Serum metabolomic profiles associated with postmenopausal hormone use

A Comparative Study of Network-Based Machine Learning Approaches for Binary Classification in Metabolomics

Background/Objectives: Metabolomics has recently emerged as a key tool in the biological sciences, offering insights into metabolic pathways and processes. Over the last decade, network-based machine learning approaches have gained significant popularity and application across various fields. While several studies have utilized metabolomics profiles for sample classification, many network-based machine learning approaches remain unexplored for metabolomic-based classification tasks. This study aims to compare the performance of various network-based machine learning approaches, including recently developed methods, in metabolomics-based classification. Methods: A standard data preprocessing procedure was applied to 17 metabolomic datasets, and Bayesian neural network (BNN), convolutional neural network (CNN), feedforward neural network (FNN), Kolmogorov-Arnold network (KAN), and spiking neural network (SNN) were evaluated on each dataset. The datasets varied widely in size, mass spectrometry method, and response variable. Results: With respect to AUC on test data, BNN, CNN, FNN, KAN, and SNN were the top-performing models in 4, 1, 5, 3, and 4 of the 17 datasets, respectively. Regarding F1-score, the top-performing models were BNN (3 datasets), CNN (3 datasets), FNN (4 datasets), KAN (4 datasets), and SNN (3 datasets). For accuracy, BNN, CNN, FNN, KAN, and SNN performed best in 4, 1, 4, 4, and 4 datasets, respectively. Conclusions: No network-based modeling approach consistently outperformed others across the metrics of AUC, F1-score, or accuracy. Our results indicate that while no single network-based modeling approach is superior for metabolomics-based classification tasks, BNN, KAN, and SNN may be underappreciated and underutilized relative to the more commonly used CNN and FNN.

MetaboLights: open data repository for metabolomics

MetaboLights is a global database for metabolomics studies including the raw experimental data and the associated metadata. The database is cross-species and cross-technique and covers metabolite structures and their reference spectra as well as their biological roles and locations where available. MetaboLights is the recommended metabolomics repository for a number of leading journals and ELIXIR, the European infrastructure for life science information. In this article, we describe the continued growth and diversity of submissions and the significant developments in recent years. In particular, we highlight MetaboLights Labs, our new Galaxy Project instance with repository-scale standardized workflows, and how data public on MetaboLights are being reused by the community. Metabolomics resources and data are available under the EMBL-EBI's Terms of Use at https://www.ebi.ac.uk/metabolights and under Apache 2.0 at https://github.com/EBI-Metabolights.

Association of serum metabolome profile with the risk of breast cancer in participants of the HUNT2 study

Background

The serum metabolome is a potential source of molecular biomarkers associated with the risk of breast cancer. Here we aimed to analyze metabolites present in pre-diagnostic serum samples collected from healthy women participating in the Norwegian Trøndelag Health Study (HUNT2 study) for whom long-term information about developing breast cancer was available.

Methods

Women participating in the HUNT2 study who developed breast cancer within a 15-year follow-up period (BC cases) and age-matched women who stayed breast cancer-free were selected (n=453 case-control pairs). Using a high-resolution mass spectrometry approach 284 compounds were quantitatively analyzed, including 30 amino acids and biogenic amines, hexoses, and 253 lipids (acylcarnitines, glycerides, phosphatidylcholines, sphingolipids, and cholesteryl esters).

Results

Age was a major confounding factor responsible for a large heterogeneity in the dataset, hence age-defined subgroups were analyzed separately. The largest number of metabolites whose serum levels differentiated BC cases and controls (82 compounds) were observed in the subgroup of younger women (<45 years old). Noteworthy, increased levels of glycerides, phosphatidylcholines, and sphingolipids were associated with reduced risk of cancer in younger and middle-aged women (≤64 years old). On the other hand, increased levels of serum lipids were associated with an enhanced risk of breast cancer in older women (>64 years old). Moreover, several metabolites could be detected whose serum levels were different between BC cases diagnosed earlier (<5 years) and later (>10 years) after sample collecting, yet these compounds were also correlated with the age of participants. Current results were coherent with the results of the NMR-based metabolomics study performed in the cohort of HUNT2 participants, where increased serum levels of VLDL subfractions were associated with reduced risk of breast cancer in premenopausal women.

Conclusions

Changes in metabolite levels detected in pre-diagnostic serum samples, which reflected an impaired lipid and amino acid metabolism, were associated with long-term risk of breast cancer in an age-dependent manner.

