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Abstract Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach—Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Additionally, we find that METRIC can still correct the random errors well even without including gut microbial compositions. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.

Abstract Background Gestational age (GEAA) estimated by newborn DNA methylation (GAmAge) is associated with maternal prenatal exposures and immediate birth outcomes. However, the association of GAmAge with long-term overweight or obesity (OWO) trajectories is yet to be determined. Methods GAmAge was calculated for 831 children from a US predominantly urban, low-income, multi-ethnic birth cohort based on cord blood DNA methylation profile using Illumina EPIC array. Repeated anthropometric measurements aligned with pediatric primary care schedule allowed us to calculate body-mass-index percentiles (BMIPCT) at specific age and to define long-term weight trajectories from birth to 18 years. Results GAmAge was associated with BMIPCT trajectories, defined by 4 groups: stable (consistent OWO: “early OWO”; constant normal weight: “NW”) or non-stable (OWO by year 1 of follow-up: “late OWO”; OWO by year 6 of follow-up: “NW to very late OWO”). GAmAge differentiated between the group with consistently normal BMIPCT pattern and the non-stable groups with late and very late OWO development. Such differentiation was observed in the age periods of birth to 1year, 3years, 6years, 10years, and 14years (p

The National Heart, Lung, and Blood Institute convened a virtual workshop in September 2022 to discuss “Optimal Instruments for Measurement of Diet, Physical Activity, and Sleep.” This report summarizes the proceedings, identifying current research gaps and future directions for measuring different lifestyle behaviors in adult population‐based studies. Key discussions centered on integrating report‐based methods, like questionnaires, with device‐based assessments, including wearables and physiological measures such as biomarkers and omics to enhance self‐reported metrics and better understand the underlying biologic mechanisms of chronic diseases. Emphasis was placed on the need for data harmonization, including the adoption of standard terminology, reproducible metrics, and accessible raw data, to enhance the analysis through artificial intelligence and machine learning techniques. The workshop highlighted the importance of standardizing procedures for integrated behavioral phenotypes using time‐series data. These efforts aim to refine data accuracy and comparability across studies and populations, thereby advancing our understanding of lifestyle behaviors and their impact on chronic disease outcomes over the life course.

Summary: Background: Prospective associations between total and groups of ultra-processed foods (UPF) and cardiovascular disease (CVD) remained to be characterised. Our aim was to assess the association of total and group-specific UPF intakes with CVD, coronary heart disease (CHD), and stroke in three large prospective cohorts of US adults. Additionally, we conducted a systematic review and meta-analyses on the existing evidence on the associations of total UPF intake with these outcomes. Methods: UPF intake was assessed through food frequency questionnaires in the Nurses’ Health Study (NHS; n = 75,735), Nurses’ Health Study II (NHSII; n = 90,813), and Health Professionals Follow-Up Study (HPFS; n = 40,409). Cox regression estimated cohort-specific associations of total and group-specific UPF intake with risk of CVD (cases = 16,800), CHD (cases = 10,401), and stroke (cases = 6758), subsequently pooled through fixed-effect models. Random-effects meta-analyses pooled existing prospective findings on the UPF-CVD association identified on Medline and Embase up to April 5, 2024, without language restrictions. Risk of bias was assessed with the Newcastle–Ottawa Scale, funnel plots, and Egger’s tests, and meta-evidence was evaluated using NutriGrade. Findings: The baseline mean (SD) age was 50.8 years (7.2) for the NHS, 36.7 years (4.6) for the NHSII, and 53.4 years (9.6) for the HPFS. The proportion of participants of White race was 97.7% in the NHS, 96.4% in the NHSII, and 94.9% in the HPFS. Among the three cohorts, multivariable-adjusted hazard ratios [HRs (95% CIs)] for CVD, CHD, and stroke for the highest (vs. lowest) total UPF intake quintile were 1.11 (1.06–1.16), 1.16 (1.09–1.24), and 1.04 (0.96–1.12), respectively. UPF groups demonstrated divergent associations. Sugar-/artificially-sweetened drinks and processed meats were associated with higher CVD risk, whereas inverse associations were observed for bread/cold cereals, yoghurt/dairy desserts, and savoury snacks. Meta-analysing 22 prospective studies showed that total UPF intake at the highest category (vs. lowest) was associated with 17% (11%–24%), 23% (12%–34%), and 9% (3%–15%) higher CVD, CHD, and stroke risk. Meta-evidence quality was high for CHD, moderate for CVD, and low for stroke. Interpretation: Total UPF intake was adversely associated with CVD and CHD risk in US adults, corroborated by prospective studies from multiple countries, also suggesting a small excess stroke risk. Nutritional advice for cardiovascular health should consider differential consequences of group-specific UPF. Replication is needed in racially/ethnically-diverse populations. Funding: National Institutes of Health (NIH) grants supported the NHS, NHSII, and HPFS.

