OBJECTIVE

Type 2 diabetes (T2D) is increasingly diagnosed at younger ages. We investigated the association of adolescent obesity with incident T2D at early adulthood.

RESEARCH DESIGN AND METHODS

A nationwide, population-based study evaluated 1,462,362 adolescents (59% men, mean age 17.4 years) during 1996–2016. Data were linked to the Israeli National Diabetes Registry. Weight and height were measured at study entry. Cox proportional models were applied.

RESULTS

During 15,810,751 person-years, 2,177 people (69% men) developed T2D (mean age at diagnosis 27 years). There was an interaction among BMI, sex, and incident T2D (Pinteraction = 0.023). In a model adjusted for sociodemographic variables, the hazard ratios for diabetes diagnosis were 1.7 (95% CI 1.4–2.0), 2.8 (2.3–3.5), 5.8 (4.9–6.9), 13.4 (11.5–15.7), and 25.8 (21.0–31.6) among men in the 50th–74th percentile, 75th–84th percentile, overweight, mild obesity, and severe obesity groups, respectively, and 2.2 (1.6–2.9), 3.4 (2.5–4.6), 10.6 (8.3–13.6), 21.1 (16.0–27.8), and 44.7 (32.4–61.5), respectively, in women. An inverse graded relationship was observed between baseline BMI and mean age of T2D diagnosis: 27.8 and 25.9 years among men and women with severe obesity, respectively, and 29.5 and 28.5 years among low-normal BMI (5th–49th percentile; reference), respectively. The projected fractions of adult-onset T2D that were attributed to high BMI (≥85th percentile) at adolescence were 56.9% (53.8–59.9%) and 61.1% (56.8–65.2%) in men and women, respectively.

CONCLUSIONS

Severe obesity significantly increases the risk for incidence of T2D in early adulthood in both sexes. The rise in adolescent severe obesity is likely to increase diabetes incidence in young adults in coming decades.

Diabetes is a major public health burden associated with high rates of morbidity, hospitalization, health care service utilization, and mortality. Diabetes affects over 400 million people worldwide, and the global prevalence has been growing, with a disproportionate increase at young ages (1). For example, in the U.S., the prevalence of type 2 diabetes (T2D) in people age <45 years doubled during two decades, reaching a prevalence of 4.0% (representing 12 million adults) by 2015 (2,3). Notably, younger age of T2D onset was linked to more severe disease, as recently exemplified by increased risk for cardiovascular mortality (4).

Several epidemiologic studies have reported a strong relationship between obesity in youth and subsequent T2D (512). However, these studies generally focused on people who developed T2D after age 40 years and used self-recall of body weight during adolescence as a measure of risk factor (6,9,10,12). Moreover, they generally excluded diagnoses of diabetes before the age of 30 years, were limited to men (5,8), and did not capture the impact on diabetes incidence of the rapid rise in the prevalence of severe obesity in adolescents. Here, we analyzed associations of BMI in adolescent males and females with incident diabetes in a nationwide cohort of 1.46 million adolescents with a follow-up into young adulthood.

Study Population

This study included all Israeli male and female adolescents who underwent a medical evaluation in the year before their mandatory military service. Individuals were between the ages of 16 and 19 years. Males were included if they were evaluated between 1 January 1996 and 31 December 2016 and females if evaluated between 1 January 1997 and 31 December 2016. Individuals were excluded from the analysis if they had diabetes or dysglycemia at the time of their examination (as reported by their primary care physician on the basis of fasting plasma glucose ≥100 mg/dL or glycated hemoglobin ≥5.7%), if they died before the Israel National Diabetes Registry (INDR) was established in 2012 (because diabetes status before their death could not be determined) (Fig. 1), or if BMI data were not available. The final study sample included 1,462,362 examinees for whom we had a continuous follow-up from age 17 years until diabetes onset, death, or 31 December 2016, whichever came first. Of note, some minorities (mostly Arab adolescents of both sexes) and Orthodox and ultra-Orthodox Jewish females are not obligated to serve in the army and are thus underrepresented in this cohort. On the other hand, the sample of Jewish men can be regarded as nationally representative (13). The Israel Defense Forces Medical Corps institutional review board approved this study, waiving the requirement for informed consent.

Figure 1

Study design and cohort buildup. The main analysis comprised 2,177 individuals with incident diagnoses of T2D. Additionally, for 405 diagnosed with diabetes, the type of diagnosis was uncertain; these individuals were analyzed separately. Another 777 individuals were diagnosed with type 1 diabetes during the study period. Data were missing for 406 diagnoses of incident T2D; of these, 267 were analyzed separately on the basis of the first year the individuals appeared in the INDR. IQ, intelligence quotient.

Figure 1

Study design and cohort buildup. The main analysis comprised 2,177 individuals with incident diagnoses of T2D. Additionally, for 405 diagnosed with diabetes, the type of diagnosis was uncertain; these individuals were analyzed separately. Another 777 individuals were diagnosed with type 1 diabetes during the study period. Data were missing for 406 diagnoses of incident T2D; of these, 267 were analyzed separately on the basis of the first year the individuals appeared in the INDR. IQ, intelligence quotient.

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The INDR and Diagnosis of Diabetes

The primary outcomes of the study were incident T2D that was diagnosed either during military service or later in life as recorded by the INDR. All health medical organizations in Israel have been requested by law to annually report prevalent cases of diabetes to the INDR since 2012.

For the purpose of this research, we used data from the INDR to attain the recordings of all diabetes diagnoses that were recorded in Israel between 1 January 2000 and 31 December 2011 and to attain the prospective recording of all new diagnoses from 1 January 2012 onward. In addition, the INDR database includes a set of clinical variables that are updated annually on the basis of measurements obtained during the previous calendar year. Data in the INDR were linked to the Israel Defense Forces database using national identity numbers. This enables linkage of medical data obtained at adolescence, including weight and height measurements, with diabetes incidence recorded later in life. Health medical organizations were required to report diabetes to the INDR when one or more of the following criteria were met in the previous year of the report to the registry: 1) glycated hemoglobin ≥6.5% (47.5 mmol/mol), 2) serum glucose concentrations of ≥200 mg/dL (11.1 mmol/L) in two tests performed at an interval of at least 1 month, and 3) greater than or equal to three purchases of glucose-lowering medications in different months. Individuals who did not meet these criteria were assumed not to have diabetes. The registry captures nearly 100% of all diabetes diagnoses among permanent residents in Israel on the basis of these criteria. The sensitivity of the INDR is 95%, and the specificity is 93%.

