Influence of Fibroblast Growth Factor 21 on Delayed Glycemic Improvement Following Acute Exercise in Type 2 Diabetes Mellitus
Article information
Abstract
Background
Exercise positively influences glycemic control. Some individuals experience greater glycemic stability on the day after exercise, even without additional physical activity. However, the mechanisms underlying this delayed glycemic improvement remain unclear.
Methods
Seventy-one patients with type 2 diabetes mellitus were assigned to either a 60-minute exercise group or a resting group. Serum fibroblast growth factor 21 (FGF21) levels and untargeted metabolomic profiles were assessed at multiple time points before and after exercise. Interstitial glucose levels were monitored using continuous glucose monitoring system. FGF21 knockout mice and wild-type littermates fed a high-fat diet underwent a 3-week exercise intervention and received FGF21 supplementation.
Results
Individuals exhibiting delayed glycemic improvement (responders) displayed a significantly stronger FGF21 response than non-responders. Baseline metabolites, including p-cresol sulfate and dimethylglycine, differed between responders and non-responders and were associated with the FGF21 response. Longitudinal time-series analyses revealed post-exercise differences in acylcarnitines, fatty acids, and complex lipids between responders and non-responders. Dynamic correlation and mediation analyses supported the role of FGF21 in modulating delayed glycemic improvement through regulation of lipid metabolism. In vivo FGF21 knockout and rescue experiments demonstrated that FGF21 is necessary for these metabolic shifts and the associated improvements in glucose tolerance and insulin sensitivity.
Conclusion
This study suggests that the baseline metabolome is associated with the magnitude of the post-exercise FGF21 response, which influences delayed glycemic improvement through regulation of lipid metabolism pathways.
Highlights
• Heterogeneous post-exercise glycemic responses were observed in T2DM.
• Delayed glycemic improvement was associated with a greater FGF21 response.
• Glycemic responders showed distinct shifts in lipid metabolism after exercise.
• FGF21 knockout eliminated the glycemic benefit of exercise, with rescue by recombinant FGF21.
INTRODUCTION
Exercise elicits both acute and long-term changes in blood pressure, heart rate, and various hematological parameters [1]. Acute aerobic exercise increases whole-body oxygen consumption, with some studies reporting sustained elevation in oxygen uptake (VO2) for up to 24 hours or longer after exercise [2,3]. The advent of continuous glucose monitoring (CGM) has shown that even a single exercise session can improve 24-hour glycemic control in individuals with obesity and type 2 diabetes mellitus (T2DM) [4,5]. However, the mechanisms underlying the delayed glycemic benefits of exercise remain poorly understood.
Fibroblast growth factor 21 (FGF21) has emerged as a promising therapeutic target for obesity and diabetes because of its rapid and potent effects on insulin sensitivity [6]. FGF21 has recently been identified as an exercise-responsive factor, with circulating levels increasing after endurance exercise [7,8]. Nevertheless, evidence suggests that exercise-induced FGF21 secretion is attenuated in individuals with obesity and T2DM [9,10]. This highlights the need to investigate whether the FGF21 response to exercise varies among individuals with T2DM and whether this variation influences post-exercise glycemic control.
Acute exercise triggers complex molecular responses and modifies biological processes, including cellular energy metabolism (e.g., glycolysis and the tricarboxylic acid [TCA] cycle), whole-body substrate metabolism (e.g., amino acid breakdown and fatty acid [FA] oxidation), oxidative stress, inflammation, and tissue repair [11,12]. Metabolomics has provided valuable insights into post-exercise metabolic profiles, revealing that many of these processes are dampened or reversed in insulin-resistant individuals [11].
In this study, we conducted an acute exercise test in patients with T2DM and measured serum FGF21 levels at multiple time points before and after exercise, along with untargeted metabolomic profiling. Given the observed variability in post-exercise glucose responses, we aimed to elucidate the mechanisms underlying the delayed effects of exercise on glycemic improvement in patients with T2DM.
METHODS
Study participants
This study was based on a clinical trial focused on exercise intervention in patients with T2DM (ChiCTR2100046148). Inclusion criteria included an age of 35 to 65 years, abdominal obesity, body mass index (BMI) ≤35 kg/m2, use of ≤three antidiabetic agents for at least 6 weeks, and no regular physical activity. Exclusion criteria included glycosylated hemoglobin (HbA1c) <6.5% or ≥9%, insulin use, severe proliferative diabetic retinopathy or worse, macroalbuminuria, renal dysfunction, a history of major adverse cardiovascular events, cerebrovascular disease, musculoskeletal injuries preventing exercise, or pregnancy. At baseline, 71 participants were randomized in a 4:1 ratio to either an acute exercise group (n=56), which performed a 60-minute aerobic exercise session or a resting group (n=15), which remained inactive (Fig. 1A). The trial was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Shanghai Sixth People’s Hospital. All participants provided written informed consent.
