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Background/Objectives: The incidence of metabolic dysfunction-associated steatotic liver disease (MASLD), a condition linked to the ongoing obesity pandemic, is rapidly increasing worldwide. In turn, its multifactorial etiology is consistently associated with low dietary quality. Changing dietary macronutrient and phytochemical quality via incorporating cooked common bean into an obesogenic diet formulation has measurable health benefits on the occurrence of both obesity and hepatic steatosis in C57BL/6 mice. Methods: A cohort of C57BL/6 mice were randomized into experimental diets containing multiple dietary concentrations of common bean. The primary endpoint of this study was comparing metabolomic analyses from liver and plasma of different treatment groups. Additionally, RNA sequencing and protein expression analysis via nanocapillary immunoelectrophoresis were used to elucidate signaling mediators involved. Results: Herein, global metabolomic profiling of liver and plasma identified sphingolipids as a lipid subcategory on which bean consumption exerted significant effects. Of note, C16 and C18 ceramides were significantly decreased in bean-fed animals. Hepatic RNAseq data revealed patterns of transcript expression of genes involved in sphingolipid metabolism that were consistent with metabolite profiles. Conclusions: Bean incorporation into an otherwise obesogenic diet induces effects on synthesis, biotransformation, and degradation of sphingolipids that inhibit the accumulation of ceramide species that exert pathological activity. These effects are consistent with a mechanistic role for altered sphingolipid metabolism in explaining how bean inhibits the development of MASLD.

Background: Exposures to perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA), environmentally persistent chemicals detectable in the blood of most Americans, have been associated with several health outcomes. To offer insight into their possible biologic effects, we evaluated the metabolomic correlates of circulating PFOS and PFOA among 3,647 participants in eight nested case-control serum metabolomic profiling studies from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Methods: Metabolomic profiling was conducted by Metabolon Inc., using ultra high-performance liquid chromatography/tandem accurate mass spectrometry. We conducted study-specific multivariable linear regression analyses estimating the associations of metabolite levels with levels of PFOS or PFOA. For metabolites measured in at least 3 of 8 nested case-control studies, random effects meta-analysis was used to summarize study-specific results (1,038 metabolites in PFOS analyses and 1,100 in PFOA analyses). Results: The meta-analysis identified 51 and 38 metabolites associated with PFOS and PFOA, respectively, at a Bonferroni-corrected significance level (4.8x10-5 and 4.6x10-5, respectively). For both PFOS and PFOA, the most common types of associated metabolites were lipids (sphingolipids, fatty acid metabolites) and xenobiotics (xanthine metabolites, chemicals). Positive associations were commonly observed with lipid metabolites sphingomyelin (d18:1/18:0) (P = 2.0x10-10 and 2.0x10-8, respectively), 3-carboxy-4-methyl-5-pentyl-2-furanpropionate (P = 2.7x10-15, 1.1x10-17), and lignoceroylcarnitine (C24) (P = 2.6x10-8, 6.2x10-6). The strongest positive associations were observed for chemicals 3,5-dichloro-2,6-dihydroxybenzoic acid (P = 3.0x10-112 and 6.8x10-13, respectively) and 3-bromo-5-chloro-2,6-dihydroxybenzoic acid (P = 1.6x10-14, 2.3x10-6). Other metabolites positively associated with PFOS included D-glucose (carbohydrate), carotene diol (vitamin A metabolism), and L-alpha-aminobutyric acid (glutathione metabolism), while uric acid (purine metabolite) was positively associated with PFOA. PFOS associations were consistent even after adjusting for PFOA as a covariate, while PFOA associations were greatly attenuated with PFOS adjustment. Conclusions: In this large metabolomic study, we observed robust positive associations with PFOS for several molecules. Further investigation of these metabolites may offer insight into PFOS-related biologic effects.

