Nutrition Data System For Research Research Articles

Racial Disparities in Fluid Intake and Plasma Copeptin in Young Adults

INTRO: Underhydration, or habitual low water intake, is associated with increased plasma copeptin, a surrogate marker of arginine vasopressin. Importantly, plasma copeptin is associated with development of cardiovascular disease (CVD) and chronic kidney disease (CKD). Compared with White individuals, Black individuals are more likely to be underhydrated, to develop CVD and end-stage CKD, and exhibit higher plasma copeptin in middle-age and older adulthood. However, there is limited information on potential racial differences in plasma copeptin in young apparently healthy adults, which may have implications for incident CVD and CKD. Therefore, we sought to determine whether young Black adults reported lower fluid intake and exhibited higher plasma copeptin concentration when compared with young White adults. Methods: We analyzed pre-existing data from two registered clinical trials (NCTs 04576338 and 05132556). Participants (n=86, sex; 40F/46M, race; White n=48, Black n=38, age; 21±2 years, BMI; 25±4 kg/m2) completed a three-day food and fluid log and underwent a venous blood draw to measure plasma copeptin concentration (BRAHMS Copeptin proAVP KRYPTOR). Food and fluid records were analyzed using the Nutrition Data System for Research for % fluid from food or water. Separately, records were used to operationalize beverage intake into 8 categories (water, sugar-sweetened beverages (SSB), milk/non-dairy alternatives, 100% juice, non-caloric beverages, plain coffee or tea, milk/sugar added coffee or tea, and alcohol). Statistical analyses included Mann-Whitney U tests for fluid intake data and ANCOVA for plasma copeptin concentration (controlled for site). Plasma copeptin was log transformed. Additionally, we assessed relations between fluid intakes and plasma copeptin concentration using Spearman’s (ρ) correlations. We set α at ≤ 0.05. Results: Compared with White adults, Black adults consumed less total fluid (Black; 1981 ±1035 vs. White; 2780±1242mL), water, alcohol, and coffee/tea (plain and milk/sugar-added), but more juice (all p’s<0.05). There were no differences in % fluid from food or water, milk/non-dairy alternatives, non-caloric beverages, or SSBs. Black individuals exhibited ~22% higher plasma copeptin concentration (Black; 5.5±2.8 vs. White; 4.5±2.8pmol/L, ANCOVA p=0.022). Irrespective of race, copeptin was negatively correlated with water intake (ρ=-0.230, p=0.034). CONCLUSION: Black young adults reported a significantly lower fluid intake and exhibited higher plasma copeptin concentration when compared with White young adults. Future studies should attempt to address racial disparities in hydration practices, which may attenuate racial differences in copeptin and corresponding health disparities. NIH-K01HL147998 (ATR), Auburn University Offce of Vice President for Research Support Program (ATR). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Abstract P194: Consumption of Added Sugar and Added Sugar-Rich Foods and Beverages is Associated With Accelerated Epigenetic Aging in the Coronary Artery Risk Development in Young Adults (CARDIA) Study

Background: Added sugar (AS) and AS-rich foods and beverages have shown unfavorable associations with overall and cardiovascular health; however, their associations with epigenetic age has not been studied. Aim: To examine the associations of AS intake and AS-rich carbohydrate (CHO) food score with the pace of epigenetic aging in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Methods: There were 850 adults included in this cross-sectional study using data from Year 20 (2005-06). Trained interviewers administered a diet history, and foods were coded using Nutrition Data System for Research (NDSR), which outputs nutrients, including AS, and food groups. An AS-rich CHO food score was created including refined grain breads and rolls, quick breads, sweet bakery products, ready-to-eat cereals, candy, sugar products, and sugar-sweetened beverages. Year 20 epigenetic age measures were calculated from DNA methylation data, generating acceleration measures GrimAge acceleration (GrimAA) and PhenoAge acceleration (PhenoAA). Multivariable linear regression evaluated the associations of GrimAA and PhenoAA across quartiles of AS and AS-rich CHO food score, adjusted for demographic characteristics and lifestyle factors (see footnotes in table). Results: Participants who consumed more AS completed fewer years of education, were less likely to be current drinkers or smokers, and more likely to be ever smokers. They had lower intakes of total CHO, protein, total fat, and saturated fat, and fewer servings of vegetables, red and processed meat, eggs, and diet beverages. Consuming more AS was positively associated with PhenoAA (p trend =0.029), but not GrimAA (p trend =0.85). The AS-rich CHO food score was also positively associated with PhenoAA (p trend <0.001), but not GrimAA (p trend =0.14). Conclusions: Consuming greater amounts of AS and AS-rich CHO foods and beverages were associated with an increased risk for accelerated epigenetic aging.

