Background: Saturated fatty acid (SFA) intake increases plasma LDL-cholesterol concentrations; therefore, intake should be reduced to prevent coronar

Objective : To examine the distribution of patterns of macronutrient density in relation to obesity. Design : Cross sectional. Setting : Denmark. Subjects : 323 men and women aged 35-65 years, selected randomly from a larger population sample of Danish adults. Main outcome measure : Bias in dietary reporting of energy and protein intake in relation to percentage body fat, assessed by comparison of data from an interview on dietary intake with data estimated from 24 hour nitrogen output, validated by administering p-aminobenzoic acid, and estimated 24 hour energy expenditure. Results : Degree of obesity was positively associated with underreporting of total energy and protein, whereas compared with total energy reported, protein was overreported by the obese subjects. Conclusion : Errors in dietary reporting of protein seem to occur disproportionately with respect to total energy, suggesting a differential reporting pattern of different foods. Although, on average, all subjects showed a greater underreporting of energy than of protein, this was most common in the obese subjects. Snack-type foods may be preferentially forgotten when obese people omit food items in dietary reporting. These results seem to agree with the general assumption that obese people tend to underreport fatty foods and foods rich in carbohydrates rather than underreport their total dietary intake. These results may have implications for the interpretation of studies of diet and comorbidities related to obesity.

Dietary fiber may reduce the risk of coronary heart disease (CHD) through a variety of mechanisms, such as improving blood lipid profiles,^1-3 lowering blood pressure,^4,5 and improving insulin sensitivity^6,7 and fibrinolytic activity.⁸ Dietary fiber has been found to be inversely associated with risk factors for CHD in observational studies.^9-12

Objective: To investigate whether the association between BMI and all‐cause mortality could be disentangled into opposite effects of body fat and fat‐free mass (FFM).Research Methods and Procedures: All‐cause mortality was studied in the Danish follow‐up study “Diet, Cancer and Health” with 27, 178 men and 29, 875 women 50 to 64 years old recruited from 1993 to 1997. By the end of year 2001, the median follow‐up was 5.8 years, and 1851 had died. Body composition was assessed by bioelectrical impedance. Cox regression models were used to estimate the relationships among body fat mass index (body fat mass divided by height squared), FFM index (FFM divided by height squared), and mortality. All analyses were adjusted for smoking habits.Results: Men and women showed similar associations. J‐shaped associations were found between body fat mass index and mortality adjusted for FFM and smoking. The mortality rate ratios in the upper part of body fat mass were 1.12 per kg/m² (95% confidence interval: 1.07, 1.18) in men and 1.06 per kg/m² (95% confidence interval: 1.02, 1.10) in women. Reversed J‐shaped associations were found between FFM index and mortality with a tendency to level off for high values of FFM.Discussion: Our findings suggest that BMI represents joint but opposite associations of body fat and FFM with mortality. Both high body fat and low FFM are independent predictors of all‐cause mortality.

OBJECTIVE: To review studies of patterns of food intake, as assessed by diet index, factor analysis or cluster analysis, and their associations with body mass index or obesity (BMI/Ob). DESIGN: Systematic literature review MEDLINE search with crosscheck of references. STUDIES: Thirty observational studies relating food intake patterns to anthropometric information were identified and reviewed. Food intake patterns were defined using a diet index, factor or cluster analysis in 12, nine and nine studies, respectively. Measures of body mass were made concurrently with the diet assessment in all studies, and only in a few cases were the primary outcomes related to BMI/Ob. RESULTS: The food intake patterns identified could, in most factor or cluster analysis studies, be categorised as: (a) meat, fatty, sweet or energy dense foods; (b) vegetables, fruit, whole grain and low-fat foods; or (c) by high alcohol consumption. The diet indexes were designed to capture a high diversity and/or food combinations matching the recommendations. The relationships with BMI/Ob were inconsistent—ten studies found that intake patterns, which we categorised as fatty, sweet or energy dense were positively associated with BMI/Ob, while similar patterns in four other studies were negatively associated with BMI. The significant associations between diet index score and BMI/Ob were consistently negative, while the associations between factor scores or cluster membership and BMI/Ob were less clear in terms of food intake pattern. Men and women had similar food intake patterns, but food intake patterns were less often positively associated with BMI/Ob in women. In 11 studies, there were no significant associations between food intake pattern and BMI/Ob. CONCLUSION: This review showed that no consistent associations could be identified between BMI or Ob and food intake patterns, derived from diet index, factor analysis or cluster analysis. However, the heterogeneity of food intake patterns identified by such analyses and the lack of gold standards for the application of these techniques hampers consistent analysis of a relation between food intake patterns and health.