Plasma metabolite biomarkers of boiled and filtered coffee intake and their association with type 2 diabetes risk

Dietary biomarkers-an update on their validity and applicability in epidemiological studies

The aim of this literature review was to identify and provide a summary update on the validity and applicability of the most promising dietary biomarkers reflecting the intake of important foods in the Western diet for application in epidemiological studies. Many dietary biomarker candidates, reflecting intake of common foods and their specific constituents, have been discovered from intervention and observational studies in humans, but few have been validated. The literature search was targeted for biomarker candidates previously reported to reflect intakes of specific food groups or components that are of major importance in health and disease. Their validity was evaluated according to 8 predefined validation criteria and adapted to epidemiological studies; we summarized the findings and listed the most promising food intake biomarkers based on the evaluation. Biomarker candidates for alcohol, cereals, coffee, dairy, fats and oils, fruits, legumes, meat, seafood, sugar, tea, and vegetables were identified. Top candidates for all categories are specific to certain foods, have defined parent compounds, and their concentrations are unaffected by nonfood determinants. The correlations of candidate dietary biomarkers with habitual food intake were moderate to strong and their reproducibility over time ranged from low to high. For many biomarker candidates, critical information regarding dose response, correlation with habitual food intake, and reproducibility over time is yet unknown. The nutritional epidemiology field will benefit from the development of novel methods to combine single biomarkers to generate biomarker panels in combination with self-reported data. The most promising dietary biomarker candidates that reflect commonly consumed foods and food components for application in epidemiological studies were identified, and research required for their full validation was summarized.

Fasting plasma metabolites reflecting meat consumption and their associations with incident type 2 diabetes in two Swedish cohorts

BACKGROUND

Consumption of processed red meat has been associated with increased risk of developing type 2 diabetes (T2D), but challenges in dietary assessment call for objective intake biomarkers.

OBJECTIVES

This study aimed to investigate metabolite biomarkers of meat intake and their associations with T2D risk.

METHODS

Fasting plasma samples were collected from a case-control study nested within Västerbotten Intervention Program (VIP) (214 females and 189 males) who developed T2D after a median follow-up of 7 years. Panels of biomarker candidates reflecting the consumption of total, processed, and unprocessed red meat and poultry were selected from the untargeted metabolomics data collected on the controls. Observed associations were then replicated in Swedish Mammography clinical subcohort in Uppsala (SMCC) (n = 4457 females). Replicated metabolites were assessed for potential association with T2D risk using multivariable conditional logistic regression in the discovery and Cox regression in the replication cohorts.

RESULTS

In total, 15 metabolites were associated with ≥1 meat group in both cohorts. Acylcarnitines 8:1, 8:2, 10:3, reflecting higher processed meat intake [r > 0.22, false discovery rate (FDR) < 0.001 for VIP and r > 0.05; FDR < 0.001 for SMCC) were consistently associated with higher T2D risk in both data sets. Conversely, lysophosphatidylcholine 17:1 and phosphatidylcholine (PC) 15:0/18:2 were associated with lower processed meat intake (r < -0.12; FDR < 0.023, for VIP and r < -0.05; FDR < 0.001, for SMCC) and with lower T2D risk in both data sets, except for PC 15:0/18:2, which was significant only in the VIP cohort. All associations were attenuated after adjustment for BMI (kg/m2).

CONCLUSIONS

Consistent associations of biomarker candidates involved in lipid metabolism between higher processed red meat intake with higher T2D risk and between those reflecting lower intake with the lower risk may suggest a relationship between processed meat intake and higher T2D risk. However, attenuated associations after adjusting for BMI indicates that such a relationship may at least partly be mediated or confounded by BMI.

Consumption of Roasted Coffee Leads to Conjugated Metabolites of Atractyligenin in Human Plasma

