Comparison of Methods Used to Correct Self-Reported Protein Intake for Systematic Variation in Reported Energy Intake Using Quantitative Biomarkers of Dietary Intake

Smell, taste and food habits changes along body mass index increase: an observational study

PURPOSE

To evaluate the changes in gustatory and olfactory sensitivity and dietary habits between healthy lean subjects (LS) and participants affected by overweight (OW), stage I and II obesity and to estimate possible impact of these factors on body mass index (BMI).

METHODS

After a general and ear-nose-throat evaluation, taste and olfactory function testing by means of taste strips and sniffin' stick tests, respectively, and food habits analysis by means of food frequency questionnaire (FFQ), 221 participants (68 LS [33 female; mean age = 53.01 ± 7.54 years]; 51 OW [26 female; mean age = 51.5 ± 12.16 years]; 50 stage I obesity [24 female; mean age = 50.78 ± 13.71 years] and 52 stage II obesity [24 female; mean age = 52.21 ± 13.35 years]) were enrolled in the study.

RESULTS

Significant (p < 0.008) reductions in total and subtest taste and smell scores were found in stage I and II obesity when compared to LS and OW participants. FFQ depicted a progressive intake increase of nutrients along the BMI stages. Significant associations were found between BMI and taste/smell subtests sugar taste carbs, saturated, monounsaturated and polyunsaturated fatty acids.

CONCLUSIONS

These data demonstrated for the first time a parallel impairment in smell and taste in a large sample size of participants from lean to stage II obesity and could reinforce those previous theories claiming that the greater the ability in taste or smell qualities perception, the lower the preference for them, resulting in a lower intake of specific foods.

Taste Function in Adult Humans from Lean Condition to Stage II Obesity: Interactions with Biochemical Regulators, Dietary Habits, and Clinical Aspects

Differences in gustatory sensitivity, nutritional habits, circulating levels of modulators, anthropometric measures, and metabolic assays may be involved in overweight (OW) development. The present study aimed at evaluating the differences in these aspects between 39 OW (19 female; mean age = 53.51 ± 11.17), 18 stage I (11 female; mean age = 54.3 ± 13.1 years), and 20 II (10 female; mean age = 54.5 ± 11.9) obesity participants when compared with 60 lean subjects (LS; 29 female; mean age = 54.04 ± 10.27). Participants were evaluated based on taste function scores, nutritional habits, levels of modulators (leptin, insulin, ghrelin, and glucose), and bioelectrical impedance analysis measurements. Significant reductions in total and subtests taste scores were found between LS and stage I and II obesity participants. Significant reductions in total and all subtests taste scores were found between OW and stage II obesity participants. Together with the progressive increase in plasmatic leptin levels, insulin, and serum glucose, decrease in plasmatic ghrelin levels, and changes in anthropometric measures and nutritional habits along with body mass index, these data for the first time demonstrated that taste sensitivity, biochemical regulators, and food habits play a parallel, concurring role along the stages evolving to obesity.

Doubly labelled water-calibration approach attenuates the underestimation of energy intake calculated from self-reported dietary assessment data in Japanese older adults

OBJECTIVE

Self-reported energy intake (EI) estimation may incur systematic errors that could be attenuated through biomarker calibration. We aimed to confirm whether calibrated EI was comparable to total energy expenditure (TEE) measured using the doubly labelled water (DLW) technique.

DESIGN

Cross-sectional study.

SETTING

General older population from the Kyoto-Kameoka Study, Japan.

PARTICIPANTS

This study included sub- and main cohorts of 72 and 8058 participants aged≥ 65 years, respectively. EI was evaluated using a validated FFQ, and calibrated EI was obtained using a previously developed equation based on the DLW method. TEE was considered representative of true EI and also measured using the DLW method. We used a Wilcoxon signed-rank test and correlation analysis to compare the uncalibrated and calibrated EI with TEE.

RESULTS

In the sub-cohort, the median TEE, uncalibrated EI and calibrated EI were 8559 kJ, 7088 kJ and 9269 kJ, respectively. The uncalibrated EI was significantly lower than the TEE (median difference = -1847 kJ; interquartile range (IQR): -2785 to -1096), although the calibrated EI was not (median difference = 463 kJ; IQR: -330 to 1541). The uncalibrated (r = 0·275) and calibrated EI (r = 0·517) significantly correlated with TEE. The reproducibility was higher for calibrated EI (interclass correlation coefficient (ICC) = 0·982) than for uncalibrated EI (ICC = 0·637). Similar findings were observed when stratifying the sample by sex. For medians, uncalibrated EI was lower (about 17 %) than calibrated EI in the main cohort.