Effects of Macronutrient Intake and Number of Meals on Glycemic Outcomes Over 1 Year in Youth with Type 1 Diabetes.

Objective: Insulin bolus doses derive from glucose levels and planned carbohydrate intake, although fat and protein impact glycemic excursions. We examined the impact of macronutrients and number of daily meals/snacks on glycemic outcomes in youth with type 1 diabetes. Methods: Youth (N = 136, ages 8-17) with type 1 diabetes completed 3-day food records, wore 3-day masked continuous glucose monitoring, and had A1c measurements every 3 months for 1 year. Diet data were analyzed using Nutrition Data System for Research. Longitudinal mixed models assessed effects of macronutrient intake and number of meals/snacks on glycemic outcomes. Results: At baseline, youth (48% male) had mean age of 12.8 ± 2.5 years and diabetes duration of 5.9 ± 3.1 years; 73% used insulin pumps. Baseline A1c was 8.1% ± 1.0%, percent time in range 70-180 mg/dL (%TIR) was 49% ± 17%, % time below range <70 mg/dL (%TBR) was 6% ± 8%, % time above range >180 mg/dL (%TAR) was 44% ± 20%, and glycemic variability as coefficient of variation (CV) was 41% ± 8%; macronutrient intake included 48% ± 5% carbohydrate, 36% ± 5% fat, and 16% ± 2% protein. Most youth (56%) reported 3-4 meals/snacks daily (range 1-9). Over 1 year, greater carbohydrate intake was associated with lower A1c (P = 0.0003), more %TBR (P = 0.0006), less %TAR (P = 0.002), and higher CV (P = 0.03). Greater fat intake was associated with higher A1c (P = 0.006), less %TBR (P = 0.002), and more %TAR (P = 0.005). Greater protein intake was associated with higher A1c (P = 0.01). More daily meals/snacks were associated with lower A1c (P = 0.001), higher %TIR (P = 0.0006), and less %TAR (P = 0.0001). Conclusions: Both fat and protein impact glycemic outcomes. Future automated insulin delivery systems should consider all macronutrients for timely insulin provision. The present research study derived from secondary analysis of the study registered under NCT00999375.

Altered Nutrient Composition of Lactose-Reduced Infant Formula.

This research comprehensively examines 88 infant formulas available in the US market, with an emphasis on their diverging nutritional attributes based on lactose content. We stratified formulas into three categories: lactose-free, lactose-reduced, and entirely lactose-based. The formulas' nutritional content for 58 nutrients was obtained from the Nutrition Data System for Research (NDSR). Nutritional analysis revealed significant differences in nutrient composition across formula categories. For example, the results showed significant associations between the lactose content and glycemic index (GI) of the formula as well as 25 other nutrients. Specifically, we showed that for every gram of lactose per 100 g of formula that is removed, there was a 10.1% increase in GI (β = -10.12, p ≤ 0.000), a 19%,5%, and a 2% increase in added sugar (β = -0.19, p < 0.01), protein (β = -0.05, p < 0.001), and polyunsaturated fatty acids (β = -0.01, p < 0.01). The substitution of lactose in infant formulas significantly alters their nutritional profile, inducing changes in GI, added sugar, protein, and polyunsaturated fatty acids. These modifications have potential consequences for infant growth and metabolic responses and could influence long-term health trajectories. The clinical relevance of the composition differences between formulas should be further explored.

Analysis of Nutritional Composition and Glycemic Control in Patients with Gestational Diabetes Mellitus

Background:The association between meal content and glycemic control is not well-understood in pregnancy, limits our ability to counsel patients regarding the optimal diet. We therefore sought to evaluate the relationship between maternal dietary content and glycemic control.&#x0D; Methods:This is a secondary analysis of the GDM-MOMS study, which was a randomized controlled pilot trial that compared glycemic targets in 60 pregnant individuals with GDM and either overweight or obesity. During the pilot trial, participants wore a blinded continuous glucose monitor (CGM) for two five-day periods, with the first data collection between 12-32 weeks and the second data collection between 32-36 weeks. During the time that participants wore their CGM, they also collected 3-day food diaries with detailed information regarding intake and cooking technique. These food diaries are being entered into the Nutrition Data System for Research (NDSR) software, which analyzes nutritional composition for each meal. Glycemic control as assessed by CGM will then be assessed based on maternal nutritional intake.&#x0D; Results:The American Diabetes Association recommends a 2,000-calorie daily diet with a minimum of 175g of carbohydrates (with 35% of the total calories coming from carbohydrates), 71g of protein, and 28g of fat. Preliminary data extracted from NDSR includes nutritional analysis of 38 daily food diaries from 14 patients. 12/38 (31.6%) food logs show consumption of less than 35% of their total calories from carbohydrates, with the other 26 consuming 36-62%. Glycemic load can be used to assess how a patient’s diet affects their glycemic levels. 25/38 (65.8%) of food logs demonstrate a daily glycemic load (GL) of 100, with the other 13 showing daily GLs rangingfrom 108-252.&#x0D; Conclusions and Further Directions:Further analyses will assess post-meal glycemic response using both patient-monitored glucose values and reports from their CGM to determine which types of diets allow for optimal glycemic control.