While the INDR does not receive data regarding the types of diabetes, it includes data regarding prescribed diabetes medications. Using this information, we excluded from the analysis individuals with a diagnoses of type 1 diabetes according to the application of the following criteria to those who were actively treated with short-acting insulin: 1) the treatment with short-acting insulin was initiated within 1 year of diabetes onset, and 2) insulin treatment, but not oral antidiabetic drugs, was prescribed. If information on antidiabetes medications was missing, the diagnosis was referred to as diabetes of uncertain type. Gestational diabetes is not reported to the INDR and was therefore not included in this study. The INDR also includes weight and height measurements that were obtained during routine clinic visits and were available at the time that an individual with diabetes was recorded in the diabetes registry. Post–baseline BMI levels were not available for people who were not diagnosed with diabetes.

Data Collection and Study Variables

Weight and height were measured (barefoot and in underwear) at baseline by trained medics using a beam balance and stadiometer to the nearest 0.1 kg and centimeter, respectively. BMI was calculated (weight [in kilograms] divided by squared height [in meters]). The health examination was performed by military physicians who reviewed the examinees’ medical records and provided diagnostic codes when applicable. Data regarding education, socioeconomic status (SES), cognitive performance score, and country of origin were recorded as well.

Age at examination and year of birth were treated as continuous variables. Education was divided into three groups: ≤10, 11, or 12 years of formal schooling. SES was based on place of residence at the time of examination and classified into low, medium, and high, as reported previously (14). Cognitive performance was assessed by a general intelligence score, which was shown to correlate by >85% with intelligence quotient (14). Cognitive performance categories were low (<−1 SD), medium (−1 to <1 SD), and high (≥1 SD), as reported previously (14). Country of origin (classified by father’s or grandfather’s country of birth if the father was born abroad) and country of birth were grouped as reported previously (13). BMI was classified according to the U.S. Centers for Disease Control and Prevention–established percentiles, which were validated for Israeli adolescents (15), for age (by month) and sex for the following subgroups: BMI <5th (underweight), 5th ≤ BMI < 50th (low-normal), 50th ≤ BMI < 75th, 75th ≤ BMI < 85th, 85th ≤ BMI < 95th (overweight), mild obesity (also referred to as class I obesity) (≥95th percentile to <120% of the 95th percentile), and severe obesity that grouped both class II obesity (≥120% to <140% of the 95th percentile or BMI ≥35 kg/m2, whichever was lower) and class III obesity (≥140% of the 95th percentile or BMI ≥40 kg/m2, whichever was lower) (16).

Statistical Analysis

The incidence rate of T2D was calculated per person-years of follow-up. Kaplan-Meier survival curves were computed for the BMI percentile categories with 95% CIs. Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% CIs for incident diabetes using the 5th–49th BMI percentile group as the reference or the entire normal BMI range in specific sensitivity analyses. Covariates were added in a stepwise manner to a model adjusted for age and birth year. Variables that appear in Table 1 and were significant (P < 0.05) in the minimally adjusted model were included in the final multivariable analysis. Analysis was stratified by sex and by the interaction between sex and BMI (treated as a continuous variable). Incident T2D was tested in unadjusted models and multivariable models that were adjusted for age at study entry, birth year, education, and cognitive performance. The assumption of proportionality of the hazards was visually confirmed for all variables. There was no interaction between BMI or any of the study covariates included in the multivariable model with time and incident T2D (P > 0.2 for all tests in both sexes).

