Web Extra material. Comparison of non-laboratory-based risk scores for predicting the occurrence of type 2

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1 Web Extra material Comparison of non-laboratory-based risk scores for predicting the occurrence of type 2 diabetes in the general population: the EPIC-InterAct Study Outline Supplementary Figures Legend... 3 Supplementary Box 1 Derivation of the EPIC-InterAct Case-cohort sample... 4 Supplementary Box 2 Assessment of diabetes predictors at baseline in the EPIC-InterAct study... 5 Supplementary Box 3 - Derivation of proxy variables in the EPIC-InterAct study... 6 Supplementary Box 4 Full equation for incident diabetes risk prediction models as applied to the EPIC- InterAct cohort... 7 Supplementary Box 5 Implementation of the prediction model recalibration through simple intercept adjustment in the EPIC-InterAct study... 9 Supplementary Box 6 Implementation of the blow-up approach in the EPIC-InterAct study Supplementary Table 1 Incident diabetes risk prediction models and EPIC-InterAct countries where they were applied Supplementary Table 2 Correction factors for implementing Incident diabetes risk prediction models recalibration at 10 years of follow-up through simple intercept adjustment across EPIC-InterAct countries Supplementary Table 3 Discrimination and calibration statistics for model performance in men and women by country, for the prediction of incident type 2 diabetes at 10 years of follow-up in the EPIC-InterAct study Supplementary Table 4 C-statistic and 95% confidence interval overall and by subgroups of participants,, for the prediction of incident type 2 diabetes at 5 years of follow-up in the EPIC-InterAct study BMI, body mass index; WC, waist circumference Supplementary Table 5 Ratios expected/observed 5-year incident diabetes rates and 95% confidence interval overall and by subgroups of participants in the EPIC-InterAct study BMI, body mass index; WC, waist circumference Supplementary Table 6 Discrimination and calibration statistics for model performance in men and women by country, for the prediction of incident type 2 diabetes at 5 years of follow-up in the EPIC-InterAct study Supplementary Figure 1 - Calibration curves per model without intercept adjustment at the total cohort level, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC-InterAct study Supplementary Figure 2 Calibration curves by country for the ARIC 2005 model with intercept adjustment, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC-InterAct study Supplementary Figure 3 Calibration curves by country for the ARIC 2009 model with intercept adjustment, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC-InterAct study Supplementary Figure 4 Calibration curves by country for the AUSDRISK model with intercept adjustment, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC-InterAct study Supplementary Figure 5 Calibration curves by country for the Cambridge model with intercept adjustment, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC-InterAct study Supplementary Figure 6 Calibration curves by country for the D.E.S.I.R. model with intercept adjustment, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC-InterAct study Supplementary Figure 7 Calibration curves by country for the DPoRT model with intercept adjustment, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC-InterAct study Supplementary Figure 8 Calibration curves by country for the FINDRISK concise model with intercept adjustment, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC-InterAct study Supplementary Figure 9 Calibration curves by country for the FINDRISK full model with intercept adjustment, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC-InterAct study Supplementary Figure 10 Calibration curves by country for the Framingham personal variables model with intercept adjustment, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC- InterAct study Supplementary Figure 11 Calibration curves by country for the KORA model with intercept adjustment, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC-InterAct study

2 Supplementary Figure 12 Calibration curves by country for the Potsdam model with intercept adjustment, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC-InterAct study Supplementary Figure 13 Calibration curves by country for the QDScore model with intercept adjustment, for the prediction of incident type 2 diabetes at 10 years of follow-up in the in the EPIC-InterAct study Supplementary Figure 14 - Calibration curves per model without intercept adjustment at the total cohort level, for the prediction of incident type 2 diabetes at 5 years of follow-up in the in the EPIC-InterAct study Supplementary Figure 15 - Calibration curves per model with intercept adjustment at the total cohort level, for the prediction of incident type 2 diabetes at 5 years of follow-up in the in the EPIC-InterAct study Figure 16 Discrimination of models by country and overall, for the prediction of incident type 2 diabetes at 5 years of follow-up in the in the EPIC-InterAct study References

3 Supplementary Figures Legend Supplementary Figures 1 to 15: Calibration curves Calibration of a model describes the extent to which the expected probability (E) of diabetes reflects the observed probability (O) of acquiring a diagnosis of diabetes during follow-up. The ideal calibration (perfect E- O agreement) is graphically represented by the dotted diagonal line at 45 o. For each panel, the smooth superimposed non-parametric lowess-estimated calibration curves describe the agreement between predicted and observed risk across the continuum of predicted risk in the test population. The vertical lines at the bottom of graph depict the frequency distribution of the calibrated probabilities of diabetes. The triangles (grouped observations) represent the groups of participants across increasing deciles of estimated risk (n=10). The calibration curves were produced with the use of the val.prob function of the Design package of R 1. For each figure panel, the ratio Expected/Observed (E/O) event rates and the accompanying 95% confidence interval (estimated by assuming a Poisson variance 2 ), the Yates slope (which is the difference between mean predicted probability for participants with and without incident diabetes) and the Brier score (the quadratic difference between predicted probability and actual outcome [0 or 1] for each participants) 3 are also shown. The p-values in Supplementary Figure 1 are for the test of heterogeneity in the Expected/Observed event rates across countries. Supplementary Figure 16: Models discrimination by country and overall at 5-year of follow-up Discrimination refers to the ability of the model to distinguish between participants who developed diabetes and those who remained diabetes free during a five-year of follow-up. Theoretically, the C-statistic ranges from 0 5 (no predictive ability at all) to 1 (perfect predictive ability). Black boxes denote the C-statistics and the horizontal bars represent the 95% confidence intervals. For each model, the diamond is the overall C-statistic derived from a random effects model. For each model, the dotted vertical line centered on the diamond has been added to assist visual interpretation. The I-squared, tau-squared and p-values for heterogeneity across countries are also shown. 3

