New Exponential Scoring Functions for Diet Quality Indexes Solve Problems Caused by Truncation

BACKGROUND

Diet quality indexes, including the Healthy Eating Index (HEI), assess diets based on usual dietary intakes and a scoring function. Nearly all diet quality indexes use scoring functions that have floors and ceilings, thereby truncating the scores and losing information about intakes outside the scoring range. This score truncation has two important impacts: 1) the index does not reflect all intakes; and 2) the assumption that measurement error in intake reporting has a neutral impact on the diet quality score cannot be upheld.

OBJECTIVE

The main objective was to devise new diet quality scoring functions that eliminate truncation and its attendant problems.

METHODS

Seven desirable properties of a new scoring function were identified: 1) avoid truncations in component scoring to prevent information loss and to provide scoring sensitivity in the currently truncated regions; 2) reduce dependency on the accuracy of dietary standards; 3) minimize measurement error bias and subsequent misclassification; 4) relate plausibly to biological processes; 5) possess desirable mathematical and statistical properties; 6) have simple representations which are easy to calculate and add minimum artifacts of their own; and 7) otherwise closely mimic existing scoring functions.

RESULTS

The recommended replacement for piecewise-linear scoring is a family of scoring functions based on exponentials. For components where higher intakes are better, the function is a single exponential. For components where lower intakes are better, the function is a concave-convex mirrored pair of exponentials. The proposed exponential scoring functions have all seven desired properties.

CONCLUSIONS

The proposed exponential scoring functions will improve the usefulness of dietary scoring indexes by eliminating truncations. Compared to existing scoring functions, the use of exponentials makes the scores more inclusive of very high and very low intakes, reduces measurement error bias, and is less sensitive to the exact placement of the scoring standards.

A narrative review of nutrient based indexes to assess diet quality and the proposed total nutrient index that reflects total dietary exposures

A priori dietary indices provide a standardized, reproducible way to evaluate adherence to dietary recommendations across different populations. Existing nutrient-based indices were developed to reflect food/beverage intake; however, given the high prevalence of dietary supplement (DS) use and its potentially large contribution to nutrient intakes for those that use them, exposure classification without accounting for DS is incomplete. The purpose of this article is to review existing nutrient-based indices and describe the development of the Total Nutrient Index (TNI), an index developed to capture usual intakes from all sources of under-consumed micronutrients among the U.S. population. The TNI assesses U.S. adults' total nutrient intakes relative to recommended nutrient standards for eight under-consumed micronutrients identified by the Dietary Guidelines for Americans: calcium, magnesium, potassium, choline, and vitamins A, C, D, E. The TNI is scored from 0 to 100 (truncated at 100). The mean TNI score of U.S. adults (≥19 y; n = 9,954) based on dietary data from NHANES 2011-2014, was 75.4; the mean score for the index ignoring DS contributions was only 69.0 (t-test; p < 0.001). The TNI extends existing measures of diet quality by including nutrient intakes from all sources and was developed for research, monitoring, and policy purposes.Supplemental data for this article is available online at https://doi.org/10.1080/10408398.2021.1967872.

Dietary Data in the Malmö Offspring Study-Reproducibility, Method Comparison and Validation against Objective Biomarkers

Irregular dietary intakes impairs estimations from food records. Biomarkers and method combinations can be used to improve estimates. Our aim was to examine reproducibility from two assessment methods, compare them, and validate intakes against objective biomarkers. We used the Malmö Offspring Study (55% women, 18–71 y) with data from a 4-day food record (4DFR) and a short food frequency questionnaire (SFFQ) to compare (1) repeated intakes (n = 180), (2) intakes from 4DFR and SFFQ (n = 1601), and (3) intakes of fatty fish, fruits and vegetables, and citrus with plasma biomarkers (n = 1433) (3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid [CMPF], β-carotene and proline betaine). We also combined 4DFR and SFFQ estimates using principal component analysis (PCA). Moderate correlations were seen between repeated intakes (4DFR median ρ = 0.41, SFFQ median ρ = 0.59) although lower for specific 4DFR-items, especially fatty/lean fish (ρ ≤ 0.08). Between-method correlations (median ρ = 0.33) were higher for intakes of overall food groups compared to specific foods. PCA scores for citrus (proline betaine ρ = 0.53) and fruits and vegetables (β-carotene: ρ = 0.39) showed the highest biomarker correlations, whereas fatty fish intake from the SFFQ per se showed the highest correlation with CMPF (ρ = 0.46). To conclude, the reproducibility of SFFQ data was superior to 4DFR data regarding irregularly consumed foods. Method combination could slightly improve fruit and vegetable estimates, whereas SFFQ data gave most valid fatty fish intake.

