Circulating levels of succinate are elevated in obesity and associated with a worse metabolic profile

In a cohort of 91 patients stratified according to obesity and T2DM (cohort 1), plasma succinate levels were significantly higher in obese than in lean individuals (Fig. 1a, Supplementary Table S1), and a comparable increase was detected in BMI-matched T2DM patients, in line with a recent report [11]. These results suggest that systemic succinate is also associated with body weight status. Accordingly, we found a positive association between circulating succinate levels and BMI (Fig. 1b), but also insulin, glucose, homeostasis model assessment of insulin resistance (HOMA-IR), and triglycerides (Fig. 1b). Consistent with the documented role of succinate in blood pressure regulation [7, 40], circulating succinate also correlated positively with diastolic blood pressure (R = 0.386, p = 0.039). A multiple regression analysis model (R2 = 0.295) adjusted for age and gender showed that BMI and glucose (β = 0.495 p < 0.001 and β = 0.279 p = 0.013, respectively) were the main determinants of circulating succinate levels.

Fig. 1 Circulating succinate levels are increased in obesity and type 2 diabetes. a Circulating plasma levels in lean, obese and type 2 diabetes (T2DM) individuals. Data are expressed as median and interquartile range. Differences were analyzed by the Kruskal–Wallis test with post hoc Dunn’s multiple comparison test. *p < 0.0001 vs. lean. b Positive correlation between succinate levels and BMI, insulin, glucose, HOMA-IR, and triglycerides using the entire cohort. c Negative correlation between succinate levels and levels of SAT ATGL, SAT ABHD5, SAT HSL, and SAT ZAG. d Positive correlation between succinate levels and SAT HIF1A and SAT CD163. SAT; subcutaneous adipose tissue. Statistical analyses for b–d: Spearman’s correlation analysis. See also Table Supplementary S1 for all clinical characteristics of this cohort Full size image

Succinate has been shown to have antilipolytic actions in adipose tissue via engagement with SUCNR1, inhibiting the release of fatty acids from adipocytes [12, 41]. Consistent with this scenario, metabolic gene expression profiling in SAT from a representative subset of cohort I (n = 42) revealed a negative association between systemic succinate levels and genes encoding key enzymes involved in intracellular degradation of triacylglycerols, including adipose triglyceride lipase (ATGL), abhydrolase domain containing (ABHD5), and hormone-sensitive lipase (HSL) (Fig. 1c). A similar negative association was found for the gene encoding the secreted AT lipolytic factor zinc-alpha-2-glycoprotein (ZAG) (Fig. 1c). Conversely, we found a positive association between succinate and hypoxia-inducible factor HIF-1α (Fig. 1d), a key transcription factor underlying chronic inflammation and AT dysfunction in obesity [42, 43]. Indeed, a clear function for succinate has been established in innate immune signaling, where it enhances interleukin-1 beta (IL-1β) production via stabilization of HIF-1α [6, 44]. Nevertheless, we found that systemic succinate levels were associated with the expression of the anti-inflammatory macrophage marker CD163 in SAT (Fig. 1d), but not with inflammatory markers such as IL-1β or MCP-1 (R = 0.116 p = 0.466; R = 0.039 p = 0.809, respectively), supporting the notion that succinate might have differential intracellular and extracellular functions as previously noted for other stress-related factors such as osteopontin [45, 46] and heat shock proteins [47]. Of note, although some associations were also found in visceral adipose tissue, stronger correlations were detected in SAT, suggesting that the subcutaneous fat depot is more responsive to succinate than visceral fat.

Gut microbiota composition is associated with circulating succinate levels

In an independent cohort (cohort II, clinical, and anthropometrical characteristics are summarized in Supplementary Table S2), the serum concentration of succinate was significantly higher in obese than in non-obese individuals (43.93 ± 6.16 μM vs. 23.2 ± 1.57 μM, p = 0.0020). Of note, the concentration of succinate in serum is about one-third lower than that found in plasma ([13] and this study).