Table 1

Characteristics of the study cohort at baseline, according to BMI groups

BMI category
Total<5th percentile5th–49th percentile50th–74th percentile75th–84th percentile85th–94th percentileMild obesitySevere obesityP value for linear trend
Men          
 Examinees, n 834,050 62,569 351,425 190,002 79,194 87,456 49,990 13,414  
 Mean age ± SD (years) 17.3 ± 0.5 17.5 ± 0.6 17.3 ± 0.5 17.3 ± 0.5 17.3 ± 0.5 17.3 ± 0.5 17.3 ± 0.5 17.3 ± 0.5 0.21 
 Mean BMI ± SD (kg/m222.2 ± 4.0 17.1 ± 0.8 19.8 ± 1.0 22.4 ± 0.7 24.3 ± 0.6 26.5 ± 1.0 30.6 ± 1.6 37.1 ± 2.7 <0.001 
 BMI range (kg/m2)* 12.1–47.1 12.1–17.8 17.9–21.4 21.4–23.6 23.6–25.1 25.1–28.4 28.4–34.1 34.1–47.1  
 Mean weight ± SD (kg) 67.5 ± 13.3 51.5 ± 4.8 60.0 ± 5.6 68.0 ± 5.7 73.8 ± 6.0 80.6 ± 7.1 93.3 ± 8.9 113.2 ± 12.2 <0.001 
 Mean height ± SD (cm) 174.1 ± 6.8 173.7 ± 7.0 174.0 ± 6.8 174.1 ± 6.8 174.2 ± 6.8 174.2 ± 6.9 174.4 ± 6.9 174.5 ± 7.1 <0.001 
 Full education, % 85 81 84 86 86 85 83 78 0.156 
 High SES, % 19 19 20 20 19 18 16 14 0.033 
 High cognitive score, % 14 12 15 16 15 13 11 0.102 
 Israeli born, % 83 80 82 83 84 85 85 86 <0.001 
Women          
 Examinees, n 592,312 27,684 249,547 155,965 65,843 65,810 21,136 6,327  
 Mean age ± SD (years) 17.2 ± 0.4 17.3 ± 0.5 17.2 ± 0.4 17.2 ± 0.4 17.2 ± 0.4 17.2 ± 0.4 17.2 ± 0.4 17.3 ± 0.5 0.51 
 Mean BMI ± SD (kg/m222.0 ± 3.8 16.5 ± 0.7 19.4 ± 1.0 22.1 ± 0.7 24.3 ± 0.6 27.0 ± 1.3 31.8 ± 1.5 37.9 ± 2.6 <0.001 
 BMI range (kg/m2)* 12.6–47.7 12.6–17.3 17.3–21.0 21.0–23.5 23.5–25.3 25.3–29.8 29.8–35.0 35.0–47.7  
 Mean weight ± SD (kg) 57.8 ± 11.0 44.0 ± 3.8 51.0 ± 4.6 57.9 ± 4.8 63.6 ± 5.1 70.9 ± 6.5 83.8 ± 7.7 100.1 ± 10.7 <0.001 
 Mean height ± SD (cm) 162.1 ± 6.3 163.1 ± 6.5 162.3 ± 6.2 161.7 ± 6.2 161.8 ± 6.2 161.8 ± 6.3 162.3 ± 6.4 162.4 ± 6.6 0.49 
 Full education, % 90 88 91 91 91 90 89 84 0.29 
 High SES, % 23 22 24 24 23 20 18 17 0.025 
 High cognitive score, % 10 11 11 10 0.063 
 Israeli born, % 83 78 82 83 84 85 87 88 <0.001 
BMI category
Total<5th percentile5th–49th percentile50th–74th percentile75th–84th percentile85th–94th percentileMild obesitySevere obesityP value for linear trend
Men          
 Examinees, n 834,050 62,569 351,425 190,002 79,194 87,456 49,990 13,414  
 Mean age ± SD (years) 17.3 ± 0.5 17.5 ± 0.6 17.3 ± 0.5 17.3 ± 0.5 17.3 ± 0.5 17.3 ± 0.5 17.3 ± 0.5 17.3 ± 0.5 0.21 
 Mean BMI ± SD (kg/m222.2 ± 4.0 17.1 ± 0.8 19.8 ± 1.0 22.4 ± 0.7 24.3 ± 0.6 26.5 ± 1.0 30.6 ± 1.6 37.1 ± 2.7 <0.001 
 BMI range (kg/m2)* 12.1–47.1 12.1–17.8 17.9–21.4 21.4–23.6 23.6–25.1 25.1–28.4 28.4–34.1 34.1–47.1  
 Mean weight ± SD (kg) 67.5 ± 13.3 51.5 ± 4.8 60.0 ± 5.6 68.0 ± 5.7 73.8 ± 6.0 80.6 ± 7.1 93.3 ± 8.9 113.2 ± 12.2 <0.001 
 Mean height ± SD (cm) 174.1 ± 6.8 173.7 ± 7.0 174.0 ± 6.8 174.1 ± 6.8 174.2 ± 6.8 174.2 ± 6.9 174.4 ± 6.9 174.5 ± 7.1 <0.001 
 Full education, % 85 81 84 86 86 85 83 78 0.156 
 High SES, % 19 19 20 20 19 18 16 14 0.033 
 High cognitive score, % 14 12 15 16 15 13 11 0.102 
 Israeli born, % 83 80 82 83 84 85 85 86 <0.001 
Women          
 Examinees, n 592,312 27,684 249,547 155,965 65,843 65,810 21,136 6,327  
 Mean age ± SD (years) 17.2 ± 0.4 17.3 ± 0.5 17.2 ± 0.4 17.2 ± 0.4 17.2 ± 0.4 17.2 ± 0.4 17.2 ± 0.4 17.3 ± 0.5 0.51 
 Mean BMI ± SD (kg/m222.0 ± 3.8 16.5 ± 0.7 19.4 ± 1.0 22.1 ± 0.7 24.3 ± 0.6 27.0 ± 1.3 31.8 ± 1.5 37.9 ± 2.6 <0.001 
 BMI range (kg/m2)* 12.6–47.7 12.6–17.3 17.3–21.0 21.0–23.5 23.5–25.3 25.3–29.8 29.8–35.0 35.0–47.7  
 Mean weight ± SD (kg) 57.8 ± 11.0 44.0 ± 3.8 51.0 ± 4.6 57.9 ± 4.8 63.6 ± 5.1 70.9 ± 6.5 83.8 ± 7.7 100.1 ± 10.7 <0.001 
 Mean height ± SD (cm) 162.1 ± 6.3 163.1 ± 6.5 162.3 ± 6.2 161.7 ± 6.2 161.8 ± 6.2 161.8 ± 6.3 162.3 ± 6.4 162.4 ± 6.6 0.49 
 Full education, % 90 88 91 91 91 90 89 84 0.29 
 High SES, % 23 22 24 24 23 20 18 17 0.025 
 High cognitive score, % 10 11 11 10 0.063 
 Israeli born, % 83 78 82 83 84 85 87 88 <0.001 
*

The BMI range refers to a mean age of 17.3 years. Note that for each of the 48 months between ages 16.0 and 19.99 years, different sex- and age-specific BMI ranges determine the U.S. Centers for Disease Control and Prevention percentiles.

Full education: either a higher school student at the time of the examination or completed 12 years of formal education.

Several subanalyses were conducted. First, we restricted the Cox analysis to those with unimpaired health status at study entry (i.e., absence of any chronic comorbidity that requires medical therapy or any history of cancer or major operation) to minimize residual confounding by coexisting morbidities (Supplementary Table 1). Second, the study outcome was T2D onset by age 25 years (Supplementary Table 1). Third, we considered the contribution to the model of diabetes diagnoses that were classified as uncertain type (Supplementary Table 2). Finally, we accounted for cases with missing date of T2D diagnosis (Supplementary Table 3).

The population-attributable risk percent (PAR%) of T2D incidence (and 95% CI) was calculated for overweight and obesity (≥85th BMI percentile) as follows:

formula

in which HR is the unadjusted HR for diabetes of the overweight and obesity group, and Pe is its average prevalence. To capture the effect of the secular trend of the increasing prevalence of adolescent obesity (16), we also reestimated PAR% for every 5-year interval. Individuals with missing data (10,401, 0.7% of the cohort) were excluded from multivariable analysis. Analyses were performed using SPSS version 25.0 statistical software.

Baseline characteristics of the 834,050 men and 592,312 women included in the study are presented in Table 1. Ages at study entry were similar across all BMI groups. As BMI increased from the low-normal range to severe obesity, proportions increased in both sexes of individuals from lower residential SES, individuals with lower scores on cognitive tests, and Israeli-born individuals.

In total, 2,177 people (1,490 men, 68%) were diagnosed with incident T2D during 15,810,751 person-years. The median follow-up periods were 11.3 years (interquartile range 5.8–16.6) and 10.9 years (5.7–15.9) among men and women, respectively. The mean follow-up length was shorter for individuals with a higher BMI, reflecting the rising obesity prevalence in this cohort in recent years (Supplementary Fig. 1). A significant interaction was observed among sex, BMI, and T2D incidence (unadjusted model P = 0.004, multivariable model P = 0.023).

