Comparing glycemic indicators of prediabetes: a prospective study of obese Latino Youth

Neighborhood Environment and Longitudinal Follow-Up of Glycosylated Hemoglobin for Youth with Overweight or Obesity

Background: Neighborhood environment, which includes multiple social drivers of health, has been associated with a higher incidence of chronic conditions in adult cohorts. We examine if neighborhood environment is associated with glycosylated hemoglobin (HbA1c) and body mass index (BMI) as a percentage of the 95th percentile (BMIp95) for youth with overweight and obesity. Methods: Cohort study using electronic health record data from a large Midwestern Children's Hospital. Youth aged 8-16 years qualified for the study with a documented BMI ≥ 85th percentile and two HbA1c test results between January 1, 2017, and December 31, 2019. Neighborhood environment was measured using area deprivation index (ADI). Results: Of the 1,309 youth that met eligibility, mean age was 14.0 ± 3.2 years, 58% female, 48% Black, and 39% White. At baseline, the average (SD) of BMIp95 was 126.1 (26.14) and HbA1c5.4 (0.46). 670 (51%) lived in a more deprived (MD) area. The median time to follow-up was 15-months. Youth that lived in a MD area had a significantly higher follow-up HbA1c (β = 0.034, p = 0.03, 95% confidence interval [CI]: [0.00, 0.06]) and BMIp95 (β = 1.283, p = 0.03, 95% CI: [0.13, 2.44]). An increase in BMIp95 was associated with worse HbA1c for most youth that lived in a MD area. Conclusions: Youth that lived in an MD area had a small but statistically significant higher level of HbA1c and BMIp95 at follow-up. Public health surveillance systems should include ADI as a risk factor for longitudinal progression of cardiometabolic diseases.

A combined strategy of feature selection and machine learning to identify predictors of prediabetes

OBJECTIVE

To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population.

MATERIALS AND METHODS

We analyzed n = 6346 men and women enrolled in the National Health and Nutrition Examination Survey 2013-2014. Prediabetes was defined using American Diabetes Association guidelines. The sample was randomly partitioned to training (n = 3174) and internal validation (n = 3172) sets. Feature selection algorithms were run on training data containing 156 preselected exposure variables. Four machine learning algorithms were applied on 46 exposure variables in original and resampled training datasets built using 4 resampling methods. Predictive models were tested on internal validation data (n = 3172) and external validation data (n = 3000) prepared from National Health and Nutrition Examination Survey 2011-2012. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Predictors were assessed by odds ratios in logistic models and variable importance in others. The Centers for Disease Control (CDC) prediabetes screening tool was the benchmark to compare model performance.

RESULTS

Prediabetes prevalence was 23.43%. The CDC prediabetes screening tool produced 64.40% AUROC. Seven optimal (≥ 70% AUROC) models identified 25 predictors including 4 potentially novel associations; 20 by both logistic and other nonlinear/ensemble models and 5 solely by the latter. All optimal models outperformed the CDC prediabetes screening tool (P < 0.05).

DISCUSSION

Combined use of feature selection and machine learning increased predictive performance outperforming the recommended screening tool. A range of predictors of prediabetes was identified.

CONCLUSION

This work demonstrated the value of combining feature selection with machine learning to identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making.

Role of exercise on insulin sensitivity and beta-cell function: is exercise sufficient for the prevention of youth-onset type 2 diabetes?

Parallel with the current pediatric obesity epidemic, the escalating rates of youthonset type 2 diabetes mellitus (T2DM) have become a major public health burden. Although lifestyle modification can be the first-line prevention for T2DM in youths, there is a lack of evidence to establish optimal specific exercise strategies for obese youths at high risk for T2DM. The purpose of this narrative review is to summarize the potential impact of exercise on 2 key pathophysiological risk factors for T2DM, insulin sensitivity and β-cell function, among obese youths. The studies cited are grouped by use of metabolic tests, i.e., direct and indirect measures of insulin sensitivity and β-cell function. In general, there are an increasing number of studies that demonstrate positive effects of aerobic exercise, resistance exercise, and the 2 combined on insulin sensitivity. However, a lack of evidence exists for the effect of any exercise modality on β-cell functional improvement. We also suggest a future direction for research into exercise medical prevention of youth-onset T2DM. These suggestions focus on the effects of exercise modalities on emerging biomarkers of T2DM risk.