Analysis of gut microbiota composition by 16S rRNA gene sequencing revealed an increase in the Firmicutes/Bacteroidetes ratio in obese subjects (Supplementary Figure 1A), and decreased richness and biodiversity at the phylum and genus level (Supplementary Figure 1B–C) [48,49,50,51]. We found that the relative abundance (RA) of Prevotellaceae (37.52 ± 3.86% vs. 12.93 ± 3.97%, p = 0.0005) and Veillonellaceae (36.08 ± 9.52% vs. 19.51 ± 4.26%, p = 0.03), known succinate producers [20, 52, 53], was higher in obese than in non-obese individuals (Fig. 2a). Accordingly, serum succinate levels positively correlated with Prevotellaceae (R = 0.465; p = 0.039). Conversely, the RA of Odoribacteraceae (1.58 ± 0.68% vs. 6.18 ± 1.64%, p = 0.005) and Clostridaceae (0.09 ± 0.04% vs. 1.02 ± 0.36%, p = 0.05) families, known succinate consumers [54, 55], was significantly lower in obese than in non-obese individuals (Fig. 2a). No differences were detected in other bacterial families such as Paraprevotellaceae, Bacteroidaceae, or Ruminococcaceae, which are also related to succinate metabolism [52, 54, 56,57,58]. Consequently, the ratio of (Prevotellaceae + Veillonellaceae/Odoribacteraceae + Clostridaceae) (fam(P + V/O + C)), specific succinate producers per consumers, was significantly higher in obese subjects (Fig. 2b) and correlated positively with succinate serum levels (Fig. 2c). At the genus level, we found that the succinate-producing member Mitsuokella spp. was enriched in fecal samples of obese subjects (9.67 ± 5.37% vs. 0.11 ± 0.11%, p = 0.08), which was accompanied by a significant decrease in the succinate-consuming members Phascolarctobacterium spp. (7.27 ± 2.29% vs. 24.15 ± 6.12%, p = 0.018) and Odoribacter spp. (0.8 ± 0.27% vs. 3.66 ± 1.81%, p = 0.017) (Supplementary Figure 1D). Correspondingly, the ratio of specific succinate producers/succinate consumers at the genus level was also significantly higher in obese than in non-obese individuals (Supplementary Figure 1E).

Fig. 2 Obese gut microbiota composition is associated with circulating succinate levels. a Percentage of incidence within Bacteroidetes and Firmicutes families in non-obese and obese individuals. b Differences between non-obese and obese individuals at the family level: families(Prevotellaceae plus Veillonellaceae/Odoribacteriaceae plus Clostridaceae) (fam(P + V/O + C)) ratio. c Positive correlation between succinate serum levels and fam(P + V/O + C) ratio. d Positive correlation between succinate serum levels and circulating zonulin levels. e Validation studies were performed using cohort III. Percentage of incidence within Bacteroidetes and Firmicutes families in lean and obese individuals. f Positive correlation between succinate plasma levels and Veilloneaceae. g Differences between lean and obese individuals in the fam(P + V/O + C) ratio in the cohort III study. h Positive correlation between succinate serum levels and log fam(P + V/O + C)) ratio in the cohort III study. See also Supplementary Table S2 for all clinical characteristics of cohorts II and III. Data information: for a and e, values are expressed as mean ± SD. For b and g, data are represented in box and whisker plot format (whiskers: min to max). Statistical analyses: Mann–Whitney U-test. *p < 0.05 vs. non-obese or lean. For c, d, f and h Spearman’s or Pearson’s correlation analysis with Bonferroni adjustment were used Full size image

According to the “leaky gut” hypothesis, intestinal dysbiosis characteristic of obesity is directly related to translocation of bacteria and their products into systemic circulation [28]. As expected, circulating levels of zonulin, a useful biomarker of intestinal permeability [59,60,61,62], were significantly higher in obese than in non-obese individuals (869.33 ± 199.013 ng/ml vs. 500.87 ± 44.61 ng/ml, p = 0.04). A positive correlation was found between serum succinate and circulating zonulin (R = 0.61; p = 0.011) (Fig. 2d), suggesting that akin to the elevated levels of circulating lipopolysaccharide in obesity [63, 64], intestinal permeability might be closely associated with the presence of succinate in systemic circulation.

To further investigate the relationship between serum succinate and the gut microbiome, we performed whole-genome shotgun sequencing of fecal DNA in an independent cohort (confirmatory cohort III; clinical and anthropometrical characteristics summarized in Supplementary Table S2). As noted in previous cohorts, succinate plasma levels were significantly higher in obese than in lean individuals (101.72 ± 9.37 μM vs. 78.24 ± 4.4 μM, p = 0.043). Furthermore, we detected a significant increase in the family Veillonellaceae (2.37 ± 0.39% vs. 1.41 ± 0.24%, p = 0.043) in obese subjects (Fig. 2e), as well as a positive correlation between Veillonellaceae and succinate levels in plasma (R = 0.773; p < 0.001) (Fig. 2f). Accordingly, obese subjects had a higher gen(P + V/O + C) ratio (Fig. 2g), which positively correlated with plasma succinate levels (Fig. 2h). Similar to cohort II, obese individuals had higher zonulin levels (Supplementary Table S2), which also positively associated with circulating succinate levels (R = 0.59; p = 0.0152).

