Early Metabolic Markers of Dysglycemia and Type 2 Diabetes
Data of RISC participants are grouped in Table 1 in 1,115 NGT subjects by quartile of insulin sensitivity (M value from clamp), IGR, and T2D subjects. Familial diabetes, age, BMI, 2-h glucose, fasting insulin, and fasting FFA concentrations were progressively higher along M quartiles in NGT, IGR, and T2D subjects, whereas β-GS was progressively lower. Across these groups, fasting levels of α-HB were progressively higher, whereas L-GPC concentrations showed an inverse gradient, with respective highest and lowest levels observed in T2D.
After adjusting for sex, age, BMI, and study site, α-HB and L-GPC concentrations were directly and inversely associated with insulin sensitivity (M), respectively (adjusted r = 0.33 and 0.34, respectively, both P < 0.0001; Supplementary Fig. 1). In the same model, α-HB was reciprocally related to β-cell function (β-GS, partial r = −0.11, P = 0.0002), whereas L-GPC was unrelated. By defining IR as an M value in the bottom quartile of NGT subjects (<39 μmol · min · kgFFM), α-HB concentration in the top quartile of its own distribution (>5.48 μg/mL) confers an IR risk of 2.84 (95% CI 2.04–3.95), whereas an L-GPC concentration in the bottom quartile of its distribution (<11.78 μg/mL) confers a risk of 3.14 (2.19–4.52). In the 122 NGT subjects falling in the highest α-HB quartile and the lowest L-GPC quartile, the risk of IR is 4.14 (2.60–6.70). Prediction of IR (defined as above) increases from a ROC of 0.801 when using familial diabetes, sex, age, and BMI as predictors to a ROC of 0.837 upon adding α-HB and L-GPC measurements.
(Enlarge Image)
Figure 1.
Multivariate logistic regression for incident dysglycemia (RISC) or T2D (Botnia). Odds ratios (95% CI) for α-HB are 1.25 (1.00–1.60) and 1.26 (1.07–1.48), respectively, for RISC and Botnia cohorts. The corresponding odds ratios (95% CI) for L-GPC are 0.64 (0.48–0.85) and 0.67 (0.54–0.84). Odds ratios are calculated for 1 SD of the explanatory variables. (A high-quality color representation of this figure is available in the online issue.)
Data of Botnia participants are given in Table 2 by quartile of eM (Stumvoll index) for the 1,811 NGT subjects and separately for 642 IGR subjects. By study design, prevalence of familial diabetes was high in this cohort and similar across eM quartiles in NGT. Age, BMI, fasting and 2-h glucose concentrations, and fasting insulin concentrations were progressively higher across eM quartiles and IGR, whereas β-cell function (as measured by I-to-G ratio) was highest in the most IR quartile and lower in IGR subjects, indicating incipient β-cell failure. α-HB levels were progressively higher across these groups, whereas L-GPC concentrations showed an inverse gradient. In the whole cohort, eM was negatively associated with α-HB and positively related to L-GPC (partial r = –0.19 and 0.22, respectively, both P < 0.0001) after adjusting for familial diabetes, sex, age, and BMI. In the same adjusted model, the I-to-G ratio was reciprocally related to α-HB (partial r = −0.09, P < 0.0001), whereas L-GPC was unrelated.
In RISC (Table 3), GT was still normal in 779 subjects (stable NGT) and had deteriorated in 123 (progressors). The baseline clinical phenotype of progressors included more familial diabetes, higher age, BMI, fasting glucose, 2-h glucose, and fasting insulin concentrations. Furthermore, progressors were more IR and (β-cell)–glucose insensitive and had higher α-HB and lower L-GPC concentrations. At follow-up, the α-HB had decreased in stable NGT subjects (by 0.27 [interquartile range, 2.00] μg/mL) and increased in progressors (by 0.41 [2.1] μg/mL), the difference being significant (P = 0.0003). By contrast, the L-GPC had increased in stable NGT (by 0.69 [6.4] μg/mL) and decreased in progressors (by 0.04 [4.8] μg/mL), this difference too being significant (P < 0.05; Table 3).
Among the 2,580 Botnia participants, 151 had developed T2D at the 9.5-year follow-up visit (Table 4). The baseline clinical and metabolic characteristics of Botnia T2D progressors versus nonprogressors were very similar to those of RISC progressors; again, α-HB levels were higher and L-GPC levels were lower.
