Long-term risk of incident type 2 diabetes and measures of overall and regional obesity: the EPIC-InterAct case-cohort study.
BACKGROUND
Waist circumference (WC) is a simple and reliable measure of fat distribution that may add to the prediction of type 2 diabetes (T2D), but previous studies have been too small to reliably quantify the relative and absolute risk of future diabetes by WC at different levels of body mass index (BMI).


RESULTS
The prospective InterAct case-cohort study was conducted in 26 centres in eight European countries and consists of 12,403 incident T2D cases and a stratified subcohort of 16,154 individuals from a total cohort of 340,234 participants with 3.99 million person-years of follow-up. We used Prentice-weighted Cox regression and random effects meta-analysis methods to estimate hazard ratios for T2D. Kaplan-Meier estimates of the cumulative incidence of T2D were calculated. BMI and WC were each independently associated with T2D, with WC being a stronger risk factor in women than in men. Risk increased across groups defined by BMI and WC; compared to low normal weight individuals (BMI 18.5-22.4 kg/m(2)) with a low WC (<94/80 cm in men/women), the hazard ratio of T2D was 22.0 (95% confidence interval 14.3; 33.8) in men and 31.8 (25.2; 40.2) in women with grade 2 obesity (BMI≥35 kg/m(2)) and a high WC (>102/88 cm). Among the large group of overweight individuals, WC measurement was highly informative and facilitated the identification of a subgroup of overweight people with high WC whose 10-y T2D cumulative incidence (men, 70 per 1,000 person-years; women, 44 per 1,000 person-years) was comparable to that of the obese group (50-103 per 1,000 person-years in men and 28-74 per 1,000 person-years in women).


CONCLUSIONS
WC is independently and strongly associated with T2D, particularly in women, and should be more widely measured for risk stratification. If targeted measurement is necessary for reasons of resource scarcity, measuring WC in overweight individuals may be an effective strategy, since it identifies a high-risk subgroup of individuals who could benefit from individualised preventive action.
Claudia
C
Langenberg
Stephen J
SJ
Sharp
Matthias B
MB
Schulze
Olov
O
Rolandsson
Kim
K
Overvad
Nita G
NG
Forouhi
Joachim
J
Spranger
Dagmar
D
Drogan
José María
JM
Huerta
Larraitz
L
Arriola
Blandine
B
de Lauzon-Guillan
Maria-Jose
MJ
Tormo
Eva
E
Ardanaz
Beverley
B
Balkau
Joline W J
JW
Beulens
Heiner
H
Boeing
H Bas
HB
Bueno-de-Mesquita
Françoise
F
Clavel-Chapelon
Francesca L
FL
Crowe
Paul W
PW
Franks
Carlos A
CA
Gonzalez
Sara
S
Grioni
Jytte
J
Halkjaer
Goran
G
Hallmans
Rudolf
R
Kaaks
Nicola D
ND
Kerrison
Timothy J
TJ
Key
Kay Tee
KT
Khaw
Amalia
A
Mattiello
Peter
P
Nilsson
Teresa
T
Norat
Luigi
L
Palla
Domenico
D
Palli
Salvatore
S
Panico
J Ramón
JR
Quirós
Dora
D
Romaguera
Isabelle
I
Romieu
Carlotta
C
Sacerdote
María-José
MJ
Sánchez
Nadia
N
Slimani
Ivonne
I
Sluijs
Annemieke M W
AM
Spijkerman
Birgit
B
Teucher
Anne
A
Tjonneland
Rosario
R
Tumino
Daphne L
DL
van der A
Yvonne T
YT
van der Schouw
Edith J  M
EJ
Feskens
Elio
E
Riboli
Nicholas J
NJ
Wareham
D000886:Anthropometry; D015992:Body Mass Index; D015331:Cohort Studies; D003924:Diabetes Mellitus, Type 2 / Q000150:complications* / Q000453:epidemiology*; D005060:Europe / Q000453:epidemiology; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000150:complications* / Q000503:physiopathology*; D016016:Proportional Hazards Models; D012307:Risk Factors; D013997:Time Factors; D055105:Waist Circumference / Q000502:physiology