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Published in: Diabetologia 9/2017

01-09-2017 | Article

One-hour and two-hour postload plasma glucose concentrations are comparable predictors of type 2 diabetes mellitus in Southwestern Native Americans

Authors: Ethan Paddock, Maximilian G. Hohenadel, Paolo Piaggi, Pavithra Vijayakumar, Robert L. Hanson, William C. Knowler, Jonathan Krakoff, Douglas C. Chang

Published in: Diabetologia | Issue 9/2017

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Abstract

Aims/hypothesis

Elevated 2-h plasma glucose concentration (2 h-PG) during a 75 g OGTT predict the development of type 2 diabetes mellitus. However, 1-h plasma glucose concentration (1 h-PG) is associated with insulin secretion and may be a better predictor of type 2 diabetes. We aimed to investigate the association between 1 h-PG and 2 h-PG using gold standard methods for measuring insulin secretion and action. We also compared 1 h-PG and 2 h-PG as predictors of type 2 diabetes mellitus.

Methods

This analysis included adult volunteers without diabetes, predominantly Native Americans of Southwestern heritage, who were involved in a longitudinal epidemiological study from 1965 to 2007, with a baseline OGTT that included measurement of 1 h-PG. Group 1 (n = 716) underwent an IVGTT and hyperinsulinaemic–euglycaemic clamp for measurement of acute insulin response (AIR) and insulin-stimulated glucose disposal (M), respectively. Some members of Group 1 (n = 490 of 716) and members of a second, larger, group (Group 2; n = 1946) were followed-up to assess the development of type 2 diabetes (median 9.0 and 12.8 years follow-up, respectively).

Results

Compared with 2 h-PG (r = −0.281), 1 h-PG (r = −0.384) was more closely associated with AIR, whereas, compared with 1 h-PG (r = −0.340), 2 h-PG (r = −0.408) was more closely associated with M. Measures of 1 h-PG and 2 h-PG had similar abilities to predict type 2 diabetes, which did not change when both were included in the model. A 1 h-PG cut-off of 9.3 mmol/l provided similar levels of sensitivity and specificity as a 2 h-PG cut-off of 7.8 mmol/l; the latter is used to define impaired glucose tolerance, a recognised predictor of type 2 diabetes mellitus.