The crude diabetes rate showed a graded increase according to BMI groups from underweight to severe obesity (5.8–120.9 diagnoses per 100,000 person-years, respectively, among men and 3.2–124.4 diagnoses per 100,000 person-years, respectively, among women). Kaplan-Meier survival analysis (Fig. 2A) and unadjusted Cox modeling further characterized this trend (Supplementary Table 1). The cumulative incidence for T2D was more than twofold higher among those with severe obesity than those with mild obesity (Fig. 2B). Adjustment for age at study entry, birth year, education, and cognitive performance had a minimal effect on the BMI-defined risk. HRs were 1.7 (95% CI 1.4–2.0), 2.8 (2.3–3.5), 5.8 (4.9–6.9), 13.4 (11.5–15.7), and 25.8 (21.0–31.6) among men in the 50th–74th percentile, 75th–84th percentile, overweight, mild obesity, and severe obesity groups, respectively, and 2.2 (1.6–2.9), 3.4 (2.5–4.6), 10.6 (8.3–13.6), 21.1 (16.0–27.8), and 44.7 (32.4–61.5) among women, respectively. These results persisted when the study sample was limited to individuals with unimpaired health at baseline to minimize confounding by pre- or coexisting morbidities (Supplementary Table 1). High HRs for overweight and mild and severe obesity also withstood when diabetes onset before age 25 years served as the outcome: 3.2 (2.1–4.9), 12.1 (8.5–17.5), and 27.0 (17.9–40.7) among men, respectively, and 6.6 (4.4–9.8), 12.8 (8.2–20.0), and 45.3 (28.9–71.0) among women, respectively (Supplementary Table 1). We analyzed separately 405 diagnoses of diabetes of uncertain type and found a consistent increase in point estimates in both sexes across BMI groups; HRs were 6.0 (3.5–10.5) and 9.0 (3.8–21.4) among men and women with severe obesity, respectively (Supplementary Table 2). To minimize misclassification of type 1 diabetes as T2D cases, we set as the outcome diabetes cases in which treatment did not include insulin and found similar results to those presented in Table 2 (Supplementary Table 2).

Figure 2

The association between adolescent obesity and incident T2D in young adulthood. A: Kaplan-Meier one-minus survival curves are plotted with 95% CIs. The number of individuals at risk is indicated below each panel for the given BMI category. B: The histogram shows the cumulative incidence values (with 95% CIs) at ages 25 and 30 years (corresponding to 7.7 and 12.7 years of follow-up, respectively).

Figure 2

The association between adolescent obesity and incident T2D in young adulthood. A: Kaplan-Meier one-minus survival curves are plotted with 95% CIs. The number of individuals at risk is indicated below each panel for the given BMI category. B: The histogram shows the cumulative incidence values (with 95% CIs) at ages 25 and 30 years (corresponding to 7.7 and 12.7 years of follow-up, respectively).

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Table 2

Risk estimates of the association between adolescent BMI and incident T2D in young adulthood