Abstract Background Metabolic syndrome (MetS), a group of risk factors that define metabolic dysfunction in adults, is strongly associated with obesity and is an emerging risk factor for cancer. However, the association of MetS and degree of metabolic dysfunction with obesity‐related cancer is unknown. Methods Using National Health and Nutrition Examination Survey data from 1999 to 2018, we identified 528 obesity‐related cancer cases and 18,972 cancer‐free participants. MetS was defined as the presence of or treatment for ≥3 of hyperglycemia, hypertension, hypertriglyceridemia, low HDL–cholesterol, and abdominal obesity. A metabolic syndrome score (MSS) was computed as the total number of abnormal MetS parameters to determine the severity of metabolic dysfunction. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression models, adjusting for sociodemographic and lifestyle factors. Results About 45.7% of obesity‐related cancer cases were classified as having MetS compared with only 33.0% of cancer‐free participants. Overall, MetS and MSS were not associated with obesity‐related cancer. However, MSS was associated with higher obesity‐related cancer risk among participants under 50 years of age (OR [95% CI] = 1.28 [1.08–1.52]). When evaluating MSS categorically, compared with healthy participants with no abnormal MetS parameters (MSS = 0), participants with one or two abnormal parameters had a statistically significant higher risk of obesity‐related cancer (OR [95% CI] = 1.73 [1.06–2.83]). Conclusions Metabolic dysfunction is associated with a higher risk of obesity‐related cancer, particularly in young adults under 50 years of age, and among participants with one or two abnormal metabolic parameters. A more accurate indicator of metabolic dysfunction, beyond metabolic syndrome, is needed to better assist in stratifying individuals for obesity‐related cancer risk.

Abstract Introduction Body mass index (BMI) fails to identify up to one‐third of normal weight individuals with metabolic dysfunction who may be at increased risk of obesity‐related cancer (ORC). Metabolic obesity phenotypes, an alternate metric to assess metabolic dysfunction with or without obesity, were evaluated for association with ORC risk. Methods National Health and Nutrition Examination Survey participants from 1999 to 2018 (N = 19,500) were categorized into phenotypes according to the metabolic syndrome (MetS) criteria and BMI: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obese (MHO) and metabolically unhealthy overweight/obese (MUO). Adjusted multivariable logistic regression models were used to evaluate associations with ORC. Results With metabolic dysfunction defined as ≥1 MetS criteria, ORC cases (n = 528) had higher proportions of MUNW (28.2% vs. 17.4%) and MUO (62.6% vs. 60.9%) phenotypes than cancer‐free individuals (n = 18,972). Compared with MHNW participants, MUNW participants had a 2.2‐times higher ORC risk [OR (95%CI) = 2.21 (1.27–3.85)]. MHO and MUO participants demonstrated a 43% and 56% increased ORC risk, respectively, compared to MHNW, but these did not reach statistical significance [OR (95% CI) = 1.43 (0.46–4.42), 1.56 (0.91–2.67), respectively]. Hyperglycaemia, hypertension and central obesity were all independently associated with higher ORC risk compared to MHNW. Conclusions MUNW participants have a higher risk of ORC than other abnormal phenotypes, compared with MHNW participants. Incorporating metabolic health measures in addition to assessing BMI may improve ORC risk stratification. Further research on the relationship between metabolic dysfunction and ORC is warranted.

The effects of vitamin E supplementation on cancer and other chronic diseases are not clear. We compared the serum metabolomic profile of differing vitamin E dosages in order to re-examine the previously observed changes in a novel C22 lactone sulfate compound, androgenic steroids, and other metabolites. A total of 3409 women and men previously selected for metabolomics studies in the PLCO Cancer Screening Trial were included in this investigation. Serum metabolites were profiled using ultrahigh-performance liquid and gas chromatography/tandem mass spectrometry. Seventy known metabolites including C22 lactone sulfate and androgens were significantly associated with vitamin E supplementation. In the sex-stratified analysis, 10 cofactors and vitamins (e.g., alpha-CEHC sulfate and alpha-CEHC glucuronide), two carbohydrates (glyceric and oxalic acids), and one lipid (glycocholenate sulfate) were significantly associated with vitamin E dose in both males and females (FDR-adjusted p-value < 0.01). However, the inverse association between C22 lactone sulfate and daily vitamin E supplementation was evident in females only, as were two androgenic steroids, 5-androstenediol and androsterone glucuronide. Our study provides evidence of distinct steroid hormone pathway responses based on vitamin E dosages. Further studies are needed to gain biological insights into vitamin E biochemical effects relevant to cancer and other chronic diseases.

