Metabolomic Signatures of Long-term Coffee Consumption and Risk of Type 2 Diabetes in Women

Elucidation of endogenous and exogenous chemicals in maternal serum using high-resolution mass spectrometry

The increasing exposure to environmental chemicals calls for comprehensive non-targeted analysis to detect unrecognized substances in human samples. We examined human serum samples to classify compounds as endogenous or exogenous using public databases and to explore the relationships between exposure markers and metabolic patterns. Serum samples from 84 pregnant women at 32 weeks gestation were analyzed using LC-QToFMS. Using the PubChemLite for Exposomics database, we annotated and classified 106 compounds (51 endogenous, 55 exogenous). The compound patterns were analyzed using three dimensional reduction methods: Principal Component Analysis (PCA), regularized Generalized Canonical Correlation Analysis (rGCCA), and Uniform Manifold Approximation and Projection (UMAP). OPTICS clustering applied to these methods revealed two distinct clusters, with 89 % of significant compounds overlapping between clusters. The detected exogenous compounds included dietary substances, phthalates, nitrogenous compounds, and parabens. Pathway enrichment analysis showed that chemical exposure was linked to changes in amino acid metabolism, protein and mineral transport, and energy metabolism. While we found associations between exposure and metabolite changes, we could not establish causality. Our approach of analyzing both exogenous and endogenous chemicals from the same dataset using PubChemLite database presents a new method for exposome research, despite limitations in sample size and peak annotation accuracy. These findings contribute to understanding multiple chemical exposures and their metabolic effects in human biomonitoring.

Impact of coffee and its bioactive compounds on the risks of type 2 diabetes and its complications: A comprehensive review

BACKGROUND

Coffee beans have a long history of use as traditional medicine by various indigenous people. Recent focus has been given to the health benefits of coffee beans and its bioactive compounds. Research on the bioactivities, applications, and effects of processing methods on coffee beans' phytochemical composition and activities has been conducted extensively. The current review attempts to provide an update on the biological effects of coffee on type 2 diabetes (T2D) and its comorbidities.

METHODS

Comprehensive literature search was carried out on peer-reviewed published data on biological activities of coffee on in vitro, in vivo and epidemiological research results published from January 2015 to December 2022, using online databases such as PubMed, Google Scholar and ScienceDirect for our searches.

RESULTS

The main findings were: firstly, coffee may contribute to the prevention of oxidative stress and T2D-related illnesses such as cardiovascular disease, retinopathy, obesity, and metabolic syndrome; secondly, consuming up to 400 mg/day (1-4 cups per day) of coffee is associated with lower risks of T2D; thirdly, caffeine consumed between 0.5 and 4 h before a meal may inhibit acute metabolic rate; and finally, both caffeinated and decaffeinated coffee are associated with reducing the risks of T2D.

CONCLUSION

Available evidence indicates that long-term consumption of coffee is associated with decreased risk of T2D and its complications as well as decreased body weight. This has been attributed to the consumption of coffee with the abundance of bioactive chemicals.

Metabolic shifts during coffee consumption refresh the immune response: insight from comprehensive multiomics analysis

Coffee, a widely consumed beverage, has shown benefits for human health but lacks sufficient basic and clinical evidence to fully understand its impacts and mechanisms. Here, we conducted a cross-sectional observational study of coffee consumption and a 1-month clinical trial in humans. We found that coffee consumption significantly reshaped the immune system and metabolism, including reduced levels of inflammatory factors and a reduced frequency of senescent T cells. The frequency of senescent T cells and the levels of the senescence-associated secretory phenotype were lower in both long-term coffee consumers and new coffee consumers than in coffee nondrinking subjects, suggesting that coffee has anti-immunosenescence effects. Moreover, coffee consumption downregulated the activities of the The Janus kinase/signal transduction and activator of transcription (JAK/STAT) and mitogen-activated protein kinases (MAPK) signaling pathways and reduced systemic proinflammatory cytokine levels. Mechanistically, coffee-associated metabolites, such as 1-methylxanthine, 3-methylxanthine, paraxanthine, and ceramide, reduced the frequency of senescent CD4+CD57+ T cells in vitro. Finally, in vivo, coffee intake alleviated inflammation and immunosenescence in imiquimod-induced psoriasis-like mice. Our results provide novel evidence of the anti-inflammatory and anti-immunosenescence effects of coffee, suggesting that coffee consumption could be considered a healthy habit.

24-h urinary sodium and potassium excretions, plasma metabolomic profiles, and cardiometabolic biomarkers in the United States adults: a cross-sectional study

BACKGROUND

High-sodium and low-potassium intakes are associated with a higher risk of hypertension and cardiovascular disease, but there are limited data on the circulating metabolomics profiles of 24-h urinary sodium and potassium excretions in free-living individuals.

OBJECTIVES

We aimed to characterize the metabolomics signatures of a high-sodium and low-potassium diet in a cross-sectional study.

METHODS

In 1028 healthy older adults from the Women's and Men's Lifestyle Validation Studies, we investigated the association of habitual sodium and potassium intakes measured by 2 to 4 24-h urine samples with plasma metabolites (quantified using liquid chromatography-tandem mass spectrometry) and metabolomic pathways. Our primary exposures were energy-adjusted 24-h urinary sodium excretion, potassium excretion, and sodium-to-potassium ratio, calculated based on energy expenditure derived from the doubly labeled water method. We then assessed the partial correlations of their metabolomics scores, derived from elastic net regressions, with cardiometabolic biomarkers.

