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Anthropometric markers and their association with incident type 2 diabetes mellitus: which marker is best for prediction? Pooled analysis of four German population-based cohort studies and comparison with a nationwide cohort study

OBJECTIVE: To compare the association between different anthropometric measurements and incident type 2 diabetes mellitus (T2DM) and to assess their predictive ability in different regions of Germany. METHODS: Data of 10 258 participants from 4 prospective population-based cohorts were pooled to ass...

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Autores principales: Hartwig, Saskia, Kluttig, Alexander, Tiller, Daniel, Fricke, Julia, Müller, Grit, Schipf, Sabine, Völzke, Henry, Schunk, Michaela, Meisinger, Christa, Schienkiewitz, Anja, Heidemann, Christin, Moebus, Susanne, Pechlivanis, Sonali, Werdan, Karl, Kuss, Oliver, Tamayo, Teresa, Haerting, Johannes, Greiser, Karin Halina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4735317/
https://www.ncbi.nlm.nih.gov/pubmed/26792214
http://dx.doi.org/10.1136/bmjopen-2015-009266
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author Hartwig, Saskia
Kluttig, Alexander
Tiller, Daniel
Fricke, Julia
Müller, Grit
Schipf, Sabine
Völzke, Henry
Schunk, Michaela
Meisinger, Christa
Schienkiewitz, Anja
Heidemann, Christin
Moebus, Susanne
Pechlivanis, Sonali
Werdan, Karl
Kuss, Oliver
Tamayo, Teresa
Haerting, Johannes
Greiser, Karin Halina
author_facet Hartwig, Saskia
Kluttig, Alexander
Tiller, Daniel
Fricke, Julia
Müller, Grit
Schipf, Sabine
Völzke, Henry
Schunk, Michaela
Meisinger, Christa
Schienkiewitz, Anja
Heidemann, Christin
Moebus, Susanne
Pechlivanis, Sonali
Werdan, Karl
Kuss, Oliver
Tamayo, Teresa
Haerting, Johannes
Greiser, Karin Halina
author_sort Hartwig, Saskia
collection PubMed
description OBJECTIVE: To compare the association between different anthropometric measurements and incident type 2 diabetes mellitus (T2DM) and to assess their predictive ability in different regions of Germany. METHODS: Data of 10 258 participants from 4 prospective population-based cohorts were pooled to assess the association of body weight, body mass index (BMI), waist circumference (WC), waist-to-hip-ratio (WHR) and waist-to-height-ratio (WHtR) with incident T2DM by calculating HRs of the crude, adjusted and standardised markers, as well as providing receiver operator characteristic (ROC) curves. Differences between HRs and ROCs for the different anthropometric markers were calculated to compare their predictive ability. In addition, data of 3105 participants from the nationwide survey were analysed separately using the same methods to provide a nationally representative comparison. RESULTS: Strong associations were found for each anthropometric marker and incidence of T2DM. Among the standardised anthropometric measures, we found the strongest effect on incident T2DM for WC and WHtR in the pooled sample (HR for 1 SD difference in WC 1.97, 95% CI 1.75 to 2.22, HR for WHtR 1.93, 95% CI 1.71 to 2.17 in women) and in female DEGS participants (HR for WC 2.24, 95% CI 1.91 to 2.63, HR for WHtR 2.10, 95% CI 1.81 to 2.44), whereas the strongest association in men was found for WHR among DEGS participants (HR 2.29, 95% CI 1.89 to 2.78). ROC analysis showed WHtR to be the strongest predictor for incident T2DM. Differences in HR and ROCs between the different markers confirmed WC and WHtR to be the best predictors of incident T2DM. Findings were consistent across study regions and age groups (<65 vs ≥65 years). CONCLUSIONS: We found stronger associations between anthropometric markers that reflect abdominal obesity (ie, WC and WHtR) and incident T2DM than for BMI and weight. The use of these measurements in risk prediction should be encouraged.
