The Metabolomic Effects of Tripeptide Gut Hormone Infusion Compared to Roux-en-Y Gastric Bypass and Caloric Restriction

This cohort of patients took part in a mechanistic study at the National Institute for Health Research (NIHR) Imperial Clinical Research Unit Facility at Hammersmith Hospital (London, UK) from July 2016 to October 2018. See Figure 1A for patient flow through the study. Inclusion and exclusion criteria and demographic characteristics as well as data on weight, fructosamine, fasting glucose, and insulin levels at baseline and 4 weeks post each intervention have been published previously (6). Briefly, the study was a single-blind, randomized, placebo-controlled study comparing two 28-day infusion groups: tripeptide GOP or 0.9% saline vehicle (SAL) in patients with obesity and prediabetes or T2D. The weight-adjusted doses of each GOP component were 4 pmol/kg/min (GLP-1), 4 pmol/kg/min (OXM), and 0.4 pmol/kg/min (PYY), delivered for 12 h per day, starting an hour prior to breakfast and finishing after the last meal of the day. All infusion participants also received dietetic advice on healthy eating and weight loss from a qualified dietician.

Two similar nonblinded groups of patients, either undergoing RYGB surgery or following a VLCD meal replacement diet (approximately 800 kcal/day for 4 weeks), were also recruited as comparators. The participants undergoing RYGB attended the research unit for a baseline visit prior to surgery and were reviewed 2, 4, and 12 weeks postsurgery. VLCD participants attended the research unit for a baseline visit, before starting a complete meal replacement VLCD of 800 kcal/day for 4 weeks (Cambridge Weight Plan). They were reviewed by a dietician prediet and then on a weekly basis until completion.

Blood samples for metabolomic analysis were collected in the fasting state into lithium heparin tubes with no protease inhibitors, prior to and 4 weeks into each intervention. For the GOP and SAL groups, the second sample was collected on the last day of the study, at least 2 h after the initiation of the infusion. Plasma was obtained after centrifuging the blood samples at 2500 × g for 10 min at 4°C. Urinary samples were collected at a fasting state on the same study days as the plasma samples. All the samples were kept at −80°C before metabolomic analysis.

A total of 136 plasma and 136 urine samples from 68 subjects [see Supplementary Figure 1A (30)] were analyzed using ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) and ¹H NMR spectroscopy. Full analytical details, following previously described sample preparation, analytical, and quality control procedures (31-33), are provided in the Supplementary Material (30).

NMR and UPLC-MS assays were applied to maximize coverage of a broad range of metabolite classes including lipophilic, hydrophilic, small, and macromolecular analytes [see Supplementary Figure 1B (30)]. Specifically, UPLC-MS assays for plasma analysis were tailored for the separation of lipophilic analytes (eg, complex and neutral lipids) by reversed-phase chromatography (RPC) assay and the separation of hydrophilic analytes (eg, polar and charged metabolites) by hydrophilic interaction liquid chromatography (HILIC assay). UPLC-MS assays for urine analysis were tailored for broad small molecule coverage using RPC (SmMol RPC) and the separation of hydrophilic analytes by HILIC. When coupled to positive- and or negative-mode ionization the following data sets were produced: plasma lipid positive (lipid RPC+), lipid negative (lipid RPC−) and HILIC positive (HILIC+); urine small molecule positive (SmMol RPC+), and negative (SmMol RPC−) and HILIC positive (HILIC+). For both plasma and urine samples, a standard 1-dimensional (1D) ¹H NMR profile experiment was acquired using the 1D-nuclear magnetic resonance nuclear Overhauser effect spectroscopy presat pulse sequence with water presaturation. For plasma, an additional spin-echo experiment using the 1D Carr-Purcell-Meiboom-Gill pre-sat pulse sequence was carried out to better visualize signals of the small metabolites.