Elevated adipose differentiation-related protein level in ovariectomized mice correlates with tissue-specific regulation of estrogen

OBJECTIVE

Redistribution of adipose tissue in the abdomen during the menopausal transition is attributable mostly to estrogen drop with aging. Adipose differentiation-related protein (ADRP), a major component of lipid droplets, is closely related to the onset of lipid accumulation. We hypothesized that estrogen exerted its tissue-specific effect in reducing abdominal fat accumulation by regulation of ADRP.

METHODS

Twenty-four female C57/BL6 mice aged 8 weeks were randomly divided into 3 groups: sham operation (Sham), bilateral ovariectomy (OVX), and OVX plus 17β-estradiol (OVX + E2). After being fed 8 weeks of a high-fat diet, plasma lipid profiles including total cholesterol (TC), total triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels, body weight gain, visceral, and subcutaneous adipose tissue, adipocyte size, and ADRP expression were measured.

RESULTS

In comparison to sham-operated mice, OVX mice presented a weight gain with higher plasma TC, TG, LDL-C levels, and lower HDL-C levels. E2 supplement ameliorated the increase in weight and lipid profiles. Elevated ADRP expression was observed in visceral adipose tissue of OVX mice, whereas treatment of estrogen suppressed the ADPR expression and reversed the fat accumulation in the abdomen. However, no significant difference of ADRP expression in subcutaneous adipose tissue was detected between sham, OVX, and OVX + E2 mice.

CONCLUSIONS

Our findings suggested that enhanced ADRP expression in ovariectomized mice correlates with the tissue-specific regulation of estrogen, which may provide useful clues for further exploring the regulatory mechanism and corresponding anti-abdominal obesity treatment in postmenopausal women.

CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets

Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among chemicals reported for human specimens. With an increase in the number of compounds for these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. We have developed the Chemical Correlation Database (CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 35 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.

IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets

Generating comprehensive and high-fidelity metabolomics data matrices from LC/HRMS data remains to be extremely challenging for population-scale large studies (n > 200). Here, we present a new data processing pipeline, the Intrinsic Peak Analysis (IDSL.IPA) R package (https://ipa.idsl.me), to generate such data matrices specifically for organic compounds. The IDSL.IPA pipeline incorporates (1) identifying potential ¹²C and ¹³C ion pairs in individual mass spectra; (2) detecting and characterizing chromatographic peaks using a new sensitive and versatile approach to perform mass correction, peak smoothing, baseline development for local noise measurement, and peak quality determination; (3) correcting retention time and cross-referencing peaks from multiple samples by a dynamic retention index marker approach; (4) annotating peaks using a reference database of m/z and retention time; and (5) accelerating data processing using a parallel computation of the peak detection and alignment steps for larger studies. This pipeline has been successfully evaluated for studies ranging from 200 to 1600 samples. By specifically isolating high quality and reliable signals pertaining to carbon-containing compounds in untargeted LC/HRMS data sets from larger studies, IDSL.IPA opens new opportunities for discovering new biological insights in the population-scale metabolomics and exposomics projects. The package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.IPA.

Integrative metabolomic characterization identifies plasma metabolomic signature in the diagnosis of papillary thyroid cancer

Discrimination of malignancy from thyroid nodules poses challenges in clinical practice. We aimed to identify the plasma metabolomic biomarkers in discriminating papillary thyroid cancer (PTC) from benign thyroid nodule (BTN). Metabolomics profiling of plasma was performed in two independent cohorts of 651 subjects of PTC (n = 215), BTN (n = 230), and healthy controls (n = 206). In addition, 132 patients with thyroid micronodules (<1 cm) and 44 patients with BTN suspected malignancy by ultrasound were used for biomarker validation. Recursive feature elimination algorithm was used for metabolic biomarkers selecting. Significant differential metabolites were demonstrated in patients with thyroid nodules (PTC and BTN) from healthy controls (P = 0.0001). A metabolic biomarker panel (17 differential metabolites) was identified to discriminate PTC from BTN with an AUC of 97.03% (95% CI: 95.28-98.79%), 91.89% sensitivity, and 92.63% specificity in discovery cohort. The panel had an AUC of 92.72% (95% CI: 87.46-97.99%), 86.57% sensitivity, and 92.50% specificity in validation cohort. The metabolic biomarker signature could correctly identify 84.09% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. Moreover, high accuracy of 87.88% for diagnosis of papillary thyroid microcarcinoma was displayed by this panel and showed significant improvement in accuracy, AUC and specificity when compared with ultrasound. We identified a novel metabolic biomarker signature to discriminate PTC from BTN. The clinical use of this biomarker panel would have improved diagnosis stratification of thyroid microcarcinoma in comparison to ultrasound.