Roasted coffee contains atractyligenin-2-O-β-d-glucoside and 3'-O-β-d-glucosyl-2'-O-isovaleryl-2-O-β-d-glucosylatractyligenin, which are ingested with the brew. Known metabolites are atractyligenin, atractyligenin-19-O-β-d-glucuronide (M1), 2β-hydroxy-15-oxoatractylan-4α-carboxy-19-O-β-d-glucuronide (M2), and 2β-hydroxy-15-oxoatractylan-4α-carboxylic acid-2-O-β-d-glucuronide (M3), but the appearance and pharmacokinetic properties are unknown. Therefore, first time-resolved quantitative data of atractyligenin glycosides and their metabolites in plasma samples from a pilot human intervention study (n = 10) were acquired. None of the compounds were found in the control samples and before coffee consumption (t = 0 h). After coffee, neither of the atractyligenin glycosides appeared in the plasma, but the aglycone atractyligenin and the conjugated metabolite M1 reached an estimated cmax of 41.9 ± 12.5 and 25.1 ± 4.9 nM, respectively, after 1 h. M2 and M3 were not quantifiable until their concentration enormously increased ≥4 h after coffee consumption, reaching an estimated cmax of 2.5 ± 1.9 and 55.0 ± 57.7 nM at t = 10 h. The data suggest that metabolites of atractyligenin could be exploited to indicate coffee consumption.

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.

Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion

MOTIVATION

Metabolomics has developed rapidly in recent years, and metabolism-related databases are also gradually constructed. Nowadays, more and more studies are being carried out on diverse microbes, metabolites and diseases. However, the logics of various associations among microbes, metabolites and diseases are limited understanding in the biomedicine of gut microbial system. The collection and analysis of relevant microbial bioinformation play an important role in the revelation of microbe-metabolite-disease associations. Therefore, the dataset that integrates multiple relationships and the method based on complex heterogeneous graphs need to be developed.

RESULTS

In this study, we integrated some databases and extracted a variety of associations data among microbes, metabolites and diseases. After obtaining the three interconnected bilateral association data (microbe-metabolite, metabolite-disease and disease-microbe), we considered building a heterogeneous graph to describe the association data. In our model, microbes were used as a bridge between diseases and metabolites. In order to fuse the information of disease-microbe-metabolite graph, we used the bipartite graph attention network on the disease-microbe and metabolite-microbe bipartite graph. The experimental results show that our model has good performance in the prediction of various disease-metabolite associations. Through the case study of type 2 diabetes mellitus, Parkinson's disease, inflammatory bowel disease and liver cirrhosis, it is noted that our proposed methodology are valuable for the mining of other associations and the prediction of biomarkers for different human diseases.Availability and implementation: https://github.com/Selenefreeze/DiMiMe.git.

The association between plasma metabolites and future risk of all-cause mortality

BACKGROUND

Metabolite profiles provide snapshots of the overall effect of numerous exposures accumulated over life courses, which may lead to health outcomes in the future.

OBJECTIVE

We hypothesized that the risk of all-cause mortality is linked to alterations in metabolism earlier in life, which are reflected in plasma metabolite profiles. We aimed to identify plasma metabolites associated with future risk of all-cause mortality.

METHODS

Through metabolomics, 110 metabolites were measured in 3833 individuals from the Malmö Diet and Cancer-Cardiovascular Cohort (MDC-CC). A total of 1574 deaths occurred within an average follow-up time of 22.2 years. Metabolites that were significantly associated with all-cause mortality in MDC-CC were replicated in 1500 individuals from Malmö Preventive Project re-examination (MPP), among whom 715 deaths occurred within an average follow-up time of 11.3 years.

RESULTS

Twenty two metabolites were significantly associated with all-cause mortality in MDC-CC, of which 13 were replicated in MPP. Levels of trigonelline, glutamate, dimethylglycine, C18-1-carnitine, C16-1-carnitine, C14-1-carnitine, and 1-methyladenosine were associated with an increased risk, while levels of valine, tryptophan, lysine, leucine, histidine, and 2-aminoisobutyrate were associated with a decreased risk of all-cause mortality.

CONCLUSION

We used metabolomics in two Swedish prospective cohorts and identified replicable associations between 13 metabolites and future risk of all-cause mortality. Novel associations between five metabolites-C18-1-carnitine, C16-1-carnitine, C14-1-carnitine, trigonelline, and 2-aminoisobutyrate-and all-cause mortality were discovered. These findings suggest potential new biomarkers for the prediction of mortality and provide insights for understanding the biochemical pathways that lead to mortality.