Overall, these data demonstrate that despite the interindividual heterogeneity, circulating succinate levels are associated with specific components of gut microbiota. Interestingly, the microorganisms linked to circulating succinate levels have been previously related to CVD and/or its risk factors. Thus, succinate-consuming genera such as Odoribacter and Clostridium have been linked to a decrease in clinical parameters associated with CVD risk [65, 66]. By contrast, the Prevotella genus, which we found to be increased in obese individuals, has been recently associated with hypertension [67] and TMAO-induced atherosclerosis [68, 69]. Along these lines, Chen and colleagues have demonstrated that resveratrol modulates gut microbiota by inhibiting the Prevotella genus, which in turn induces a decrease in circulating TMAO levels [70], pointing to gut microbiota as an attractive target for pharmacological or dietary interventions to decrease the risk of developing CVD.

Modification of gut microbiota by dietary weight loss intervention affects circulating succinate levels

To determine whether diet-induced modifications in gut microbiota could be reflected in variations in circulating succinate levels, we carried out a prospective 12-week dietary intervention study in obese patients aimed to weight loss (cohort IV, Supplementary Table S3). Serum succinate levels decreased after the intervention (Fig. 3a) in parallel with an increase in genus and family richness (Supplementary Figure 2A). Although no significant differences were detected in genus or family diversity (Supplementary Figure 2B), we identified a decrease in the Firmicutes/Bacteroidetes ratio (Supplementary Figure 2C), similar to that reported in a previous dietary weight loss intervention study [71,72,73].

Fig. 3 Weight loss induced by dietary intervention modifies specific gut microbiota and impacts circulating succinate levels. a Circulating serum succinate levels in basal state and after a 12-week dietary intervention (12-wDI) from cohort IV. b Percentage of incidence within Bacteroidetes and Firmicutes families in obese individuals in basal state and after 12-wDI. c Positive correlation between the change in succinate serum levels (12-wDI[succinate]-basal[succinate]) and the change in Prevotellaceae (12-wDI [% abundance Prevotellaceae]-basal[% abundance Prevotellaceae]). d Differences between basal state and 12-wDI in the fam(P + V/O + C) ratio. e Positive correlation between the change in succinate serum levels (12-wDI[succinate]-basal[succinate]) and the change in the (12-wDI fam(P + V/O + C)–basal fam(P + V/O + C)) ratio. See also Supplementary Table S3 for all clinical characteristics of cohort IV. Data information: for a and b values are expressed as mean ± SD. For d, data are represented in box and whisker plot format (whiskers: min to max). Statistical analyses: Wilcoxon signed-rank test. *p < 0.05 vs. basal. For c and d, Spearman’s correlation analysis with Bonferroni adjustment was used Full size image

In accordance with the results of the two previous cohorts (cohort II and III), we found a significant decrease in the succinate-producing families Prevotellaceae (17.91 ± 6.43% vs. 7.15 ± 2.47%, p = 0.019) and Veillonellaceae (13.11 ± 2.76% vs. 3.73 ± 1.48%, p = 0.027) after the dietary intervention (Fig. 3b). Comparable to that observed in cohort III, we found a positive correlation between the change in the incidence of Prevotellaceae ([Prevotellaceae] post-intervention – [Prevotellaceae] basal ) and succinate levels (R = 0.751; p = 0.019) (Fig. 3c). Correspondingly, the fam(P + V/O + C) ratio significantly decreased after weight loss (Fig. 3d) in parallel with a decrease in succinate, which was reflected in a positive correlation between the change in the fam(P + V/O + C) ratio and the change in circulating succinate (post-intervention–basal) (Fig. 3e). Similar observations were found at the genus level (Supplementary Figure 2D), and the gen(P + V/O + C) ratio significantly decreased after the intervention (Supplementary Figure 2E).

Taken together, these results indicate that a short-term dietary weight loss intervention impacts different members of the gut commensal community related to succinate metabolism. Specifically, a decrease in succinate producers concomitant with an increase in succinate consumers at two taxonomic levels, which correlates with the decrease in systemic succinate levels observed, pointing to circulating succinate as a new dysbiosis-associated metabolite in the context of obesity.

