Beta Estimates Research Articles

The associations between oxidative balance score and serum Klotho level in the U.S. population aged 40–79 years

The relationship between the oxidative balance score (OBS) and the serum Klotho level has yet to be defined. We sought to investigate the potential relationship between OBS and the serum Klotho level in the U.S. population aged 40–79 years. This study included 8,145 participants from the National Health and Nutrition Examination Surveys (NHANES) database spanning from 2007 to 2016. The OBS consisted of the dietary OBS and the lifestyle OBS, based on 16 dietary components and 4 lifestyle components. Weighted multiple linear regressions were performed to explore the association between OBS and serum Klotho level. Furthermore, nonlinear relationships were analyzed through the application of restricted cubic splines (RCS). In the multivariate linear regression model with adjustment for such as demographics, economic income and dietary intake, a higher OBS was associated with a higher serum Klotho, with the beta estimate and 95%CI of 2.85 (1.03–4.68, p < 0.01). Compared with the lowest tertile group, the highest group was associated with a higher Klotho level (30.35, 3.43–57.28, p < 0.05). Furthermore, higher dietary OBS and lifestyle OBS were similarly associated with higher Klotho level (beta (95%CI): 1.27 (0.79–3.32); 14.23 (9.53–18.92), respectively). The RCS exhibited a linear dose-response association between OBS, dietary OBS and lifestyle OBS with serum Klotho concentration (Pnon−linearity>0.05). The association between OBS and serum Klotho level was consistent across age, sex, education, marital status, energy intake and poverty income ratio (PIR) (Pinteraction>0.05). The study reported significant association between OBS and klotho, indicating that adherence to antioxidant behaviors may be linked to slower aging and better health.

CCAFE: Estimating Case and Control Allele Frequencies from GWAS Summary Statistics.

Methods involving summary statistics in genetics can be quite powerful but can be limited in utility. For instance, many post-hoc analyses of disease studies require case and control allele frequencies (AFs), which are not always published. We present two frameworks to derive case and control AFs from GWAS summary statistics using the odds ratio, case and control sample sizes, and either the total (case and control aggregated) AF or standard error (SE). In simulations and real data, derivations of case and controls AFs using total AF is highly accurate across all settings (e.g., minor AF, condition prevalence). Conversely, derivations using SE underestimate common variant AFs (e.g. minor allele frequency >0.3) in the presence of covariates. We develop an adjustment using gnomAD AFs as a proxy for true AFs, which reduces the bias when using SE. While estimating case and control AFs using the total AF is preferred due to its high accuracy, estimating from the SE can be used more broadly since SE can be derived from p-values and beta estimates, which are commonly provided. The methods provided here expand the utility of publicly available genetic summary statistics and promote the reusability of genomic data. The R package CCAFE , with implementations of both methods, is freely available on Bioconductor and GitHub.

Practical Applications of Investigating the Influence of News Sources and Language Models on Climate Beta Estimates

Practical Applications of Investigating the Influence of News Sources and Language Models on Climate Beta Estimates

A Comparison of Green, Delta, and Monte Carlo Methods to Select an Optimal Approach for Calculating the 95% Confidence Interval of the Population-attributable Fraction: Guidance for Epidemiological Research.

This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the "GDM-PAF CI Explorer," was developed to facilitate the analysis and visualization of these computations. No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland's method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

A Graphical and Beta Analysis of the Effect of Increased Ethanol Production on the Volatility of Corn Prices

Increased demand for corn-based ethanol puts upward pressure on prices of corn and other commodities, such as soybeans, and possibly worsens their price volatility. The paper investigates the changes in agricultural commodities' standard deviation and beta sizes due to ethanol production in the US. Standard deviations and beta estimations are compared for the ethanol pre-expansion and expansion periods. The results indicate a high level of price volatility in the second period, which could be attributed to ethanol expansion.

Extrapolation beyond peers: An asset pricing perspective

We introduce a novel measure that captures the beta deviation of individual firms from their industry peers within China's stock market. Our analysis reveals that stocks with greater beta deviation generate significantly higher future returns. This predictive power is unaffected by established return predictors and remains robust across alternative peer identification methods, beta estimation techniques, and subsample tests. Our findings suggest a behavioral interpretation, linking positive predictability to mispricing driven by investors' extrapolation biases. Overall, our research highlights the critical role of incorporating peer firms into asset pricing, particularly in emerging markets.

