Targeted proteomic response to coffee consumption

European Journal of Nutrition https://doi.org/10.1007/s00394-019-02009-1 ORIGINAL CONTRIBUTION Targeted proteomic response to coffee consumption Alan Kuang1 · Iris Erlund2 · Christian Herder3,4,5 · Johan A. Westerhuis6,7 · Jaakko Tuomilehto8,9,10 · Marilyn C. Cornelis1 Received: 8 January 2019 / Accepted: 23 May 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Purpose Coffee is widely consumed and implicated in numerous health outcomes but the mechanisms by which coffee contributes to health is unclear. The purpose of this study was to test the effect of coffee drinking on candidate proteins involved in cardiovascular, immuno-oncological and neurological pathways. Methods We examined fasting serum samples collected from a previously reported single blinded, three-stage clinical trial. Forty-seven habitual coffee consumers refrained from drinking coffee for 1 month, consumed 4 cups of coffee/day in the second month and 8 cups/day in the third month. Samples collected after each coffee stage were analyzed using three multiplex proximity extension assays that, after quality control, measured a total of 247 proteins implicated in cardiovascular, immuno-oncological and neurological pathways and of which 59 were previously linked to coffee exposure. Repeated measures ANOVA was used to test the relationship between coffee treatment and each protein. Results Two neurology-related proteins including carboxypeptidase M (CPM) and neutral ceramidase (N-CDase or ASAH2), significantly increased after coffee intake (P < 0.05 and Q < 0.05). An additional 46 proteins were nominally associated with coffee intake (P < 0.05 and Q > 0.05); 9, 8 and 29 of these proteins related to cardiovascular, immuno-oncological and neurological pathways, respectively, and the levels of 41 increased with coffee intake. Conclusions CPM and N-CDase levels increased in response to coffee intake. These proteins have not previously been linked to coffee and are thus novel markers of coffee response worthy of further study. Clinical trial registry http://www.isrct​n.com/ISRCT​N1254​7806. Keywords Coffee · Caffeine · Proteomics · Biomarkers · Trial Electronic supplementary material The online version of this article (https​://doi.org/10.1007/s0039​4-019-02009​-1) contains supplementary material, which is available to authorized users. * Marilyn C. Cornelis [email protected] 1 Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 North Lake Shore Drive, Suite 1400, Chicago, IL 60611, USA 2 Genomics and Biomarkers Unit, National Institute for Health and Welfare, P.O. Box 30, 00271 Helsinki, Finland 3 Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany 4 German Center for Diabetes Research (DZD), Partner Düsseldorf, Düsseldorf, Germany 5 Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany 6 Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands 7 Centre for Human Metabolomics, Faculty of Natural Sciences, North-West University (Potchefstroom Campus), Private Bag X6001, Potchefstroom, South Africa 8 Disease Risk Unit, National Institute for Health and Welfare, 00271 Helsinki, Finland 9 Department of Public Health, University of Helsinki, 00014 Helsinki, Finland 10 Saudi Diabetes Research Group, King Abdulaziz University, Jidda 21589, Saudi Arabia 13 Vol.:(0123456789) European Journal of Nutrition Introduction Coffee is one of the most widely consumed beverages in the world and has been implicated in numerous health outcomes [1, 2]. Epidemiological data support more benefit than harm when coffee is consumed in moderation, particularly for metabolic, neurological and liver-related outcomes [2]. Heavy coffee intake is likely harmful on pregnancy outcomes and its impact on bone health is unclear [2]. The causal and precise molecular mechanisms by which coffee affects the human body thus contributing to these health effects remain unclear. While coffee is the major source of caffeine for many populations [1], it also contains significant amounts of chlorogenic acid (CGA), trigonelline, cafestol, kahweol and many other known and possibly unknown compounds, some of which might impact pathways related to disease development or prevention [3]. High-throughput omic profiling techniques enable thorough studies of an individual’s response to coffee intake and provide potentially new mechanistic insight