4 Supplementary Box 1 Derivation of the EPIC-InterAct Case-cohort sample A total of 455,569 EPIC participants were followed up for an average (range) of 11 7 (0-17 5) years for diabetes occurrence. Individuals without stored blood (n=109,574), with prevalent diabetes (n=10,293) or without reported- (n=5,821) or with unverified (n=3,078) status for diabetes during follow-up, were excluded. Therefore, 326,805 were eligible, in whom 14,980 verified incident cases of T2DM were recorded. Of the EPIC participants with blood stored at baseline (345,944), a center-stratified, random subcohort of 16,835 individuals was selected. After exclusion of 548 individuals with prevalent diabetes and 133 with uncertain diabetes status, the subcohort included 16,154 individuals for analysis. Due to the random selection, this subcohort also included a random set of 778 individuals who had developed incident T2DM during follow-up. Of the remaining 14,202 incident diabetes cases recorded in the non-subcohort sample, 4,485 (36 6%) were registered in Denmark centers alone. To limit the influence of Denmark on the dataset, only a random sample of non-subcohort incident diabetes cases in Denmark (1908; 42 5%) were included in the InterAct case-cohort dataset. Therefore, the final EPIC-InterAct dataset included 27,779 participants (12,403 with incident diabetes). Participants in the random subcohort were similar to all EPIC participants eligible for inclusion in EPIC-InterAct. All participants gave written informed consent, and the study was approved by the local ethics committee in the participating countries and the Internal Review Board of the International Agency for Research on Cancer. 4

5 Supplementary Box 2 Assessment of diabetes predictors at baseline in the EPIC-InterAct study Weight, height and waist circumference were recorded by trained health professionals during a visit to the study centre, with the exception of Oxford (U.K.) and France where self-reported waist circumference and/or height and weight were obtained from all participants, and were measured only in a restricted number. In Umea (Sweden) only weight and height were measured 4. Smoking status was self-reported as never, current or former smoking. Information on coronary heart disease and stroke at baseline was obtained from self-reported diagnosis or from hospital discharge registries. Presence of hypertension and hyperlipidemia were based on self-reported diagnosis and/or use of medication. Physical activity was assessed by questionnaire and classified into inactive, moderately inactive, moderately active, and active, according to the Cambridge Physical Activity Index 5. Diet, including alcohol intake over the 12 months before enrolment was measured by centre-specific dietary assessment instruments (mainly food frequency questionnaires [FFQ] and dietary history) designed to capture local dietary habits with high compliance 6. Most centres adopted a self-administered dietary questionnaire including food items. 5

6 Supplementary Box 3 - Derivation of proxy variables in the EPIC-InterAct study Family history of diabetes: four models used parental history of diabetes (yes/no) as predictor, two used family history of diabetes (yes/no) as predictor and one model used a 3-level family history of diabetes (no first-degree relative with diabetes/parent or sibling with diabetes/parent and sibling with diabetes). The 3 rd level in this last case could not be computed for the InterAct participants, and was therefore ignored in the validation of the Cambridge model 8. Fruit consumption: The FINDRISK full model 9 used daily consumption of fruits, vegetables or berries as a predictor (categorical variable yes/no). Consumption of fruits, vegetables and berries in InterAct was recorded as a continuous variable (gram/day). Individual levels of those were summed and divided by 80 (the approximated portion weight) to obtain the number of portions consumed per day. Then intake of 3 or more portion sizes was used to characterize daily consumption. Sport, biking, or gardening: This was used as a continuous predictor (hours/week) in the Potsdam model 10. Sport, biking and gardening in InterAct were recorded as continuous variables in MET-hours/week. The Compendium of Physical Activities codes 11 were used to convert the MET-hours/week into hours/week. Status for hypertension: Five models used treated hypertension as a predictor and 5 other models used any hypertension as predictor. Status for treated hypertension was inconsistently reported across EPIC cohorts. Therefore any hypertension was invariably used in all models for the validation purpose. Townsend deprivation score (TDS): Education, a well-documented personal indicator of the socioeconomic position 12, was used as a proxy for this variable, a predictor in the QDScore 13. Equivalence between education (categorical variable in the InterAct dataset) and Townsend score [continuous variables ranging from -6 (most affluent) to 11 (most deprived) in the original population 13 ] was established as followed (education Townsend score): none 10, primary school 6, technical/professional school 2, secondary school -2, longer education -5. These derived values were then standardized to obtain the predictor used in the equation 13. Standardization was done by taking the ratio (Individual level of TDS minus Mean level of TSD in InterAct)/Standard deviation of TDS in InterAct. Ethnicity: The AUSDRISK model 14 had a predictor on ethnicity to indicate a higher risk of diabetes for the Aboriginal, Asian and Southern European migrant Australians. All participants from cohorts in Italy and Spain (Southern Europe) were assigned this status. 6