Harnessding the Power of Machine Learning to Superchange CAPM Forecasts

This paper investigates the improvement of the Capital Asset Pricing Model (CAPM) by incorporating machine learning techniques. The objective is to address the model's conventional constraints in accurately predicting stock returns. The conventional Capital Asset Pricing Model (CAPM), which heavily depends on the beta coefficient to explain returns, frequently proves inadequate in emerging markets and neglects to consider market irregularities such as size and value effects. Our methodology enhances the estimation of beta by refining the Capital Asset Pricing Model (CAPM) to exclude the intercept term and incorporating a dynamic rolling regression method. This approach captures the fluctuations of beta more effectively across different periods, resulting in a more precise estimation. This study utilises ten years of weekly closing price data from companies listed on the NSE Nifty 50 index. It employs rolling regression and two-stage regression analysis to improve the prediction of excess returns above the risk-free rate. The revised CAPM model, which omits the intercept, exhibits a superior level of accuracy, as evidenced by higher adjusted R-squared values and improved F-statistics. In addition, the paper analyses the Fama-French models within the Chinese stock market and explores the potential of advanced machine learning models, such as Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), to improve forecasting methods in unpredictable markets. Integrating LSTM-RNN models presents a promising approach to capturing intricate patterns in financial time series data, demonstrating substantial enhancement in forecasting accuracy compared to conventional methods such as OLS.

TICS-M scores in an oldest-old normative cohort identified by computable phenotype

Objective: To (1) examine the distribution of Telephone Interview for Cognitive Status modified (TICS-m) scores in oldest-old individuals (age 85 and above) identified as cognitively healthy by a previously validated electronic health records-based computable phenotype (CP) and (2) to compare different cutoff scores for cognitive impairment in this population. Method: CP identified 24,024 persons, 470 were contacted and 252 consented and completed the assessment. Associations of TICS-m score with age, sex, and educational categories (<10 years, 11–15 years, and >16 years) were examined. The number of participants perceived as impaired was studied with commonly used cutoff scores (27–31). Results: TICS-m score ranged from 18 to 44 with a mean of 32.6 (SD = 4.7) in older adults aged 85–99 years old. A linear regression model including (range-restricted) age, education, and sex, showed beta estimates comparable to previous findings. Different cutoff scores (27 to 31) generated slightly lower MCI and dementia prevalence rates of participants meeting the criteria for the impairments than studies of younger elderly using traditional recruitment methods. Conclusions: The use of validated computable phenotype to identify a normative cohort generated a normative distribution for the TICS-m consistent with prior findings from more effortful approaches to cohort identification and established expected TICS-m performance in the oldest-old population.

Exogenous factors associated with legacy PFAS concentrations in the general U.S. population: NHANES 1999-2018

To reduce per- and polyfluoroalkyl substances (PFAS) exposure through legislation and other interventions, we must understand factors contributing to individual body burden. Identifying factors associated with serum perfluorinated compounds (PFAS) concentrations also aids in identifying groups at higher risk of adverse health outcomes due to elevated exposure. This exploratory analysis provides initial findings on exposure-related factors associated with legacy PFAS concentrations in the general United States (U.S.) population. We obtained National Health and Nutrition Examination Survey (NHANES) datasets with individual serum PFAS measurements from cycle years 1999-2018 (N = 14,961), excluding the 2001-2002 cycle due to pooled sampling. Over 100 features were evaluated for associations with PFAS concentrations. Data were singly imputed using hot deck and predictive mean matching (PMM) methods, and model performance by imputation method was compared using elastic net regression (ENR) models. Hot-deck imputation explained the most variance in predictive models; thus, a dataset imputed via hot-deck was used for feature selection. ENR models were employed to identify the top variables associated with legacy PFAS concentrations, and selected features were put into linear mixed models to obtain beta estimates and standard errors. Survey year, demographic, income, place of birth and citizenship status, household size, dietary, health, food insecurity, general health and healthcare, housing, social and behavioral, and other characteristics were important factors associated with legacy PFAS concentrations in this nationally representative study population. A better understanding of exogenous factors associated with PFAS concentrations can influence future epidemiological studies, guiding decisions on adjustment for confounding, and advancing our understanding of factors that affect chemical half-lives and toxicokinetics.

