High‐throughput metabolite profiling techniques enable thorough studies of an individual's metabolic response to diet and could provide new mechanistic insight to the role diet plays in health 7 . Knowledge of a ‘metabolite signature’ reflecting diet response or exposure may also be used to optimize clinical and epidemiological tools for diet assessment in nutrition research 7 . In this study, we conduct the first metabolomic study of controlled coffee intake; leveraging serum samples collected from a previously reported coffee trial 8 . Our primary objective was to identify individual metabolite changes in response to coffee in order to gain insight into biological mechanisms by which coffee impacts health. Our secondary objective was to identify metabolite signatures that discriminate periods of coffee consumption from periods of no coffee consumption which thus have potential utility in clinical or epidemiological studies of coffee and health.

Coffee is one of the most widely consumed beverages in the world 1 . A growing body of experimental and epidemiological literature points to the premise that coffee may be an inexpensive naturally preventive agent against development of certain diseases, particularly type 2 diabetes (T2D) and Parkinson's disease 2 , 3 . However, the literature also points to potential adverse effects of coffee 2 , 4 , and these need to be carefully considered before broadly encouraging coffee consumption. Importantly, the causal and precise molecular mechanisms that underlie the beneficial and adverse effects of coffee remain unclear. Coffee is the major source of caffeine for many populations 5 , but it also contains 100s of other compounds, any of which might impact pathways related to disease development or prevention 6 .

Multilevel partial least squares discriminant analysis (MPLSDA) 14 was performed to examine whether coffee consumption led to systemic metabolic changes and which metabolites were the most differentiating biomarkers across the stages of the coffee trial. Because repeated measure multivariate methods that model three or more levels simultaneously are prone to bias 15 , we performed separate MPLSDA for each treatment comparison (4 cups vs. 0 cups, 8 vs. 0 and 8 vs. 4) 14 . The prediction error of the MPLSDA model was determined and expressed in terms of number of misclassifications (NMC) and Q 2 by a fivefold CMV scheme 16 . To obtain stable class predictions, and stable metabolite selections, the average result of 20 CMVs was calculated. To validate whether the prediction error of the MPLSDA model was not obtained by chance, a comparison was made with the prediction errors from 1000 randomly permutated data sets representing the H 0 ‐distribution of no‐effect. The treatment effect was considered statistically significant if the P value obtained from this permutation test was <0.05. To select the most discriminative metabolites for coffee consumption from the model, the metabolites were ranked according to their absolute size in the MPLSDA regression coefficient based on the average result of 20 CMVs. Metabolites with the lowest rank product (RP) values have the strongest discriminative power. The RP values of the MPLSDA model were compared with the RP values obtained from 1000 permutations, and those with a P value <0.05 were considered significantly discriminative. Random Forest (RF) analysis, a nonparametric technique unaffected by feature scale and applicable to two or more treatment levels, was also implemented as a secondary multivariate analysis (Appendix S1 ).

Repeated measures anova was used to test the relationship between coffee treatment and each individual metabolite. Statistical significance was defined as P < 0.05 and FDR ( Q ‐value) <0.05. The proportion of variability explained by each metabolite was estimated by the generalized eta‐squared statistic 12 . Pathway enrichment analysis was performed using MetaboLync (Metabolon Inc.), with all metabolites and their pre‐assigned pathways as background and reference pathways, respectively. Analyses were restricted to the 42 pathways containing at least five metabolite members. Correction for multiple hypothesis testing in pathway enrichment analysis was performed using an FDR of 5%. We computed both ordinary Pearson correlations and pairwise partial correlations to explore the latent relationships of changes in identified coffee metabolites across treatments (see Appendix S1 ). Correlation networks were constructed using Cytoscape 13 .

Fasting serum samples collected after each coffee stage were subject to nontargeted metabolomic profiling using UPLC‐ESI‐MS/MS (Metabolon Inc., Durham, NC, USA) as previously described 9 , 10 and detailed in the Appendix S1 . Mass spectral peaks, retention times and m / z were used to determine the relative quantities of each metabolite. Missing values were imputed with the observed minimum value following normalization and scaling steps. Individual metabolites that contained more than 20% missing values across the first (zero cups/day) and third (eight cups/day) trial stages were not included for statistical analysis (160 metabolites). All known (named) compounds of the final 733 metabolites analysed in this study are listed in Table S2 . Those identified based on mass and fragmentation analysis but not confirmed with standards are indicated by asterisks (*).