to the role coffee plays in health [4]. In metabolomic and lipidomic analysis of serum samples collected during a coffee trial, we previously identified over 100 metabolites and lipids associated with coffee intake; several mapping to xanthine, benzoate, steroid, endocannabinoid, lysophosphatidylcholine and fatty acid metabolism [5–7]. Proteins can also provide insights into biological processes at the functional level. The quantification of a small subset of proteins has been routinely used for the diagnosis of diseases and treatment monitoring but recently has extended to broader panels of proteins to facilitate discovery of new mechanisms underlying disease as well as risk factors [8]. Our objective was to test the effect of coffee drinking on candidate proteins involved in cardiovascular, immuno-oncological and neurological pathways. Materials and methods Coffee trial Serum samples analyzed in the current study were obtained from participants completing an investigatorblinded, three-stage controlled trial in 2009–2010 that lasted for 3 months (Online Resource 1, Supplementary Note, registration: http://www.isrct​n.com/ISRCT​N1254​ 7806) [5]. Briefly, habitual coffee consumers < 65 years of age, residing in Finland, free of type 2 diabetes (T2D), but with an elevated risk of T2D were eligible for participation. The participants received packages of coffee 13 (Juhla Mokka brand: medium roast, 100% Arabica blend of Brazilian, Columbian, Central American and African 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 4 cups 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 Online Resource 1 (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, Helsinki, Finland (ref 302/E5/05). Written informed consent was obtained from all participants. Proteomic profiling and quality control Serum samples collected after each trial stage were analyzed with the highly specific Proximity Extension Assay (PEA) technique using three Olink Multiplex 96 × 96 panels, (i) Cardiovascular III, (ii) Immuno-Oncology and (iii) Neurology. Each panel assay simultaneously measures concentrations of 92 candidate proteins. The panels were chosen to provide a balance between hypothesis testing and discovery. A total of 68 proteins across the panels have previously been linked to some aspect of coffee exposure (i.e., metabolism, response) based on animal experiments or human observational or clinical studies (Online Resource 1, Table S2). For each assay, oligonucleotide-labeled antibody probe pairs are allowed to bind to their respective target present in the sample [9, 10]. Only correctly matched antibody pairs give rise to a signal, yielding exceptionally high specificity. Since the PEA is a PCR-based methodology, it is also very sensitive. The amplicons are quantified on a Fluidigm BioMark™ HD real-time PCR platform. All assay runs include internal controls for monitoring incubation, extension and detection. An inter-plate control (external/technical control) is set in triplicate on each plate and used for normalization. Sample quality is assessed by evaluation of the deviation from the median value for the incubation and detection controls. A sample will fail quality control (QC) if controls deviate more than ± 0.3 from the median value of all samples. The quality of the entire run is evaluated by calculating the standard deviations of incubation and detection controls and should be below the pre-determined quality threshold 0.2. Six samples from six different individuals failed QC and were excluded from the immuno-oncology panel while two samples from two different individuals failed QC and were excluded from the neurology panel. For one individual, all three samples (one per treatment) for these two panels failed QC. We also randomly selected 39 of 47 ‘baseline’ European Journal of Nutrition samples (i.e., collected before a participant began his/her 1 month of coffee abstinence) to assay alongside the treatment samples and made efficient use of empty plate wells. These were not included for statistical analysis but provided a crude measure of participant basal levels of proteins to allow additional interpretation of the results. All panel assay characteristics including the list of protein markers, detection limits and measurements of assay performance and validations are available from the manufacturer (http:// www.olink.