ASSOCIATIONS BETWEEN GENETICALLY PREDICTED PULSE PRESSURE AND TARGET ORGAN DAMAGE: A MENDELIAN RANDOMIZATION STUDY

Objective: Large-artery stiffness (LAS) leads to an increased pulse pressure (PP) and is thought to induce target organ damage (TOD). However, causal associations of PP have not been unequivocally assessed in humans. We aimed to investigate the bi-directional associations between genetically predicted increased PP and phenotypes of TOD using Mendelian randomization (MR). Design and method: Bi-directional MR analyses were performed to investigate associations between genetically predicted PP and: left ventricular mass (LVM), myocardial interstitial fibrosis, heart failure (HF), chronic kidney disease (CKD), cognitive performance, brain white matter hyperintensities (WMH), type 2 diabetes (T2D) and preeclampsia (PrE). Multivariable MR was conducted to investigate the effect of PP on significant outcomes, independently of mean arterial pressure (MAP). Results: For a 10-mmHg increase in genetically-predicted PP, the odds ratios (ORs) were 1.113 (95%CI=1.085-1.137, P&lt;0.001) for HF, 1.075 (95%CI=1.048-1.103, P&lt;0.001) for CKD, 1.137 (95%CI=1.102-1.173, P&lt;0.001) for T2D, and 1.435 (95%CI=1.256-1.638, P&lt;0.001) for PrE. Similarly, a 10-mmHg increase in genetically-predicted PP was associated with a greater LVM (beta estimate=3.527 g/m2, 95%CI=3.026-4.028, P&lt;0.001). The association of genetically predicted PP on risk of HF, CKD and T2D was independent of genetically-predicted MAP. Bidirectional MR also supported a causal effect of genetically predicted log-odds of T2D on PP (beta estimate=0.527 mmHg, 0.396-0.658, P&lt;0.001), and PrE on PP (beta estimate=2.365 mmHg, 0.783-3.947, P=0.003) indicating a potential bidirectional relationship. Conclusions: We provide evidence supporting causal associations between PP and T2D, CKD, HF and increased risk of LV hypertrophy in humans, consistent with the deleterious effects of LAS on LV afterload and on microvascular integrity in low-resistance target organs. Early interventions aimed at lowering LAS may lead to lowering the risk of adverse outcomes.

First-trimester fetal size, accelerated growth in utero, and child neurodevelopment in a cohort study.

Early pregnancy is a critical window for neural system programming; however, the association of first-trimester fetal size with children's neurodevelopment remains to be assessed. This study aimed to explore the association between first-trimester fetal size and children's neurodevelopment and to examine whether intrauterine accelerated growth could compensate for the detrimental effects of first-trimester restricted growth on childhood neurodevelopment. The participants were from a birth cohort enrolled from March 2014 to March 2019 in Wuhan, China. A total of 2058 fetuses with crown to rump length (CRL) (a proxy of first-trimester fetal size) measurements in the first trimester and neurodevelopmental assessment at age 2 years were included. We measured the first-trimester CRL and defined three fetal growth patterns based on the growth rate of estimated fetal weight from mid to late pregnancy. The neurodevelopment was assessed using the Bayley Scales of Infant Development of China Revision at 2 years. Each unit (a Z score) increase of first-trimester CRL was associated with increased scores in mental developmental index (MDI) (adjusted beta estimate = 1.19, (95% CI: 0.42, 1.95), P = 0.03) and psychomotor developmental index (PDI) (adjusted beta estimate = 1.36, (95% CI: 0.46, 2.26), P < 0.01) at age 2 years, respectively. No significant association was observed between fetal growth rate and PDI. For children with restricted first-trimester fetal size (the lowest tertile of first-trimester CRL), those with "intrauterine accelerated growth" pattern (higher growth rates) had significantly higher MDI (adjusted beta estimate = 6.14, (95% CI: 3.80, 8.49), P < 0.001) but indistinguishable PDI compared to those with "intrauterine faltering growth" pattern (lower growth rates). Main limitations of this study included potential misclassification of gestational age due to recall bias of the last menstrual period and residual confounding. The current study suggests that restricted first-trimester fetal size is associated with mental and psychomotor developmental delay in childhood. However, in children with restricted first-trimester fetal size, intrauterine accelerated growth was associated with improved mental development but had little effect on psychomotor development. Additional studies are needed to validate the results in diverse populations.