BMI category at adolescence
Total<5th percentile (underweight)5th–49th percentile (low normal)50th–74th percentile75th–84th percentile85th–94th percentile (overweight)Mild obesitySevere obesityP value for trend
Men          
 Incident cases, n 1,490 44 302 217 139 292 353 143  
 Mean follow-up ± SD (years) 11.3 ± 6.1 12.0 ± 6.1 11.8 ± 6.1 11.2 ± 6.0 10.8 ± 6.0 10.3 ± 5.9 9.8 ± 5.8 8.8 ± 5.5 <0.001 
 Person-years of follow-up 9,412,455 753,711 4,158,394 2,136,497 851,800 902,214 491,455 118,382  
 Incidence* (per 10−5 person-years) 15.8 (15.0–16.7) 5.8 (4.2–7.8) 7.3 (6.5–8.1) 10.2 (8.9–11.6) 16.3 (13.7–9.3) 32.3 (28.8–36.3) 71.8 (64.5–79.7) 120.9 (102–142) 0.008 
 Mean age at end of follow-up ± SD (years) 28.6 ± 6.1 29.5 ± 6.2 29.2 ± 6.1 28.5 ± 6.1 28.0 ± 6.1 27.6 ± 6.0 27.1 ± 5.9 26.1 ± 5.6 <0.001 
 Mean age at diagnosis ± SD (years) 30.0 ± 4.3 31.3 ± 4.0 30.4 ± 4.6 30.6 ± 4.1 30.8 ± 4.0 30.4 ± 4.0 29.3 ± 4.1 27.8 ± 4.6 0.015 
 Mean BMI at year of diagnosis ± SD (kg/m231.1 ± 6.3 23.7 ± 4.7 25.8 ± 4.3 29.7 ± 5.1 30.3 ± 4.6 31.9 ± 5.1 34.4 ± 5.5 37.3 ± 6.4 <0.001 
 Overweight or obese at year of diagnosis, % 83.8 36.4 52.7 85.7 92.8 93.8 97.5 98.1 0.008 
 HRadjusted  0.61 1.66 2.82 5.81 13.42 25.8  
 95% CI  0.44–0.84  1.39–1.98 2.30–3.47 4.92–6.85 11.47–15.72 21.0–31.61  
 P value  0.003  7.4 * 10−9 2.5 * 10−23 9.2 * 10−97 8.3 * 10−229 3.5 * 10−213  
Women          
 Incident cases, n 687 10 92 115 69 209 123 69  
 Mean follow-up ± SD (years) 10.8 ± 5.8 11.1 ± 5.7 11.2 ± 5.7 10.8 ± 5.8 10.5 ± 5.8 10.1 ± 5.8 9.6 ± 5.7 8.8 ± 5.4 0.001 
 Person-year follow-up 6,407,296 308,543 2,797,131 1,686,554 691,622 665,386 202,436 55,624  
 Incidence* (per 10−5 person-year) 10.7 (9.9–11.6) 3.2 (1.6–6.0) 3.3 (2.7–4.0) 6.8 (5.6–8.2) 10.0 (7.8–12.6) 31.4 (27.3–36.0) 60.8 (50.5–72.5) 124.4 (96.5–157) 0.011 
 Age at end of follow-up ± SD (years) 28.0 ± 5.9 28.4 ± 5.8 28.4 ± 5.8 28.0 ± 5.9 27.7 ± 5.9 27.3 ± 5.9 26.7 ± 5.8 26.0 ± 5.5 <0.001 
 Mean age at diagnosis ± SD (years) 27.7 ± 5.9 29.0 ± 3.8 29.0 ± 4.7 28.2 ± 4.3 28.0 ± 3.8 28.1 ± 4.0 28.0 ± 4.5 25.9 ± 4.7 0.181 
 Mean BMI at year of diagnosis ± SD (kg/m232.1 ± 7.2 23.0 ± 4.7 25.1 ± 5.2 28.5 ± 5.6 30.2 ± 5.0 32.7 ± 5.4 37.7 ± 6.6 39.3 ± 6.5 <0.001 
 Overweight or obese at year of diagnosis, % 82.3 25 46.4 70.3 86 91.1 98.1 100 0.001 
  HR adjusted  0.90 2.17 3.38 10.63 21.08 44.66  
  95% CI  0.47–1.78  1.64–2.87 2.46–4.63 8.29–13.64 16.00–27.77 32.42–61.52  
  P value  0.76  5.3 * 10−8 3.6 * 10−14 4.4 * 10−77 3.0 * 10−104 1.4 * 10−119  
BMI category at adolescence
Total<5th percentile (underweight)5th–49th percentile (low normal)50th–74th percentile75th–84th percentile85th–94th percentile (overweight)Mild obesitySevere obesityP value for trend
Men          
 Incident cases, n 1,490 44 302 217 139 292 353 143  
 Mean follow-up ± SD (years) 11.3 ± 6.1 12.0 ± 6.1 11.8 ± 6.1 11.2 ± 6.0 10.8 ± 6.0 10.3 ± 5.9 9.8 ± 5.8 8.8 ± 5.5 <0.001 
 Person-years of follow-up 9,412,455 753,711 4,158,394 2,136,497 851,800 902,214 491,455 118,382  
 Incidence* (per 10−5 person-years) 15.8 (15.0–16.7) 5.8 (4.2–7.8) 7.3 (6.5–8.1) 10.2 (8.9–11.6) 16.3 (13.7–9.3) 32.3 (28.8–36.3) 71.8 (64.5–79.7) 120.9 (102–142) 0.008 
 Mean age at end of follow-up ± SD (years) 28.6 ± 6.1 29.5 ± 6.2 29.2 ± 6.1 28.5 ± 6.1 28.0 ± 6.1 27.6 ± 6.0 27.1 ± 5.9 26.1 ± 5.6 <0.001 
 Mean age at diagnosis ± SD (years) 30.0 ± 4.3 31.3 ± 4.0 30.4 ± 4.6 30.6 ± 4.1 30.8 ± 4.0 30.4 ± 4.0 29.3 ± 4.1 27.8 ± 4.6 0.015 
 Mean BMI at year of diagnosis ± SD (kg/m231.1 ± 6.3 23.7 ± 4.7 25.8 ± 4.3 29.7 ± 5.1 30.3 ± 4.6 31.9 ± 5.1 34.4 ± 5.5 37.3 ± 6.4 <0.001 
 Overweight or obese at year of diagnosis, % 83.8 36.4 52.7 85.7 92.8 93.8 97.5 98.1 0.008 
 HRadjusted  0.61 1.66 2.82 5.81 13.42 25.8  
 95% CI  0.44–0.84  1.39–1.98 2.30–3.47 4.92–6.85 11.47–15.72 21.0–31.61  
 P value  0.003  7.4 * 10−9 2.5 * 10−23 9.2 * 10−97 8.3 * 10−229 3.5 * 10−213  
Women          
 Incident cases, n 687 10 92 115 69 209 123 69  
 Mean follow-up ± SD (years) 10.8 ± 5.8 11.1 ± 5.7 11.2 ± 5.7 10.8 ± 5.8 10.5 ± 5.8 10.1 ± 5.8 9.6 ± 5.7 8.8 ± 5.4 0.001 
 Person-year follow-up 6,407,296 308,543 2,797,131 1,686,554 691,622 665,386 202,436 55,624  
 Incidence* (per 10−5 person-year) 10.7 (9.9–11.6) 3.2 (1.6–6.0) 3.3 (2.7–4.0) 6.8 (5.6–8.2) 10.0 (7.8–12.6) 31.4 (27.3–36.0) 60.8 (50.5–72.5) 124.4 (96.5–157) 0.011 
 Age at end of follow-up ± SD (years) 28.0 ± 5.9 28.4 ± 5.8 28.4 ± 5.8 28.0 ± 5.9 27.7 ± 5.9 27.3 ± 5.9 26.7 ± 5.8 26.0 ± 5.5 <0.001 
 Mean age at diagnosis ± SD (years) 27.7 ± 5.9 29.0 ± 3.8 29.0 ± 4.7 28.2 ± 4.3 28.0 ± 3.8 28.1 ± 4.0 28.0 ± 4.5 25.9 ± 4.7 0.181 
 Mean BMI at year of diagnosis ± SD (kg/m232.1 ± 7.2 23.0 ± 4.7 25.1 ± 5.2 28.5 ± 5.6 30.2 ± 5.0 32.7 ± 5.4 37.7 ± 6.6 39.3 ± 6.5 <0.001 
 Overweight or obese at year of diagnosis, % 82.3 25 46.4 70.3 86 91.1 98.1 100 0.001 
  HR adjusted  0.90 2.17 3.38 10.63 21.08 44.66  
  95% CI  0.47–1.78  1.64–2.87 2.46–4.63 8.29–13.64 16.00–27.77 32.42–61.52  
  P value  0.76  5.3 * 10−8 3.6 * 10−14 4.4 * 10−77 3.0 * 10−104 1.4 * 10−119  
*

Parentheses denote 95% CI.

The model was adjusted for age at study entry, birth year, education, and cognitive performance score.

For 406 incident diagnoses of diabetes, the date of diagnosis was missing. Of these, 267 were not reported to the INDR in its first year but during 2013 or later. Therefore, the date of diabetes diagnosis was assumed to be 1 July, 1 year before reporting to the INDR. We found similar point estimates in a subanalysis that included only these diagnoses (Supplementary Table 3).