7 Supplementary Box 4 Full equation for incident diabetes risk prediction models as applied to the EPIC- InterAct cohort The ARIC Clinical variables logistic model (ARIC 2005) 15 The probability of developing diabetes was calculated as exp(x)/(1 + exp(x)); Where X= age (years) (if black race, else 0) (if parental history of diabetes, else 0) systolic blood pressure (mmhg) waist (cm) height (cm) The Australian type 2 diabetes risk assessment tool (AUSDRISK) logistic regression model 14 The probability of developing diabetes was calculated as exp(x)/(1 + exp(x)); Where X= (if aged years) (if aged years) (if aged years) (if aged years) (if age >65 years) + 0 (if BMI <25 kg/m 2 ) (if 25<BMI<30 kg/m 2 ) (if 30<BMI<35 kg/m 2 ) (if BMI>35 kg/m 2 ) + 0 (if waist circumference (cm) <90 in men or <80 in women (if 90<waist circumference (cm)<100 in men, or 80<waist circumference (cm)<90 in women) (if waist circumference (cm) >100 in men or > 90 in women) (if Southern European, Asian, Aboriginal background, else 0) (if parental history of diabetes, else 0) (if positive history of high glucose, else 0) (if using blood pressure medication, else 0) (if current smoker, else 0) (if physical inactivity, else 0) The Cambridge logistic regression model 16 The probability of developing diabetes was calculated as exp(x)/(1 + exp(x)); Where X= (if female, else 0) (if prescribed antihypertensive medication, else 0) (if prescribed steroids, else 0) x age (years) + 0 (if BMI <25 kg/m 2 ) (if 25<BMI<27 5 kg/m 2 ) (if 27 5<BMI<30 kg/m 2 ) (if BMI>30 kg/m 2 ) + 0 (if no first degree relative with diabetes) (if parent or sibling with diabetes) (if parent and sibling with diabetes) + 0 (if non-smoker) (if ex-smoker) (if current smoker The D.E.S.I.R. clinical logistic regression model 17 The probability of developing diabetes was calculated as exp(x)/(1 + exp(x)); Where X(men)= (if current smoker, else 0) x waist circumference (cm) (if hypertension, else 0) X(women)= (if family history of diabetes, else 0) x waist circumference (cm) (if hypertension, else 0) The DPoRT Weibull regression model 18 The probability of developing diabetes was calculated as 1 exp(-exp m ); Where, exp denotes the exponential function, m = [log (follow-up time) μ]/scale ( ) μ = (if hypertension, else 0) (if non-white ethnicity, else 0) (if history of heart disease, else 0) (if current smoker, else 0) (if post-secondary education or higher, else 0) 0 (if BMI <23 kg/m 2 and age <45 years) (if BMI kg/m 2 and age <45 years) (if BMI kg/m 2 and age <45 years) (if BMI kg/m 2 and age <45 years) (if BMI >35 kg/m 2 and age <45 years) (if BMI <23 kg/m 2 and age >45 years) (if BMI kg/m 2 and age >45 years) (if BMI kg/m 2 and age >45 years) (if BMI kg/m 2 and age >45 years) (if BMI >35 kg/m 2 and age >45 years) The FINDRISK concise logistic regression model 9 The probability of developing diabetes was calculated as exp(x)/(1 + exp(x)); Where X= (if aged years) (if aged years) (if 25<BMI<30 kg/m 2 ) (if BMI>30 kg/m 2 ) (if 94<waist circumference (cm)<102 in men, or 80<waist circumference (cm)<88 in women) (if waist circumference (cm) >102 in men or > 88 in women) (if using blood pressure medication, else 0) (if positive history of high glucose, else 0) The FINDRISK full logistic regression model 9 The probability of developing diabetes was calculated as exp(x)/(1 + exp(x)); Where X= (if aged years) (if aged years) (if 25<BMI<30 kg/m 2 ) (if BMI>30 kg/m 2 ) (if 94<waist circumference (cm)<102 in men, or 80<waist circumference (cm)<88 in women) (if waist circumference (cm) >102 in men or > 88 in women) (if using blood pressure medication, else 0) (if positive history of high glucose, else 0) (if physical activity <4h/week, else 0) (if daily consumption of vegetable, fruits, or berries; else 0) The Framingham personal variables logistic regression model 19 The probability of developing diabetes was calculated as exp(x)/(1 + exp(x)); Where X = (if aged <50 years) + log(1 54) [if aged years] + log(1 74) [if aged >65 years] + log(1 25) [if men, else 0] + log(1 87) [if parental history of diabetes] + 0 [if BMI <25 kg/m 2 ] + log(2 35) [if BMI kg/m 2 ] + log(6 41) [if BMI >30 kg/m 2 ] The KORA S4/F4 logistic regression model 20 The probability of developing diabetes was calculated as exp(x)/(1 + exp(x)); Where 7

8 X = x age (years) x body mass index (kg/m 2 ) (if men, else 0) + 0 (for never smoker) (if former smoker) (if current smoker, else 0) (if parental history of diabetes) (if hypertension, else 0) The Potsdam Cox regression model 10 The probability of developing diabetes was calculated as 1 S 0 (5) exp(lp) ; Where, exp denotes the exponential function; S 0 (5)= LP=0 074 x waist circumference (cm) x Height (cm) x age (years) (if hypertension, else 0) x read meat intake (per 150 g/d) x whole-grain bread intake ( per 50 g/day) x coffee consumption (per 150 g/day) (if moderate alcohol consumption [10 to 40 g/day], else 0] x sports/biking or gardening (hours/week) (if former smoker, else 0) (if heavy smoker [>20 cigarettes/days], else 0) The QDScore Cox regression model 13 The probability of developing diabetes was calculated as 1 S 0 (10) exp(lp) ; Where, exp denotes the exponential function, S 0 (10)= (for men) and (for women) LP= LP(continuous) + LP (boolean) + LP (interaction); With LP(continuous)=age_1* age_2* bmi_1* bmi_2* *(town ) for men, and LP(continuous)= age_1* age_2* bmi_1* bmi_2* *(town ) for women LP(boolean)= (if history of heart disease, else 0) (if hypertension, else 0) (if first degree relative with diabetes, else 0) (if current smoker, else 0) for men, and LP(boolean)= (if history of heart disease, else 0) (if hypertension, else 0) (if first degree relative with diabetes, else 0) (if current smoker, else 0) for women LP (interaction) = age_1*bmi_1* age_1*bmi_2* age_1* (if first degree relative with diabetes, else 0) + age_1* (if current smoker, else 0) -age_2*bmi_1* age_2*bmi_2* age_2* (if first degree relative with diabetes, else 0) - age_2* (if current smoker, else 0) for men, and LP (interaction) = age_1*bmi_1* age_1*bmi_2* age_1* (if first degree relative with diabetes, else 0) + age_1* (if current smoker, else 0) -age_2*bmi_1* age_2*bmi_2* age_2* (if first degree relative with diabetes, else 0) - age_2* (if current smoker, else 0) for women Where Age_1 = log(age [years] /10) (for men) and sqrt(age [years] / ) (for women) Age_2= (age [years]/10)^ (for men) and (age [years] 10)^ (for women) BMI_1 = (BMI [kg/m 2 ]/10)^ (for men) and BMI [kg/m 2 ]/ (for women) BMI_2 = (BMI [kg/m 2 ]/10)^ (for men) and (BMI [kg/m 2 ]/10)^ (for women) * denotes the multiplication sign; ^ the power sign; exp, the exponential function; log, the natural logarithm function; sqrt, the square root function The beta coefficients for QDScore, not included in the original paper 13, were downloaded in January 2011 from the website of the model using a link obtained from the investigators: Unpublished coefficients and parameters for the ARIC 2009 Weibull model 21 were obtained from the investigators and are therefore not included. ARIC, Atherosclerosis Risk in Communities; AUSDRISK, Australian Type 2 Diabetes Risk Assessment Tool; D.E.S.I.R., Epidemiological Study on the Insulin Resistance Syndrome; DPoRT, Diabetes Population Risk Tool; EPIC, European Investigation into Cancer and Nutrition; FINDRISK, Finnish Diabetes Risk Score; KORA S4/F4, Cooperative Health Research in the Region of Augsburg (KORA), Survey 4 8