Individual variability in delayed glycemic improvement following exercise. (A) Study design. Changes in 24-hour (B) coefficient of variation (CV), (C) standard deviation (SD), (D) mean sensor glucose (MSG), (E) time in range (TIR), and (F) time in tight range (TITR) before and after exercise showed individual heterogeneity. (G) Interstitial glucose trends throughout the post-exercise day in the response (n=30) and non-response (n=26) groups. (H-L) Changes in 24-hour (H) CV, (I) SD, (J) MSG, (K) TIR, and (L) TITR between the response and non-response groups after adjustment for clinical variables and exercise intensity. Data are presented as mean±SD. Gray trace and area, non-response group; orange trace and area, response group. T2DM, type 2 diabetes mellitus; Pre_Day, pre-exercise day; EX_Day, exercise day; Post_Day, post-exercise day. aP<0.05 for significant differences between groups, bP<0.01, cP<0.001.
To validate our findings, we used an independent cohort of patients with T2DM (n=16) who underwent a single aerobic exercise intervention similar to that in the primary cohort. Serum FGF21 levels and untargeted metabolomic profiles were measured before and after exercise. For further information about participants in this cohort, please contact the corresponding author.
Detailed aspects of the study design and clinical and laboratory measurements are outlined in the Supplementary Methods.
Animals
FGF21 knockout (KO) mice on a C57BL/6J background were generated as previously described [13]. All mice were housed in a temperature-controlled room (23°C±2°C) with a 12-hour light-dark cycle and had free access to water and rodent diet. At 8 weeks of age, mice were switched to a high-fat, high-cholesterol (HFHC) diet (Research Diet, D12079B, containing 40% fat, 17% protein, and 43% carbohydrate [kcal%]) for 12 weeks to establish diet-induced obesity. All animal experiments were conducted in male mice and were approved by the Animal Ethics Committee of Shanghai Sixth People’s Hospital. Detailed methods for the animal studies are provided in the Supplementary Methods.
Statistical analysis
Clinical characteristics of participants are presented as mean± standard deviation (SD) or median (interquartile range) for continuous variables and as frequencies (%) for categorical variables. Normality (Shapiro-Wilk test) and homoscedasticity (Levene’s test) were checked before statistical analysis. Untargeted metabolomics data were standardized using logarithm transformation and auto-scaling. Mixed-effects linear regression models, adjusted for clinical variables and exercise intensity, were used to analyze post-exercise glucose-related indices, cytokine responses, and metabolite changes. Bonferroni post hoc tests were used to assess differences between conditions at each time point. Fuzzy c-means clustering was performed using the R package “Mfuzz.” Anthropometric measures, the area under the curve (AUC) for cytokines, and glucose or insulin tolerance test results were compared using unpaired t-tests or Welch’s t-tests. Pearson correlation coefficients were used to evaluate relationships between FGF21 and metabolites. Time-delayed analysis was used to dynamically investigate correlations between FGF21 and metabolites at different time points in the time series. Mediation effects of metabolites on the relationship between FGF21 levels and participant grouping were investigated using the R package “mediation.” All analyses were performed using MetaboAnalyst version 6.0 (https://www.metaboanalyst.ca/), GraphPad Prism 10 (GraphPad Software Inc., San Diego, CA, USA), and R software version 4.3.3 (R Foundation for Statistical Computing, Vienna, Austria).
RESULTS
Individual variability in delayed glycemic improvement following exercise
A total of 71 participants with T2DM were included in the analysis: 56 were assigned to the acute exercise group and 15 to the resting group (Supplementary Table 1). After 60 minutes of aerobic exercise, the acute exercise group exhibited significant reductions in blood glucose, insulin, and C-peptide levels, whereas the resting group showed gradual decreases (Supplementary Fig. 1A–C). No significant differences in 24-hour glycemic control were observed between the acute exercise and resting groups on the following day (Supplementary Fig. 1D-H). Within the acute exercise group, substantial individual differences in 24-hour coefficient of variation (CV) and SD of glucose levels were observed after exercise (Fig. 1B-F). Approximately half of the participants demonstrated a reduction in 24-hour CV compared with pre-exercise levels, indicating improved glucose variability, whereas the others did not (Fig. 1B). Based on these findings, participants in the acute exercise group were classified into two categories: those exhibiting delayed glycemic improvement (response group, n=30) and those without delayed glycemic improvement (non-response group, n=26). Immediate post-exercise changes in blood glucose, insulin, and C-peptide levels were similar between the two groups (Supplementary Table 2). However, on the following day, the response group exhibited lower amplitudes of glucose fluctuation than the non-response group (Fig. 1G). Specifically, the response group showed significant reductions in 24-hour CV (P<0.001) and SD (P<0.001), whereas the non-response group exhibited significant increases in both 24-hour CV and SD after acute exercise (Fig. 1H and I). Moreover, significant improvements in 24-hour mean sensor glucose (MSG), time in range, and time in tight range were observed exclusively in the response group on the following day (Fig. 1J-L).
