Divergence between HbA1c and fasting glucose through childhood: implications for diagnosis of impaired fasting glucose (Early Bird 52)

Which Is the Most Appropriate Cut-Off of HbA1c for Prediabetes Screening in Caucasian Youths with Overweight or Obesity?

This cross-sectional study aimed to assess the best cut-off of HbA1c for detection of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), beta-cell impairment and cardiometabolic risk (CMR) profile in overweight or obese (OW/OB) Caucasian youths. Two-hour oral glucose tolerance test was available in 1549 youths, one-hour glucose (G₆₀) in 1430 youths and disposition index (DI) in 972 youths. Insulin resistance (IR) was calculated as Homeostatic Model Assessment for IR and insulin sensitivity (IS) as 1/fasting insulin. High G₆₀ was defined by a value ≥ 133 mg/dL. The best cut-off of HbA1c for IFG or IGT was 5.5%. The frequency of individuals with HbA1c ≥ 5.5% was 32.5%, compared to 16.3% with HbA1c ≥ 5.7% (as proposed by the American Diabetes Association). HbA1c ≥ 5.5% showed higher sensitivity and lower specificity with respect to HbA1c ≥ 5.7% for all the abnormalities examined (IFG, IGT, high G₆₀, IR, low IS, DI and CMR factors). In conclusion, this lower cut-off might represent a more appropriate screening marker of glucose dysmetabolism in youths with OW/OB. Prospective studies are needed to validate this cut-off for predicting prediabetes/diabetes in youths with OW/OB.

Effect of fasting hyperglycemia and insulin resistance on bone turnover markers in children aged 9-11 years

AIM

Impaired regulation of glucose metabolism in childhood adversely affects bone health. We assessed the effect of fasting hyperglycemia and insulin resistance on bone turnover markers in prepubertal children with normal glycemia (<100 mg/dL) and fasting hyperglycemia (100-125 mg/dL).

METHODS

Glucose, hemoglobin A1c, IGF-I (insulin-like growth factor I), iP1NP (N-terminal propeptide of type I procollagen), CTX-1 (C-terminal telopeptide of type I collagen) and insulin were measured. Bone turnover index (BTI) and HOMA-IR (homeostasis model assessment) were calculated.

RESULTS

Bone resorption marker (CTX) levels were decreased by 26.5% in boys with hyperglycemia, though only 7% in girls. Hyperglycemia had no effect on the bone formation marker iP1NP. IGF-1, the best predictor of bone marker variance accounted for 25% of iP1NP and 5% of CTX variance. Girls presented significantly higher BTI indicating the predominance of bone formation over resorption. Insulin resistance significantly decreased CTX. In girls, HOMA-IR and IGF-1 predicted 15% of CTX variance.

CONSLUSIONS

Fasting hyperglycemia and insulin resistance in children impact bone turnover suppressing bone resorption. Hyperglycemia decreased resorption, particularly in boys, while suppression of resorption by insulin resistance was more pronounced in girls. We suggest that the progression of disturbances accompanying prediabetes, may interfere with bone modelling and be deleterious to bone quality in later life.

Contributions of Fat and Carbohydrate Metabolism to Glucose Homeostasis in Childhood Change With Age and Puberty: A 12-Years Cohort Study (EARLYBIRD 77)

Puberty-a period when susceptibility to the onset of Type 2 diabetes (T2D) increases-is marked with profound physiological and metabolic changes. In the EarlyBird cohort, children who developed impaired fasting glycemia in adolescence already exhibited higher fasting blood glucose at 5 years of age, independent of their body mass index (BMI), suggesting that pubertal factors may modify existing predisposition. Understanding how the physiological changes during childhood influence glucose homeostasis and how the central energy metabolism may help deciphering the mechanisms that underlie the risk of developing T2D in children and adults. We investigated these associations by analyzing glycemic variations with molecular markers of central energy metabolism, substrate oxidation status and pubertal stages in the EarlyBird cohort. The EarlyBird study is a non-interventional, prospective cohort study, that recruited 307 healthy UK children at age 5, and followed them annually throughout childhood for 12 years. Longitudinal data on blood biochemistry, respiratory exchange ratio, and anthropometry, available from 150 children were integrated with fasting glycemia. The gradual rise in blood glucose during childhood associates with age-dependent changes in molecular processes and substrate oxidation status, namely (i) greater pre-pubertal fat utilization, ketogenesis, and fatty acid oxidation, and (ii) greater pubertal carbohydrate oxidation and glycolytic metabolism (Cori and Cahill Cycles) associated with different amino acid exchanges between muscle and other tissues (proline, glutamine, alanine). Since children's metabolic and nutritional requirements evolve during childhood, this study has potential clinical implications for the development of nutritional strategies for disease prevention in children.