Age at diagnosis showed an inverse graded relationship. The mean ages of diabetes onset were 27.8 and 25.9 years among men and women with severe obesity, respectively, compared with 30.4 and 29.0 years in the normal BMI reference group. Notably, approximately one-half of those who developed T2D and had a normal BMI at adolescence were overweight or obese at the year of diagnosis (Table 2).

The fractions of incident T2D that were attributed to overweight and obesity were 51.5% (48.3–54.7%) for men and 54.6% (50.2−59.0%) for women on the basis of mean prevalences of 17.9% and 15.6% during the study period, respectively. The PAR% calculated for 5-year intervals has gradually risen in both sexes (Supplementary Fig. 2). The projected PAR% for the 23.0% and 19.8% prevalences of overweight and obesity among men and women, respectively, for the years 2015–2016 were 56.9% (53.8–59.9%) and 60.6% (56.2–65.6%).

This nationwide study showed increased incidence of T2D in early adulthood among individuals with severe obesity in late adolescence. HRs were 26 and 45 among men and women, respectively, compared with their peers with normal BMI at adolescence. The absolute risk for incident T2D was substantially higher in severe versus mild obesity in both sexes. This is evident by the >1.5% incidence rate for diabetes development before the age of 30 years among those with severe obesity compared with less than one-half the fraction among those with mild obesity. The constancy of the risk estimates following adjustment by sociodemographic background, country of origin, and baseline medical status suggests that the results are likely generalizable to populations of other sociodemographic backgrounds.

Previous works that emphasized the importance of adolescent BMI to future cardiometabolic health excluded diagnoses of incident T2D in the first three decades of life (5,8). Other studies that examined the effect of changes in BMI during the life course showed that even modest weight gain might be associated with an increased risk of T2D. However, these studies included diagnoses in the sixth decade of life and later (10,12).

In other studies, relative risks for incident T2D among young adults with obesity were reported to span between 4.2 and 27.8 compared with their counterparts with normal BMI (17,18). Longer duration of follow-up, higher female-to-male ratio, and larger study size were usually associated with higher risk estimates (19). Several studies on the BMI-diabetes association were based on recall weight and height, which might lead to underestimation of the actual HR (20,21). Finally, early versus mid-adulthood age at study entry was associated with higher risk estimates (9). These are likely due to lower prevalences of concomitant diabetes risk factors (22) and lead to a minimal incidence in the reference group. The higher risk estimates among female adolescents with obesity, observed herein, is consistent with a meta-analysis that reported relative risks of 8.4 and 6.5 among women and men with obesity, respectively, compared with their sex-matched counterparts with normal BMI (19). U.S. data also show that girls, but not boys, who became obese before age 16 years had a more than twofold higher risk for diabetes in adulthood than those who became obese at or after the age of 18 years (23,24). In addition, among adolescents who developed T2D at a mean age of 13–14 years, girls are more susceptible than boys, with an overall female-to-male ratio of 1.7:1, regardless of race (25). Early age of onset of puberty among girls with decreased insulin sensitivity during puberty as well as decreased physical activity among girls are possible explanations (26). Another potential explanation is that BMI may better reflect fat mass in female adolescents than in males because of increased muscle mass in the latter, especially among those in the normal BMI range (27). In addition to biological differences, sex differences may be attributed to social and cultural factors. Our findings are important because females with T2D have a higher mortality rate, with BMI being the leading modifiable risk factor (28).

While adolescent BMI is recognized as a strong risk factor for incident T2D (29), relatively few works have examined associations between various degrees of adolescent obesity and incident diabetes at early adulthood (30). Severe obesity became much more prevalent in recent years and has increased disproportionally compared with mild obesity. For example, among both U.S. (31,32) and Israeli (16) adolescents, mild obesity has less than doubled in the past two decades, whereas severe obesity has increased by almost fourfold. In parallel to this increase, a disproportionate rise in incident T2D in early adulthood has been reported in the U.S. for both sexes (33). The reported prevalence is in the range of 2.5–4.4% among adults age <35 years (23,34).

We report a projected PAR% for diabetes related to overweight and obesity in adolescence as 57% and 61% for men and women, respectively, on the basis of the mean prevalence of overweight and obesity between 2015 and 2016. Previous studies reported a wide range of PAR%s for diabetes related to overweight and obesity (3–66%), albeit in middle-aged adults from age 35 to ≥40 years (35,36). These estimates deserve public health attention given the implications of diabetes on disability (37), morbidity (38), and health care expenditures (39), especially when diagnosed among young adults (40).

Our work has limitations. We lacked data on repeated anthropometric measurements to allow assessment of cumulative exposure to high BMI as well as longitudinal data into adulthood for those who remained diabetes free. Nevertheless, the effect of BMI was well evident before age 25 years, during which a meaningful drift in BMI percentiles is less likely to occur (8). We also lacked lifestyle data and other measures of adiposity, such as waist circumference, which might be a more sensitive measure than BMI and better define the population at risk (41), especially among men, as mentioned above. Nevertheless, BMI is considered as the preferable method for screening according to the U.S. Preventive Services Task Force (42). In addition, while individuals with known dysglycemia at baseline were excluded, we did not have data regarding fasting blood glucose at adolescence, and therefore we could not assess the risk associated with increased glucose levels within the normal range and diabetes incident at adulthood (43). We cannot exclude the possibility that individuals with type 1 diabetes, including latent autoimmune diabetes in adults, were included. Yet, such misclassification is likely to result in underestimation of the point estimates in relation to obesity. The definition of type 1 diabetes used in this study, on the basis of the primary use of insulin, likely minimized such misclassification. Our study predominantly represents non-Hispanic whites, and is unrepresentative of other ethnicities, such as Native Americans, Hispanics, African Americans, and East Asians. Glucose-lowering drugs such as metformin might have been prescribed for indications other than diabetes, such as menstrual irregularities. The strengths of this study include the linkage of two detailed national databases with systematic data collection of sociodemographic variables; measured, rather than reported, weight and height values; strict control of coexisting morbidities; and heterogenous genetic ancestry (44).

The transition from adolescence to early adulthood is a sensitive period for the development of diabetes (23,45). Data regarding actual risk for incident diabetes is of clinical value to better define populations at risk, to identify early abnormal weight gain patterns, and to direct health care policy in the evaluation and management of obesity among youth. Among adolescents with obesity, and particularly females with severe obesity, the higher risk for incident T2D is of clinical and public health importance, especially given the ongoing increasing size of this subpopulation.