RESULTS

Higher sodium excretion was associated with 38 metabolites including higher piperine, phosphatidylethanolamine, and C5:1 carnitine. In pathway analysis, higher sodium excretion was associated with enhanced biotin and propanoate metabolism and enhanced degradation of lysine and branched-chain amino acids (BCAAs). Metabolites associated with higher potassium and lower sodium-to-potassium ratio included quinic acid and proline-betaine. After adjusting for confounding factors, the metabolomics score for sodium-to-potassium ratio positively correlated with fasting insulin (Spearman's rank correlation coefficient ρ = 0.27), C-peptide (ρ = 0.30), and triglyceride (ρ = 0.46), and negatively with adiponectin (ρ = -0.40), and high-density lipoprotein cholesterol (ρ = -0.42).

CONCLUSIONS

We discovered metabolites and metabolomics pathways associated with a high-sodium diet, including metabolites related to biotin, propanoate, lysine, and BCAA pathways. The metabolomics signature for a higher sodium low-potassium diet is associated with multiple components of elevated cardiometabolic risk.

Caffeic acid and diabetic neuropathy: Investigating protective effects and insulin-like growth factor 1 (IGF-1)-related antioxidative and anti-inflammatory mechanisms in mice

Diabetic neuropathy (DN) represents a common and debilitating complication of diabetes, affecting a significant proportion of patients. Despite available treatments focusing on symptom management, there remains an unmet need for therapies that address the underlying pathophysiology. In pursuit of novel interventions, this study evaluated the therapeutic effects of caffeic acid-a natural phenolic compound prevalent in various foods-on diabetic neuropathy using a mouse model, particularly examining its interaction with the Insulin-like Growth Factor 1 (IGF-1) signaling pathway. Caffeic acid was administered orally at two dosages (5 mg/kg and 10 mg/kg), and a comprehensive set of outcomes including fasting blood glucose levels, body weight, sensory behavior, spinal cord oxidative stress markers, inflammatory cytokines, and components of the IGF-1 signaling cascade were assessed. Additionally, to determine the specific contribution of IGF-1 signaling to the observed benefits, IGF1R inhibitor Picropodophyllin (PPP) was co-administered with caffeic acid. Our results demonstrated that caffeic acid, at both dosages, effectively reduced hyperglycemia and alleviated sensory behavioral deficits in diabetic mice. This was accompanied by a marked decrease in oxidative stress markers and an increase in antioxidant enzyme activities within the spinal cord. Significantly lowered microglial activation and inflammatory cytokine expression highlighted the potent antioxidative and anti-inflammatory effects of caffeic acid. Moreover, increases in both serum and spinal levels of IGF-1, along with elevated phosphorylated IGF1R, implicated the IGF-1 signaling pathway as a mediator of caffeic acid's neuroprotective actions. The partial reversal of caffeic acid's benefits by PPP substantiated the pivotal engagement of IGF-1 signaling in mediating its effects. Our findings delineate the capability of caffeic acid to mitigate DN symptoms, particularly through reducing spinal oxidative stress and inflammation, and pinpoint the integral role of IGF-1 signaling in these protective mechanisms. The insights gleaned from this study not only position caffeic acid as a promising dietary adjunct for managing diabetic neuropathy but also highlight the therapeutic potential of targeting spinal IGF-1 signaling as part of a strategic treatment approach.

Habitual coffee drinking and the chance of prediabetes remission: findings from a population with low coffee consumption

Introduction

We aimed to investigate the association between coffee drinking and total caffeine intakes with the chance of prediabetes (Pre-DM) regression and progression over 9-years of follow-up.

Research design and methods

This cohort study included 334 Pre-DM individuals (mean age of 49.4 ± 12.8 years and 51.5% men) who participated in the third phase of the Tehran Lipid and Glucose Study (2006-2008). A validated food frequency questionnaire at baseline assessed habitual coffee consumption. All measurements were done at baseline and all subsequent examinations with 3-year follow-up intervals. The odds ratios (OR) and 95% confidence intervals (CIs) of Pre-DM regression to normal glycemia or progression to type 2 diabetes (T2D) in coffee drinkers/non-drinkers were estimated using multinomial logistic regression analysis.

Results

During the study follow-up 39.8% of the study participants were progressed to T2D and 39.8% returned to normal glycemia. Coffee consumption nearly doubled the chance of returning to normal (OR = 2.26, 95% CI = 1.03-4.97). Total caffeine intake was not related to Pre-DM progression and regression. Compared to non-drinkers, coffee drinkers had significantly lower 2-hour serum glucose concentrations over time (152, 95% CI = 144-159 vs. 162, 95% CI = 155-169 mg/dL, P = 0.05).

Conclusions

Habitual coffee drinking may increase the chance of returning to normal glycemia in Pre-DM subjects.

Metabolite signatures associated with microRNA miR-143-3p serve as drivers of poor lung function trajectories in childhood asthma

BACKGROUND

Lung function trajectories (LFTs) have been shown to be an important measure of long-term health in asthma. While there is a growing body of metabolomic studies on asthma status and other phenotypes, there are no prospective studies of the relationship between metabolomics and LFTs or their genomic determinants.