Baseline metabolic profiles are associated with delayed post-exercise glycemic improvement
No significant differences were observed between the response and non-response groups in age, sex, BMI, HbA1c levels, VO2max, maximum heart rate during cardiopulmonary exercise testing, or actual heart rate during acute exercise (Table 1, Supplementary Fig. 2). Both groups maintained consistent dietary patterns over the 3-day period (Supplementary Table 3). We identified 20 metabolites that differed significantly at baseline between the response and non-response groups (Supplementary Table 4). These included gut microbiota-derived metabolites, such as phenol sulfate, p-cresol sulfate, and indoxyl sulfate; dimethylglycine; N-oleoyltaurine; 2-hydroxyarachidate; propionylcarnitine; 6-bromotryptophan, hexanoylglutamine; phenylacetylglutamine; and the bilirubin metabolite L-urobilin. These results suggest that baseline metabolic differences may be associated with delayed glycemic improvement following exercise.
Summary of clinical characteristics of participants in the response and non-response groups
Association between FGF21 and delayed glycemic improvement
FGF21 is a known exercise-responsive factor. In this study, serum FGF21 levels increased significantly after exercise, peaking at 30 minutes post-exercise before gradually declining (Supplementary Fig. 1I). Notably, the post-exercise FGF21 response was significantly higher in the response group than those in the non-response group (P<0.001) (Fig. 2A). Similarly, free fatty acid (FFA) levels also increased significantly following acute exercise (Supplementary Fig. 1J), with the response group demonstrating a significantly higher AUC for FFA elevation than the non-response group (Fig. 2B). These findings suggest that post-exercise elevations in serum FGF21 and FFA are closely associated with delayed glycemic improvement in individuals with T2DM. To validate this finding, we included an external cohort of patients with T2DM who underwent aerobic exercise and had serum FGF21 levels measured before and after exercise. Results were consistent with those of the primary cohort: some participants showed delayed glycemic improvement one day after exercise, and these participants exhibited a more pronounced post-exercise FGF21 response (Supplementary Fig. 3).
Post-exercise serum fibroblast growth factor 21 (FGF21), free fatty acid (FFA), and fibroblast activation protein (FAP) responses in the response (R) and non-response (NR) groups. Time-course changes and the area under the curve (AUC) for the fold change in (A) serum FGF21, (B) FFA, and (C) FAP levels following exercise are shown for the R and NR groups. Data are presented as mean±standard error of the mean. Analyses were conducted after adjustment for clinical variables and exercise intensity. Blue points and lines, NR group; red points and lines, R group; shaded areas, exercise (EX) periods. (D) Correlation between baseline serum FAP levels and baseline serum FGF21 levels. (E) Correlation between baseline serum FAP levels and the AUC for the fold change in the post-exercise serum FGF21 response. aP<0.05 for significant differences between groups, bP<0.01, cP<0.001.
Fibroblast activation protein (FAP) has been reported to cleave FGF21 and potentially impair its signaling pathways. In this study, we observed no significant change in serum FAP levels before or after exercise (Fig. 2C), suggesting that acute aerobic exercise does not immediately alter circulating FAP. However, baseline serum FAP levels were inversely correlated with baseline FGF21 levels (P=0.038) (Fig. 2D) and with the AUC for the post-exercise FGF21 response (P=0.009) (Fig. 2E), suggesting that inter-individual variability in the post-exercise FGF21 response may be related to circulating FAP.
Specific baseline metabolites associated with post-exercise FGF21 response
To investigate potential associations between baseline differentially abundant metabolites and post-exercise FGF21 responses, we conducted additional analyses, correlating baseline metabolite levels with the AUC for the post-exercise FGF21 response. We observed significant positive correlations between the AUC for the post-exercise FGF21 response and three baseline metabolites: p-cresol sulfate, a gut microbiota-derived metabolite (r=0.301, P=0.042); dimethylglycine, a methylation-related metabolite (r=0.338, P=0.022), and 6-bromotryptophan, an amino acid metabolism-related metabolite (r=0.373, P=0.011) (Supplementary Fig. 4). These findings suggest that specific baseline metabolic differences may contribute to delayed glycemic improvement by modulating the FGF21 response following acute exercise.
Post-exercise metabolomic profiles differed between responders and non-responders
In addition to baseline measurements, we constructed longitudinal metabolomics datasets at multiple time points before and after exercise. These datasets were used to evaluate differential metabolomic responses to acute exercise between the response and non-response groups and to explore associations among FGF21, metabolites, and delayed glycemic improvement. After data processing and annotation, the final datasets included 632 metabolites. Comparative analyses of metabolomic profiles between the acute exercise and resting groups were shown in Supplementary Fig. 5.