Adipose Tissue Insulin Resistance in Youth on the Spectrum From Normal Weight to Obese and From Normal Glucose Tolerance to Impaired Glucose Tolerance to Type 2 Diabetes

OBJECTIVE

Adipose tissue insulin resistance is one of the pathophysiological components of type 2 diabetes. Herein we investigated: 1) adipose insulin resistance index (Adipose-IR) (calculated as fasting insulin × free fatty acids [FFAs]) in youth across the spectrum of adiposity from normal weight to obese and the spectrum from normal glucose tolerance (NGT) to impaired glucose tolerance (IGT) to type 2 diabetes, 2) the relationship of Adipose-IR with physical and metabolic characteristics, and 3) the predictive power of Adipose-IR for determining dysglycemia in youth.

RESEARCH DESIGN AND METHODS

A total of 205 youth had fasting glucose, insulin, FFA, Adipose-IR, body composition, visceral adipose tissue (VAT), leptin, and adiponectin evaluated.

RESULTS

Adipose-IR was 2.2-fold higher in obese NGT, 4.3-fold higher in IGT, and 4.6-fold higher in type 2 diabetes compared with that in normal-weight peers (all P < 0.05). Females with dysglycemia (IGT and type 2 diabetes) had higher Adipose-IR than their male counterparts (P < 0.001). Adipose-IR correlated positively with total body and visceral adiposity, fasting glucose, HOMA-IR, and leptin and negatively with adiponectin. Receiver operating characteristic curve analysis yielded an optimal cutoff for Adipose-IR of 9.3 μU/mL × mmol/L for determining dysglycemia with 80% predictive power.

CONCLUSIONS

Adipose-IR is a simple surrogate estimate that reflects pathophysiological alterations in adipose tissue insulin sensitivity in youth, with progressive deterioration from normal weight to obese and from NGT to IGT to type 2 diabetes. Adipose-IR can be applied in large-scale epidemiological/observational studies of the natural history of youth-onset type 2 diabetes and its progression or reversal with intervention strategies.

Biological and socioeconomic determinants of prediabetes in youth: an analysis using 2007 to 2011 Canadian Health Measures Surveys

OBJECTIVES

To describe rates of prediabetes among youth in Canada and the associated social and biological characteristics.

METHODS

We analyzed the cross-sectional data from the first (2007-2009) and second (2009-2011) cycles of the Canadian Health Measures Survey (CHMS) for youth aged 6-19 years. Prediabetes was defined using the glycated hemoglobin (A1C) guidelines set out by the American Diabetes Association (ADA) and the Canadian Diabetes Association (CDA) of A1C ranges 5.7-6.4% (38.8-46.4 mmol/mol) and 6.0-6.4% (42.1-46.4 mmol/mol), respectively.

RESULTS

An elevated A1C was observed in 22.8% of our sample (n = 3449) based on the ADA definition and 5.2% of youth using the CDA definition. Independent predictors in a fully adjusted model for prediabetes were non-White (odds ratio (OR) 2.62: 95% Confidence intervals 2.05-3.35), obese (OR 1.53: 1.19-1.96), less physically active youth (0.97: 0.95-0.99), and parents with high school education or less (1.34: 1.02-1.74). Moreover, significant regional variations were noted with higher rates for all regions except Ontario.

CONCLUSION

Prediabetes is relatively common in Canada and associated with common biologic and socioeconomic factors. Importantly, regular physical activity was significantly associated with reduced odds of prediabetes. Targeted screening and continued emphasis on physical activity may help curb the increasing rates of prediabetes.

The prognosis for glycemic status among children and youth with obesity 2 years after entering a weight management program

BACKGROUND

To address gaps in knowledge of the longitudinal trajectory of dysglycemia in children with obesity, this study aimed to: (1) describe the changes in glycemic status over 2 years; (2) establish a predictive model for development of prediabetes among children with euglycemia; and (3) evaluate the influence of change in body mass index (BMI) z-score on glycemic status.

METHODS

Children aged 5 to 17 years entered this prospective, longitudinal study at the time of entry to a weight management program. Measures included a 75-g oral glucose tolerance test (OGTT), fasting blood glucose, hemoglobin A1c (HbA1c), lipid profile, liver enzymes and anthropometric measures at baseline, 1 and 2 years. Cox proportional hazard was used to build a predictive model for prediabetes.

RESULTS

The cohort included 270 children, mean age: 11.6 ± 2.7 years and BMI z-score: 3.1. The baseline prevalence of prediabetes, based upon elevated 2-hour glucose in OGTT or HbA1c, was 100/270 (37.0%). Among children with prediabetes at baseline, 53 (53.0%) continued to have prediabetes over the following 2 years, 15 (15.0%) were euglycemic at 1 year and had prediabetes at 2 years, 20 (20.0%) became euglycemic and remained so. Change in BMI z-score predicted dysglycemic status at 2 years. Among those euglycemic at baseline, the incidence of prediabetes was 14 (8.2%) after 1 year, 20 (12.8%) at 2 years. Predictors of incident prediabetes were baseline BMI z-score; hazard ratio (HR): 1.72, 95th confidence interval (CI: 1.08, 2.74) and baseline HbA1c HR: 1.26, 95th CI (1.02-1.56) when controlling for age, family history of diabetes and sex.