An online atlas of human plasma metabolite signatures of gut microbiome composition

Human gut microbiota produce a variety of molecules, some of which enter the bloodstream and impact health. Conversely, dietary or pharmacological compounds may affect the microbiota before entering the circulation. Characterization of these interactions is an important step towards understanding the effects of the gut microbiota on health. In this cross-sectional study, we used deep metagenomic sequencing and ultra-high-performance liquid chromatography linked to mass spectrometry for a detailed characterization of the gut microbiota and plasma metabolome, respectively, of 8583 participants invited at age 50 to 64 from the population-based Swedish CArdioPulmonary bioImage Study. Here, we find that the gut microbiota explain up to 58% of the variance of individual plasma metabolites and we present 997 associations between alpha diversity and plasma metabolites and 546,819 associations between specific gut metagenomic species and plasma metabolites in an online atlas (https://gutsyatlas.serve.scilifelab.se/). We exemplify the potential of this resource by presenting novel associations between dietary factors and oral medication with the gut microbiome, and microbial species strongly associated with the uremic toxin p-cresol sulfate. This resource can be used as the basis for targeted studies of perturbation of specific metabolites and for identification of candidate plasma biomarkers of gut microbiota composition.

Here, Dekkers et al. characterize associations of 1528 gut metagenomic species with the plasma metabolome in 8583 participants of the SCAPIS Study, and find that gut microbiota explain up to 58% of the variance of individual plasma metabolites.

Plasma metabolite profiles related to plant-based diets and the risk of type 2 diabetes

AIMS/HYPOTHESIS

Plant-based diets, especially when rich in healthy plant foods, have been associated with a lower risk of type 2 diabetes. However, whether plasma metabolite profiles related to plant-based diets reflect this association was unknown. The aim of this study was to identify the plasma metabolite profiles related to plant-based diets, and to evaluate the associations between the identified metabolite profiles and the risk of type 2 diabetes.

METHODS

Within three prospective cohorts (Nurses' Health Study, Nurses' Health Study II and Health Professionals Follow-up Study), we measured plasma metabolites from 10,684 participants using high-throughput LC MS. Adherence to plant-based diets was assessed by three indices derived from the food frequency questionnaire: an overall Plant-based Diet Index (PDI), a Healthy Plant-based Diet Index (hPDI), and an Unhealthy Plant-based Diet Index (uPDI). Multi-metabolite profiles related to plant-based diet were identified using elastic net regression with a training/testing approach. The prospective associations between metabolite profiles and incident type 2 diabetes were evaluated using multivariable Cox proportional hazards regression. Metabolites potentially mediating the association between plant-based diets and type 2 diabetes risk were further identified.

RESULTS

We identified multi-metabolite profiles comprising 55 metabolites for PDI, 93 metabolites for hPDI and 75 metabolites for uPDI. Metabolite profile scores based on the identified metabolite profiles were correlated with the corresponding diet index (Pearson r = 0.33-0.35 for PDI, 0.41-0.45 for hPDI, and 0.37-0.38 for uPDI, all p<0.001). Metabolite profile scores of PDI (HR per 1 SD higher = 0.81 [95% CI 0.75, 0.88]) and hPDI (HR per 1 SD higher = 0.77 [95% CI 0.71, 0.84]) showed an inverse association with incident type 2 diabetes, whereas the metabolite profile score for uPDI was not associated with the risk. Mutual adjustment for metabolites selected in the metabolite profiles, including trigonelline, hippurate, isoleucine and a subset of triacylglycerols, attenuated the associations of diet indices PDI and hPDI with lower type 2 diabetes risk. The explainable proportion of PDI/hPDI-related diabetes risk by these metabolites ranged between 8.5% and 37.2% (all p<0.05).

CONCLUSIONS/INTERPRETATION

Plasma metabolite profiles related to plant-based diets, especially a healthy plant-based diet, were associated with a lower risk of type 2 diabetes among a generally healthy population. Our findings support the beneficial role of healthy plant-based diets in diabetes prevention and provide new insights for future investigation.

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.

Coffee constituents with antiadipogenic and antidiabetic potentials: A narrative review

Coffee consumption has been associated with the reduction of several chronic diseases, including type 2 diabetes mellitus (T2DM) and obesity. The aim of this review was to summarize the research conducted in the last five years (or older, when appropriate) on the relationship between the consumption of coffee bioactive compounds, obesity, and T2DM. A bibliographic search was performed using the Web of Sciences, Scopus, and Google Scholar. Keywords used were "caffeine," "coffee," "coffee consumption," "coffee extraction," "coffee bioactive components," "chlorogenic acid," "obesity," "antidiabetic," and "antiadipogenic." Epidemiological, clinical, animal, and cell culture studies were reviewed. Caffeine, chlorogenic acid, and diterpenes have been identified as potential bioactive compounds in coffee that exhibit antiadipogenic and antidiabetic effects. The concentration of these compounds in coffee depends on the coffee preparation method. The relationship between coffee consumption and obesity risk is inconsistent, as not all results report a positive association. The addition of sugar and cream may be responsible for these mixed results. The consumption of coffee and its constituents is consistently associated with a lower T2DM risk. Caffeine, chlorogenic acids, and diterpenes have antidiabetic properties and are associated with these effects. The available data do not allow us to draw a conclusion on the effect of coffee or its constituents on adipogenesis. Therefore, more tightly controlled human intervention studies are required for a deeper understanding about this relationship.

