Associations of the Dietary Inflammatory Index with total adiposity and ectopic fat through the gut microbiota, LPS, and C-reactive protein in the Multiethnic Cohort-Adiposity Phenotype Study

BACKGROUND

Mechanisms linking a proinflammatory diet to obesity remain under investigation. The ability of diet to influence the gut microbiome (GM) in creating chronic low-grade systemic inflammation provides a plausible connection to adiposity.

OBJECTIVES

Assess whether any associations seen between the Energy-Adjusted Dietary Inflammatory Index (E-DII score), total fat mass, visceral adipose tissue (VAT), or liver fat (percentage volume) operated through the GM or microbial related inflammatory factors, in a multiethnic cross-sectional study.

METHODS

In the Multiethnic Cohort-Adiposity Phenotype Study (812 men, 843 women, aged 60-77 y) we tested whether associations between the E-DII and total adiposity, VAT, and liver fat function through the GM, LPS, and high-sensitivity C-reactive protein (hs-CRP). DXA-derived total fat mass, MRI-measured VAT, and MRI-based liver fat were measured. Participants provided stool and fasting blood samples and completed an FFQ. Stool bacterial DNA was amplified and the 16S rRNA gene was sequenced at the V1-V3 region. E-DII score was computed from FFQ data, with a higher E-DII representing a more proinflammatory diet. The associations between E-DII score, GM (10 phyla, 28 genera, α diversity), and adiposity phenotypes were examined using linear regression and mediation analyses, adjusting for confounders.

RESULTS

There were positive total effects (c) between E-DII and total fat mass (c = 0.68; 95% CI: 0.47, 0.90), VAT (c = 4.61; 95% CI: 2.95, 6.27), and liver fat (c = 0.40; 95% CI: 0.27, 0.53). The association between E-DII score and total fat mass was mediated by LPS, Flavonifractor, [Ruminococcus] gnavus group, and Tyzzerella. The association between E-DII score and ectopic fat occurred indirectly through Fusobacteria, Christensenellaceae R-7 group, Coprococcus 2, Escherichia-Shigella, [Eubacterium] xylanophilum group, Flavonifractor, Lachnoclostridium, [Ruminococcus] gnavus group, Tyzzerella, [Ruminococcus] gnavus group (VAT only), and α diversity (liver fat only). There was no significant association between E-DII score and adiposity phenotype through hs-CRP.

CONCLUSIONS

Associations found between E-DII and adiposity phenotypes occurred through the GM and LPS.

Severity of Candida-associated denture stomatitis is improved in institutionalized elders who consume Lactobacillus rhamnosus SP1

BACKGROUND

This study aimed to determine the effect of the consumption of a probiotic on the clinical characteristics of Candida-associated denture stomatitis (DS) and to determine the impact of a protocol of self-care measures in oral/prosthetic hygiene in institutionalized elders that wear removable prostheses (RP). The current treatment for DS is the systemic use of antifungals, as well as the replacement of these devices together with oral/prosthetic hygiene instructions.

METHODS

This study was a randomized, controlled and triple-blind trial. Thirty-six elders presenting DS of different severity and who carried RP consumed milk with/without the probiotic L. rhamnosus SP1. The prevalence and severity of DS and prevalence of Candida were determined and all participants/caregivers were trained in oral/prosthetic hygiene.

RESULTS

In both groups a decrease in the prevalence of DS was observed (P < 0.05) but only the group that consumed probiotic throughout the trial had a significant reduction in the severity of DS and reduced Candida counts (P < 0.05). Educational instruction in oral/prosthetic hygiene was relevant for the participants, regarding the prevalence of this lesion.

CONCLUSIONS

The frequent consumption of Lactobacillus rhamnosus SP1 and the establishment of a protocol of oral/prosthetic hygiene drastically reduced the severity of DS in institutionalized elders who wore RP.

Change in the inflammatory potential of diet over 10 years and subsequent mortality: the Multiethnic Cohort Study

Dietary inflammatory potential assessed by the Dietary Inflammatory Index (DII®) has been associated with health outcomes. However, longitudinal changes in the DII in relation to health outcomes rarely have been studied. This study aimed to examine change in the DII score over 10 years and its association with subsequent mortality in the Multiethnic Cohort. The analysis included 56 263 African American, Japanese American, Latino, Native Hawaiian and White participants who completed baseline (45-75 years) and 10-year follow-up surveys, including a FFQ. Mean energy-adjusted DII (E-DII) decreased over 10 years in men (from -0·85 to -1·61) and women (from -1·80 to -2·47), reflecting changes towards a more anti-inflammatory diet. During an average follow-up of 13·0 years, 16 363 deaths were identified. In multivariable Cox models, compared with anti-inflammatory stable individuals, risk of all-cause mortality was increased with pro-inflammatory change in men (hazard ratio (HR) = 1·13, 95 % CI 1·03, 1·23) and women (HR = 1·22, 95 % CI 1·13, 1·32). Per one-point increase in E-DII score over time, HR was 1·02 (95 % CI 1·00, 1·03) for men and 1·06 (95 % CI 1·04, 1·07) for women (P for heterogeneity < 0·001). While no heterogeneity by race and ethnicity was observed for men, the increased risk per one-point increase among women was stronger in non-Whites than in Whites (P for heterogeneity = 0·004). Our findings suggest that a change towards a more pro-inflammatory diet is associated with an increased risk of mortality both in men and women, and that the association is stronger in women, especially non-White women, than in men.