Age-specific associations of glycated haemoglobin variability with cardiovascular disease and mortality in patients with type 2 diabetes mellitus: A 10- year cohort study

AIMS

To investigate the associations of increased variability in glycated haemoglobin (HbA1c) with cardiovascular disease (CVD) and mortality risk in patients with diabetes.

MATERIALS AND METHODS

This prospective cohort study included 147 811 patients aged 45 to 84 years with type 2 diabetes mellitus, without CVD and with at least three HbA1c values recorded before baseline in the period 2008 to 2010. HbA1c variability was evaluated using a mixed effects model to reduce regression dilution bias. Age-specific associations (45- 54, 55- 64, 65- 74 and 75- 84 years) between HbA1c variability and risk of CVD and mortality were assessed by Cox regression, adjusted for patient characteristics and usual HbA1c.

RESULTS

After a median follow-up of 7.4 years(1.02 million person-years), an overall incidence of 40 785 events including CVD (incidence 27  793) and all-cause mortalities (incidence 23 175) were identified. Positive log-linear associations between HbA1c variability and CVD and mortality were identified in all age groups. The hazard ratios (HRs) for the composite of CVD and all-cause mortality showed that age was inversely associated with HbA1c variability, with a 28% higher risk per 1% increase in HbA1c variability in the age group 45 to 54 years (all composite outcomes: HR 1.28, 95% confidence interval [CI] 1.21, 1.35), whereas only a 14% higher risk in the 75- 84 age group (all composite outcomes: HR 1.14, 95% CI 1.11, 1.17). Subgroup analysis showed the risk in patients with usual HbA1c <53mmol/mol was about eight times higher than in those with usual HbA1c ≥64mmol/mol.

CONCLUSIONS

HbA1c variability was strongly related to CVD and mortality in patients with diabetes across all age groups. Whilst pursuing optimal HbA1c targets, attention should be given to patients with high HbA1c variability, especially younger patients with good HbA1c control.

Comparison of HbA1c and OGTT for the diagnosis of type 2 diabetes in children at risk of diabetes

BACKGROUND

The aim of this study was to evaluate the correlation between plasma glucose and HbA1c and the diagnostic accuracy of HbA1c as a screening tool to identify asymptomatic diabetes mellitus in children and adolescents with obesity or asymptomatic glucosuria.

METHODS

A total of 190 subjects who underwent an oral glucose tolerance test (OGTT) to confirm diabetes were categorized into normal glucose tolerance (NGT; n = 117), impaired glucose tolerance (IGT; n = 33), and diabetes (DM; n = 40) according to the OGTT. Forty-seven patients with DM were diagnosed by either OGTT or HbA1c levels. The diagnostic accuracy for the detection of diabetes is based on 47 patients. Laboratory tests were performed after 12 h of fasting.

RESULTS

According to the HbA1c criterion, 107 (55.3%) subjects were in the NGT group, 41 (21.6%) were in the IGT group, and 42 (22.1%) were in the DM group. Diagnostic sensitivities of HbA1c and 2-hour plasma glucose level following OGTT (2-h OGTT) for DM were significantly higher than that of fasting plasma glucose, FPG (89.4, 85.1 vs. 63.8%). In addition, the area under the curves of diagnostic criteria was 0.970 for HbA1c, 0.939 for FPG and 0.977 for 2-h OGTT. Mean FPG and 2-h OGTT for HbA1c level >6.5% were 115.2 mg/dL and 181.8 mg/dL, respectively. The optimal HbA1c level cut-off point for predicting DM is 6.15%, with a sensitivity of 95.7% in Korean children and adolescents.

CONCLUSION

The HbA1c criterion ≥6.5% was adequate to detect DM among Korean children and adolescents with obesity or asymptomatic glucosuria. We also recommend HbA1c level of 6.15% as the optimal cut-off point for detecting DM in Korean children and adolescents.

Discrepancies between Glycosylated Hemoglobin and Fasting Plasma Glucose for Diagnosing Impaired Fasting Glucose and Diabetes Mellitus in Korean Youth and Young Adults

BACKGROUND

Glycosylated hemoglobin (HbA1c) has been recommended as a diagnostic test for prediabetes and diabetes. Here, we evaluated the level of agreement between diagnoses based on fasting plasma glucose (FPG) versus HbA1c levels and determined optimal HbA1c cutoff values for these diseases in youth and young adults.

METHODS

The study included 7,332 subjects (n=4,129, aged 10 to 19 years in youth group; and n=3,203 aged 20 to 29 years in young adult group) from the 2011 to 2016 Korea National Health and Nutrition Examination Survey. Prediabetes and diabetes were defined as 100 to 125 mg/dL (impaired fasting glucose [IFG]) and ≥126 mg/dL for FPG (diabetes mellitus [DM] by FPG [DM_FPG]), and 5.7% to 6.4% and ≥6.5% for HbA1c, respectively.