Predicting Peak Productivity in Urologic Medicare Practice via Work-Relative Value Units

ObjectivesTo explore factors associated with productivity in urologic practice. Work-relative value units (wRVUs), the basis for Center for Medicare & Medicaid Services (CMS) and private payer reimbursements, commonly serve to estimate physician productivity. Limited data describes which practice factors predict increased wRVU productivity. MethodsThe 2017 and 2018 CMS databases were retrospectively queried for urologic Medicare provider demographics and procedural/service details. Medical school graduation year was used to estimate years in practice and generation (Millennial, Gen X, Baby Boomer, or Post-War). Treated patients’ demographics were obtained. Adjusted and unadjusted linear mixed models were performed to predict wRVU production. ResultsIncluded were 6,773 Medicare-participating urologists across the United States. Millennials produced 1115 wRVUs per year, while Gen X and Baby Boomers produced significantly more (1997 and 2104, respectively, p<.01). Post-War urologists produced numerically more (1287, p=.88). In adjusted analyses, predictors of Medicare wRVU productivity included FPMRS (exponentiated beta estimate (β) 1.46, 95% CI 1.32-1.60), men’s health (β 1.22, 95% CI 1.13-1.32), and oncologic sub-specialization (β 1.08, 95% CI 1.02-1.14), female gender (β 0.87, 95% CI 0.82-0.92), wRVUs generated from inpatient procedures (β 1.08, 95% CI 1.06-1.09) and office visits (β 0.88, 95% CI 0.87-0.89), and the level of education (β 1.10, 95% CI 1.07-1.14) and percent impoverished patients (β 0.85, 95% CI 0.83-0.88) in provider’s practice zip code. ConclusionsUrologic experience, specialization, demographics, practice patterns, and patient demographics are significantly associated with wRVU productivity in Medicare settings. Further work should incorporate quality metrics into wRVUs and ensure patient demographics do not affect reimbursement.

Child eating behavior predicts body mass index after 1 year: results from the Swiss Preschooler's Health Study (SPLASHY).

Child obesity is a growing global issue. Preventing early development of overweight and obesity requires identifying reliable risk factors for high body mass index (BMI) in children. Child eating behavior might be an important and malleable risk factor that can be reliably assessed with the parent-report Child Eating Behavior Questionnaire (CEBQ). Using a hierarchical dataset (children nested within child care centers) from a representative cohort of Swiss preschool children, we tested whether eating behavior, assessed with a 7-factor solution of the CEBQ, and BMI at baseline predicted the outcome BMI after 1 year, controlling for socioeconomic status (n = 555; 47% female; mean age = 3.9 years, range: 2.2-6.6; mean BMI = 16 kg/m2, range: 11.2-23; mean age- and sex-corrected z-transformed BMI, zBMI = 0.4, range -4 to +4.7). The statistical model explained 65.2% of zBMI at follow-up. Baseline zBMI was a strong positive predictor, uniquely explaining 48.8% of outcome variance. A linear combination of all CEBQ scales, taken together, explained 10.7% of outcome variance. Due to their intercorrelations, uniquely explained variance by any individual scale was of negligible clinical relevance. Only food responsiveness was a significant predictor, when accounting for all other predictors and covariates in the model, and uniquely explained only 0.4% of outcome variance. Altogether, our results confirm, extend, and refine previous research on eating behavior and zBMI in preschool children, by adjusting for covariates, accounting for intercorrelations between predictors, partitioning explained outcome variance, and providing standardized beta estimates. Our findings show the importance of carefully examining the contribution of predictors in multiple regression models for clinically relevant outcomes.

Non-invasive assessment of stimulation-specific changes in cerebral glucose metabolism with functional PET