An Overview of Traditional and Novel Tools to Assess Diet

Effective dietary interventions are vital for combating morbidity and mortality, necessitating reliable assessment tools. This article explores diverse dietary assessment methods, emphasizing their complexities and applications. Food Frequency Questionnaires (FFQs) offer insights into dietary habits over specified periods but require validation for target populations. Traditional Food Records provide detailed insights but are labor-intensive and prone to underreporting. Technology-based and technology-assisted records offer efficient alternatives, leveraging mobile apps and wearable sensors, albeit with access and privacy concerns. 24 hour Dietary Recall (24HR) methods capture detailed intake within a day, with traditional and technology-assisted approaches strengthening population studies. The Automated Multi-Pass Method (AMPM) and technology-assisted ASA24 represent traditional and contemporary 24HR methodologies, respectively, both yielding comprehensive dietary data. In conclusion, dietary assessments are crucial for understanding dietary patterns and health implications. Integration of novel technologies streamlines data collection and analysis, enhancing researchers' ability to accurately gauge short- and long-term dietary impacts. Proper utilization of these tools empowers researchers to make informed decisions regarding dietary interventions and public health initiatives.

Evaluating the Validity of the PortionSize Smartphone Application for Estimating Dietary Intake in Free-Living Conditions: A Pilot Study

OBJECTIVE

Evaluate the validity of the PortionSize application.

METHODS

In this pilot study, 14 adults used PortionSize to record their free-living food intake over 3 consecutive days. Digital photography was the criterion measure, and the main outcomes were estimated intake of food (grams), energy (kilocalories), and food groups. Equivalence tests with ±25% equivalence bounds and Bland-Altman analysis were performed.

RESULTS

Estimated gram intake from PortionSize was equivalent (P < 0.001) to digital photography estimates. PortionSize and digital photography estimated energy intake, however, were not equivalent (P = 0.08), with larger estimates from PortionSize. In addition, PortionSize and digital photography were equivalent for vegetable intake (P = 0.01), but PortionSize had larger estimates of fruits, grains, dairy, and protein intake (P >0.07; error range 11% to 23%).

CONCLUSIONS AND IMPLICATIONS

Compared with digital photography, PortionSize accurately estimated food intake and had reasonable error rates for other nutrients; however, it overestimated energy intake, indicating further application improvements are needed for free-living conditions.

Validity of an Artificial Intelligence-Based Application to Identify Foods and Estimate Energy Intake Among Adults: A Pilot Study

Background

The commercial application Openfit allows for automatic identification and quantification of food intake through short video capture without a physical reference marker. There are no known peer-reviewed publications on the validity of this Nutrition Artificial Intelligence (AI).

Objectives

To test the validity of Openfit to identify food automatically and semiautomatically (with user correction), test the validity of Openfit at quantifying energy intake (kcal) automatically and semiautomatically, and assess satisfaction and usability of Openfit.

Methods

During a laboratory-based visit, adults (7 male and 17 female), used Openfit to automatically and semiautomatically record provided meals, which were covertly weighed. Foods logged were identified as an "exact match," "far match," or an "intrusion" using Food and Nutrient Database for Dietary Studies (FNDDS) codes. Descriptive data were stratified by meal, food item, and FNDDS group, and presented with or without beverages. Bland-Altman analyses assessed errors over levels of energy intake. Participants completed a User Satisfaction Survey (USS) and the Computer Systems Usability Questionnaire (CSUQ). Open-ended questions were assessed with qualitative methods.

Results

Exact matches, far matches, and intrusions were 46%, 41%, and 13% for automated identification, and 87%, 23%, and 0% for semiautomated identification, respectively. Error for automated and semiautomated energy estimates were 43% and 33% with beverages, and 16% and 42% without beverages. Bland-Altman analyses indicated larger error for higher energy meals. Overall mean scores were 2.4 for the CSUQ and subscale means scores ranged from 4.1 to 5.5. for the USS. Participants recommended improvements to Openfit's Nutrition AI, manual estimation, and overall app.

Conclusion

Openfit worked relatively well for automatically and semiautomatically identifying foods. Error in automated energy estimates was relatively high; however, after excluding beverages, error was relatively low (16%). For semiautomated energy estimates, error was comparable to previous studies. Improvements to the Nutrition AI, manual estimation and overall application may increase Openfit's usability and validity.This trial was registered at clinicaltrials.gov as NCT05343585.

Diet, Activity and Sleep Clusters Associated With Obesity Markers of Children in the US-Affiliated Pacific

AIM

Among children in the US-Affiliated Pacific, we aimed to identify lifestyle clusters and associations with obesity.