RESULTS

In the youth group, 32.5% with IFG had an HbA1c level of 5.7% to 6.4%, and 72.2% with DM_FPG had an HbA1c ≥6.5%. In the young adult group, 27.5% with IFG had an HbA1c level of 5.7% to 6.4%, and 66.6% with DM_FPG had an HbA1c ≥6.5%. Kappa coefficients for agreement between the FPG and HbA1c results were 0.12 for the youth group and 0.19 for the young adult group. In receiver operating characteristic curve analysis, the optimal HbA1c cutoff for IFG and DM_FPG were 5.6% and 5.9% in youths and 5.5% and 5.8% in young adults, respectively.

CONCLUSION

Usefulness of HbA1c for diagnosis of IFG and DM_FPG in Koreans aged <30 years remains to be determined due to discrepancies between the results of glucose- and HbA1c-based tests. Additional testing might be warranted at lower HbA1c levels to detect IFG and DM_FPG in this age group.

HbA1c Cutoff for Prediabetes and Diabetes Based on Oral Glucose Tolerance Test in Obese Children and Adolescents

BACKGROUND

Oral glucose tolerance test (OGTT) is a traditional diagnostic tool for diabetes. Hemoglobin A1c (HbA1c) is an alternative method used in adults; however, its application in youths has been controversial. We evaluated the diagnostic performance of HbA1c and determined optimal cutoff points for detecting prediabetes and diabetes in youth.

METHODS

This retrospective study included 389 obese children (217 boys, 55.8%) who had undergone simultaneous OGTT and HbA1c testing at six hospitals, Korea, between 2010 and 2016. Subjects were diagnosed with diabetes (fasting glucose ≥ 7.0 mmol/L; 2-hour glucose ≥ 11.1 mmol/L) or prediabetes (fasting glucose 5.6-6.9 mmol/L; 2-hour glucose 7.8-11.0 mmol/L). The diagnostic performance of HbA1c for prediabetes and diabetes was determined using the area under the receiver operating characteristic curve (AUC).

RESULTS

At diagnosis, 197 (50.6%) subjects had normoglycemia, 121 (31.1%) had prediabetes, and 71 (18.3%) had diabetes. The kappa coefficient for agreement between OGTT and HbA1c was 0.464. The optimal HbA1c cutoff points were 5.8% (AUC, 0.795; a sensitivity of 64.1% and a specificity of 83.8%) for prediabetes and 6.2% (AUC, 0.972; a sensitivity of 91.5% and a specificity of 93.7%) for diabetes. When HbA1c (≥ 6.2%) and 2-hour glucose level were used to diagnose diabetes, 100% were detected.

CONCLUSION

Pediatric criteria for HbA1c remain unclear, therefore, we recommend the combination of fasting and 2-hour glucose levels, in addition to HbA1c, in the diagnosis of childhood prediabetes and diabetes.

Consensus Clustering of temporal profiles for the identification of metabolic markers of pre-diabetes in childhood (EarlyBird 73)

In longitudinal clinical studies, methodologies available for the analysis of multivariate data with multivariate methods are relatively limited. Here, we present Consensus Clustering (CClust) a new computational method based on clustering of time profiles and posterior identification of correlation between clusters and predictors. Subjects are first clustered in groups according to a response variable temporal profile, using a robust consensus-based strategy. To discover which of the remaining variables are associated with the resulting groups, a non-parametric hypothesis test is performed between groups at every time point, and then the results are aggregated according to the Fisher method. Our approach is tested through its application to the EarlyBird cohort database, which contains temporal variations of clinical, metabolic, and anthropometric profiles in a population of 150 children followed-up annually from age 5 to age 16. Our results show that our consensus-based method is able to overcome the problem of the approach-dependent results produced by current clustering algorithms, producing groups defined according to Insulin Resistance (IR) and biological age (Tanner Score). Moreover, it provides meaningful biological results confirmed by hypothesis testing with most of the main clinical variables. These results position CClust as a valid alternative for the analysis of multivariate longitudinal data.

A Novel Physiology-Based Mathematical Model to Estimate Red Blood Cell Lifespan in Different Human Age Groups

Direct measurement of red blood cell (RBC) survival in humans has improved from the original accurate but limited differential agglutination technique to the current reliable, safe, and accurate biotin method. Despite this, all of these methods are time consuming and require blood sampling over several months to determine the RBC lifespan. For situations in which RBC survival information must be obtained quickly, these methods are not suitable. With the exception of adults and infants, RBC survival has not been extensively investigated in other age groups. To address this need, we developed a novel, physiology-based mathematical model that quickly estimates RBC lifespan in healthy individuals at any age. The model is based on the assumption that the total number of RBC recirculations during the lifespan of each RBC (denoted by N max) is relatively constant for all age groups. The model was initially validated using the data from our prior infant and adult biotin-labeled red blood cell studies and then extended to the other age groups. The model generated the following estimated RBC lifespans in 2-year-old, 5-year-old, 8-year-old, and 10-year-old children: 62, 74, 82, and 86 days, respectively. We speculate that this model has useful clinical applications. For example, HbA1c testing is not reliable in identifying children with diabetes because HbA1c is directly affected by RBC lifespan. Because our model can estimate RBC lifespan in children at any age, corrections to HbA1c values based on the model-generated RBC lifespan could improve diabetes diagnosis as well as therapy in children.