Urinary metabotype of severe asthma evidences decreased carnitine metabolism independent of oral corticosteroid treatment in the U-BIOPRED study

INTRODUCTION

Asthma is a heterogeneous disease with poorly defined phenotypes. Severe asthmatics often receive multiple treatments including oral corticosteroids (OCS). Treatment may modify the observed metabotype, rendering it challenging to investigate underlying disease mechanisms. Here, we aimed to identify dysregulated metabolic processes in relation to asthma severity and medication.

METHODS

Baseline urine was collected prospectively from healthy participants (n=100), mild-to-moderate asthmatics (n=87) and severe asthmatics (n=418) in the cross-sectional U-BIOPRED cohort; 12-18-month longitudinal samples were collected from severe asthmatics (n=305). Metabolomics data were acquired using high-resolution mass spectrometry and analysed using univariate and multivariate methods.

RESULTS

Ninety metabolites were identified, with 40 significantly altered (p<0.05, FDR<0.05) in severe asthma and 23 by OCS use. Multivariate modelling showed that observed metabotypes in healthy participants and mild-to-moderate asthmatics differed significantly from severe asthmatics (p=2.6×10^-20), OCS-treated asthmatics differed significantly from non-treated (p=9.5×10^-4), and longitudinal metabotypes demonstrated temporal stability. Carnitine levels evidenced the strongest OCS-independent decrease in severe asthma. Reduced carnitine levels were associated with mitochondrial dysfunction via decreases in pathway enrichment scores of fatty acid metabolism and reduced expression of the carnitine transporter SLC22A5 in sputum and bronchial brushings.

CONCLUSIONS

This is the first large-scale study to delineate disease- and OCS-associated metabolic differences in asthma. The widespread associations with different therapies upon the observed metabotypes demonstrate the necessity to evaluate potential modulating effects on a treatment- and metabolite-specific basis. Altered carnitine metabolism is a potentially actionable therapeutic target that is independent of OCS treatment, highlighting the role of mitochondrial dysfunction in severe asthma.

Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases (KEGG, Reactome, and BioCyc), led to vastly different results in both the number and function of significantly enriched pathways. Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.

Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine

Networks and network analyses are fundamental tools of systems biology. Networks are built by inferring pair-wise relationships among biological entities from a large number of samples such that subject-specific information is lost. The possibility of constructing these sample (individual)-specific networks from single molecular profiles might offer new insights in systems and personalized medicine and as a consequence is attracting more and more research interest. In this study, we evaluated and compared LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) and ssPCC (single sample network based on Pearson correlation) in the metabolomics context of metabolite–metabolite association networks. We illustrated and explored the characteristics of these two methods on (i) simulated data, (ii) data generated from a dynamic metabolic model to simulate real-life observed metabolite concentration profiles, and (iii) 22 metabolomic data sets and (iv) we applied single sample network inference to a study case pertaining to the investigation of necrotizing soft tissue infections to show how these methods can be applied in metabolomics. We also proposed some adaptations of the methods that can be used for data exploration. Overall, despite some limitations, we found single sample networks to be a promising tool for the analysis of metabolomics data.

Validation of plasma metabolites associated with breast cancer risk among Mexican Americans

In our previous breast cancer case control study in Hispanics, we found 14 metabolites whose levels differed between cases and controls. To validate the results, we carried out a nested case control study of 100 incident breast cancer and 100 matched healthy women identified from the Mano-A-Mano Mexican American Cohort study. With the adjustment of parity, education, birth place, language acculturation, BMI category, smoking, drinking, physical activity, and sitting time, 4 metabolites were associated with breast cancer risk: 3-hydroxyoctanoate (Odds ratio (OR) = 1.51, 95% confidence interval (CI): 1.10, 3.47), 3-hydroxybutyrate (BHBA) (OR = 1.42, 95%CI: 1.01, 3.72), linoleate (18:2n6) (OR = 1.39, 95% CI: 1.07, 4.04), and bilirubin (OR = 0.54, 95%CI: 0.42, 0.95). Then, we used 3 non-redundant metabolites, namely 3-hydroxyoctanoate, linoleate (18:2n6), and bilirubin, to generate a metabolic risk score. Increased metabolites risk score was associated with a 1.67-fold increased risk of breast cancer (OR = 1.67, 95%CI: 1.32, 3.94). And the significant association was more evident among those who were diagnosed with cancer earlier during the follow-up (≤ 5 years) than their counterparts. In conclusion, we identified four significant metabolites which may help elucidate metabolic pathways that contribute to breast carcinogenesis. Our findings warrant further replication efforts.