METHODS

We utilized ordinal logistic regression to identify plasma metabolite principal components associated with four previously-published LFTs in children from the Childhood Asthma Management Program (CAMP) (n = 660). The top significant metabolite principal component (PCLF) was evaluated in an independent cross-sectional child cohort, the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (n = 1151) and evaluated for association with spirometric measures. Using meta-analysis of CAMP and GACRS, we identified associations between PCLF and microRNA, and SNPs in their target genes. Statistical significance was determined using an false discovery rate-adjusted Q-value.

FINDINGS

The top metabolite principal component, PCLF, was significantly associated with better LFTs after multiple-testing correction (Q-value = 0.03). PCLF is composed of the urea cycle, caffeine, corticosteroid, carnitine, and potential microbial (secondary bile acid, tryptophan, linoleate, histidine metabolism) metabolites. Higher levels of PCLF were also associated with increases in lung function measures and decreased circulating neutrophil percentage in both CAMP and GACRS. PCLF was also significantly associated with microRNA miR-143-3p, and SNPs in three miR-143-3p target genes; CCZ1 (P-value = 2.6 × 10-5), SLC8A1 (P-value = 3.9 × 10-5); and TENM4 (P-value = 4.9 × 10-5).

INTERPRETATION

This study reveals associations between metabolites, miR-143-3p and LFTs in children with asthma, offering insights into asthma physiology and possible interventions to enhance lung function and long-term health.

FUNDING

Molecular data for CAMP and GACRS via the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI).

Identification of New Diterpenoids from the Pulp of Coffea arabica and Their α-Glucosidase Inhibition Activity

Six new (1, 2, 3, 5, 6, and 8) and seven known (4, 7, 9, 10, 11, 12, and 13) diterpenoids have been identified in the pulp of Coffea arabica. The structures of new diterpenoids were elucidated by extensive spectroscopic analysis, including 1D, 2D NMR (HSQC, HMBC, 1H-1H COSY, and ROESY), HRESIMS, IR, DP4+, electronic circular dichroism, and X-ray crystallography analysis. Compound 1 is ent-labdane-type diterpenoid, whereas compounds (2-13) are ent-kaurane diterpenoids. The result of α-glucosidase inhibitory assay demonstrated that compounds (1, 3, 5, 7, and 10) have moderate inhibitory activity with IC50 values of 55.23 ± 0.84, 74.02 ± 0.89, 66.46 ± 1.05, 49.70 ± 1.02, and 76.34 ± 0.46 μM, respectively, compared to the positive control (acarbose, 51.62 ± 0.21 μM). Furthermore, molecular docking analysis has been conducted to investigate the interaction between the compounds and the receptors of α-glucosidase to interpret their mechanism of activity. This study is the first investigation that successfully discovered the presence of diterpenoids within the coffee pulp.

The metabolic potential of inflammatory and insulinaemic dietary patterns and risk of type 2 diabetes

AIMS/HYPOTHESIS

Diets with higher inflammatory and insulinaemic potential have been associated with an increased risk of type 2 diabetes. However, it remains unknown whether plasma metabolomic profiles related to proinflammatory/hyperinsulinaemic diets and to inflammatory/insulin biomarkers are associated with type 2 diabetes risk.

METHODS

We analysed 6840 participants from the Nurses' Health Study and Health Professionals Follow-up Study to identify the plasma metabolome related to empirical dietary inflammatory pattern (EDIP), empirical dietary index for hyperinsulinemia (EDIH), four circulating inflammatory biomarkers and C-peptide. Dietary intakes were assessed using validated food frequency questionnaires. Plasma metabolomic profiling was conducted by LC-MS/MS. Metabolomic signatures were derived using elastic net regression. Multivariable Cox regression was used to examine associations of the metabolomic profiles with type 2 diabetes risk.

RESULTS

We identified 27 metabolites commonly associated with both EDIP and inflammatory biomarker z score and 21 commonly associated with both EDIH and C-peptide. Higher metabolomic dietary inflammatory potential (MDIP), reflecting higher metabolic potential of both an inflammatory dietary pattern and circulating inflammatory biomarkers, was associated with higher type 2 diabetes risk. The HR comparing highest vs lowest quartiles of MDIP was 3.26 (95% CI 2.39, 4.44). We observed a strong positive association with type 2 diabetes risk for the metabolomic signature associated with EDIP-only (HR 3.75; 95% CI 2.71, 5.17) or inflammatory biomarkers-only (HR 4.07; 95% CI 2.91, 5.69). In addition, higher metabolomic dietary index for hyperinsulinaemia (MDIH), reflecting higher metabolic potential of both an insulinaemic dietary pattern and circulating C-peptide, was associated with greater type 2 diabetes risk (HR 3.00; 95% CI 2.22, 4.06); further associations with type 2 diabetes were HR 2.79 (95% CI 2.07, 3.76) for EDIH-only signature and HR 3.89 (95% CI 2.82, 5.35) for C-peptide-only signature. The diet scores were significantly associated with risk, although adjustment for the corresponding metabolomic signature scores attenuated the associations with type 2 diabetes, these remained significant.

CONCLUSIONS/INTERPRETATION

The metabolomic signatures reflecting proinflammatory or hyperinsulinaemic diets and related biomarkers were positively associated with type 2 diabetes risk, supporting that these dietary patterns may influence type 2 diabetes risk via the regulation of metabolism.

