as assessed by Healthy Eating Index (HEI) scores. The clusters with higher HEI scores contained significantly higher amounts of most micronutrients. Both methods consistently clustered subgroups with high energy contribution (eg, fats and oils and dairy desserts) with a lower HEI score. Clusters resulting from the percent energy method were less likely to differentiate fruit and vegetable subgroups. The higher diet quality dietary pattern derived from the number of servings method resulted in more favorable weight status. CONCLUSIONS: Cluster analysis of food subgroups using two different methods on the same data yielded similarities and dissimilarities in dietary patterns. Dietary patterns characterized by the number of servings method of analysis provided stronger association with weight status and was more sensitive to fruit and vegetable intake with regard to a more healthful dietary pattern within this sample. Public health recommendations should evaluate the methodology used to derive dietary patterns.     

Associations between microalbuminuria and animal foods, plant foods, and dietary patterns in the Multiethnic Study of Atherosclerosis

BACKGROUND: The balance between the intake of animal and the intake of plant foods may influence renal vascular integrity as reflected by urinary albumin excretion. OBJECTIVE: We assessed cross-sectional associations between urinary albumin excretion and dietary patterns and intake of plant and animal foods. DESIGN: At baseline, diet (food-frequency questionnaire) and the urinary albumin-to-creatinine ratio (ACR; spot urine collection) were measured in 5042 participants in the Multi-Ethnic Study of Atherosclerosis who were aged 45-84 y and were without clinical cardiovascular disease, diabetes, or macroalbuminuria (sex-adjusted ACR >or= 250). We derived dietary patterns by principal components analysis. We also summed food groups to characterize plant food intake (fruit, fruit juice, vegetables, nuts, legumes, whole grains, and refined grains), animal food intake (red meat, processed meat, poultry, fish, high-fat dairy, and low-fat dairy), and nondairy animal food intake. RESULTS: After adjustment for multiple demographic and lifestyle confounders, a dietary pattern characterized by high consumption of whole grains, fruit, vegetables, and low-fat dairy foods was associated with 20% lower ACR across quintiles (P for trend = 0.004). Neither total animal nor total plant food intake was associated with ACR. However, greater low-fat dairy consumption was associated with 13% lower ACR across quartiles (P for trend = 0.03). Total nondairy animal food consumption was associated with 11% higher ACR across quintiles (P for trend = 0.03). CONCLUSIONS: A high intake of low-fat dairy foods and a dietary pattern rich in whole grains, fruit, and low-fat dairy foods were both associated with lower ACR. In contrast, collectively, nondairy animal food intake was positively associated with ACR.     

Adults with healthier dietary patterns have healthier beverage patterns

There is an absence of research examining associations between food and beverage intake patterns and most research has centered on soft drinks, whereas research on overall beverage patterns is absent. Using data from the National Health and Nutrition Examination Survey 99-02 for adults aged 19 y and older, we independently examined beverage and food intake patterns, as well as their interrelations. Cluster analysis generated mutually exclusive intake patterns for beverages and foods. Multinomial logistic regression models provided the odds of a given beverage pattern for each food pattern; we then compared the probability of a given beverage pattern for each food pattern. Six beverage and 6 food patterns emerged. Beverage patterns revealed that calorically sweetened, non caloric, and diet beverages tended to be consumed independently of one another. Being in the Snacks and High-Fat Foods cluster increased the odds of being in the Coffee and Soda (odds ratio (OR): 1.62 [95% CI: 1.27-2.06]) or Nutrients and Soda (OR: 1.51 [95% CI: 1.14-2.00]) beverage clusters and decreased the odds of being in the Water and Tea (OR: 0.51 [95% CI: 0.52-0.97]) cluster relative to the odds of being in the Water, Coffee, and Tea cluster. The opposite was true for the Vegetable pattern. Furthermore, persons who had a healthier food pattern had a higher probability of having a non caloric beverage pattern than persons who did not. Increasing awareness of both the contribution of calorie-containing beverages to overall energy intake and dietary patterns associated with these beverages helps inform policies targeted at reducing energy intake in the population.     