​ com/produc​ ts/docume​ nt-downlo​ ad-center​ /). The platform provides normalized protein expression (NPX) data where a high protein value corresponds to a high protein concentration, but not an absolute quantification. Each one NPX change corresponds to a doubling in protein amount. The Multiplex panels applied in the current study have been shown to have high reproducibility and repeatability with mean intra-assay and inter-assay coefficients of variation of < 8.4% and < 11.6%, respectively. Proteins that contained more than 20% missing values across the first (0 cups/day) and third (8 cups/day) trial stages were excluded from statistical analysis (23 proteins, Online Resource 1, Table S3). This approach to missingness minimizes the number of proteins with missing values yet retains potentially legitimate missing data as a consequence of extreme differences in coffee exposure [11]. CRTAM, GZMA, IL12, MCP-1, TNFRSF12A and TNFRSF21 were included on more than one of the three assay panels and correlations between measures were 0.93, 0.90, 0.95, 0.89, 0.90 and 0.78, respectively. Repeated proteins were thus excluded leaving a total of 247 unique proteins for analysis (Online Resource 1, Table S4). Missing values for these remaining proteins were imputed with half the limit of detection value (LOD/2). Eighty-three KEGG pathways were captured by combinations of five or more analyzed proteins (Online Resource 1, Table S5). Statistical analysis All statistical analyses were performed using R or SAS version 9.4 (SAS Institute Inc, Cary, NC, USA). Repeated measures ANOVA was used to test the relationship between coffee treatment and each protein. P values were further adjusted for multiple comparisons by the Benjamini–Hochberg procedure and the false discovery rate (FDR)-adjusted P values, expressed as Q values, are reported [12]. All nominal (P < 0.05) associations are presented but only those with a Q value < 0.05 are defined as statistically significant. Protein fold-change with each treatment comparison was calculated as follows: 2^|(mean NPX of treatment 2) − (mean NPX of treatment 1)|; with the fold-change having the same direction as the treatment difference. We also computed ordinary Pearson correlations to explore the latent relationships of changes in identified coffee proteins across treatments. This analysis were additionally supplemented with data for 82 metabolites, 3 lipids and 6 clinical biomarkers that changed in response to coffee in this coffee trial as we previously reported (Online Resource 1, Table S6) [5–7]. Formal cross-platform integration analysis will be the focus of another report. Correlation networks were constructed using Cytoscape [13]. Results Serum levels of 48 proteins were at least nominally associated with coffee intake (P < 0.05, Table 1, Fig. 1, Online Resource 1, Fig. S1). Serum levels of 43 of these proteins increased with coffee consumption. These increases were most evident after the 8 cups/day stage of the trial. Only VEGFA presented with dose–response increases after the 4 and 8 cups/day trial stages. Serum levels of FASLG and IL12 decreased after 4 and 8 cups of coffee per day. Response patterns for DLK-1, CTSC, and TNFRSF21 were difficult to interpret. When applying an FDR correction, two of the 48 proteins remained significantly associated with coffee intake including carboxypeptidase M (CPM) and neutral ceramidase (N-CDase) (Fig. 1). Baseline levels of these two proteins were similar to those observed after coffee drinking stages of the trial. Note the average habitual coffee intake for participants before the trial was 4 cups/day (Online Resource 1, Table S1). The set of 48 proteins were enriched for proteins mapping to twenty Kegg pathways (FDR < 0.05, Online Resource 1, Table S7). The top pathway: ‘cytokine–cytokine receptor interaction’ included 12 proteins that were distributed across the three panels (FDR = 2.4 × 10−10, annotated in Table 1). Results of correlation analysis of changes among previously identified clinical [5], metabolite [6] and lipid [7] markers of coffee response and the nominal to significant proteins identified here (Table 1) are presented in Online Resource 1, Fig. S2. Generally, proteins on the same panel clustered together, but notable cross-platform correlations were also observed and this involved CPA2, CPA1, ADA, VEGFA and MMP7. As anticipated by the