Two Risk-Scoring Systems for Predicting Incident Diabetes Mellitus in U.S. Adults Age 45 to 64 Years

Context Prediction scores that combine multiple factors could help identify people who are at risk for diabetes. Contribution Using data from a large prospective study, these investigators derived and tested scoring systems for predicting development of diabetes within 10 years among middle-age adults. A basic system, which included such items as waist circumference, parental diabetes, hypertension, short stature, and black race, identified groups with varying risk for diabetes. An enhanced system that included such blood tests as fasting glucose, lipids, and uric acid better identified those at highest risk. Caution These scoring systems need to be validated in different populations and their utility evaluated before widespread application. The Editors The incidence of diabetes among U.S. adults continues to increase (1, 2). Against this disturbing trend, good evidence (3, 4) now indicates that dietary and exercise interventions offered to nondiabetic adults at high risk can materially reduce or delay their conversion to type 2 diabetes, and a recent study (5) indicates that such interventions are most effective among people with the highest a priori risk for diabetes. These interventions, however, may be burdensome to some patients or their families, and the substantial societal costs of these interventions could eventually be borne by insurers or public health agencies (6, 7). For these reasons, it may be difficult to justify providing expensive intervention programs to people who are at relatively low risk for diabetes. Clinicians who provide medical care to adults need simple, validated tools with which to identify those most likely to benefit from participation in a diabetes prevention program. Research protocols for estimating a person's future risk for type 2 diabetes have depended primarily on identifying impaired glucose tolerance through a 2-hour oral glucose tolerance test (811). This 2-hour challenge test, however, is relatively costly, inconvenient, and labor-intensive, and its reliability has been questioned (12, 13). We sought to develop simple scoring systems that could efficiently identify adults at high risk for diabetes without requiring the 2-hour challenge test. To do so, we used 14.9 years of longitudinal data from the ARIC (Atherosclerosis Risk in Communities) Study to derive and validate a basic diabetes prediction scoring system that depended on each participant's baseline anthropometric characteristics, sex, parental history of diabetes, and other clinical variables but did not require a blood specimen. We also derived and validated an enhanced scoring system that evaluated the same variables as the basic system along with baseline concentrations of glucose and other analytes that are commonly assessed in a fasting blood sample. Methods Population Sample The ARIC Study used probability sampling of white and black adults in 4 U.S. communities to assemble a population-based cohort of 15792 adults age 45 to 64 years at baseline in 1987 to 1989 (1416). To comply with the requirements of human subject research review committees, ARIC participants provided written consent for their involvement and data sharing. We obtained public-use, limited-access data released by the Collaborative Studies Coordinating Center of the University of North Carolina at Chapel Hill (17) for our analysis. We excluded 60 persons who did not consent to sharing their information. We excluded another 3003 persons because they had prevalent diabetes at baseline or because we lacked complete data (Figure 1). Figure 1. Study flow diagram. ARIC = Atherosclerosis Risk in Communities. Assessment of Risk Factors and Outcomes We chose candidate baseline variables for their simplicity, low cost, common availability, and mention in previous models of diabetes risk. At the baseline interview, the diabetes status of participants' natural parents was determined from their response to a question about whether either parent had ever had diabetes (sugar in the blood). Racial group was categorized as either nonblack (reference, of whom <0.4% were other than white) or black (16); the public use data set provided no information on ethnic descriptions. Baseline age was categorized initially in 5-year intervals, beginning with 45 to 49 years (reference). Smoking history was categorized as none (reference), moderate (<24 cigarette pack-years or a similar exposure), or heavy (24 cigarette pack-years or similar exposure) on the basis of responses to extensive questions about lifetime exposure to cigarettes, pipes, and cigars or cigarillos. Alcohol consumption status was categorized as current drinker (reference), former drinker, or never drinker. Educational attainment was categorized as high (17 to 21 years; reference), moderate (12 to 16 years), or low (<12 years). Height and weight were measured at the baseline examination without shoes or heavy clothing. Waist circumference (standing) was measured once at the level of the umbilicus while the examiner held the tape horizontally and instructed the participant to breathe quietly. Systolic and diastolic blood pressure values were the average of the second and third readings obtained in a seated position after a 5-minute rest; participants were considered to have hypertension if they had a systolic value of at least 140 mm Hg or a diastolic value of at least 90 mm Hg or reported recent use of antihypertensive medication. Pulse rate was determined from a supine electrocardiogram obtained at rest. Central laboratories performed analyses on baseline fasting serum and plasma specimens by using conventional assays (17). The ARIC Lipid Laboratory participated in the Centers for Disease Control and PreventionNational Heart, Lung, and Blood Institute Lipid Standardization Program. No glycated hemoglobin assay or glucose tolerance test was performed at the baseline examination. Diabetes status was reassessed primarily at triennial follow-up encounters that included fasting blood specimens (year 3 [in 1990 to 1992], year 6 [1993 to 1995], and year 9 [1996 to 1998]). The year 9 follow-up examination also provided a 2-hour oral glucose (75 g) tolerance test. Participants were considered to have diabetes if they reported a history of physician-diagnosed diabetes (sugar in the blood) or if their fasting glucose level was at least 7.0 mmol/L (126 mg/dL), their nonfasting glucose level was at least 11.1 mmol/L (200 mg/dL), or their 2-hour glucose value at the year 9 follow-up was at least 11.1 mmol/L (200 mg/dL). Additional cases of incident diabetes were identified by criterion-based abstractions of hospital records (obtained through surveillance of hospital admissions) and from responses to annual postal or telephone questionnaires distributed during follow-up years 11, 12, 13, and 14 (17). Statistical Analysis From the eligible study population of 12729 participants, we randomly selected a 75% derivation sample (9587 persons) in which to model the incidence of diabetes in relation to baseline risk factors. We used the LIFEREG procedure in SAS, version 9.1 (SAS Institute, Cary, North Carolina) to create Weibull proportional hazard regression models for interval-censored outcome data because the ARIC records provided only an interval for each diabetes diagnosis rather than a precise date. We categorized all continuous variables into quintiles (sex-specific for all except fasting glucose) so that the estimated contribution of these variables to diabetes risk could be expressed through simplified point scores assigned to each of the quintile categories. For anthropometric variables, we used weight, height, and waist circumference rather than complex indices, such as body mass index or waist-to-height ratio. We then identified a best basic predictive model (without baseline blood information) and a best enhanced predictive model (which included data from analysis of a baseline fasting blood specimen). For each variable in these models, we considered adjacent categories of the variable to be overlapping if their estimated Weibull coefficients were within 1 SE of each other. We joined any overlapping categories into a single combined categorical term, which we retained if it contributed to the model (P< 0.05). For the 2 final models, each retained categorical variable was represented by a point score proportional to the estimate of its Weibull coefficient. We standardized the point score values so that the maximum total score equaled 100 and rounded each point score assignment to the nearest integer. We then evaluated the performance of these best basic and enhanced scoring systems in a validation sample that comprised the remaining 25% of the eligible ARIC cohort (3142 participants). For these evaluations, we used interval-censored Weibull models to estimate the 10-year diabetes incidence rates. Applying each scoring system, we calculated the area under the receiver-operating characteristic (ROC) curve on the basis of the 10-year incidence outcome (18). Through repetitive sampling and replacement of participants in the validation sample, we generated a bootstrapped estimate of the confidence limits for our areas under the ROC curves (19). We also identified the empirical quintiles of basic or enhanced score values and estimated the 10-year diabetes incidence among members of the validation sample with scores in each of the 5 quintiles by using the LIFETEST procedure of SAS with the KaplanMeier survival function. For the purpose of comparison with our derived scoring systems, we used the same validation sample to test 2 alternative simple scoring systems (20, 21) that similarly used integer points for predicting diabetes. Role of the Funding Source The National Heart, Lung, and Blood Institute funded the ARIC Study, including its participant follow-up. The Centers for Disease Control and Prevention supported the secondary analysis of ARIC data. Neither funding source had a role in the design of our secondary