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spelling pubmed-47353172016-02-09 Anthropometric markers and their association with incident type 2 diabetes mellitus: which marker is best for prediction? Pooled analysis of four German population-based cohort studies and comparison with a nationwide cohort study Hartwig, Saskia Kluttig, Alexander Tiller, Daniel Fricke, Julia Müller, Grit Schipf, Sabine Völzke, Henry Schunk, Michaela Meisinger, Christa Schienkiewitz, Anja Heidemann, Christin Moebus, Susanne Pechlivanis, Sonali Werdan, Karl Kuss, Oliver Tamayo, Teresa Haerting, Johannes Greiser, Karin Halina BMJ Open Epidemiology OBJECTIVE: To compare the association between different anthropometric measurements and incident type 2 diabetes mellitus (T2DM) and to assess their predictive ability in different regions of Germany. METHODS: Data of 10 258 participants from 4 prospective population-based cohorts were pooled to assess the association of body weight, body mass index (BMI), waist circumference (WC), waist-to-hip-ratio (WHR) and waist-to-height-ratio (WHtR) with incident T2DM by calculating HRs of the crude, adjusted and standardised markers, as well as providing receiver operator characteristic (ROC) curves. Differences between HRs and ROCs for the different anthropometric markers were calculated to compare their predictive ability. In addition, data of 3105 participants from the nationwide survey were analysed separately using the same methods to provide a nationally representative comparison. RESULTS: Strong associations were found for each anthropometric marker and incidence of T2DM. Among the standardised anthropometric measures, we found the strongest effect on incident T2DM for WC and WHtR in the pooled sample (HR for 1 SD difference in WC 1.97, 95% CI 1.75 to 2.22, HR for WHtR 1.93, 95% CI 1.71 to 2.17 in women) and in female DEGS participants (HR for WC 2.24, 95% CI 1.91 to 2.63, HR for WHtR 2.10, 95% CI 1.81 to 2.44), whereas the strongest association in men was found for WHR among DEGS participants (HR 2.29, 95% CI 1.89 to 2.78). ROC analysis showed WHtR to be the strongest predictor for incident T2DM. Differences in HR and ROCs between the different markers confirmed WC and WHtR to be the best predictors of incident T2DM. Findings were consistent across study regions and age groups (<65 vs ≥65 years). CONCLUSIONS: We found stronger associations between anthropometric markers that reflect abdominal obesity (ie, WC and WHtR) and incident T2DM than for BMI and weight. The use of these measurements in risk prediction should be encouraged. BMJ Publishing Group 2016-01-20 /pmc/articles/PMC4735317/ /pubmed/26792214 http://dx.doi.org/10.1136/bmjopen-2015-009266 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Epidemiology
Hartwig, Saskia
Kluttig, Alexander
Tiller, Daniel
Fricke, Julia
Müller, Grit
Schipf, Sabine
Völzke, Henry
Schunk, Michaela
Meisinger, Christa
Schienkiewitz, Anja
Heidemann, Christin
Moebus, Susanne
Pechlivanis, Sonali
Werdan, Karl
Kuss, Oliver
Tamayo, Teresa
Haerting, Johannes
Greiser, Karin Halina
Anthropometric markers and their association with incident type 2 diabetes mellitus: which marker is best for prediction? Pooled analysis of four German population-based cohort studies and comparison with a nationwide cohort study
title Anthropometric markers and their association with incident type 2 diabetes mellitus: which marker is best for prediction? Pooled analysis of four German population-based cohort studies and comparison with a nationwide cohort study
title_full Anthropometric markers and their association with incident type 2 diabetes mellitus: which marker is best for prediction? Pooled analysis of four German population-based cohort studies and comparison with a nationwide cohort study
title_fullStr Anthropometric markers and their association with incident type 2 diabetes mellitus: which marker is best for prediction? Pooled analysis of four German population-based cohort studies and comparison with a nationwide cohort study
title_full_unstemmed Anthropometric markers and their association with incident type 2 diabetes mellitus: which marker is best for prediction? Pooled analysis of four German population-based cohort studies and comparison with a nationwide cohort study
title_short Anthropometric markers and their association with incident type 2 diabetes mellitus: which marker is best for prediction? Pooled analysis of four German population-based cohort studies and comparison with a nationwide cohort study
title_sort anthropometric markers and their association with incident type 2 diabetes mellitus: which marker is best for prediction? pooled analysis of four german population-based cohort studies and comparison with a nationwide cohort study
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4735317/
https://www.ncbi.nlm.nih.gov/pubmed/26792214
http://dx.doi.org/10.1136/bmjopen-2015-009266
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