Dietary patterns predict the development of overweight in women: The Framingham Nutrition Studies

OBJECTIVE: To investigate relationships between dietary patterns and the development of overweight. DESIGN: Longitudinal analyses during 12 years of follow-up involved the identification of dietary patterns at baseline using cluster analysis applied to a 145-item semiquantitative food frequency questionnaire. SUBJECTS/SETTING: 737 non-overweight women in the Framingham Offspring/Spouse cohort (mean age, 45 years). MAIN OUTCOME MEASURE: Development of overweight (BMI> or =25) at follow-up. STATISTICAL ANALYSES: Relative risks were calculated using Proc Genmod and multivariate models comprehensively considered potential confounders. RESULTS: Five dietary patterns were identified among the cohort at baseline: Heart Healthy, Light Eating, Wine and Moderate Eating, High Fat, and Empty Calorie. Over 12 years, the crude risk of becoming overweight was 29% overall, ranging from 22% of women in the Wine and Moderate Eating cluster to 41% of women in the Empty Calorie cluster. Compared with women who ate a lower-fat, nutritionally varied Heart Healthy diet, women who ate an Empty Calorie diet that was rich in sweets and fats with fewer servings of nutrient-dense fruits, vegetables, and lean food choices were at higher risk for developing overweight [RR 1.4, 95% CI (0.9, 2.2)] after adjusting for age, smoking status, physical activity, menopausal status, energy intake, intentional dieting, and usual weight pattern. Women who ate an Empty Calorie dietary pattern were also younger and were more likely to smoke. CONCLUSIONS: Behavioral interventions for weight management and obesity prevention may be enhanced by creatively targeting differences in eating patterns, dietary quality, and other lifestyle behaviors of distinct subgroups of the population.     

More favorable dietary patterns are associated with lower glycemic load in older adults

OBJECTIVE: Glycemic load represents the total glycemic effect of the diet and may reduce the risk for chronic disease by affecting the risk for obesity and by altering metabolic endpoints. The food choices associated with lower-glycemic-load diets have received little investigation. Therefore, the purpose of this research was to examine the food patterns associated with lower-glycemic-load diets to establish targeted intervention messages. SUBJECTS/SETTING: A random sample (n=179; 81 male and 98 female subjects) of older adults > or =65 years of age in the Geisinger Rural Aging study, a nutritional risk screening study. METHODS: Standardized methodology was used to calculate the glycemic load from data obtained in five 24-hour recalls. Statistical analysis t tests compared dietary patterns between male and female subjects from two eating pattern clusters identified in previous cluster analysis based on food group intake. RESULTS: The mean (+/-standard deviation) glycemic load for the entire sample was 115.6 (+/-39.9). Two clusters were identified, and male and female subjects in one cluster had a lower glycemic load (113.7+/-44.2 and 94.0+/-27.5, respectively) than male and female subjects in the second cluster (139.9+/-38.8 and 110.7+/-35.9, respectively) ( P <.01). Participants in the lower-glycemic-load cluster consumed more carbohydrate from cereal, fruits, vegetables, and milk, whereas those in the higher-glycemic-load cluster consumed more breads and desserts. CONCLUSIONS: Promoting appropriate portions of nutrient-dense carbohydrate foods, particularly whole grain, fruits, vegetables, and dairy foods, may offer reasonable guidance for lowering the glycemic load of the diet among older adults. A lower-glycemic-load diet may reduce the risk for obesity and many chronic diseases.     

Dietary patterns predict mortality in a national cohort: the National Health Interview Surveys, 1987 and 1992

We examined the association of mortality and dietary patterns using data from the National Health Interview Surveys of 1987 and 1992 (n = 10,084), aged >/=45 y at baseline (with 2287 deaths due to all causes over 5.9 median years of follow-up). The approximately 60-item FFQ administered at baseline was examined for mentions of foods and dietary behaviors recommended in current dietary guidance (fruits, vegetables, lean poultry and alternates, low-fat dairy, and whole grains), and the resulting patterns were expressed as follows: 1) a Recommended Foods and Behavior Score (RFBS), 2) factor scores from factor analysis, and 3) clusters from cluster analysis. The multivariate-adjusted relative risk (RR) of mortality for each of the 3 types of dietary patterns was examined using Cox proportional hazards regression analysis. In men, RR of all-cause mortality was 0.72 (95% CI: 0.56, 0.92, P for trend < 0.001) for RFBS, and 0.74 (95% CI: 0.57, 0.95, P for trend = 0.002) for the fruit-vegetable-whole grain factor score when comparing extreme quartiles. Membership in 1 of the 4 clusters also was associated with lower risk in men (RR = 0.82, 95% CI: 0.66, 1.01). For women, the RFBS was a modest inverse predictor of mortality after multivariate adjustment (RR = 0.80, 95% CI: 0.61, 1.04, P for trend = 0.04), but estimates for factor and cluster patterns were attenuated. The population-attributable fraction due to diet was 0.16 in men and 0.09 in women. Dietary patterns characterized by compliance with prevailing food-based dietary guidance were associated with a lower risk of all-cause mortality.     