While diet and nutrition are modifiable risk factors for many chronic and infectious diseases, their role in cancer prevention and control remains under investigation. The lack of clarity of some diet–cancer relationships reflects the ongoing debate about the relative contribution of genetic factors, environmental exposures, and replicative errors in stem cell division as determinate drivers of cancer risk. In addition, dietary guidance has often been based upon research assuming that the effects of diet and nutrition on carcinogenesis would be uniform across populations and for various tumor types arising in a specific organ, i.e., that one size fits all. Herein, we present a paradigm for investigating precision dietary patterns that leverages the approaches that led to successful small-molecule inhibitors in cancer treatment, namely understanding the pharmacokinetics and pharmacodynamics of small molecules for targeting carcinogenic mechanisms. We challenge the scientific community to refine the paradigm presented and to conduct proof-in-concept experiments that integrate existing knowledge (drug development, natural products, and the food metabolome) with developments in artificial intelligence to design and then test dietary patterns predicted to elicit drug-like effects on target tissues for cancer prevention and control. We refer to this precision approach as dietary oncopharmacognosy and envision it as the crosswalk between the currently defined fields of precision oncology and precision nutrition with the goal of reducing cancer deaths.

Cancer-related fatigue (CRF) is considered one of the most frequent and distressing symptoms for cancer survivors. Despite its high prevalence, factors that predispose, precipitate, and perpetuate CRF are poorly understood. Emerging research focuses on cancer and treatment-related nutritional complications, changes in body composition, and nutritional deficiencies that can compound CRF. Nutritional metabolomics, the novel study of diet-related metabolites in cells, tissues, and biofluids, offers a promising tool to further address these research gaps. In this position paper, we examine CRF risk factors, summarize metabolomics studies of CRF, outline dietary recommendations for the prevention and management of CRF in cancer survivorship, and identify knowledge gaps and challenges in applying nutritional metabolomics to understand dietary contributions to CRF over the cancer survivorship trajectory.

The study of natural plant molecules and their medicinal properties, pharmacognosy, provides a taxonomy for botanical families that represent diverse chemical groupings with potentially distinct functions in relation to human health. Yet, this reservoir of knowledge has not been systematically applied to elucidating the role of patterns of plant food consumption on gut microbial ecology and function. All chemical classes of dietary phytochemicals can affect the composition of the microbes that colonize the gut and their function. In turn, the gut microbiome affects the host via multiple mechanisms including gut barrier function, immune function, satiety and taste regulation and the activity of biological signaling pathways that influence health and disease. Herein, we report the development of a botanical diversity index (BDI) to evaluate plant food consumption as a novel metric for identifying and quantifying phytochemicals to which an individual is exposed. A rationale is advanced for using the BDI to investigate how plant food diversity impacts gut microbial ecology and functionality.

Breast cancer is the most common cancer in women, but its incidence can only be partially explained through established risk factors. Our aim was to use metabolomics to identify novel risk factors for breast cancer and to validate recently reported metabolite-breast cancer findings. We measured levels of 1275 metabolites in prediagnostic serum in a nested case-control study of 782 postmenopausal breast cancer cases and 782 matched controls. Metabolomics analysis was performed by Metabolon Inc using ultra-performance liquid chromatography and a Q-Exactive high resolution/accurate mass spectrometer. Controls were matched by birth date, date of blood draw, and race/ethnicity. Odds ratios (ORs) and 95% confidence intervals (CIs) of breast cancer at the 90th versus 10th percentile (modeled on a continuous basis) of metabolite levels were estimated using conditional logistic regression, with adjustment for age. Twenty-four metabolites were significantly associated with breast cancer risk at a false discovery rate