Hemoglobin A1c (HbA1c) changes over time among adolescent and young adult participants in the T1D exchange clinic registry

OBJECTIVE

Hemoglobin A1c (HbA1c) levels among individuals with type 1 diabetes (T1D) influence the longitudinal risk for diabetes-related complications. Few studies have examined HbA1c trends across time in children, adolescents, and young adults with T1D. This study examines changes in glycemic control across the specific transition periods of pre-adolescence-to-adolescence and adolescence-to-young adulthood, and the demographic and clinical factors associated with these changes.

RESEARCH DESIGN AND METHODS

Available HbA1c lab results for up to 10 yr were collected from medical records at 67 T1D Exchange clinics. Two retrospective cohorts were evaluated: the pre-adolescent-to-adolescent cohort consisting of 85 016 HbA1c measurements from 6574 participants collected when the participants were 8-18 yr old and the adolescent-to-young adult cohort, 2200 participants who were 16-26 yr old at the time of 17 279 HbA1c measurements.

RESULTS

HbA1c in the 8-18 cohort increased over time after age 10 yr until ages 16-17; followed by a plateau. HbA1c levels in the 16-26 cohort remained steady from 16-18, and then gradually declined. For both cohorts, race/ethnicity, income, health insurance, and pump use were all significant in explaining individual variations in age-centered HbA1c (p < 0.001). For the 8-18 cohort, insulin pump use, age of onset, and health insurance were significant in predicting individual HbA1c trajectory.

CONCLUSIONS

Glycemic control among patients 8-18 yr old worsens over time, through age 16. Elevated HbA1c levels observed in 18 yr-olds begin a steady improvement into early adulthood. Focused interventions to prevent deterioration in glucose control in pre-adolescence, adolescence, and early adulthood are needed.

Staying Young at Heart: Cardiovascular Disease Prevention in Adolescents and Young Adults

OPINION STATEMENT

Approaches to the prevention and management of cardiovascular disease (CVD) are often too narrow in scope and initiated too late. While the majority of adolescents are free of CVD, far fewer are free of CVD risk factors, especially lifestyle factors such as poor exercise and dietary habits. Most clinicians are familiar with behavioral and pharmacologic strategies for modifying these and other traditional CVD risk factors such as hypertension, hypercholesterolemia, and diabetes. In this review, we highlight those strategies most applicable to teens and also propose fundamental reframing that recognizes the importance of early choices and life experiences to achieving cardiovascular health. Population- and individual-level approaches that support the establishment of positive health behaviors early in life are the foundation of preserving ideal cardiovascular health and promoting positive cardiovascular outcomes. The Positive Youth Development movement supports a frame shift away from seeing young people as merely the sum of their risk factors and instead as developmentally dynamic youth capable of making healthy choices. Informed by the Positive Youth Development framework, our approach to cardiovascular prevention among adolescents is both broad based and proactive, paying heed as early as possible to social, familial, and developmental factors that underlie health behaviors and employing evidence-based behavioral, pharmacologic, and surgical treatments when needed.

Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research

Systems biology is an important approach for deciphering the complex processes in health maintenance and the etiology of metabolic diseases. Such integrative methodologies will help better understand the molecular mechanisms involved in growth and development throughout childhood, and consequently will result in new insights about metabolic and nutritional requirements of infants, children and adults. To achieve this, a better understanding of the physiological processes at anthropometric, cellular and molecular level for any given individual is needed. In this respect, novel omics technologies in combination with sophisticated data modeling techniques are key. Due to the highly complex network of influential factors determining individual trajectories, it becomes imperative to develop proper tools and solutions that will comprehensively model biological information related to growth and maturation of our body functions. The aim of this review and perspective is to evaluate, succinctly, promising data analysis approaches to enable data integration for clinical research, with an emphasis on the longitudinal component. Approaches based on empirical and mechanistic modeling of omics data are essential to leverage findings from high dimensional omics datasets and enable biological interpretation and clinical translation. On the one hand, empirical methods, which provide quantitative descriptions of patterns in the data, are mostly used for exploring and mining datasets. On the other hand, mechanistic models are based on an understanding of the behavior of a system's components and condense information about the known functions, allowing robust and reliable analyses to be performed by bioinformatics pipelines and similar tools. Herein, we will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research.