Patients with obesity have a high risk of developing diabetes. There is, however, heterogeneity in the susceptibility to diabetes. Particularly young patients, children and adolescents can show a more rapid deterioration of glycemic control with increased mortality risk.1 On the other hand, there is a window of opportunity for sustained delay or prevention of diabetes, if treatment is initiated early and aggressively.2,3 It is, therefore, important to identify these patients at highest risk for precision prevention. For this, the right parameters with the correct cut-offs are essential.
We appreciate the interest of Adam-Hassan et al.4 in our recently published cut-offs for fasting indices of glucose-insulin metabolism5 and we completely agree with the potential benefits that an insulin-independent index offers. We here expand the portfolio of fasting indices on the TyG index.6 Of note, there is a controversy regarding the right formula of TyG index between initial publication6 and subsequent correction7 with the later formula used in here (Table 1) propagated to be used. In addition, we have incorporated another insulin-independent index: the “single point insulin sensitivity estimator” (SPISE index).8 Similar to the TyG index, the SPISE index employs easily accessible and frequently measured fasting parameters of blood lipids (triglycerides (TG), high-density lipoprotein cholesterol (HDL-C)) along with body mass index (BMI). It has been validated in adults and adolescents in relation to the clamp method8 and serves as a surrogate marker for nonalcoholic fatty liver disease, type 2 diabetes and abdominal obesity.9, 10, 11, 12 Cut-off values covering childhood to old age are lacking for both TyG and SPISE index.
Table 1.
Age-specific reference values for TyG and SPISE index in children and adults.
| Age groups (years) | N | P2.5 (CI 90%) | P5 (CI 90%) | P10 (CI 90%) | P25 (CI 90%) | P50 (CI 90%) | P75 (CI 90%) | P90 (CI 90%) | P95 (CI 90%) | P97.5 (CI 90%) |
|---|---|---|---|---|---|---|---|---|---|---|
| TyG index | ||||||||||
| 5–8 | 436 | 3.9 (3.8–3.9) | 3.9 (3.9–3.9) | 4.0 (3.9–4.0) | 4.1 (4.0–4.1) | 4.2 (4.2–4.2) | 4.3 (4.3–4.3) | 4.5 (4.4–4.5) | 4.6 (4.6–4.7) | 4.7 (4.6–4.8) |
| 9–10 | 446 | 3.9 (3.8–3.9) | 3.9 (3.9–4.0) | 4.0 (4.0–4.0) | 4.1 (4.1–4.1) | 4.3 (4.3–4.3) | 4.4 (4.4–4.5) | 4.6 (4.6–4.7) | 4.8 (4.7–4.9) | 4.9 (4.9–5) |
| 11–15 | 888 | 4.0 (3.9–4.0) | 4.0 (4.0–4.0) | 4.1 (4.1–4.1) | 4.2 (4.2–4.2) | 4.3 (4.3–4.3) | 4.5 (4.5–4.5) | 4.7 (4.6–4.7) | 4.8 (4.7–4.8) | 4.8 (4.8–4.9) |
| 16–23 | 324 | 4.0 (3.9–4.0) | 4.0 (4.0–4.1) | 4.1 (4.1–4.1) | 4.2 (4.2–4.3) | 4.4 (4.4–4.4) | 4.5 (4.5–4.6) | 4.7 (4.6–4.7) | 4.8 (4.7–4.8) | 4.8 (4.8–4.9) |
| 24–59 | 2644 | 4.1 (4.1–4.1) | 4.1 (4.1–4.2) | 4.2 (4.2–4.2) | 4.3 (4.3–4.3) | 4.5 (4.5–4.5) | 4.7 (4.7–4.7) | 4.9 (4.9–4.9) | 5.0 (5.0–5.0) | 5.1 (5.1–5.1) |
| 60–80 | 1047 | 4.2 (4.2–4.2) | 4.3 (4.2–4.3) | 4.3 (4.3–4.3) | 4.4 (4.4–4.4) | 4.6 (4.6–4.6) | 4.7 (4.7–4.7) | 4.9 (4.8–4.9) | 5.0 (4.9–5.0) | 5.0 (5.0–5.1) |
