The aim of this cross-sectional study was to examine the relative validity of food and nutrient intakes and overall diet quality scores derived using a newly developed dietary assessment questionnaire (food combination questionnaire, FCQ). Dietary data were collected from 222 Japanese adults (111 for each sex) aged 30–76 years using the online FCQ and then the 4-non-consective-day weighed dietary record (DR). The median of Spearman correlation coefficients for sixteen food groups was 0⋅32 among women and 0⋅38 among men. The median of Pearson correlation coefficients for forty-six nutrients was 0⋅34 among women and 0⋅31 among men. The Pearson correlation coefficient between the total scores of Healthy Eating Index-2015 (HEI-2015) derived from the DR and FCQ was 0⋅37 among women and 0⋅39 among men. The corresponding value for the Nutrient-Rich Food Index 9.3 (NRF9.3) total scores was 0⋅39 among women and 0⋅46 among men. Bland–Altman plots for these diet quality scores showed poor agreement at the individual level, although mean difference was small for the HEI-2015 (but not NRF9.3). Similar results were obtained using the paper version of FCQ, which was answered after conducting the DR, except for somewhat high Pearson correlation coefficients for the total scores of HEI-2015 (0⋅50 among both women and men) and NRF9.3 (0⋅37 among women and 0⋅53 among men). In conclusion, this analysis may lend support to the possible use of the FCQ as a rapid dietary assessment tool in large-scale epidemiologic studies in Japan, but further refinement of this tool should be pursued.

Healthy longevity is a tangible possibility for many individuals and populations, with nutritional and other lifestyle factors playing a key role in modulating the likelihood of healthy ageing. Nevertheless, studies of effects of nutrients or single foods on ageing often show inconsistent results and ignore the overall framework of dietary habits. Therefore, the use of dietary patterns (e.g. a Mediterranean dietary pattern) and the specific dietary recommendations (e.g. dietary approaches to stop hypertension, Polymeal and the American Healthy Eating Index) are becoming more widespread in promoting lifelong health. A posteriori defined dietary patterns are described frequently in relation to age-related diseases but their generalisability is often a challenge since these are developed specifically for the population under study. Conversely, the dietary guidelines are often developed based on prevention of disease or nutrient deficiency, but often less attention is paid to how well these dietary guidelines promote health outcomes. In the present paper, we provide an overview of the state of the art of dietary patterns and dietary recommendations in relation to life expectancy and the risk of age-related disorders (with emphasis on cardiometabolic diseases and cognitive outcomes). According to both a posteriori and a priori dietary patterns, some key ‘ingredients’ can be identified that are associated consistently with longevity and better cardiometabolic and cognitive health. These include high intake of fruit, vegetables, fish, (whole) grains and legumes/pulses and potatoes, whereas dietary patterns rich in red meat and sugar-rich foods have been associated with an increased risk of mortality and cardiometabolic outcomes.

This paper aims to describe different approaches for studying the overall diet with advantages and limitations. Studies of the overall diet have emerged because the relationship between dietary intake and health is very complex with all kinds of interactions. These cannot be captured well by studying single dietary components. Three main approaches to study the overall diet can be distinguished. The first method is researcher-defined scores or indices of diet quality. These are usually based on guidelines for a healthy diet or on diets known to be healthy. The second approach, using principal component or cluster analysis, is driven by the underlying dietary data. In principal component analysis, scales are derived based on the underlying relationships between food groups, whereas in cluster analysis, subgroups of the population are created with people that cluster together based on their dietary intake. A third approach includes methods that are driven by a combination of biological pathways and the underlying dietary data. Reduced rank regression defines linear combinations of food intakes that maximally explain nutrient intakes or intermediate markers of disease. Decision tree analysis identifies subgroups of a population whose members share dietary characteristics that influence (intermediate markers of) disease. It is concluded that all approaches have advantages and limitations and essentially answer different questions. The third approach is still more in an exploration phase, but seems to have great potential with complementary value. More insight into the utility of conducting studies on the overall diet can be gained if more attention is given to methodological issues.