The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Diabetes can be classified into the following general categories:

  1. Type 1 diabetes (due to autoimmune β-cell destruction, usually leading to absolute insulin deficiency, including latent autoimmune diabetes of adulthood)

  2. Type 2 diabetes (due to a non-autoimmune progressive loss of adequate β-cell insulin secretion frequently on the background of insulin resistance and metabolic syndrome)

  3. Specific types of diabetes due to other causes, e.g., monogenic diabetes syndromes (such as neonatal diabetes and maturity-onset diabetes of the young), diseases of the exocrine pancreas (such as cystic fibrosis and pancreatitis), and drug- or chemical-induced diabetes (such as with glucocorticoid use, in the treatment of HIV/AIDS, or after organ transplantation)

  4. Gestational diabetes mellitus (diabetes diagnosed in the second or third trimester of pregnancy that was not clearly overt diabetes prior to gestation)

This section reviews most common forms of diabetes but is not comprehensive. For additional information, see the American Diabetes Association (ADA) position statement “Diagnosis and Classification of Diabetes Mellitus” (1).

Type 1 diabetes and type 2 diabetes are heterogeneous diseases in which clinical presentation and disease progression may vary considerably. Classification is important for determining therapy, but some individuals cannot be clearly classified as having type 1 or type 2 diabetes at the time of diagnosis. The traditional paradigms of type 2 diabetes occurring only in adults and type 1 diabetes only in children are no longer accurate, as both diseases occur in both age groups. Children with type 1 diabetes often present with the hallmark symptoms of polyuria/polydipsia, and approximately half present with diabetic ketoacidosis (DKA) (24). The onset of type 1 diabetes may be more variable in adults; they may not present with the classic symptoms seen in children and may experience temporary remission from the need for insulin (57). The features most useful in discrimination of type 1 diabetes include younger age at diagnosis (<35 years) with lower BMI (<25 kg/m2), unintentional weight loss, ketoacidosis, and glucose >360 mg/dL (20 mmol/L) at presentation (8). Occasionally, people with type 2 diabetes may present with DKA (9,10), particularly members of ethnic and racial minorities (11). It is important for the health care professional to realize that classification of diabetes type is not always straightforward at presentation and that misdiagnosis is common (e.g., adults with type 1 diabetes misdiagnosed as having type 2 diabetes, individuals with maturity-onset diabetes of the young [MODY] misdiagnosed as having type 1 diabetes). Although difficulties in distinguishing diabetes type may occur in all age groups at onset, the diagnosis becomes more obvious over time in people with β-cell deficiency as the degree of β-cell deficiency becomes clear.

In both type 1 and type 2 diabetes, various genetic and environmental factors can result in the progressive loss of β-cell mass and/or function that manifests clinically as hyperglycemia. Once hyperglycemia occurs, people with all forms of diabetes are at risk for developing the same chronic complications, although rates of progression may differ. The identification of individualized therapies for diabetes in the future will be informed by better characterization of the many paths to β-cell demise or dysfunction (12). Across the globe, many groups are working on combining clinical, pathophysiological, and genetic characteristics to more precisely define the subsets of diabetes that are currently clustered into the type 1 diabetes versus type 2 diabetes nomenclature with the goal of optimizing personalized treatment approaches. Many of these studies show great promise and may soon be incorporated into the diabetes classification system (13).

Characterization of the underlying pathophysiology is more precisely developed in type 1 diabetes than in type 2 diabetes. It is now clear from prospective studies that the persistent presence of two or more islet autoantibodies is a near-certain predictor of clinical diabetes (14). The rate of progression is dependent on the age at first detection of autoantibody, number of autoantibodies, autoantibody specificity, and autoantibody titer. Glucose and A1C levels rise well before the clinical onset of diabetes, making diagnosis feasible well before the onset of DKA. Three distinct stages of type 1 diabetes can be identified (Table 2.1 ) and serve as a framework for research and regulatory decision-making (12,15). There is debate as to whether slowly progressive autoimmune diabetes with an adult onset should be termed latent autoimmune diabetes in adults (LADA) or type 1 diabetes. The clinical priority with detection of LADA is awareness that slow autoimmune β-cell destruction can occur in adults leading to a long duration of marginal insulin secretory capacity. For the purpose of this classification, all forms of diabetes mediated by autoimmune β-cell destruction are included under the rubric of type 1 diabetes. Use of the term LADA is common and acceptable in clinical practice and has the practical impact of heightening awareness of a population of adults likely to have progressive autoimmune β-cell destruction (16), thus accelerating insulin initiation prior to deterioration of glucose management or development of DKA (6,17).

Table 2.1

Staging of type 1 diabetes (12,16)

Stage 1Stage 2Stage 3
Characteristics • Autoimmunity • Autoimmunity • Autoimmunity 
• Normoglycemia • Dysglycemia • Overt hyperglycemia 
• Presymptomatic • Presymptomatic • Symptomatic 
Diagnostic criteria • Multiple islet autoantibodies
• No IGT or IFG 
• Islet autoantibodies (usually multiple)
• Dysglycemia: IFG and/or IGT
• FPG 100–125 mg/dL (5.6–6.9 mmol/L)
• 2-h PG 140–199 mg/dL (7.8–11.0 mmol/L)
• A1C 5.7–6.4% (39–47 mmol/mol) or ≥10% increase in A1C 
• Autoantibodies may become absent
• Diabetes by standard criteria 
Stage 1Stage 2Stage 3
Characteristics • Autoimmunity • Autoimmunity • Autoimmunity 
• Normoglycemia • Dysglycemia • Overt hyperglycemia 
• Presymptomatic • Presymptomatic • Symptomatic 
Diagnostic criteria • Multiple islet autoantibodies
• No IGT or IFG 
• Islet autoantibodies (usually multiple)
• Dysglycemia: IFG and/or IGT
• FPG 100–125 mg/dL (5.6–6.9 mmol/L)
• 2-h PG 140–199 mg/dL (7.8–11.0 mmol/L)
• A1C 5.7–6.4% (39–47 mmol/mol) or ≥10% increase in A1C 
• Autoantibodies may become absent
• Diabetes by standard criteria 

FPG, fasting plasma glucose; IFG, impaired fasting glucose; IGT, impaired glucose tolerance; 2-h PG, 2-h plasma glucose.

The paths to β-cell demise and dysfunction are less well defined in type 2 diabetes, but deficient β-cell insulin secretion, frequently in the setting of insulin resistance, appears to be the common denominator. Type 2 diabetes is associated with insulin secretory defects related to genetics, inflammation, and metabolic stress. Future classification schemes for diabetes will likely focus on the pathophysiology of the underlying β-cell dysfunction (12,13,1820).

Diabetes may be diagnosed based on plasma glucose criteria, either the fasting plasma glucose (FPG) value or the 2-h plasma glucose (2-h PG) value during a 75-g oral glucose tolerance test (OGTT) or A1C criteria (21) (Table 2.2 ).

Table 2.2

Criteria for the diagnosis of diabetes

FPG ≥126 mg/dL (7.0 mmol/L). Fasting is defined as no caloric intake for at least 8 h.* 
OR 
2-h PG ≥200 mg/dL (11.1 mmol/L) during OGTT. The test should be performed as described by WHO, using a glucose load containing the equivalent of 75 g anhydrous glucose dissolved in water.* 
OR 
A1C ≥6.5% (48 mmol/mol). The test should be performed in a laboratory using a method that is NGSP certified and standardized to the DCCT assay.* 
OR 
In a patient with classic symptoms of hyperglycemia or hyperglycemic crisis, a random plasma glucose ≥200 mg/dL (11.1 mmol/L). 
FPG ≥126 mg/dL (7.0 mmol/L). Fasting is defined as no caloric intake for at least 8 h.* 
OR 
2-h PG ≥200 mg/dL (11.1 mmol/L) during OGTT. The test should be performed as described by WHO, using a glucose load containing the equivalent of 75 g anhydrous glucose dissolved in water.* 
OR 
A1C ≥6.5% (48 mmol/mol). The test should be performed in a laboratory using a method that is NGSP certified and standardized to the DCCT assay.* 
OR 
In a patient with classic symptoms of hyperglycemia or hyperglycemic crisis, a random plasma glucose ≥200 mg/dL (11.1 mmol/L). 

DCCT, Diabetes Control and Complications Trial; FPG, fasting plasma glucose; OGTT, oral glucose tolerance test; NGSP, National Glycohemoglobin Standardization Program; WHO, World Health Organization; 2-h PG, 2-h plasma glucose.

*

In the absence of unequivocal hyperglycemia, diagnosis requires two abnormal test results from the same sample or in two separate test samples.

Generally, FPG, 2-h PG during 75-g OGTT, and A1C are equally appropriate for diagnostic screening. It should be noted that detection rates of different screening tests vary in both populations and individuals. Moreover, the efficacy of interventions for primary prevention of type 2 diabetes has mainly been demonstrated among individuals who have impaired glucose tolerance (IGT) with or without elevated fasting glucose, not for individuals with isolated impaired fasting glucose (IFG) or for those with prediabetes defined by A1C criteria (22,23).

The same tests may be used to screen for and diagnose diabetes and to detect individuals with prediabetes (Table 2.2  and Table 2.5 ) (24). Diabetes may be identified anywhere along the spectrum of clinical scenarios—in seemingly low-risk individuals who happen to have glucose testing, in individuals screened based on diabetes risk assessment, and in symptomatic patients. For additional details on the evidence used to establish the criteria for the diagnosis of diabetes, prediabetes, and abnormal glucose tolerance (IFG, IGT), see the ADA position statement “Diagnosis and Classification of Diabetes Mellitus” (1) and other reports (21,25,26).

Fasting and 2-Hour Plasma Glucose

The FPG and 2-h PG may be used to diagnose diabetes (Table 2.2 ). The concordance between the FPG and 2-h PG tests is imperfect, as is the concordance between A1C and either glucose-based test. Compared with FPG and A1C cut points, the 2-h PG value diagnoses more people with prediabetes and diabetes (27). In people in whom there is discordance between A1C values and glucose values, FPG and 2-h PG are more accurate (28).

A1C

Recommendations

  • 2.1a To avoid misdiagnosis or missed diagnosis, the A1C test should be performed using a method that is certified by the National Glycohemoglobin Standardization Program (NGSP) and standardized to the Diabetes Control and Complications Trial (DCCT) assay. B

  • 2.1b Point-of-care A1C testing for diabetes screening and diagnosis should be restricted to U.S. Food and Drug Administration–approved devices at laboratories proficient in performing testing of moderate complexity or higher by trained personnel. B

  • 2.2 Marked discordance between measured A1C and plasma glucose levels should raise the possibility of A1C assay interference and consideration of using an assay without interference or plasma blood glucose criteria to diagnose diabetes. B

  • 2.3 In conditions associated with an altered relationship between A1C and glycemia, such as hemoglobinopathies including sickle cell disease, pregnancy (second and third trimesters and the postpartum period), glucose-6-phosphate dehydrogenase deficiency, HIV, hemodialysis, recent blood loss or transfusion, or erythropoietin therapy, only plasma blood glucose criteria should be used to diagnose diabetes. B

  • 2.4 Adequate carbohydrate intake (at least 150 g/day) should be assured for 3 days prior to oral glucose tolerance testing as a screen for diabetes. A

The A1C test should be performed using a method that is certified by the NGSP (ngsp.org) and standardized or traceable to the Diabetes Control and Complications Trial (DCCT) reference assay. Point-of-care A1C assays may be NGSP certified and cleared by the U.S. Food and Drug Administration (FDA) for use in monitoring glycemic control in people with diabetes in both Clinical Laboratory Improvement Amendments (CLIA)-regulated and CLIA-waived settings. FDA-approved point-of-care A1C testing can be used in laboratories or sites that are CLIA certified, are inspected, and meet the CLIA quality standards. These standards include specified personnel requirements (including documented annual competency assessments) and participation three times per year in an approved proficiency testing program (2932). As discussed in Section 6, “Glycemic Targets,” point-of-care A1C assays may be more generally applied for assessment of glycemic stability in the clinic.

A1C has several advantages compared with FPG and OGTT, including greater convenience (fasting not required), greater preanalytical stability, and fewer day-to-day perturbations during stress, changes in nutrition, or illness. However, these advantages may be offset by the lower sensitivity of A1C at the designated cut point, greater cost, limited availability of A1C testing in certain regions of the developing world, and the imperfect correlation between A1C and average glucose in certain individuals. The A1C test, with a diagnostic threshold of ≥6.5% (48 mmol/mol), diagnoses only 30% of the diabetes cases identified collectively using A1C, FPG, or 2-h PG, according to National Health and Nutrition Examination Survey (NHANES) data (33). Despite these limitations with A1C, in 2009, the International Expert Committee added A1C to the diagnostic criteria with the goal of increased screening (21).

When using A1C to diagnose diabetes, it is important to recognize that A1C is an indirect measure of average blood glucose levels and to take other factors into consideration that may impact hemoglobin glycation independently of glycemia, such as hemodialysis, pregnancy, HIV treatment (34,35), age, race/ethnicity, genetic background, and anemia/hemoglobinopathies. (See other conditions altering the relationship of a1c and glycemia below for more information.)

Age

The epidemiologic studies that formed the basis for recommending A1C to diagnose diabetes included only adult populations (33). However, recent ADA clinical guidance concluded that A1C, FPG, or 2-h PG could be used to test for prediabetes or type 2 diabetes in children and adolescents (see screening and testing for prediabetes and type 2 diabetes in children and adolescents below for additional information) (36).

Race/Ethnicity/Hemoglobinopathies

Hemoglobin variants can interfere with the measurement of A1C, although most assays in use in the U.S. are unaffected by the most common variants. Marked discrepancies between measured A1C and plasma glucose levels should prompt consideration that the A1C assay may not be reliable for that individual. For individuals with a hemoglobin variant but normal red blood cell turnover, such as those with the sickle cell trait, an A1C assay without interference from hemoglobin variants should be used. An updated list of A1C assays with interferences is available at ngsp.org/interf.asp.

African American individuals heterozygous for the common hemoglobin variant HbS may have, for any given level of mean glycemia, lower A1C by about 0.3% compared with those without the trait (37). Another genetic variant, X-linked glucose-6-phosphate dehydrogenase G202A, carried by 11% of African American individuals, was associated with a decrease in A1C of about 0.8% in homozygous men and 0.7% in homozygous women compared with those without the variant (38). For example, in Tanzania, where there is a high likelihood of hemoglobinopathies in people with HIV, A1C may be lower than expected based on glucose, limiting its usefulness for screening (39).

Even in the absence of hemoglobin variants, A1C levels may vary with race/ethnicity independently of glycemia (4042). For example, African American individuals may have higher A1C levels than non-Hispanic White individuals with similar fasting and post–glucose load glucose levels (43). Though conflicting data exist, African American individuals may also have higher levels of fructosamine and glycated albumin and lower levels of 1,5-anhydroglucitol, suggesting that their glycemic burden (particularly postprandially) may be higher (44,45). Similarly, A1C levels may be higher for a given mean glucose concentration when measured with continuous glucose monitoring (46). A recent report in Afro-Caribbean people demonstrated a lower A1C than predicted by glucose levels (47). Despite these and other reported differences, the association of A1C with risk for complications appears to be similar in African American and non-Hispanic White populations (42,48). In the Taiwanese population, age and sex have been reported to be associated with increased A1C in men (49); the clinical implications of this finding are unclear at this time.

Other Conditions Altering the Relationship of A1C and Glycemia

In conditions associated with increased red blood cell turnover, such as sickle cell disease, pregnancy (second and third trimesters), glucose-6-phosphate dehydrogenase deficiency (50,51), hemodialysis, recent blood loss or transfusion, or erythropoietin therapy, only plasma blood glucose criteria should be used to diagnose diabetes (52). A1C is less reliable than blood glucose measurement in other conditions such as the postpartum state (5355), HIV treated with certain protease inhibitors (PIs) and nucleoside reverse transcriptase inhibitors (NRTIs) (34), and iron-deficient anemia (56).