Conclusions/interpretation

The 1 h-PG was associated with important physiological predictors of type 2 diabetes and was as effective as 2 h-PG for predicting type 2 diabetes mellitus. The 1 h-PG is, therefore, an alternative method of identifying individuals with an elevated risk of type 2 diabetes mellitus.
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Literature
1.
go back to reference American Diabetes Association (2016) 2. Classification and diagnosis of diabetes. Diabetes Care 39:S13–S22CrossRef American Diabetes Association (2016) 2. Classification and diagnosis of diabetes. Diabetes Care 39:S13–S22CrossRef
2.
go back to reference National Diabetes Data Group (1979) Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 28:1039–1057CrossRef National Diabetes Data Group (1979) Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 28:1039–1057CrossRef
3.
go back to reference World Health Organization (1980) World Health Organization expert committee on diabetes mellitus: second report. World Health Organ Tech Rep Ser 646:1–180 World Health Organization (1980) World Health Organization expert committee on diabetes mellitus: second report. World Health Organ Tech Rep Ser 646:1–180
4.
go back to reference World Health Organization (1985) Diabetes mellitus: report of a WHO study group. World Health Org Tech Rep Ser 727:1–113 World Health Organization (1985) Diabetes mellitus: report of a WHO study group. World Health Org Tech Rep Ser 727:1–113
5.
go back to reference Abdul-Ghani MA, Williams K, DeFronzo RA, Stern M (2007) What is the best predictor of future type 2 diabetes? Diabetes Care 30:1544–1548CrossRefPubMed Abdul-Ghani MA, Williams K, DeFronzo RA, Stern M (2007) What is the best predictor of future type 2 diabetes? Diabetes Care 30:1544–1548CrossRefPubMed
6.
go back to reference Alyass A, Almgren P, Akerlund M et al (2015) Modelling of OGTT curve identifies 1 h plasma glucose level as a strong predictor of incident type 2 diabetes: results from two prospective cohorts. Diabetologia 58:87–97CrossRefPubMed Alyass A, Almgren P, Akerlund M et al (2015) Modelling of OGTT curve identifies 1 h plasma glucose level as a strong predictor of incident type 2 diabetes: results from two prospective cohorts. Diabetologia 58:87–97CrossRefPubMed
7.
go back to reference Nielsen ML, Pareek M, Leósdóttir M et al (2016) Follow-up duration influences the relative importance of OGTT and optimal timing of glucose measurements for predicting future type 2 diabetes. Eur J Endocrinol 174:591–600CrossRefPubMed Nielsen ML, Pareek M, Leósdóttir M et al (2016) Follow-up duration influences the relative importance of OGTT and optimal timing of glucose measurements for predicting future type 2 diabetes. Eur J Endocrinol 174:591–600CrossRefPubMed
8.
go back to reference Fiorentino TV, Marini MA, Andreozzi F et al (2015) One-hour post-load hyperglycemia is a stronger predictor of type 2 diabetes than impaired fasting glucose. J Clin Endocrinol Metab 100:3744–3751CrossRefPubMed Fiorentino TV, Marini MA, Andreozzi F et al (2015) One-hour post-load hyperglycemia is a stronger predictor of type 2 diabetes than impaired fasting glucose. J Clin Endocrinol Metab 100:3744–3751CrossRefPubMed
9.
go back to reference Manco M, Panunzi S, Macfarlane DP et al (2010) One-hour plasma glucose identifies insulin resistance and β-cell dysfunction in individuals with normal glucose tolerance. Cross-sectional data from the Relationship between Insulin Sensitivity and Cardiovascular Risk (RISC) study. Diabetes Care 33:2090–2097CrossRefPubMedPubMedCentral Manco M, Panunzi S, Macfarlane DP et al (2010) One-hour plasma glucose identifies insulin resistance and β-cell dysfunction in individuals with normal glucose tolerance. Cross-sectional data from the Relationship between Insulin Sensitivity and Cardiovascular Risk (RISC) study. Diabetes Care 33:2090–2097CrossRefPubMedPubMedCentral
10.
go back to reference Hanson RL, Pratley RE, Bogardus C et al (2000) Evaluation of simple indices of insulin sensitivity and insulin secretion for use in epidemioiogic studies. Am J Epidemiol 151:190–198CrossRefPubMed Hanson RL, Pratley RE, Bogardus C et al (2000) Evaluation of simple indices of insulin sensitivity and insulin secretion for use in epidemioiogic studies. Am J Epidemiol 151:190–198CrossRefPubMed
11.
go back to reference Marini MA, Succerro E, Frontoni S et al (2012) Insulin sensitivity, β-cell function, and incretin effect in individuals with elevated 1-hour postload plasma glucose levels. Diabetes Care 35:869–872 Marini MA, Succerro E, Frontoni S et al (2012) Insulin sensitivity, β-cell function, and incretin effect in individuals with elevated 1-hour postload plasma glucose levels. Diabetes Care 35:869–872
12.
go back to reference Abdul-Ghani MA, Abdul-Ghani T, Ali N, Defronzo RA (2008) One-hour plasma glucose concentration and the metabolic syndrome identify subjects at high risk for future type 2 diabetes. Diabetes Care 31:1650–1655CrossRefPubMedPubMedCentral Abdul-Ghani MA, Abdul-Ghani T, Ali N, Defronzo RA (2008) One-hour plasma glucose concentration and the metabolic syndrome identify subjects at high risk for future type 2 diabetes. Diabetes Care 31:1650–1655CrossRefPubMedPubMedCentral