Utilising the precision nutrition toolkit in the path towards precision medicine

The overall aim of precision nutrition is to replace the 'one size fits all' approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more 'precision' dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene-diet or microbiota-diet interactions. Third, precision nutrition can be used for 'personalised nutrition' advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet-disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.

Association of coffee consumption with type 2 diabetes and glycemic traits: a Mendelian randomization study

BACKGROUND/OBJECTIVES

Habitual coffee consumption was inversely associated with type 2 diabetes (T2D) and hyperglycemia in observational studies, but the causality of the association remains uncertain. This study tested a causal association of genetically predicted coffee consumption with T2D using the Mendelian randomization (MR) method.

SUBJECTS/METHODS

We used five single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) associated with habitual coffee consumption in a previous genome-wide association study among Koreans. We analyzed the associations between IVs and T2D, fasting blood glucose (FBG), 2h-postprandial glucose (2h-PG), and glycated haemoglobin (HbA1C) levels. The MR results were further evaluated by standard sensitivity tests for possible pleiotropism.

RESULTS

MR analysis revealed that increased genetically predicted coffee consumption was associated with a reduced prevalence of T2D; ORs per one-unit increment of log-transformed cup per day of coffee consumption ranged from 0.75 (0.62-0.90) for the weighted mode-based method to 0.79 (0.62-0.99) for Wald ratio estimator. We also used the inverse-variance-weighted method, weighted median-based method, MR-Egger method, and MR-PRESSO method. Similarly, genetically predicted coffee consumption was inversely associated with FBG and 2h-PG levels but not with HbA1c. Sensitivity measures gave similar results without evidence of pleiotropy.

CONCLUSIONS

A genetic predisposition to habitual coffee consumption was inversely associated with T2D prevalence and lower levels of FBG and 2h-PG profiles. Our study warrants further exploration.

The Association of Metabolomic Profiles of a Healthy Lifestyle with Heart Failure Risk in a Prospective Study

Lifestyle has been linked to the incidence of heart failure, but the underlying biological mechanisms remain unclear. Using the metabolomic, lifestyle, and heart failure data of the UK Biobank, we identified and validated healthy lifestyle-related metabolites in a matched case-control and cohort study, respectively. We then evaluated the association of healthy lifestyle-related metabolites with heart failure (HF) risk and the added predictivity of these healthy lifestyle-associated metabolites for HF. Of 161 metabolites, 8 were identified to be significantly related to healthy lifestyle. Notably, omega-3 fatty acids and docosahexaenoic acid (DHA) positively associated with a healthy lifestyle score (HLS) and exhibited a negative association with heart failure risk. Conversely, creatinine negatively associated with a HLS, but was positively correlated with the risk of HF. Adding these three metabolites to the classical risk factor prediction model, the prediction accuracy of heart failure incidence can be improved as assessed by the C-statistic (increasing from 0.806 [95% CI, 0.796-0.816] to 0.844 [95% CI, 0.834-0.854], p-value < 0.001). A healthy lifestyle is associated with significant metabolic alterations, among which metabolites related to healthy lifestyle may be critical for the relationship between healthy lifestyle and HF. Healthy lifestyle-related metabolites might enhance HF prediction, but additional validation studies are necessary.

In utero metabolomic signatures of refined grain intake and risk of gestational diabetes: A metabolome-wide association study

BACKGROUND

Epidemiologic evidence has linked refined grain intake to a higher risk of gestational diabetes (GDM), but the biological underpinnings remain unclear.

OBJECTIVES

We aimed to identify and validate refined grain-related metabolomic biomarkers for GDM risk.

METHODS

In a metabolome-wide association study of 91 cases with GDM and 180 matched controls without GDM (discovery set) nested in the prospective Pregnancy Environment and Lifestyle Study (PETALS), refined grain intake during preconception and early pregnancy and serum untargeted metabolomics were assessed at gestational weeks 10-13. We identified refined grain-related metabolites using multivariable linear regression and examined their prospective associations with GDM risk using conditional logistic regression. We further examined the predictivity of refined grain-related metabolites selected by least absolute shrinkage and selection operator regression in the discovery set and validation set (a random PETALS subsample of 38 individuals with and 336 without GDM).

RESULTS

Among 821 annotated serum (87.4% fasting) metabolites, 42 were associated with refined grain intake, of which 17 (70.6% in glycerolipids, glycerophospholipids, and sphingolipids clusters) were associated with subsequent GDM risk (all false discovery rate-adjusted P values <0.05). Adding 7 of 17 metabolites to a conventional risk factor-based prediction model increased the C-statistic for GDM risk in the discovery set from 0.71 (95% CI: 0.64, 0.77) to 0.77 (95% CI: 0.71, 0.83) and in the validation set from 0.77 (95% CI: 0.69, 0.86) to 0.81 (95% CI: 0.74, 0.89), both with P-for-difference <0.05.

CONCLUSIONS

Clusters of glycerolipids, glycerophospholipids, and sphingolipids may be implicated in the association between refined grain intake and GDM risk, as demonstrated by the significant associations of these metabolites with both refined grains and GDM risk and the incremental predictive value of these metabolites for GDM risk beyond the conventional risk factors. These findings provide evidence on the potential biological underpinnings linking refined grain intake to the risk of GDM and help identify novel disease-related dietary biomarkers to inform diet-related preventive strategies for GDM.