Comparison of metabolomic responses to exercise revealed clear segregation between the response and non-response groups in the partial least squares-discriminant analysis (PLS-DA) score plot (Fig. 3A). A total of 216 differential metabolites were identified (Fig. 3B, Supplementary Table 5), approximately half of which were lipids and lipid-like molecules. Based on their longitudinal trajectories, metabolites were categorized into six distinct temporal patterns (Fig. 3C). The AUCs for metabolite clusters corresponding to patterns 1, 2, 3, and 5 differed significantly between the response and non-response groups (Supplementary Table 6). Metabolites in pattern 1 exhibited rapid responses, peaking immediately post-exercise. These included glycolysis and TCA cycle-related metabolites; intermediates of amino acid metabolism, such as glutaric acid; and xanthine, which is related to adenosine triphosphate-turnover. These metabolites accumulated to a greater extent in responders and correlated significantly with body-fat and lean mass percentages. Patterns 2 and 3 were enriched for metabolites involved in FA oxidation and complex lipid metabolism, including acylcarnitines (e.g., carnitine[2:0], carnitine[8:0]), phosphatidylcholines (PCs) (e.g., PC[33:2], PC[34:1]), and FAs (e.g., FA[20:5], FA [20:3]). Metabolites in pattern 2 increased immediately post-exercise and remained elevated for up to 2 hours; they were significantly associated with exercise-induced changes in 24-hour SD and CV. Metabolites in pattern 3 showed a delayed increase and were closely associated with changes in 24-hour glucose CV, waist circumference, and baseline insulin and C-peptide levels (Supplementary Table 7). Metabolites in patterns 4, 5, and 6 were down-regulated post-exercise, with a more pronounced decline observed in the response group. These included L-carnitine and certain lysophosphatidylcholines (LPCs). These findings suggest that individuals in the response group experience more pronounced activation of energy metabolism, particularly lipid metabolism, following acute exercise.
Untargeted metabolomics identified metabolic differences between responders and non-responders following acute exercise. (A) Score plot of partial least squares-discriminant analysis for participants in the response and non-response groups at seven time points. Blue points, non-response group; red points, response group. (B) Heatmap of the 216 metabolites with significant inter-group differences. Each metabolite is annotated according to its superclass. (C) Post-exercise response patterns of differentially abundant metabolites identified in responder and non-responder groups using fuzzy c-means clustering. Solid lines represent the mean, and dashed lines represent the 95% confidence interval. Blue trace and area, non-response group; red trace and area, response group. n indicates the number of metabolites categorized within each pattern. (D) Pearson correlation coefficients for 126 differential metabolites correlated with fibroblast growth factor 21 (FGF21).
Post-exercise FGF21 response influences delayed glycemic improvement through lipid metabolism
To explore the relationship between specific metabolite profiles and FGF21 responses following acute exercise, we conducted analyses. We observed significant correlations between FGF21 and 125 of the 216 differential metabolites, with more than 70% showing positive correlations (Fig. 3D). By calculating correlations between FGF21 and each metabolite at various temporal lag points, we identified 79 metabolites with positive lag coefficients associated with FGF21 (Supplementary Table 8), suggesting that the FGF21 response acts as an upstream event that triggers fluctuations in these metabolites.
Mediation analysis identified 19 metabolites as mediators in the association between FGF21 and participant grouping (response vs. non-response groups) (Supplementary Table 9). Key mediators included L-acetylcarnitine with a mediation proportion of 12.56% (P=0.01) (Fig. 4A), and L-carnitine, with a mediation proportion of 11.58% (P=0.016) (Fig. 4B), both of which are associated with FA oxidation. Additional mediators included FA(24:6), with a mediation proportion of 12.54% (P=0.01) (Fig. 4C), and FA(20:5), with a mediation proportion of 8.46% (P=0.026) (Fig. 4D), which are linked to fat mobilization and lipolysis. Complex lipids, such as LPC(15:0), with a mediation proportion of 9.29% (P=0.024) (Fig. 4E) and LPC(18:0), with a mediation proportion of 16.50% (P=0.022) (Fig. 4F), also served as mediators. These metabolites were more strongly activated in the response group, with L-acetylcarnitine, FAs, and PCs showing sustained increases following exercise. This indicates that FGF21 enhances delayed glycemic improvement through pathways involving fat mobilization, lipolysis, and FA oxidation. In addition to lipid metabolism, several intermediates of the TCA cycle and amino acid metabolism also mediated the relationship between FGF21 and participant grouping. These included aminoadipic acid (mediation proportion: 15.36%, P<0.001), threonine (16.89%, P<0.001), and tyrosine (16.71%, P=0.036). These findings suggest that FGF21 influences post-exercise glucose homeostasis through multiple pathways, as illustrated in Fig. 4G. Analysis of metabolite time-course changes revealed distinct temporal patterns in responders. Glucose metabolism-related pathways were activated shortly after acute exercise and returned to baseline within 60 minutes. In contrast, lipid-metabolism responses were activated later, following the FGF21 response, and continued to rise for up to 2 hours post-exercise. We also performed untargeted metabolomic profiling in the independent validation cohort (Supplementary Fig. 6). The findings mirrored those of the primary cohort, providing additional evidence that the post-exercise FGF21 response primarily modulates delayed glycemic improvement by regulating lipid metabolism.