Serum samples examined for this study were obtained from participants completing an investigator‐blinded, three‐stage clinical trial that lasted for 3 months (Appendix S1 , registration: http://www.isrctn.com/ISRCTN12547806 ) 8 . Briefly, habitual coffee consumers <65 years of age, residing in Finland, free of T2D, but with an elevated risk of T2D were eligible for participation. The participants received packages of coffee and brewed the coffee daily at home with their own coffee machine using paper filters. During the first month, participants refrained from drinking coffee, whereas in the second month, they were instructed to consume four cups of coffee/day (1 cup = 150 mL) and in the third month 8 cups/day. Of the 49 participants recruited, 47 completed the trial. Baseline characteristics of these 47 participants are shown in Table S1 . The trial was conducted in accordance with the Declaration of Helsinki (1964), as amended in South Africa (1996), and approved by Joint Authority for the Hospital District of Helsinki and Uusimaa Ethics Committee, Department of Medicine. Written informed consent was obtained from all participants.

Figures S5 a–c presents the multilevel PCA score plots for each coffee stage comparison. Comparing the nondrinking stage (zero cups/day) to four cups/day and to eight cups/day, on average 0.7 (1.4%) and 0.2 (0.4%) samples, respectively, of 47 were misclassified (Figure S5 D–E). Misclassifications increased when comparing the eight cups/day to four cups/day periods: 11 (23%) samples of 47 were misclassified (Figure S5 F). All three classification models were statistically significant ( P < 0.05). Significant treatment effects were also observed based on the Q 2 classification criteria (data not shown). As caffeine and caffeine metabolites were anticipated biomarkers of coffee consumption, we repeated the MPLSDA after excluding all xanthine metabolites from the data set. Prediction errors remained low across models: 3% (4 vs. 0 cups/day), 1% (8 vs. 0 cups/day) and 28% (8 vs. 4 cups/day).

The metabolomics data set was significantly enriched for metabolite members of xanthine metabolism ( P = 4.50 × 10 −11 , 5.0‐fold enrichment), fatty acid metabolism (acylcholine, P = 4.36 × 10 −5 , 5.0‐fold enrichment), endocannabinoid ( P = 2.37 × 10 −4 , 5.0‐fold enrichment), benzoate metabolism ( P = 1.31 × 10 −4 , 3.3‐fold enrichment) and steroid ( P = 0.02, 2.2‐fold enrichment) pathways ( Q < 0.05). Relationships of changes amongst identified coffee metabolites in response to coffee intake were largely consistent with pathway membership and pathway enrichment analysis (Fig. 1 , Figure S3 ). Strong connections between xanthine and benzoate metabolism and between steroid and acylcholine pathways were also evident. Figure S4 presents the partial correlation networks for changes in metabolite levels across coffee stages and, as expected, is sparser then their corresponding ordinary correlation networks (Figure S3 ) because they aim to capture only direct connections between metabolites.

Discussion

Within the context of a clinical trial lasting 3 months, the serum levels of over 100 metabolites changed with increased coffee consumption. Dose–response elevations were observed for coffee‐derived metabolites, a subset of which were products of microbial metabolism. Metabolites mapping to the endocannabinoid and fatty acid acylcholine pathway decreased in response to coffee consumption whilst those of the steroid pathway generally increased. Subsets of these metabolites significantly discriminated the coffee‐drinking period from the noncoffee‐drinking periods precisely, and this precision persisted even after excluding caffeine and other xanthines.

Our discussion focuses on only key metabolic pathways altered by coffee consumption and significant metabolites potentially underlying reported associations between coffee and health. Hypothesized connections between other significant metabolites and coffee are presented in Table S4. The current study confirms the strong association between circulating levels of the known coffee constituents – caffeine, theophylline and theobromine – and their xanthine metabolites with coffee consumption. Other significant metabolites previously shown to originate from brewed coffee include dihydroferulic acid, campesterol, N‐(2‐furoyl) glycine, trigonelline and quinate (Table 1, Table S4). Coffee is the richest source of chlorogenic acid (CGA) 17, 18 and several CGA metabolites (assigned to the benzoate metabolism pathway) that have previously been shown to derive from cleavage products of gut microbiota 19, 20 also increased with coffee intake. Coffee and polyphenols have been shown to promote the growth of certain species of bacteria such as Bifidobacterium spp. and Akkermansia muciniphila 20-25. As intestinal microbiota also alter the metabolism of other exogenous and endogenous substances 26, 27, the microbiota signature potentially induced by coffee may contribute to significant changes in other metabolites reported in the current study. For example, preclinical evidence supports a role for the gut microbiota in regulating the availability of tryptophan for kynurenine metabolism, and in the current study, serum levels of kynurenine, indoleacetate and 5‐bromotryptophan were significantly altered with coffee intake (Tables 1, S4). Microbiota may also impact the metabolism and bioavailability of choline 28, which decreased with coffee intake. The gut microbiota has been implicated in various diseases of which coffee has also been implicated, such as diabetes and Parkinson's disease 29-31. Indeed, a proposed hypothesis for the protective association of coffee on Parkinson's disease is via compositional changes of the microbiota that mitigates inflammation 32.