9 Supplementary Box 5 Implementation of the prediction model recalibration through simple intercept adjustment in the EPIC-InterAct study The recalibrated incident diabetes risk estimates for logistic regression models was calculated as exp(x+corr_factor)/(1 + exp(x+corr_factor)) 22 ; where X is as estimated in Supplementary Box 5 and corr_factor (the correction factor) is estimated by the formula corr_factor=log((obs/(1-obs))/(pred/(1-pred))). Obs is the observed incident diabetes rate for each country and duration of follow-up (five or 10 years) Pred is the mean estimated incident diabetes risk from the original model (see Supplementary Box 5) at the country level and for a given duration of follow-up. log denotes the natural logarithm. The recalibrated incident diabetes risk estimates for survival regression models was calculated as 1 exp(- exp( +log(-log(1-r model (t))) 23 ; where exp denotes the exponential function and log the natural logarithmic function. R model (t) is the incident diabetes risk estimate from the original model for each individual and follow-up duration (five or ten years) as described in the Supplementary Box 5., the correction factor is estimated at the country level by the formula = log(-log(1-obs_km))-log(-log(1-r model )). Obs_KM is the incident diabetes rate at the country level, for a given follow-up duration (five or ten years), estimated from the Kaplan-Meier methods. R model is the mean estimated incident diabetes risk for each country and duration of follow-up, from the original model as described in the Supplementary Box 5. 9

10 Supplementary Box 6 Implementation of the blow-up approach in the EPIC-InterAct study 24 In the case-cohort design the disease incidence (diabetes in our case) is artificially inflated. To arrive at the true incidences from the original cohort (which we need to assess calibration), the EPIC cohort for each participating center within country was reconstituted by applying a blow-up approach to extrapolate the case-cohort data to a full cohort 24. This is achieved by extrapolating the non-cases of the random subcohort to the number of noncases in the full cohort. To do this, we sampled at random and with replacement non-cases from the random subcohort until we reached the size of non-cases in the full cohort. Next, we merged the extrapolated data from non-cases to those from all cases, recreating the size and composition of the full cohort. In Denmark centres, this approach was also applied to blow-up the included sample of non-subcohort cases to the full size of recorded non-subcohort cases. 10

11 Supplementary Table 1 Incident diabetes risk prediction models and EPIC-InterAct countries where they were applied InterAct Countries ARIC 2005 model 15 ARIC 2009 model 21 AUSDRISK 14 Cambridge model 16 D.E.S.I.R. model 17 DPoRT 18 FinRisk concise model 9 FinRisk full model 9 Framingham personal variable model 19 KORA S4/F4 model 20 EPIC- Potsdam model 10 QDScore 13 Denmark Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes France Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Germany Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Italy No No No No Yes (men) Yes Yes Yes No No Yes No The Netherlands Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Spain No No No No Yes (men) Yes Yes Yes No No Yes No Sweden Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes United Kingdom Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes ARIC, Atherosclerosis Risk in Communities; AUSDRISK, Australian Type 2 Diabetes Risk Assessment Tool; D.E.S.I.R., Epidemiological Study on the Insulin Resistance Syndrome; DPoRT, Diabetes Population Risk Tool; EPIC, European Investigation into Cancer and Nutrition; HBP, hypertension; FINDRISK, Finnish Diabetes Risk Score; KORA S4/F4, Cooperative Health Research in the Region of Augsburg (KORA), Survey 4 11

12 Supplementary Table 2 Correction factors for implementing Incident diabetes risk prediction models recalibration at 10 years of follow-up through simple intercept adjustment across EPIC-InterAct countries InterAct Countries ARIC 2005 model 15 ARIC 2009 model 21 AUSDRISK 14 Cambridge model 16 D.E.S.I.R. model 17 (Men) D.E.S.I.R. model 17 (Women) DPoRT 1 8 (Men) DPoRT 18 (Women ) FINDRIS K concise model 9 FINDRIS K full model 9 Framingha m personal variable model 19 KORA S4/F4 model 20 EPIC- Potsdam model 10 QDScore 13 (Men) QDScore 13 (Women) Denmark NA France NA NA NA Germany Italy NA NA NA NA NA NA NA NA The Netherlands Spain NA NA NA NA NA NA NA Sweden NA United Kingdom ARIC, Atherosclerosis Risk in Communities; AUSDRISK, Australian Type 2 Diabetes Risk Assessment Tool; D.E.S.I.R., Epidemiological Study on the Insulin Resistance Syndrome; DPoRT, Diabetes Population Risk Tool; EPIC, European Investigation into Cancer and Nutrition; HBP, hypertension; FINDRISK, Finnish Diabetes Risk Score; KORA S4/F4, Cooperative Health Research in the Region of Augsburg (KORA), Survey 12