Fibroblast growth factor 21 (FGF21)-driven metabolomic mediators and post-exercise pathway divergence between responders and non-responders. (A) L-acetylcarnitine, (B) L-carnitine, (C) fatty acid (FA)(24:6), (D) FA(20:5), (E) lysophosphatidylcholine (LPC)(15:0), and (F) LPC(18:0) significantly mediated the relationship between serum FGF21 and participant grouping. Estimates are presented as standardized coefficients with P values and 95% confidence intervals (CIs). (G) Schematic representations of selected metabolites: amino acid metabolism (orange), glucose metabolism (yellow), FA oxidation and mitochondrial energy production (blue), and complex lipid metabolism (green). Individual metabolite responses are shown for each of the colored pathway. Data are presented as mean±standard error of the mean. Blue point and line, non-response group (NR); red point and line, response group (R); shaded area, exercise (EX) periods. ACME, average causal mediation effect; ADE, average direct effect; CoA, coenzyme A; TCA, tricarboxylic acid; FFA, free fatty acids; TG, triglycerides; SM, sphingomyelin; DG, diacylglycerol; PC, phosphatidylcholine; LPS, lysophosphatidylserine; PA, phosphatidic acid; PE, phosphatidylethanolamine; LPE, lysophosphatidylethanolamine; LPA, lysophosphatidic acid; PS, phosphatidylserine; LPC, lysophosphatidylcholine. aP<0.05, bP<0.01, and cP<0.001 indicate significant differences between the response and non-response groups, assessed using a mixed model with Bonferroni post hoc correction for multiple comparisons.
FGF21 KO and rescue experiments establish its necessity for exercise-induced metabolic and glycemic benefits
To further validate the necessity and mechanistic role of FGF21 in mediating exercise-induced metabolite changes and glycemic regulation, we compared FGF21 KO mice with their wild-type (WT) littermates. Eight-week-old male mice of each genotype were fed an HFHC diet for 12 weeks; thereafter, one cohort underwent 3 weeks of treadmill training while sedentary controls remained untrained. In parallel, a subset of FGF21 KO mice received subcutaneous osmotic pumps delivering recombinant mouse FGF21 (rmFGF21) throughout the training period to restore circulating FGF21 levels (Fig. 5A). After training, WT mice exhibited significant improvements in glucose tolerance and insulin sensitivity compared with sedentary controls (Fig. 5B and E), whereas FGF21 KO mice showed markedly attenuated responses (Fig. 5C and F). Remarkably, rmFGF21 supplementation rescued these glycemic benefits in FGF21 KO mice, indicating that FGF21 signaling is required for exercise-induced improvements in glycemic control (Fig. 5D and G).
Fibroblast growth factor 21 (FGF21) is required for exercise-induced improvements in glucose tolerance and the metabolomics response in obese mice. (A) Experimental workflow. (B) Glucose tolerance tests (GTTs) in wild-type sedentary (WT-SE, n=6) and exercised (WT-EX, n=6) mice. (C) GTTs in FGF21 knockout sedentary (KO-SE, n=6), exercised (KO-EX, n=6), and exercised recombinant mouse FGF21-supplemented (KO-rmFGF21-EX, n=6) mice. (D) Area under the glucose curve (AUC) quantification corresponding to panels B and C. (E) Insulin tolerance tests (ITTs) in WT-SE and WT-EX mice. (F) ITTs in KO-SE, KO-EX, and KO-rmFGF21-EX mice. (G) AUC quantification corresponding to panels E and F. Data are presented as mean± standard error of the mean. (H) Score plot of partial least squares-discriminant analysis of serum metabolomic profiles from the five groups (n=9). Ellipses represent 95% confidence intervals. (I) Volcano plots of differential metabolites between WT-EX and WT-SE. (J) Volcano plots of differential metabolites between KO-EX and KO-SE. (K) Volcano plots of differential metabolites between KO-rmFGF21-EX and KO-EX. (L) Volcano plots of differential metabolites between KO-EX and WT-EX. Green points indicate metabolites mapped from key human metabolites. (M) Heatmap of metabolites associated with exercise that exhibited FGF21-related patterns. HFHC, high-fat, high-cholesterol; FDR, false discovery rate; FC, fold change; LPC, lysophosphatidylcholine; NS, not significant; PC, phosphatidylcholine; FA, fatty acid. aP<0.05, bP<0.01.
We then performed untargeted metabolomic profiling of post-intervention serum samples. PLS-DA revealed clear separation among the five experimental groups (Fig. 5H). Exercise significantly altered 46 metabolites in WT mice, whereas only four metabolites changed in exercised FGF21 KO mice (Fig. 5I and J), underscoring the critical role of FGF21 in exercise-induced metabolic remodeling. Under exercise conditions, the KO metabolome deviated from that of WT mice, and rmFGF21 largely reversed this divergence (Fig. 5K and L). Pathway enrichment analysis in exercised WT mice identified significant enrichment of pantothenate and coenzyme A biosynthesis, glutathione metabolism, ether lipid metabolism, and thiamine metabolism. These responses were attenuated or altered in KO mice but were restored towards WT levels by rmFGF21 treatment (Supplementary Fig. 7). Through group comparisons, we identified 23 exercise-related metabolites regulated by FGF21. Heatmap visualization revealed coherent patterns across the five groups (Fig. 5M). To link mouse and human data, we mapped 19 key human metabolites onto the mouse dataset; seven showed significant group-wise differences (Supplementary Table 10). Notably, two metabolites highlighted in the human analysis, L-acetylcarnitine and LPC(18:0), decreased with exercise in WT mice, showed no significant change in KO mice, and were rescued by rmFGF21 (Supplementary Fig. 8), exemplifying an FGF21-dependent lipid-carnitine signature. Collectively, these results demonstrate that FGF21 is essential for exercise-induced remodeling of energy and lipid metabolism, and that rmFGF21 restores these effects toward WT levels.