Data from each assay was processed to include both global profiling and targeted extraction data sets. These 2 approaches are highly complementary. Global profiling provides a comprehensive analysis of all measurable metabolites in a sample, but results in data sets with multiple variables per analyte, the identities of which are typically unknown. In contrast, by targeted extraction of a predefined set of metabolites, preannotated data sets are immediately more interpretable but are limited in coverage to those metabolites in the predefined set. For UPLC-MS, targeted extraction of metabolites from each assay was performed using PeakPantheR (34) (https://github.com/phenomecentre/peakPantheR↗) and for NMR targeted extraction was performed using the in vitro diagnostics platform (IVDr) from Bruker Biospin (www.bruker.com↗) to generate plasma SmMol IVDr, plasma lipoprotein IVDr and urine SmMol IVDr data sets.

The study design and participant disposition are summarized in Figure 1A. Baseline demographic and clinical characteristics are shown in Supplementary Table 1 (30). We previously reported that tripeptide GOP infusion led to better glucose tolerance in response to a mixed meal test but less weight loss than VLCD and RYGB (6). Individual participant changes from baseline in weight, fasting glucose, insulin, and lipid parameters from the current subanalysis, which includes 1 additional RYGB participant that had not completed follow-up in the original study, are presented in Figure 1B.

We first performed untargeted metabolomic analysis on pre- and postintervention plasma and urine samples to identify patterns of change in an unbiased manner, without focusing on specific metabolites. It is immediately apparent from visual inspection of the metabolomic changes between pre- and postintervention timepoints (Fig. 2A) that the impact of GOP was less marked than of RYGB and VLCD and, in many cases, similar to that seen with SAL. This was further investigated using LME modeling to formally determine the proportion of changes that were significantly different between interventions (Fig. 2B). This indicated that, when compared to SAL, up to one third of the observed plasma LC-MS features were distinctly altered by RYGB or VLCD, whereas no features were significantly altered by GOP after FDR correction. However, calculating the similarity between the effects of each intervention by correlating average fold changes for each variable showed that VLCD and RYGB effects were highly congruent (Fig. 2C). In fact, even features that were statistically significantly altered only by VLCD or by RYGB still showed a high degree of correlation (ie, the directions of change, if not the magnitudes, were generally consistent) [Supplementary Figure 2A (30)]. On the other hand, urine LC-MS features identified as “uniquely” affected by VLCD or RYGB were not consistently as well correlated [Supplementary Figure 2B (30)].

As an additional analysis aiming to identify patterns in the metabolomic effects of each treatment, PCA was also performed on all recorded log₂ fold changes. No principal components in any biofluid were significantly different between GOP and SAL, whereas VLCD and RYGB showed a number of differences when compared to SAL, GOP, and each other [Supplementary Figure 2C and 2D (30)].

This main message from this global analysis is that GOP treatment has a far more minor effect on the metabolome than VLCD or RYGB, despite being more effective for lowering blood glucose. A second message is that the metabolomic impacts of VLCD and RYGB share many common elements.

Following this analysis of the untargeted profiling data, we next repeated the LME modeling to determine the proportion of changes that were significantly different between interventions in specific groups of metabolites from the targeted extraction data sets. Overall changes across major plasma metabolite classes relative to SAL are summarized in Figure 3A. See Supplementary Figure 1B (30) for information about the plasma and urine data sets and Supplementary Figures 3 to 7 (30) for full characterization of individual metabolites, statistical analysis of differences between groups, and associations with clinical responses. As for the untargeted data sets, no metabolites were affected by GOP compared to SAL with FDR correction (Fig. 3A). In contrast, VLCD and RYGB led to significant changes in a substantial proportion of lipid species compared to SAL (Fig. 3A). From the lipids quantified, VLCD led to a larger number of changes in acylglycerols, fatty acids, phospholipids, and lysophospholipids whereas RYGB affected ceramides and sphingomyelins to a greater extent. However, despite some variation in the statistical significance obtained for SAL-VLCD and SAL-RYGB comparisons, the lipidomic effects of these 2 interventions were very well correlated (Fig. 3B), suggesting overlap between the mechanisms driving these changes.