The association of coffee consumption rate with serum 25-hydroxyvitamin D, non-HDL levels, and TC/HDL ratio in females with vitamin D deficiency

OBJECTIVES

The purpose of this study was to evaluate the association of coffee consumption rate with serum 25-hydroxyvitamin D, non-high-density lipoprotein cholesterol levels, and total cholesterol to high-density lipoprotein cholesterol ratio in females with vitamin D deficiency.

METHODS

This retrospective cross-sectional study was carried out by studying the records of 270 Jordanian females aged 18-65 years with varying degrees of vitamin D deficiency. Following completion of the questionnaire regarding their anthropometric characteristics and coffee consumption rate during the preceding 3 months, the participants were required to provide blood samples for analysis to measure 25-hydroxyvitamin D and lipid profile levels including non-high-density lipoprotein cholesterol and total cholesterol to high-density lipoprotein cholesterol ratio.

RESULTS

The current study demonstrated that coffee consumption rate and vitamin D deficiency were significantly positively connected with the total cholesterol to high-density lipoprotein cholesterol ratio (p = .003) in women with vitamin D deficiency. In addition, vitamin D deficiency alone correlated positively with non-high-density lipoprotein cholesterol levels and the total cholesterol to high-density lipoprotein cholesterol ratio (p = .010) and (p = .002), respectively.

CONCLUSION

Higher coffee consumption rate among women with vitamin D deficiency significantly elevated total cholesterol to high-density lipoprotein cholesterol ratio that may increase woman's risk of hyperlipidemia.

A New Pipeline for the Normalization and Pooling of Metabolomics Data

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples' originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

Alteration of lipids and amino acids in plasma distinguish schizophrenia patients from controls: A targeted metabolomics study

BACKGROUND

Schizophrenia (SCZ) is a serious psychiatric disorder. Metabolite disturbance is an important pathogenic factor in schizophrenic patients. In this study, we aim to identify plasma lipid and amino acid biomarkers for SCZ using targeted metabolomics.

METHODS

Plasma from 76 SCZ patients and 50 matched controls were analyzed using the LC/MS-based multiple reaction monitoring (MRM) metabolomics approach. A total of 182 targeted metabolites, including 22 amino acids and 160 lipids or lipid-related metabolites were observed. We used binary logistic regression analysis to determine whether the lipid and amino acid biomarkers could discriminate SCZ patients from controls. The area under the curve (AUC) from receiver operation characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance of the biomarkers panel.

RESULTS

We identified 19 significantly differentially expressed metabolites between the SCZ patients and the controls (false discovery rate < 0.05), including one amino acid and 18 lipids or lipid-related metabolites. The binary logistic regression-selected panel showed good diagnostic performance in the drug-naïve group (AUC = 0.936) and all SCZ patients (AUC = 0.948), especially in the drug-treated group (AUC = 0.963).

CONCLUSIONS

Plasma lipids and amino acids showed significant dysregulation in SCZ, which could effectively discriminate SCZ patients from controls. The LC/MS/MS-based approach provides reliable data for the objective diagnosis of SCZ.

Combining Field-Enabled Capillary Vibrating Sharp-Edge Spray Ionization with Microflow Liquid Chromatography and Mass Spectrometry to Enhance 'Omics Analyses