Nutrition assessment and MASH severity in children using the Healthy Eating Index.

Pediatric metabolic-associated fatty liver disease (MAFLD) is a global health problem, with lifestyle modification as its major therapeutic strategy. Rigorous characterization of dietary content on MAFLD in children is lacking. We hypothesized an objectively measured healthier diet would positively modulate MAFLD. Diet was assessed using the Nutrition Data System for Research in children enrolled from 10 tertiary clinical centers to determine the Healthy Eating Index (HEI, 0-100) and individual food components. In all, 119 children were included (13.3 ± 2.7y), 80 (67%) male, 67 (18%) White, and 90 (76%) Hispanic, with an average body mass index Z-score of 2.2 ± 0.5. Diet was classified as low HEI < 47.94 (n = 39), mid HEI ≥ 47.94 and < 58.89 (n = 41), or high HEI ≥ 58.89 (n=39). Children with high HEI (healthier diet) had lower body weight (p = 0.005) and more favorable lipids. Mean serum triglycerides for low, mid, and high HEI were 163, 148, and 120mg/dL, respectively; p = 0.04 mid versus high, p = 0.01 low versus high. Mean HDL was 38, 41 and 43mg/dL; p = 0.02 low vs high. Less severe steatosis was noted with added sugar ≤ 10% of calories (p = 0.03). Higher lobular inflammation is associated with a higher percentage of calories from fat (OR (95% CI) = 0.95 (0.91-1.00), p = 0.04). In children with MAFLD, high HEI is associated with lower body weight and more favorable lipids, while added sugar and fat intake has individual histologic features. Differential consumption of major dietary components may modify both metabolic risk factors and histologic liver injury, highlighting the importance of objective diet assessments in children with MAFLD.

Comparing Self-Reported Dietary Intake to Provided Diet during a Randomized Controlled Feeding Intervention: A Pilot Study.

Systematic and random errors based on self-reported diet may bias estimates of dietary intake. The objective of this pilot study was to describe errors in self-reported dietary intake by comparing 24 h dietary recalls to provided menu items in a controlled feeding study. This feeding study was a parallel randomized block design consisting of a standard diet (STD; 15% protein, 50% carbohydrate, 35% fat) followed by either a high-fat (HF; 15% protein, 25% carbohydrate, 60% fat) or a high-carbohydrate (HC; 15% protein, 75% carbohydrate, 10% fat) diet. During the intervention, participants reported dietary intake in 24 h recalls. Participants included 12 males (seven HC, five HF) and 12 females (six HC, six HF). The Nutrition Data System for Research was utilized to quantify energy, macronutrients, and serving size of food groups. Statistical analyses assessed differences in 24 h dietary recalls vs. provided menu items, considering intervention type (STD vs. HF vs. HC) (Student's t-test). Caloric intake was consistent between self-reported intake and provided meals. Participants in the HF diet underreported energy-adjusted dietary fat and participants in the HC diet underreported energy-adjusted dietary carbohydrates. Energy-adjusted protein intake was overreported in each dietary intervention, specifically overreporting beef and poultry. Classifying misreported dietary components can lead to strategies to mitigate self-report errors for accurate dietary assessment.

Abstract 16940: Nutrient Intake of Patients With Heart Failure: Different Profiles Reveal Different Intervention Targets

