Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models

Andre Pascal Kengne; Joline Wj Beulens; Linda M Peelen; Karel Gm Moons; Yvonne T van der Schouw; Matthias B Schulze; Annemieke Mw Spijkerman; Simon J Griffin; Diederick E Grobbee; Luigi Palla; Maria-Jose Tormo; Larraitz Arriola; Noël C Barengo; Aurelio Barricarte; Heiner Boeing; Catalina Bonet; Françoise Clavel-Chapelon; Laureen Dartois; Guy Fagherazzi; Paul W Franks; José María Huerta; Rudolf Kaaks; Timothy J Key; Kay Tee Khaw; Kuanrong Li; Kristin Mühlenbruch; Peter M Nilsson; Kim Overvad; Thure F Overvad; Domenico Palli; Salvatore Panico; J Ramón Quirós; Olov Rolandsson; Nina Roswall; Carlotta Sacerdote; María-José Sánchez; Nadia Slimani; Giovanna Tagliabue; Anne Tjønneland; Rosario Tumino; Daphne L van der A; Nita G Forouhi; Stephen J Sharp; Claudia Langenberg; Elio Riboli; Nicholas J Wareham

doi:10.1016/S2213-8587(13)70103-7

Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models

Andre Pascal Kengne, Joline Wj Beulens, Linda M Peelen, Karel Gm Moons, Yvonne T van der Schouw, Matthias B Schulze, Annemieke Mw Spijkerman, Simon J Griffin, Diederick E Grobbee, Luigi Palla, Maria-Jose Tormo, Larraitz Arriola, Noël C Barengo, Aurelio Barricarte, Heiner Boeing, Catalina Bonet, Françoise Clavel-Chapelon, Laureen Dartois, Guy Fagherazzi, Paul W FranksJosé María Huerta, Rudolf Kaaks, Timothy J Key, Kay Tee Khaw, Kuanrong Li, Kristin Mühlenbruch, Peter M Nilsson, Kim Overvad, Thure F Overvad, Domenico Palli, Salvatore Panico, J Ramón Quirós, Olov Rolandsson, Nina Roswall, Carlotta Sacerdote, María-José Sánchez, Nadia Slimani, Giovanna Tagliabue, Anne Tjønneland, Rosario Tumino, Daphne L van der A, Nita G Forouhi, Stephen J Sharp, Claudia Langenberg, Elio Riboli, Nicholas J Wareham

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

BACKGROUND: The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations.

METHODS: We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27 779 individuals from eight European countries, of whom 12 403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (<60 years vs ≥60 years), BMI (<25 kg/m(2)vs ≥25 kg/m(2)), and waist circumference (men <102 cm vs ≥102 cm; women <88 cm vs ≥88 cm).

FINDINGS: We validated 12 prediction models. Discrimination was acceptable to good: C statistics ranged from 0·76 (95% CI 0·72-0·80) to 0·81 (0·77-0·84) overall, from 0·73 (0·70-0·76) to 0·79 (0·74-0·83) in men, and from 0·78 (0·74-0·82) to 0·81 (0·80-0·82) in women. We noted significant heterogeneity in discrimination (pheterogeneity<0·0001) in all but one model. Calibration was good for most models, and consistent across countries (pheterogeneity>0·05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups.

INTERPRETATION: Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity.

FUNDING: The European Union.

Original language	English
Journal	The Lancet Diabetes & Endocrinology
Volume	2
Issue number	1
Pages (from-to)	19-29
Number of pages	11
ISSN	2213-8587
DOIs	https://doi.org/10.1016/S2213-8587(13)70103-7
Publication status	Published - Jan 2014

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Kengne, A. P., Beulens, J. W., Peelen, L. M., Moons, K. G., van der Schouw, Y. T., Schulze, M. B., Spijkerman, A. M., Griffin, S. J., Grobbee, D. E., Palla, L., Tormo, M-J., Arriola, L., Barengo, N. C., Barricarte, A., Boeing, H., Bonet, C., Clavel-Chapelon, F., Dartois, L., Fagherazzi, G., ... Wareham, N. J. (2014). Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. The Lancet Diabetes & Endocrinology, 2(1), 19-29. https://doi.org/10.1016/S2213-8587(13)70103-7