Blood Metabolomic Profiling Confirms and Identifies Biomarkers of Food Intake

Metabolomics can be a tool to identify dietary biomarkers. However, reported food-metabolite associations have been inconsistent, and there is a need to explore further associations. Our aims were to confirm previously reported food-metabolite associations and to identify novel food-metabolite associations. We conducted a cross-sectional analysis of data from 849 participants (57% men) of the PopGen cohort. Dietary intake was obtained using FFQ and serum metabolites were profiled by an untargeted metabolomics approach. We conducted a systematic literature search to identify previously reported food-metabolite associations and analyzed these associations using linear regression. To identify potential novel food-metabolite associations, datasets were split into training and test datasets and linear regression models were fitted to the training datasets. Significant food-metabolite associations were evaluated in the test datasets. Models were adjusted for covariates. In the literature, we identified 82 food-metabolite associations. Of these, 44 associations were testable in our data and confirmed associations of coffee with 12 metabolites, of fish with five, of chocolate with two, of alcohol with four, and of butter, poultry and wine with one metabolite each. We did not identify novel food-metabolite associations; however, some associations were sex-specific. Potential use of some metabolites as biomarkers should consider sex differences in metabolism.

Under-reporting of the energy intake in patients with type 2 diabetes

BACKGROUND

In patients with type 2 diabetes mellitus (DM), an accurate assessment of food intake is essential for clinical nutritional management. Tools such as the food frequency questionnaire (FFQ) and 24-h food record (24HR) identify dietary habits in support of dietary planning. However, it is possible that these tools have reporting errors with respect to assessing food intake, particularly energy intake (EI).

METHODS

A cross-sectional study was conducted in patients with type 2 DM. EI was assessed by the FFQ and 24HR tools. Resting energy expenditure (REE) was measured by indirect calorimetry. Data were analysed using a kappa test, t-test and Spearman's correlation coefficients. Under-reporting was assessed using the EI/REE ratio. Patients with values <1.18 and <1.10 for FFQ and 24HR, respectively, were considered as under-reporting.

RESULTS

We evaluated 55 patients [mean (SD) 62.7 (5.3) years old, duration of diabetes 11.2 (7.3) years, 52.7% female]. The mean (SD) EI assessed by FFQ was 1797.7 (641.3) and as assessed by 24HR was 1624 (484.8) kcal day^-1 . The mean (SD) REE was 1641.3 (322.3) kcal day^-1 . The mean (SD) ratios FFQ/REE and 24HR/REE were 1.11 (0.38) and 1.01 (0.30), respectively. The tools showed a moderate agreement for under-reporting of EI (kappa = 0.404; P = 0.003). Moderate and positive correlations between REE were observed with FFQ (r = 0.321; P = 0.017) and 24HR (r = 0.364; P = 0.006). According to the tools, the under-reporting was observed in approximately 65% of patients.

CONCLUSIONS

The majority of patients with type 2 DM under-reported their calorie intake, as assessed by FFQ and 24HR. REE showed a positive correlation with both tools.

Perspective: The Dietary Inflammatory Index (DII)-Lessons Learned, Improvements Made, and Future Directions

The literature on the role of inflammation in health has grown exponentially over the past several decades. Paralleling this growth has been an equally intense focus on the role of diet in modulating inflammation, with a doubling in the size of the literature approximately every 4 y. The Dietary Inflammatory Index (DII) was developed to provide a quantitative means for assessing the role of diet in relation to health outcomes ranging from blood concentrations of inflammatory cytokines to chronic diseases. Based on literature from a variety of different study designs ranging from cell culture to observational and experimental studies in humans, the DII was designed to be universally applicable across all human studies with adequate dietary assessment. Over the past 4 y, the DII has been used in >200 studies and forms the basis for 12 meta-analyses. In the process of conducting this work, lessons were learned with regard to methodologic issues related to total energy and nutrient intake and energy and nutrient densities. Accordingly, refinements to the original algorithm have been made. In this article we discuss these improvements and observations that we made with regard to misuse and misinterpretation of the DII and provide suggestions for future developments.