This article contains supplementary material online at https://doi.org/10.2337/dc20-1234/suppl.12077391.

This article is featured in a podcast available at https://www.diabetesjournals.org/content/diabetes-core-update-podcasts.

Acknowledgments. The authors are grateful to members of the steering committee of the INDR for support in facilitating the linkage between the two databases described herein. The authors thank the administrative staff of the Department of Military Medicine, Hebrew University, for technical support throughout this study.

Funding. This study was supported by a research grant from the Medical Corps Israel Defense Forces (Israel) awarded to G.T.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. G.T. conceived and designed the study; analyzed and interpreted the data; wrote the first draft of and revised the manuscript, incorporating contributions from coauthors; and decided on submission. I.Z., M.L., T.S., and D.T. conducted database management quality assurance, interpreted the data, contributed to the discussion, and critically revised the manuscript. A.A., T.C.-Y., O.M., O.P.-H., I.R., and H.C.G. interpreted the data, contributed to the discussion, and critically revised the manuscript. C.D.B. and S.T. analyzed the data, contributed to the discussion, and critically revised the manuscript. E.D. conducted the statistical analysis and critically revised the manuscript. A.T. designed the study, analyzed and interpreted the data, wrote the first draft, and decided on submission. G.T. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

1.
Lascar
N
,
Brown
J
,
Pattison
H
,
Barnett
AH
,
Bailey
CJ
,
Bellary
S
.
Type 2 diabetes in adolescents and young adults
.
Lancet Diabetes Endocrinol
2018
;
6
:
69
80
2.
Centers for Disease Control and Prevention
.
National Diabetes Statistics Report, 2017
.
Atlanta, GA
,
Centers for Disease Control and Prevention, US Dept of Health and Human Services
,
2017
3.
Menke
A
,
Casagrande
S
,
Geiss
L
,
Cowie
CC
.
Prevalence of and trends in diabetes among adults in the United States, 1988-2012
.
JAMA
2015
;
314
:
1021
1029
4.
Sattar
N
,
Rawshani
A
,
Franzén
S
, et al
.
Age at diagnosis of type 2 diabetes mellitus and associations with cardiovascular and mortality risks
.
Circulation
2019
;
139
:
2228
2237
5.
Bjerregaard
LG
,
Jensen
BW
,
Ängquist
L
,
Osler
M
,
Sørensen
TIA
,
Baker
JL
.
Change in overweight from childhood to early adulthood and risk of type 2 diabetes
.
N Engl J Med
2018
;
378
:
1302
1312
6.
de Mutsert
R
,
Sun
Q
,
Willett
WC
,
Hu
FB
,
van Dam
RM
.
Overweight in early adulthood, adult weight change, and risk of type 2 diabetes, cardiovascular diseases, and certain cancers in men: a cohort study
.
Am J Epidemiol
2014
;
179
:
1353
1365
7.
Schmidt
M
,
Johannesdottir
SA
,
Lemeshow
S
, et al
.
Obesity in young men, and individual and combined risks of type 2 diabetes, cardiovascular morbidity and death before 55 years of age: a Danish 33-year follow-up study
.
BMJ Open
2013
;
3
:
e002698
8.
Tirosh
A
,
Shai
I
,
Afek
A
, et al
.
Adolescent BMI trajectory and risk of diabetes versus coronary disease
.
N Engl J Med
2011
;
364
:
1315
1325
9.
Wei
GS
,
Coady
SA
,
Reis
JP
, et al
.
Duration and degree of weight gain and incident diabetes in younger versus middle-aged black and white adults: ARIC, CARDIA, and the Framingham Heart Study
.
Diabetes Care
2015
;
38
:
2042
2049
10.
Zheng
Y
,
Manson
JE
,
Yuan
C
, et al
.
Associations of weight gain from early to middle adulthood with major health outcomes later in life
.
JAMA
2017
;
318
:
255
269
11.
Zimmermann
E
,
Bjerregaard
LG
,
Gamborg
M
,
Vaag
AA
,
Sørensen
TIA
,
Baker
JL
.
Childhood body mass index and development of type 2 diabetes throughout adult life—a large-scale Danish cohort study
.
Obesity (Silver Spring)
2017
;
25
:
965
971
12.
Stokes
A
,
Collins
JM
,
Grant
BF
, et al
.
Obesity progression between young adulthood and midlife and incident diabetes: a retrospective cohort study of U.S. adults
.
Diabetes Care
2018
;
41
:
1025
1031
13.
Twig
G
,
Yaniv
G
,
Levine
H
, et al
.
Body-mass index in 2.3 million adolescents and cardiovascular death in adulthood
.
N Engl J Med
2016
;
374
:
2430
2440
14.
Twig
G
,
Gluzman
I
,
Tirosh
A
, et al
.
Cognitive function and the risk for diabetes among young men
.
Diabetes Care
2014
;
37
:
2982
2988
15.
Goldstein
A
,
Haelyon
U
,
Krolik
E
,
Sack
J
.
Comparison of body weight and height of Israeli schoolchildren with the Tanner and Centers for Disease Control and Prevention growth charts
.
Pediatrics
2001
;
108
:
E108
16.
Twig
G
,
Reichman
B
,
Afek
A
, et al
.
Severe obesity and cardio-metabolic comorbidities: a nationwide study of 2.8 million adolescents
.
Int J Obes
2019
;
43
:
1391
1399
17.
Meisinger
C
,
Döring
A
,
Thorand
B
,
Heier
M
,
Löwel
H
.
Body fat distribution and risk of type 2 diabetes in the general population: are there differences between men and women? The MONICA/KORA Augsburg cohort study
.
Am J Clin Nutr
2006
;
84
:
483
489
18.
Hu
FB
,
Manson
JE
,
Stampfer
MJ
, et al
.
Diet, lifestyle, and the risk of type 2 diabetes mellitus in women
.
N Engl J Med
2001
;
345
:
790
797
19.
Abdullah
A
,