Background Evidence from cohort studies indicates a bidirectional relationship between periodontal disease and type 2 diabetes (T2D), but the underlying mechanisms remain unknown. In this study, we aimed to (1) identify saliva, plasma, and multifluid metabolomic signatures associated with periodontal disease and (2) determine if these signatures predict T2D progression and cardiometabolic biomarkers at year 3. Methods and Results We included participants from the SOALS (San Juan Overweight Adult Longitudinal Study) (n=911). Metabolites from saliva (k=635) and plasma (k=1051) were quantified using liquid chromatography–mass spectrometry. We applied elastic net regression with 10‐fold cross‐validation to identify baseline metabolomic signatures of periodontal disease. Multivariable Cox proportional hazards regression and linear regression were used to evaluate the association with T2D progression and biomarker concentrations. Metabolomic profiles included highly weighted metabolites related to lysine and pyrimidine metabolism. Periodontal disease or its 3 metabolomic signatures were not associated with T2D progression in 3 years. Prospectively, 1‐SD increments in the multifluid and saliva metabolomic signatures were associated with higher low‐density lipoprotein (multifluid: 12.9±5.70, P=0.02; saliva: 13.3±5.11, P=0.009). A 1‐SD increment in the plasma metabolomic signature was also associated with Homeostatic Model Assessment for Insulin Resistance (2.67±1.14, P=0.02) and triglyceride (0.52±0.18, P=0.002). Conclusions Although metabolomic signatures of periodontal disease could not predict T2D progression, they were associated with low‐density lipoprotein, triglyceride, and Homeostatic Model Assessment for Insulin Resistance levels at year 3.

Abstract Background Legume consumption has been linked to a reduced risk of type 2 diabetes (T2D) and cardiovascular disease (CVD), while the potential association between plasma metabolites associated with legume consumption and the risk of cardiometabolic diseases has never been explored. Therefore, we aimed to identify a metabolite signature of legume consumption, and subsequently investigate its potential association with the incidence of T2D and CVD. Methods The current cross-sectional and longitudinal analysis was conducted in 1833 PREDIMED study participants (mean age 67 years, 57.6% women) with available baseline metabolomic data. A subset of these participants with 1-year follow-up metabolomics data (n = 1522) was used for internal validation. Plasma metabolites were assessed through liquid chromatography-tandem mass spectrometry. Cross-sectional associations between 382 different known metabolites and legume consumption were performed using elastic net regression. Associations between the identified metabolite profile and incident T2D and CVD were estimated using multivariable Cox regression models. Results Specific metabolic signatures of legume consumption were identified, these included amino acids, cortisol, and various classes of lipid metabolites including diacylglycerols, triacylglycerols, plasmalogens, sphingomyelins and other metabolites. Among these identified metabolites, 22 were negatively and 18 were positively associated with legume consumption. After adjustment for recognized risk factors and legume consumption, the identified legume metabolite profile was inversely associated with T2D incidence (hazard ratio (HR) per 1 SD: 0.75, 95% CI 0.61–0.94; p = 0.017), but not with CVD incidence risk (1.01, 95% CI 0.86–1.19; p = 0.817) over the follow-up period. Conclusions This study identified a set of 40 metabolites associated with legume consumption and with a reduced risk of T2D development in a Mediterranean population at high risk of cardiovascular disease. Trial registration: ISRCTN35739639.

Summary: Background: Ultra-processed foods (UPFs) are emerging as a risk factor for colorectal cancer (CRC), yet how post-diagnostic UPF intake may impact CRC prognosis remains unexplored. Methods: Data collected from food frequency questionnaires were used to estimate intakes of total UPFs and UPF subgroups (serving/d) at least 6 months but less than 4 years post-diagnosis among 2498 patients diagnosed with stages I–III CRC within the Nurses’ Health Study and Health Professionals Follow-up Study during 1980–2016. Hazard ratios (HR) and 95% confidence intervals (CIs) of all-cause, CRC- and cardiovascular disease (CVD)-specific mortality in association with UPF consumption were estimated using an inverse probability weighted multivariable Cox proportional hazards regression model, adjusted for confounders. Findings: The mean (SD) age of patients at diagnosis was 68.5 (9.4) years. A total of 1661 deaths were documented, including 321 from CRC and 335 from CVD. Compared to those in the lowest quintile (median = 3.6 servings/d), patients in the highest quintile (median = 10 servings/d) of post-diagnostic UPF intake had higher CVD mortality (HR = 1.65, 95% CI = 1.13–2.40) but not CRC or all-cause mortality. Among UPF subgroups, higher consumption of fats/condiments/sauces was associated with a higher risk of CVD-specific mortality (highest vs. lowest quintile of intake, HR = 1.96, 95% CI = 1.41–2.73), and higher intake of ice cream/sherbet was associated with an increased risk of CRC-specific mortality (highest vs. lowest quintile, HR = 1.86, 95% CI: 1.33–2.61). No statistically significant association was found between UPF subgroups and overall mortality. Interpretation: Higher post-diagnostic intake of total UPFs and fats/condiments/sauces in CRC survivors is associated with higher CVD mortality, and higher ice cream/sherbet intake is linked to higher CRC mortality. Funding: US National Institutes of Health and the American Cancer Society.