three-stage trial design, stronger correlations among proteins were observed with 1-month changes (i.e., from 0 to 4 cups/day and 4 to 8 cups/day) than with 2-month changes (i.e., from 0 to 8 cups/day). Changes in N-CDase and CPM were moderately correlated from 0 to 4 cups/day (r = 0.70) and shared most other protein correlations (also presented in Online Resource 1, Fig. S3; a sub-network of Fig. S2). Increasing levels of CPM after 4 cups/day, were strongly (r > 0.8) correlated with increasing levels of Nr-CAM, RGMB, PRTG and PDGFRA. A similar but weaker CPM network persisted when switching from 4 to 8 cups/day and when differences in proteins levels at 0 cups/day and 8 cups/day were considered. Increasing levels of N-CDase after 4 cups/day strongly 13 European Journal of Nutrition Table 1  Significant proteins markers of coffee consumption Panel a CARD Protein name CCL15 0.04 0.33 CPA1 0.002 0.15 1.04 2.27 1.13 carboxypeptidase B1 CPB1 0.007 0.19 1.04 2.24 1.12 protein delta homolog 1 DLK-1 0.05 0.33 -1.07 -2.01 1.07 Gal-3 0.03 0.33 -1.01 2.09 1.07 Insulin-like growth factorbinding protein 1 monocyte chemotactic protein 1 IGFBP-1 0.01 0.24 1.02 2.57 1.34 MCP-1c 0.02 0.24 -1.02 2.09 1.09 tissue-type plasminogen activator t-PA 0.005 0.19 1.01 2.23 1.14 tartrate-resistant acid phosphatase type 5 adenosine deaminase TR-AP 0.01 0.24 -1.03 2.18 1.16 ADA 0.04 0.41 -1.02 2.08 1.08 C-X-C motif chemokine ligand 17 CD40 molecule CCL17 c 0.05 0.41 1.05 2.15 1.06 CD40 c 0.003 0.22 1.01 2.15 1.09 CD70 molecule CD70 c 0.04 0.41 1.05 2.12 1.03 FASLG c 0.02 0.39 -1.05 -2.11 -1.02 MMP7 0.008 0.26 1.02 2.20 1.12 TRAIL c 0.05 0.41 1.02 2.11 1.06 VEGFAc 0.02 0.40 1.09 2.12 -1 ADAM_23 0.01 0.09 -1.07 2.09 1.13 BMP-4 0.01 0.09 1.01 2.13 1.09 CLEC10A 0.02 0.09 1.02 2.21 1.12 matrix metallopeptidase 7 TNF-related apoptosis-inducing ligand vascular endothelial growth factor A disintegrin and metalloproteinase domain-containing protein 23 bone morphogenetic protein 4 C-type lectin domain family 10 member A carboxypeptidase A2 8 cups/ 0 cup 2.12 8 cups/ 4 cups 1.08 CPA2 0.005 0.09 1.09 2.34 1.13 carboxypeptidase M CPM 0.0002 0.02 1.01 2.11 1.06 cathepsin C CTSC 0.008 0.09 -1.1 2.02 1.11 cathepsin S CTSS 0.03 0.12 1.01 2.06 1.04 EDA2R c 0.02 0.10 1.01 2.1 1.06 EFNA4 0.03 0.11 1.01 2.14 1.08 EZR 0.03 0.11 1.02 2.1 1.05 IL12A/Bb,c 0.02 0.09 -1.1 -2.1 1.03 ectodysplasin A2 receptor ephrin A4 ezrin interleukin 12 junctional adhesion molecule 2 JAM-B 0.05 0.14 -1.01 2.08 1.07 layilin LAYN 0.005 0.09 1.01 2.15 1.09 MDGA1 0.01 0.09 -1.02 2.12 1.11 N-CDase 0.0007 0.03 1.02 2.17 1.1 N2DL-2 0.008 0.09 1.03 2.15 1.07 NBL1 0.01 0.09 1.01 2.05 1.03 Nr-CAM 0.007 0.09 1 2.06 1.04 NRP2 0.02 0.09 1.04 2.18 1.08 NTRK2 0.02 0.09 -1.02 2.06 1.06 MAM domain-containing glycosylphosphatidylinositol anchor protein 1 neutral ceramidase (Nacylsphingosine amidohydrolase 2) ULBP2; UL16 binding protein 2 neuroblastoma suppressor of tumorigenicity 1 neuronal cell adhesion molecule neuropilin-2 neurotrophic receptor tyrosine kinase 2 platelet derived growth factor receptor alpha plexin B1 PDGFRAc 0.02 0.09 1.01 2.1 1.06 PLXNB1 0.05 0.14 1.03 2.21 1.11 protogenin PRTG 0.03 0.11 -1.01 2.08 1.06 repulsive guidance molecule A RGMA 0.02 0.09 -1.03 2.07 1.08 repulsive guidance molecule B RGMB 0.01 0.09 1 2.11 1.08 SCARA5 0.03 0.11 -1.03 2.08 1.09 SCARB2 0.04 0.14 1.01 2.1 1.07 SKR3 0.03 0.11 -1.01 2.08 1.07 SMOC2 0.003 0.09 1.02 2.19 1.1 TNFRSF21c 0.04 0.14 -1.05 -2.01 1.04 UNC5C 0.03 0.11 -1.03 2.08 1.09 scavenger receptor class A member 5 scavenger receptor class B member 2 serine/threonine-protein kinase receptor R3 SPARC-related modular calciumbinding protein 2 TNF receptor superfamily member 21 netrin receptor UNC5C 13 q value C-X-C motif chemokine ligand 15 carboxypeptidase A1 fas ligand NEUR Fold of Changec Group Effect p value 4 cups/ 0 cup 1.01 Galectin-3 IMMU Protein symbol European Journal of Nutrition Table 1  (continued) Shown are results from RMA that meet nominal significance (P < 0.05, column 3). Bold-faced lipid species meet significance threshold of P < 0.05 (column 3) and Q < 0.05 (column 4) a ANOVA contrasts: lipid levels that increase in response to coffee are shaded red (P < 0.05) or pink (0.05 < P < 0.10) and lipid levels that decrease are colored green (P < 0.05) or light green (0.05 < P < 0.10) b c Heterodimer (PEA was unable to distinguish between