Confirming the Diagnosis

Unless there is a clear clinical diagnosis (e.g., patient in a hyperglycemic crisis or with classic symptoms of hyperglycemia and a random plasma glucose ≥200 mg/dL [11.1 mmol/L]), diagnosis requires two abnormal screening test results, either from the same sample (57) or in two separate test samples. If using two separate test samples, it is recommended that the second test, which may either be a repeat of the initial test or a different test, be performed without delay. For example, if the A1C is 7.0% (53 mmol/mol) and a repeat result is 6.8% (51 mmol/mol), the diagnosis of diabetes is confirmed. If two different tests (such as A1C and FPG) are both above the diagnostic threshold when analyzed from the same sample or in two different test samples, this also confirms the diagnosis. On the other hand, if a patient has discordant results from two different tests, then the test result that is above the diagnostic cut point should be repeated, with careful consideration of the possibility of A1C assay interference. The diagnosis is made on the basis of the confirmatory screening test. For example, if a patient meets the diabetes criterion of the A1C (two results ≥6.5% [48 mmol/mol]) but not FPG (<126 mg/dL [7.0 mmol/L]), that person should nevertheless be considered to have diabetes.

Each of the screening tests has preanalytic and analytic variability, so it is possible that a test yielding an abnormal result (i.e., above the diagnostic threshold), when repeated, will produce a value below the diagnostic cut point. This scenario is likely for FPG and 2-h PG if the glucose samples remain at room temperature and are not centrifuged promptly. Because of the potential for preanalytic variability, it is critical that samples for plasma glucose be spun and separated immediately after they are drawn. If patients have test results near the margins of the diagnostic threshold, the health care professional should discuss signs and symptoms with the patient and repeat the test in 3–6 months.

People should consume a mixed diet with at least 150 g of carbohydrates on the 3 days prior to oral glucose tolerance testing (5860). Fasting and carbohydrate restriction can falsely elevate glucose level with an oral glucose challenge.

Diagnosis

In a patient with classic symptoms, measurement of plasma glucose is sufficient to diagnose diabetes (symptoms of hyperglycemia or hyperglycemic crisis plus a random plasma glucose ≥200 mg/dL [11.1 mmol/L]). In these cases, knowing the plasma glucose level is critical because, in addition to confirming that symptoms are due to diabetes, it will inform management decisions. Some health care professionals may also want to know the A1C to determine the chronicity of the hyperglycemia. The criteria to diagnose diabetes are listed in Table 2.2 .

Recommendations

  • 2.5 Screening for presymptomatic type 1 diabetes using screening tests that detect autoantibodies to insulin, glutamic acid decarboxylase (GAD), islet antigen 2, or zinc transporter 8 is currently recommended in the setting of a research study or can be considered an option for first-degree family members of a proband with type 1 diabetes. B

  • 2.6 Development of and persistence of multiple islet autoantibodies is a risk factor for clinical diabetes and may serve as an indication for intervention in the setting of a clinical trial or screening for stage 2 type 1 diabetes. B

Immune-Mediated Diabetes

This form, previously called “insulin-dependent diabetes” or “juvenile-onset diabetes,” accounts for 5–10% of diabetes and is due to cell-mediated autoimmune destruction of the pancreatic β-cells. Autoimmune markers include islet cell autoantibodies and autoantibodies to GAD (glutamic acid decarboxylase, GAD65), insulin, the tyrosine phosphatases islet antigen 2 (IA-2) and IA-2β, and zinc transporter 8. Numerous clinical studies are being conducted to test various methods of preventing type 1 diabetes in those with evidence of islet autoimmunity (trialnet.org/our-research/prevention-studies) (14,17,6164). Stage 1 of type 1 diabetes is defined by the presence of two or more of these autoimmune markers. The disease has strong HLA associations, with linkage to the DQB1 and DRB1 haplotypes, and genetic screening has been used in some research studies to identify high-risk populations. Specific alleles in these genes can be either predisposing or protective (Table 2.1 ).

The rate of β-cell destruction is quite variable, being rapid in some individuals (particularly but not exclusively in infants and children) and slow in others (mainly but not exclusively adults) (65,66). Children and adolescents often present with DKA as the first manifestation of the disease, and the rates in the U.S. have increased dramatically over the past 20 years (24). Others have modest fasting hyperglycemia that can rapidly change to severe hyperglycemia and/or DKA with infection or other stress. Adults may retain sufficient β-cell function to prevent DKA for many years; such individuals may have remission or decreased insulin needs for months or years and eventually become dependent on insulin for survival and are at risk for DKA (57,67,68). At this later stage of the disease, there is little or no insulin secretion, as manifested by low or undetectable levels of plasma C-peptide. Immune-mediated diabetes is the most common form of diabetes in childhood and adolescence, but it can occur at any age, even in the 8th and 9th decades of life.

Autoimmune destruction of β-cells has multiple genetic factors and is also related to environmental factors that are still poorly defined. Although individuals do not typically have obesity when they present with type 1 diabetes, obesity is increasingly common in the general population; as such, obesity should not preclude testing for type 1 diabetes. People with type 1 diabetes are also prone to other autoimmune disorders such as Hashimoto thyroiditis, Graves disease, celiac disease, Addison disease, vitiligo, autoimmune hepatitis, myasthenia gravis, and pernicious anemia (see Section 4, “Comprehensive Medical Evaluation and Assessment of Comorbidities”). Type 1 diabetes can be associated with monogenic polyglandular autoimmune syndromes, including immune dysregulation, polyendocrinopathy, enteropathy, and X-linked (IPEX) syndrome, which is an early-onset systemic autoimmune, genetic disorder caused by mutation of the forkhead box protein 3 (FOXP3) gene, and another caused by the autoimmune regulator (AIRE) gene mutation (69,70). As indicated by the names, these disorders are associated with other autoimmune and rheumatological diseases.

Introduction of immunotherapy, specifically checkpoint inhibitors, for cancer treatment has led to unexpected adverse events, including immune system activation precipitating autoimmune disease. Fulminant onset of type 1 diabetes can develop, with DKA and low or undetectable levels of C-peptide as a marker of endogenous β-cell function (71,72). Fewer than half of these patients have autoantibodies that are seen in type 1 diabetes, supporting alternate pathobiology. This immune-related adverse event occurs in just under 1% of checkpoint inhibitor-treated patients but most commonly occurs with agents that block the programmed cell death protein 1/programmed cell death ligand 1 pathway alone or in combination with other checkpoint inhibitors (73). To date, the majority of immune checkpoint inhibitor–related cases of type 1 diabetes occur in people with high-risk HLA-DR4 (present in 76% of patients), whereas other high-risk HLA alleles are not more common than those in the general population (73). To date, risk cannot be predicted by family history or autoantibodies, so all health care professionals administering these medications should be mindful of this adverse effect and educate patients appropriately.

Idiopathic Type 1 Diabetes

Some forms of type 1 diabetes have no known etiologies. These individuals have permanent insulinopenia and are prone to DKA but have no evidence of β-cell autoimmunity. However, only a minority of people with type 1 diabetes fall into this category. Individuals with autoantibody-negative type 1 diabetes of African or Asian ancestry may suffer from episodic DKA and exhibit varying degrees of insulin deficiency between episodes (possibly ketosis-prone diabetes) (74). This form of diabetes is strongly inherited and is not HLA associated. An absolute requirement for insulin replacement therapy in affected individuals may be intermittent. Future research is needed to determine the cause of β-cell destruction in this rare clinical scenario.

Screening for Type 1 Diabetes Risk

The incidence and prevalence of type 1 diabetes are increasing (75). People with type 1 diabetes often present with acute symptoms of diabetes and markedly elevated blood glucose levels, and 40–60% are diagnosed with life-threatening DKA (24). Multiple studies indicate that measuring islet autoantibodies in relatives of those with type 1 diabetes (15) or in children from the general population (76,77) can effectively identify those who will develop type 1 diabetes. A study reported the risk of progression to type 1 diabetes from the time of seroconversion to autoantibody positivity in three pediatric cohorts from Finland, Germany, and the U.S. Of the 585 children who developed more than two autoantibodies, nearly 70% developed type 1 diabetes within 10 years and 84% within 15 years (14). These findings are highly significant because while the German group was recruited from offspring of parents with type 1 diabetes, the Finnish and American groups were recruited from the general population. Remarkably, the findings in all three groups were the same, suggesting that the same sequence of events led to clinical disease in both “sporadic” and familial cases of type 1 diabetes. Indeed, the risk of type 1 diabetes increases as the number of relevant autoantibodies detected increases (63,78,79). In The Environmental Determinants of Diabetes in the Young (TEDDY) study, type 1 diabetes developed in 21% of 363 subjects with at least one autoantibody at 3 years of age (80). Such testing, coupled with education about diabetes symptoms and close follow-up, has been shown to enable earlier diagnosis and prevent DKA (81,82).

While widespread clinical screening of asymptomatic low-risk individuals is not currently recommended due to lack of approved therapeutic interventions, several innovative research screening programs are available in Europe (e.g., Fr1da, gppad.org) and the U.S. (trialnet.org, askhealth.org). Participation should be encouraged to accelerate development of evidence-based clinical guidelines for the general population and relatives of those with type 1 diabetes. Individuals who test positive should be counseled about the risk of developing diabetes, diabetes symptoms, and DKA prevention. Numerous clinical studies are being conducted to test various methods of preventing and treating stage 2 type 1 diabetes in those with evidence of autoimmunity with promising results (see clinicaltrials.gov and trialnet.org). Delay of overt diabetes development in stage 2 type 1 diabetes with the anti-CD3 antibody teplizumab in relatives at risk for type 1 diabetes was reported in 2019, with an extension of the randomized controlled trial in 2021 (83,84). Based on these data, this agent has been submitted to the FDA for the indication of delay or prevention of clinical type 1 diabetes in at-risk individuals. Neither this agent nor others in this category are currently available for clinical use.

Recommendations

  • 2.7 Screening for prediabetes and type 2 diabetes with an informal assessment of risk factors or validated risk calculator should be done in asymptomatic adults. B

  • 2.8 Testing for prediabetes and/or type 2 diabetes in asymptomatic people should be considered in adults of any age with overweight or obesity (BMI ≥25 kg/m2 or ≥23 kg/m2 in Asian American individuals) who have one or more risk factors (Table 2.3 ). B

  • 2.9 For all people, screening should begin at age 35 years. B

  • 2.10 If tests are normal, repeat screening recommended at a minimum of 3-year intervals is reasonable, sooner with symptoms or change in risk (i.e., weight gain). C

  • 2.11 To screen for prediabetes and type 2 diabetes, fasting plasma glucose, 2-h plasma glucose during 75-g oral glucose tolerance test, and A1C are each appropriate (Table 2.2  and Table 2.5 ). B

  • 2.12 When using oral glucose tolerance testing as a screen for diabetes, adequate carbohydrate intake (at least 150 g/day) should be assured for 3 days prior to testing. A

  • 2.13 In people with prediabetes and type 2 diabetes, identify and treat cardiovascular disease risk factors. A

  • 2.14 Risk-based screening for prediabetes and/or type 2 diabetes should be considered after the onset of puberty or after 10 years of age, whichever occurs earlier, in children and adolescents with overweight (BMI ≥85th percentile) or obesity (BMI ≥95th percentile) and who have one or more risk factors for diabetes. (See Table 2.4  for evidence grading of risk factors.) B

  • 2.15 People with HIV should be screened for diabetes and prediabetes with a fasting glucose test before starting antiretroviral therapy, at the time of switching antiretroviral therapy, and 3–6 months after starting or switching antiretroviral therapy. If initial screening results are normal, fasting glucose should be checked annually. E

Table 2.3

Criteria for screening for diabetes or prediabetes in asymptomatic adults

1. Testing should be considered in adults with overweight or obesity (BMI ≥25 kg/m2 or ≥23 kg/m2 in Asian American individuals) who have one or more of the following risk factors: 
 • First-degree relative with diabetes 
 • High-risk race/ethnicity (e.g., African American, Latino, Native American, Asian American, Pacific Islander) 
 • History of CVD 
 • Hypertension (≥140/90 mmHg or on therapy for hypertension) 
 • HDL cholesterol level <35 mg/dL (0.90 mmol/L) and/or a triglyceride level >250 mg/dL (2.82 mmol/L) 
 • Individuals with polycystic ovary syndrome 
 • Physical inactivity 
 • Other clinical conditions associated with insulin resistance (e.g., severe obesity, acanthosis nigricans) 
2. People with prediabetes (A1C ≥5.7% [39 mmol/mol], IGT, or IFG) should be tested yearly. 
3. People who were diagnosed with GDM should have lifelong testing at least every 3 years. 
4. For all other people, testing should begin at age 35 years. 
5. If results are normal, testing should be repeated at a minimum of 3-year intervals, with consideration of more frequent testing depending on initial results and risk status. 
6. People with HIV 
1. Testing should be considered in adults with overweight or obesity (BMI ≥25 kg/m2 or ≥23 kg/m2 in Asian American individuals) who have one or more of the following risk factors: 
 • First-degree relative with diabetes 
 • High-risk race/ethnicity (e.g., African American, Latino, Native American, Asian American, Pacific Islander) 
 • History of CVD 
 • Hypertension (≥140/90 mmHg or on therapy for hypertension) 
 • HDL cholesterol level <35 mg/dL (0.90 mmol/L) and/or a triglyceride level >250 mg/dL (2.82 mmol/L) 
 • Individuals with polycystic ovary syndrome 
 • Physical inactivity 
 • Other clinical conditions associated with insulin resistance (e.g., severe obesity, acanthosis nigricans) 
2. People with prediabetes (A1C ≥5.7% [39 mmol/mol], IGT, or IFG) should be tested yearly. 
3. People who were diagnosed with GDM should have lifelong testing at least every 3 years. 
4. For all other people, testing should begin at age 35 years. 
5. If results are normal, testing should be repeated at a minimum of 3-year intervals, with consideration of more frequent testing depending on initial results and risk status. 
6. People with HIV 

CVD, cardiovascular disease; GDM, gestational diabetes mellitus; IFG, impaired fasting glucose; IGT, impaired glucose tolerance.

Table 2.4

Risk-based screening for type 2 diabetes or prediabetes in asymptomatic children and adolescents in a clinical setting

Screening should be considered in youth* who have overweight (≥85th percentile) or obesity (≥95th percentile) A and who have one or more additional risk factors based on the strength of their association with diabetes: 
 • Maternal history of diabetes or GDM during the child’s gestation A 
 • Family history of type 2 diabetes in first- or second-degree relative A 
 • Race/ethnicity (Native American, African American, Latino, Asian American, Pacific Islander) A 
 • Signs of insulin resistance or conditions associated with insulin resistance (acanthosis nigricans, hypertension, dyslipidemia, polycystic ovary syndrome, or small-for-gestational-age birth weight) B 
Screening should be considered in youth* who have overweight (≥85th percentile) or obesity (≥95th percentile) A and who have one or more additional risk factors based on the strength of their association with diabetes: 
 • Maternal history of diabetes or GDM during the child’s gestation A 
 • Family history of type 2 diabetes in first- or second-degree relative A 
 • Race/ethnicity (Native American, African American, Latino, Asian American, Pacific Islander) A 
 • Signs of insulin resistance or conditions associated with insulin resistance (acanthosis nigricans, hypertension, dyslipidemia, polycystic ovary syndrome, or small-for-gestational-age birth weight) B 

GDM, gestational diabetes mellitus.

*

After the onset of puberty or after 10 years of age, whichever occurs earlier. If tests are normal, repeat testing at a minimum of 3-year intervals (or more frequently if BMI is increasing or risk factor profile deteriorating) is recommended. Reports of type 2 diabetes before age 10 years exist, and this can be considered with numerous risk factors.

Prediabetes

“Prediabetes” is the term used for individuals whose glucose levels do not meet the criteria for diabetes yet have abnormal carbohydrate metabolism (48,85). People with prediabetes are defined by the presence of IFG and/or IGT and/or A1C 5.7–6.4% (39–47 mmol/mol) (Table 2.5 ). Prediabetes should not be viewed as a clinical entity in its own right but rather as a risk factor for progression to diabetes and cardiovascular disease (CVD). Criteria for screening for diabetes or prediabetes in asymptomatic adults are outlined in Table 2.3 . Prediabetes is associated with obesity (especially abdominal or visceral obesity), dyslipidemia with high triglycerides and/or low HDL cholesterol, and hypertension. The presence of prediabetes should prompt comprehensive screening for cardiovascular risk factors.