Assessing the effect of underreporting energy intake on dietary patterns and weight status

OBJECTIVES: To identify misreporting among older rural adults using a prediction algorithm and to compare dietary patterns of underreporters and plausible reporters. DESIGN: In this cross-sectional study, diet information was assessed by five 24-hour recalls collected over 10 months. All foods were classified into 24 food subgroups. Demographic, health, and anthropometric data were collected via home visit. SETTING: Rural Pennsylvania. SUBJECTS: One hundred seventy-nine community-dwelling adults, aged 66 to 87 years. STATISTICAL ANALYSIS: Cluster analysis. RESULTS: Underreporters (n=43) were more likely than plausible reporters (n=133) to be overweight and less educated but did not differ by sex. Underreporters consumed fewer servings across the majority of food groups. Two dietary patterns were determined for all and plausible reporters, in both cases one of higher and one of lower nutrient density. Using only plausible reporters to determine dietary patterns was very similar to using all reporters. The correlation between energy intake and weight status was improved for plausible-reporting women, but not men. CONCLUSIONS: Dietary patterns of plausible reporters were generally similar to that of all reporters; however, correlations with energy intake and weight status improved for women using only plausible reporters. Individuals may not accurately report dietary intake. Those obtaining diet reports should be aware of reporting errors before making decisions about dietary adequacy.     

中国慢性病控制中膳食关键因素的研究

目的分析膳食结构对健康的影响，探讨预防人群慢性病和促进健康的膳食关键因素。方法以“2002年中国居民营养与健康状况调查”数据为基础，首先根据粮谷类食物供能比、碳水化合物供能比、脂肪供能比等膳食结构指标的人群分布及已有的推荐标准，将人群分为若干水平组，然后采用协方差分析方法分析不同组别在调整年龄、性别、地区等因素后常见慢性病指标的差异，并用logistic回归模型计算控制混杂因素（年龄、性别、地区、能量摄入、非睡眠静态牛活时间等）后，人群慢性病患病率随膳食结构变化的趋势及不同膳食结构下人群患各种慢性病的相对危险度（OR），进行趋势检验。结果随着粮谷类食物供能比的增加，人群体重指数（BMI）、血浆总胆固醇（TC）、低密度脂蛋白月日固醇（LDL—C）水平均显著下降，患超重、肥胖、高血压、高胆固醇血症、高甘油三酯血症、高低密度脂蛋白胆固醇血症的风险显著降低，人群低体重患病率则上升；随着碳水化合物供能比的增加，人群BMI、TC、LDL—C水平下降，相应人群超重及肥胖、高胆固醇血症、高低密度脂蛋白胆固醇血症的患病风险越低，但在粮谷类食物供能比〉75％时，人群低体重患病率则明显卜升；而脂肪供能比越高，人群BMI、TC、LDL—C水平均显著上升，相应人群超重及肥胖、高胆固醇血症、高低密度脂蛋白胆固醇血症的患病风险也增加，人群低体重率则降低。结论保持粮谷类食物占适宜比例的膳食结构，坚持按照中国居民膳食指南的建议，对于维护健康和预防慢性病具有重要意义；现有中国居民的膳食结构如能加以适当调整，是一种较好的膳食结构。     

Correlation between food intake change patterns and body weight loss in middle-aged women in Japan

The patterns of food intake change which are effective for weight loss have not been clearly researched yet. The objective of this study was to examine the correlation between food intake change patterns and body weight loss. One hundred and two overweight women completed a 2-day dietary record before and after a 12-week weight-reduction program, and 28 food groups were classified. Patterns of food intake change were derived by cluster analysis, and compared with the changes of physical measurements and nutrition intake. As a result, decreasing Japanese foods pattern (DJP), increasing healthy foods pattern (IHP) and changing staple foods pattern (CSP) were classified. DJP and CSP mainly changed staple food intake. IHP decreased sugars, oils, beans except soybeans and meat intake, and increased fruits and seafood intake. DJP decreased fat and carbohydrate intake most but CSP showed least change. IHP also decreased fat and carbohydrate intake but maintained protein intake. Although no significant differences were seen in the change of energy intake between IHP and DJP or CSP, subjects of IHP showed the largest reduction in mean body weight, BMI, %body fat, waist circumference and serum triacylglycerol after adjusting for age and baseline values. Body weight, BMI and %body fat maintained their significance further adjusting for changes in energy intake and the number of walking steps.Food intake change patterns affected the magnitude of body weight loss independent of energy intake. In addition to energy intake, assessment of food intake change patterns could be useful for effective weight loss.     