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title = "Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models",

abstract = "BACKGROUND: The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations.METHODS: We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27 779 individuals from eight European countries, of whom 12 403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (<60 years vs ≥60 years), BMI (<25 kg/m(2)vs ≥25 kg/m(2)), and waist circumference (men <102 cm vs ≥102 cm; women <88 cm vs ≥88 cm).FINDINGS: We validated 12 prediction models. Discrimination was acceptable to good: C statistics ranged from 0·76 (95% CI 0·72-0·80) to 0·81 (0·77-0·84) overall, from 0·73 (0·70-0·76) to 0·79 (0·74-0·83) in men, and from 0·78 (0·74-0·82) to 0·81 (0·80-0·82) in women. We noted significant heterogeneity in discrimination (pheterogeneity<0·0001) in all but one model. Calibration was good for most models, and consistent across countries (pheterogeneity>0·05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups.INTERPRETATION: Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity.FUNDING: The European Union.",

author = "Kengne, {Andre Pascal} and Beulens, {Joline Wj} and Peelen, {Linda M} and Moons, {Karel Gm} and {van der Schouw}, {Yvonne T} and Schulze, {Matthias B} and Spijkerman, {Annemieke Mw} and Griffin, {Simon J} and Grobbee, {Diederick E} and Luigi Palla and Maria-Jose Tormo and Larraitz Arriola and Barengo, {No{\"e}l C} and Aurelio Barricarte and Heiner Boeing and Catalina Bonet and Fran{\c c}oise Clavel-Chapelon and Laureen Dartois and Guy Fagherazzi and Franks, {Paul W} and Huerta, {Jos{\'e} Mar{\'i}a} and Rudolf Kaaks and Key, {Timothy J} and Khaw, {Kay Tee} and Kuanrong Li and Kristin M{\"u}hlenbruch and Nilsson, {Peter M} and Kim Overvad and Overvad, {Thure F} and Domenico Palli and Salvatore Panico and Quir{\'o}s, {J Ram{\'o}n} and Olov Rolandsson and Nina Roswall and Carlotta Sacerdote and Mar{\'i}a-Jos{\'e} S{\'a}nchez and Nadia Slimani and Giovanna Tagliabue and Anne Tj{\o}nneland and Rosario Tumino and {van der A}, {Daphne L} and Forouhi, {Nita G} and Sharp, {Stephen J} and Claudia Langenberg and Elio Riboli and Wareham, {Nicholas J}",

year = "2014",

month = jan,

doi = "10.1016/S2213-8587(13)70103-7",

language = "English",

volume = "2",

pages = "19--29",

journal = "The Lancet Diabetes & Endocrinology",

issn = "2213-8587",

publisher = "The Lancet Publishing Group",

number = "1",

}

Kengne, AP, Beulens, JW, Peelen, LM, Moons, KG, van der Schouw, YT, Schulze, MB, Spijkerman, AM, Griffin, SJ, Grobbee, DE, Palla, L, Tormo, M-J, Arriola, L, Barengo, NC, Barricarte, A, Boeing, H, Bonet, C, Clavel-Chapelon, F, Dartois, L, Fagherazzi, G, Franks, PW, Huerta, JM, Kaaks, R, Key, TJ, Khaw, KT, Li, K, Mühlenbruch, K, Nilsson, PM, Overvad, K, Overvad, TF, Palli, D, Panico, S, Quirós, JR, Rolandsson, O, Roswall, N, Sacerdote, C, Sánchez, M-J, Slimani, N, Tagliabue, G, Tjønneland, A, Tumino, R, van der A, DL, Forouhi, NG, Sharp, SJ, Langenberg, C, Riboli, E & Wareham, NJ 2014, 'Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models', The Lancet Diabetes & Endocrinology, vol. 2, no. 1, pp. 19-29. https://doi.org/10.1016/S2213-8587(13)70103-7