We observed a significant enrichment of coffee‐metabolite associations mapping to the steroid pathway (Fig. 3) and which we hypothesize to be attributable to at least one phytosterol present in coffee: campesterol 33, 34. The cholesterol‐lowering action of campesterol, however, is unlikely contributing to the altered steroid metabolism we observed as changes in serum campesterol levels did not correlate with changes in serum cholesterol (r < |0.11|, P > 0.73), and the anticipated reduction in cholesterol was not observed. Campesterol also acts as a precursor of the anabolic steroid boldenone, an androstadiene and testosterone derivative 35. Production of glucuronate or sulphate conjugates of steroids facilitate their excretion 36, 37, and most of those that increased with coffee intake in the current study are metabolites of DHEA (not analysed in current study). DHEA sulphate levels were not altered by changes in coffee intake, suggesting moderate coffee intake unlikely contributes to DHEA synthesis but rather metabolism of its direct metabolites (Fig. 3). A smaller randomized crossover trial also reported no differences in DHEA and androstenedione following 7 days of 400 mg of caffeine/day 38. Furthermore, although weak ordinary correlations (r < |0.30|) were observed between changes in serum campesterol and steroid metabolites, partial correlation analysis suggest a direct connection between campesterol and etiocholanolone glucuronide (Figure S4B). The clinical implications of our findings linking coffee to the androgen pathway are unclear. Pregnenolone sulphate (PREGS) levels decreased with coffee intake and, given its position in the steroid pathway relative to DHEA metabolites, may have been altered by some other mechanism. PREGS is an active neurosteroid in the brain with cognitive enhancing, promnesic, antistress and antidepressant effects 39, 40, which we may expect to lessen as serum levels decrease. The implications of this inhibition in the presence of the well‐known psychostimulant effects of caffeine warrant further study.

Figure 3 Open in figure viewer PowerPoint Steroid pathway. Metabolites in red increased in response to coffee consumption whilst metabolites in green decreased. Those without p and q statistics met prespecified criteria for significance (P < 0.05 and q < 0.05). Metabolites in black bold‐face were measured but did not change in response to coffee.

Five metabolites of the endocannabinoid (eCB) pathway were detected in the current study and serum levels of four significantly decreased with coffee intake (Table 1), particularly with eight cups/day. The fifth, oleoylethanolamide (OEA), was nominally significant (P = 0.05, Q = 0.13) and also decreased with coffee intake. PEA has been detected in both green and roasted coffee beans and Moka infusions 41, but this would not explain the inverse relationship between PEA and coffee intake, and thus, our findings likely result from an endogenous response to coffee. PEA, LEA, SEA and OEA are long‐chain N‐acylethanolamines (NAEs) that structurally resemble and share biosynthesis and degradation pathways with prototypical (or ‘true’) endocannabinoids, such as N‐arachidonoylethanolamine (AEA) and 2‐arachidonoylglycerol (2‐AG), but exert biological actions without directly activating the cannabinoid (CB) receptors 42, 43. AEA and 2‐AG were below detection thresholds in the current study, and thus, a direct relationship between CBs and coffee intake cannot be drawn. However, the strong correlation amongst NAEs observed in the current study (Fig. 3) agrees with earlier work using targeted metabolite assays which also reported positive correlations between AEA and other NAEs 44, 45. Several precursors to NAEs, AEA and 2‐AG were detected in the current study (Table S2) 42 but only arachidonate, a moiety of AEA and 2‐AG, was significantly decreased with increased coffee intake. Interestingly, palmitoylcholine, oleoylcholine, arachidonoylcholine and all other metabolite members of the acyl choline pathway also decreased with coffee intake and their changes correlated with those of NAEs (Fig. 1). Whether acyl cholines are linked to endocannabinoid synthesis, which might be impaired by coffee consumption is unknown. Alternatively, much evidence suggests that the eCB system is a key regulator of the stress response 46. Certain eCBs decrease in the presence of chronic stress possibly contributing to stress adaptation 46. The increased coffee consumption over the 2‐month span of the trial may have induced sufficient stress to elicit this metabolic response. The eCB system regulates a wide range of functions in the central and peripheral systems that have also been affected by or correlated with coffee intakes such as cognition, blood pressure, immunity, addiction, sleep, appetite and energy and glucose homoeostasis 47-50. Whether coffee consumption elicits a chronic effect on the eCB system warrants further study.