The predictivity of α-HB and L-GPC for incident dysglycemia (RISC) or T2D (Botnia) was evaluated in multivariate models including classical predictors. We found generally similar odds ratios between the two populations. BMI and fasting glucose were positive predictors in both cohorts, whereas familial diabetes and age were stronger positive predictors in Botnia than RISC. In both cohorts, baseline α-HB was a positive predictor and L-GPC a negative predictor of almost superimposable strength (Fig. 1).
We next compared the ability of α-HB and L-GPC to predict dysglycemia/T2D with that of traditional clinical models. As detailed in Table 5, adding fasting plasma glucose to the standard clinical predictors (familial diabetes, sex, age, and BMI) increased the ROC area under the curve by 0.044 in RISC and 0.017 in Botnia. Upon adding also the 2-h postglucose plasma glucose concentration, ROC area rose by a further 0.024 in RISC and 0.022 in Botnia. By replacing 2-h glucose with α-HB and L-GPC, very similar ROCs were observed in RISC (0.790) and Botnia (0.783). Finally, adding α-HB and L-GPC to both fasting and 2-h glucose improved the ROC by 0.018 in RISC and by 0.008 in Botnia. Thus the two biomarkers matched the predictivity of an OGTT (fasting insulin making a negligible contribution) in both cohorts. Interestingly, including the actual measurements of insulin sensitivity and β-cell function in the 2-h glucose model yielded ROC values of 0.817 and 0.794 in RISC and Botnia, respectively (i.e., only 0.016 and 0.011 higher than the fasting biomarkers model).
Because levels of α-HB and L-GPC were predictive of dysglycemia, we also tested their activity on insulin secretion in INS-1e cells. Insulin release increased as glucose concentrations rose from 3.3 to 20.0 mmol/L and was potentiated by adding arginine to 20.0 mmol/L glucose. Overall, preincubation with α-HB inhibited, and preincubation with L-GPC potentiated, glucose- and glucose/arginine-induced insulin release in a dose-dependent manner (Supplementary Fig. 2). More specifically, the effects of α-HB and L-GPC both appeared to be exerted on insulin secretion at low glucose concentrations. For example, L-GPC dose-dependently increased insulin release at 3.3 mmol/L glucose (from 42 ± 6 to 73 ± 13 ng/mL at the highest L-GPC dose, P < 0.01), and α-HB tended to dose-dependently decrease insulin release at 3 mmol/L glucose.
(Enlarge Image)
Figure 2.
Relationship between L-GPC, α-HB, BCAA, and oleate levels and insulin sensitivity. Fasting plasma concentrations of the BCAAs (sum of leucine, isoleucine, and valine) by quartile of α-HB concentrations in 542 subjects from the Botnia study are shown. Also plotted are plasma L-GPC and oleate concentrations, and eM values by quartile of α-HB. Plots are mean ± SEM. Note that the horizontal scale for oleate and L-GPC has been shifted to avoid overlapping symbols.
To further examine these biomarkers in the context of other metabolic pathways in vivo, we measured amino acids and fatty acids in 542 representative subjects from Botnia (Supplementary Table 1), with progressors and nonprogressors having a similar clinical phenotype as the entire cohort (compared with Table 2). Notably, branched-chain amino acids (BCAAs; leucine, isoleucine, valine) and three major glucogenic amino acids (alanine, glutamate, arginine) were increased, whereas glycine was significantly decreased, in progressors versus nonprogressors. Increased concentrations of BCAAs and fatty acids, such as oleate, were positively related to α-HB, whereas L-GPC and insulin sensitivity were reciprocally related to α-HB (Fig. 2). In addition, oleate and L-GPC were reciprocally related to one another (with partial r of –0.23, P < 0.0001 after adjusting for sex, age, BMI; Supplementary Fig. 3).
(Enlarge Image)
Figure 3.
Reconstruction of the metabolic pathway featuring α-HB and L-GPC. Unmeasured metabolites are in italic, and statistically significant changes between progressors and nonprogressors are indicated in red. α-KB, α-ketobutyrate; GSH, glutathione; oc-FFA, odd-chain FFA. See text for further explanation.