Introduction The Department of Veterans Affairs (VA) Million Veteran Program (MVP) nutrition data is derived from dietary food/beverage intake information collected through a semiquantitative food frequency questionnaire (SFFQ). Methods Estimates of dietary energy, nutrient, and non-nutritive food components intakes data were derived from an extensively validated SFFQ, which assessed the habitual frequency of consumption of 61 food items, added sugar, fried food frequency, and 21 nutritional supplements over the 12 months preceding questionnaire administration. Results Complete nutrition data was available for 353,418 MVP participants as of 30th September 2021. Overall, 91.5% of MVP participants with nutrition data were male with an average age of 65.7 years at enrollment. Participants who completed the SFFQ were primarily White (82.5%), and Blacks accounted for 13.2% of the responders. Mean ± SD energy intake for 353, 418 MVP participants was 1428 ±616 kcal/day, which was 1434 ±617 kcal/day for males and 1364 ±601 kcal/day for females. Energy intake and information on 322 nutrients and non-nutritive food components is available through contact with MVP for research collaborations at www.research.va.gov/mvp. Conclusions The energy and nutrient data derived from MVP SFFQ are an invaluable resource for Veteran health and research. In conjunction with the MVP Lifestyle Survey, electronic health records, and genomic data, MVP nutrition data may be used to assess nutritional status and related risk factors, disease prevalence, and determinants of health that can provide scientific support for the development of evidence-based public health policy and health promotion programs and services for Veterans and general population.

We aimed to investigate the associations between maternal intake of folate, vitamin B12, B6, B2, methionine, choline, phosphatidylcholine and betaine during the period surrounding pregnancy and offspring weight outcomes from birth to early adulthood. These associations were examined among 2454 mother–child pairs from the Nurses’ Health Study II and Growing Up Today Study. Maternal energy-adjusted nutrient intakes were derived from food frequency questionnaires. Birth weight, body size at age 5 and repeated BMI measurements were considered. Overweight/obesity was defined according to the International Obesity Task Force (p-trend: 0.003). The association was stronger among offspring born to mothers with high red meat intake (high red meat RRQ5vsQ1 = 1.50 [1.14–1.98], p-trend: 0.001; low red meat RRQ5vsQ1 = 1.05 [0.87–1.27], p-trend: 0.46; p-interaction = 0.13). Future studies confirming the association between a higher maternal phosphatidylcholine intake during pregnancy and offspring risk of being overweight or obese are needed.

Background: Dietary quality plays an important role in disease development and prognosis, and diet is also a key contributor to disparities in many chronic diseases and health conditions. Objectives: This study aimed to assess racial and ethnic disparities experienced by veterans; we examined food intake and dietary quality across different racial and ethnic groups of United States veterans. Methods: The study included 420,730 males and females aged 19–107 y (91.2% males) enrolled in the Veterans Affairs Million Veteran Program with plausible dietary intake measured by food frequency questionnaire. Dietary quality was evaluated with dietary approaches to stop hypertension (DASH) score. Dietary intakes of various race and ethnicity groups were standardized to the age distribution of non-Hispanic White participants, separately for males and females. Differences across race and ethnicity groups were compared using general linear regression models after adjustment for socioeconomic and lifestyle factors as well as military service. Results: Compared to non-Hispanic White males, non-Hispanic Black males had a relatively lower DASH score, higher red and processed meats, higher sugar-sweetened beverages (SSBs), and lower low-fat dairy intakes. Non-Hispanic Asian males had a relatively higher DASH score as compared to non-Hispanic White males with relatively higher intakes of fruits and vegetables and relatively lower intakes of sodium, red meat and SSBs. Age-standardized DASH scores of Hispanic males and “Other” race/ethnicity groups were not statistically different from non-Hispanic White males. Similar race and ethnicity dietary patterns were found in females, although not all reached a statistically significant level. Conclusions: A modest difference in overall dietary quality (i.e., DASH score) was observed, but unique differences in food preferences across the different racial/ethnic groups were identified. Findings from our study may provide insight for the potential development of specific interventions to help address nutritional disparities experienced among veterans.