Diagnosis

IFG is defined as FPG levels from 100 to 125 mg/dL (from 5.6 to 6.9 mmol/L) (82,83) and IGT as 2-h PG levels during 75-g OGTT from 140 to 199 mg/dL (from 7.8 to 11.0 mmol/L) (25). It should be noted that the World Health Organization and numerous other diabetes organizations define the IFG lower limit at 110 mg/dL (6.1 mmol/L).

As with the glucose measures, several prospective studies that used A1C to predict the progression to diabetes as defined by A1C criteria demonstrated a strong, continuous association between A1C and subsequent diabetes. In a systematic review of 44,203 individuals from 16 cohort studies with a follow-up interval averaging 5.6 years (range 2.8–12 years), those with A1C between 5.5% and 6.0% (between 37 and 42 mmol/mol) had a substantially increased risk of diabetes (5-year incidence from 9% to 25%). Those with an A1C range of 6.0–6.5% (42–48 mmol/mol) had a 5-year risk of developing diabetes between 25% and 50% and a relative risk 20 times higher compared with A1C of 5.0% (31 mmol/mol) (86). In a community-based study of African American and non-Hispanic White adults without diabetes, baseline A1C was a stronger predictor of subsequent diabetes and cardiovascular events than fasting glucose (87). Other analyses suggest that A1C of 5.7% (39 mmol/mol) or higher is associated with a diabetes risk similar to that of the high-risk participants in the Diabetes Prevention Program (DPP) (88), and A1C at baseline was a strong predictor of the development of glucose-defined diabetes during the DPP and its follow-up (89).

Hence, it is reasonable to consider an A1C range of 5.7–6.4% (39–47 mmol/mol) as identifying individuals with prediabetes. Similar to those with IFG and/or IGT, individuals with A1C of 5.7–6.4% (39–47 mmol/mol) should be informed of their increased risk for diabetes and CVD and counseled about effective strategies to lower their risks (see Section 3, “Prevention or Delay of Type 2 Diabetes and Associated Comorbidities”). Similar to glucose measurements, the continuum of risk is curvilinear, so as A1C rises, the diabetes risk rises disproportionately (86). Aggressive interventions and vigilant follow-up should be pursued for those considered at very high risk (e.g., those with A1C >6.0% [42 mmol/mol]).

Table 2.5  summarizes the categories of prediabetes, and Table 2.3  outlines the criteria for screening for prediabetes. The ADA Diabetes Risk Test is an additional option for assessment to determine the appropriateness of screening for diabetes or prediabetes in asymptomatic adults (Fig. 2.1 ) (diabetes.org/socrisktest). For additional background regarding risk factors and screening for prediabetes, see screening and testing for prediabetes and type 2 diabetes in asymptomatic adults and also screening and testing for prediabetes and type 2 diabetes in children and adolescents below. For details regarding individuals with prediabetes most likely to benefit from a formal behavioral or lifestyle intervention, see Section 3, “Prevention or Delay of Type 2 Diabetes and Associated Comorbidities.”

Table 2.5

Criteria defining prediabetes*

FPG 100 mg/dL (5.6 mmol/L) to 125 mg/dL (6.9 mmol/L) (IFG) 
OR 
2-h PG during 75-g OGTT 140 mg/dL (7.8 mmol/L) to 199 mg/dL (11.0 mmol/L) (IGT) 
OR 
A1C 5.7–6.4% (39–47 mmol/mol) 
FPG 100 mg/dL (5.6 mmol/L) to 125 mg/dL (6.9 mmol/L) (IFG) 
OR 
2-h PG during 75-g OGTT 140 mg/dL (7.8 mmol/L) to 199 mg/dL (11.0 mmol/L) (IGT) 
OR 
A1C 5.7–6.4% (39–47 mmol/mol) 

FPG, fasting plasma glucose; IFG, impaired fasting glucose; IGT, impaired glucose tolerance; OGTT, oral glucose tolerance test; 2-h PG, 2-h plasma glucose.

*

For all three tests, risk is continuous, extending below the lower limit of the range and becoming disproportionately greater at the higher end of the range.

Type 2 Diabetes

Type 2 diabetes, previously referred to as “non-insulin-dependent diabetes” or “adult-onset diabetes,” accounts for 90–95% of all diabetes. This form encompasses individuals who have relative (rather than absolute) insulin deficiency and have peripheral insulin resistance. At least initially, and often throughout their lifetime, these individuals may not need insulin treatment to survive.

There are various causes of type 2 diabetes. Although the specific etiologies are not known, autoimmune destruction of β-cells does not occur, and patients do not have any of the other known causes of diabetes. Most, but not all, people with type 2 diabetes have overweight or obesity. Excess weight itself causes some degree of insulin resistance. Individuals who do not have obesity or overweight by traditional weight criteria may have an increased percentage of body fat distributed predominantly in the abdominal region.

DKA seldom occurs spontaneously in type 2 diabetes; when seen, it usually arises in association with the stress of another illness such as infection or myocardial infarction or with the use of certain drugs (e.g., corticosteroids, atypical antipsychotics, and sodium–glucose cotransporter 2 inhibitors) (90,91). Type 2 diabetes frequently goes undiagnosed for many years because hyperglycemia develops gradually and, at earlier stages, is often not severe enough for the patient to notice the classic diabetes symptoms caused by hyperglycemia, such as dehydration or unintentional weight loss. Nevertheless, even undiagnosed people with diabetes are at increased risk of developing macrovascular and microvascular complications.

People with type 2 diabetes may have insulin levels that appear normal or elevated, yet the failure to normalize blood glucose reflects a relative defect in glucose-stimulated insulin secretion. Thus, insulin secretion is defective in these individuals and insufficient to compensate for insulin resistance. Insulin resistance may improve with weight reduction, physical activity, and/or pharmacologic treatment of hyperglycemia but is seldom restored to normal. Recent interventions with intensive diet and exercise or surgical weight loss have led to diabetes remission (9298) (see Section 8, “Obesity and Weight Management for the Prevention and Treatment of Type 2 Diabetes”).

The risk of developing type 2 diabetes increases with age, obesity, and lack of physical activity (99,100). It occurs more frequently in individuals with prior gestational diabetes mellitus (GDM) or polycystic ovary syndrome. It is also more common in people with hypertension or dyslipidemia and in certain racial/ethnic subgroups (African American, Native American, Hispanic/Latino, and Asian American). It is often associated with a strong genetic predisposition or family history in first-degree relatives (more so than type 1 diabetes). However, the genetics of type 2 diabetes are poorly understood and under intense investigation in this era of precision medicine (18). In adults without traditional risk factors for type 2 diabetes and/or of younger age, consider islet autoantibody testing (e.g., GAD65 autoantibodies) to exclude the diagnosis of type 1 diabetes (8).

Screening and Testing for Prediabetes and Type 2 Diabetes in Asymptomatic Adults

Screening for prediabetes and type 2 diabetes risk through an informal assessment of risk factors (Table 2.3 ) or with an assessment tool, such as the ADA risk test (Fig. 2.1 ) (online at diabetes.org/socrisktest), is recommended to guide health care professionals on whether performing a diagnostic test (Table 2.2 ) is appropriate. Prediabetes and type 2 diabetes meet criteria for conditions in which early detection via screening is appropriate. Both conditions are common and impose significant clinical and public health burdens. There is often a long presymptomatic phase before the diagnosis of type 2 diabetes. Simple tests to detect preclinical disease are readily available (101). The duration of glycemic burden is a strong predictor of adverse outcomes. There are effective interventions that prevent progression from prediabetes to diabetes. It is important to individualize risk/benefit of formal intervention for people with prediabetes and consider patient-centered goals. Risk models have explored the benefit, in general finding higher benefit of intervention in those at highest risk (102) (see Section 3, “Prevention or Delay of Type 2 Diabetes and Associated Comorbidities”) and reduce the risk of diabetes complications (103) (see Section 10, “Cardiovascular Disease and Risk Management,” Section 11, “Chronic Kidney Disease and Risk Management,” and Section 12, “Retinopathy, Neuropathy, and Foot Care”). In the most recent National Institutes of Health (NIH) Diabetes Prevention Program Outcomes Study (DPPOS) report, prevention of progression from prediabetes to diabetes (104) resulted in lower rates of developing retinopathy and nephropathy (105). Similar impact on diabetes complications was reported with screening, diagnosis, and comprehensive risk factor management in the U.K. Clinical Practice Research Datalink database (103). In that report, progression from prediabetes to diabetes augmented risk of complications.

Approximately one-quarter of people with diabetes in the U.S. and nearly half of Asian and Hispanic American people with diabetes are undiagnosed (106,107). Although screening of asymptomatic individuals to identify those with prediabetes or diabetes might seem reasonable, rigorous clinical trials to prove the effectiveness of such screening have not been conducted and are unlikely to occur. Clinical conditions, such as hypertension, hypertensive pregnancy, and obesity, enhance risk (108). Based on a population estimate, diabetes in people of childbearing age is underdiagnosed (109). Employing a probabilistic model, Peterson et al. (110) demonstrated cost and health benefits of preconception screening.

A large European randomized controlled trial compared the impact of screening for diabetes and intensive multifactorial intervention with that of screening and routine care (111). General practice patients between the ages of 40 and 69 years were screened for diabetes and randomly assigned by practice to intensive treatment of multiple risk factors or routine diabetes care. After 5.3 years of follow-up, CVD risk factors were modestly but significantly improved with intensive treatment compared with routine care, but the incidence of first CVD events or mortality was not significantly different between the groups (26). The excellent care provided to patients in the routine care group and the lack of an unscreened control arm limited the authors’ ability to determine whether screening and early treatment improved outcomes compared with no screening and later treatment after clinical diagnoses. Computer simulation modeling studies suggest that major benefits are likely to accrue from the early diagnosis and treatment of hyperglycemia and cardiovascular risk factors in type 2 diabetes (112); moreover, screening, beginning at age 30 or 45 years and independent of risk factors, may be cost-effective (<$11,000 per quality-adjusted life year gained—2010 modeling data) (113). Cost-effectiveness of screening has been reinforced in cohort studies (114,115).

Additional considerations regarding testing for type 2 diabetes and prediabetes in asymptomatic individuals include the following.

Age

Age is a major risk factor for diabetes. Testing should begin at no later than age 35 years for all people (116). Screening should be considered in adults of any age with overweight or obesity and one or more risk factors for diabetes.

BMI and Ethnicity

In general, BMI ≥25 kg/m2 is a risk factor for diabetes. However, data suggest that the BMI cut point should be lower for the Asian American population (117,118). The BMI cut points fall consistently between 23 and 24 kg/m2 (sensitivity of 80%) for nearly all Asian American subgroups (with levels slightly lower for Japanese American individuals). This makes a rounded cut point of 23 kg/m2 practical. An argument can be made to push the BMI cut point to lower than 23 kg/m2 in favor of increased sensitivity; however, this would lead to an unacceptably low specificity (13.1%). Data from the World Health Organization also suggest that a BMI of ≥23 kg/m2 should be used to define increased risk in Asian American individuals (119). The finding that one-third to one-half of diabetes in Asian American people is undiagnosed suggests that testing is not occurring at lower BMI thresholds (99,120).

Evidence also suggests that other populations may benefit from lower BMI cut points. For example, in a large multiethnic cohort study, for an equivalent incidence rate of diabetes, a BMI of 30 kg/m2 in non-Hispanic White individuals was equivalent to a BMI of 26 kg/m2 in African American individuals (121).

Medications

Certain medications, such as glucocorticoids, thiazide diuretics, some HIV medications (34), and atypical antipsychotics (92), are known to increase the risk of diabetes and should be considered when deciding whether to screen.

HIV

Individuals with HIV are at higher risk for developing prediabetes and diabetes on antiretroviral (ARV) therapies; a screening protocol is therefore recommended (122). The A1C test may underestimate glycemia in people with HIV; it is not recommended for diagnosis and may present challenges for monitoring (35). In those with prediabetes, weight loss through healthy nutrition and physical activity may reduce the progression toward diabetes. Among people with HIV and diabetes, preventive health care using an approach used in people without HIV is critical to reduce the risks of microvascular and macrovascular complications. Diabetes risk is increased with certain PIs and NRTIs. New-onset diabetes is estimated to occur in more than 5% of individuals infected with HIV on PIs, whereas more than 15% may have prediabetes (123).

PIs are associated with insulin resistance and may also lead to apoptosis of pancreatic β-cells. NRTIs also affect fat distribution (both lipohypertrophy and lipoatrophy), which is associated with insulin resistance. For people with HIV and ARV-associated hyperglycemia, it may be appropriate to consider discontinuing the problematic ARV agents if safe and effective alternatives are available (124). Before making ARV substitutions, carefully consider the possible effect on HIV virological control and the potential adverse effects of new ARV agents. In some cases, antihyperglycemic agents may still be necessary.

Testing Interval

The appropriate interval between screening tests is not known (125). The rationale for the 3-year interval is that with this interval, the number of false-positive tests that require confirmatory testing will be reduced, and individuals with false-negative tests will be retested before substantial time elapses and complications develop (125). In especially high-risk individuals, particularly with weight gain, shorter intervals between screening may be useful.

Community Screening

Ideally, screening should be carried out within a health care setting because of the need for follow-up and treatment. Community screening outside a health care setting is generally not recommended because people with positive tests may not seek, or have access to, appropriate follow-up testing and care. However, in specific situations where an adequate referral system is established beforehand for positive tests, community screening may be considered. Community screening may also be poorly targeted; i.e., it may fail to reach the groups most at risk and inappropriately test those at very low risk or even those who have already been diagnosed (126).

Screening in Dental Practices

Because periodontal disease is associated with diabetes, the utility of screening in a dental setting and referral to primary care as a means to improve the diagnosis of prediabetes and diabetes has been explored (127129), with one study estimating that 30% of patients ≥30 years of age seen in general dental practices had dysglycemia (129,130). A similar study in 1,150 dental patients >40 years old in India reported 20.69% and 14.60% meeting criteria for prediabetes and diabetes, respectively, using random blood glucose. Further research is needed to demonstrate the feasibility, effectiveness, and cost-effectiveness of screening in this setting.

Screening and Testing for Prediabetes and Type 2 Diabetes in Children and Adolescents

In the last decade, the incidence and prevalence of type 2 diabetes in children and adolescents has increased dramatically, especially in racial and ethnic minority populations (75). See Table 2.4  for recommendations on risk-based screening for type 2 diabetes or prediabetes in asymptomatic children and adolescents in a clinical setting (36). See Table 2.2  and Table 2.5  for the criteria for the diagnosis of diabetes and prediabetes, respectively, that apply to children, adolescents, and adults. See Section 14, “Children and Adolescents,” for additional information on type 2 diabetes in children and adolescents.

Some studies question the validity of A1C in the pediatric population, especially among certain ethnicities, and suggest OGTT or FPG as more suitable diagnostic tests (131). However, many of these studies do not recognize that diabetes diagnostic criteria are based on long-term health outcomes, and validations are not currently available in the pediatric population (132). The ADA acknowledges the limited data supporting A1C for diagnosing type 2 diabetes in children and adolescents. Although A1C is not recommended for diagnosis of diabetes in children with cystic fibrosis or symptoms suggestive of acute onset of type 1 diabetes, and only A1C assays without interference are appropriate for children with hemoglobinopathies, the ADA continues to recommend A1C and the criteria in Table 2.2  for diagnosis of type 2 diabetes in this cohort to decrease barriers to screening (133,134).