DISCUSSION
In this study, only a subset of patients with T2DM exhibited a reduction in 24-hour glucose CV the day after a 60-minute aerobic exercise session, whereas others did not. Untargeted metabolomics identified 20 baseline metabolites that differed significantly between the two groups. Responders displayed a significantly greater post-exercise increase in serum FGF21 than non-responders. Three baseline metabolites, p-cresol sulfate, dimethylglycine, and 6-bromotryptophan, were strongly associated with the magnitude of the FGF21 response. Longitudinal metabolomic profiling revealed that metabolic divergence between responders and non-responders was driven chiefly by lipid metabolism. Mediation analysis implicated L-acetylcarnitine, L-carnitine, several FAs, and complex lipids as intermediaries linking FGF21 to participant classification. In vivo experiments supported the indispensability of FGF21 for the activation of energy and lipid metabolic pathways induced by exercise. Genetic deletion of FGF21 abolished exercise-induced metabolic reprogramming, whereas administration of rmFGF21 reinstated these effects. These findings indicate a mechanistic link among baseline metabolites, the post-exercise FGF21 surge, downstream metabolic responses, and subsequent glycemic outcomes.
Several studies show that the glucose-lowering effect of exercise is highly heterogeneous [14,15]. This variability is modulated by baseline glycemic status (e.g., higher HbA1c), cardiorespiratory fitness (e.g., VO2max), exercise timing, and molecular features including gut microbiota composition and the plasma proteome [16-18]. Notably, elevated day-to-day glucose variability-reflected by higher 24-hour CV and SD predicts more frequent hypoglycemia and increased vascular stiffness, even when mean glucose levels are comparable [19,20]. Clarifying the determinants of this variability is therefore of considerable clinical importance.
In our cohort, exercise exerted a greater effect on 24-hour CV and SD than on MSG, indicating a delayed improvement in glycemic stability. Correlation and mediation analyses attributed this benefit to FGF21-driven lipid reprogramming. In responders, the post-exercise surge in FGF21 activated the carnitine shuttle (e.g., L-acetylcarnitine, L-carnitine) and stimulated FA oxidation (e.g., FA[20:5], FA[24:6]). Consistent with its known actions, FGF21 enhances hepatic FA oxidation, suppresses lipogenesis and gluconeogenesis, and improves dyslipidemia and hyperglycemia by promoting adipose lipolysis, mitochondrial activity, and glucose uptake [21–24]. Mechanistically, FGF21 engages several key signaling nodes, including adenosine monophosphate-activated protein kinase-sirtuin 1 (AMPK-Sirt1) [25], peroxisome proliferator-activated receptor (PPAR) alpha [26,27] and gamma [28], Jumonji domain-containing protein 3 [29], the mechanistic target of rapamycin complex 1 (mTORC1) [30], and the cyclic adenosine monophosphate response element-binding protein (CREB)/CREB-regulated transcription coactivator 2 [31]. By accelerating mitochondrial lipid utilization, this program limits the accumulation of lipotoxic intermediates and enhances hepatic and skeletal-muscle insulin sensitivity [32,33]. Enhanced peripheral insulin sensitivity may therefore explain post-exercise glucose stability, with improvements persisting for up to 24 to 48 hours [34,35]. Collectively, these adaptations probably underpin the improved glucose variability observed on the following day. Our murine experiments corroborated this mechanism: FGF21 KO abolished the lipid-metabolism response to exercise, whereas rmFGF21 reinstated it and restored glucose tolerance. Taken together, these data position FGF21 as a central regulator of post-exercise metabolic adaptation, orchestrating a lipid-centered network that safeguards subsequent glycemic control.
Several baseline metabolites were strongly correlated with the post-exercise increase in FGF21, suggesting that they may reflect gut microbiota or hepatic metabolic status, which may modulate hepatic FGF21 expression via the gut-liver axis. For instance, dimethylglycine is an intermediate in betaine catabolism [36]. Dietary betaine supplementation elevates hepatic and circulating FGF21, improves glucose homeostasis, reduces hepatic lipid accumulation, and enhances white adipose tissue oxidation in mice [37]. Similarly, the gut-derived metabolite p-cresol sulfate increases when the intestinal barrier is compromised or microbial metabolic pathways are altered, and its accumulation may potentiate the hepatic response to exercise [38]. FAP, a member of the dipeptidyl peptidase 4 (DPP4) gene family, cleaves both the N- and C-termini of FGF21, thereby attenuating its signaling pathways [39]. DPP4 has recently been identified as a microbial-host isoenzyme; gut microbial DPP4 disrupts glucose homeostasis by reducing active glucagon-like peptide 1 levels in high-fat diet mouse models [40]. Collectively, these observations implicate specific microbial and hepatic metabolites as potential upstream modulators of the FGF21 response to exercise.