Introduction: Diet quality is an important, but often underappreciated part of heart failure (HF) care. Tailoring of dietary interventions must take into consideration how patients with HF eat. Purpose: To identify distinct dietary profiles representing eating patterns in patients with HF. Methods: This was a secondary analysis of data from a randomized controlled double-blind clinical trial of a nutrition intervention with patients with HF. Baseline dietary data were collected using 24-hour dietary recall that included a weekend and two weekdays. Nutritional data were generated from the Nutrition Data Systems for Research. Nine unique diet components were quantified (along with a total Healthy Eating Index) and included in modeling based on theoretical and empirical knowledge: sodium, added sugars, trans-fat, vitamin C, vitamin D, vitamin E, folate, beta-cryptoxanthin, and lutein + zeaxanthin. Latent class mixture modeling was used to identify distinct dietary profiles among these 9 components representing different eating patterns that could serve as the bases of future interventions. Results: There were 112 participants with an average age 63 ± 12, 59.8% male, and 62.5% with class III or IV HF. Two dietary profiles were identified and were labeled by the prominent dimensional characteristic. The “higher intake” profile (n=79 (69.9% of the sample)) had significantly higher intake levels of sodium, vitamin D, vitamin E, vitamin C, beta-cryptoxanthin, lutein + zeaxanthin, and folate, and a “lower intake” profile (n=34 (30.1% of the sample) had significantly lower intake of these same nutrients. Patients with HF in the lower intake profile also had significant higher quality diets (Healthy Eating Index = 63.7± 13.2) compared with those in the higher intake profile (Healthy Eating Index = 53.9 ± 14.2, p&lt;0.001). Conclusion: Two different nutrient patterns were identified that would be amenable to different interventions; one focused on total calorie reduction and the other focused on optimizing nutrient intake.

The diet quality of well adolescents: Do they really eat poorly?

BackgroundA common assumption is that adolescents have poor eating habits. However, there is minimal data regarding adolescents’ intake of macronutrients (i.e. protein, carbohydrate, and fats) and micronutrients (i.e. vitamins and minerals). In addition, few studies have used the Healthy Eating Index-2010 (HEI-2010) to assess diet quality of well adolescents. ObjectiveTo determine diet quality among patients presenting for well care by using NDSR (Nutrition Data Systems for Research) nutrition analysis software and by calculating HEI-2010 scores. MethodsA convenience sample of adolescents presenting to clinic for well care was recruited. Participants completed a 24 hour dietary recall using the multiple pass method, a validated interviewing technique. Results for each participant were entered into NDSR version 2014, which provides a detailed nutrition analysis, and these results were also used to calculate a HEI-2010 score, which measures how well dietary intake conforms to the Dietary Guidelines for Americans in terms of amounts consumed from each food group. ResultsData from 48 adolescents (30 female, 18 male; mean age 16.5, range 12–20 years; 18 Hispanic, 11 Black, 5 White, 14 other) were analyzed. Body-mass-index (BMI) ranged from 16.0 to 38.1. Average total calories were 2054 (but there was a wide range of 683 to 4801 calories). Average calories for females and males were 1723 and 2604, respectively. Average percentages of calories from macronutrients were 33.2% from fat, 50.5% from carbohydrate, and 16.3% from protein. Micronutrient content was very low (< 53% of recommended Daily Values) for vitamins E and D; moderately low (61–76%) for zinc, magnesium, potassium and copper; minimally low (86–97%) for calcium, iron and vitamin B12; and normal (≥100%) for thiamin, riboflavin, niacin, folic acid, phosphorus, manganese, selenium and vitamins C, K, and B6. HEI scores were <50 (“poor”) for 20 subjects and 51–79 (“needs improvement”) for 13 subjects. Calorie consumption was insufficient to calculate an HEI score for 15 subjects and no subjects had an HEI score >80 (“good”). ConclusionOn some measures (distribution of macronutrients, intake of most vitamins and minerals), the adolescents in this study had sufficient diets by NDSR calculations. However, no subject had an HEI score indicative of good diet quality. It is possible that adolescents are consuming adequate macro- and micronutrients in the setting of processed, fortified foods, but that optimal consumption from each food group is lacking, resulting in lower diet quality scores on the HEI.

Association of free-living diet composition with plasma lipoprotein(a) levels in healthy adults

BackgroundLipoprotein (a) [Lp(a)] is an apoB100-containing lipoprotein with high levels being positively associated with atherosclerotic cardiovascular disease. Lp(a) levels are genetically determined. However, previous studies report a negative association between Lp(a) and saturated fatty acid intake. Currently, apoB100 lowering therapies are used to lower Lp(a) levels, and apheresis therapy is FDA approved for patients with extreme elevations of Lp(a). The current study analyzed the association of free-living diet components with plasma Lp(a) levels.MethodsDietary composition data was collected during screening visits for enrollment in previously completed lipid and lipoprotein metabolism studies at Columbia University Irving Medical Center via a standardized protocol by registered dietitians using 24 hour recalls. Data were analyzed with the Nutrition Data System for Research (Version 2018). Diet quality was calculated using the Healthy Eating Index (HEI) score. Fasting plasma Lp(a) levels were measured via an isoform-independent ELISA and apo(a) isoforms were measured using gel electrophoresis.ResultsWe enrolled 28 subjects [Black (n = 18); Hispanic (n = 7); White (n = 3)]. The mean age was 48.3 ± 12.5 years with 17 males. Median level of Lp(a) was 79.9 nmol/L (34.4–146.0) and it was negatively associated with absolute (grams/day) and relative (percent of total calories) intake of dietary saturated fatty acids (SFA) (R = -0.43, P = 0.02, SFA …(% CAL): R = -0.38, P = 0.04), palmitic acid intake (R = -0.38, P = 0.05), and stearic acid intake (R = -0.40, P = 0.03). Analyses of associations with HEI score when stratified based on Lp(a) levels > or ≤ 100 nmol/L revealed no significant associations with any of the constituent factors.ConclusionsUsing 24 hour recall, we confirm previous findings that Lp(a) levels are negatively associated with dietary saturated fatty acid intake. Additionally, Lp(a) levels are not related to diet quality, as assessed by the HEI score. The mechanisms underlying the relationship of SFA with Lp(a) require further investigation.