The current study is the first clinical trial‐based metabolomic study of coffee intake. Of the 82 named metabolites that changed with coffee intake, 24 were also identified as associated with habitual coffee consumption in observational, population‐based, metabolomic studies (Table S5). Most of these metabolites originated from coffee. We additionally performed less conservative ‘look‐ups’ in DietMetab, a comprehensive resource containing population‐based metabolite associations with habitual intake of an array of foods and beverages 51. Several of our significant coffee‐metabolite associations were also associated with habitual coffee intake (P < 0.05) but not always in the direction consistent with that of the current study (Table S5). Conversely, population‐based metabolomic studies of habitual coffee intake have reported additional metabolites associated with habitual coffee intake (Table S5) 51-58 that were not amongst the 115 identified in the current study. Only four of these were analysed in the current study – serum levels of pyridoxate, a vitamin B6 cofactor, decreased with coffee intake (P = 0.04, Q = 0.11); phenylalanine, pseudouridine and erythronate levels did not change with coffee intake (P > 0.22). We cannot exclude the possibility that the duration of the coffee trial was insufficient to mimic the habitual coffee consumption patterns observed in population‐based studies; a similar argument used to explain discrepancies between clinical and population studies of coffee more generally.

The application of metabolomics to a clinical study of coffee intake with repeated measures, large treatment contrasts, excellent participant compliance and standardized protocols for sample handling and storage are major strengths of the current study. As a clinical trial, it addresses many of the limitations of observational studies such as bias and confounding. In addition, the composition of brewed coffee varies as a function of bean type, roast and preparation methods; factors for which detailed information is rarely collected in population‐based studies of coffee. Participants of our clinical trial were all provided the same coffee: Juhla Mokka, which is a medium roast, 100% Arabica blend of Brazilian, Columbian, Central American and African coffee and is the most popular coffee consumed in Finland. Despite these strengths, several weaknesses of the study should be acknowledged. Our one‐group study design without randomization, blinding of participants and placebo control were limitations. We cannot rule out an impact of time‐varying factors that may induce significant associations due to correlations with coffee or the possibility of delayed metabolite responses leading to misalignment of coffee dose and response. No specific guidelines were provided on coffee additives (i.e. sugar, cream) or beverages to consume in the place of coffee during the month of coffee abstinence. The very low levels of xanthine metabolites in the first month suggest participants largely refrained from consuming any caffeine‐containing beverages. Our findings also do not share any obvious overlap with potential metabolite markers of dairy or tea consumption 51, 59. Participants were advised to maintain their normal physical activities and diet during the trial, but direct measures of compliance were not recorded. Body weight, a proxy for energy balance, remained stable throughout the trial. With the possible exception of a few amino acids, none of the 115 metabolites we identified as linked to coffee were candidates or known markers of some other dietary factor or exposure that might have changed over the course of the trial (Table S4). Furthermore, potential metabolite markers of other lifestyle factors 51, 59, 60 (Table S2) were not changed with coffee intake. All participants for the current study were Finnish habitual coffee drinkers at increased risk of T2D which may limit the generalizability of our findings to other groups. We previously reported significant changes in inflammatory biomarkers and HDL cholesterol; particularly after the eight cups/day period 8. We cannot discount the possibility that the significant changes in endogenous metabolites we report in the current analysis are a cause or an effect of changes in inflammation or cholesterol resulting from coffee consumption. However, the temporal relationship does not support this hypothesis, as changes in inflammatory markers and HDL cholesterol occurred in parallel with changes in endogenous metabolites. The metabolism of several of the identified coffee metabolites is under significant genetic regulatory control. Genetic factors were not measured, but the dependent sampling design and analysis of the current study reduce the potential bias introduced by genetics.

Human research on coffee and health has largely focused on relationships between self‐reported coffee consumption and disease end‐points or biomarkers and without attention to underlying mechanisms. The current study reports novel biological pathways impacted by coffee which may mediate, in part, the effects of coffee on health. As tens of thousands of participants in prospective cohorts now have metabolomics data, these pathways can be investigated as potential mediators of the associations between coffee and health in populations. Our refined metabolite signature of coffee consumption could also facilitate the monitoring of coffee consumption in epidemiological or coffee intervention studies, or to enhance and validate current coffee intake assessment methods. Most promising is the contribution this signature may have in separating consumers of regular brewed, Arabica and filtered type coffee from consumers of other coffee types or preparations. Degree of coffee roast might also be inferred since several of the coffee‐derived metabolites vary as a function of roast type. We caution, however, that supervised discriminative techniques tend to over‐fit the data, and thus, an independent study will be necessary to validate the performance of our identified metabolite signature of coffee consumption. A broader finding from our single‐metabolite and classification analysis is that similar metabolic changes occur in response to four or eight cups of coffee per day. This interpretation should take into account the staged design of the study which reflects realistic changes in coffee‐drinking behaviour, as some degree of adaptation or tolerance to coffee is likely necessary before increasing habitual intake.