Metabolic responses after exercise exhibit a temporal cascade: metabolites in pattern 1 (glycolytic and TCA cycle-related metabolites) demonstrate a rapid response (0 to 60 minutes), which may improve acute glycemic control, whereas lipid metabolites in patterns 2 and 3 increase with a delay (60 to 120 minutes), thereby facilitating glycemic control the following day. It is well-established that the body prioritizes glycogen as an energy source during aerobic exercise, while lipid metabolism also serves as a significant energy source; moreover, alterations in lipid-metabolism after exercise often persist for an extended period [41,42]. The temporal cascade identified in our study reveals a potential action pattern of FGF21: its secretion occurs later than the immediate glucose metabolic response following exercise, but promotes FA oxidation and activates the carnitine shuttle system, thereby shifting the principal energy substrates from carbohydrates to lipids during the post-exercise recovery phase and ultimately enhancing insulin sensitivity in target tissues [43,44]. Therefore, from a clinical perspective, regulating specific microbial and hepatic metabolites, as well as the bioavailability of FGF21, may optimize the effects of exercise on glycemic control.
The limitations of this study should also be acknowledged. First, the study examined only a single session of aerobic exercise; the behavior of FGF21 during long-term exercise intervention or alternative exercise modalities remains unexplored. Second, although we identified several baseline metabolites, particularly metabolites of microbial origin, that were associated with delayed glycemic improvement, causality has not been established. Additional research is required to define the relationship between gut microbiota composition and post-exercise glycemic control. Third, several other candidate metabolites that may mediate the link between the FGF21 response and delayed glycemic improvement were identified only statistically. These associations require further validation.
SUPPLEMENTARY MATERIALS
Supplementary materials related to this article can be found online at https://doi.org/10.4093/dmj.2024.0814.
Characteristics of participants in the resting and acute exercise groups
Changes in glucose metabolism indicators of participants in the response and non-response groups
Dietary intake of participants in the response and non-response groups before and after the intervention over a period of 3 days
Metabolites that differed significantly between the response and non-response groups at baseline
Results of one-way ANOVA and mixed model between the response and non-response groups
Comparison of the area under the curve for metabolite clusters between the response and non-response groups
Correlations between the area under the curve of each metabolite cluster and clinical characteristics
Time-delayed correlation analysis between differential metabolites and FGF21
Metabolites serve as mediators in the association between FGF21 with grouping (response or non-response groups)
Group-wise contrasts for human key metabolites mapped to the mouse model
Time-course changes in metabolic parameters following acute exercise. Time-course changes in (A) glucose, (B) insulin, and (C) C-peptide levels in the resting group (RE; n=15) and the acute exercise groups (AE; n=56) are depicted. (D-H) present the changes in 24-hour mean sensor glucose (MSG), standard deviation (SD), coefficient of variation (CV), time in range (TIR), and time in tight range (TITR) in the RE and AE groups. Time-course changes and the area under the curve (AUC) for the fold-change in (I) serum fibroblast growth factor 21 (FGF21) and (J) free fatty acids (FFA) post-exercise in the RE and AE groups. Data are presented as mean±standard error of the mean. Analyses were conducted after adjusting for clinical variables and exercise intensity. White circles represent the RE group, while black squares represent the AE group. Pre_Day, pre-exercise day; Post_Day, post-exercise day; EX, exercise. aP<0.05 for significant differences between groups, bP<0.01, cP<0.001.
Heart rates of participants in the response (R) and non-response (NR) groups during the intervention. (A) Changes in heart rate before and after exercise for participants in the R and NR groups. (B) Mean heart rate and (C) maximum heart rate (HRmax) between the R and NR groups. Data are presented as mean±standard error of the mean. Statistical comparisons were performed using unpaired t-tests. Red traces and points represent the R group, while blue traces and points represent the NR group.
An external cohort also showed heterogeneity in delayed glycemic improvement after exercise in patients with type 2 diabetes mellitus, and this heterogeneity was associated with the post-exercise response of fibroblast growth factor 21 (FGF21). (A-E) illustrate the changes in 24-hour mean sensor glucose (MSG), standard deviation (SD), coefficient of variation (CV), time in range (TIR), and time in tight range (TITR) in the response (R; n=6) and non-response (NR; n=10) groups after adjustments for clinical variables and exercise intensity; (F, G) show the time-course changes and the area under the curve (AUC) for the fold-change in serum FGF21 and free fatty acids (FFA) post-exercise in the R and NR groups. Data are presented as mean±standard error of the mean. Pre_Day, pre-exercise day; Post_Day, post-exercise day; EX, exercise. aP<0.05 for significant differences between groups, bP<0.01.