Identifying Plasma and Urinary Biomarkers of Fermented Food Intake and Their Associations with Cardiometabolic Health in a Dutch Observational Cohort

Identification of food intake biomarkers (FIBs) for fermented foods could help improve their dietary assessment and clarify their associations with cardiometabolic health. We aimed to identify novel FIBs for fermented foods in the plasma and urine metabolomes of 246 free-living Dutch adults using nontargeted LC-MS and GC-MS. Furthermore, associations between identified metabolites and several cardiometabolic risk factors were explored. In total, 37 metabolites were identified corresponding to the intakes of coffee, wine, and beer (none were identified for cocoa, bread, cheese, or yoghurt intake). While some of these metabolites appeared to originate from raw food (e.g., niacin and trigonelline for coffee), others overlapped different fermented foods (e.g., 4-hydroxybenzeneacetic acid for both wine and beer). In addition, several fermentation-dependent metabolites were identified (erythritol and citramalate). Associations between these identified metabolites with cardiometabolic parameters were weak and inconclusive. Further evaluation is warranted to confirm their relationships with cardiometabolic disease risk.

Coffee and metabolic phenotypes: A cross-sectional analysis of the Japan multi-institutional collaborative cohort (J-MICC) study

BACKGROUND AND AIMS

To date, the relationship between coffee consumption and metabolic phenotypes has hardly been investigated and remains controversial. Therefore, the aim of this cross-sectional study is to examine the associations between coffee consumption and metabolic phenotypes in a Japanese population.

METHODS AND RESULTS

We analyzed the data of 26,363 subjects (aged 35-69 years) in the baseline survey of the Japan Multi-Institutional Collaborative Cohort Study. Coffee consumption was assessed using a questionnaire. Metabolic Syndrome (MetS) was defined according to the Joint Interim Statement Criteria of 2009, using body mass index (BMI) instead of waist circumference. Subjects stratified by the presence or absence of obesity (normal weight: BMI <25 kg/m²; obesity: BMI ≥25 kg/m²) were classified by the number of MetS components (metabolically healthy: no components; metabolically unhealthy: one or more components) other than BMI. In multiple logistic regression analyses adjusted for sex, age, and other potential confounders, high coffee consumption (≥3 cups/day) was associated with a lower prevalence of MetS and metabolically unhealthy phenotypes both in normal weight (OR 0.83, 95% CI 0.76-0.90) and obese subjects (OR 0.83, 95% CI 0.69-0.99). Filtered/instant coffee consumption was inversely associated with the prevalence of MetS and metabolically unhealthy phenotypes, whereas canned/bottled/packed coffee consumption was not.

CONCLUSION

The present results suggest that high coffee consumption, particularly filtered/instant coffee, is inversely associated with the prevalence of metabolically unhealthy phenotypes in both normal weight and obese Japanese adults.

Machine Learning in Nutrition Research

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.

Prediagnostic Plasma Metabolomics and the Risk of Exfoliation Glaucoma

Purpose

The etiology of exfoliation glaucoma (XFG) is poorly understood. We aimed to identify a prediagnostic plasma metabolomic signature associated with XFG.

Methods

We conducted a 1:1 matched case-control study nested within the Nurses' Health Study and Health Professionals Follow-up Study. We collected blood samples in 1989-1990 (Nurses' Health Study) and 1993-1995 (Health Professionals Follow-up Study). We identified 205 incident XFG cases through 2016 (average time to diagnosis from blood draw = 11.8 years) who self-reported glaucoma and were confirmed as XFG cases with medical records. We profiled plasma metabolites using liquid chromatography-mass spectrometry. We evaluated 379 known metabolites (transformed for normality using probit scores) using multiple conditional logistic models. Metabolite set enrichment analysis was used to identify metabolite classes associated with XFG. To adjust for multiple comparisons, we used number of effective tests (NEF) and the false discovery rate (FDR).

Results

Mean age of cases (n = 205) at diagnosis was 71 years; 85% were women and more than 99% were Caucasian; controls (n = 205) reported eye examinations as of the matched cases' index date. Thirty-three metabolites were nominally significantly associated with XFG (P < 0.05), and 4 metabolite classes were FDR-significantly associated. We observed positive associations for lysophosphatidylcholines (FDR = 0.02) and phosphatidylethanolamine plasmalogens (FDR = 0.004) and inverse associations for triacylglycerols (FDR < 0.0001) and steroids (FDR = 0.03). In particular, the multivariable-adjusted odds ratio with each 1 standard deviation higher plasma cortisone levels was 0.49 (95% confidence interval, 0.32-0.74; NEF = 0.05).

Conclusions

In plasma from a decade before diagnosis, lysophosphatidylcholines and phosphatidylethanolamine plasmalogens were positively associated and triacylglycerols and steroids (e.g., cortisone) were inversely associated with XFG risk.

Lipidomic Signatures of Dairy Consumption and Associated Changes in Blood Pressure and Other Cardiovascular Risk Factors Among Chinese Adults

BACKGROUND

Omics data may provide a unique opportunity to discover dairy-related biomarkers and their linked cardiovascular health.

METHODS

Dairy-related lipidomic signatures were discovered in baseline data from a Chinese cohort study (n=2140) and replicated in another Chinese study (n=212). Dairy intake was estimated by a validated food-frequency questionnaire. Lipidomics was profiled by high-coverage liquid chromatography-tandem mass spectrometry. Associations of dairy-related lipids with 6-year changes in cardiovascular risk factors were examined in the discovery cohort, and their causalities were analyzed by 2-sample Mendelian randomization using available genome-wide summary data.