Recommendations

  • 2.16 Annual screening for cystic fibrosis–related diabetes with an oral glucose tolerance test should begin by age 10 years in all people with cystic fibrosis not previously diagnosed with cystic fibrosis–related diabetes. B

  • 2.17 A1C is not recommended as a screening test for cystic fibrosis–related diabetes. B

  • 2.18 People with cystic fibrosis–related diabetes should be treated with insulin to attain individualized glycemic goals. A

  • 2.19 Beginning 5 years after the diagnosis of cystic fibrosis–related diabetes, annual monitoring for complications of diabetes is recommended. E

Cystic fibrosis–related diabetes (CFRD) is the most common comorbidity in people with cystic fibrosis, occurring in about 20% of adolescents and 40–50% of adults (135). Diabetes in this population, compared with individuals with type 1 or type 2 diabetes, is associated with worse nutritional status, more severe inflammatory lung disease, and greater mortality. Insulin insufficiency is the primary defect in CFRD. Genetically determined β-cell function and insulin resistance associated with infection and inflammation may also contribute to the development of CFRD. Milder abnormalities of glucose tolerance are even more common and occur at earlier ages than CFRD. Whether individuals with IGT should be treated with insulin replacement has not currently been determined. Although screening for diabetes before the age of 10 years can identify risk for progression to CFRD in those with abnormal glucose tolerance, no benefit has been established with respect to weight, height, BMI, or lung function. OGTT is the recommended screening test; however, recent publications suggest that an A1C cut point threshold of 5.5% (5.8% in a second study) would detect more than 90% of cases and reduce patient screening burden (136,137). Ongoing studies are underway to validate this approach, and A1C is not recommended for screening (138). Regardless of age, weight loss or failure of expected weight gain is a risk for CFRD and should prompt screening (136,137). The Cystic Fibrosis Foundation Patient Registry (139) evaluated 3,553 people with cystic fibrosis and diagnosed 445 (13%) with CFRD. Early diagnosis and treatment of CFRD was associated with preservation of lung function. The European Cystic Fibrosis Society Patient Registry reported an increase in CFRD with age (increased 10% per decade), genotype, decreased lung function, and female sex (140,141). Continuous glucose monitoring or HOMA of β-cell function (142) may be more sensitive than OGTT to detect risk for progression to CFRD; however, evidence linking these results to long-term outcomes is lacking, and these tests are not recommended for screening outside of the research setting (143).

CFRD mortality has significantly decreased over time, and the gap in mortality between people with cystic fibrosis with and without diabetes has considerably narrowed (144). There are limited clinical trial data on therapy for CFRD. The largest study compared three regimens: premeal insulin aspart, repaglinide, or oral placebo in people with cystic fibrosis and diabetes or abnormal glucose tolerance. Participants all had weight loss in the year preceding treatment; however, in the insulin-treated group, this pattern was reversed, and participants gained 0.39 (± 0.21) BMI units (P = 0.02). The repaglinide-treated group had initial weight gain, but it was not sustained by 6 months. The placebo group continued to lose weight (144). Insulin remains the most widely used therapy for CFRD (145). The primary rationale for the use of insulin in people with CFRD is to induce an anabolic state while promoting macronutrient retention and weight gain.

Additional resources for the clinical management of CFRD can be found in the position statement “Clinical Care Guidelines for Cystic Fibrosis–Related Diabetes: A Position Statement of the American Diabetes Association and a Clinical Practice Guideline of the Cystic Fibrosis Foundation, Endorsed by the Pediatric Endocrine Society” (146) and in the International Society for Pediatric and Adolescent Diabetes 2018 clinical practice consensus guidelines (135).

Recommendations

  • 2.20 After organ transplantation, screening for hyperglycemia should be done. A formal diagnosis of posttransplantation diabetes mellitus is best made once the individual is stable on an immunosuppressive regimen and in the absence of an acute infection. B

  • 2.21 The oral glucose tolerance test is the preferred test to make a diagnosis of posttransplantation diabetes mellitus. B

  • 2.22 Immunosuppressive regimens shown to provide the best outcomes for patient and graft survival should be used, irrespective of posttransplantation diabetes mellitus risk. E

Several terms are used in the literature to describe the presence of diabetes following organ transplantation (147). “New-onset diabetes after transplantation” (NODAT) is one such designation that describes individuals who develop new-onset diabetes following transplant. NODAT excludes people with pretransplant diabetes that was undiagnosed as well as posttransplant hyperglycemia that resolves by the time of discharge (148). Another term, “posttransplantation diabetes mellitus” (PTDM) (148,149), describes the presence of diabetes in the posttransplant setting irrespective of the timing of diabetes onset.

Hyperglycemia is very common during the early posttransplant period, with ∼90% of kidney allograft recipients exhibiting hyperglycemia in the first few weeks following transplant (148151). In most cases, such stress- or steroid-induced hyperglycemia resolves by the time of discharge (151,152). Although the use of immunosuppressive therapies is a major contributor to the development of PTDM, the risks of transplant rejection outweigh the risks of PTDM, and the role of the diabetes care health care professional is to treat hyperglycemia appropriately regardless of the type of immunosuppression (148). Risk factors for PTDM include both general diabetes risks (such as age, family history of diabetes, etc.) as well as transplant-specific factors, such as use of immunosuppressant agents (153155). Whereas posttransplantation hyperglycemia is an important risk factor for subsequent PTDM, a formal diagnosis of PTDM is optimally made once the patient is stable on maintenance mmunosuppression and in the absence of acute infection (151153,156). In a recent study of 152 heart transplant recipients, 38% had PTDM at 1 year. Risk factors for PTDM included elevated BMI, discharge from the hospital on insulin, and glucose values in the 24 h prior to hospital discharge (157). In an Iranian cohort, 19% had PTDM after heart and lung transplant (158). The OGTT is considered the gold-standard test for the diagnosis of PTDM (1 year posttransplant) (148,149,159,160). Pretransplant elevation in hs-CRP was associated with PTDM in the setting of renal transplant (161,162). However, screening people with fasting glucose and/or A1C can identify high-risk individuals requiring further assessment and may reduce the number of overall OGTTs required.

Few randomized controlled studies have reported on the short- and long-term use of antihyperglycemic agents in the setting of PTDM (153,163,164). Most studies have reported that transplant patients with hyperglycemia and PTDM after transplantation have higher rates of rejection, infection, and rehospitalization (151,153,165). Insulin therapy is the agent of choice for the management of hyperglycemia, PTDM, and preexisting diabetes and diabetes in the hospital setting. After discharge, people with preexisting diabetes could go back on their pretransplant regimen if they were in good control before transplantation. Those with previously poor glycemic stability or with persistent hyperglycemia should continue insulin with frequent home glucose monitoring to determine when insulin dose reductions may be needed and when it may be appropriate to switch to noninsulin agents.

No studies to date have established which noninsulin agents are safest or most efficacious in PTDM. The choice of agent is usually made based on the side effect profile of the medication and possible interactions with the patient’s immunosuppression regimen (153). Drug dose adjustments may be required because of decreases in the glomerular filtration rate, a relatively common complication in transplant patients. A small short-term pilot study reported that metformin was safe to use in renal transplant recipients (166), but its safety has not been determined in other types of organ transplant. Thiazolidinediones have been used successfully in people with liver and kidney transplants, but side effects include fluid retention, heart failure, and osteopenia (167,168). Dipeptidyl peptidase 4 inhibitors do not interact with immunosuppressant drugs and have demonstrated safety in small clinical trials (169,170). Well-designed intervention trials examining the efficacy and safety of these and other antihyperglycemic agents in people with PTDM are needed.

Recommendations

  • 2.23 Regardless of current age, all people diagnosed with diabetes in the first 6 months of life should have immediate genetic testing for neonatal diabetes. A

  • 2.24 Children and young adults who do not have typical characteristics of type 1 or type 2 diabetes and who often have a family history of diabetes in successive generations (suggestive of an autosomal dominant pattern of inheritance) should have genetic testing for maturity-onset diabetes of the young. A

  • 2.25 In both instances, consultation with a center specializing in diabetes genetics is recommended to understand the significance of genetic mutations and how best to approach further evaluation, treatment, and genetic counseling. E

Monogenic defects that cause β-cell dysfunction, such as neonatal diabetes and MODY, represent a small fraction of people with diabetes (<5%). Table 2.6  describes the most common causes of monogenic diabetes. For a comprehensive list of causes, see Genetic Diagnosis of Endocrine Disorders (171).

Table 2.6

Most common causes of monogenic diabetes (171)

GeneInheritanceClinical features
MODY HNF1A AD HNF1A-MODY: progressive insulin secretory defect with presentation in adolescence or early adulthood; lowered renal threshold for glucosuria; large rise in 2-h PG level on OGTT (>90 mg/dL [5 mmol/L]); sensitive to sulfonylureas 
 HNF4A AD HNF4A-MODY: progressive insulin secretory defect with presentation in adolescence or early adulthood; may have large birth weight and transient neonatal hypoglycemia; sensitive to sulfonylureas 
 HNF1B AD HNF1B-MODY: developmental renal disease (typically cystic); genitourinary abnormalities; atrophy of the pancreas; hyperuricemia; gout 
 GCK AD GCK-MODY: higher glucose threshold (set point) for glucose-stimulated insulin secretion, causing stable, nonprogressive elevated fasting blood glucose; typically, does not require treatment; microvascular complications are rare; small rise in 2-h PG level on OGTT (<54 mg/dL [3 mmol/L]) 
Neonatal diabetes KCNJ11 AD Permanent or transient: IUGR; possible developmental delay and seizures; responsive to sulfonylureas 
 INS AD Permanent: IUGR; insulin requiring 
 ABCC8 AD Permanent or transient: IUGR; rarely developmental delay; responsive to sulfonylureas 
 6q24 (PLAGL1, HYMA1AD for paternal duplications Transient: IUGR; macroglossia; umbilical hernia; mechanisms include UPD6, paternal duplication, or maternal methylation defect; may be treatable with medications other than insulin 
 GATA6 AD Permanent: pancreatic hypoplasia; cardiac malformations; pancreatic exocrine insufficiency; insulin requiring 
 EIF2AK3 AR Permanent: Wolcott-Rallison syndrome: epiphyseal dysplasia; pancreatic exocrine insufficiency; insulin requiring 
 EIF2B1 AD Permanent diabetes: can be associated with fluctuating liver function (172
 FOXP3 X-linked Permanent: immunodysregulation, polyendocrinopathy, enteropathy X-linked (IPEX) syndrome: autoimmune diabetes, autoimmune thyroid disease, exfoliative dermatitis; insulin requiring 
GeneInheritanceClinical features
MODY HNF1A AD HNF1A-MODY: progressive insulin secretory defect with presentation in adolescence or early adulthood; lowered renal threshold for glucosuria; large rise in 2-h PG level on OGTT (>90 mg/dL [5 mmol/L]); sensitive to sulfonylureas 
 HNF4A AD HNF4A-MODY: progressive insulin secretory defect with presentation in adolescence or early adulthood; may have large birth weight and transient neonatal hypoglycemia; sensitive to sulfonylureas 
 HNF1B AD HNF1B-MODY: developmental renal disease (typically cystic); genitourinary abnormalities; atrophy of the pancreas; hyperuricemia; gout 
 GCK AD GCK-MODY: higher glucose threshold (set point) for glucose-stimulated insulin secretion, causing stable, nonprogressive elevated fasting blood glucose; typically, does not require treatment; microvascular complications are rare; small rise in 2-h PG level on OGTT (<54 mg/dL [3 mmol/L]) 
Neonatal diabetes KCNJ11 AD Permanent or transient: IUGR; possible developmental delay and seizures; responsive to sulfonylureas 
 INS AD Permanent: IUGR; insulin requiring 
 ABCC8 AD Permanent or transient: IUGR; rarely developmental delay; responsive to sulfonylureas 
 6q24 (PLAGL1, HYMA1AD for paternal duplications Transient: IUGR; macroglossia; umbilical hernia; mechanisms include UPD6, paternal duplication, or maternal methylation defect; may be treatable with medications other than insulin 
 GATA6 AD Permanent: pancreatic hypoplasia; cardiac malformations; pancreatic exocrine insufficiency; insulin requiring 
 EIF2AK3 AR Permanent: Wolcott-Rallison syndrome: epiphyseal dysplasia; pancreatic exocrine insufficiency; insulin requiring 
 EIF2B1 AD Permanent diabetes: can be associated with fluctuating liver function (172
 FOXP3 X-linked Permanent: immunodysregulation, polyendocrinopathy, enteropathy X-linked (IPEX) syndrome: autoimmune diabetes, autoimmune thyroid disease, exfoliative dermatitis; insulin requiring 

AD, autosomal dominant; AR, autosomal recessive; IUGR, intrauterine growth restriction; OGTT, oral glucose tolerance test; UPD6, uniparental disomy of chromosome 6; 2-h PG, 2-h plasma glucose.

Neonatal Diabetes

Diabetes occurring under 6 months of age is termed “neonatal” or “congenital” diabetes, and about 80–85% of cases can be found to have an underlying monogenic cause (8,172175). Neonatal diabetes occurs much less often after 6 months of age, whereas autoimmune type 1 diabetes rarely occurs before 6 months of age. Neonatal diabetes can either be transient or permanent. Transient diabetes is most often due to overexpression of genes on chromosome 6q24, is recurrent in about half of cases, and may be treatable with medications other than insulin. Permanent neonatal diabetes is most commonly due to autosomal dominant mutations in the genes encoding the Kir6.2 subunit (KCNJ11) and SUR1 subunit (ABCC8) of the β-cell KATP channel. A recent report details a de novo mutation in EIF2B1 affecting eIF2 signaling associated with permanent neonatal diabetes and hepatic dysfunction, similar to Wolcott-Rallison syndrome but with few severe comorbidities (176). The recent ADA-European Association for the Study of Diabetes type 1 diabetes consensus report recommends that regardless of current age, individuals diagnosed under 6 months of age should have genetic testing (8). Correct diagnosis has critical implications because 30–50% of people with KATP-related neonatal diabetes will exhibit improved blood glucose levels when treated with high-dose oral sulfonylureas instead of insulin. Insulin gene (INS) mutations are the second most common cause of permanent neonatal diabetes, and while intensive insulin management is currently the preferred treatment strategy, there are important genetic counseling considerations, as most of the mutations that cause diabetes are dominantly inherited.

Maturity-Onset Diabetes of the Young

MODY is frequently characterized by onset of hyperglycemia at an early age (classically before age 25 years, although diagnosis may occur at older ages). MODY is characterized by impaired insulin secretion with minimal or no defects in insulin action (in the absence of coexistent obesity). It is inherited in an autosomal dominant pattern with abnormalities in at least 13 genes on different chromosomes identified to date (177). The most commonly reported forms are GCK-MODY (MODY2), HNF1A-MODY (MODY3), and HNF4A-MODY (MODY1).

For individuals with MODY, the treatment implications are considerable and warrant genetic testing (178,179). Clinically, people with GCK-MODY exhibit mild, stable fasting hyperglycemia and do not require antihyperglycemic therapy except commonly during pregnancy. Individuals with HNF1A- or HNF4A-MODY usually respond well to low doses of sulfonylureas, which are considered first-line therapy; in some instances, insulin will be required over time. Mutations or deletions in HNF1B are associated with renal cysts and uterine malformations (renal cysts and diabetes [RCAD] syndrome). Other extremely rare forms of MODY have been reported to involve other transcription factor genes, including PDX1 (IPF1) and NEUROD1.

Diagnosis of Monogenic Diabetes

A diagnosis of one of the three most common forms of MODY, including HFN1A-MODY, GCK-MODY, and HNF4A-MODY, allows for more cost-effective therapy (no therapy for GCK-MODY; sulfonylureas as first-line therapy for HNF1A-MODY and HNF4A-MODY). Additionally, diagnosis can lead to identification of other affected family members. Genetic screening is increasingly available and cost-effective (176,178).

A diagnosis of MODY should be considered in individuals who have atypical diabetes and multiple family members with diabetes not characteristic of type 1 or type 2 diabetes, although admittedly, “atypical diabetes” is becoming increasingly difficult to precisely define in the absence of a definitive set of tests for either type of diabetes (173175,178184). In most cases, the presence of autoantibodies for type 1 diabetes precludes further testing for monogenic diabetes, but the presence of autoantibodies in people with monogenic diabetes has been reported (185). Individuals in whom monogenic diabetes is suspected should be referred to a specialist for further evaluation if available, and consultation can be obtained from several centers. Readily available commercial genetic testing following the criteria listed below now enables a cost-effective (186), often cost-saving, genetic diagnosis that is increasingly supported by health insurance. A biomarker screening pathway, such as the combination of urinary C-peptide/creatinine ratio and antibody screening, may aid in determining who should get genetic testing for MODY (187). It is critical to correctly diagnose one of the monogenic forms of diabetes because these individuals may be incorrectly diagnosed with type 1 or type 2 diabetes, leading to suboptimal, even potentially harmful, treatment plans and delays in diagnosing other family members (188). The correct diagnosis is especially critical for those with GCK-MODY mutations, where multiple studies have shown that no complications ensue in the absence of glucose-lowering therapy (189). The risks of microvascular and macrovascular complications with HNFIA- and HNF4A-MODY are similar to those observed in people with type 1 and type 2 diabetes (190,191). Genetic counseling is recommended to ensure that affected individuals understand the patterns of inheritance and the importance of a correct diagnosis and addressing comprehensive cardiovascular risk.