Adapting a US dietary analysis software and nutrient database for use in Brazil.

In many countries, assessing food and nutrient intake for research and surveillance purposes is difficult due to the lack of comprehensive, country-specific food and nutrient databases and/or a dietary analysis software program. In this case study, we describe the approach used to adapt a United States (US) dietary analysis software and nutrient database (Nutrition Data System for Research [NDSR]) for use in analyzing 24-hour dietary recalls collected for the Brazil Kids Nutrition and Health Study (KNHS). A team of experts that included individuals knowledgeable about Brazil and US eating traditions was assembled to devise solutions for between-country differences in eating habits, food supply, food nomenclature, and language. Solutions devised relied on several key resources, including the Brazilian Food Composition Table (TBCA) and a list of 200 foods commonly consumed in Brazil. These solutions included creating data entry rules that specified how each reported food should be entered into NDSR, creation of User Recipes for foods lacking a close nutritionally matching food in the NDSR database, and adjustment for food fortification differences as part of the analysis of study data. This case study illustrates that NDSR can be adapted for use outside of the United States through a structured process.

Evaluating a Campus Food Pantry’s Potential Impact on Nutrition Security using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) Framework

BackgroundCampus food pantries are uniquely positioned to promote health equity with the foods they make accessible to food-insecure students. ObjectivesThis study assessed the implementation and effectiveness of a client-choice campus food pantry to understand its potential impact on nutrition security and to inform future implementation. MethodsThis observational study was designed using the reach, effectiveness, adoption, implementation, and maintenance framework, leveraging administrative data collected from a client-choice campus food pantry at a large Midwest university from August 2020 to May 2022. Pantry staff tracked student usage and item inventory. Items were analyzed for specific nutrients using the Nutrition Data System for Research. The mean nutrients and servings per food group distributed per visit were calculated and compared with dietary recommendations (effectiveness). Characteristics of pantry operation and setup were detailed (adoption). The percentage of openings with adequate stock to meet distribution guidelines was determined (implementation). Descriptive statistics were calculated, and multiple linear regressions determined whether significant changes in nutrients and food were distributed based on distribution guidelines and stock available. ResultsEffectiveness: Vitamin D, fruits, vegetables, and whole grains were below 3 d of the recommended intake for all students, and energy, fiber, potassium, vitamin A, and grains were low for male students. Adoption: The pantry was established through a collaboration with a pre-existing community food pantry and operates as a 501(c) and is managed by campus recreation staff. Implementation: The pantry supplied enough produce for 72% of openings to meet distribution guidelines and enough dairy for 63% of openings. ConclusionsThe food and nutrients distributed in limited amounts are consistent with those underconsumed according to the Dietary Guidelines for Americans. More research is needed to understand how pantry implementation can best support nutrition security through the adequate distribution of key nutrients and food groups.

Comparison of skin carotenoid content scores and dietary intake between cognitively normal older adults and older adults with Alzheimer’s Disease: A matched‐pair analysis