RESULTS

Of 350 lipid metabolites, 4 sphingomyelins, namely sphingomyelin (OH) C32:2, sphingomyelin C32:1, sphingomyelin (2OH) C30:2, and sphingomyelin (OH) C38:2, were identified and replicated to be positively associated with total dairy consumption (β=0.130 to 0.148; P<1.43×10^-4), but not or weakly with nondairy food items. The score of 4 sphingomyelins showed inverse associations with 6-year changes in systolic (-2.68 [95% CI, -4.92 to -0.43]; P=0.019), diastolic blood pressures (-1.86 [95% CI, -3.12 to -0.61]; P=0.004), and fasting glucose (-0.25 [95% CI, -0.41 to -0.08]; P=0.003). Mendelian randomization analyses further revealed that genetically inferred sphingomyelin (OH) C32:2 was inversely associated with systolic (-0.57 [95% CI, -0.85 to -0.28]; P=9.16×10^-5) and diastolic blood pressures (-0.39 [95% CI, -0.59 to -0.20]; P=7.09×10^-5).

CONCLUSIONS

The beneficial effects of dairy products on cardiovascular health might be mediated through specific sphingomyelins among Chinese with overall low dairy consumption.

Untargeted plasma metabolomics for risk prediction of hepatocellular carcinoma: A prospective study in two Chinese cohorts

Characterization of metabolic perturbation prior to hepatocellular carcinoma (HCC) may deepen the understanding of causal pathways and identify novel biomarkers for early prevention. We conducted two 1:1 matched nested case-control studies (108 and 55 pairs) to examine the association of plasma metabolome (profiled using LC-MS) with the risk of HCC based on two prospective cohorts in China. Differential metabolites were identified by paired t-tests and orthogonal partial least-squares discriminant analysis (OPLS-DA). Weighted gene co-expression network analysis (WGCNA) was performed to classify metabolites into modules for identifying biological pathways involved in hepatocarcinogenesis. We assessed the risk predictivity of metabolites using multivariable logistic regression models. Among 612 named metabolites, 44 differential metabolites were identified between cases and controls, including 12 androgenic/progestin steroid hormones, 8 bile acids, 10 amino acids, 6 phospholipids, and 8 others. These metabolites were associated with HCC in the multivariable logistic regression analyses, with odds ratios ranging from 0.19 (95% CI: 0.11-0.35) to 5.09 (95% CI: 2.73-9.50). WGCNA including 612 metabolites showed 8 significant modules related to HCC risk, including those representing metabolic pathways of androgen and progestin, primary and secondary bile acids, and amino acids. A combination of 18 metabolites of independent effects showed the potential to predict HCC risk, with an AUC of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.93) in the training and validation sets, respectively. In conclusion, we identified a panel of plasma metabolites that could be implicated in hepatocellular carcinogenesis and have the potential to predict HCC risk.

Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies

BACKGROUND

Due to the rapidly increasing availability of metabolomics data in prospective studies, an update of the meta evidence on metabolomics and type 2 diabetes risk is warranted.

PURPOSE

To conduct an updated systematic review and meta-analysis of plasma, serum, and urine metabolite markers and incident type 2 diabetes.

DATA SOURCES

We searched PubMed and Embase until 6 March 2021.

STUDY SELECTION

We selected prospective observational studies where investigators used high-throughput techniques to investigate the relationship between plasma, serum, or urine metabolites and incident type 2 diabetes.

DATA EXTRACTION

Baseline metabolites per-SD risk estimates and 95% CIs for incident type 2 diabetes were extracted from all eligible studies.

DATA SYNTHESIS

A total of 61 reports with 71,196 participants and 11,771 type 2 diabetes cases/events were included in the updated review. Meta-analysis was performed for 412 metabolites, of which 123 were statistically significantly associated (false discovery rate-corrected P < 0.05) with type 2 diabetes risk. Higher plasma and serum levels of certain amino acids (branched-chain, aromatic, alanine, glutamate, lysine, and methionine), carbohydrates and energy-related metabolites (mannose, trehalose, and pyruvate), acylcarnitines (C4-DC, C4-OH, C5, C5-OH, and C8:1), the majority of glycerolipids (di- and triacylglycerols), (lyso)phosphatidylethanolamines, and ceramides included in meta-analysis were associated with higher risk of type 2 diabetes (hazard ratio 1.07-2.58). Higher levels of glycine, glutamine, betaine, indolepropionate, and (lyso)phosphatidylcholines were associated with lower type 2 diabetes risk (hazard ratio 0.69-0.90).

LIMITATIONS

Substantial heterogeneity (I2 > 50%, τ2 > 0.1) was observed for some of the metabolites.

CONCLUSIONS

Several plasma and serum metabolites, including amino acids, lipids, and carbohydrates, are associated with type 2 diabetes risk.