| SPISE index | ||||||||||
| 5–8 | 444 | 8.7 (8.1–9.1) | 9.8 (9.3–10.3) | 11.0 (10.6–11.6) | 13.0 (12.6–13.4) | 14.8 (14.5–15.1) | 16.4 (16.2–16.6) | 18.0 (17.7–18.4) | 18.7 (18.5–19.1) | 19.2 (18.9–19.5) |
| 9–10 | 449 | 6.8 (6.4–6.9) | 7.4 (7.0–7.8) | 8.2 (7.9–8.4) | 10.8 (10.4–11.3) | 12.8 (12.6–13.0) | 14.9 (14.6–15.3) | 16.6 (16.1–17.0) | 17.6 (17.3–17.9) | 18.2 (17.9–18.6) |
| 11–15 | 896 | 6.1 (6.0–6.3) | 6.3 (6.1–6.5) | 7.0 (6.8–7.2) | 8.4 (8.2–8.5) | 10.2 (10.0–10.4) | 12.2 (12.0–12.4) | 14.1 (13.7–14.3) | 15.5 (15.4–16.0) | 16.2 (15.6–16.6) |
| 16–23 | 322 | 5.6 (5.3–6.1) | 5.9 (5.6–6.0) | 6.4 (6.0–6.5) | 7.6 (7.3–7.8) | 9.2 (8.9–9.5) | 10.3 (10.1–10.4) | 11.6 (11.4–12.1) | 12.2 (11.9–12.6) | 12.8 (12.5–13.3) |
| 24–59 | 2658 | 4.6 (4.5–4.7) | 4.9 (4.8–5.0) | 5.3 (5.3–5.4) | 6.1 (6.0–6.2) | 7.2 (7.2–7.3) | 8.7 (8.5–8.7) | 10.0 (10.0–10.2) | 10.8 (10.7–10.9) | 11.5 (11.2–11.6) |
| 60–80 | 1047 | 4.7 (4.6–4.8) | 5.0 (4.9–5.0) | 5.3 (5.2–5.4) | 5.9 (5.8–6.0) | 6.7 (6.6–6.8) | 7.7 (7.6–7.8) | 8.9 (8.8–9.0) | 9.5 (9.3–9.6) | 10.2 (10.0–10.7) |
Reference ranges were determined non-parametrically.
CI, confidence interval; N, number of subjects (multiple measurements of one subject within the same age group were randomly excluded); P, percentile; TyG index, triglyceride glucose index (Ln[fasting triglycerides (mg/dL) × fasting glucose (mg/dL)]/2); SPISE index, single-point insulin sensitivity estimator index (600 × HDL-C (mg/dL)0.185/[TG (mg/dL)0.2 × BMI (kg/m2)1.338]).
Using exactly the same methodology and data set as in our original manuscript,5 we provide age-dependent reference ranges for those two indices in Table 1. In line with our previous research, both indices show remarkable dynamics with age. However, their patterns differ from those of the insulin-based indices (Table 1, Supplementary Figure S1). Particularly the well-established pubertal peak in insulin resistance is not reflected by the two insulin-independent indices. There is a strong decline of SPISE index during childhood and adolescence, which is mainly driven by the increase of absolute BMI during that age (data not shown).
When applying the new reference ranges of TyG index and SPISE index to the Leipzig obesity cohort (N = 2538 with 3551 observations) as described previously,5 differences between the reference population and the obesity population were less pronounced for TyG index as compared to SPISE index and insulin-dependent indices (Supplementary Figure S1). E.g., a maximum of 29.2% of subjects with obesity were classified having pathological levels when applying the 97.5th percentile of TyG index as cut-off, compared with 56.6% pathological values of HOMA-IR. On the opposite, the vast majority of subjects with obesity revealed a pathological SPISE index (e.g., 81.9% of preschoolers). These pronounced differences are due to the BMI as a component of SPISE index.