AbstractBackgroundAlzheimer’s Disease (AD) is a systemic metabolic disease with a variable number and type of clinical symptoms mostly impacting the brain. Skin carotenoid content (SCC) is an objective measure of carotenoid‐containing fruit and vegetable intake that has been validated in diverse populations. Our previous findings suggest SCC scores are impacted by various metabolic disease states. Therefore, our objective was to analyze SCC scores and dietary intake across matched pairs including unimpaired adults and those with AD.MethodBaseline data from two dietary interventions (NCT03860792, NCT03841539) were analyzed. Pairs are exact matches for sex, race/ethnicity, and education and are matched within two years of age and 4 kg/m2 body mass index. Each pair (n = 12 pairs) includes one cognitively normal (MMSE &gt; 25), community dwelling older adult (‘CN’) and one community dwelling older adult with AD (‘AD’). Baseline self‐reported dietary intake data (3‐day food records) were collected, reviewed, and entered into the Nutrition Data System for Research (NDSR 2019) database by a Registered Dietitian. SCC scores were collected through pressure‐mediated reflection spectroscopy via VeggieMeter® (Longevity Link Corporation, Holladay, UT). Two‐sample independent t‐tests assuming unequal variances were used to compare differences in SCC scores and dietary intake. Significance was set at p = 0.05.ResultThere was a significant difference in SCC scores between matched pairs (SCC scores: CN 284.25, AD 207.67, p = 0.008). There was no difference between pairs for total daily energy (kcal/day; p = 0.60), fat (g/day; p = 0.91), carbohydrate (g/day; p = 0.85), or protein (g/day; p = 0.26) intake. Both groups had similar food group intake (p‐value range 0.09‐0.79), including all fruit and vegetable groups except CN had higher intake of non‐citrus fruit (p = 0.046). Total fruit (p = 0.09) and fruit plus vegetable (p = 0.24) were not different between pairs. Dietary intake by individual carotenoid was not significantly different (total vitamin A, p = 0.48; beta‐carotene, p = 0.49; alpha‐carotene, p = 0.57; beta‐cryptoxanthin, p = 0.60; lutein plus zeaxanthin, p = 0.07; lycopene, p = 0.37).ConclusionIndividuals with AD have lower carotenoid skin content compared to matched cognitively unimpaired individuals. These differences are not explained by differences in diet suggesting that AD may be associated with disease‐associated biochemical features that are metabolism‐related and not limited to the brain.

Evaluation of a mobile behavior change program for weight loss in breast cancer survivors: A prospective single-arm pilot study.

12070 Background: Post-diagnosis weight gain is common among women with early-stage breast cancer and is associated with increased risk of recurrence, breast cancer death, and all-cause mortality. Intentional weight loss following diagnosis is difficult to maintain. Targeted lifestyle interventions focusing on behavioral changes and education that can be implemented long-term are needed. Methods: This was a prospective single-arm pilot study in patients with history of early-stage breast cancer (NCT04753268). Key eligibility criteria included body mass index (BMI) ≥27.5 kg/m2, stage I-III breast cancer, and completion of active cancer treatment ≥6 months prior to study enrollment. Patients were recruited at the Dempsey Center in Lewiston, ME and via social media. Participants were given access to the Noom mobile application, which utilizes cognitive behavioral therapy, motivational interviewing, and self-determination theory to induce behavior change using an educational curriculum of daily articles, virtual health coaching, social networking, food records, and physical activity logs. The primary endpoint was change in self-reported weight from baseline to 26 weeks. Secondary endpoints included change in physical activity measured using step count and the Global Physical Activity Questionnaire (GPAQ), change in diet patterns analyzed using the Nutrition Data System for Research, patient-reported outcome (PRO) measures, and metrics for engagement with the mobile application. Results: 31 patients were enrolled; 24 at the Dempsey Center and 7 via social media. Mean age was 56.8 and mean baseline BMI was 33.5 kg/m2. Mean weight change was -4.8 kg (range +0.6 to -19.9 kg, p &lt; 0.001) and mean percent weight change was -5.6%; 11/31 patients (35.5%) lost ≥5% of their initial weight. Average daily step count increased by 54.7% (p = 0.004). Mean GPAQ score for physical activity increased from 990 to 1773 (p = 0.01), and the proportion of patients who met physical activity guidelines increased from 45.1% to 74.2%. Mean energy intake did not change significantly over 26 weeks (p = 0.779). Improvements were observed in PROMIS-Physical Functioning T-scores and Body Image Scale scores (p = 0.04, p &lt; 0.001 respectively). Metrics of engagement with Noom that were predictive of weight loss ≥5% included total articles read (p = 0.012), total weights logged (p = 0.006), total food records logged (p = 0.001), messages sent to coach (p = 0.001), and number of times the application was opened (p = 0.014). Conclusions: In this pilot study, breast cancer survivors lost weight and became more physically active using a commercially-available mobile application. This digital weight loss program is a scalable intervention, and these findings support future studies investigating impact on breast cancer outcomes. Clinical trial information: NCT04753268 .