The pattern of lipidomic changes seen with RYGB and VLCD, characterized by decreases in acylglycerols, increases in fatty acids and acylcarnitines, but decreases in phospholipids and their lysophospholipid derivatives (Fig. 3A), is consistent with the known effects of caloric restriction (19,38). These changes reflect an energetic switch to beta oxidation, facilitated by mobilization of fatty acids from diverse sources (acylglycerols, phospholipids, and their derivatives), which are then converted to acylcarnitines for transport into mitochondria, where they undergo beta oxidation (Fig. 3C). Generation of acetyl-coenzyme A from beta oxidation provides a substrate for both ketogenesis and the tricarboxylic acid (TCA) cycles, and accordingly, elevations in ketones (eg, 3-hydroxybutyric acid and acetoacetic acid) and circulating TCA cycle intermediates (eg, citric and succinic acid) were observed, particularly after VLCD [Fig. 3D; also see Supplementary Figure 3 (30)]. Ketones in urine tended to also be increased after VLCD and RYGB [Supplementary Figure 4 (30)]. The marked reductions in triacylglycerol levels with VLCD and RYGB tended to apply particularly to shorter chain, saturated forms [Fig. 3E; also see Supplementary Figure 5 (30)], a pattern previously noted during caloric restriction (38).

Resolution of individual acyl chain carbon content in phospholipids and lysophospholipids enabled us to observe how these lipid species changed in parallel to fatty acids and acylcarnitines with the same chain length (Figs. 3F and 3G). Note that phosphatidylcholines (a major group of phospholipids) contain 2 acyl chains (sn-1 and sn-2 positions); in this case, we matched free fatty acids to the acyl chain at the sn-2 position, meaning there is more than 1 match for many fatty acids, with the mean change for each treatment represented by a single data point for each match (Fig. 3G, left panel). Similarly, inclusion of several different lysophospholipid subclasses (Fig. 3G, right panel) means that each fatty acid has a number of lysophospholipid matches. With these caveats in mind, it was still apparent that phospholipids and lysophospholipids tended to reduce in parallel with increases in free fatty acids of the same chain length (Fig. 3G), which is consistent with the notion that (lyso)phospholipids are a key source of fatty acids during caloric restriction (eg, sourced from cell membranes). Lysophosphatidylethanolamines and lysophosphatidylinositides showed the clearest correlations (r = 0.68 and 0.62, respectively) with their matched fatty acids.

While a majority of fatty acids and acylcarnitines were increased with VLCD and RYGB, we observed that the very long chain C24:0 (lignoceric acid) or C26:0 (cerotic acid) species tended to be reduced [Fig. 3F; also see Supplementary Figures 5 and 6 (30)]. The divergent effects on these very long chain fatty acids vs shorter chain counterparts may be a feature of caloric restriction (19,38) and are compatible with the observation that reductions in triglycerides seen with VLCD and RYGB applied preferentially to species with a lesser carbon content (Fig. 3E), as these are presumably unlikely to contain very long acyl chains.

Showing the opposite pattern to that observed with phospholipids, ceramide, and sphingomyelin species across the entire cohort tended to increase or decrease in tandem with fatty acids or acylcarnitines sharing the same acyl chain length (Fig. 3H). C24:0- and C26:0-containing sphingomyelins and ceramides were well represented in our preannotated lipidomic panel but not in the phospholipid or lysophospholipid panels; due to the contrasting direction of change seen with these very long chain species compared to their shorter counterparts, this could explain why the effects of RYGB and VLCD on sphingomyelin/ceramide lipids appeared (somewhat artefactually) more heterogenous than for phospholipids/lysophospholipids (Fig. 3A).