Metabolomic Effects of Hormone Therapy and Associations With Coronary Heart Disease Among Postmenopausal Women

BACKGROUND

In the WHI-HT trials (Women's Health Initiative Hormone Therapy), treatment with oral conjugated equine estrogens and medroxyprogesterone acetate (CEE+MPA) resulted in increased risk of coronary heart disease (CHD), whereas oral conjugated equine estrogens (CEE) did not.

METHODS

Four hundred eighty-one metabolites were measured at baseline and at 1-year in 503 and 431 participants in the WHI CEE and CEE+MPA trials, respectively. The effects of randomized HT on the metabolite profiles at 1-year was evaluated in linear models adjusting for baseline metabolite levels, age, body mass index, race, incident CHD, prevalent hypertension, and diabetes. Metabolites with discordant effects by HT type were evaluated for association with incident CHD in 944 participants (472 CHD cases) in the WHI-OS (Women's Health Initiative Observational Study), with replication in an independent cohort of 980 men and women at high risk for cardiovascular disease.

RESULTS

HT effects on the metabolome were profound; 62% of metabolites significantly changed with randomized CEE and 52% with CEE+MPA (false discovery rate-adjusted P value<0.05) in multivariable models. Concerted increases in abundance were seen within various metabolite classes including triacylglycerols, phosphatidylethanolamines, and phosphatidylcholines; decreases in abundance was observed for acylcarnitines, lysophosphatidylcholines, quaternary amines, and cholesteryl/cholesteryl esters. Twelve metabolites had discordant effects by HT type and were associated with incident CHD in the WHI-OS; a metabolite score estimated in a Least Absolute Shrinkage and Selection Operator regression was associated with CHD risk with an odds ratio of 1.47 per SD increase (95% CI, 1.27-1.70, P<10-6). All twelve metabolites were altered in the CHD protective direction by CEE treatment. One metabolite (lysine) was significantly altered in the direction of increased CHD risk by CEE+MPA; the remaining 11 metabolites were not significantly changed by CEE+MPA. The CHD associations of a subset of 4 metabolites including C58:11 triacylglycerol, C54:9 triacylglycerol, C36:1 phosphatidylcholine and sucrose replicated in an independent dataset of 980 participants in the PREDIMED trial (Prevención con Dieta Mediterránea).

CONCLUSIONS

Randomized treatment with oral HT resulted in large metabolome shifts that generally favored CEE alone over CEE+MPA in term of CHD risk. Discordant metabolite effects between HT regimens may partially mediate the differences in CHD risk between the 2 WHI-HT trials.

On the Use of Correlation and MI as a Measure of Metabolite-Metabolite Association for Network Differential Connectivity Analysis

Metabolite differential connectivity analysis has been successful in investigating potential molecular mechanisms underlying different conditions in biological systems. Correlation and Mutual Information (MI) are two of the most common measures to quantify association and for building metabolite-metabolite association networks and to calculate differential connectivity. In this study, we investigated the performance of correlation and MI to identify significantly differentially connected metabolites. These association measures were compared on (i) 23 publicly available metabolomic data sets and 7 data sets from other fields, (ii) simulated data with known correlation structures, and (iii) data generated using a dynamic metabolic model to simulate real-life observed metabolite concentration profiles. In all cases, we found more differentially connected metabolites when using correlation indices as a measure for association than MI. We also observed that different MI estimation algorithms resulted in difference in performance when applied to data generated using a dynamic model. We concluded that there is no significant benefit in using MI as a replacement for standard Pearson's or Spearman's correlation when the application is to quantify and detect differentially connected metabolites.