Remarkably, joint analysis of both microbiota cohorts (cohort II and IV) validated the strong positive correlation between the fam(P + V/O + C) ratio and circulating serum succinate levels (n = 38, R = 0.646; p < 0.001). Reassuringly, multiple regression analysis revealed that our proposed ratio based on (succinate-producing) vs. (succinate-consuming) families was the main determinant of systemic succinate levels (R2 = 0.744, β = 0.597; p = 0.007). Notwithstanding these strong correlations, exactly how microbial communities interact and use succinate is currently unknown. Moreover, other microbial groups could be responsible for succinate production (e.g., Succinovibrio spp., Ruminococcus spp., or Fibrobacter succinogenes) and consumption (e.g., Dialister spp., Phascolartobacterium succinatutes) [52, 54, 56,57,58]. Nevertheless, our results strongly link the specific fam(P + V/O + C) ratio to circulating succinate.

Microbiota spontaneous evolution drives changes in systemic succinate

Finally, to evaluate the spontaneous evolution of microbiota, we studied 19 subjects in whom general healthy habits counseling was provided: at baseline and at 2 years thereafter (see Materials and methods section, cohort V description in Supplementary Table S4). No significant differences in body weight were observed in these patients in the follow-up. We used a metagenomic approach rather than 16S sequencing to analyze gut microbiota in this cohort. At the end of follow-up, subjects were classified into two groups in terms of changes in the ratio (succinate-producing) vs. (succinate-consuming) families (group 1, decreased ratio vs. group 2, increased ratio). A reduction in fam(P + V/O + C) was associated with a significant decrease in succinate levels (Table 1, group 1), whereas a significant increase in this ratio was related to a rise in systemic succinate (Table 1, group 2). These results show that variation in gut microbial composition independent of body weight changes are directly related to circulating succinate. Of note, elevated systemic succinate was paralleled with an impairment of glucose homeostasis, which contrasts with recently reported findings in animal models showing that microbiota-produced succinate is directly related to an improvement of glucose homeostasis [16]. Indeed, high succinate levels have been associated with various human pathological settings including CVD [8] and T2DM [7, 9,10,11].

Table 1 Anthropometric and analytical characteristics in the cohort V Full size table

Multivariate analyses identified statistically significant associations between the expression of 64 genes encoding metabolic enzymes, and the fam(P + V/O + C) ratio. Hierarchical clustering of these metagenomic data and the associations among fam(P + V/O + C) ratio, circulating succinate and succinate-related microbial species, identified two clusters (labeled as A and B in Fig. 4a) with a clear relationship with the fam(P + V/O + C) ratio (positive and negative correlations represented in green and red, respectively), which was mostly reflected by succinate levels. The metagenomic-derived clusters were also confirmed when associations with Prevotellaceae and Clostridaceae were analyzed, and a strong inverse relationship was detected. The main positive associations in cluster A were with genes encoding metabolic enzymes involved in amino-acid transport and metabolism ([E]), whereas cluster B showed a predominance of associations with genes related to energy production and conversion ([C]). Robust relationships with genes related to carbohydrate transport and metabolism ([G]) were revealed in both clusters. Interestingly, subclusters A1/A2 and B1/B2 were segregated on the basis of inverse associations with Veillonellaceae and Clostridaceae. These results link the fam(P + V/O + C) ratio, specific gut microbiota and circulating succinate levels with a specific molecular entity and metabolic function.

Fig. 4 Associations of gut microbiota interactions, circulating succinate levels and metabolic bacterial functions. a Spearman’s rank correlation between 64 genes encoding metabolic enzymes and fam(P + V/O + C) ratio, circulating succinate levels, Prevotellaceae, Veillonellaceae, and Clostridaceae visualized as a heatmap. Annotation of heatmap: metabolic pathway-based gene classification according to KEGG database; [C] Energy production and conversion; [E] Amino-acid transport and metabolism; [F] Nucleotide transport and metabolism; [G] Carbohydrate transport and metabolism; [H] Coenzyme transport and metabolism; [I] Lipid transport and metabolism; [P] Inorganic ion transport and metabolism and [Q] Secondary metabolites, biosynthesis and catabolism. b Positive and negative associations with the fam(P + V/O + C) ratio and metabolic enzymes as adapted from the KEGG metabolic pathways. See also Supplementary Table S4 and Table 1 for all clinical characteristics of cohort V Full size image