We also investigated the relationship between changes in the previously mentioned lipids or energetic metabolites and changes in clinical parameters including weight, fasting glucose, insulin, triglycerides, and cholesterol across the whole cohort and within individual interventions. Approximately 60% of quantified lipids showed a significant correlation with weight loss across the entire cohort, with a smaller number (~23%; mainly fatty acids) significantly correlated with glucose, and 56%, 44%, and 40%, respectively, correlated with changes in insulin, cholesterol, and triglycerides [Supplementary Figures 5 and 6 (30)]. However, using LME modeling, owing to the observed group-specific differences in these clinical parameters (Fig. 1B), it is hard to mathematically separate the impact of treatment from the impact of differences in clinical values on the metabolome. This was therefore further investigated using PLS analysis by building models between the metabolomic data and each clinical parameter across all samples (ie, all treatment groups combined) and separately for individual interventions. Interestingly, even where valid models were obtained, for the majority of lipids the observed associations did not meet an FDR-corrected P-value threshold of 0.05 [Supplementary Figures 5 and 6 (30)]. Those lipid species that did show a statistically significant association with weight loss (but not glucose) across the whole cohort included CAR(24:0) and selected ceramide, sphingomyelin, and phosphatidylcholine species. Of these however, only the 2 ceramide species (d16:1/22:0 andd16:1/24:0) showed any associations with weight loss when individual groups were modeled separately (Fig. 3I); significant Pearson’s correlations with blood glucose were seen for these lipids in the SAL group, but these were not significant by PLS modeling [Supplementary Figures 5 and 6 (30)]. Acetoacetic acid remained predictive of weight changes across the entire group (Fig. 3I).

In summary, we observed that VLCD and RYGB led to marked and well-correlated changes in several lipid parameters that can be explained by caloric restriction. A relationship between some of these changes and weight loss was observed. The effects of GOP on the lipidome were minor.

Caloric restriction can affect amino acid metabolism, in part as several amino acids serve as substrates for gluconeogenesis, ketogenesis, or both (19,38,39). In our study, only 1 amino acid (tyrosine) was significantly altered by an active treatment (RYGB) compared to SAL treatment [Fig. 3A; also see Supplementary Figure 3 (30)]. However, there were a number of other statistically significant intergroup comparisons and nonsignificant but informative trends [Fig. 4A; also see Supplementary Figure 3 (30)]. For example, with VLCD and RYGB, trends toward reductions in alanine and glutamic acid, and increases in glycine were observed. This pattern is reported in the context of starvation (38,39), with the drop in alanine levels thought to represent increased alanine utilization as a gluconeogenic substrate, thereby fitting with reduced levels of pyruvic acid (another gluconeogenic substrate) after VLCD (Fig. 3D). Reductions in blood glucose for the RYGB group, but not weight loss, were associated with greater reductions in alanine in the PLS [Fig. 4B; also see Supplementary Figure 3 (30)]; this did not apply to other interventions or across the cohort as a whole. Changes in glycine were inversely associated with weight loss across the whole cohort by Pearson’s correlation (Fig. 4B), but this was not significant by PLS modeling for the entire group or for individual interventions [Supplementary Figure 3 (30)]. The fact that alanine and glycine, both considered gluconeogenic amino acids, showed divergent changes with VLCD indicates that the effects of caloric restriction on amino acid metabolism cannot be considered only within the framework of fuel utilization.

RYGB has previously been associated with a specific lowering effect on BCAAs, with this effect predictive of improvements in glucose homeostasis (23). Notably, in our study, all 3 BCAAs (valine, leucine, isoleucine) were numerically decreased, albeit nonsignificantly, after RYGB compared to SAL (Fig. 4A), whereas for VLCD there was no change. Interestingly, isoleucine was also reduced after GOP treatment compared to baseline, although this was driven primarily by 3 participants who showed quite marked reductions in this BCAA (Fig. 4B). The same 3 GOP participants also showed the greatest reductions in blood glucose, and accordingly, changes in isoleucine were correlated with changes in blood glucose across the whole cohort (Fig. 4B). This was the case for leucine [Supplementary Figure 3 (30)], and the glucose-isoleucine association within the GOP treatment group remained significant in the PLS model [Supplementary Figure 3 (30)]. The effects of RYGB and GOP on BCAAs are unlikely to be explained by caloric restriction, which is known to cause an initial increase in circulating BCAAs and a subsequent decline if starvation is prolonged over many days (38,39). Compared to VLCD, for which the metabolomic effects of caloric restriction are most pronounced (Fig. 3), consistently lower BCAAs in the RYGB group are suggestive of a distinct mechanism unrelated to caloric restriction.

The RYGB-mediated reduction in plasma tyrosine (Fig. 4A) was associated with weight loss across the entire cohort, both by Pearson’s correlation (Fig. 4B) and by PLS analysis [Supplementary Figure 3 (30)]. Urinary tyrosine was also significantly reduced after RYGB vs SAL [Supplementary Figure 4 (30)], similar to a previous study of sleeve gastrectomy (40).