13 Supplementary Table 3 Discrimination and calibration statistics for model performance in men and women by country, for the prediction of incident type 2 diabetes at 10 years of follow-up in the EPIC-InterAct study Models Statistic Denmark France Germany Italy Netherlands Spain Sweden UK MEN ARIC 2005 E/O (95% CI) 1 12 ( ) NA 1 02 ( ) NA 1 30 ( ) NA 1 04 ( ) 1 04 ( ) Brier score NA NA NA Yates slope NA NA NA C-statistic (95% CI) 0 72 ( ) NA 0 76 ( ) NA 0 83 ( ) NA 0 73 ( ) 0 74 ( ) ARIC 2009 E/O (95% CI) NA NA 0 91 ( ) NA 1 35 ( ) NA NA 0 86 ( ) Brier score NA NA NA NA NA Yates slope NA NA NA NA NA C- statistic (95% CI) NA NA 0 76 ( ) NA 0 84 ( ) NA NA 0 76 ( ) AUSDRISK E/O (95% CI) 1 09 ( ) NA 1 04 ( ) NA 1 59 ( ) NA 1 07 ( ) 1 04 ( ) Brier score NA NA NA Yates slope NA NA NA C- statistic (95% CI) 0 70 ( ) NA 0 74 ( ) NA 0 81 ( ) NA 0 71 ( ) 0 71 ( ) Cambridge model E/O (95% CI) 1 55 ( ) NA 1 89 ( ) NA 2 38 ( ) NA 1 69 ( ) 1 57 ( ) Brier score NA NA NA Yates slope NA NA NA C- statistic (95% CI) 0 71 ( ) NA 0 76 ( ) NA 0 82 ( ) NA 0 77 ( ) 0 74 ( ) D E S I R Model E/O (95% CI) 1 03 ( ) NA 1 09 ( ) 1 07 ( ) 1 13 ( ) 1 07 ( ) 1 08 ( ) 1 05 ( ) Brier score NA Yates slope NA C- statistic (95% CI) 0 71 ( ) NA 0 76 ( ) 0 71 ( ) 0 78 ( ) 0 69 ( ) 0 74 ( ) 0 75 ( ) DPoRT model E/O (95% CI) 1 49 ( ) NA 1 62 ( ) 1 58 ( ) 1 75 ( ) 1 50 ( ) 1 65 ( ) 1 60 ( ) Brier score NA Yates slope NA C- statistic (95% CI) 0 69 ( ) NA 0 74 ( ) 0 72 ( ) 0 79 ( ) 0 68 ( ) 0 75 ( ) 0 76 ( ) FINDRISK concise E/O (95% CI) 0 94 ( ) NA 0 86 ( ) 0 79 ( ) 1 04 ( ) 0 78 ( ) 0 82 ( ) 0 78 ( ) Brier score NA Yates slope NA C- statistic (95% CI) 0 71 ( ) NA 0 76 ( ) 0 74 ( ) 0 83 ( ) 0 69 ( ) 0 75 ( ) 0 76 ( ) FINDRISK full model E/O (95% CI) 0 95 ( ) NA 0 90 ( ) 0 87 ( ) 1 16 ( ) 0 85 ( ) 0 86 ( ) 0 81 ( ) Brier score NA Yates slope NA C- statistic (95% CI) 0 70 ( ) NA 0 76 ( ) 0 73 ( ) 0 82 ( ) 0 69 ( ) 0 74 ( ) 0 76 ( ) Framingham personal E/O (95% CI) 0 86 ( ) NA 0 72 ( ) NA 0 96 ( ) NA 0 76 ( ) 0 65 ( ) Brier score NA NA NA Yates slope NA NA NA

14 KORA model Potsdam model QDScore WOMEN ARIC 2005 ARIC 2009 AUSDRISK Cambridge model D E S I R Model DPoRT model FINDRISK concise C- statistic (95% CI) 0 68 ( ) NA 0 73 ( ) NA ) NA 0 74 ( ) 0 74 ( ) E/O (95% CI) 1 09 ( ) NA 1 09 ( ) NA 1 20 ( ) NA 1 10 ( ) 1 09 ( ) Brier score NA NA NA Yates slope NA NA NA C- statistic (95% CI) 0 71 ( ) NA 0 75 ( ) NA 0 85 ( ) NA 0 74 ( ) 0 72 ( ) E/O (95% CI) 1 10 ( ) NA 1 04 ( ) 1 05 ( ) 1 31 ( ) 1 01 ( ) 0 97 ( ) 1 10 ( ) Brier score NA Yates slope NA C- statistic (95% CI) 0 70 ( ) NA 0 77 ( ) 0 73 ( ) 0 82 ( ) 0 70 ( ) 0 74 ( ) 0 75 ( ) E/O (95% CI) 1 08 ( ) NA 1 09 ( ) NA 1 05 ( ) NA 1 06 ( ) 1 07 ( ) Brier score NA NA NA Yates slope NA NA NA C- statistic (95% CI) 0 73 ( ) NA 0 77 ( ) NA 0 86 ( ) NA 0 78 ( ) 0 77 ( ) E/O (95% CI) 0 92 ( ) 1 05 ( ) 1 13 ( ) NA 1 00 ( ) NA 1 12 ( ) 1 14 ( ) Brier score NA NA Yates slope NA NA C- statistic (95% CI) 0 73 ( ) 0 83 ( ) 0 84 ( ) NA 0 83 ( ) NA 0 79 ( ) 0 81 ( ) E/O (95% CI) NA 1 05 ( ) 1 26 ( ) NA 0 95 ( ) NA NA 1 26 ( ) Brier score NA NA NA NA Yates slope NA NA NA NA C- statistic (95% CI) NA 0 81 ( ) 0 81 ( ) NA 0 80 ( ) NA NA 0 79 ( ) E/O (95% CI) 0 82 ( ) 1 01 ( ) 0 97 ( ) NA 0 87 ( ) NA 0 95 ( ) 0 98 ( ) Brier score NA NA Yates slope NA NA C- statistic (95% CI) 0 72 ( ) 0 80 ( ) 0 82 ( ) NA 0 81 ( ) NA 0 75 ( ) 0 77 ( ) E/O (95% CI) 0 87 ( ) 1 22 ( ) 1 20 ( ) NA 1 15 ( ) NA 1 22 ( ) 1 19 ( ) Brier score NA NA Yates slope NA NA C- statistic (95% CI) 0 72 ( ) 0 82 ( ) 0 83 ( ) NA 0 81 ( ) NA 0 78 ( ) 0 80 ( ) E/O (95% CI) 0 95 ( ) 1 05 ( ) 1 13 ( ) NA 1 09 ( ) NA 1 11 ( ) 1 04 ( ) Brier score NA NA Yates slope NA NA C- statistic (95% CI) 0 74 ( ) 0 85 ( ) 0 84 ( ) NA 0 83 ( ) NA 0 79 ( ) 0 82 ( ) E/O (95% CI) 1 13 ( ) 1 15 ( ) 1 17 ( ) 1 14 ( ) 1 14 ( ) 1 14 ( ) 1 13 ( ) 1 13 ( ) Brier score Yates slope C- statistic (95% CI) 0 71 ( ) 0 81 ( ) 0 82 ( ) 0 78 ( ) 0 80 ( ) 0 76 ( ) 0 77 ( ) 0 78 ( ) 14