Women who are obese at the time of breast cancer diagnosis have higher overall mortality than normal weight women and some evidence implicates adiponectin and leptin as contributing to prognostic disadvantage. While intentional weight loss is thought to improve prognosis, its impact on these adipokines is unclear. This study compared the pattern of change in plasma leptin and adiponectin in overweight-to-obese post-menopausal breast cancer survivors during weight loss. Given the controversies about what dietary pattern is most appropriate for breast cancer control and regulation of adipokine metabolism, the effect of a low fat versus a low carbohydrate pattern was evaluated using a non-randomized, controlled study design. Anthropometric data and fasted plasma were obtained monthly during the six-month weight loss intervention. While leptin was associated with fat mass, adiponectin was not, and the lack of correlation between leptin and adiponectin concentrations throughout weight loss implies independent mechanisms of regulation. The temporal pattern of change in leptin but not adiponectin was affected by magnitude of weight loss. Dietary pattern was without effect on either adipokine. Mechanisms not directly related to dietary pattern, weight loss, or fat mass appear to play dominant roles in the regulation of circulating levels of these adipokines.

The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.

ObjectiveTo assess the relationship between time-of-day of exercise training and changes in relevant cancer health outcomes among cancer survivors.MethodsRetrospective analysis of data collected from 2016-2019 from a hospital-based exercise oncology program. Descriptive statistics were calculated for demographic, clinical, and exercise timing characteristics (e.g. AM, PM, or mix) among survivors with available data for exercise training time (n = 233). For the total sample and a breast cancer sub-analysis, univariate analysis of covariance, adjusted for age, was carried out by exercise training time, for change in the following outcomes collected during the program's assessment sessions: cardiorespiratory fitness and muscular endurance (human performance variables), physical function, anthropometrics, self-reported fatigue, and quality of life (QoL). Change in body mass index (BMI) and body weight was included in the breast cancer analysis.ResultsOverall, 37.3% of survivors habitually engaged in AM exercise (e.g. ≥ 75% AM training), 34.3% in PM exercise, and 28.3% in a mix of AM and PM exercise training throughout the program. Median time in the program was 17 weeks. Significant improvements in most human performance and physical function variables were observed in the total sample regardless of exercise training time-of-day. Among breast cancer survivors, PM but not AM or mixed was associated with improvements in fitness, and lower-body muscular endurance and function. Mixed exercise timing was linked with greater increase in waist circumference (total sample: 3.02cm, 95%CI 1.55, 4.49; breast cancer: 3.57cm 95%CI 0.96, 6.18), body weight (breast cancer: 1.6kg, 95%CI 0.3, 2.8) and BMI (breast cancer: 0.6kg/m2, 95%CI 0.1, 1.0). AM and PM exercise, but not mixed, was associated with improvements in fatigue and QoL.ConclusionTime-of-day of exercise training may differentially impact changes in human performance and physical function variables. Mixed exercise training time may result in less favorable outcomes related of weight management variables among cancer survivors.

Body weight management is not emphasized in clinical practice guidelines for breast cancer survivors, reflecting the lack of evidence that weight loss improves prognosis. Even if this situation changes, the optimal design for weight loss interventions is unclear. We conducted a 6-month non-randomized, controlled weight loss intervention in 249 post-menopausal breast cancer survivors. This paper reports effects on two secondary endpoints, change in body weight and composition. Participants were predominantly non-Hispanic whites (89%) with a mean age of 54.9 ± 9.2 years, a mean BMI of 29.0 ± 2.6 kg/m: (2) and an average of 43 ± 5% body fat. Two dietary interventions, low fat or low carbohydrate, were investigated and consisted of a 42 day cycle of menus and recipes. Weight loss counseling and anthropometric assessment were provided at monthly clinic visits. One hundred ninety-two women completed the trial (77% retention). In comparison to the nonintervention control, both intervention arms achieved significant decreases in body weight (12.5%), body fat (27.5%), waist circumference (9.5%), and hip circumference (7.8%) (all p < 0.001) with minimal effects on lean mass (1.3% decrease). Median time to 5 and 10% weight loss was 2 (95% confidence interval = 1 to 3) and 4 (95% confidence interval = 3 to 5) months, respectively, and 23% of participants experienced ≥ 15% weight loss. Loss of body weight and fat mass was rapid and substantial irrespective of dietary approach when a structured program was provided with monthly anthropometric assessment and weight loss counseling.ClinicalTrials.gov NCT01315483.