IL12A (p35) and IL12B (p40) isoforms) Protein members of the ‘cytokine–cytokine receptor interaction’ Kegg pathway Fig. 1  Box plots of a CPM and b N-CDase serum levels at baseline (pre-trial, N = 37) and after each coffee treatment (N = 47) correlated with changes in NTRK2 (r = 0.80) and N2DL-2 (r = 0.79) levels. Changes of N-CDase when switching from 4 to 8 cups/day or when differences in protein serum levels after 0 cups/day and 8 cups/day were determined, presented with moderate correlations (0.5 < r < 0.72) with changes in other proteins. Serum changes in two caffeine metabolites (1,3-dimethylurate and 5-acetylamino-6-amino-3-methyluracil), palmitoylethanolamide, creatine, kynurenine, N-acetylputrescine, palmitoyl dihydrosphingomyelin, 3 hydroxypyridine sulfate, LPC (22:1), HDL and IL18, which associated with coffee response in our previous reports, were each correlated (0.50 < r < 0.60) with changes in one or more proteins but these correlations were inconsistent over the course of the coffee trial (Online Resource 1, Fig. S2). Discussion In this clinical trial-based proteomic assessment of coffee intake, we examined proteins mapping to three broad biological pathways that enabled us to follow-up candidate proteins as well as related but non-candidate proteins. Serum levels of two proteins, CPM and N-CDase, significantly increased after consuming 4 and 8 cups of coffee per day for a month. To our knowledge, CPM and N-CDase have never previously been linked to coffee response. Several other proteins showed nominally significant changes and a subset of these are discussed below since they are plausible candidates in light of previous literature. Carboxypeptidase M (CPM), is a membrane-bound protein that cleaves C-terminal Arg or Lys residues from peptides and proteins; potentially altering their binding affinity to their physiological targets. It is expressed in a variety of tissues particularly the lung, kidney and adipose [14]. Over 60 endogenous human peptides or proteins have been identified as potential CPM substrates and among these are growth factors, chemokines, bone morphogenetic proteins, plasminogen-binding proteins, fibrin cleavage products, complement proteins, kinin peptides, opioid peptides and bradykinin [15–21]. Several potential CPM substrates were measured in the current study, some of which were a priori candidates, but were not detected (i.e., BDNF, CXCL12) or did not respond to coffee intake (i.e., EGF). Changes in CPM levels in response to coffee correlated with changes in NrCAM, RGMB, PRTG and PDGFRA but biological mechanisms underlying these correlations are unknown. Because of the varied role of CPM in controlling peptide hormone activity, CPM may participate in a variety of processes. For example, by regulating the kinin receptor and opioid receptor signaling, CPM may play roles in inflammation and nociception [17, 20, 22, 23]. While coffee or caffeine have been implicated in inflammation, pain and other physiological 13 European Journal of Nutrition pathways involving CPM, no direct link between this protein and coffee has been observed until now [24–26]. Neutral ceramidase (N-CDase) is highly expressed in the gastrointestinal tract, liver and kidney and is a key enzyme in the catabolism of dietary sphingolipids and regulation of the cellular levels of ceramide and sphingosine [14, 27–33]. Dietary sphingolipids are commonly found in the form of sphingomyelin (SM) which is present in significant quantities in animal-derived food products, such as egg and dairy products [34]. Dietary SM is hydrolyzed in the small intestine by alkaline sphingomyelinase yielding ceramide and phosphocholine. Ceramide is subsequently hydrolyzed by N-CDase to yield sphingosine and fatty acid, which are readily absorbed by enterocytes [35]. Our previous lipidomic and metabolomic analyses of these trial samples included a comprehensive array of sphingolipids and bioactive sphingolipid metabolites. Palmitoyl dihydrosphingomyelin levels significantly increased in response to coffee [6]. Dihydroceramide (24:0) and lactosylceramide (26:1) also increased but were only nominally significant [7]. SM is also produced endogenously. Although serum SM species did not respond to coffee intake in this trial [7], population-based studies [36, 37] report positive correlations between particular plasma SM species and habitual