15 FINDRISK full model Framingham personal KORA model Potsdam model E/O (95% CI) 0 95 ( ) 1 01 ( ) 1 19 ( ) 1 12 ( ) 1 00 ( ) 1 20 ( ) 1 18 ( ) 1 24 ( ) Brier score Yates slope C- statistic (95% CI) 0 73 ( ) 0 80 ( ) 0 83 ( ) 0 79 ( ) 0 80 ( ) 0 78 ( ) 0 77 ( ) 0 80 ( ) E/O (95% CI) 0 93 ( ) 1 01 ( ) 1 15 ( ) 1 06 ( ) 0 96 ( ) 1 13 ( ) 1 14 ( ) 1 20 ( ) Brier score Yates slope C- statistic (95% CI) 0 73 ( ) 0 80 ( ) 0 83 ( ) 0 79 ( ) 0 80 ( ) 0 77 ( ) 0 76 ( ) 0 79 ( ) E/O (95% CI) 1 16 ( ) 1 02 ( ) 1 44 ( ) NA 1 03 ( ) NA 1 29 ( ) 1 42 ( ) Brier score NA NA Yates slope NA NA C- statistic (95% CI) 0 70 ( ) 0 82 ( ) 0 81 ( ) NA 0 80 ( ) NA 0 75 ( ) 0 79 ( ) E/O (95% CI) 0 79 ( ) 1 01 ( ) 0 88 ( ) NA 0 97 ( ) NA 0 92 ( ) 0 95 ( ) Brier score NA NA Yates slope NA NA C- statistic (95% CI) 0 72 ( ) 0 82 ( ) 0 83 ( ) NA 0 81 ( ) NA 0 76 ( ) 0 79 ( ) E/O (95% CI) 0 74 ( ) 1 03 ( ) 0 93 ( ( ) 0 91 ( ) 0 96 ( ) 1 00 ( ) 0 93 ( ) Brier score Yates slope C- statistic (95% CI) 0 75 ( ) 0 84 ( ) 0 84 ( ) 0 81 ( ) 0 83 ( ) 0 79 ( ) 0 79 ( ) 0 81 ( ) QDScore E/O (95% CI) 1 03 ( ) 1 06 ( ) 1 05 ( ) NA 1 04 ( ) NA 1 03 ( ) 1 04 ( ) Brier score NA NA Yates slope NA NA C-statistic (95% CI) 0 73 ( ) 0 85 ( ) 0 85 ( ) NA 0 84 ( ) NA 0 80 ( ) 0 82 ( ) C-statistic, area under the receiver-operating characteristic curves; 95% CI, 95% confidence interval; E/O, ratio expected/observed event rate; Brier scores and Yates slopes are scaled from 0 to 1. Higher Yates slope, as well as lower Brier Scores or higher C-statistic indicates higher discrimination. ARIC, Atherosclerosis Risk in Communities; AUSDRISK, Australian Type 2 Diabetes Risk Assessment Tool; D.E.S.I.R., Epidemiological Study on the Insulin Resistance Syndrome; DPoRT, Diabetes Population Risk Tool; EPIC, European Investigation into Cancer and Nutrition; KORA S4/F4, Cooperative Health Research in the Region of Augsburg (KORA), Survey 4 15

16 Supplementary Table 4 C-statistic and 95% confidence interval overall and by subgroups of participants,, for the prediction of incident type 2 diabetes at 5 years of follow-up in the EPIC-InterAct study Models Overall Men Women Age<60 years Age>=60 years BMI<25 kg/m 2 BMI>=25 kg/m 2 WC<102 (88) cm WC>=102 (88) cm ARIC ( )*** 0 78 ( )** 0 83 ( )*** 0 83 ( )*** 0 75 ( )*** 0 74 ( )*** 0 74 ( ) 0 76 ( ) 0 68 ( ) ARIC ( ) 0 80 ( )** 0 82 ( ) 0 82 ( ) 0 76 ( )** 0 74 ( ) 0 74 ( ) 0 75 ( )* 0 67 ( )* AUSDRISK 0 79 ( )*** 0 76 ( )** 0 81 ( ) *** 0 80 ( )*** 0 72 ( ) 0 70 ( )* 0 74 ( ) 0 70 ( ) 0 66 ( ) Cambridge model 0 81 ( )*** 0 78 ( )*** 0 82 ( ) *** 0 82 ( )*** 0 74 ( ) 0 71 ( )*** 0 73 ( ) 0 75 ( ) 0 68 ( ) D E S I R model 0 81 ( )*** 0 76 ( ) 0 83 ( ) *** 0 83 ( )*** 0 77 ( )*** 0 71 ( )*** 0 74 ( )*** 0 73 ( ) 0 67 ( )* DPoRT model 0 75 ( )*** 0 76 ( )** 0 80 ( ) *** 0 79 ( )*** 0 72 ( )*** 0 64 ( )*** 0 70 ( )*** 0 73 ( ) 0 65 ( )** FINDRISK concise 0 80 ( )*** 0 78 ( ) *** 0 81 ( ) *** 0 81 ( )*** 0 73 ( )** 0 70 ( )*** 0 73 ( ) 0 74 ( ) 0 65 ( ) FINDRISK full model 0 80 ( )*** 0 77 ( ) *** 0 81 ( ) *** 0 81 ( )*** 0 72 ( ) 0 69 ( )*** 0 73 ( ) 0 72 ( ) 0 65 ( )*** Framingham personal 0 78 ( ) 0 76 ( ) 0 80 ( ) 0 80 ( ) 0 72 ( )*** 0 64 ( ) 0 68 ( ) 0 71 ( ) 0 62 ( )** KORA model 0 81 ( )*** 0 78 ( ) *** 0 82 ( ) *** 0 82 ( )*** 0 74 ( )* 0 74 ( )** 0 74 ( )*** 0 73 ( ) 0 69 ( )*** Potsdam model 0 81 ( )*** 0 77 ( ) *** 0 83 ( )** 0 82 ( )*** 0 75 ( )** 0 76 ( )*** 0 75 ( ) 0 76 ( ) 0 68 ( ) QDScore 0 83 ( )*** 0 80 ( ) *** 0 84 ( ) *** 0 84 ( )*** 0 77 ( )*** 0 75 ( )*** 0 75 ( )*** 0 78 ( ) 0 70 ( ) BMI, body mass index; WC, waist circumference p-value for heterogeneity across countries: *<0 05; **<0 01; ***<0 001; <

