The study population consisted of men and women aged 30–64 years who participated in the 9-year follow-up study, DESIR. Participants were recruited from volunteers who were offered periodic health examinations free of charge by the French Social Security at 10 health examination centers in western France. All subjects provided informed written consent, and the protocol was approved by an ethics committee.
Incident cases of diabetes were identified by treatment for diabetes or a fasting plasma glucose ≥7.0 mmol/l at one of the 3-yearly examinations; after exclusion of individuals with diabetes at baseline and those with unknown diabetes status at the 9-year examination, 1,863 men and 1,954 women with glucose, BMI, and waist circumference measures available at baseline were included in the study.
Two measures of blood pressure, using a mercury sphygmomanometer, and heart rate were taken in a supine position after 5 min of rest, and mean values were used for analyses. Weight and height were measured in lightly clad participants, and BMI was calculated. Waist circumference, the smallest circumference between the lower ribs and the iliac crest, was also measured.
The examining physician noted the family history of diabetes and menopausal status in a clinical questionnaire; treatment for diabetes and hypertension and lipids were recorded. Hypertension was defined by systolic/diastolic blood pressure of at least 140/90 mmHg or being on antihypertensive medication. Smoking habits, alcohol consumption (glasses of wine, beer, cider, and spirits per day), and degree of physical activity (at home, at work, and sport) were assessed using a self-administered questionnaire.
All biochemical measurements were from one of four health center laboratories located in France at Blois, Chartres, LA Riche, or Orléans. Fasting plasma glucose, measured by the glucose oxidase method, was applied to fluoro-oxalated plasma using a Technicon RA100 (Bayer Diagnostics, Puteaux, France) or a Specific or a Delta device (Konelab, Evry, France). Total cholesterol, HDL cholesterol, triglycerides, alanine aminotransferase (ALT), γ-glutamyltransferase (GGT), and creatinine were assayed by DAX 24 (Bayer Diagnostics) or KONE (Konelab). Insulin was quantified by microparticle enzyme immunoassay with an automated analyzer (IMX; Abbott, Rungis, France). White cell counts were determined by a Technicon H* or Technicon H3RTX (Bayer Diagnostics), a JT2 (Beckman/Coulter, Roissy, France), or an Argos (ABX, Montpellier, France). Interlaboratory variability was assessed monthly on normal and pathological values for each biologic variable.
Single nucleotide polymorphism (SNP) genotyping was performed with SNPlex Technology (Applied Biosystems, Foster City, CA) based on oligonucleotide ligation assay combined with multiplex PCR target amplification (http://www.appliedbiosystems.com
Statistical analysis was performed using SAS version 9.1 (SAS Institute, Cary, NC). Alcohol intake, BMI, fasting glucose, insulin, ALT, GGT, triglycerides, and white blood cell count were log-transformed because of their skewed distributions.
Characteristics of men and women with and without incident diabetes are shown as means ± SD or n (%) and compared by t or χ2 tests or by linear regression for the polymorphisms with additive models. The logistic model was used to test for interactions with sex, and P are reported; significant interactions (P < 0.01) provided the rational for sex-specific models.
The linearity of continuous parameters in logistic analyses was studied by adding a squared term and comparing nested models by likelihood ratio tests; all variables were linearly related with the logit of diabetes incidence except for fasting glucose (log transformed); in the models, glucose(log) was centered by subtracting its mean, and its square was systematically included.
Parsimonious logistic regression models were selected using forwards and backwards as well best model selection criteria using all parameters; the Hosmer-Lemeshow goodness-of-fit test was the principal criteria for selection of a model. Interactions with sex were tested. The area under the receiver operating characteristic curve (AROC) for sensitivity-specificity quantified the discrimination between diabetic and nondiabetic participants. Bootstrap sampling was used to validate the choice of variables in the models, with 1,000 samples of the same sizes as the study populations. The choice of variables was also validated in the Cox model.
To derive a simple clinical score from the clinical equations, we used the β-coefficients from the logistic regression analysis; for waist circumference, four groups were defined, linearly, from the approximate sex-specific quartiles. The score was validated in two French cohorts: E3N and SU.VI.MAX (15
) (online appendix Fig. 1 [available at http://dx.doi.org/10.2337/dc08-0368
]). The first study identified incident diabetes by self-questionnaire or treatment reimbursement, the second by fasting glucose or treatment.
Four polymorphisms were chosen for study (glucokinase: GCK-30
G/A rs1799884, interleukine 6: IL6-174
G/C rs1800795, and Kir6.2: KCNJ11
E23K rs5219 and TCF7L2
rs7903146) following previous analyses in this population (14
). Additive models discriminated best between diabetic and nondiabetic people. For the two polymorphisms found to be the most related with incident diabetes (IL6 and TCF7L2), the number of deleterious alleles (as a continuous variable) was calculated and added as a variable to the (clinical + biological) equations chosen above. As this analysis aimed to determine those who should be screened for diabetes, we have analyzed all individuals and have not excluded those born outside of mainland France. This analysis was on a smaller population (1,655 men and 1,740 women), where these two polymorphisms were available.
We compared our clinical risk score with the FINDRISC score (3
) using the AROC statistic; FINDRISC includes age, BMI, waist circumference, antihypertensive medication, physical activity, previously known high glucose, and daily consumption of vegetables, and fruits or berries; we were not able to include the latter two items. Our (clinical + biological) equation was compared with the Stern equation, including age, sex, fasting glucose, systolic blood pressure, HDL cholesterol, BMI, and diabetes in the family; we did not include the factor for coding Mexican Americans (9