Background: Prior study reported that mushroom consumption was associated with a lower incidence of hyperuricemia, but there is limited evidence on this association. We conducted a collaborative study to investigate the association between mushroom intake and hyperuricemia in middle-aged and older populations. Methods: We used data from the National Health and Nutrition Examination Survey (NHANES) in the U.S. (2007–2018) and the National Institute for Longevity Sciences-Longitudinal Study of Aging (NILS-LSA) in Japan (1997–2012). Consumption of mushroom (g/day) were measured by one- or two-day dietary recall in NHANES and by 3-day dietary records in the NILS-LSA. Hyperuricemia was defined using uric acid levels as > 420 μmol/L and > 350 μmol/L in NHANES for men and women, respectively; in the NILS-LSA, serum uric acid was repeatedly measured at baseline and follow-up surveys. Hyperuricemia was defined as uric acid levels > 416.4 μmol/L for men and ≥ 356.9 μmol/L for women. Logistic regression models in NHANES (cross-sectionally) and Generalized Estimation Equations in NILS-LSA (longitudinally) were performed. Results: A total of 5,778 NHANES participants (mean (SD) age: 53.2 (9.6) years) and 1,738 NILS-LSA (mean (SD) age: 53.5 (11.2) years) were included. Mushrooms were consumed by 5.7% of participants in NHANES and 81.2% in NILS-LSA. We did not observe a significant association between mushroom intakes and hyperuricemia in the NHANES men and women. However, in the NILS-LSA, compared to non-consumers, a higher mushroom intake was associated with a lower risk of incident hyperuricemia in men under 65 years old. The adjusted odds ratio (95% CI) for non-consumers, participants with middle, and the highest consumption of mushrooms were 1.00 (Ref.), 0.77 (0.44, 1.36), and 0.55 (0.31, 0.99), respectively (P-trend = 0.036). No association was found in women in NILS-LSA. Conclusions: Mushroom consumption was associated with a lower risk of incident hyperuricemia in Japanese men.

Background: Multiple, independent lines of research on self-regulation point to executive cognitive ability (executive function/cognitive control) as a factor that underlies the capacity to successfully regulate one’s thoughts, emotion, and behavior. Critically, while leading theories point to the role of executive control in modifying ‘in-the-moment’ regulatory processes (e.g., enacting physical activity, resisting a poor dietary choice, reflecting on a stressful experience), few studies have tested the executive hypothesis at this timescale. Moreover, given normative changes in executive control across the adult lifespan, it is essential to understand how cognitive aging might impact these processes. Objective: The Physical Activity, Imaging, and Ambulatory Testing (PHIAT) project was designed to test how variation in executive control at multiple timescales (from moment-to-moment within-days to age-differences across decades) influences self-regulation across the adult lifespan. Methods: A 14-day, high-frequency, ambulatory assessment protocol was designed for the current study. The study was conducted in a measurement burst design and included a 14-day ecological momentary assessment (EMA) protocol involving 6 assessments per day. Ultra-brief ambulatory cognitive assessments of multiple domains of cognition were included in the EMA protocol. Throughout the measurement burst, participants also wore 3 activity monitors on the hip, thigh, and wrist to measure physical activity/exercise, sedentary behavior, and to self-monitor physical activity behavior, respectively. Results: A total of N=221 participants ranging from 18-89 years of age completed the PHIAT protocol over the course of 2021 to 2024. EMA data was collected from participants reflecting a wide range of psychosocial factors surrounding participation in health-promoting behaviors (motivation, intention, stress, built environment, social cognitive factors). This EMA data stream is complemented by data from high-frequency, ambulatory cognitive assessments measuring processing speed, working memory capacity, inhibitory control, and divided/sustained attention administered 5 times per day throughout the 14-day burst. In addition, health promoting behaviors, including sleep, diet, hydration, physical activity, and exercise, were assessed throughout the 14-day burst through a combination of EMA self-report and continuous activity monitoring. Conclusions: A rich, high-frequency dataset was generated by the PHIAT project that will provide a range of novel insights into the motivational, information processing, and environmental factors that surround self-regulation of health-promoting behavior.