The diagnosis of monogenic diabetes should be considered in children and adults diagnosed with diabetes in early adulthood with the following findings:

  • Diabetes diagnosed within the first 6 months of life (with occasional cases presenting later, mostly INS and ABCC8 mutations) (172,192)

  • Diabetes without typical features of type 1 or type 2 diabetes (negative diabetes-associated autoantibodies, no obesity, lacking other metabolic features, especially with strong family history of diabetes)

  • Stable, mild fasting hyperglycemia (100–150 mg/dL [5.5–8.5 mmol/L]), stable A1C between 5.6% and 7.6% (between 38 and 60 mmol/mol), especially if no obesity

Pancreatic diabetes includes both structural and functional loss of glucose-normalizing insulin secretion in the context of exocrine pancreatic dysfunction and is commonly misdiagnosed as type 2 diabetes. Hyperglycemia due to general pancreatic dysfunction has been called “type 3c diabetes,” and, more recently, diabetes in the context of disease of the exocrine pancreas has been termed pancreoprivic diabetes (1). The diverse set of etiologies includes pancreatitis (acute and chronic), trauma or pancreatectomy, neoplasia, cystic fibrosis (addressed elsewhere in this chapter), hemochromatosis, fibrocalculous pancreatopathy, rare genetic disorders (193), and idiopathic forms (1); as such, pancreatic diabetes is the preferred umbrella terminology.

Pancreatitis, even a single bout, can lead to postpancreatitis diabetes mellitus (PPDM). Both acute and chronic pancreatitis can lead to PPDM, and the risk is highest with recurrent bouts. A distinguishing feature is concurrent pancreatic exocrine insufficiency (according to the monoclonal fecal elastase 1 test or direct function tests), pathological pancreatic imaging (endoscopic ultrasound, MRI, computed tomography), and absence of type 1 diabetes-associated autoimmunity (194199). There is loss of both insulin and glucagon secretion and often higher-than-expected insulin requirements. Risk for microvascular complications appears to be similar to that of other forms of diabetes. In the context of pancreatectomy, islet autotransplantation can be done to retain insulin secretion (200,201). In some cases, autotransplant can lead to insulin independence. In others, it may decrease insulin requirements (202).

Recommendations

  • 2.26a In individuals who are planning pregnancy, screen those with risk factors B and consider testing all individuals of childbearing potential for undiagnosed diabetes. E

  • 2.26b Before 15 weeks of gestation, test individuals with risk factors B and consider testing all individuals E for undiagnosed diabetes at the first prenatal visit using standard diagnostic criteria if not screened preconception.

  • 2.26c Individuals of childbearing potential identified as having diabetes should be treated as such. A

  • 2.26d Before 15 weeks of gestation, screen for abnormal glucose metabolism to identify individuals who are at higher risk of adverse pregnancy and neonatal outcomes, are more likely to need insulin, and are at high risk of a later gestational diabetes mellitus diagnosis. B Treatment may provide some benefit. E

  • 2.26e Screen for early abnormal glucose metabolism using fasting glucose of 110–125 mg/dL (6.1 mmol/L) or A1C 5.9–6.4% (41–47 mmol/mol). B

  • 2.27 Screen for gestational diabetes mellitus at 24–28 weeks of gestation in pregnant individuals not previously found to have diabetes or high-risk abnormal glucose metabolism detected earlier in the current pregnancy. A

  • 2.28 Screen individuals with gestational diabetes mellitus for prediabetes or diabetes at 4–12 weeks postpartum, using the 75-g oral glucose tolerance test and clinically appropriate nonpregnancy diagnostic criteria. B

  • 2.29 Individuals with a history of gestational diabetes mellitus should have lifelong screening for the development of diabetes or prediabetes at least every 3 years. B

  • 2.30 Individuals with a history of gestational diabetes mellitus found to have prediabetes should receive intensive lifestyle interventions and/or metformin to prevent diabetes. A

Definition

For many years, GDM was defined as any degree of glucose intolerance that was first recognized during pregnancy (86), regardless of the degree of hyperglycemia. This definition facilitated a uniform strategy for detection and classification of GDM, but this definition has serious limitations (203). First, the best available evidence reveals that many cases of GDM represent preexisting hyperglycemia that is detected by routine screening in pregnancy, as routine screening is not widely performed in nonpregnant individuals of reproductive age. It is the severity of hyperglycemia that is clinically important with regard to both short- and long-term maternal and fetal risks.

The ongoing epidemic of obesity and diabetes has led to more type 2 diabetes in people of reproductive age, with an increase in the number of pregnant individuals with undiagnosed type 2 diabetes in early pregnancy (204206). Ideally, undiagnosed diabetes should be identified preconception in individuals with risk factors or in high-risk populations (207212), as the preconception care of people with preexisting diabetes results in lower A1C and reduced risk of birth defects, preterm delivery, perinatal mortality, small-for-gestational-age birth weight, and neonatal intensive care unit admission (213). If individuals are not screened prior to pregnancy, universal early screening at <15 weeks of gestation for undiagnosed diabetes may be considered over selective screening (Table 2.3 ), particularly in populations with high prevalence of risk factors and undiagnosed diabetes in people of childbearing age. Strong racial and ethnic disparities exist in the prevalence of undiagnosed diabetes. Therefore, early screening provides an initial step to identify these health disparities so that they can begin to be addressed (209212). Standard diagnostic criteria for identifying undiagnosed diabetes in early pregnancy are the same as those used in the nonpregnant population (Table 2.2 ). Individuals found to have diabetes by the standard diagnostic criteria used outside of pregnancy should be classified as having diabetes complicating pregnancy (most often type 2 diabetes, rarely type 1 diabetes or monogenic diabetes) and managed accordingly.

Early abnormal glucose metabolism, defined as fasting glucose threshold of 110 mg/dL (6.1 mmol/L) or an A1C of 5.9% (39 mmol/mol), may identify individuals who are at higher risk of adverse pregnancy and neonatal outcomes (preeclampsia, macrosomia, shoulder dystocia, perinatal death), are more likely to need insulin treatment, and are at high risk of a later GDM diagnosis (214220). An A1C threshold of 5.7% has not been shown to be associated with adverse perinatal outcomes (221,222).

If early screening is negative, individuals should be rescreened for GDM between 24 and 28 weeks of gestation (see Section 15, “Management of Diabetes in Pregnancy”). The International Association of the Diabetes and Pregnancy Study Groups (IADPSG) GDM diagnostic criteria for the 75-g OGTT, as well as the GDM screening and diagnostic criteria used in the two-step approach, were not derived from data in the first half of pregnancy and should not be used for early screening (223). To date, most randomized controlled trials of treatment of early abnormal glucose metabolism have been underpowered for outcomes. Therefore, the benefits of treatment for early abnormal glucose metabolism remain uncertain. Nutrition counseling and periodic “block” testing of glucose levels weekly to identify individuals with high glucose levels are suggested. Testing frequency may proceed to daily, and treatment may be intensified, if the fasting glucose is predominantly >110 mg/dL prior to 18 weeks of gestation.

Both the fasting glucose and A1C are low-cost tests. An advantage of the A1C is its convenience, as it can be added to the prenatal laboratories and does not require an early-morning fasting appointment. Disadvantages include inaccuracies in the presence of increased red blood cell turnover and hemoglobinopathies (usually reads lower) and higher values with anemia and reduced red blood cell turnover (224). A1C is not reliable to screen for GDM or for preexisting diabetes at 15 weeks of gestation or later. See Recommendation 2.3 above.

GDM is often indicative of underlying β-cell dysfunction (225), which confers marked increased risk for later development of diabetes, generally but not always type 2 diabetes, in the mother after delivery (226,227). As effective prevention interventions are available (228,229), individuals diagnosed with GDM should receive lifelong screening for prediabetes to allow interventions to reduce diabetes risk and for type 2 diabetes to allow treatment at the earliest possible time (230).

Diagnosis

GDM carries risks for the mother, fetus, and neonate. The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study (231), a large-scale multinational cohort study completed by more than 23,000 pregnant individuals, demonstrated that risk of adverse maternal, fetal, and neonatal outcomes continuously increased as a function of maternal glycemia at 24–28 weeks of gestation, even within ranges previously considered normal for pregnancy. For most complications, there was no threshold for risk. These results have led to careful reconsideration of the diagnostic criteria for GDM.

GDM diagnosis (Table 2.7 ) can be accomplished with either of two strategies:

  1. The “one-step” 75-g OGTT derived from the IADPSG criteria, or

  2. The older “two-step” approach with a 50-g (nonfasting) screen followed by a 100-g OGTT for those who screen positive based on the work of Carpenter-Coustan’s interpretation of the older O’Sullivan and Mahan (232) criteria.

Table 2.7

Screening for and diagnosis of GDM

One-step strategy 
Perform a 75-g OGTT, with plasma glucose measurement when patient is fasting and at 1 and 2 h, at 24–28 weeks of gestation in individuals not previously diagnosed with diabetes. 
The OGTT should be performed in the morning after an overnight fast of at least 8 h. 
The diagnosis of GDM is made when any of the following plasma glucose values are met or exceeded: 
 • Fasting: 92 mg/dL (5.1 mmol/L) 
 • 1 h: 180 mg/dL (10.0 mmol/L) 
 • 2 h: 153 mg/dL (8.5 mmol/L) 
Two-step strategy 
Step 1: Perform a 50-g GLT (nonfasting), with plasma glucose measurement at 1 h, at 24–28 weeks of gestation in individuals not previously diagnosed with diabetes. 
If the plasma glucose level measured 1 h after the load is ≥130, 135, or 140 mg/dL (7.2, 7.5, or 7.8 mmol/L, respectively), proceed to a 100-g OGTT. 
Step 2: The 100-g OGTT should be performed when the patient is fasting. 
The diagnosis of GDM is made when at least two* of the following four plasma glucose levels (measured fasting and at 1, 2, and 3 h during OGTT) are met or exceeded (Carpenter-Coustan criteria [251]): 
 • Fasting: 95 mg/dL (5.3 mmol/L) 
 • 1 h: 180 mg/dL (10.0 mmol/L) 
 • 2 h: 155 mg/dL (8.6 mmol/L) 
 • 3 h: 140 mg/dL (7.8 mmol/L) 
One-step strategy 
Perform a 75-g OGTT, with plasma glucose measurement when patient is fasting and at 1 and 2 h, at 24–28 weeks of gestation in individuals not previously diagnosed with diabetes. 
The OGTT should be performed in the morning after an overnight fast of at least 8 h. 
The diagnosis of GDM is made when any of the following plasma glucose values are met or exceeded: 
 • Fasting: 92 mg/dL (5.1 mmol/L) 
 • 1 h: 180 mg/dL (10.0 mmol/L) 
 • 2 h: 153 mg/dL (8.5 mmol/L) 
Two-step strategy 
Step 1: Perform a 50-g GLT (nonfasting), with plasma glucose measurement at 1 h, at 24–28 weeks of gestation in individuals not previously diagnosed with diabetes. 
If the plasma glucose level measured 1 h after the load is ≥130, 135, or 140 mg/dL (7.2, 7.5, or 7.8 mmol/L, respectively), proceed to a 100-g OGTT. 
Step 2: The 100-g OGTT should be performed when the patient is fasting. 
The diagnosis of GDM is made when at least two* of the following four plasma glucose levels (measured fasting and at 1, 2, and 3 h during OGTT) are met or exceeded (Carpenter-Coustan criteria [251]): 
 • Fasting: 95 mg/dL (5.3 mmol/L) 
 • 1 h: 180 mg/dL (10.0 mmol/L) 
 • 2 h: 155 mg/dL (8.6 mmol/L) 
 • 3 h: 140 mg/dL (7.8 mmol/L) 

GDM, gestational diabetes mellitus; GLT, glucose load test; OGTT, oral glucose tolerance test.

*

American College of Obstetricians and Gynecologists notes that one elevated value can be used for diagnosis (247).

Different diagnostic criteria will identify different degrees of maternal hyperglycemia and maternal/fetal risk, leading some experts to debate, and disagree on, optimal strategies for the diagnosis of GDM.

One-Step Strategy

The IADPSG defined diagnostic cut points for GDM as the average fasting, 1-h, and 2-h PG values during a 75-g OGTT in individuals at 24–28 weeks of gestation who participated in the HAPO study at which odds for adverse outcomes reached 1.75 times the estimated odds of these outcomes at the mean fasting, 1-h, and 2-h PG levels of the study population. This one-step strategy was anticipated to significantly increase the incidence of GDM (from 5–6% to 15–20%), primarily because only one abnormal value, not two, became sufficient to make the diagnosis (233). Many regional studies have investigated the impact of adopting the IADPSG criteria on prevalence and have seen a roughly one- to threefold increase (234). The anticipated increase in the incidence of GDM could have a substantial impact on costs and medical infrastructure needs and has the potential to “medicalize” pregnancies previously categorized as normal. A recent follow-up study of individuals participating in a blinded study of pregnancy OGTTs found that 11 years after their pregnancies, individuals who would have been diagnosed with GDM by the one-step approach, as compared with those without, were at 3.4-fold higher risk of developing prediabetes and type 2 diabetes and had children with a higher risk of obesity and increased body fat, suggesting that the larger group of individuals identified by the one-step approach would benefit from the increased screening for diabetes and prediabetes that would accompany a history of GDM (235,236). The ADA recommends the IADPSG diagnostic criteria with the intent of optimizing gestational outcomes because these criteria are the only ones based on pregnancy outcomes rather than end points such as prediction of subsequent maternal diabetes.

The expected benefits of using IADPSG criteria to the offspring are inferred from intervention trials that focused on individuals with lower levels of hyperglycemia than identified using older GDM diagnostic criteria. Those trials found modest benefits including reduced rates of large-for-gestational-age births and preeclampsia (237,238). It is important to note that 80–90% of participants being treated for mild GDM in these two randomized controlled trials could be managed with lifestyle therapy alone. The OGTT glucose cutoffs in these two trials overlapped the thresholds recommended by the IADPSG, and in one trial (238), the 2-h PG threshold (140 mg/dL [7.8 mmol/L]) was lower than the cutoff recommended by the IADPSG (153 mg/dL [8.5 mmol/L]).

No randomized controlled trials of treating versus not treating GDM diagnosed by the IADPSG criteria but not the Carpenter-Coustan criteria have been published to date. However, a recent randomized trial of testing for GDM at 24–28 weeks of gestation by the one-step method using IADPSG criteria versus the two-step method using a 1-h 50-g glucose loading test (GLT) and, if positive, a 3-h OGTT by Carpenter-Coustan criteria identified twice as many individuals with GDM using the one-step method compared with the two-step method. Despite treating more individuals for GDM using the one-step method, there was no difference in pregnancy and perinatal complications (239). However, concerns have been raised about sample size estimates and unanticipated suboptimal engagement with the protocol with regard to screening and treatment. For example, in the two-step group, 165 participants who did not get counted as having GDM were treated for isolated elevated fasting glucose >95 mg/dL (240). The high prevalence of prediabetes in people of childbearing age may support the more inclusive IADPSG criteria. NHANES data demonstrate a 21.5% prevalence of prediabetes in people of reproductive age 20–44 years, which is comparable to or higher than the prevalence of GDM diagnosed by the one-step method (241).

The one-step method identifies the long-term risks of maternal prediabetes and diabetes and offspring abnormal glucose metabolism and adiposity. Post hoc GDM in individuals diagnosed by the one-step method in the HAPO cohort was associated with higher prevalence of IGT; higher 30-min, 1-h, and 2-h glucoses during the OGTT; and reduced insulin sensitivity and oral disposition index in their offspring at 10–14 years of age compared with offspring of mothers without GDM. Associations of mother’s fasting, 1-h, and 2-h values on the 75-g OGTT were continuous with a comprehensive panel of offspring metabolic outcomes (236,242). In addition, HAPO Follow-up Study (HAPO FUS) data demonstrate that neonatal adiposity and fetal hyperinsulinemia (cord C-peptide), both higher across the continuum of maternal hyperglycemia, are mediators of childhood body fat (243).