17 Supplementary Table 5 Ratios expected/observed 5-year incident diabetes rates and 95% confidence interval overall and by subgroups of participants in the EPIC-InterAct study Models Overall Men Women Age>=60 years Age<60 years BMI>=25 kg/m 2 BMI<25 kg/m 2 WC<102 (88) cm WC>=102 (88) cm ARIC ( ) 1 09 ( )** 1 09 ( ) 1 06 ( ) 1-9 ( ) 0 83 ( ) 2 47 ( ) 1 71 ( ) 0 69 ( ) ARIC ( ) 1 02 ( )*** 1 08 ( ) 0 71 ( )* 1 23 ( ) 0 66 ( ) 2 90 ( ) 1 82 ( ) 0 51 ( ) AUSDRISK 1 03 ( ) 1 13 ( )*** 0 95 ( ) 0 90 ( )* 1 10 ( ) 0 76 ( ) 2 55 ( )* 1 47 ( ) 0 73 ( ) Cambridge model 1 56 ( ) 1 99 ( )*** 1 23 ( )** 1 61 ( ) 1 51 ( )** 1 56 ( ) 1 41 ( ) 1 87 ( ) 1 37 ( ) D E S I R model 1 15 ( ) 1 13 ( ) 1 18 ( ) 0 90 ( ) 1 34 ( ) 1 10 ( ) 1 53 ( )* 1 14 ( ) 1 16 ( )* DPoRT model 1 26 ( ) 1 23 ( ) 1 05 ( ) 0 88 ( )** 1 27 ( ) 1 09 ( ) 1 44 ( ) 1 72 ( ) 0 80 ( ) FINDRISK concise 1 02 ( ) 0 89 ( )** 1 15 ( )** 0 91 ( ) 1 08 ( ) 0 96 ( )* 1 37 ( )** 0 97 ( ) 1 04 ( ) FINDRISK full model 1 02 ( ) 0 94 ( )** 1 11 ( )** 0 91 ( ) 1 08 ( ) 0 95 ( )* 1 48 ( )** 1 01 ( ) 1 02 ( ) Framingham personal 1 02 ( ( )*** 1 27 ( ) 0 77 ( ) 1 19 ( ) 0 88 ( )** 1 85 ( )** 1 55 ( ) 0 69 ( )* KORA model 1 05 ( ) 1 14 ( ) 0 96 ( ) 1 25 ( ) 0 90 ( ) 0 91 ( )* 1 87 ( ) 1 54 ( ) 0 76 ( ) Potsdam model 1 01 ( ) 1 09 ( )* 0 93 ( ) 1 10 ( ) 0 95 ( ) 0 93 ( )** 1 45 ( )** 1 15 ( )* 0 91 ( )** QDScore 1 02 ( ) 1 03 ( ) 1 01 ( ) 0 96 ( ) 1 05 ( ) 0 94 ( ) 1 43 ( ) 1 41 ( ) 0 78 ( ) BMI, body mass index; WC, waist circumference p-value for heterogeneity across countries: *<0 05; **<0 01; ***<0 001; <