Peeters
A
,
de Courten
M
,
Stoelwinder
J
.
The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies
.
Diabetes Res Clin Pract
2010
;
89
:
309
319
20.
Keith
SW
,
Fontaine
KR
,
Pajewski
NM
,
Mehta
T
,
Allison
DB
.
Use of self-reported height and weight biases the body mass index-mortality association
.
Int J Obes
2011
;
35
:
401
408
21.
Flegal
KM
,
Kit
BK
,
Graubard
BI
.
Bias in hazard ratios arising from misclassification according to self-reported weight and height in observational studies of body mass index and mortality
.
Am J Epidemiol
2018
;
187
:
125
134
22.
Twig
G
,
Afek
A
,
Derazne
E
, et al
.
Diabetes risk among overweight and obese metabolically healthy young adults
.
Diabetes Care
2014
;
37
:
2989
2995
23.
The
NS
,
Richardson
AS
,
Gordon-Larsen
P
.
Timing and duration of obesity in relation to diabetes: findings from an ethnically diverse, nationally representative sample
.
Diabetes Care
2013
;
36
:
865
872
24.
Yeung
EH
,
Zhang
C
,
Louis
GM
,
Willett
WC
,
Hu
FB
.
Childhood size and life course weight characteristics in association with the risk of incident type 2 diabetes
.
Diabetes Care
2010
;
33
:
1364
1369
25.
Fagot-Campagna
A
.
Emergence of type 2 diabetes mellitus in children: epidemiological evidence
.
J Pediatr Endocrinol Metab
2000
;
13
(
Suppl. 6
):
1395
1402
26.
Zeitler
P
,
Arslanian
S
,
Fu
J
, et al
.
ISPAD Clinical Practice Consensus Guidelines 2018: type 2 diabetes mellitus in youth
.
Pediatr Diabetes
2018
;
19
(
Suppl. 27
):
28
46
27.
Lindsay
RS
,
Hanson
RL
,
Roumain
J
,
Ravussin
E
,
Knowler
WC
,
Tataranni
PA
.
Body mass index as a measure of adiposity in children and adolescents: relationship to adiposity by dual energy x-ray absorptiometry and to cardiovascular risk factors
.
J Clin Endocrinol Metab
2001
;
86
:
4061
4067
28.
Elling
D
,
Surkan
PJ
,
El-Khatib
Z
.
Sex differences and risk factors for diabetes mellitus - an international study from 193 countries
.
Global Health
2018
;
14
:
118
29.
Geng
T
,
Smith
CE
,
Li
C
,
Huang
T
.
Childhood BMI and adult type 2 diabetes, coronary artery diseases, chronic kidney disease, and cardiometabolic traits: a Mendelian randomization analysis
.
Diabetes Care
2018
;
41
:
1089
1096
30.
Skinner
AC
,
Perrin
EM
,
Moss
LA
,
Skelton
JA
.
Cardiometabolic risks and severity of obesity in children and young adults
.
N Engl J Med
2015
;
373
:
1307
1317
31.
Ogden
CL
,
Carroll
MD
,
Lawman
HG
, et al
.
Trends in obesity prevalence among children and adolescents in the United States, 1988-1994 through 2013-2014
.
JAMA
2016
;
315
:
2292
2299
32.
Skinner
AC
,
Skelton
JA
.
Prevalence and trends in obesity and severe obesity among children in the United States, 1999-2012
.
JAMA Pediatr
2014
;
168
:
561
566
33.
Geiss
LS
,
Wang
J
,
Cheng
YJ
, et al
.
Prevalence and incidence trends for diagnosed diabetes among adults aged 20 to 79 years, United States, 1980-2012
.
JAMA
2014
;
312
:
1218
1226
34.
Liese
AD
,
D’Agostino
RB
 Jr
,
Hamman
RF
, et al.;
SEARCH for Diabetes in Youth Study Group
.
The burden of diabetes mellitus among US youth: prevalence estimates from the SEARCH for Diabetes in Youth Study
.
Pediatrics
2006
;
118
:
1510
1518
35.
Wilson
PWF
,
D’Agostino
RB
,
Sullivan
L
,
Parise
H
,
Kannel
WB
.
Overweight and obesity as determinants of cardiovascular risk: the Framingham experience
.
Arch Intern Med
2002
;
162
:
1867
1872
36.
Chan
JM
,
Rimm
EB
,
Colditz
GA
,
Stampfer
MJ
,
Willett
WC
.
Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men
.
Diabetes Care
1994
;
17
:
961
969
37.
Fishman
EI
.
Incident diabetes and mobility limitations: reducing bias through risk-set matching
.
J Gerontol A Biol Sci Med Sci
2015
;
70
:
860
865
38.
Engelgau
MM
,
Geiss
LS
,
Saaddine
JB
, et al
.
The evolving diabetes burden in the United States
.
Ann Intern Med
2004
;
140
:
945
950
39.
Dieleman
JL
,
Baral
R
,
Birger
M
, et al
.
US spending on personal health care and public health, 1996-2013
.
JAMA
2016
;
316
:
2627
2646
40.
Magliano
DJ
,
Martin
VJ
,
Owen
AJ
,
Zomer
E
,
Liew
D
.
The productivity burden of diabetes at a population level
.
Diabetes Care
2018
;
41
:
979
984
41.
Langenberg
C
,
Sharp
SJ
,
Schulze
MB
, et al.;
InterAct Consortium
.
Long-term risk of incident type 2 diabetes and measures of overall and regional obesity: the EPIC-InterAct case-cohort study
.
PLoS Med
2012
;
9
:
e1001230
42.
Grossman
DC
,
Bibbins-Domingo
K
,
Curry
SJ
, et al.;
US Preventive Services Task Force
.
Screening for obesity in children and adolescents: US Preventive Services Task Force recommendation statement
.
JAMA
2017
;
317
:
2417
2426
43.
Tirosh
A
,
Shai
I
,
Tekes-Manova
D
, et al.;
Israeli Diabetes Research Group
.
Normal fasting plasma glucose levels and type 2 diabetes in young men
.
N Engl J Med
2005
;
353
:
1454
1462
44.
Behar
DM
,
Yunusbayev
B
,
Metspalu
M
, et al
.
The genome-wide structure of the Jewish people
.
Nature
2010
;
466
:
238
242
45.
RISE Consortium
.
Metabolic contrasts between youth and adults with impaired glucose tolerance or recently diagnosed type 2 diabetes: II. Observations using the oral glucose tolerance test
.
Diabetes Care
2018
;
41
:
1707
1716
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