Furthermore, we tested the clinical relevance of the new insulin-independent indices for the prediction of dysglycemia in children and adolescents with obesity in the same manner as we did in the original manuscript. Stratifying the baseline cohort according to the 90th percentile of TyG index revealed a moderately elevated risk of subjects with high TyG index for emerging dysglycemia (adjusted hazard ratio (HR) 1.59 (95% CI 0.98–2.59), p-value of Log rank test 0.027), whereas all other proposed cut-offs of both TyG index and SPISE index did not show any significant results (Supplementary Table S1). In comparison, stratification based on HOMA-IR and fasting insulin as in our original manuscript5 nearly doubled the risk for glycemic deterioration (HOMA-IR HR 1.88 (95% CI 1.1–3.21), fasting insulin HR 1.89 (95% CI 1.11–3.23)), whereas fasting glucose levels did not facilitate any significant prediction.
Hence, TyG index was less precise in predicting dysglycemia than insulin-based indices, but still better than established diabetes markers such as fasting glucose. TyG index showed a lower prevalence of pathological values in the obesity population compared with HOMA-IR and fasting insulin and accordingly was not as sensitive in detecting patients at risk. E.g., out of 77 subjects who developed dysglycemia at follow-up, two thirds (67.1%) would have been detected already at baseline when applying the 90th percentile of insulin-based indices,5 whereas less than half of the subjects (42.9%) showed an elevated TyG index at baseline. On the other hand, due to the high prevalence of pathological SPISE index values among the obesity population no meaningful distinction between “normal” and “pathological” could be achieved by this index. Of note, in a previous analysis of children with obesity, comparing the highest vs. the lowest quartile of SPISE index resulted in better prediction of emerging dysglycemia than using HOMA-IR.12 Thus, SPISE index conveys useful information regarding glucose-insulin-metabolism in general, but detecting a specific cut-off point appears to be difficult.
We, therefore, conclude that also for insulin-independent indices age-dependent cut-offs should be applied. Even less precise, they may be useful as a proxy for insulin resistance and sensitivity if no insulin measures are available. However, insulin-based fasting indices are more powerful in predicting glycemic deterioration and should therefore be preferred for the detection of early disease stages like insulin resistance.
Contributors
RS designed the study, analysed and interpreted the data and drafted the manuscript. EG and CH interpreted the data and drafted the manuscript. CH and RS have directly accessed and verified the underlying data reported in the manuscript. MV was involved in conception of the study, data analysis, and revised the manuscript. WK acquired funding, was involved in study design and revised the manuscript. DW interpreted the data and revised the manuscript. AK designed the study, acquired funding, interpreted the results and revised the manuscript. All authors approved the final version of the manuscript and agreed to be accountable for all aspects of the work.
Declaration of interests
DW received consulting fees, as well as honoraria for lectures and support for attending meetings from Novo Nordisk Austria/Switzerland. All other authors have no conflicts of interest relevant to this article to disclose.
Acknowledgements
This work was supported by the German Research Foundation (DFG) for the Clinical Research Center “Obesity Mechanisms” (grant CRC1052, project number 209933838, subproject C05) and project KO3512/3-1; the Federal Ministry of Education and Research, Germany (grant 01GL1906 SUCCEED); and Leipzig Research Center for Civilization Diseases (LIFE Child), University of Leipzig, which was supported by the European Union, the European Regional Development Fund, and the Free State of Saxony within the framework of the excellence initiative. The assessment of participants from the Leipzig Childhood Obesity Cohort, who reached adulthood during follow-up was facilitated by the clinical trial unit of the Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig. Furthermore, AK acknowledges support by the German Diabetes Association (DDG), the JPI HDHL Metadis funded CarbHealth consortium (01EA1908B). RS was funded by the joint Clinician Scientist Programme of the Medical Faculty and the Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig. EG was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG); project number 493646873—MD-LEICS.