Plasma metabolomic profiles for colorectal cancer precursors in women

How metabolome changes influence the early process of colorectal cancer (CRC) development remains unknown. We conducted a 1:2 matched nested case-control study to examine the associations of pre-diagnostic plasma metabolome (profiled using LC-MS) with risk of CRC precursors, including conventional adenomas (n = 586 vs. 1141) and serrated polyps (n = 509 vs. 993), in the Nurses' Health Study (NHS) and NHSII. Conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). We used the permutation-based Westfall and Young approach to account for multiple testing. Subgroup analyses were performed for advanced conventional adenomas (defined as at least one adenoma of ≥ 10 mm or with high-grade dysplasia, or tubulovillous or villous histology) and high-risk serrated polyps that were located in the proximal colon or with size of ≥ 10 mm. After multiple testing correction, among 207 metabolites, higher levels of C36:3 phosphatidylcholine (PC) plasmalogen were associated with lower risk of conventional adenomas, with the OR (95% CI) comparing the 90th to the 10th percentile of 0.62 (0.48-0.81); C54:8 triglyceride (TAG) was associated with higher risk of serrated polyps (OR = 1.79, 95% CI: 1.31-2.43), and phenylacetylglutamine (PAG) was associated with lower risk (OR = 0.57, 95% CI:0.43-0.77). PAG was also inversely associated with advanced adenomas (OR = 0.57, 95% CI: 0.36-0.89) and high-risk serrated polyps (OR = 0.54, 95% CI: 0.32-0.89), although the multiple testing-corrected p value was > 0.05. Our findings suggest potential roles of lipid metabolism and phenylacetylglutamine, a microbial metabolite, in the early stage of colorectal carcinogenesis, particularly for the serrated pathway.

Perspective: Novel Approaches to Evaluate Dietary Quality: Combining Methods to Enhance Measurement for Dietary Surveillance and Interventions

Refining existing dietary assessment methods to reduce measurement error and facilitate the routine evaluation of dietary quality is essential to inform health policy. Notable advancements in technology in the past decade have enhanced the precision and transformation of dietary assessment methods with applications toward both population health and precision nutrition. Within population health, innovative applications of big data including use of automatically collected food purchasing data, quantitative measurement of food environments, and novel, yet simplified dietary quality metrics provide important complementary data to traditional self-report methods. Precision nutrition is similarly advancing with greater use of validated biomarkers for assessing dietary patterns and understanding individual variability in metabolism. Concurrently enhancing our understanding of diet-disease relations at the population health and precision nutrition levels provides tremendous potential to generate evidence needed to advance public health nutrition policy. This commentary highlights the importance of these advances toward progressing the field of dietary assessment and discusses the application of food purchasing data, data analytics, alternative dietary quality metrics, and -omics technology in population and clinical medicine.

Metabolites Associated with Coffee Consumption and Incident Chronic Kidney Disease

BACKGROUND AND OBJECTIVES

Moderate coffee consumption has been associated with lower risk of CKD; however, the exact biologic mechanisms underlying this association are unknown. Metabolomic profiling may identify metabolic pathways that explain the association between coffee and CKD. The goal of this study was to identify serum metabolites associated with coffee consumption and examine the association between these coffee-associated metabolites and incident CKD.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Using multivariable linear regression, we identified coffee-associated metabolites among 372 serum metabolites available in two subsamples of the Atherosclerosis Risk in Communities study (ARIC; n=3811). Fixed effects meta-analysis was used to pool the results from the two ARIC study subsamples. Associations between coffee and metabolites were replicated in the Bogalusa Heart Study (n=1043). Metabolites with significant associations with coffee in both cohorts were then evaluated for their prospective associations with incident CKD in the ARIC study using Cox proportional hazards regression.

RESULTS

In the ARIC study, mean (SD) age was 54 (6) years, 56% were daily coffee drinkers, and 32% drank >2 cups per day. In the Bogalusa Heart Study, mean (SD) age was 48 (5) years, 57% were daily coffee drinkers, and 38% drank >2 cups per day. In a meta-analysis of two subsamples of the ARIC study, 41 metabolites were associated with coffee consumption, of which 20 metabolites replicated in the Bogalusa Heart Study. Three of these 20 coffee-associated metabolites were associated with incident CKD in the ARIC study.

CONCLUSIONS

We detected 20 unique serum metabolites associated with coffee consumption in both the ARIC study and the Bogalusa Heart Study, and three of these 20 candidate biomarkers of coffee consumption were associated with incident CKD. One metabolite (glycochenodeoxycholate), a lipid involved in primary bile acid metabolism, may contribute to the favorable kidney health outcomes associated with coffee consumption. Two metabolites (O-methylcatechol sulfate and 3-methyl catechol sulfate), both of which are xenobiotics involved in benzoate metabolism, may represent potential harmful aspects of coffee on kidney health.

Chemical Analysis, Toxicity Study, and Free-Radical Scavenging and Iron-Binding Assays Involving Coffee (Coffea arabica) Extracts

We aimed to analyze the chemical compositions in Arabica coffee bean extracts, assess the relevant antioxidant and iron-chelating activities in coffee extracts and instant coffee, and evaluate the toxicity in roasted coffee. Coffee beans were extracted using boiling, drip-filtered and espresso brewing methods. Certain phenolics were investigated including trigonelline, caffeic acid and their derivatives, gallic acid, epicatechin, chlorogenic acid (CGA) and their derivatives, p-coumaroylquinic acid, p-coumaroyl glucoside, the rutin and syringic acid that exist in green and roasted coffee extracts, along with dimethoxycinnamic acid, caffeoylarbutin and cymaroside that may be present in green coffee bean extracts. Different phytochemicals were also detected in all of the coffee extracts. Roasted coffee extracts and instant coffees exhibited free-radical scavenging properties in a dose-dependent manner, for which drip coffee was observed to be the most effective (p < 0.05). All coffee extracts, instant coffee varieties and CGA could effectively bind ferric ion in a concentration-dependent manner resulting in an iron-bound complex. Roasted coffee extracts were neither toxic to normal mononuclear cells nor breast cancer cells. The findings indicate that phenolics, particularly CGA, could effectively contribute to the iron-chelating and free-radical scavenging properties observed in coffee brews. Thus, coffee may possess high pharmacological value and could be utilized as a health beverage.