18 Supplementary Table 6 Discrimination and calibration statistics for model performance in men and women by country, for the prediction of incident type 2 diabetes at 5 years of follow-up in the EPIC-InterAct study Models Statistic Denmark France Germany Italy Netherlands Spain Sweden UK MEN ARIC 2005 E/O (95% CI) 1 10 ( ) NA 1 01 ( ) NA 1 56 ( ) NA 1 03 ( ) 1 11 ( ) Brier score NA NA NA Yates slope NA NA NA C-statistic (95% CI) 0 76 ( ) NA 0 77 ( ) NA 0 84 ( ) NA 0 78 ( ) 0 76 ( ) ARIC 2009 E/O (95% CI) NA NA 0 85 ( ) NA 1 57 ( ) NA NA 0 86 ( ) Brier score NA NA NA NA NA Yates slope NA NA NA NA NA C- statistic (95% CI) NA NA 0 77 ( ) NA 0 86 ( ) NA NA 0 78 ( ) AUSDRISK E/O (95% CI) 1 08 ( ) NA 1 03 ( ) NA 1 93 ( ) NA 1 06 ( ) 1 11 ( ) Brier score NA NA NA Yates slope NA NA NA C- statistic (95% CI) 0 75 ( ) NA 0 75 ( ) NA 0 83 ( ) NA 0 75 ( ) 0 74 ( ) Cambridge model E/O (95% CI) 1 75 ( ) NA 2 04 ( ) NA 3 07 ( ) NA 1 93 ( ) 1 84 ( ) Brier score NA NA NA Yates slope NA NA NA C- statistic (95% CI) 0 76 ( ) NA 0 76 ( ) NA 0 84 ( ) NA 0 81 ( ) 0 77 ( ) D E S I R Model E/O (95% CI) 1 12 ( ) NA 1 13 ( ) 1 11 ( ) 1 16 ( ) 1 15 ( ) 1 20 ( ) 1 11 ( ) Brier score NA Yates slope NA C- statistic (95% CI) 0 75 ( ) NA 0 77 ( ) 0 70 ( ) 0 78 ( ) 0 76 ( ) 0 78 ( ) 0 77 ( ) DPoRT model E/O (95% CI) 1 20 ( ) NA 1 25 ( ) 1 23 ( ) 1 33 ( ) 1 20 ( ) 1 29 ( ) 1 21 ( ) Brier score NA Yates slope NA C- statistic (95% CI) 0 74 ( ) NA 0 74 ( ) 0 74 ( ) 0 80 ( ) 0 73 ( ) 0 79 ( ) 0 77 ( ) FINDRISK concise E/O (95% CI) 0 94 ( ) NA 0 86 ( ) 0 80 ( ) 1 25 ( ) 0 93 ( ) 0 82 ( ) 0 84 ( ) Brier score NA Yates slope NA C- statistic (95% CI) 0 76 ( ) NA 0 77 ( ) 0 73 ( ) 0 84 ( ) 0 75 ( ) 0 80 ( ) 0 78 ( ) FINDRISK full model E/O (95% CI) 0 95 ( ) NA 0 90 ( ) 0 89 ( ) 1 41 ( ) 1 01 ( ) 0 86 ( ) 0 88 ( ) Brier score NA Yates slope NA C- statistic (95% CI) 0 75 ( ) NA 0 76 ( ) 0 72 ( ) 0 84 ( ) 0 75 ( ) 0 79 ( ) 0 77 ( ) Framingham personal E/O (95% CI) 0 82 ( ) NA 0 71 ( ) NA 1 14 ( ) NA 0 74 ( ) 0 69 ( ) Brier score NA NA NA

19 KORA model Potsdam model QDScore WOMEN ARIC 2005 ARIC 2009 AUSDRISK Cambridge model D E S I R Model DPoRT model Yates slope NA NA NA C- statistic (95% CI) 0 72 ( ) NA 0 73 ( ) NA 0 82 ( ) NA 0 77 ( ) 0 77 ( ) E/O (95% CI) 1 09 ( ) NA 1 10 ( ) NA 1 46 ( ) NA 1 15 ( ) 1 20 ( ) Brier score NA NA NA Yates slope NA NA NA C- statistic (95% CI) 0 75 ( ) NA 0 76 ( ) NA 0 87 ( ) NA 0 78 ( ) 0 77 ( ) E/O (95% CI) 1 08 ( ) NA 1 01 ( ) 1 03 ( ) 1 56 ( ) 1 19 ( ) 1 01 ( ) 1 15 ( ) Brier score NA Yates slope NA C- statistic (95% CI) 0 75 ( ) NA 0 78 ( ) 0 72 ( ) 0 83 ( ) 0 77 ( ) 0 78 ( ) 0 78 ( ) E/O (95% CI) 1 03 ( ) NA 1 02 ( ) NA 1 02 ( ) NA 1 04 ( ) 1 01 ( ) Brier score NA NA NA Yates slope NA NA NA C- statistic (95% CI) 0 77 ( ) NA 0 77 ( ) NA 0 87 ( ) NA 0 82 ( ) 0 79 ( ) E/O (95% CI) 1 04 ( ) 1 06 ( ) 1 20 ( ) NA 0 98 ( ) NA 1 21 ( ) 1 10 ( ) Brier score ) NA NA Yates slope NA NA C- statistic (95% CI) 0 80 ( ) 0 84 ( ) 0 86 ( ) NA 0 83 ( ) NA 0 83 ( ) 0 82 ( ) E/O (95% CI) NA 1 01 ( ) 1 27 ( ) NA 0 89 ( ) NA NA 1 15 ( ) Brier score NA NA NA NA Yates slope NA NA NA NA C- statistic (95% CI) NA 0 83 ( ) 0 84 ( ) NA 0 81 ( ) NA NA 0 79 ( ) E/O (95% CI) 0 93 ( ) 1 02 ( ) 1 02 ( ) NA 0 84 ( ) NA 1 02 ( ) 0 94 ( ) Brier score NA NA Yates slope NA NA C- statistic (95% CI) 0 77 ( ) 0 83 ( ) 0 84 ( ) NA 0 82 ( ) NA 0 80 ( ) 0 78 ( ) E/O (95% CI) 1 07 ( ) 1 28 ( ) 1 31 ( ) NA 1 17 ( ) NA 1 42 ( ) 1 21 ( ) Brier score NA NA Yates slope NA NA C- statistic (95% CI) 0 78 ( ) 0 83 ( ) 0 86 ( ) NA 0 82 ( ) NA 0 83 ( ) 0 79 ( ) E/O (95% CI) 1 16 ( ) 1 10 ( ) 1 23 ( ) NA 1 16 ( ) NA 1 29 ( ) 1 14 ( ) Brier score NA NA Yates slope NA NA C- statistic (95% CI) 0 80 ( ) 0 86 ( ) 0 86 ( ) NA 0 85 ( ) NA 0 82 ( ) 0 81 ( ) E/O (95% CI) 1 04 ( ) 1 05 ( ) 1 05 ( ) 1 03 ( ) 1 05 ( ) 1 04 ( ) 1 05 ( ) 1 05 ( ) Brier score Yates slope C- statistic (95% CI) 0 78 ( ) 0 81 ( ) 0 85 ( ) 0 79 ( ) 0 80 ( ) 0 79 ( ) 0 81 ( ) 0 76 ( ) 19