Results
Baseline
Data of RISC participants are grouped in Table 1 in 1,115 NGT subjects by quartile of insulin sensitivity (M value from clamp), IGR, and T2D subjects. Familial diabetes, age, BMI, 2-h glucose, fasting insulin, and fasting FFA concentrations were progressively higher along M quartiles in NGT, IGR, and T2D subjects, whereas β-GS was progressively lower. Across these groups, fasting levels of α-HB were progressively higher, whereas L-GPC concentrations showed an inverse gradient, with respective highest and lowest levels observed in T2D.
After adjusting for sex, age, BMI, and study site, α-HB and L-GPC concentrations were directly and inversely associated with insulin sensitivity (M), respectively (adjusted r = 0.33 and 0.34, respectively, both P < 0.0001; Supplementary Fig. 1). In the same model, α-HB was reciprocally related to β-cell function (β-GS, partial r = −0.11, P = 0.0002), whereas L-GPC was unrelated. By defining IR as an M value in the bottom quartile of NGT subjects (<39 μmol · min · kgFFM), α-HB concentration in the top quartile of its own distribution (>5.48 μg/mL) confers an IR risk of 2.84 (95% CI 2.04–3.95), whereas an L-GPC concentration in the bottom quartile of its distribution (<11.78 μg/mL) confers a risk of 3.14 (2.19–4.52). In the 122 NGT subjects falling in the highest α-HB quartile and the lowest L-GPC quartile, the risk of IR is 4.14 (2.60–6.70). Prediction of IR (defined as above) increases from a ROC of 0.801 when using familial diabetes, sex, age, and BMI as predictors to a ROC of 0.837 upon adding α-HB and L-GPC measurements.
(Enlarge Image)
Figure 1.
Multivariate logistic regression for incident dysglycemia (RISC) or T2D (Botnia). Odds ratios (95% CI) for α-HB are 1.25 (1.00–1.60) and 1.26 (1.07–1.48), respectively, for RISC and Botnia cohorts. The corresponding odds ratios (95% CI) for L-GPC are 0.64 (0.48–0.85) and 0.67 (0.54–0.84). Odds ratios are calculated for 1 SD of the explanatory variables. (A high-quality color representation of this figure is available in the online issue.)
Data of Botnia participants are given in Table 2 by quartile of eM (Stumvoll index) for the 1,811 NGT subjects and separately for 642 IGR subjects. By study design, prevalence of familial diabetes was high in this cohort and similar across eM quartiles in NGT. Age, BMI, fasting and 2-h glucose concentrations, and fasting insulin concentrations were progressively higher across eM quartiles and IGR, whereas β-cell function (as measured by I-to-G ratio) was highest in the most IR quartile and lower in IGR subjects, indicating incipient β-cell failure. α-HB levels were progressively higher across these groups, whereas L-GPC concentrations showed an inverse gradient. In the whole cohort, eM was negatively associated with α-HB and positively related to L-GPC (partial r = –0.19 and 0.22, respectively, both P < 0.0001) after adjusting for familial diabetes, sex, age, and BMI. In the same adjusted model, the I-to-G ratio was reciprocally related to α-HB (partial r = −0.09, P < 0.0001), whereas L-GPC was unrelated.
Follow-up
In RISC (Table 3), GT was still normal in 779 subjects (stable NGT) and had deteriorated in 123 (progressors). The baseline clinical phenotype of progressors included more familial diabetes, higher age, BMI, fasting glucose, 2-h glucose, and fasting insulin concentrations. Furthermore, progressors were more IR and (β-cell)–glucose insensitive and had higher α-HB and lower L-GPC concentrations. At follow-up, the α-HB had decreased in stable NGT subjects (by 0.27 [interquartile range, 2.00] μg/mL) and increased in progressors (by 0.41 [2.1] μg/mL), the difference being significant (P = 0.0003). By contrast, the L-GPC had increased in stable NGT (by 0.69 [6.4] μg/mL) and decreased in progressors (by 0.04 [4.8] μg/mL), this difference too being significant (P < 0.05; Table 3).
Among the 2,580 Botnia participants, 151 had developed T2D at the 9.5-year follow-up visit (Table 4). The baseline clinical and metabolic characteristics of Botnia T2D progressors versus nonprogressors were very similar to those of RISC progressors; again, α-HB levels were higher and L-GPC levels were lower.