Data are lacking on how the treatment of mother’s hyperglycemia in pregnancy affects her offspring’s risk for obesity, diabetes, and other metabolic disorders. Additional well-designed clinical studies are needed to determine the optimal intensity of monitoring and treatment of individuals with GDM diagnosed by the one-step strategy (244,245).

Two-Step Strategy

In 2013, the NIH convened a consensus development conference to consider diagnostic criteria for diagnosing GDM (246). The 15-member panel had representatives from obstetrics and gynecology, maternal-fetal medicine, pediatrics, diabetes research, biostatistics, and other related fields. The panel recommended a two-step approach to screening that used a 1-h 50-g GLT followed by a 3-h 100-g OGTT for those who screened positive. The American College of Obstetricians and Gynecologists (ACOG) recommends any of the commonly used thresholds of 130, 135, or 140 mg/dL for the 1-h 50-g GLT (247). Updated from 2014, a 2021 U.S. Preventive Services Task Force systematic review continues to conclude that one-step versus two-step screening is associated with increased likelihood of GDM (11.5% vs. 4.9%) but without improved health outcomes. It reports that the oral glucose challenge test using 140 or 135 mg/dL thresholds had sensitivities of 82% and 93% and specificities of 82% and 79%, respectively, against Carpenter-Coustan criteria. Fasting plasma glucose cutoffs of 85 mg/dL or 90 mg/dL had sensitivities of 88% and 81% and specificities of 73% and 82%, respectively, against Carpenter-Coustan criteria (248). The use of A1C at 24–28 weeks of gestation as a screening test for GDM does not function as well as the GLT (249).

Key factors cited by the NIH panel in their decision-making process were the lack of clinical trial data demonstrating the benefits of the one-step strategy and the potential negative consequences of identifying a large group of individuals with GDM, including medicalization of pregnancy with increased health care utilization and costs. Moreover, screening with a 50-g GLT does not require fasting and therefore is easier to accomplish for many individuals. Treatment of higher-threshold maternal hyperglycemia, as identified by the two-step approach, reduces rates of neonatal macrosomia, large-for-gestational-age births (250), and shoulder dystocia without increasing small-for-gestational-age births. ACOG currently supports the two-step approach but notes that one elevated value, as opposed to two, may be used for the diagnosis of GDM (247). If this approach is implemented, the incidence of GDM by the two-step strategy will likely increase markedly. ACOG recommends either of two sets of diagnostic thresholds for the 3-h 100-g OGTT–Carpenter-Coustan or National Diabetes Data Group (251,252). Each is based on different mathematical conversions of the original recommended thresholds by O’Sullivan and Mahan (232), which used whole blood and nonenzymatic methods for glucose determination. A secondary analysis of data from a randomized clinical trial of identification and treatment of mild GDM (253) demonstrated that treatment was similarly beneficial in people meeting only the lower thresholds per Carpenter-Coustan (251) and in those meeting only the higher thresholds per National Diabetes Data Group (252). If the two-step approach is used, it would appear advantageous to use the Carpenter-Coustan lower diagnostic thresholds, as shown in step 2 in Table 2.7 .

Future Considerations

The conflicting recommendations from expert groups underscore the fact that there are data to support each strategy. A systematic review of economic evaluations of GDM screening found that the one-step method identified more cases of GDM and was more likely to be cost-effective than the two-step method (254). The decision of which strategy to implement must therefore be made based on the relative values placed on factors that have yet to be measured (e.g., willingness to change practice based on correlation studies rather than intervention trial results, available infrastructure, and importance of cost considerations).

The IADPSG criteria (“one-step strategy”) have been adopted internationally as the preferred approach. Data comparing population-wide outcomes with one-step versus two-step approaches have been inconsistent to date (239,255257). In addition, pregnancies complicated by GDM per the IADPSG criteria, but not recognized as such, have outcomes comparable to pregnancies with diagnosed GDM by the more stringent two-step criteria (258,259). There remains strong consensus that establishing a uniform approach to diagnosing GDM will benefit patients, caregivers, and policymakers. Longer-term outcome studies are currently underway.

Disclosure information for each author is available at https://doi.org/10.2337/dc23-SDIS.

Suggested citation: ElSayed NA, Aleppo G, Aroda VR, et al., American Diabetes Association. 2. Classification and diagnosis of diabetes: Standards of Care in Diabetes—2023. Diabetes Care 2023;46(Suppl. 1):S19–S40