TY - JOUR

T1 - Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct)

T2 - a validation of existing models

AU - Kengne, Andre Pascal

AU - Beulens, Joline Wj

AU - Peelen, Linda M

AU - Moons, Karel Gm

AU - van der Schouw, Yvonne T

AU - Schulze, Matthias B

AU - Spijkerman, Annemieke Mw

AU - Griffin, Simon J

AU - Grobbee, Diederick E

AU - Palla, Luigi

AU - Tormo, Maria-Jose

AU - Arriola, Larraitz

AU - Barengo, Noël C

AU - Barricarte, Aurelio

AU - Boeing, Heiner

AU - Bonet, Catalina

AU - Clavel-Chapelon, Françoise

AU - Dartois, Laureen

AU - Fagherazzi, Guy

AU - Franks, Paul W

AU - Huerta, José María

AU - Kaaks, Rudolf

AU - Key, Timothy J

AU - Khaw, Kay Tee

AU - Li, Kuanrong

AU - Mühlenbruch, Kristin

AU - Nilsson, Peter M

AU - Overvad, Kim

AU - Overvad, Thure F

AU - Palli, Domenico

AU - Panico, Salvatore

AU - Quirós, J Ramón

AU - Rolandsson, Olov

AU - Roswall, Nina

AU - Sacerdote, Carlotta

AU - Sánchez, María-José

AU - Slimani, Nadia

AU - Tagliabue, Giovanna

AU - Tjønneland, Anne

AU - Tumino, Rosario

AU - van der A, Daphne L

AU - Forouhi, Nita G

AU - Sharp, Stephen J

AU - Langenberg, Claudia

AU - Riboli, Elio

AU - Wareham, Nicholas J

PY - 2014/1

Y1 - 2014/1

N2 - BACKGROUND: The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations.METHODS: We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27 779 individuals from eight European countries, of whom 12 403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (<60 years vs ≥60 years), BMI (<25 kg/m(2)vs ≥25 kg/m(2)), and waist circumference (men <102 cm vs ≥102 cm; women <88 cm vs ≥88 cm).FINDINGS: We validated 12 prediction models. Discrimination was acceptable to good: C statistics ranged from 0·76 (95% CI 0·72-0·80) to 0·81 (0·77-0·84) overall, from 0·73 (0·70-0·76) to 0·79 (0·74-0·83) in men, and from 0·78 (0·74-0·82) to 0·81 (0·80-0·82) in women. We noted significant heterogeneity in discrimination (pheterogeneity<0·0001) in all but one model. Calibration was good for most models, and consistent across countries (pheterogeneity>0·05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups.INTERPRETATION: Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity.FUNDING: The European Union.

AB - BACKGROUND: The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations.METHODS: We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27 779 individuals from eight European countries, of whom 12 403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (<60 years vs ≥60 years), BMI (<25 kg/m(2)vs ≥25 kg/m(2)), and waist circumference (men <102 cm vs ≥102 cm; women <88 cm vs ≥88 cm).FINDINGS: We validated 12 prediction models. Discrimination was acceptable to good: C statistics ranged from 0·76 (95% CI 0·72-0·80) to 0·81 (0·77-0·84) overall, from 0·73 (0·70-0·76) to 0·79 (0·74-0·83) in men, and from 0·78 (0·74-0·82) to 0·81 (0·80-0·82) in women. We noted significant heterogeneity in discrimination (pheterogeneity<0·0001) in all but one model. Calibration was good for most models, and consistent across countries (pheterogeneity>0·05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups.INTERPRETATION: Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity.FUNDING: The European Union.

U2 - 10.1016/S2213-8587(13)70103-7

DO - 10.1016/S2213-8587(13)70103-7

M3 - Journal article

C2 - 24622666

SN - 2213-8587

VL - 2

SP - 19

EP - 29

JO - The Lancet Diabetes & Endocrinology

JF - The Lancet Diabetes & Endocrinology

IS - 1

ER -

Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models

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