Footnotes
Supplementary data related to this article can be found at https://doi.org/10.1016/j.lanepe.2023.100734.
Appendix A. Supplementary data
References
- 1.Hannon T.S., Arslanian S.A. The changing face of diabetes in youth: lessons learned from studies of type 2 diabetes. Ann N Y Acad Sci. 2015;1353(1):113–137. doi: 10.1111/nyas.12939. [DOI] [PubMed] [Google Scholar]
- 2.Inge T.H., Courcoulas A.P., Jenkins T.M., et al. Five-year outcomes of gastric bypass in adolescents as compared with adults. N Engl J Med. 2019;380(22):2136–2145. doi: 10.1056/NEJMoa1813909. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Körner A., Tschöp M.H., Blüher M. Five-year outcomes of gastric bypass in adolescents as compared with adults. N Engl J Med. 2019;381(9) doi: 10.1056/NEJMc1908751. [DOI] [PubMed] [Google Scholar]
- 4.Adam-Hassan F., Dridi-Brahimi I., Bastard J.-P. Are there relevant thresholds of insulin-independent indices across the lifespan to predict alterations in glycemic control? Lancet Reg Health Eur. 2023:100728. doi: 10.1016/j.lanepe.2023.100728. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Hammel M.C., Stein R., Kratzsch J., et al. Fasting indices of glucose-insulin-metabolism across life span and prediction of glycemic deterioration in children with obesity from new diagnostic cut-offs. Lancet Reg Health Eur. 2023 doi: 10.1016/j.lanepe.2023.100652. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Guerrero-Romero F., Simental-Mendía L.E., González-Ortiz M., et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95(7):3347–3351. doi: 10.1210/jc.2010-0288. [DOI] [PubMed] [Google Scholar]
- 7.Simental-Mendía L.E., Guerrero-Romero F. The correct formula for the triglycerides and glucose index. Eur J Pediatr. 2020;179(7):1171. doi: 10.1007/s00431-020-03644-1. [DOI] [PubMed] [Google Scholar]
- 8.Paulmichl K., Hatunic M., Højlund K., et al. Modification and validation of the triglyceride-to-HDL cholesterol ratio as a surrogate of insulin sensitivity in white juveniles and adults without diabetes mellitus: the single point insulin sensitivity estimator (SPISE) Clin Chem. 2016;62(9):1211–1219. doi: 10.1373/clinchem.2016.257436. [DOI] [PubMed] [Google Scholar]
- 9.Cederholm J., Zethelius B. SPISE and other fasting indexes of insulin resistance: risks of coronary heart disease or type 2 diabetes. Comparative cross-sectional and longitudinal aspects. Ups J Med Sci. 2019;124(4):265–272. doi: 10.1080/03009734.2019.1680583. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Furthner D., Anderwald C.H., Bergsten P., et al. Single point insulin sensitivity estimator in pediatric non-alcoholic fatty liver disease. Front Endocrinol. 2022;13 doi: 10.3389/fendo.2022.830012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Correa-Burrows P., Blanco E., Gahagan S., Burrows R. Validity assessment of the single-point insulin sensitivity estimator (spise) for diagnosis of cardiometabolic risk in post-pubertal hispanic adolescents. Sci Rep. 2020;10(1) doi: 10.1038/s41598-020-71074-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Stein R., Koutny F., Riedel J., et al. Single point insulin sensitivity estimator (SPISE) as a prognostic marker for emerging dysglycemia in children with overweight or obesity. Metabolites. 2023;13(1):100. doi: 10.3390/metabo13010100. [DOI] [PMC free article] [PubMed] [Google Scholar]
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