13.
go back to reference Bergman M, Chetrit A, Roth J, Dankner R (2016) One-hour post-load plasma glucose level during the OGTT predicts mortality: observations from the Israel Study of Glucose Intolerance, Obesity and Hypertension. Diabet Med 33:1060–1066CrossRefPubMed Bergman M, Chetrit A, Roth J, Dankner R (2016) One-hour post-load plasma glucose level during the OGTT predicts mortality: observations from the Israel Study of Glucose Intolerance, Obesity and Hypertension. Diabet Med 33:1060–1066CrossRefPubMed
14.
go back to reference Bardini G, Dicembrini I, Cresci B, Rotella CM (2010) Inflammation markers and metabolic characteristics of subjects with 1-h plasma glucose levels. Diabetes Care 33:411–413CrossRefPubMed Bardini G, Dicembrini I, Cresci B, Rotella CM (2010) Inflammation markers and metabolic characteristics of subjects with 1-h plasma glucose levels. Diabetes Care 33:411–413CrossRefPubMed
15.
go back to reference Succurro E, Marini MA, Arturi F et al (2009) Elevated one-hour post-load plasma glucose levels identifies subjects with normal glucose tolerance but early carotid atherosclerosis. Atherosclerosis 207:245–249CrossRefPubMed Succurro E, Marini MA, Arturi F et al (2009) Elevated one-hour post-load plasma glucose levels identifies subjects with normal glucose tolerance but early carotid atherosclerosis. Atherosclerosis 207:245–249CrossRefPubMed
16.
go back to reference Sciacqua A, Miceli S, Greco L et al (2011) One-hour postload plasma glucose levels and diastolic function in hypertensive patients. Diabetes Care 34:2291–2296CrossRefPubMedPubMedCentral Sciacqua A, Miceli S, Greco L et al (2011) One-hour postload plasma glucose levels and diastolic function in hypertensive patients. Diabetes Care 34:2291–2296CrossRefPubMedPubMedCentral
17.
go back to reference Succurro E, Arturi F, Lugarà M et al (2010) One-hour postload plasma glucose levels are associated with kidney dysfunction. Clin J Am Soc Nephrol 5:1922–1927CrossRefPubMedPubMedCentral Succurro E, Arturi F, Lugarà M et al (2010) One-hour postload plasma glucose levels are associated with kidney dysfunction. Clin J Am Soc Nephrol 5:1922–1927CrossRefPubMedPubMedCentral
18.
go back to reference Weyer C, Tataranni PA, Bogardus C, Pratley RE (2001) Insulin resistance and insulin secretory dysfunction are independent predictors of worsening of glucose tolerance during each stage of type 2 diabetes development. Diabetes Care 24:89–94CrossRefPubMed Weyer C, Tataranni PA, Bogardus C, Pratley RE (2001) Insulin resistance and insulin secretory dysfunction are independent predictors of worsening of glucose tolerance during each stage of type 2 diabetes development. Diabetes Care 24:89–94CrossRefPubMed
19.
go back to reference Rushforth NB, Bennett PH, Steinberg AG, Miller M (1975) Comparison of the value of the two- and one-hour glucose levels of the oral GTT in diagnosis of diabetes in Pima Indians. Diabetes 24:538–546CrossRefPubMed Rushforth NB, Bennett PH, Steinberg AG, Miller M (1975) Comparison of the value of the two- and one-hour glucose levels of the oral GTT in diagnosis of diabetes in Pima Indians. Diabetes 24:538–546CrossRefPubMed
20.
go back to reference Lillioja S, Mott DM, Spraul M et al (1993) Insulin resistance and insulin secretory dysfunction as precursors of non-insulin-dependent diabetes mellitus: prospective studies of Pima Indians. N Engl J Med 329:1988–1992CrossRefPubMed Lillioja S, Mott DM, Spraul M et al (1993) Insulin resistance and insulin secretory dysfunction as precursors of non-insulin-dependent diabetes mellitus: prospective studies of Pima Indians. N Engl J Med 329:1988–1992CrossRefPubMed
21.
go back to reference Genuth S, Alberti KG, Bennett P (Expert Committee on the Diagnosis and Classification of Diabetes Mellitus) et al (2003) Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 26: 3160–3167CrossRefPubMed Genuth S, Alberti KG, Bennett P (Expert Committee on the Diagnosis and Classification of Diabetes Mellitus) et al (2003) Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 26: 3160–3167CrossRefPubMed
22.
go back to reference Goldman R, Buskirk E (1961) A method for underwater weighing and the determination of body density. In: Brozekand J, Hershel A (eds) Techniques for measuring body composition. National Academy of Science, Washington DC, pp 78–106 Goldman R, Buskirk E (1961) A method for underwater weighing and the determination of body density. In: Brozekand J, Hershel A (eds) Techniques for measuring body composition. National Academy of Science, Washington DC, pp 78–106
23.
go back to reference Tataranni P, Ravussin E (1995) Use of dual-energy X-ray absorptiometry in obese individuals. Am J Clin Nutr 62:730–734PubMed Tataranni P, Ravussin E (1995) Use of dual-energy X-ray absorptiometry in obese individuals. Am J Clin Nutr 62:730–734PubMed
24.