These data therefore reflect the effects of caloric restriction on amino acid metabolism, along with superimposed intervention-specific effects, mainly of RYGB, apparently achieved via a different mechanism.

Analysis of both global profiling and preannotated data sets thus far highlight many similarities between the effects of VLCD and RYGB, in many cases (especially for lipid species) explicable by caloric restriction. On the other hand, differences in plasma and urine amino acid responses indicate these interventions are not entirely equivalent at the metabolomic level (Fig. 4). We therefore sought to identify additional metabolites showing differential responses between RYGB and VLCD, focussing on the nonlipid small molecule preannotated data sets in plasma and urine (the corresponding analysis for plasma lipids is already shown in Figure 3B and shows a high degree of correlation).

We identified metabolites that were significantly affected by either VLCD or RYGB, compared to SAL, and categorized these changes as “uniquely significant” to 1 intervention or “shared” (Fig. 5A). A number of metabolites, such as plasma pantothenate, plasma creatine, urine succinic acid, and urine kynurenine, showed concordant responses (ie, both significantly altered compared to SAL and both in the same direction). Other metabolites clearly showed similar direction of change but were only significant for 1 intervention compared to SAL (eg, plasma 3-hydroxybutyric acid, plasma citric acid, and urine suberic acid). We interpret these as reflecting a lack of statistical power rather than true intervention-specific changes. However, a smaller number of metabolites were identified that did not show a consistent pattern between interventions. For example, plasma caffeine and its metabolites trigonelline and paraxanthine were all decreased with RYGB but unchanged with VLCD (Fig. 5A), with trends in the same direction for the same metabolites (and also theophylline) in urine [Supplementary Figure 4 (30)]. Plasma trimethylamine-N-oxide (TMAO) was increased with RYGB and slightly decreased with VLCD (Fig. 5A); this effect of RYGB has been described on other occasions (41,42). Urine indoxyl glucuronide, a metabolite of tryptophan, was increased with RYGB but unchanged with VLCD (Fig. 5A). This may reflect accelerated tryptophan metabolism as urinary tryptophan was particularly reduced with RYGB [Supplementary Figure 4 (30)]. Of these highlighted metabolite changes, only urinary indoxyl glucuronide showed an association with changes in clinical parameters (weight, fasting glucose) across the entire cohort (Fig. 5B), but this was not significant by PLS modeling [Supplementary Figure 4 (30)].

In the original description of this trial, minor changes only were observed in the standard clinical lipid profile after 4 weeks of each intervention, with the most notable effects being a reduction in triglycerides with VLCD and a paradoxical increase with RYGB, compared to SAL (6). This pattern was confirmed using NMR lipoprotein analysis, which is able to subclassify a large number of lipoprotein subfractions and their lipid content [Fig. 6A; also see Supplementary Figure 7 (30)]. VLCD also reduced total apolipoprotein B100 (apoB), the protein compound of very low-density lipoprotein (VLDL) and low-density lipoprotein (LDL), whereas RYGB did not (Fig. 6B). Examining the abundance and content of differently sized apoB-containing lipoprotein subfractions revealed a similar pattern for both GOP and VLCD, in which particle number and carriage of triglycerides, cholesterol, free cholesterol, and phospholipids on the smaller, denser LDL subfractions (LDL4-6) tended to be reduced (albeit not significantly compared to SAL), while the larger, lighter subfractions (LDL1-3) were less affected (Fig. 6C). Moreover, the effect of VLCD on VLDL-associated lipids particularly affected the very lowest density subfraction (VLDL1) (Fig. 6C), for which insulin is a key regulator of production (43), and thus improvements in insulin sensitivity may be expected to exert a prominent effect. Although no lipoprotein-related differences passed adjustment for weight loss [Supplementary Figure 7 (30)], no valid models were obtained for PLS between the lipoprotein data and weight (either across all groups or for individual groups), perhaps indicating that these changes are indeed treatment-associated rather than weight loss–associated.

The data generated and analyzed during this study is available upon reasonable request from the authors.