Blood Pressure and Self-management in Black Women With Hypertension: Protocol Revisions to the Brain Relationships Among Information, Neuroprocessing, and Self-Management Study Due to the COVID-19 Pandemic

The COVID-19 pandemic and the halt to in-person research activities beginning in March 2020 brought new challenges to protocol development and implementation. Due to the pandemic, we had to revise our protocol for the Brain Relationships Among Information, Neuroprocessing, and Self-Management (BRAINS) study, which was designed to examine health information behavior, brain activity, diabetes status, and self-management behavior among Black women with hypertension. This report outlines 7 steps describing how our research team (1) revised the BRAINS study protocol, (2) implemented a remote method of data collection, and (3) mitigated the challenges we faced. Prior to March 2020, Black women with hypertension were invited to participate in the BRAINS study to undergo a functional magnetic resonance imaging scan, complete surveys, have their blood pressure measured, and have their blood drawn. After these measures were collected, participants would receive phone calls from a dietician to complete two 24-hour dietary recalls using the Nutrition Data System for Research. Our revised protocol relied on a web-based, interactive approach. Participants received a study kit that included an Omron automatic home blood pressure monitor and a hemoglobin A1c kit from the DTIL laboratory. In a Zoom meeting with each participant, our team played an introductory video, administered surveys (via Qualtrics), and guided participants through measuring their blood pressure and performing a finger stick to collect a blood sample for hemoglobin A1c testing. We examined cognitive function using the TestMyBrain Digital Neuropsychology Toolkit, as we were unable to access the functional magnetic resonance imaging laboratory to assess brain activity. The 7 steps in revising our protocol were as follows: conceptualizing the move from in-person to remote study activities (step 1); contacting the funders (step 2); submitting changes for Institutional Review Board approval (step 3); preparing to implement the revised protocol (step 4); implementing the study changes (step 5); mitigating challenges (step 6); and evaluating protocol implementation (step 7). Approximately 1700 individuals responded to web-based advertisements about the BRAINS study. A total of 131 individuals completed our eligibility screener. We conducted our first Zoom appointment in July 2020 and our last Zoom appointment in September 2020. Using our revised strategies, a total of 99 participants completed all study measures within a 3-month period. In this report, we discuss successes and challenges in revising our protocol and reaching our population of interest remotely, safely, and effectively. The information we have outlined can help researchers create similar protocols to reach and conduct research remotely with diverse populations, such as individuals who are unable to participate in studies in person. DERR1-10.2196/43849.

Relationship between short-term self-reported dietary magnesium intake and whole blood ionized magnesium (iMg2+) or serum magnesium (s-Mg) concentrations

Objective Since we and others have shown that supplemental magnesium raises whole blood ionized magnesium (iMg2+) we investigated the relationships between self-reported dietary magnesium intake and concentrations of whole blood iMg2+ and serum magnesium (s-Mg). Methods We obtained whole blood iMg2+ concentrations, as well as s-Mg concentrations, from a pilot, three-arm, randomized, controlled, crossover bioavailability study of magnesium supplements (n = 23; 105 measures). Dietary magnesium intake was assessed using three-day food records and the Nutrition Data System for Research (NDSR, University of Minnesota, MN, USA). Whole blood iMg2+ was measured with an electrode analyser (NOVA Biochemical, Waltham, MA, USA), whereas s-Mg was measured using atomic absorption spectroscopy. A linear mixed-effects model was employed with dietary magnesium as the outcome variable and iMg2+, s-Mg, study treatment and study visit as fixed effects. We adjusted age, gender, race and body mass index covariates. Results Values for dietary magnesium, iMg2+ and s-Mg were 303.8 ± 118.9 mg/day, 1.3 ± 0.1 mg/dL and 2.2 ± 4.1 mg/dL, respectively. No association was found between dietary magnesium intake and iMg2+ −125 ± 176.95 (p = .49) or s-Mg −9.33 ± 5.04 (p = .08). Conclusions Whole blood iMg2+ and s-Mg concentrations do not reflect short-term self-reported dietary intake in adults. Further research is needed to determine whether blood biomarkers of magnesium may reflect dietary magnesium intake. Key messages Dietary intake of magnesium, a shortfall nutrient, may be objectively measured using blood biomarkers of magnesium. Serum magnesium and whole blood iMg2+ were not associated with short-term dietary intake of magnesium.

The association between hydration status and body composition in healthy children and adolescents.

Children 10-20years old in the US are currently obese, showing suboptimal hydration as 60% fail to meet the US Dietary Reference Intakes for water. Studies have shown a significant inverse association between hydration status and body composition in children, although most failed to use the Dual-X-Ray Absorptiometry Scan (DEXA), the gold standard for body composition. Limited studies used an objective marker to measure hydration, such as urine specific gravity (USG) from a 24-h urine collection. Therefore, this study aimed toexamine the association between hydration status (measured from USG in a 24-h urine sample and assessed from three 24-h dietary recalls) and body fat % and lean mass (assessed from a DEXA scan) in children (10-13 years, n=34) and adolescents (18-20 years, n=34). Body composition was measured using DEXA, total water intake (mL/d) was assessed from three 24-h dietary recalls and analyzed using the Nutrition Data System for Research (NDSR). Hydration status was objectively measured using USG via 24-h urine collection. Overall body fat % was 31.7±7.31, total water intake was 1746±762.0mL/d, and USG score was 1.020±0.011 uG. Linear regressions showed significance between totalwater intake and lean mass (B=12.2, p<0.05). Logistic regressions showed no significant association between body composition and USG and total water intake. Findings showed total water intake was significantly associated with lean mass. Future research should be conducted to explore other objective markers of hydration and with a larger sample.