Metabolic Fingerprint of Turner Syndrome

Girls with Turner syndrome (TS) are at increased risk of developing insulin resistance and coronary artery disease as a result of hypertension and obesity frequently seen in these patients. On the other hand, it is known that obesity is associated with increased serum levels of branched-chain amino acids (BCAAs: valine; leucine and isoleucine) and aromatic amino acids. The aim of the study is to compare the metabolic fingerprint of girls with TS to the metabolic fingerprint of girls with obesity. Metabolic fingerprinting using an untargeted metabolomic approach was examined in plasma from 46 girls with TS (study group) and 22 age-matched girls with obesity (control group). The mean values of BCAAs, methionine, phenylalanine, lysine, tryptophan, histidine, tyrosine, alanine and ornithine were significantly lower in the study group than in the control (p from 0.0025 to <0.000001). Strong significant correlation between BCAAs, phenylalanine, arginine, tyrosine, glutamic acid, citrulline and alanine, and body mass index expressed as standard deviation score BMI-SDS in the patients with obesity (p from 0.049 to 0.0005) was found. In contrast; there was no correlation between these amino acids and BMI-SDS in the girls with TS. It is suggested that obesity in patients with TS is not associated with altered amino acids metabolism.

A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification

INTRODUCTION

Metabolomics is increasingly being used in the clinical setting for disease diagnosis, prognosis and risk prediction. Machine learning algorithms are particularly important in the construction of multivariate metabolite prediction. Historically, partial least squares (PLS) regression has been the gold standard for binary classification. Nonlinear machine learning methods such as random forests (RF), kernel support vector machines (SVM) and artificial neural networks (ANN) may be more suited to modelling possible nonlinear metabolite covariance, and thus provide better predictive models.

OBJECTIVES

We hypothesise that for binary classification using metabolomics data, non-linear machine learning methods will provide superior generalised predictive ability when compared to linear alternatives, in particular when compared with the current gold standard PLS discriminant analysis.

METHODS

We compared the general predictive performance of eight archetypal machine learning algorithms across ten publicly available clinical metabolomics data sets. The algorithms were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks.

RESULTS

There was only marginal improvement in predictive ability for SVM and ANN over PLS across all data sets. RF performance was comparatively poor. The use of out-of-bag bootstrap confidence intervals provided a measure of uncertainty of model prediction such that the quality of metabolomics data was observed to be a bigger influence on generalised performance than model choice.

CONCLUSION

The size of the data set, and choice of performance metric, had a greater influence on generalised predictive performance than the choice of machine learning algorithm.

Prospective analysis of circulating metabolites and breast cancer in EPIC

BACKGROUND

Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively investigated the associations between metabolite concentrations in plasma and breast cancer risk.

METHODS

A nested case-control study was established within the European Prospective Investigation into Cancer cohort, which included 1624 first primary incident invasive breast cancer cases (with known estrogen and progesterone receptor and HER2 status) and 1624 matched controls. Metabolites (n = 127, acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose, sphingolipids) were measured by mass spectrometry in pre-diagnostic plasma samples and tested for associations with breast cancer incidence using multivariable conditional logistic regression.

RESULTS

Among women not using hormones at baseline (n = 2248), and after control for multiple tests, concentrations of arginine (odds ratio [OR] per SD = 0.79, 95% confidence interval [CI] = 0.70-0.90), asparagine (OR = 0.83 (0.74-0.92)), and phosphatidylcholines (PCs) ae C36:3 (OR = 0.83 (0.76-0.90)), aa C36:3 (OR = 0.84 (0.77-0.93)), ae C34:2 (OR = 0.85 (0.78-0.94)), ae C36:2 (OR = 0.85 (0.78-0.88)), and ae C38:2 (OR = 0.84 (0.76-0.93)) were inversely associated with breast cancer risk, while the acylcarnitine C2 (OR = 1.23 (1.11-1.35)) was positively associated with disease risk. In the overall population, C2 (OR = 1.15 (1.06-1.24)) and PC ae C36:3 (OR = 0.88 (0.82-0.95)) were associated with risk of breast cancer, and these relationships did not differ by breast cancer subtype, age at diagnosis, fasting status, menopausal status, or adiposity.

CONCLUSIONS

These findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies.

Neuroendocrine drivers of risk and resilience: The influence of metabolism & mitochondria

The manifestation of risk versus resilience has been considered from varying perspectives including genetics, epigenetics, early life experiences, and type and intensity of the challenge with which the organism is faced. Although all of these factors are central to determining risk and resilience, the current review focuses on what may be a final common pathway: metabolism. When an organism is faced with a perturbation to the environment, whether internal or external, appropriate energy allocation is essential to resolving the divergence from equilibrium. This review examines the potential role of metabolism in the manifestation of stress-induced neural compromise. In addition, this review details the current state of knowledge on neuroendocrine factors which are poised to set the tone of the metabolic response to a systemic challenge. The goal is to provide an essential framework for understanding stress in a metabolic context and appreciation for key neuroendocrine signals.