PurposeFunctional positron emission tomography (fPET) with [18F]FDG allows quantification of stimulation-induced changes in glucose metabolism independent of neurovascular coupling. However, the gold standard for quantification requires invasive arterial blood sampling, limiting its widespread use. Here, we introduce a novel fPET method without the need for an input function.MethodsWe validated the approach using two datasets (DS). For DS1, 52 volunteers (23.2 ± 3.3 years, 24 females) performed Tetris® during a [18F]FDG fPET scan (bolus + constant infusion). For DS2, 18 participants (24.2 ± 4.3 years, 8 females) performed an eyes-open/finger tapping task (constant infusion). Task-specific changes in metabolism were assessed with the general linear model (GLM) and cerebral metabolic rate of glucose (CMRGlu) was quantified with the Patlak plot as reference. We then estimated simplified outcome parameters, including GLM beta values and percent signal change (%SC), and compared them, region and whole-brain-wise.ResultsWe observed higher agreement with the reference for DS1 than DS2. Both DS resulted in strong correlations between regional task-specific beta estimates and CMRGlu (r = 0.763…0.912). %SC of beta values exhibited strong agreement with %SC of CMRGlu (r = 0.909…0.999). Average activation maps showed a high spatial similarity between CMRGlu and beta estimates (Dice = 0.870…0.979) as well as %SC (Dice = 0.932…0.997), respectively.ConclusionThe non-invasive method reliably estimates task-specific changes in glucose metabolism without blood sampling. This streamlines fPET, albeit with the trade-off of being unable to quantify baseline metabolism. The simplification enhances its applicability in research and clinical settings.

Optimal Inference for Spot Regressions

Betas from return regressions are commonly used to measure systematic financial market risks. “Good” beta measurements are essential for a range of empirical inquiries in finance and macroeconomics. We introduce a novel econometric framework for the nonparametric estimation of time-varying betas with high-frequency data. The “local Gaussian” property of the generic continuous-time benchmark model enables optimal “finite-sample” inference in a well-defined sense. It also affords more reliable inference in empirically realistic settings compared to conventional large-sample approaches. Two applications pertaining to the tracking performance of leveraged ETFs and an intraday event study illustrate the practical usefulness of the new procedures. (JEL C22, C58, G12, G23)

Estimating Stock Market Betas via Machine Learning

Abstract Machine learning-based stock market beta estimators outperform established benchmark models both statistically and economically. Analyzing the predictability of time-varying market betas of U.S. stocks, we document that machine learning-based estimators produce the lowest forecast and hedging errors. They also help to create better market-neutral anomaly strategies and minimum variance portfolios. Among the various techniques, random forests perform the best overall. Model complexity is highly time-varying. Historical stock market betas, turnover, and size are the most important predictors. Compared to linear regressions, allowing for nonlinearity and interactions significantly improves predictive performance.

Dynamic vagal-mediated connectivity of cortical and subcortical central autonomic hubs predicts chronotropic response to submaximal exercise in healthy adults

BackgroundDespite accumulation of a substantial body of literature supporting the role of exercise on frontal lobe functioning, relatively less is understood of the interconnectivity of ventromedial prefrontal cortical (vmPFC) regions that underpin cardio-autonomic regulation predict cardiac chronotropic competence (CC) in response to sub-maximal exercise. MethodsEligibility of 161 adults (mean age = 48.6, SD = 18.3, 68% female) was based upon completion of resting state brain scan and sub-maximal bike test. Sliding window analysis of the resting state signal was conducted over 45-s windows, with 50% overlap, to assess how changes in photoplethysmography-derived HRV relate to vmPFC functional connectivity with the whole brain. CC was assessed based upon heart rate (HR) changes during submaximal exercise (HR change /HRmax (206–0.88 × age) – HRrest). ResultsDuring states of elevated HRV the vmPFC showed greater rsFC with an 83-voxel region of the hypothalamus (p < 0.001, uncorrected). Beta estimates of vmPFC connectivity extracted from a 6-mm sphere around this region emerged as the strongest predictor of CC (b = 0.283, p <.001) than age, BMI, and resting HRV F(8,144) = 6.30, p <.001. ConclusionExtensive glutamatergic innervation of the hypothalamus by the vmPFC allows for top-down control of the hypothalamus and its various autonomic efferents which facilitate chronotropic response during sub-maximal exercise.

Robust Realized Integrated Beta Estimator with Application to Dynamic Analysis of Integrated Beta

Robust Realized Integrated Beta Estimator with Application to Dynamic Analysis of Integrated Beta

Subclinical Vascular Risk Composites Predict Cardiovascular Disease, Stroke, and Dementia: The Multi‐Ethnic Study of Atherosclerosis (MESA)