Field-enabled capillary vibrating sharp-edge spray ionization (cVSSI) has been combined with high-flow liquid chromatography (LC) and mass spectrometry (MS) to establish current ionization capabilities for metabolomics and proteomics investigations. Comparisons are made between experiments employing cVSSI and a heated electrospray ionization probe representing the state-of-the-art in microflow LC-MS methods for 'omics studies. For metabolomics standards, cVSSI is shown to provide an ionization enhancement by factors of 4 ± 2 for both negative and positive ion mode analyses. For chymotryptic peptides, cVSSI is shown to provide an ionization enhancement by factors of 5 ± 2 and 2 ± 1 for negative and positive ion mode analyses, respectively. Slightly broader high-performance liquid chromatography peaks are observed in the cVSSI datasets, and several studies suggest that this results from a slightly decreased post-split flow rate. This may result from partial obstruction of the pulled-tip emitter over time. Such a challenge can be remedied with the use of LC pumps that operate in the 10 to 100 μL·min-1 flow regime. At this early stage, the proof-of-principle studies already show ion signal advantages over state-of-the-art electrospray ionization (ESI) for a wide variety of analytes in both positive and negative ion mode. Overall, this represents a ∼20-50-fold improvement over the first demonstration of LC-MS analyses by voltage-free cVSSI. Separate comparisons of the ion abundances of compounds eluting under identical solvent conditions reveal ionization efficiency differences between cVSSI and ESI and may suggest varied contributions to ionization from different physicochemical properties of the compounds. Future investigations of parameters that could further increase ionization gains in negative and positive ion mode analyses with the use of cVSSI are briefly presented.

Metabolomic Changes after Coffee Consumption: New Paths on the Block

SCOPE

Several studies suggest that regular coffee consumption may help preventing chronic diseases, but the impact of daily intake and the contribution of coffee metabolites in disease prevention are still unclear. The present study aims at evaluating whether and how different patterns of coffee intake (one cup of espresso coffee/day, three cups of espresso coffee/day, and one cup of espresso coffee/day and two cocoa-based products containing coffee two times per day) may impact endogenous molecular pathways.

METHODS AND RESULTS

A three-arm, randomized, crossover trial is performed in 21 healthy volunteers who consumed each treatment for one month. Urine samples are collected to perform untargeted metabolomics based on UHPLC-IMS-HRMS. A total of 153 discriminant metabolites are identified. Several molecular features are associated with coffee consumption, while others are linked with different metabolic pathways, such as phenylalanine, tyrosine, energy metabolism, steroid hormone biosynthesis, and arginine biosynthesis and metabolism.

CONCLUSION

This information has provided new insights into the metabolic routes by which coffee and coffee-related metabolites may exert effects on human health.

Coffee, Black Tea, and Green Tea Consumption in Relation to Plasma Metabolites in an Asian Population

SCOPE

Coffee and tea are among the most popular beverages in the world. However, the association between habitual coffee, green tea, and black tea consumption with metabolomics profiles in Asian populations remain largely unknown.

METHODS AND RESULTS

158 metabolites (14 amino acids, 45 acylcarnitines, and 99 sphingolipids) in the blood plasma of participants are measured from the population-based Singapore Prospective Study Program cohort using mass spectrometry (MS). Linear regression models are used to obtain the estimates for the association between coffee and tea consumption with metabolite levels, adjusted for potential confounders and false discovery rate (FDR). Coffee consumption is significantly associated with higher levels of 63 sphingolipids (29 sphingomyelins, 32 ceramides, a sphingosine-1-phosphate, and a sphingosine) and lower levels of 13 acylcarnitines and alanine. Black tea consumption is significantly associated with higher levels of eight sphingolipids, and lower levels of an amino acid, whereas green tea is significantly inversely associated with four metabolites (C8:1-OH acylcarnitine, ganglioside GM3 d18:1/16:0, sphingomyelins d18:2/18:0 and d18:1/14:0).

CONCLUSIONS

Coffee, black tea, and green tea consumption are associated with plasma levels of certain classes of sphingolipids and acylcarnitines in an Asian population, particularly sphingomyelins, which may mediate the health benefits of these beverages.

Prevention and Management of Type 2 Diabetes and Metabolic Syndrome in the Time of COVID-19: Should We Add a Cup of Coffee?

Recent evidence shows that COVID-19 patients with existing metabolic disorders, such as diabetes and metabolic syndrome, are exposed to a high risk of morbidity and mortality. At the same time, in order to manage the pandemic, the health authorities around the world are advising people to stay at home. This results in decreased physical activity and an increased consumption of an unhealthy diet, which often leads to an increase in body weight, risk for diabetes, insulin resistance, and metabolic syndrome, and thus, paradoxically, to a high risk of morbidity and mortality due to COVID-19 complications. Here we summarize the evidence demonstrating that the promotion of a healthy life style, including physical activity and a dietary intake of natural polyphenols present in coffee and tea, has the potential to improve the prevention and management of insulin resistance and diabetes in the time of COVID-19 pandemic. Particularly, it would be pertinent to evaluate further the potential positive effects of coffee beverages, rich in natural polyphenols, as an adjuvant therapy for COVID-19, which appear not to be studied sufficiently.