Parents' Reports of Preschoolers' Diets: Relative Validity of a Food Frequency Questionnaire and Dietary Patterns

The accurate assessment of food consumption is crucial in nutritional studies. Since modern nutrition science has become more interested in diet as a whole, studies validating food frequency questionnaires (FFQs) and exploratory dietary patterns are needed. We aimed at examining the relative validity of a 47-item FFQ against three-day food records among three- to six-year-old Finnish children, as well as investigating the consistency of the dietary patterns derived using the principal component analysis (PCA), with food record and FFQ data as inputs. We conducted the PCA without forcing the food record data to match the FFQ items. Altogether, 75% or more of the participants were classified into the same or adjacent quarter of vegetables and fruits as well as sugary food consumption. Furthermore, the intake of folate and vitamin C increased linearly in the quarters of vegetable and fruit consumption, as did the intake of sucrose in quarters of sugary food consumption. Three fairly similar dietary patterns were identified from food records and FFQ data. Concerning the patterns, more than 70% of the participants were classified into the same or adjacent quarter. However, the Spearman correlation coefficients between the respective pattern scores were low (0.25–0.33). The FFQ showed acceptable validity when ranking food group consumption compared to food records. Additionally, the FFQ-derived dietary patterns were consistent with those derived using food record data.

Comparison of INTAKE24 (an Online 24-h Dietary Recall Tool) with Interviewer-Led 24-h Recall in 11-24 Year-Old

Online dietary assessment tools offer a convenient, low cost alternative to traditional dietary assessment methods such as weighed records and face-to-face interviewer-led 24-h recalls. INTAKE24 is an online multiple pass 24-h recall tool developed for use with 11-24 year-old. The aim of the study was to undertake a comparison of INTAKE24 (the test method) with interviewer-led multiple pass 24-h recalls (the comparison method) in 180 people aged 11-24 years. Each participant completed both an INTAKE24 24-h recall and an interviewer-led 24-h recall on the same day on four occasions over a one-month period. The daily energy and nutrient intakes reported in INTAKE24 were compared to those reported in the interviewer-led recall. Mean intakes reported using INTAKE24 were similar to the intakes reported in the interviewer-led recall for energy and macronutrients. INTAKE24 was found to underestimate energy intake by 1% on average compared to the interviewer-led recall with the limits of agreement ranging from minus 49% to plus 93%. Mean intakes of all macronutrients and micronutrients (except non-milk extrinsic sugars) were within 4% of the interviewer-led recall. Dietary assessment that utilises technology may offer a viable alternative and be more engaging than paper based methods, particularly for children and young adults.

Perspective: Randomized Controlled Trials Are Not a Panacea for Diet-Related Research

Research into the role of diet in health faces a number of methodologic challenges in the choice of study design, measurement methods, and analytic options. Heavier reliance on randomized controlled trial (RCT) designs is suggested as a way to solve these challenges. We present and discuss 7 inherent and practical considerations with special relevance to RCTs designed to study diet: 1) the need for narrow focus; 2) the choice of subjects and exposures; 3) blinding of the intervention; 4) perceived asymmetry of treatment in relation to need; 5) temporal relations between dietary exposures and putative outcomes; 6) strict adherence to the intervention protocol, despite potential clinical counter-indications; and 7) the need to maintain methodologic rigor, including measuring diet carefully and frequently. Alternatives, including observational studies and adaptive intervention designs, are presented and discussed. Given high noise-to-signal ratios interjected by using inaccurate assessment methods in studies with weak or inappropriate study designs (including RCTs), it is conceivable and indeed likely that effects of diet are underestimated. No matter which designs are used, studies will require continued improvement in the assessment of dietary intake. As technology continues to improve, there is potential for enhanced accuracy and reduced user burden of dietary assessments that are applicable to a wide variety of study designs, including RCTs.

Methods for nutrition monitoring in cancer patients: a cognitive perspective

In the present medical context, the evaluation and the monitoring of factors other than mere physical symptoms are an urgent demand. In particular, the issue of quality of life (QoL) has become a relevant target in the treatment of cancer. However, the approach towards these aspects is not well standardized and the actual applications in a concrete setting are fragmented, left to personal or local initiative. If this is true for QoL in general, it is particularly relevant in the specific field of nutrition. Indeed, though the growing awareness of a correlation between chronic diseases and dietary habits has led to an increased interest in nutrition, both before and after cancer, very little is still known about the methods that measure this important variable of the QoL. Indeed, good nutrition may have a relevant impact on QoL, positively affecting both the physical and psychological well-being. Targeting this issue implies using proper instruments to both monitor and educate the patients. Hence, we argue that it is vital for oncologists to be able to individuate the best tool available in a specified context, so as to achieve an important goal with little effort, also adopting standardized strategies proved to be efficacious. In this framework, we briefly reviewed the tools more frequently reported in the scientific literature. We suggest that through a cognitive approach, it is possible to achieve important clinical targets, initially by understanding the patients' needs, values, and psychosocial factors involved in nutritional behaviour and food-related decisions, in order to develop a personalized approach. Hence, this is the only way to support concrete actions for promoting healthier diets, thus preventing recurrences, monitoring chronic conditions, and supporting a good QoL.