1.
American Diabetes Association
.
Diagnosis and classification of diabetes mellitus
.
Diabetes Care
2014
;
37
(
Suppl. 1
):
S81
S90
2.
Rewers
A
,
Dong
F
,
Slover
RH
,
Klingensmith
GJ
,
Rewers
M
.
Incidence of diabetic ketoacidosis at diagnosis of type 1 diabetes in Colorado youth, 1998-2012
.
JAMA
2015
;
313
:
1570
1572
3.
Alonso
GT
,
Coakley
A
,
Pyle
L
,
Manseau
K
,
Thomas
S
,
Rewers
A
.
Diabetic ketoacidosis at diagnosis of type 1 diabetes in Colorado children, 2010–2017
.
Diabetes Care
2020
;
43
:
117
121
4.
Jensen
ET
,
Stafford
JM
,
Saydah
S
, et al
.
Increase in prevalence of diabetic ketoacidosis at diagnosis among youth with type 1 diabetes: the SEARCH for Diabetes in Youth Study
.
Diabetes Care
2021
;
44
:
1573
1578
5.
Humphreys
A
,
Bravis
V
,
Kaur
A
, et al
.
Individual and diabetes presentation characteristics associated with partial remission status in children and adults evaluated up to 12 months following diagnosis of type 1 diabetes: an ADDRESS-2 (After Diagnosis Diabetes Research Support System-2) study analysis
.
Diabetes Res Clin Pract
2019
;
155
:
107789
6.
Thomas
NJ
,
Lynam
AL
,
Hill
AV
, et al
.
Type 1 diabetes defined by severe insulin deficiency occurs after 30 years of age and is commonly treated as type 2 diabetes
.
Diabetologia
2019
;
62
:
1167
1172
7.
Hope
SV
,
Wienand-Barnett
S
,
Shepherd
M
, et al
.
Practical Classification Guidelines for Diabetes in patients treated with insulin: a cross-sectional study of the accuracy of diabetes diagnosis
.
Br J Gen Pract
2016
;
66
:
e315
e322
8.
Holt
RIG
,
DeVries
JH
,
Hess-Fischl
A
, et al
.
The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)
.
Diabetes Care
2021
;
44
:
2589
2625
9.
Zhong
VW
,
Juhaeri
J
,
Mayer-Davis
EJ
.
Trends in hospital admission for diabetic ketoacidosis in adults with type 1 and type 2 diabetes in England, 1998–2013: a retrospective cohort study
.
Diabetes Care
2018
;
41
:
1870
1877
10.
Lawrence
JM
,
Slezak
JM
,
Quesenberry
C
, et al
.
Incidence and predictors of type 1 diabetes among younger adults aged 20–45 years: the Diabetes in Young Adults (DiYA) study
.
Diabetes Res Clin Pract
2021
;
171
:
108624
11.
Newton
CA
,
Raskin
P
.
Diabetic ketoacidosis in type 1 and type 2 diabetes mellitus: clinical and biochemical differences
.
Arch Intern Med
2004
;
164
:
1925
1931
12.
Skyler
JS
,
Bakris
GL
,
Bonifacio
E
, et al
.
Differentiation of diabetes by pathophysiology, natural history, and prognosis
.
Diabetes
2017
;
66
:
241
255
13.
Lynam
AL
,
Dennis
JM
,
Owen
KR
, et al
.
Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults
.
Diagn Progn Res
2020
;
4
:
6
14.
Ziegler
AG
,
Rewers
M
,
Simell
O
, et al
.
Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children
.
JAMA
2013
;
309
:
2473
2479
15.
Insel
RA
,
Dunne
JL
,
Atkinson
MA
, et al
.
Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association
.
Diabetes Care
2015
;
38
:
1964
1974
16.
Zhu
Y
,
Qian
L
,
Liu
Q
, et al
.
Glutamic acid decarboxylase autoantibody detection by electrochemiluminescence assay identifies latent autoimmune diabetes in adults with poor islet function
.
Diabetes Metab J
2020
;
44
:
260
266
17.
Lynam
A
,
McDonald
T
,
Hill
A
, et al
.
Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18–50 years
.
BMJ Open
2019
;
9
:
e031586
18.
Chung
WK
,
Erion
K
,
Florez
JC
, et al
.
Precision medicine in diabetes: a consensus report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)
.
Diabetes Care
2020
;
43
:
1617
1635
19.
Gale
EA
.
Declassifying diabetes
.
Diabetologia
2006
;
49
:
1989
1995
20.
Schwartz
SS
,
Epstein
S
,
Corkey
BE
,
Grant
SFA
,
Gavin
JR
3rd
,
Aguilar
RB
.
The time is right for a new classification system for diabetes: rationale and implications of the β-cell–centric classification schema
.
Diabetes Care
2016
;
39
:
179
186
21.
International Expert Committee
.
International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes
.
Diabetes Care
2009
;
32
:
1327
1334
22.
Knowler
WC
,
Barrett-Connor
E
,
Fowler
SE
, et al.;
Diabetes Prevention Program Research Group
.
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin
.
N Engl J Med
2002
;
346
:
393
403
23.
Tuomilehto
J
,
Lindström
J
,
Eriksson
JG
, et al.;
Finnish Diabetes Prevention Study Group
.
Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance
.
N Engl J Med
2001
;
344
:
1343
1350
24.
Chadha
C
,
Pittas
AG
,
Lary
CW
, et al.;
D2d Research Group
.
Reproducibility of a prediabetes classification in a contemporary population
.
Metabol Open
2020
;
6
:
100031
25.
Expert Committee on the Diagnosis and Classification of Diabetes Mellitus
.
Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus
.
Diabetes Care
1997
;
20
:
1183
1197
26.
Expert Committee on the Diagnosis and Classification of Diabetes Mellitus
.
Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus
.
Diabetes Care
2003
;
26
(
Suppl. 1
):
S5
S20
27.
Meijnikman
AS
,
De Block
CEM
,
Dirinck
E
, et al
.
Not performing an OGTT results in significant underdiagnosis of (pre)diabetes in a high risk adult Caucasian population
.
Int J Obes
2017
;
41
:
1615
1620
28.
Gonzalez
A
,
Deng
Y
,
Lane
AN
, et al
.
Impact of mismatches in HbA1c vs glucose values on the diagnostic classification of diabetes and prediabetes
.
Diabet Med
2020
;
37
:
689
696
29.
Lenters-Westra
E
,
Slingerland
RJ
.
Six of eight hemoglobin A1c point-of-care instruments do not meet the general accepted analytical performance criteria
.
Clin Chem
2010
;
56
:
44
52
30.
Hirst
JA
,
McLellan
JH
,
Price
CP
, et al
.
Performance of point-of-care HbA1c test devices: implications for use in clinical practice—a systematic review and meta-analysis
.
Clin Chem Lab Med
2017
;
55
:
167
180
31.
Nathan
DM
,
Griffin
A
,
Perez
FM
,
Basque
E
,
Do
L
,
Steiner
B
.
Accuracy of a point-of-care hemoglobinA1c assay
.
J Diabetes Sci Technol
2019
;
13
:
1149
1153
32.
Centers for Medicare & Medicaid Services
.
CLIA Brochures
.
33.
Cowie
CC
,
Rust
KF
,
Byrd-Holt
DD
, et al
.
Prevalence of diabetes and high risk for diabetes using A1C criteria in the U.S. population in 1988–2006
.
Diabetes Care
2010
;
33
:
562
568
34.
Eckhardt
BJ
,
Holzman
RS
,
Kwan
CK
,
Baghdadi
J
,
Aberg
JA
.
Glycated hemoglobin A1c as screening for diabetes mellitus in HIV-infected individuals
.
AIDS Patient Care STDS
2012
;
26
:
197
201
35.
Kim
PS
,
Woods
C
,
Georgoff
P
, et al
.
A1C underestimates glycemia in HIV infection
.
Diabetes Care
2009
;
32
:
1591
1593
36.
Arslanian
S
,
Bacha
F
,
Grey
M
,
Marcus
MD
,
White
NH
,
Zeitler
P
.
Evaluation and management of youth-onset type 2 diabetes: a position statement by the American Diabetes Association
.
Diabetes Care
2018
;
41
:
2648
2668
37.
Lacy
ME
,
Wellenius
GA
,
Sumner
AE
,
Correa
A
,
Carnethon
MR
,
Liem
RI
, et al
.
Association of sickle cell trait with hemoglobin A1c in African Americans
.
JAMA
.
2017
07
;
317
(
5
):
507
515
.
38.
Wheeler
E
,
Leong
A
,
Liu
CT
, et al.;
EPIC-CVD Consortium
;
EPIC-InterAct Consortium
;
Lifelines Cohort Study
.
Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: a transethnic genome-wide meta-analysis
.
PLoS Med
2017
;
14
:
e1002383
39.
Kweka
B
,
Lyimo
E
,
Jeremiah
K
, et al
.
Influence of hemoglobinopathies and glucose-6-phosphate dehydrogenase deficiency on diagnosis of diabetes by HbA1c among Tanzanian adults with and without HIV: A cross-sectional study
.
PLoS One
2020
;
15
:
e0244782
40.
Ziemer
DC
,
Kolm
P
,
Weintraub
WS
, et al
.
Glucose-independent, black-white differences in hemoglobin A1c levels: a cross-sectional analysis of 2 studies
.
Ann Intern Med
2010
;
152
:
770
777
41.
Kumar
PR
,
Bhansali
A
,
Ravikiran
M
, et al
.
Utility of glycated hemoglobin in diagnosing type 2 diabetes mellitus: a community-based study
.
J Clin Endocrinol Metab
2010
;
95
:
2832
2835
42.
Herman
WH
.
Are there clinical implications of racial differences in HbA1c? Yes, to not consider can do great harm!
Diabetes Care
2016
;
39
:
1458
1461
43.
Herman
WH
,
Ma
Y
,
Uwaifo
G
, et al.;
Diabetes Prevention Program Research Group
.
Differences in A1C by race and ethnicity among patients with impaired glucose tolerance in the Diabetes Prevention Program
.
Diabetes Care
2007
;
30
:
2453
2457
44.
Selvin
E
,
Steffes
MW
,
Ballantyne
CM
,
Hoogeveen
RC
,
Coresh
J
,
Brancati
FL
.
Racial differences in glycemic markers: a cross-sectional analysis of community-based data
.
Ann Intern Med
2011
;
154
:
303
309
45.
Herman
WH
,
Dungan
KM
,
Wolffenbuttel
BHR
, et al
.
Racial and ethnic differences in mean plasma glucose, hemoglobin A1c, and 1,5-anhydroglucitol in over 2000 patients with type 2 diabetes
.
J Clin Endocrinol Metab
2009
;
94
:
1689
1694
46.
Bergenstal
RM
,
Gal
RL
,
Connor
CG
, et al.;
T1D Exchange Racial Differences Study Group
.
Racial differences in the relationship of glucose concentrations and hemoglobin A1c levels
.
Ann Intern Med
2017
;
167
:
95
102
47.
Khosla
L
,
Bhat
S
,
Fullington
LA
,
Horlyck-Romanovsky
MF
.
HbA1c performance in African descent populations in the United States with normal glucose tolerance, prediabetes, or diabetes: a scoping review
.
Prev Chronic Dis
2021
;
18
:
E22
48.
Selvin
E
.
Are there clinical implications of racial differences in HbA1c? A difference, to be a difference, must make a difference
.
Diabetes Care
2016
;
39
:
1462
1467
49.
Huang
SH
,
Huang
PJ
,
Li
JY
,
Su
YD
,
Lu
CC
,
Shih
CL
.
Hemoglobin A1c levels associated with age and gender in Taiwanese adults without prior diagnosis with diabetes
.
Int J Environ Res Public Health
2021
;
18
:
3390
50.
Paterson
AD
.
HbA1c for type 2 diabetes diagnosis in Africans and African Americans: personalized medicine NOW!
PLoS Med
2017
;
14
:
e1002384
51.
Cappellini
MD
,
Fiorelli
G
.
Glucose-6-phosphate dehydrogenase deficiency
.
Lancet
2008
;
371
:
64
74
52.
Picón
MJ
,
Murri
M
,
Muñoz
A
,
Fernández-García
JC
,
Gomez-Huelgas
R
,
Tinahones
FJ
.
Hemoglobin A1c versus oral glucose tolerance test in postpartum diabetes screening
.
Diabetes Care
2012
;
35
:
1648
1653
53.
Göbl
CS
,
Bozkurt
L
,
Yarragudi
R
,
Tura
A
,
Pacini
G
,
Kautzky-Willer
A
.
Is early postpartum HbA1c an appropriate risk predictor after pregnancy with gestational diabetes mellitus?
Acta Diabetol
2014
;
51
:
715
722
54.
Megia
A
,
Näf
S
,
Herranz
L
, et al
.
The usefulness of HbA1c in postpartum reclassification of gestational diabetes
.
BJOG
2012
;
119
:
891
894
55.
Welsh
KJ
,
Kirkman
MS
,
Sacks
DB
.
Role of glycated proteins in the diagnosis and management of diabetes: research gaps and future directions
.
Diabetes Care
2016
;
39
:
1299
1306
56.
Kim
C
,
Bullard
KM
,
Herman
WH
,
Beckles
GL
.
Association between iron deficiency and A1C levels among adults without diabetes in the National Health and Nutrition Examination Survey, 1999–2006
.
Diabetes Care
2010
;
33
:
780
785
57.
Selvin
E
,
Wang
D
,
Matsushita
K
,
Grams
ME
,
Coresh
J
.
Prognostic implications of single-sample confirmatory testing for undiagnosed diabetes: a prospective cohort study
.
Ann Intern Med
2018
;
169
:
156
164
.
58.
Klein
KR
,
Walker
CP
,
McFerren
AL
,
Huffman
H
,
Frohlich
F
,
Buse
JB
.
Carbohydrate intake prior to oral glucose tolerance testing
.
J Endocr Soc
2021
;
5
:
bvab049
59.
CoNN JW
.
Interpretation of the glucose tolerance test. The necessity of a standard preparatory diet
.
Am J Med Sci
1940
;
199
:
555
564
60.
Wilkerson
HL
,
Butler
FK
,
Francis
JO
.
The effect of prior carbohydrate intake on the oral glucose tolerance test
.
Diabetes
1960
;
9
:
386
391
61.
Ziegler
AG
;
BABYDIAB-BABYDIET Study Group
.
Age-related islet autoantibody incidence in offspring of patients with type 1 diabetes
.
Diabetologia
2012
;
55
:
1937
1943
62.
Parikka
V
,
Näntö-Salonen
K
,
Saarinen
M
, et al
.
Early seroconversion and rapidly increasing autoantibody concentrations predict prepubertal manifestation of type 1 diabetes in children at genetic risk
.
Diabetologia
2012
;
55
:
1926
1936
63.
Steck
AK
,
Vehik
K
,
Bonifacio
E
, et al.;
TEDDY Study Group
.
Predictors of Progression From the Appearance of Islet Autoantibodies to Early Childhood Diabetes: The Environmental Determinants of Diabetes in the Young (TEDDY)
.
Diabetes Care
2015
;
38
:
808
813
64.
McKeigue
PM
,
Spiliopoulou
A
,
McGurnaghan
S
,
Colombo
M
,
Blackbourn
L
,
McDonald
TJ
, et al
.
Persistent C-peptide secretion in type 1 diabetes and its relationship to the genetic architecture of diabetes
.
BMC Med
2019
;
17
:
165
65.
Bogun
MM
,
Bundy
BN
,
Goland
RS
,
Greenbaum
CJ
.
C-peptide levels in subjects followed longitudinally before and after type 1 diabetes diagnosis in TrialNet
.
Diabetes Care
2020
;
43
:
1836
1842
66.
Greenbaum
CJ
,
Beam
CA
,
Boulware
D
, et al.;
Type 1 Diabetes TrialNet Study Group
.
Fall in C-peptide during first 2 years from diagnosis: evidence of at least two distinct phases from composite Type 1 Diabetes TrialNet data
.
Diabetes
2012
;
61
:
2066
2073
67.
Mishra
R
,
Hodge
KM
,
Cousminer
DL
,
Leslie
RD
,
Grant
SFA
.
A global perspective of latent autoimmune diabetes in adults
.
Trends Endocrinol Metab
2018
;
29
:
638
650
68.
Buzzetti
R
,
Zampetti
S
,
Maddaloni
E
.
Adult-onset autoimmune diabetes: current knowledge and implications for management
.
Nat Rev Endocrinol
2017
;
13
:
674
686
69.
Ben-Skowronek
I
.
IPEX syndrome: genetics and treatment options
.
Genes (Basel)
2021
;
12
:
323
70.
Frommer
L
,
Kahaly
GJ
.
Autoimmune polyendocrinopathy
.
J Clin Endocrinol Metab
2019
;
104
:
4769
4782
71.
Smith
CJ
,
Almodallal
Y
,
Jatoi
A
.
Rare adverse events with programmed death-1 and programmed death-1 ligand inhibitors: justification and rationale for a systematic review
.
Curr Oncol Rep
2021
;
23
:
86
72.
Zhao
Z
,
Wang
X
,
Bao
XQ
,
Ning
J
,
Shang
M
,
Zhang
D
.
Autoimmune polyendocrine syndrome induced by immune checkpoint inhibitors: a systematic review
.
Cancer Immunol Immunother
2021
;
70
:
1527
1540
73.
Stamatouli
AM
,
Quandt
Z
,
Perdigoto
AL
, et al
.
Collateral damage: insulin-dependent diabetes induced with checkpoint inhibitors
.
Diabetes
2018
;
67
:
1471
1480
74.
Balasubramanyam
A
,
Nalini
R
,
Hampe
CS
,
Maldonado
M
.
Syndromes of ketosis-prone diabetes mellitus
.
Endocr Rev
2008
;
29
:
292
302
75.
Dabelea
D
,
Mayer-Davis
EJ
,
Saydah
S
, et al.;
SEARCH for Diabetes in Youth Study
.
Prevalence of type 1 and type 2 diabetes among children and adolescents from 2001 to 2009
.
JAMA
2014
;
311
:
1778
1786
76.
McQueen
RB
,
Geno Rasmussen
C
,
Waugh
K
, et al
.
Cost and cost-effectiveness of large-scale screening for type 1 diabetes in Colorado
.
Diabetes Care
2020
;
43
:
1496
1503
77.
Ziegler
AG
,
Kick
K
,
Bonifacio
E
, et al.;
Fr1da Study Group
.
Yield of a public health screening of children for islet autoantibodies in Bavaria, Germany
.
JAMA
2020
;
323
:
339
351
78.
Sosenko
JM
,
Skyler
JS
,
Palmer
JP
, et al.;
Type 1 Diabetes TrialNet Study Group
;
Diabetes Prevention Trial-Type 1 Study Group
.
The prediction of type 1 diabetes by multiple autoantibody levels and their incorporation into an autoantibody risk score in relatives of type 1 diabetic patients
.
Diabetes Care
2013
;
36
:
2615
2620
79.
Orban
T
,
Sosenko
JM
,
Cuthbertson
D
, et al.;
Diabetes Prevention Trial-Type 1 Study Group
.
Pancreatic islet autoantibodies as predictors of type 1 diabetes in the Diabetes Prevention Trial-Type 1
.
Diabetes Care
2009
;
32
:
2269
2274
80.
Jacobsen
LM
,
Larsson
HE
,
Tamura
RN
, et al.;
TEDDY Study Group
.
Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children
.
Pediatr Diabetes
2019
;
20
:
263
270
81.
Barker
JM
,
Goehrig
SH
,
Barriga
K
, et al.;
DAISY study
.
Clinical characteristics of children diagnosed with type 1 diabetes through intensive screening and follow-up
.
Diabetes Care
2004
;
27
:
1399
1404
82.
Elding Larsson
H
,
Vehik
K
,
Gesualdo
P
, et al.;
TEDDY Study Group
.
Children followed in the TEDDY study are diagnosed with type 1 diabetes at an early stage of disease
.
Pediatr Diabetes
2014
;
15
:
118
126
83.
Herold
KC
,
Bundy
BN
,
Long
SA
, et al.;
Type 1 Diabetes TrialNet Study Group
.
An anti-CD3 antibody, teplizumab, in relatives at risk for type 1 diabetes
.
N Engl J Med
2019
;
381
:
603
613
84.
Sims
EK
,
Bundy
BN
,
Stier
K
, et al.;
Type 1 Diabetes TrialNet Study Group
.
Teplizumab improves and stabilizes beta cell function in antibody-positive high-risk individuals
.
Sci Transl Med
2021
;
13
:
eabc8980
85.
Selvin
E
,
Rawlings
AM
,
Bergenstal
RM
,
Coresh
J
,
Brancati
FL
.
No racial differences in the association of glycated hemoglobin with kidney disease and cardiovascular outcomes
.
Diabetes Care
2013
;
36
:
2995
3001
86.
Zhang
X
,
Gregg
EW
,
Williamson
DF
, et al
.
A1C level and future risk of diabetes: a systematic review
.
Diabetes Care
2010
;
33
:
1665
1673
87.
Selvin
E
,
Steffes
MW
,
Zhu
H
, et al
.
Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults
.
N Engl J Med
2010
;
362
:
800
811
88.
Ackermann
RT
,
Cheng
YJ
,
Williamson
DF
,
Gregg
EW
.
Identifying adults at high risk for diabetes and cardiovascular disease using hemoglobin A1c National Health and Nutrition Examination Survey 2005–2006
.
Am J Prev Med
2011
;
40
:
11
17
89.
Diabetes Prevention Program Research Group
.
HbA1c as a predictor of diabetes and as an outcome in the diabetes prevention program: a randomized clinical trial
.
Diabetes Care
2015
;
38
:
51
58
90.
Umpierrez
G
,
Korytkowski
M
.
Diabetic emergencies—ketoacidosis, hyperglycaemic hyperosmolar state and hypoglycaemia
.
Nat Rev Endocrinol
2016
;
12
:
222
232
91.
Fadini
GP
,
Bonora
BM
,
Avogaro
A
.
SGLT2 inhibitors and diabetic ketoacidosis: data from the FDA Adverse Event Reporting System
.
Diabetologia
2017
;
60
:
1385
1389
92.
Lean
ME
,
Leslie
WS
,
Barnes
AC
, et al
.
Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial
.
Lancet
2018
;
391
:
541