go back to reference Bunt JC, Krakoff J, Ortega E, Knowler WC, Bogardus C (2007) Acute insulin response is an independent predictor of type 2 diabetes mellitus in individuals with both normal fasting and 2-h plasma glucose concentrations. Diabetes Metab Res Rev 23:304–310CrossRefPubMedPubMedCentral Bunt JC, Krakoff J, Ortega E, Knowler WC, Bogardus C (2007) Acute insulin response is an independent predictor of type 2 diabetes mellitus in individuals with both normal fasting and 2-h plasma glucose concentrations. Diabetes Metab Res Rev 23:304–310CrossRefPubMedPubMedCentral
25.
go back to reference Lillioja S, Mott DM, Howard BV et al (1988) Impaired glucose tolerance as a disorder of insulin action. Longitudinal and cross-sectional studies in Pima Indians. N Engl J Med 318:1217–1225CrossRefPubMed Lillioja S, Mott DM, Howard BV et al (1988) Impaired glucose tolerance as a disorder of insulin action. Longitudinal and cross-sectional studies in Pima Indians. N Engl J Med 318:1217–1225CrossRefPubMed
26.
go back to reference Chen M, Porte DJ (1976) The effect of rate and dose of glucose infusion on the acute insulin response in man. J Clin Endocrinol Metab 42:1168–1175CrossRefPubMed Chen M, Porte DJ (1976) The effect of rate and dose of glucose infusion on the acute insulin response in man. J Clin Endocrinol Metab 42:1168–1175CrossRefPubMed
27.
go back to reference Schwartz MW, Boyko EJ, Kahn SE, Ravussin E, Bogardus C (1995) Reduced insulin secretion: an independent predictor of body weight gain. J Clin Endocrinol Metab 80:1571–1576PubMed Schwartz MW, Boyko EJ, Kahn SE, Ravussin E, Bogardus C (1995) Reduced insulin secretion: an independent predictor of body weight gain. J Clin Endocrinol Metab 80:1571–1576PubMed
28.
go back to reference Herbert V, Lau K-S, Gottlieb CW, Bleicher SJ (1965) Coated charcoal immunoassay of insulin. J Clin Endocrinol Metab 25:1375–1384CrossRefPubMed Herbert V, Lau K-S, Gottlieb CW, Bleicher SJ (1965) Coated charcoal immunoassay of insulin. J Clin Endocrinol Metab 25:1375–1384CrossRefPubMed
29.
30.
go back to reference Steiger JH (1980) Tests for comparing elements of a correlation matrix. Psychol Bull 87:245–251CrossRef Steiger JH (1980) Tests for comparing elements of a correlation matrix. Psychol Bull 87:245–251CrossRef
31.
go back to reference Lee IA, Preacher KJ (2013) Calculation for the test of the difference between two dependent correlations with one variable in common. Available from http://quantpsy.org. Accessed Sept 2016 Lee IA, Preacher KJ (2013) Calculation for the test of the difference between two dependent correlations with one variable in common. Available from http://​quantpsy.​org. Accessed Sept 2016
32.
go back to reference Statistical Analysis Software (1983) SUGI supplemental user’s guide. SAS Institute, Cary, pp 437–466 Statistical Analysis Software (1983) SUGI supplemental user’s guide. SAS Institute, Cary, pp 437–466
33.
go back to reference Thearle MS, Muller YL, Hanson RL et al (2012) Greater impact of melanocortin-4 receptor deficiency on rates of growth and risk of type 2 diabetes during childhood compared with adulthood in Pima Indians. Diabetes 61:250–257CrossRefPubMed Thearle MS, Muller YL, Hanson RL et al (2012) Greater impact of melanocortin-4 receptor deficiency on rates of growth and risk of type 2 diabetes during childhood compared with adulthood in Pima Indians. Diabetes 61:250–257CrossRefPubMed
34.
go back to reference Pencina MJ, D’Agostino RB Sr (2015) Evaluating discrimination of risk prediction models: the C statistic. JAMA 314:1063–1064CrossRefPubMed Pencina MJ, D’Agostino RB Sr (2015) Evaluating discrimination of risk prediction models: the C statistic. JAMA 314:1063–1064CrossRefPubMed
35.
go back to reference Pencina MJ, D’Agostino RB (2004) Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med 23:2109–2123CrossRefPubMed Pencina MJ, D’Agostino RB (2004) Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med 23:2109–2123CrossRefPubMed
36.
go back to reference DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefPubMed DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefPubMed
37.
go back to reference Heagerty PJ, Lumley T, Pepe MS (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56:337–344CrossRefPubMed Heagerty PJ, Lumley T, Pepe MS (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56:337–344CrossRefPubMed
38.
go back to reference Oka R, Aizawa T, Miyamoto S, Yoneda T, Yamagishi M (2016) One-hour plasma glucose as a predictor of the development of type 2 diabetes in Japanese adults. Diabet Med 33:1399–1405CrossRefPubMed Oka R, Aizawa T, Miyamoto S, Yoneda T, Yamagishi M (2016) One-hour plasma glucose as a predictor of the development of type 2 diabetes in Japanese adults. Diabet Med 33:1399–1405CrossRefPubMed
Metadata
Title
One-hour and two-hour postload plasma glucose concentrations are comparable predictors of type 2 diabetes mellitus in Southwestern Native Americans
Authors
Ethan Paddock
Maximilian G. Hohenadel
Paolo Piaggi
Pavithra Vijayakumar
Robert L. Hanson
William C. Knowler
Jonathan Krakoff
Douglas C. Chang
Publication date
01-09-2017
Publisher
Springer Berlin Heidelberg
Published in
Diabetologia / Issue 9/2017
Print ISSN: 0012-186X
Electronic ISSN: 1432-0428
DOI
https://doi.org/10.1007/s00125-017-4332-1

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