Under-reporting of energy intake is more prevalent in a healthy dietary pattern cluster

The aim of the present study was to determine whether under-reporting rates vary between dietary pattern clusters. Subjects were sixty-five Brazilian women. During 3 weeks, anthropometric data were collected, total energy expenditure (TEE) was determined by the doubly labelled water method and diet was measured. Energy intake (EI) and the daily frequency of consumption per 1000 kJ of twenty-two food groups were obtained from a FFQ. These frequencies were entered into a cluster analysis procedure in order to obtain dietary patterns. Under-reporters were defined as those who did not lose more than 1 kg of body weight during the study and presented EI:TEE less than 0.82. Three dietary pattern clusters were identified and named according to their most recurrent food groups: sweet foods (SW), starchy foods (ST) and healthy (H). Subjects from the healthy cluster had the lowest mean EI:TEE (SW = 0.86, ST = 0.71 and H = 0.58; P = 0.003) and EI - TEE (SW = - 0.49 MJ, ST = - 3.20 MJ and H = - 5.08 MJ; P = 0.008). The proportion of under-reporters was 45.2 (95 % CI 35.5, 55.0) % in the SW cluster; 58.3 (95 % CI 48.6, 68.0) % in the ST cluster and 70.0 (95 % CI 61.0, 79) % in the H cluster (P = 0.34). Thus, in Brazilian women, under-reporting of EI is not uniformly distributed among dietary pattern clusters and tends to be more severe among subjects from the healthy cluster. This cluster is more consistent with both dietary guidelines and with what lay individuals usually consider 'healthy eating'.     

Dietary patterns in adolescence are related to adiposity in young adulthood in black and white females

Few studies have systematically used a total diet approach to classify adolescent dietary patterns. We examined dietary patterns in relation to nutrient intakes and adiposity in the National Heart, Lung, and Blood Institute Growth and Health Study cohort of 2371 black and white girls recruited at 9-10 y of age and followed for 10 y. Serial measurements were obtained for indices of anthropometry, dietary intake, physical activity, and sociodemographic variables. Dietary patterns for the 2 racial subgroups were separately identified by cluster analysis of 40 food groupings derived from 3-d food records. Nutrient intakes and measures of adiposity (BMI, percent body fat, and waist circumference) were compared by dietary pattern. We identified 4 discrete dietary patterns for black and for white adolescents. A Healthy pattern, followed by 12% of white girls and characterized by a high intake of fruits, vegetables, dairy, grains without added fats, mixed dishes and soups, and a low intake of sweetened drinks, other sweets, fried foods, burgers, and pizza, was related to more favorable nutrient intakes and a smaller increase in waist circumference. Among black girls, none of the dietary patterns appeared distinctly advantageous in terms of mitigating increases in adiposity. In conclusion, a cumulative pattern of food intake consistent with recommendations for general health appears to help prevent overweight, but this pattern was followed by only a minority of adolescent girls.     

Dietary patterns and health outcomes

A systematic review of the literature on dietary patterns (multiple dietary components operationalized as a single exposure) in relation to nutrient adequacy, lifestyle and demographic variables, and health outcome was conducted. Most of the published reports on the subject have used one of two methods to determine dietary patterns: (a) diet indexes or scores that assess compliance with prevailing dietary guidance as dietary patterns, and (b) data-driven methods that use factor or cluster analysis to derive dietary patterns. Irrespective of the approach used, patterns characterized by fruit/vegetable/whole grain/fish/poultry consumption generally have been reported to relate to micronutrient intake, and to selected biomarkers of dietary exposure and disease risk in the expected direction. Age, income, and education have been reported to be among positive predictors of the so-called more healthful dietary patterns. An inverse association of healthful dietary patterns with all-cause mortality and cardiovascular disease risk was reported in most studies. However, the magnitude of risk reduction was modest and was attenuated after control for confounders. Few published studies showed an association between risk of most incident cancers and dietary patterns. Both of the currently used approaches for extracting dietary patterns have limitations, are subject to dietary measurement errors, and have not generated new diet and disease hypotheses.     

Dietary beliefs of people with ulcerative colitis and their effect on relapse and nutrient intake

BACKGROUND & AIMS: Ulcerative colitis usually follows a relapsing and remitting course. Patients believe that dietary factors are important. We wished to determine the nature of and reasons for patients' dietary beliefs and their effect on relapse and nutrient intake. METHODS: In a 1-year prospective cohort study, patients' nutrient intake and disease activity were measured with a validated food frequency questionnaire and disease activity index. Food beliefs, demographics and disease characteristics were recorded. The influence of beliefs on the risk of relapse and nutrient intake were examined using chi-squared and Mann-Whitney U-tests. RESULTS: One hundred and eighty-three patients were studied and 52% relapsed. Sixty-eight per cent held dietary beliefs and reported modifying their intake accordingly. The most common reported behaviour was the avoidance of milk and dairy products. Food beliefs were more common amongst those who had received dietary advice. No reported behaviour reduced the risk of relapse, but patients who avoided dairy products had a significantly lower intake of calcium. Folate intake was below UK recommended levels in 13% of patients. CONCLUSIONS: Patients with ulcerative colitis believe that many foods are harmful or helpful to their disease activity. Commonly held beliefs do not modify the risk of relapse, but do adversely affect nutrient intake.