Validating Healthy Eating Index, Glycemic Index, and Glycemic Load with Modern Diets for E-Health Era

Predictors of healthy eating parameters, including the Healthy Eating Index (HEI), Glycemic Index (GI), and Glycemic Load (GL), were examined using various modern diets (n = 131) in preparation for personalized nutrition in the e-health era. Using Nutrition Data Systems for Research computerized software and artificial intelligence machine-learning-based predictive validation analyses, we included domains of HEI, caloric source, and various diets as the potentially modifiable factors. HEI predictors included whole fruits and whole grains, and empty calories. Carbohydrates were the common predictor for both GI and GL, with total fruits and Mexican diets being additional predictors for GI. The median amount of carbohydrates to reach an acceptable GL < 20 was predicted as 33.95 g per meal (median: 3.59 meals daily) with a regression coefficient of 37.33 across all daily diets. Diets with greater carbohydrates and more meals needed to reach acceptable GL < 20 included smoothies, convenient diets, and liquids. Mexican diets were the common predictor for GI and carbohydrates per meal to reach acceptable GL < 20; with smoothies (12.04), high-school (5.75), fast-food (4.48), Korean (4.30), Chinese (3.93), and liquid diets (3.71) presenting a higher median number of meals. These findings could be used to manage diets for various populations in the precision-based e-health era.

The association between hydration status and total fluid intake in healthy children and adolescents.

Children 10-20 years old in the US have suboptimal hydration status. Hydration is best assessed using an objective marker, such as urine specific gravity (USG) from a 24-h urine collection. There are limited studies associating hydration from an objective marker with intake assessed from 24-h recalls in children. The objective of the study was to evaluate which foods or beverages are significantly associated with an objective marker of hydration (USG) in a sample of children and adolescents. Intake was assessed from three 24-h dietary recalls and analyzed using the Nutrition Data System for Research (NDSR). Hydration status was objectively measured using USG via 24-h urine collection. Associations were assessed with logistic regressions. A total of 68 children and adolescents were recruited (50% females). Average overall USG score was 1.020 ± 0.011 uG with 39.7% categorized as dehydrated. After adjusting for age and sex, fruit juice (1.009, 95% CI: 1.001, 1.018) and all beverages (1.001, 95% CI: 1.000, 1.002) were significantly associated with higher odds of being euhydrated. The main predictors of hydration were fruit juice and all beverages intake. Future research should be conducted to explore differences in dietary patterns in a larger, more generalizable sample. Findings showed that the main predictors of hydration were water and fruit juice intake in children and water intake in adolescents in southern Florida. This is the first study to examine which type of beverages and foods are associated with USG, an objective marker of hydration status, in US children and adolescents. Provides further insight into the use of objective markers to assess hydration status, while providing data to assist epidemiological studies that may have limited resources to examine beverages and foods that contribute to hydration.

Validation of Diet ID™ in Predicting Nutrient Intake Compared to Dietary Recalls, Skin Carotenoid Scores, and Plasma Carotenoids in University Students.

Background and Aim: Collecting accurate dietary information in the research setting is challenging due to the inherent biases, duration, and resource-intensive nature of traditional data collection methods. Diet ID™ is a novel, rapid assessment method that uses an image-based algorithm to identify dietary patterns and estimate nutrient intake. The purpose of this analysis was to explore the criterion validity between Diet ID™ and additional measures of dietary intake. Methods: This prospective cohort study (n = 42) collected dietary information using Diet ID™, the Nutrition Data System for Research (NDSR), plasma carotenoid concentrations, and the Veggie Meter® to estimate carotenoid levels in the skin. Results: There were significant correlations between Diet ID™ and NDSR for diet quality, calories, carbohydrates, protein, fiber, and cholesterol. Vitamin A and carotenoid intake were significantly correlated, with the exception of α-carotene and lycopene. Significant correlations were observed for calcium, folate, iron, sodium, potassium, Vitamins B2, B3, B6, C, and E. Skin carotenoid scores and plasma carotenoids were correlated with carotenoid intake from Diet ID™. Conclusions: Diet ID™ may be a useful tool in nutrition research as a less time-intensive and minimally burdensome dietary data collection method for both participants and researchers.