AbstractBackgroundSubclinical cardiovascular disease (CVD) measures may reflect biological pathways that contribute to increased risk for CVD events and dementia beyond conventional risk scores. Therefore, we examined whether subclinical CVD biomarkers: 1) dissociate into composites representing distinct pathologic pathways that 2) predict future risk of clinical CVD, stroke, and dementia‐related outcomes in a diverse longitudinal cohort.MethodMESA followed 6,814 participants (45‐84 years of age) from baseline in 2000‐2002 to 2018 over 6 clinical examinations and annual follow‐up interviews. Baseline subclinical CVD biomarkers were transformed into z‐scores before factor analysis to derive composite factor scores. Time to clinical CVD events (coronary heart disease (CHD) and stroke) and dementia were modeled using Cox proportional hazards models reported as area under the curve (AUC) with 95% Confidence Intervals (95%CI). Global cognitive decline was measured by change in the Cognitive Abilities Screening Instrument from 2010‐12 to 2019‐2021, modeled by linear mixed models, and reported as beta estimates and standard error (SE). Cognitive status was adjudicated in 2019‐2021 as cognitively normal, mild cognitive impairment (MCI) and dementia from Uniform Data Set v3 tests and informant interview and modeled with multinomial logistic regression reported as odds ratios (OR). All models included all factor scores together and adjustment for conventional risk scores: Framingham risk scores for CVD or stroke or Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) for dementia.ResultSubclinical CVD measures aggregated into four distinct factors representing: blood pressure, arterial stiffness, atherosclerosis, and cardiac remodeling (Table 1). Each factor significantly predicted CVD events and dementia independent of each other and conventional risk scores (Figure). Atherosclerosis best predicted time to clinical events of CHD [AUC(95%CI) = 0.73(0.71‐0.75)], stroke [AUC(95%CI) = 0.75(0.70‐0.78)], and dementia [AUC(95%CI) = 0.74(0.71‐0.77)]. Arterial stiffness was the only the factor significantly associated with both cognitive decline (b = ‐0.95, SE = 0.11, p&lt;0.0001), a higher odds of MCI [OR(95%CI) = 1.10(1.01‐1.21)] and dementia [OR(95%CI) = 1.79(1.37‐2.35)] (Table 2). These results were consistent across sex and racial/ethnic groups.ConclusionSubclinical vascular factor composites of arterial stiffness and atherosclerosis shown to independently predict both CVD events and dementia may be useful biomarkers to inform the vascular pathways contributing to Alzheimer’s disease and related dementia etiologies.

Abstract 17418: Daily Intake of Fruits and Vegetables Moderates the Effect of a Nutrition Intervention on Symptom Burden in Patients With Heart Failure

Introduction: Patients with heart failure (HF) often remain symptomatic after optimization of medical treatment. Complementary interventions may reduce symptom burden. We conducted a 6-month randomized controlled trial of a nutrition intervention (NIHFT) to reduce fluid overload, oxidative stress, and inflammation, which are major pathologic pathways underlying HF symptoms.Aims: 1) To examine the effects of NIHFT on HF symptom burden (i.e., shortness of breath, edema, and fatigue); and 2) to determine if intake of fruits and vegetables, primary sources of antioxidants and anti-inflammatories, moderated the effect of the NIHFT on HF symptom burden. Methods: Patients with HF (n=116) with complete baseline data were included (61% male, 63±12 years old, 64% NYHA III/IV). Intervention group received 1) individualized educational intervention for sodium reduction that included promoting fruit and vegetable intake, 2) daily low sodium tomato or vegetable juice, and 3) omega-3 supplementation (1050 mg EPA, 150 mg DHA/capsule/day) for 6 months. Symptom burden was measured by the Memorial Symptom Assessment Scale-HF at baseline and 3 and 6-month. Daily intake of fruits and vegetables at baseline was dichotomized (≤1 vs ≥2 servings/day). A linear mixed-effect model was used. Results: There was no direct effect of the NIHFT on symptom burden. However, baseline intake of fruits and vegetables moderated the effect of the intervention on overall symptom burden scores (beta estimate= -4.67, SE=1.89, P=0.015). Reduction in symptom burden was greater among patients with lower baseline fruit and vegetable intake compared to those with higher intake. Among the subscales (i.e., frequency, severity, and distress) of overall symptom burden, frequency (beta estimate= -1.63, SE=0.66, P=0.014) and severity (beta estimate= -1.81, SE=0.61, P=0.004) were significantly moderated by the intake of fruits and vegetables. Conclusions: The findings indicate that the nutrition intervention was more effective in reducing symptom burden for patients with lower baseline intake of fruits and vegetables. This suggests that increased fruit and vegetable intake may further reduce HF symptom burden in patients on optimal medication therapy.