The differences in the gene expression profiles associated with specific bacterial communities were also evident when we classified the cohort into two groups according the fam(P + V/O + C) ratio (group 1 vs. group 2) (Supplementary Figure 3 A). An increase in the abundance of genes encoding enzymes associated with carbohydrate transport and metabolism ([G]), such as pectate lyase (EC:4.2.2.2), pectinesterase (EC:3.1.1.11) and glycosyl hydrolase (EC:3.2.1.52) after 2 years of follow-up, was detected in subjects in whom the fam(P + V/O + C) ratio was increased in parallel with an increase in succinate levels. Curiously, a decrease in the abundance of genes encoding enzymes connecting the pentose phosphate pathway to glycolysis, such as ribulokinase (EC:2.7.1.16) and transaldolase (EC:2.2.1.2), was also observed in these patients. Genes associated with metabolic pathways linked to the biosynthesis of secondary metabolites ([Q]) such as succinylbenzoic acid-CoA ligase (EC:6.2.1.26), or those associated with amino-acid transport and metabolism ([E]) such as phosphoribosylformimino-5-aminoimidazole carboxamide ribotide isomerase (EC:5.3.1.16) and glutamate synthase (EC:1.4.1.14) were also modified. Intriguingly, all of these genes showed the strongest association with the fam(P + V/O + C) ratio (Fig. 4a, enzyme names in red). More importantly, projection of these enzymes onto the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways map identified central metabolism as the main process associated with the fam(P + V/O + C) ratio. Among them, glyocoside hydrolase and glutamate synthase were of particular interest because of their functional roles in glycolysis activation and succinate production via the GABA shunt pathway. Also worthy of mention was the negative association of fam(P + V/O + C) ratio with ribulokinase and transaldolase, which also could promote glycolysis through inhibition of the pentose phosphate pathway (Fig. 4b). Mapping of the main enzymes positively or negatively correlated with fam(P + V/O + C) ratio uncovered a clear connection between their functional features and succinate metabolism (adapted from KEGG metabolic pathways) (Supplementary Figure 3B).

In conclusion, our study reveals for the first time a strong association between microbial community, gene composition, and metabolism and circulating succinate levels in humans. Although it has been recently shown that microbiota-derived succinate induces metabolic benefits by acting as an intestinal gluconeogenic substrate in germ-free mice [16], some caution should be exercised when extrapolating from mouse model data to humans. Moreover, a recent meta-analysis has suggested that in humans, the association between specific gut microbiota and obesity is smaller than that detected in mice and by most microbiome studies in humans [74]. Although the physiological effects of such modest differences are far from clear, they might become more apparent at the level of specific gene transcripts or metabolites. In this line, our study uncovers a clear association between succinate levels and obesity-related metabolic disturbances, similar to that found in other CVD risk factors [7,8,9,10,11, 16, 75]. Clearly, although such an increase does not necessarily qualify succinate as a disease-causing metabolite, its participation in the pathophysiology of obesity should not be entirely ruled out. Indeed, our data show a significant increase of glycemia in those patients who presented an increase in circulating succinate associated with changes in gut microbiota. Although engagement of succinate with its receptor has emerged as a link between metabolic stress and inflammation [4,5,6], it is still unclear whether succinate serves as a “harmful” signal or might have a protective role by acting as alarmin [76]. Furthermore, similar to what is seen with other microbial metabolites such as SCFAs, the beneficial and/or detrimental effects of succinate might depend on the amount synthesized [77, 78]. Although it is evident that further studies are required to prove causality, we hypothesize that obesity-related dysbiosis along with increased gut permeability might account for the higher levels of circulating succinate found in obese subjects. This may explain why colonization of germ-free mice with a probiotic succinate producer strain under normal conditions of intestinal permeability increases cecal, but not circulating, succinate levels [16]. Thus, we propose that modulation of fam(P + V/O + C) ratio in a pathological condition of increased intestinal permeability such as obesity will impact circulating succinate levels. Under this scenario, it should be considered that systemic succinate could act as a hormone-like metabolite, signaling through SUCNR1. How such levels impact host metabolism underlying obesity-related co-morbidities or whether manipulation of gut microbiota, specifically by increasing succinate-consuming families, might improve metabolic disturbances in obese patients are some of the open questions that should be explored in future work. Indeed, host or even dietary sources of succinate should not be dismissed. Our present findings point to succinate as a microbiota-derived metabolite with a potential role in obesity and metabolic-associated cardiovascular disorders, and strengthen the importance of microbial communities and their interactions when microbiota-derived bioactive compounds are studied.