Background: Multiple independent lines of research on self-regulation point to executive cognitive ability (executive function/cognitive control) as a factor that underlies the capacity to successfully regulate one’s thoughts, emotions, and behavior. Critically, while leading theories point to the role of executive control in modifying “in-the-moment” regulatory processes (eg, enacting physical activity, resisting a poor dietary choice, reflecting on a stressful experience), few studies have tested the executive hypothesis at this timescale. Moreover, given the normative changes in executive control across the adult lifespan, it is essential to understand how cognitive aging might impact these processes. Objective: The Physical Activity, Imaging, and Ambulatory Testing (PHIAT) project was designed to test how variation in executive control at multiple timescales (from moment-to-moment within days to age differences across decades) influences self-regulation across the adult lifespan. Methods: A 14-day, high-frequency, ambulatory assessment protocol was designed for this study. The study was conducted in a measurement burst design and included a 14-day ecological momentary assessment (EMA) protocol involving 6 assessments per day. Ultra-brief ambulatory cognitive assessments of multiple domains of cognition were included in the EMA protocol. Throughout the measurement burst, participants also wore 3 activity monitors on the hip, thigh, and wrist to measure physical activity/exercise, measure sedentary behavior, and self-monitor physical activity behavior, respectively. Results: A total of 221 participants ranging from 18 to 89 years of age completed the PHIAT protocol over the course of 2021-2024, and data collection is complete. EMA data were collected from participants reflecting a wide range of psychosocial factors surrounding participation in health-promoting behaviors (motivation, intention, stress, built environment, and social cognitive factors). This EMA data stream is complemented by data from high-frequency, ambulatory cognitive assessments measuring processing speed, working memory capacity, inhibitory control, and divided/sustained attention administered 5 times per day throughout the 14-day burst. In addition, health-promoting behaviors, including sleep, diet, hydration, physical activity, and exercise, were assessed throughout the 14-day burst through a combination of EMA self-report and continuous activity monitoring. Conclusions: A rich, high-frequency dataset was generated by the PHIAT project that will provide a range of novel insights into the motivational factors, information processing, and environmental factors that surround self-regulation of health-promoting behavior.

Introduction: About one-quarter of patients with acute pancreatitis (AP) will develop diabetes mellitus (DM) within 3 years, but risk factors remain unclear. This study aims to determine whether machine learning models (ML) can be trained to accurately predict new-onset DM following AP and identify key clinical features using real-world data. Methods: This retrospective cohort study used de-identified data from the TriNetX federated electronic health records (EHR) network from 1 January 2017 to 11 March 2024. A total of 58 746 patients with AP (International Classification of Diseases-10 code K85) and no prior diagnosis of DM were included. New-onset DM following AP was the main outcome of interest. Five ML models were trained across four prediction windows, including logistic regression (LR), eXtreme Gradient Boosting, Adaptive Boosting, Random Forest and support vector machine. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). Results: Among the 58 746 patients with AP (mean (SD) age, 50.2 (16.6) years), the LR model demonstrated the highest overall performance, with a mean accuracy of 0.72 (SD, 0.009) and an AUROC of 0.79 (SD, 0.009). Key clinical features across models included age, pancreatic necrosis, body weight, body mass index, systolic blood pressure, number of medical visits and prior AP laboratory values such as glucose, anion gap, blood urea nitrogen (BUN) and total protein. Conclusions: In this first real-world evidence study using EHR data, we have developed and demonstrated the feasibility of using ML to predict the new onset of DM after AP. Clinical features such as age, pancreatic necrosis, prior glucose, BUN and anion gap had the highest overall importance scores and may inform tailored prevention strategies.

Background/Objectives: Plant-based diets are widely recommended for lowering the risk of type 2 diabetes, with the caveat that their health benefits depend on the quality of the foods consumed. This analysis examined associations between five dietary patterns and pre-existing diabetes among patients with acute pancreatitis (AP), a population in which the relationship between diet and diabetes has not been well characterized. Methods: Participants were drawn from the Diabetes RElated to Acute Pancreatitis and its Mechanisms (DREAM) study, an ongoing multicenter study of adults aged 18 to 75 years with AP in the U.S. This cross-sectional analysis evaluated five dietary patterns using food frequency questionnaires (FFQ): the reverse Empirical Dietary Index for Hyperinsulinemia (rEDIH), the reverse Empirical Dietary Inflammatory Pattern (rEDIP), the overall plant-based diet index (PDI), the healthful plant-based diet index (hPDI), and the unhealthful plant-based diet index (uPDI). We used multivariable logistic regression models to estimate odds ratios (ORs) for pre-existing diabetes (i.e., prior to the AP episode). Results: A total of 647 participants (mean age 49.1 ± 14.5 years) were included. In multivariable-adjusted analyses, participants in the highest versus lowest tertile of PDI had 55% lower odds of pre-existing diabetes (adjusted OR: 0.45; 95% CI: 0.25–0.80; p-trend = 0.005). No statistically significant associations were detected for rEDIP, rEDIH, hPDI, or uPDI. Conclusions: Higher overall PDI scores were associated with lower odds of prevalent diabetes. Among patients with AP, further studies are needed to evaluate dietary contributions to future diabetes development following AP and identify potential dietary strategies for diabetes prevention.