coffee intake and so the effect of a longer duration of coffee consumption on SM levels is an open question. Bioactive sphingolipids have been implicated in neurodegenerative processes, metabolic disorders, various cancers, immune function, cardiovascular disorders and skin integrity [38]. Taken together, metabolomic, lipidomic and proteomic analysis of coffee intake appear to converge on the sphingolipid pathway and is thus a novel pathway by which coffee might be exerting its health effects and is worthy of further study. Ten candidate proteins including BDNF, FGF2, GDNF, IFN-γ, IL10, IL2, IL4, IL5, NOS3 and TNF were excluded due to missingness. This may be due to the precision of the technology used or biologically ‘real’ low levels of these proteins in fasting human serum samples. Other a priori candidates were nominally associated with response to coffee intake. Serum levels of monocyte chemoattractant protein-1 (MCP1) increased after consuming 8 cups/d and contrasts with experimental studies that have shown coffee, cafestol, CGA and caffeine, suppress inflammation-induced expression or circulating levels of MCP1 [39–45]. An 8 week randomized, cross-over, controlled study reported that daily consumption of a green/roasted coffee blend (6 g, containing 445 mg caffeoylquinic acids and 121 mg caffeine) did not impact MCP1 levels [46]. The psychostimulant effects of caffeine result from blocking the adenosine receptors which, in turn, inhibits the actions of adenosine. Serum levels of adenosine deaminase (ADA), which breaks down adenosine to inosine, increased with coffee drinking in the current study. Animal, in vitro and spectrophotometry studies report 13 increased and decreased ADA activity in the presence of coffee, caffeine, CGA and caffeic acid [47–57]. The impact coffee or caffeine might have on MCP1 and ADA activity remains inconclusive. In the current study, coffee consumption leads to a dose–response increase in levels of VEGFA, a growth factor which generally promotes angiogenesis and contributes to inflammation [58–60]. In non-neuronal cells, coffee and CGA suppress substrate-induced expression of VEGFA [61–66]. However, in neuronal cells wherein evidence suggests that VEGFA may contribute to neurotrophic and neuroprotective effects [67–72], coffee strongly induced VEGFA expression. Implications and future follow-up of a coffee-VEGFA link may need to account for possible cell or tissue-specific effects. Serum levels of the pro-inflammatory cytokine IL12 decreased in response to coffee drinking and are consistent with experimental data showing CGA and CGA-containing tea inhibit inflammation-induced IL12 levels [62, 73]. Ligand activation of adenosine 2A receptors inhibits inflammation-induced IL12 secretion [74] suggesting caffeine, via antagonism of this receptor, unlikely contribute to the IL12 findings of the current study. Nr-CAM is a cell adhesion protein that is required for normal responses to cell–cell contacts in brain and in the peripheral nervous system [75]. Nr-CAM levels increased after 8 cups/day and is interesting in light of human genetic and mouse model analysis linking genetic variation in NrCAM and high NrCAM function and expression with coffee drinking behavior and several psychiatric disorders [75, 76]. Tartrate-resistant acid phosphatase type 5 (TR-AP) is an osteoclast differentiation marker [77]. The role of caffeine and regular coffee on bone health has been controversial [78]. High caffeine intake might decrease bone mineral density, induce urinary calcium loss and stimulate intracellular ­Ca2+ release in osteoclast [78, 79]. TR-AP levels increased after 8 cups/day and together with a recent report that a physiologically relevant concentration of caffeine induced the expression of osteoclast marker genes including TR-AP [79], suggests caffeine may also directly enhances osteoclast differentiation and maturation [79]. Five studies have taken a broad approach to evaluate the impact of coffee consumption on changes in human protein levels. Three of these were population-based cross-sectional analysis of self-reported habitual coffee drinking or caffeine intake [80, 81]. The first study [82] examined the association between caffeine intake and the plasma levels of 54 proteins in young Canadian adults and reported that gelsolin (GSN) levels were significantly lower among those