The predictivity of α-HB and L-GPC for incident dysglycemia (RISC) or T2D (Botnia) was evaluated in multivariate models including classical predictors. We found generally similar odds ratios between the two populations. BMI and fasting glucose were positive predictors in both cohorts, whereas familial diabetes and age were stronger positive predictors in Botnia than RISC. In both cohorts, baseline α-HB was a positive predictor and L-GPC a negative predictor of almost superimposable strength (Fig. 1).
We next compared the ability of α-HB and L-GPC to predict dysglycemia/T2D with that of traditional clinical models. As detailed in Table 5, adding fasting plasma glucose to the standard clinical predictors (familial diabetes, sex, age, and BMI) increased the ROC area under the curve by 0.044 in RISC and 0.017 in Botnia. Upon adding also the 2-h postglucose plasma glucose concentration, ROC area rose by a further 0.024 in RISC and 0.022 in Botnia. By replacing 2-h glucose with α-HB and L-GPC, very similar ROCs were observed in RISC (0.790) and Botnia (0.783). Finally, adding α-HB and L-GPC to both fasting and 2-h glucose improved the ROC by 0.018 in RISC and by 0.008 in Botnia. Thus the two biomarkers matched the predictivity of an OGTT (fasting insulin making a negligible contribution) in both cohorts. Interestingly, including the actual measurements of insulin sensitivity and β-cell function in the 2-h glucose model yielded ROC values of 0.817 and 0.794 in RISC and Botnia, respectively (i.e., only 0.016 and 0.011 higher than the fasting biomarkers model).
In Vitro Studies
Because levels of α-HB and L-GPC were predictive of dysglycemia, we also tested their activity on insulin secretion in INS-1e cells. Insulin release increased as glucose concentrations rose from 3.3 to 20.0 mmol/L and was potentiated by adding arginine to 20.0 mmol/L glucose. Overall, preincubation with α-HB inhibited, and preincubation with L-GPC potentiated, glucose- and glucose/arginine-induced insulin release in a dose-dependent manner (Supplementary Fig. 2). More specifically, the effects of α-HB and L-GPC both appeared to be exerted on insulin secretion at low glucose concentrations. For example, L-GPC dose-dependently increased insulin release at 3.3 mmol/L glucose (from 42 ± 6 to 73 ± 13 ng/mL at the highest L-GPC dose, P < 0.01), and α-HB tended to dose-dependently decrease insulin release at 3 mmol/L glucose.
(Enlarge Image)
Figure 2.
Relationship between L-GPC, α-HB, BCAA, and oleate levels and insulin sensitivity. Fasting plasma concentrations of the BCAAs (sum of leucine, isoleucine, and valine) by quartile of α-HB concentrations in 542 subjects from the Botnia study are shown. Also plotted are plasma L-GPC and oleate concentrations, and eM values by quartile of α-HB. Plots are mean ± SEM. Note that the horizontal scale for oleate and L-GPC has been shifted to avoid overlapping symbols.
Amino Acid Profile
To further examine these biomarkers in the context of other metabolic pathways in vivo, we measured amino acids and fatty acids in 542 representative subjects from Botnia (Supplementary Table 1), with progressors and nonprogressors having a similar clinical phenotype as the entire cohort (compared with Table 2). Notably, branched-chain amino acids (BCAAs; leucine, isoleucine, valine) and three major glucogenic amino acids (alanine, glutamate, arginine) were increased, whereas glycine was significantly decreased, in progressors versus nonprogressors. Increased concentrations of BCAAs and fatty acids, such as oleate, were positively related to α-HB, whereas L-GPC and insulin sensitivity were reciprocally related to α-HB (Fig. 2). In addition, oleate and L-GPC were reciprocally related to one another (with partial r of –0.23, P < 0.0001 after adjusting for sex, age, BMI; Supplementary Fig. 3).
(Enlarge Image)
Figure 3.
Reconstruction of the metabolic pathway featuring α-HB and L-GPC. Unmeasured metabolites are in italic, and statistically significant changes between progressors and nonprogressors are indicated in red. α-KB, α-ketobutyrate; GSH, glutathione; oc-FFA, odd-chain FFA. See text for further explanation.
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