CONCLUSIONS

Biomarker calibration may improve the accuracy of self-reported dietary intake estimation.

Novel Application of Nutritional Biomarkers From a Controlled Feeding Study and an Observational Study to Characterization of Dietary Patterns in Postmenopausal Women

Dietary guidance emphasizes healthy dietary patterns, but supporting evidence comes from self-reported dietary data, which are prone to measurement error. We explored whether nutritional biomarkers from the Women's Health Initiative Nutrition and Physical Activity Assessment Study Feeding Study (NPAAS-FS) (n = 153; 2010-2014) and the Women's Health Initiative Nutrition and Physical Activity Assessment Study Observational Study (NPAAS-OS) (n = 450; 2006-2009) could identify biomarker signatures of dietary patterns for development of corresponding regression calibration equations to help mitigate measurement error. Fasting blood samples were assayed for a specific panel of vitamins, carotenoids, and phospholipid fatty acids; 24-hour urine samples were assayed for nitrogen, sodium, and potassium levels. Intake records from the NPAAS-FS were used to calculate Healthy Eating Index 2010 (HEI-2010), Alternative Healthy Eating Index 2010 (AHEI-2010), alternative Mediterranean diet (aMED), and Dietary Approaches to Stop Hypertension (DASH) scores. Scores were regressed on blood and urine nutritional measures for discovery of dietary pattern biomarkers using a cross-validated model R2 ≥ 36% criterion (stage 1). Next, stepwise models (P ≤ 0.10 for entry/removal) using NPAAS-OS data were used to regress stage 1 dietary pattern biomarkers on NPAAS-OS self-reported dietary pattern scores using a food frequency questionnaire, a 4-day food record, and a 24-hour recall (stage 2). HEI-2010 and aMED analyses met the cross-validated R2 ≥ 36% criterion in stage 1, while AHEI-2010 and DASH analyses did not. The R2 values for HEI-2010 stage 2 calibration equations were as follows: food frequency questionnaire, 63.5%; 4-day food record, 83.1%; and 24-hour recall, 77.8%. Stage 2 aMED R2 values were 34.9%-46.8%. Dietary pattern biomarkers have potential for calibrating self-reports to enhance studies of diet-disease associations.

Protein Intake and Human Health: Implications of Units of Protein Intake

Understanding the health effects of protein intake is bedeviled by a number of factors, including protein quality and source. In addition, different units, including grams, grams per kilogram body weight (g/kg BW), and percent energy, may contribute to confusion about protein's effects on health, especially BW-based units in increasingly obese populations. We aimed to review the literature and to conduct a modeling demonstration of various units of protein intake in relation to markers of cardiometabolic health. Data from the Framingham Heart Study Offspring (n = 1847; 60.3 y; 62.5% women) and Third Generation (n = 2548; 46.2 y; 55.3% women) cohorts and the NHANES 2003-04 (n = 1625; 46.2 y; 49.7% women) and 2005-06 (n = 1347; 43.7 y; 49.5% women) cycles were used to model cross-sectional associations between 7 protein units (grams, percent energy, g/kg ideal BW, g/kg actual BW, BW-adjusted g/kg actual BW, g/kg lean BW, and g/kg fat-free BW) and 9 cardiometabolic outcomes (fasting glucose, systolic and diastolic blood pressure, total and HDL cholesterol, triglycerides, BMI, waist circumference, and estimated glomerular filtration rate). The literature review indicated the use of myriad units of protein intake, with differential results on cardiometabolic outcomes. The modeling demonstration showed units expressed in BW were confounded by BW, irrespective of outcome. Units expressed in grams, percent energy, and ideal BW showed similar results, with or without adjustment for body size. After adjusting for BW, results of units expressed in BW aligned with results of grams, percent energy, and ideal BW. In conclusion, protein intake in cardiometabolic health appears to depend on protein's unit of expression. Authors should be specific about the use of WHO (g/kg ideal BW) compared with US (g/kg actual BW) units, and ideally use gram or percent energy in observational studies. In populations where overweight/obesity are prevalent, intake based on actual BW should be reevaluated.