Gestational diabetes associated with incident diabetes in childhood and youth: a retrospective cohort study

The retrospective cohort study of Blotsky et al. serves well the epidemiology of diabetes in youth.(1) Their work, and supportive studies otherwise, have used administrative databases to establish diagnoses of diabetes, and they rely on validations previously made that provide some reasonable predictive values for discharge diagnoses as being representative of a gold standard laboratory diagnosis.(2-7) Making extrapolations in this regard has its perils, however, and also has the tendency to bring us further away from the complex clinical entity we recognize as diabetes mellitus.

Decades ago, criteria for and diagnoses of diabetes mellitus were simplistic and often more clinically-based. Fundamentally, diabetes mellitus is a complex and heterogeneous group of pathologies which are unified by the concepts of disordered carbohydrate metabolism. A major difficulty with harnessing such complexity is the perceived need to create more palpable boundaries for definitions. Such definitions may be more tailored towards assisting epidemiological, administrative, and to some extent clinical usages. There was eventually a move to ascribe the gold standards to strictly laboratory parameters and descriptions. Internationally, the choice of such standards were fraught with controversy, although there was some consensus that the laboratory definitions of diabetes mellitus would strongly rely on the creation of thresholds for the diabetic state as indicated by the frequency of associated complications (then ophthalmological or cardiovascular) at any set time. What was evident nonetheless was that any such laboratory definition underestimated risk and underdiagnosed those who were to suffer any such complications. There are varying paths to diabetes, and the ‘one size-fits all’ applications were often challenged then and continue to be challenged.

Added to these dilemmae were the findings of glycemic profiles in what would appear to be normal patients. Many such patients have their blood glucose levels in the hyperglycemic range for a considerable time day-to-day.(8) Others found that glycated hemoglobin in a normal population was associated with complications.(9) Among seemingly normal young men, higher fasting blood sugars were risk factors for dysregulated glycemic control.(10) The consequence of these findings is the more balanced and robust view that diabetes mellitus is a continuous spectrum of glycemic dysregulation.(11) There is therefore the onus on the clinician to assess each individual and devise a personalized diabetic profile.(12) Such assessment will include factors such as age, metabolic parameters, family history, medication profile, past history, gestational status, among others in the least and in addition to laboratory measures. The management of this profile will depend on where the patient is on that spectral diagnostic curve.(13,14)

Effectively, the current knowledge base of molecular, cellular, and genetic bases of disease lead to a unified construct that there is a diabetic spectrum and that the decision to say that a patient has diabetes mellitus is a clinical definition. The junior diagnostician will dwell on absolute markers among fasting blood glucose, hemoglobin A1c, or glucose tolerance testing with the preliminary knowledge that these indicators will signal the initiation of a disease boundary that warrants some form of intervention. The senior diagnostician will already know that diabetes mellitus will always be a spectral disease in which the patient as a whole must be considered. The latter will establish the presence of a metabolic disorder on the basis of several criteria as above and beyond a paper diagnosis. Interventions and/or recommendations will be predicated on that intimate knowledge for the given patient. We then re-reflect on studies such as those from Blotsky et al.(1) What should then be the gold standard – an extrapolated laboratory definition or clinical diagnoses that created the foundation for hospital or outpatient discharge diagnoses?

References

1. Blotsky AL, Rahme E, Dahhou M, Nakhla M, Dasgupta K. Gestational diabetes associated with incident diabetes in childhood and youth: a retrospective cohort study. CMAJ 2019 April 15;191:E410-7.
2. Dart AB, Martens PJ, Sellers EA, Brownell MD, Rigatto C, Dean HJ. Validation of a pediatric diabetes care definition using administrative health data in Manitoba, Canada. Diabetes Care 2011;34(4):898-903.
3. Leong A, Dasgupta K, Chiasson J-L, Rahme E. Estimating the population prevalence of diagnosed and undiagnosed diabetes. Diabetes Care 2013;36(10):3002-8.
4. Bowker SL, Savu A, Donovan LE, Johnson JA, Kaul P. Validation of administrative and clinical case definitions for gestational diabetes mellitus against laboratory results. Diabet Med 2017;3(6):781-5.
5. Bowker SL, Savu A, Lam NK, Johnson JA, Kaul P. Validation of administrative data case definitions for gestational diabetes mellitus. Diabet Med 2017;34(1):51-5.
6. Guttmann A, Nakhla M, Henderson M, et al. Validation of a health administrative data algorithm for assessing the epidemiology of diabetes in Canadian children. Pediatric Diabetes 2010;11(2):122-8.
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8. Borg R, Kuenen JC, Carstensen B, et al. Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-derived average glucose (ADAG) study. Diabetologia 2010;53(8):1608-11.
9. Selvin E, Steffes MW, Zhu H, et al. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. N Engl J Med 2010;362(9):800-11.
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12. Bergman M. Prediabetes and diabetes prevention. Med Clin N A 2011;95:xi-xiii.
13. Brooks-Worrell B, Palmer JP. Is diabetes mellitus a continuous spectrum? Clin Chem 2011;57(2):158-61.
14. Heianza Y, Arase Y, Fujihara K, et al. Screening for pre-diabetes to predict future diabetes using various cut-off points for HbA1c and impaired fasting glucose: the Toranomon Hospital Health Management Center Study 4 (TOPICS 4). Diabet Med 2012;29(9):e270-85.