consuming > 200 mg/day compared to those consuming < 100 mg/day, but only among the 593 carriers of a CYP1A2 variant corresponding to slow caffeine metabolism. Loftfield et al. [80] measured serum levels of 77 proteins in a European Journal of Nutrition US population (N = 1728). Heavy coffee consumption, defined as ≥ 2.5 cups/day (1 cup = 355 mL, any coffee type), was associated with below sample median levels of IFNγ, CX3CL1, CCL4, TNF-R2 and FGF2. In three Swedish populations (N = 3869), 80 plasma proteins were measured and associations between coffee intake and lower leptin (LEP) and CHI3L1 levels was reported [81]. In the original analysis of the current coffee trial, a non-significant decrease in LEP levels in response to coffee was reported [5]. The current study did not measure LEP or GSN while IFNγ and FGF2 were excluded due to missingness. TNFR2, CCL4, CX3CL1 and CHI3L1 levels did not significantly change in response to coffee drinking. None of these population-based studies measured CPM or N-CDase. However, nominally higher levels of TRAIL with coffee consumption was reported in the Swedish study [81], which is consistent with the current study. TRAIL is a natural potent anticancer agent that preferentially induces apoptosis in cancer cells [83]. In vitro, trigonelline and kahweol sensitize TRAIL-resistant cancer cells [84, 85]. Although TRAIL in the context of cancer has been the focus of much research, recent studies demonstrate pleiotropic functions in non-cancer cells with implications in the pathogenesis of diabetes and obesity [86–89]. Taken together, with TRAIL a possible exception, there is limited consistency in results among these three population-based studies and the current trial-based study. The coffee consumption distribution in North American populations and analytical models employed may have contributed to the discrepant findings. Importantly, 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 or that other factors correlated with habitual coffee consumption contributed to the results reported in population-based studies. To our knowledge, there are no other clinical trials of coffee intake to which we can directly compare or validate our findings. Clinical trials by Wu et al. [90] and Peerapen et al. [91] examined the acute effects of instant regular coffee on the urinary proteome [90, 91] in 10 and 4 healthy individuals, respectively. The former reported that six proteins increased and five decreased in response to coffee; many of which were associated with hepatic fibrosis, glycolysis and inhibition of matrix metalloproteases [90]. Peerapen et al. [91] reported that Kininogen 1 isoform 3 precursor and fibrinogen α-chain decreased after coffee (and also excess water) compared to control. None of these 13 proteins were measured in serum of the current study. Urinary CPM did not significantly respond to coffee and N-CDase was not detected in the urine of the study by Wu et al. (personal communication with authors) [90]. Between-study differences make comparisons of results difficult. Thus far, few human clinical studies lasting longer than a week have examined the proteomic response to diet. Most were randomized cross-over studies but with sample sizes ranging from 7 to 42 [92–102]. Interventions included zinc, selenized-yeast protein, industrialized trans fat, vitamin D, marine/vegetable fed trout, cruciferous vegetable, folate, flaxseed, and fish oil. Each study employed comprehensive non-targeted proteomic analysis with individual or pooledsamples detecting hundreds of protein spots. Between 0 and 282 proteins changed in response to diet, however, no study corrected for multiple testing or applied a FDR threshold greater than 30%. The currently limited support for many or detectable changes in the human proteome in response dietary interventions may have real biological or technical underpinnings [8]. For example, interventions may require longer duration to yield broad changes in the human proteome. The use of blood proteomics might be limited both by the dynamic nature and day-to-day variation in the proteome and by the variability between subjects and methodological variability. We and others have attempted to reduce the variability to some extent, by focusing on the changes in individuals over time (where each participant acts as his or her own control). Nevertheless, dietary