CONCLUSION

Prediabetes presents significant morbidity in children with obesity. Family-based lifestyle interventions might delay prediabetes progression.

Longitudinal follow up of dysglycemia in overweight and obese pediatric patients

OBJECTIVE

To examine factors related to progression of dysglycemia in overweight and obese youth in a large primary care setting.

RESEARCH DESIGN AND METHODS

10- to 18-year-old youth with body mass index (BMI) > 85 percentile and first-time A1c 5.7%-7.9% (39-63 mmol/mol) were identified retrospectively through electronic medical records (EMR). Levels of dysglycemia were defined as low-range prediabetes (LRPD; A1c 5.7%-5.9% [39-41 mmol/mol]), high-range prediabetes (HRPD; A1c 6.0%-6.4% [42-46 mmol/mol]), or diabetes-range (A1c 6.5%-7.9% [48 mmol/mol]). Follow-up A1c and BMI were extracted from the EMR. Follow up was truncated at the time of initiation of diabetes medication.

RESULTS

Of 11 000 youth, 547 were identified with baseline dysglycemia (mean age 14.5 ± 2.2 years, 70% Hispanic, 23% non-Hispanic Black, 7% other). Of these, 206 had LRPD, 282 HRPD, and 59 diabetes. Follow-up A1c was available in 420 (77%), with median follow up of 12-22 months depending on A1c category. At follow-up testing, the percent with diabetes-range A1c was 4% in youth with baseline LRPD, 8% in youth with baseline HRPD, and 33% in youth with baseline diabetes-range A1c. There was a linear association between BMI increase and worsening A1c for LRPD (P < .001) and HRPD (P = .003).

CONCLUSIONS

Most adolescents with an initial prediabetes or diabetes-range A1c did not have a diabetes-range A1c on follow up. Moreover, prediabetes-range A1c values do not all convey equal risk for the development of diabetes, with lower rates of progression for youth with initial A1c <6%. In youth with prediabetes-range A1c, BMI stabilization was associated with improvement of glycemia.

Alternate glycemic markers reflect glycemic variability in continuous glucose monitoring in youth with prediabetes and type 2 diabetes

OBJECTIVE

To determine whether the alternate glycemic markers, fructosamine (FA), glycated albumin (GA), and 1,5-anhydroglucitol (1,5AG), predict glycemic variability captured by continuous glucose monitoring (CGM) in obese youth with prediabetes and type 2 diabetes (T2D).

STUDY DESIGN

Youth with BMI ≥85th%ile, 10-18 years, had collection of fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), FA, GA, and 1,5AG and 72 hours of CGM. Participants with HbA1c ≥5.7% were included. Relationships between glycemic markers and CGM variables were determined with Spearman correlation coefficients. Linear models were used to examine the association between alternate markers and CGM measures of glycemic variability-standard deviation (SD) and mean amplitude of glycemic excursions (MAGE)-after controlling for HbA1c.

RESULTS

Total n = 56; Median (25th%ile, 75th%ile) age = 14.3 years (12.5, 15.9), 32% male, 64% Hispanic, 20% black, 13% white, HbA1c = 5.9% (5.8, 6.3), FA=211 mmol/L (200, 226), GA= 12% (11%, 12%), and 1,5AG = 22mcg/mL (19, 26). HbA1c correlated with average sensor glucose, AUC, SD, MAGE, and %time > 140 mg/dL. FA and GA correlated with average and peak sensor glucose, %time >140 and >200 mg/dL, and MAGE. GA also correlated with SD and AUC180. 1,5AG correlated with peak glucose, AUC180, SD, and MAGE. After adjusting for HbA1c, all 3 markers independently predicted MAGE; FA and GA independently predicted SD.

CONCLUSIONS

Alternate glycemic markers predict glycemic variability as measured by CGM in youth with prediabetes and T2D. After adjusting for HbA1c, these alternate markers continued to predict components of glycemic variability detected by CGM.

Exercise for obese youth: refocusing attention from weight loss to health gains

Despite evidence to the contrary, exercise interventions for obese youth target weight loss as a means of improving health. Using Exercise is Medicine® as a framework, we present a conceptual model for the beneficial effects of exercise independent of weight loss in obese youth and highlight novel biomarkers of cardiometabolic health that could prove useful as interventional targets for this population.