METHODS

Movement behaviours, diet and anthropometrics were from the Children's Healthy Living Program (n = 1780; 2012-2015). Partitioning-around-medoids identified clusters; regression examined differences in anthropometrics.

RESULTS

Among 2-5-year-olds, boys' clusters were: (1) high %energy from (E%) fat and sedentary behaviour; (2) high screen time and energy intake and (3) long sleep. Body mass index z-score (zBMI) was lower in Cluster 3 versus 1 (-0.28 [-0.50; -0.07], p = 0.01). Girls' clusters were: (1) high energy intake; (2) low E% fat and (3) high physical activity and sleep. zBMI was lower in Cluster 3 versus 1 (-0.34 [-0.55; -0.13], p = 0.002). Among 6-8-year-olds, boys' clusters were: (1) high screen time; (2) high energy intake; (3) high E% protein; (4) long sleep and (5) high sedentary time and low E% saturated fat. Compared with Cluster 1, zBMI was lower in Clusters 3 (-0.43 [-0.84; -0.02], p = 0.04), 4 (-0.64 [-1.08; -0.20], p = 0.004) and 5 (-0.93 [-1.35; -0.51], p < 0.001). Girls' clusters were: (1) high E% fat and protein; (2) high screen time and energy intake; (3) short sleep and high physical activity (4) long sleep and (5) low sedentary time. Compared with Cluster 1, zBMI was lower in Clusters 2 (-0.57 [-0.98; -0.17], p = 0.006), 3 (-0.48 [-0.81; -0.14], p = 0.005) and 5 (-0.50 [-0.83; -0.18], p = 0.003).

CONCLUSION

Various lifestyle patterns support healthy body weight.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT01881373.

Selecting foods from FNDDS when calculating food intake: Does the reference matter?

To estimate food intake, the nutrient content of food must be known or inferred by matching it to a nutrient database. This study evaluated how different procedures for matching foods to the Food and Nutrient Database for Dietary Studies (FNDDS) affect food intake estimates. Adult participants estimated a meal with the PortionSize app, which contains a subset of FNDDS codes (PortionSize FNDDS). Meals were covertly weighed (weigh back), and intake was estimated by referencing the FNDDS dataset. Combination foods were evaluated by 1) a composite generated by summing the nutrient values (from FNDDS) from single food items and 2) using the single composite food code from FNDDS. Raters analyzed food images from the PortionSize app to estimate intake with the digital photography of foods (DPF) method. Raters estimated intake from: 1) PortionSize FNDDS and 2) full FNDDS. Using PortionSize's FNDDS database, participants' energy intake estimates were equivalent to rater estimated energy intake. Raters estimated almost identical energy intake when they used PortionSize vs. full FNDDS. Using the full FNDDS, the DPF's energy estimates were similar to weigh back. Estimation of energy intake with a single FNDDS code was not equivalent to estimations from summing the nutrient values for combination foods.

Validity of the PortionSize application compared with that of MyFitnessPal for accurately estimating intake: a randomized crossover laboratory-based evaluation

BACKGROUND

PortionSize offers real-time feedback on dietary intake, including intake of MyPlate food groups but requires further evaluation on a larger sample in a laboratory-based setting. MyFitnessPal (MFP) is a commonly used commercial dietary assessment application, and to our knowledge, no known studies have evaluated MFP in a laboratory setting.

OBJECTIVES

The overall objective was to test the validity of PortionSize and MFP to accurately measure intake compared with that of weighed food (WB) and to compare error between applications. A secondary objective was to test usability, satisfaction, and user preference between applications.

METHODS

This randomized crossover study was completed between February and October 2021. Participants (N = 43) used both applications to estimate intake in a laboratory setting. Participants were provided with a preweighed plated meal and plated leftovers. Two 1-sided t tests assessed equivalence (±21% bounds) between simulated intake from PortionSize and WB, and MFP and WB. The primary outcome was energy intake, and secondary outcome measures were portion size (in grams), food groups, and other nutrients. Differences in relative absolute error, usability, satisfaction, and user preference between applications were evaluated using dependent samples t tests. Cohen d assessed effect size.

RESULTS

For PortionSize, energy and portion size were underestimated by 13.3% and 14.0%, respectively, and were not equivalent to WB. For MFP, energy was overestimated by 7.0%, and equivalent to WB (P = 0.04). Relative absolute error for energy did not differ between applications. For PortionSize, Cohen d was small (<0.2) for fruits, grains, protein foods, and specific nutrients. No differences were seen with usability, and the only difference for satisfaction was that participants found it easier to use MFP to find foods consumed (P = 0.019), and participants preferred using MFP (P = 0.014).

CONCLUSIONS

PortionSize requires further updates to improve energy estimates and usability but demonstrates clinical utility for tracking food group and nutrient intake. PortionSize did not outperform MFP for measuring energy intake.

CLINICAL TRIAL REGISTRY

This trial was registered at clinicaltrials.gov as NCT04700904 (https://classic.

CLINICALTRIALS

gov/ct2/show/NCT04700904).