Relationship between baseline differential metabolites and the post-exercise serum fibroblast growth factor 21 (FGF21) response. Correlations of (A) p-cresol sulfate, (B) dimethylglycine, and (C) 6-bromotryptophan with the area under the curve (AUC) for the fold-change in serum FGF21 are presented.
Changes in circulating metabolites following acute exercise in patients with type 2 diabetes mellitus. (A) Score plot of partial least squares-discriminant analysis across seven time points. (B) Top 50 time-differential metabolites ranked by variable importance projection (VIP) scores from the partial least-squares discriminant analysis model. (C) Number and direction of metabolites with significant changes over intervention time in the acute exercise group and resting group (false discovery rate <0.05). (D) Clustering of longitudinal trajectories based on circulating metabolites. Post 2 min, 2-minute immediately after acute exercise. 8-ETE, 8-hydroxyeicosatetraenoic acid; FA, fatty acid; LPC, lysophosphatidylcholine.
Multi-timepoint untargeted serum metabolomics analysis in the independent validation cohort before and after exercise. (A) Score plot of partial least squares-discriminant analysis for the response group (red) and non-response group (blue) at baseline and at 2, 15, 30, 60, 90, and 120 minutes after exercise. (B) Heatmap illustrating the dynamic changes of key differential metabolites between the two groups at each time point; each row represents a metabolite and the color indicates the normalized score. (C) Pathway enrichment analysis of key differential metabolites. Dot colors indicate P values and size represents enrichment ratio. (D-G) Mediation analysis demonstrates the role of specific metabolites in mediating the effect of fibroblast growth factor 21 (FGF21) on post-exercise glycemic improvement. Examples shown are (D) L-acetylcarnitine, (E) fatty acid (FA) (24:5), (F) FA(20:5), and (G) lysophosphatidylcholine (LPC)(15:0). Statistical results for each pathway—including average causal mediation effect (ACME), average direct effect (ADE), 95% confidence interval (CI), and P value—are marked next to the arrows. LPE, lysophosphatidylethanolamine; PE, phosphatidylethanolamine; CoA, coenzyme A; TCA, tricarboxylic acid; NR, non-response group; R, response group.
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway over-representation analysis for key groupwise contrasts. (A) wild-type exercised (WT-EX) vs. wild-type sedentary (WT-SE), (B) knockout exercised (KO-EX) vs. knockout sedentary (KO-SE), (C) KO-EX vs. WT-EX, (D) exercised with recombinant mouse FGF21 supplemented (KO-rmFGF21-EX) vs. KO-EX. Dot colors indicate P values and size represents enrichment ratio. CoA, coenzyme A; TCA, tricarboxylic acid.
Comparisons of normalized metabolite intensities across mouse groups for two key cross-species-consistent metabolites. (A) L-acetylcarnitine and (B) lysophosphatidylcholine (LPC)(18:0). Boxes depict the median and interquartile range; points represent individual mice (n=9). WT-SE, wild-type sedentary; WT-EX, wild-type exercised; KO-SE, knockout sedentary; KO-EX, knockout exercised; KO-rmFGF21-EX, knockout exercised with recombinant mouse FGF21 supplemented. aFalse discovery rate (FDR) <0.05 for significant differences between groups, bFDR <0.01, cFDR <0.001.
Notes
CONFLICTS OF INTEREST
No potential conflict of interest relevant to this article was reported.
AUTHOR CONTRIBUTIONS
Conception or design: X.L., Q.F., Y.W., G.X., H.L.
Acquisition, analysis or interpretation of data: Y.Z., D.L., Y.L. (Yurun Lu), P.K., X.L., Q.W., A.C., D.C., L.W., Q.L., X.W., Y.L. (Yanli Li), Y.Y., J.Y., J.N.
Drafting the work or revising: Y.Z., D.L., X.L., Q.F., Z.H., A.X., W.J., Y.W., G.X., H.L.
Final approval of the manuscript: all authors.
FUNDING
This study was supported by the National Key Research and Development Program of China (2022YFA1004804) to Huating Li and Weiping Jia; the Excellent Young Scientists Fund of NSFC (82022012), General Fund of NSFC (82270907), Major Program of NSFC (92357305), Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX202 12700) to Huating Li; the Shanghai Municipal Key Clinical Specialty (2017ZZ01013), Shanghai Research Center for Endocrine and Metabolic Diseases (2022ZZ01002) to Weiping Jia; the Youth Innovation Promotion Association of the Chinese Academy of Sciences (2021186) to Xinyu Liu; the National Natural Science Foundation of China (82100879), Shanghai Pujiang Program (2020PJD044), and Exploration Fund Grant of Shanghai Sixth People’s Hospital (ynts202003) to Liang Wu; the National Natural Science Foundation of China (82500987) to Dan Liu.
ACKNOWLEDGMENTS
We thank all participants and investigators for their contributions to this study. We appreciate Nanjing Kuanyue Health Technology Co., Ltd. for supporting the exercise equipment. The graphical abstract was created using BioRender.com.