Longitudinal study of the relationship between coffee consumption and type 2 diabetes in Chinese adult residents: Data from China Health and Nutrition Survey

BACKGROUND

Increasing coffee intake was inversely associated with risk of type 2 diabetes in Western countries. However, in China where coffee consumption and diabetes population has been growing fast in recent years, studies on the impact of coffee intakes on the onset of type 2 diabetes are lacking. This study attempts to determine the associations between coffee consumption and type 2 diabetes in Chinese adults.

METHODS

This longitudinal study analyzed 10447 adults who had participated in at least two rounds of the China Health and Nutrition Survey (CHNS), which is a survey database of multistage, random cluster process during 1993-2011. Coffee consumption and type 2 diabetes incidence were measured in the survey. Body mass index (BMI), age, sex, place of residence, waves, education level, smoking, drinking alcohol and tea drinking frequency were adjusted as covariate. We used longitudinal fixed effects regression models to assess changes within person.

RESULTS

After adjusting confounding factors, lower risk of diabetes is observed among Chinese adults who drink coffee occasionally (Adjusted Odds Ratio (AOR)  = 0.13, 95% CI = 0.05, 0.34) and drink almost every day (AOR = 0.61, 95% CI = 0.45, 0.83), compared with those who do not or hardly drink. In the subgroup analysis, among women aged 45-59 who drink coffee one to three times a week (AOR = 0.21, 95% CI = 0.08, 0.52) and men over 60 who drink coffee almost every day (AOR = 0.19, 95% CI = 0.07, 0.53), protective effects were found. For young men aged 19-29, drinking coffee almost every day showed a risk effect (AOR = 20.21, 95% CI = 5.96-68.57).

CONCLUSIONS

Coffee drinking habit is an independent protective factor for adult on type 2 diabetes in China. And it varies among people with different ages and genders. The rapid growth of coffee consumption in China in recent years may help reduce the risk of type 2 diabetes, but at the same time, the risk of type 2 diabetes in adolescents needs attention.

Coffee and Lower Risk of Type 2 Diabetes: Arguments for a Causal Relationship

Prospective epidemiological studies concur in an association between habitual coffee consumption and a lower risk of type 2 diabetes. Several aspects of these studies support a cause–effect relationship. There is a dependency on daily coffee dose. Study outcomes are similar in different regions of the world, show no differences between sexes, between obese versus lean, young versus old, smokers versus nonsmokers, regardless of the number of confounders adjusted for. Randomized controlled intervention trials did not find a consistent impact of drinking coffee on acute metabolic control, except for effects of caffeine. Therefore, lowering of diabetes risk by coffee consumption does not involve an acute effect on the post-meal course of blood glucose, insulin or insulin resistance. Several studies in animals and humans find that the ingestion of coffee phytochemicals induces an adaptive cellular response characterized by upregulation and de novo synthesis of enzymes involved in cell defense and repair. A key regulator is the nuclear factor erythroid 2-related factor 2 (Nrf2) in association with the aryl hydrocarbon receptor, AMP-activated kinase and sirtuins. One major site of coffee actions appears to be the liver, causing improved fat oxidation and lower risk of steatosis. Another major effect of coffee intake is preservation of functional beta cell mass via enhanced mitochondrial function, lower endoplasmic reticulum stress and prevention or clearance of aggregates of misfolded proinsulin or amylin. Long-term preservation of proper liver and beta cell function may account for the association of habitual coffee drinking with a lower risk of type 2 diabetes, rather than acute improvement of metabolic control.

Promoting Human Nutrition and Health through Plant Metabolomics: Current Status and Challenges

Simple Summary

This review summarizes the status, applications, and challenges of plant metabolomics in the context of crop breeding, food quality and safety, and human nutrition and health. It also highlights the importance of plant metabolomics in elucidating biochemical and genetic bases of traits associated with nutritive and healthy beneficial foods and other plant products to secure food supply, to ensure food quality, to protect humans from malnutrition and other diseases. Meanwhile, this review calls for comprehensive collaborations to accelerate relevant researches and applications in the context of human nutrition and health.

Abstract

Plant metabolomics plays important roles in both basic and applied studies regarding all aspects of plant development and stress responses. With the improvement of living standards, people need high quality and safe food supplies. Thus, understanding the pathways involved in the biosynthesis of nutritionally and healthily associated metabolites in plants and the responses to plant-derived biohazards in humans is of equal importance to meet people’s needs. For each, metabolomics has a vital role to play, which is discussed in detail in this review. In addition, the core elements of plant metabolomics are highlighted, researches on metabolomics-based crop improvement for nutrition and safety are summarized, metabolomics studies on plant natural products including traditional Chinese medicine (TCM) for health promotion are briefly presented. Challenges are discussed and future perspectives of metabolomics as one of the most important tools to promote human nutrition and health are proposed.