interventions might also require larger sample sizes to compensate for the biological and experimental variability among samples. Availability of comprehensive proteomic assays is also a limiting factor. The current study examined over 200 proteins related to the broad pathways of cardiovascular disease, immunologyoncology and neurology; but was nevertheless non-comprehensive. The PEA technique employed is targeted and more specific than other traditional proteomic platforms. Nevertheless, protein confirmation by more precise techniques such as ELISA may be warranted. Finally, several proteins measured in the current study might still be targets for coffee but evidence for this might not be reflected in serum. The clinical study of coffee intake with repeated measures, large contrasts in coffee intake, excellent participant compliance and standardized protocols for sample handling and storage are major strengths of the current study. In addition, participants of our clinical trial were all provided the same coffee: a medium roast, 100% Arabica blend of Brazilian, Columbian, Central American and African coffee which is a popular type of coffee in Finland. Despite these strengths, several weaknesses of the study should be acknowledged. Our one-group study design without randomization, lack of blinding of participants and placebo control were limitations. If the impact of coffee on certain proteins persists for a long period after coffee abstinence, the 1 month of coffee absence may not have been sufficient to detect a difference from the following coffee-drinking stages. 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. As previously reported, a very low level of xanthine metabolites in the first month suggest participants 13 European Journal of Nutrition largely refrained from consuming any caffeine-containing beverages [5, 6] and we observed no obvious overlap with potential metabolite markers of dairy or tea consumption or lifestyle factors [6]. Body weight, a proxy for energy balance, remained stable throughout the trial. 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. In conclusion, serum levels of CMP and N-CDase increased with coffee consumption over a duration of 2 months. Whether coffee consumption influences both expression of the genes regulating translation of CPM and N-CDase and subsequent enzyme activity remains to be confirmed. Nominal findings for previous candidates including VEGFA, IL12, NrCAM, TRAIL and TRAP are also promising. The new findings are generally distinct from those uncovered by our previous metabolomic and lipidomic results for the same trial samples and thus offer novel insight to the mechanisms by which coffee may be exerting its health effects in humans. Acknowledgements We thank Paulig Oy, Helsinki, Finland for the donation of coffee for this trial. Matlab computations in this paper were run on the Quest cluster supported in part through the computational resources and staff contributions provided for the Quest highperformance computing facility at Northwestern University, which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology. Author contributions AK analyzed the data. IE, CH and JT lead the coffee trial and provided samples for the current study. MCC and JAW supervised the statistical analysis. MCC acquired the proteomics data and was responsible for the current study concept, study design and final content. MCC wrote the paper. All authors critically revised for important intellectual content and approved the final manuscript. Funding This work was supported by the American Diabetes Association (ADA, 7-13-JF-15 to MCC). The original trial was supported by a grant from the Institute of Scientific Information on Coffee, which is a consortium of major European Coffee Companies (JT). The German Diabetes Center was supported by the Ministry of Culture and Science of the State of North Rhine-Westphalia (MKW NRW), the German Federal Ministry of Health (BMG) and in part by a grant from the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD). Compliance with ethical standards Conflict of interest The authors have no conflicts of interest to declare. References 1. Reyes CM, Cornelis MC (2018) Caffeine in the diet: country-level consumption and guidelines. Nutrients. https​://doi. org/10.3390/nu101​11772​ 2. 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