551
93.
Taheri
S
,
Zaghloul
H
,
Chagoury
O
, et al
.
Effect of intensive lifestyle intervention on bodyweight and glycaemia in early type 2 diabetes (DIADEM-I): an open-label, parallel-group, randomised controlled trial
.
Lancet Diabetes Endocrinol
2020
;
8
:
477
489
94.
Lean
MEJ
,
Leslie
WS
,
Barnes
AC
, et al
.
Durability of a primary care-led weight-management intervention for remission of type 2 diabetes: 2-year results of the DiRECT open-label, cluster-randomised trial
.
Lancet Diabetes Endocrinol
2019
;
7
:
344
355
95.
Roth
AE
,
Thornley
CJ
,
Blackstone
RP
.
Outcomes in bariatric and metabolic surgery: an updated 5-year review
.
Curr Obes Rep
2020
;
9
:
380
389
96.
Conte
C
,
Lapeyre-Mestre
M
,
Hanaire
H
,
Ritz
P
.
Diabetes remission and relapse after bariatric surgery: a nationwide population-based study
.
Obes Surg
2020
;
30
:
4810
4820
97.
Yoshino
M
,
Kayser
BD
,
Yoshino
J
,
Stein
RI
,
Reeds
D
,
Eagon
JC
, et al
.
Effects of diet versus gastric bypass on metabolic function in diabetes
.
N Engl J Med
.
2020
20
;
383
(
8
):
721
32
.
98.
Cresci
B
,
Cosentino
C
,
Monami
M
,
Mannucci
E
.
Metabolic surgery for the treatment of type 2 diabetes: a network meta-analysis of randomized controlled trials
.
Diabetes Obes Metab
2020
;
22
:
1378
1387
99.
Centers for Disease Control and Prevention
.
National Diabetes Statistics Report, 2020: Estimates of Diabetes and Its Burden in the United States
.
100.
International Diabetes Federation
.
IDF Diabetes Atlas, 10th edition
.
Brussels, Belgium, International Diabetes Federation, 2021. Accessed 29 March 2022. Available from https://www.diabetesatlas.org/atlas/tenth-edition/
101.
Bardenheier
BH
,
Wu
WC
,
Zullo
AR
,
Gravenstein
S
,
Gregg
EW
.
Progression to diabetes by baseline glycemic status among middle-aged and older adults in the United States, 2006–2014
.
Diabetes Res Clin Pract
2021
;
174
:
108726
102.
Sussman
JB
,
Kent
DM
,
Nelson
JP
,
Hayward
RA
.
Improving diabetes prevention with benefit based tailored treatment: risk based reanalysis of Diabetes Prevention Program
.
BMJ
2015
;
350
:
h454
103.
Palladino
R
,
Tabak
AG
,
Khunti
K
, et al
.
Association between pre-diabetes and microvascular and macrovascular disease in newly diagnosed type 2 diabetes
.
BMJ Open Diabetes Res Care
2020
;
8
:
e001061
104.
Perreault
L
,
Pan
Q
,
Aroda
VR
, et al.;
Diabetes Prevention Program Research Group
.
Exploring residual risk for diabetes and microvascular disease in the Diabetes Prevention Program Outcomes Study (DPPOS)
.
Diabet Med
2017
;
34
:
1747
1755
105.
Nathan
DM
,
Bennett
PH
,
Crandall
JP
, et al.;
Research Group
.
Does diabetes prevention translate into reduced long-term vascular complications of diabetes?
Diabetologia
2019
;
62
:
1319
1328
106.
American Diabetes Association
.
Diagnosis and classification of diabetes mellitus
.
Diabetes Care
2011
;
34
(
Suppl. 1
):
S62
S69
107.
Genuth
S
,
Alberti
KGMM
,
Bennett
P
, et al.;
Expert Committee on the Diagnosis and Classification of Diabetes Mellitus
.
Follow-up report on the diagnosis of diabetes mellitus
.
Diabetes Care
2003
;
26
:
3160
3167
108.
Lin
C-H
,
Wei
J-N
,
Fan
K-C
, et al
.
Different cutoffs of hypertension, risk of incident diabetes and progression of insulin resistance: a prospective cohort study
.
J Formos Med Assoc
2022
;
121
:
193
201
109.
Wei
Y
,
Xu
Q
,
Yang
H
, et al
.
Preconception diabetes mellitus and adverse pregnancy outcomes in over 6.4 million women: a population-based cohort study in China
.
PLoS Med
2019
;
16
:
e1002926
110.
Peterson
C
,
Grosse
SD
,
Li
R
, et al
.
Preventable health and cost burden of adverse birth outcomes associated with pregestational diabetes in the United States
.
Am J Obstet Gynecol
2015
;
212
:
74.e1
74.e9
111.
Griffin
SJ
,
Borch-Johnsen
K
,
Davies
MJ
, et al
.
Effect of early intensive multifactorial therapy on 5-year cardiovascular outcomes in individuals with type 2 diabetes detected by screening (ADDITION-Europe): a cluster-randomised trial
.
Lancet
2011
;
378
:
156
167
112.
Herman
WH
,
Ye
W
,
Griffin
SJ
, et al
.
Early detection and treatment of type 2 diabetes reduce cardiovascular morbidity and mortality: a simulation of the results of the Anglo-Danish-Dutch Study of Intensive Treatment in People With Screen-Detected Diabetes in Primary Care (ADDITION-Europe)
.
Diabetes Care
2015
;
38
:
1449
1455
113.
Kahn
R
,
Alperin
P
,
Eddy
D
, et al
.
Age at initiation and frequency of screening to detect type 2 diabetes: a cost-effectiveness analysis
.
Lancet
2010
;
375
:
1365
1374
114.
Zhou
X
,
Siegel
KR
,
Ng
BP
, et al
.
Cost-effectiveness of diabetes prevention interventions targeting high-risk individuals and whole populations: a systematic review
.
Diabetes Care
2020
;
43
:
1593
1616
115.
Chatterjee
R
,
Narayan
KMV
,
Lipscomb
J
, et al
.
Screening for diabetes and prediabetes should be cost-saving in patients at high risk
.
Diabetes Care
2013
;
36
:
1981
1987
116.
Chung
S
,
Azar
KMJ
,
Baek
M
,
Lauderdale
DS
,
Palaniappan
LP
.
Reconsidering the age thresholds for type II diabetes screening in the U.S
.
Am J Prev Med
2014
;
47
:
375
381
117.
Araneta
MRG
,
Kanaya
A
,
Fujimoto
W
, et al
.
Optimum BMI cut points to screen Asian Americans for type 2 diabetes
.
Diabetes Care
2015
;
38
:
814
820
118.
Hsu
WC
,
Araneta
MRG
,
Kanaya
AM
,
Chiang
JL
,
Fujimoto
W
.
BMI cut points to identify at-risk Asian Americans for type 2 diabetes screening
.
Diabetes Care
2015
;
38
:
150
158
119.
WHO Expert Consultation
.
Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies
.
Lancet
2004
;
363
:
157
163
120.
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
121.
Chiu
M
,
Austin
PC
,
Manuel
DG
,
Shah
BR
,
Tu
JV
.
Deriving ethnic-specific BMI cutoff points for assessing diabetes risk
.
Diabetes Care
2011
;
34
:
1741
1748
122.
Schambelan
M
,
Benson
CA
,
Carr
A
, et al.;
International AIDS Society-USA
.
Management of metabolic complications associated with antiretroviral therapy for HIV-1 infection: recommendations of an International AIDS Society-USA panel
.
J Acquir Immune Defic Syndr
2002
;
31
:
257
275
123.
Monroe
AK
,
Glesby
MJ
,
Brown
TT
.
Diagnosing and managing diabetes in HIV-infected patients: current concepts
.
Clin Infect Dis
2015
;
60
:
453
462
124.
Wohl
DA
,
McComsey
G
,
Tebas
P
, et al
.
Current concepts in the diagnosis and management of metabolic complications of HIV infection and its therapy
.
Clin Infect Dis
2006
;
43
:
645
653
125.
Johnson
SL
,
Tabaei
BP
,
Herman
WH
.
The efficacy and cost of alternative strategies for systematic screening for type 2 diabetes in the U.S. population 45-74 years of age
.
Diabetes Care
2005
;
28
:
307
311
126.
Tabaei
BP
,
Burke
R
,
Constance
A
, et al
.
Community-based screening for diabetes in Michigan
.
Diabetes Care
2003
;
26
:
668
670
127.
Lalla
E
,
Kunzel
C
,
Burkett
S
,
Cheng
B
,
Lamster
IB
.
Identification of unrecognized diabetes and pre-diabetes in a dental setting
.
J Dent Res
2011
;
90
:
855
860
128.
Lalla
E
,
Cheng
B
,
Kunzel
C
,
Burkett
S
,
Lamster
IB
.
Dental findings and identification of undiagnosed hyperglycemia
.
J Dent Res
2013
;
92
:
888
892
129.
Herman
WH
,
Taylor
GW
,
Jacobson
JJ
,
Burke
R
,
Brown
MB
.
Screening for prediabetes and type 2 diabetes in dental offices
.
J Public Health Dent
2015
;
75
:
175
182
130.
Jadhav
AN
,
Tarte
PR
,
Puri
SK
.
Dental clinic: potential source of high-risk screening for prediabetes and type 2 diabetes
.
Indian J Dent Res
2019
;
30
:
851
854
131.
Buse
JB
,
Kaufman
FR
,
Linder
B
,
Hirst
K
,
El Ghormli
L
;
HEALTHY Study Group
.
Diabetes screening with hemoglobin A(1c) versus fasting plasma glucose in a multiethnic middle-school cohort
.
Diabetes Care
2013
;
36
:
429
435
132.
Kapadia
C
;
Drugs and Therapeutics Committee of the Pediatric Endocrine Society
.
Hemoglobin A1c measurement for the diagnosis of Type 2 diabetes in children
.
Int J Pediatr Endocrinol
2012
;
2012
:
31
133.
Kester
LM
,
Hey
H
,
Hannon
TS
.
Using hemoglobin A1c for prediabetes and diabetes diagnosis in adolescents: can adult recommendations be upheld for pediatric use?
J Adolesc Health
2012
;
50
:
321
323
134.
Wu
EL
,
Kazzi
NG
,
Lee
JM
.
Cost-effectiveness of screening strategies for identifying pediatric diabetes mellitus and dysglycemia
.
JAMA Pediatr
2013
;
167
:
32
39
135.
Moran
A
,
Pillay
K
,
Becker
D
,
Granados
A
,
Hameed
S
,
Acerini
CL
.
ISPAD clinical practice consensus guidelines 2018: management of cystic fibrosis-related diabetes in children and adolescents
.
Pediatr Diabetes
2018
;
19
(
Suppl. 27
):
64
74
136.
Gilmour
JA
.
Response to the letter to the editor from Dr. Boudreau et al., “Validation of a Stepwise Approach Using Glycated Hemoglobin Levels to Reduce the Number of Required Oral Glucose Tolerance Tests to Screen for Cystic Fibrosis-Related Diabetes in Adults”
.
Can J Diabetes
2019
;
43
:
163
137.
Gilmour
JA
,
Sykes
J
,
Etchells
E
,
Tullis
E
.
Cystic fibrosis-related diabetes screening in adults: a gap analysis and evaluation of accuracy of glycated hemoglobin levels
.
Can J Diabetes
2019
;
43
:
13
18
138.
Darukhanavala
A
,
Van Dessel
F
,
Ho
J
,
Hansen
M
,
Kremer
T
,
Alfego
D
.
Use of hemoglobin A1c to identify dysglycemia in cystic fibrosis
.
PLoS One
2021
;
16
:
e0250036
139.
Franck Thompson
E
,
Watson
D
,
Benoit
CM
,
Landvik
S
,
McNamara
J
.
The association of pediatric cystic fibrosis-related diabetes screening on clinical outcomes by center: a CF patient registry study
.
J Cyst Fibros
2020
;
19
:
316
320
140.
Olesen
HV
,
Drevinek
P
,
Gulmans
VA
, et al.;
ECFSPR Steering Group
.
Cystic fibrosis related diabetes in Europe: prevalence, risk factors and outcome
.
J Cyst Fibros
2020
;
19
:
321
327
141.
Prentice
BJ
,
Chelliah
A
,
Ooi
CY
, et al
.
Peak OGTT glucose is associated with lower lung function in young children with cystic fibrosis
.
J Cyst Fibros
2020
;
19
:
305
309
142.
Mainguy
C
,
Bellon
G
,
Delaup
V
, et al
.
Sensitivity and specificity of different methods for cystic fibrosis-related diabetes screening: is the oral glucose tolerance test still the standard?
J Pediatr Endocrinol Metab
2017
;
30
:
27
35
143.
Ode
KL
,
Moran
A
.
New insights into cystic fibrosis-related diabetes in children
.
Lancet Diabetes Endocrinol
2013
;
1
:
52
58
144.
Moran
A
,
Pekow
P
,
Grover
P
, et al
.
Insulin therapy to improve BMI in cystic fibrosis-related diabetes without fasting hyperglycemia: results of the cystic fibrosis related diabetes therapy trial
.
Diabetes Care
2009
;
32
:
1783
1788
145.
Onady
GM
,
Stolfi
A
.
Insulin and oral agents for managing cystic fibrosis-related diabetes
.
Cochrane Database Syst Rev
2016
;
4
:
CD004730
146.
Moran
A
,
Brunzell
C
,
Cohen
RC
, et al.;
CFRD Guidelines Committee
.
Clinical care guidelines for cystic fibrosis-related diabetes: a position statement of the American Diabetes Association and a clinical practice guideline of the Cystic Fibrosis Foundation, endorsed by the Pediatric Endocrine Society
.
Diabetes Care
2010
;
33
:
2697
2708
147.
Shivaswamy
V
,
Boerner
B
,
Larsen
J
.
Post-transplant diabetes mellitus: causes, treatment, and impact on outcomes
.
Endocr Rev
2016
;
37
:
37
61
148.
Sharif
A
,
Hecking
M
,
de Vries
APJ
, et al
.
Proceedings from an international consensus meeting on posttransplantation diabetes mellitus: recommendations and future directions
.
Am J Transplant
2014
;
14
:
1992
2000
149.
Hecking
M
,
Werzowa
J
,
Haidinger
M
, et al.;
European-New-Onset Diabetes After Transplantation Working Group
.
Novel views on new-onset diabetes after transplantation: development, prevention and treatment
.
Nephrol Dial Transplant
2013
;
28
:
550
566
150.
Ramirez
SC
,
Maaske
J
,
Kim
Y
, et al
.
The association between glycemic control and clinical outcomes after kidney transplantation
.
Endocr Pract
2014
;
20
:
894
900
151.
Thomas
MC
,
Moran
J
,
Mathew
TH
,
Russ
GR
,
Rao
MM
.
Early peri-operative hyperglycaemia and renal allograft rejection in patients without diabetes
.
BMC Nephrol
2000
;
1
:
1
152.
Chakkera
HA
,
Weil
EJ
,
Castro
J
, et al
.
Hyperglycemia during the immediate period after kidney transplantation
.
Clin J Am Soc Nephrol
2009
;
4
:
853
859
153.
Wallia
A
,
Illuri
V
,
Molitch
ME
.
Diabetes care after transplant: definitions, risk factors, and clinical management
.
Med Clin North Am
2016
;
100
:
535
550
154.
Kim
HD
,
Chang
JY
,
Chung
BH
, et al
.
Effect of everolimus with low-dose tacrolimus on development of new-onset diabetes after transplantation and allograft function in kidney transplantation: a multicenter, open-label, randomized trial
.
Ann Transplant
2021
;
26
:
e927984
155.
Cheng
CY
,
Chen
CH
,
Wu
MF
, et al
.
Risk factors in and long-term survival of patients with post-transplantation diabetes mellitus: a retrospective cohort study
.
Int J Environ Res Public Health
2020
;
17
:
E4581
156.
Gulsoy Kirnap
N
,
Bozkus
Y
,
Haberal
M
.
Analysis of risk factors for posttransplant diabetes mellitus after kidney transplantation: single-center experience
.
Exp Clin Transplant
2020
;
18
(
Suppl. 1
):
36
40
157.
Munshi
VN
,
Saghafian
S
,
Cook
CB
,
Eric Steidley
D
,
Hardaway
B
,
Chakkera
HA
.
Incidence, risk factors, and trends for postheart transplantation diabetes mellitus
.
Am J Cardiol
2020
;
125
:
436
440
158.
Kgosidialwa
O
,
Blake
K
,
O’Connell
O
,
Egan
J
,
O’Neill
J
,
Hatunic
M
.
Post-transplant diabetes mellitus associated with heart and lung transplant
.
Ir J Med Sci
2020
;
189
:
185
189
159.
Sharif
A
,
Moore
RH
,
Baboolal
K
.
The use of oral glucose tolerance tests to risk stratify for new-onset diabetes after transplantation: an underdiagnosed phenomenon
.
Transplantation
2006
;
82
:
1667
1672
160.
Hecking
M
,
Kainz
A
,
Werzowa
J
, et al
.
Glucose metabolism after renal transplantation
.
Diabetes Care
2013
;
36
:
2763
2771
161.
Pham Vu
T
,
Nguyen Thi Thuy
D
,
Truong Quy
K
, et al
.
Serum hs-CRP measured prior transplantation predicts of new-onset diabetes after transplantation in renal transplant recipients
.
Transpl Immunol
2021
;
66
:
101392
162.
Grundman
JB
,
Wolfsdorf
JI
,
Marks
BE
.
Post-transplantation diabetes mellitus in pediatric patients
.
Horm Res Paediatr
2020
;
93
:
510
518
163.
Galindo
RJ
,
Fried
M
,
Breen
T
,
Tamler
R
.
Hyperglycemia management in patients with posttransplantation diabetes
.
Endocr Pract
2016
;
22
:
454
465
164.
Jenssen
T
,
Hartmann
A
.
Emerging treatments for post-transplantation diabetes mellitus
.
Nat Rev Nephrol
2015
;
11
:
465
477
165.
Thomas
MC
,
Mathew
TH
,
Russ
GR
,
Rao
MM
,
Moran
J
.
Early peri-operative glycaemic control and allograft rejection in patients with diabetes mellitus: a pilot study
.
Transplantation
2001
;
72
:
1321
1324
166.
Kurian
B
,
Joshi
R
,
Helmuth
A
.
Effectiveness and long-term safety of thiazolidinediones and metformin in renal transplant recipients
.
Endocr Pract
2008
;
14
:
979
984
167.
Budde
K
,
Neumayer
HH
,
Fritsche
L
,
Sulowicz
W
,
Stompôr
T
,
Eckland
D
.
The pharmacokinetics of pioglitazone in patients with impaired renal function
.
Br J Clin Pharmacol
2003
;
55
:
368
374
168.
Luther
P
,
Baldwin
D
Jr
.
Pioglitazone in the management of diabetes mellitus after transplantation
.
Am J Transplant
2004
;
4
:
2135
2138
169.
Strøm Halden
TA
,
Åsberg
A
,
Vik
K
,
Hartmann
A
,
Jenssen
T
.
Short-term efficacy and safety of sitagliptin treatment in long-term stable renal recipients with new-onset diabetes after transplantation
.
Nephrol Dial Transplant
2014
;
29
:
926
933
170.
Lane
JT
,
Odegaard
DE
,
Haire
CE
,
Collier
DS
,
Wrenshall
LE
,
Stevens
RB
.
Sitagliptin therapy in kidney transplant recipients with new-onset diabetes after transplantation
.
Transplantation
2011
;
92
:
e56
e57
171.
Carmody
D
,
Støy
J
,
Greeley
SAW
,
Bell
GI
,
Philipson
LH
.
Chapter 2—A clinical guide to monogenic diabetes
. In
Genetic Diagnosis of Endocrine Disorders
. 2nd ed.
Weiss
RE
,
Refetoff
S
, Eds.
Academic Press
,
2016
, pp.
21
30
172.
De Franco
E
,
Flanagan
SE
,
Houghton
JAL
, et al
.
The effect of early, comprehensive genomic testing on clinical care in neonatal diabetes: an international cohort study
.
Lancet
2015
;
386
:
957
963
173.
Sanyoura
M
,
Letourneau
L
,
Knight Johnson
AE
, et al
.
GCK-MODY in the US Monogenic Diabetes Registry: description of 27 unpublished variants
.
Diabetes Res Clin Pract
2019
;
151
:
231
236
174.
Carmody
D
,
Naylor
RN
,
Bell
CD
, et al
.
GCK-MODY in the US National Monogenic Diabetes Registry: frequently misdiagnosed and unnecessarily treated
.
Acta Diabetol
2016
;
53
:
703
708
175.
Timsit
J
,
Saint-Martin
C
,
Dubois-Laforgue
D
,
Bellanné-Chantelot
C
.
Searching for maturity-onset diabetes of the young (MODY): when and what for?
Can J Diabetes
2016
;
40
:
455
461
176.
De Franco
E
,
Caswell
R
,
Johnson
MB
, et al
.
De novo mutations in EIF2B1 affecting eIF2 signaling cause neonatal/early-onset diabetes and transient hepatic dysfunction
.
Diabetes
2020
;
69
:
477
483
177.
Valkovicova
T
,
Skopkova
M
,
Stanik
J
,
Gasperikova
D
.
Novel insights into genetics and clinics of the HNF1A-MODY
.
Endocr Regul
2019
;
53
:
110
134
178.
Awa
WL
,
Schober
E
,
Wiegand
S
, et al
.
Reclassification of diabetes type in pediatric patients initially classified as type 2 diabetes mellitus: 15 years follow-up using routine data from the German/Austrian DPV database
.
Diabetes Res Clin Pract
2011
;
94
:
463
467
179.
Shields
BM
,
Hicks
S
,
Shepherd
MH
,
Colclough
K
,
Hattersley
AT
,
Ellard
S
.
Maturity-onset diabetes of the young (MODY): how many cases are we missing?
Diabetologia
2010
;
53
:
2504
2508
180.
Shepherd
M
,
Shields
B
,
Hammersley
S
, et al.;
UNITED Team
.
Systematic population screening, using biomarkers and genetic testing, identifies 2.5% of the U.K. Pediatric Diabetes Population With Monogenic Diabetes
.
Diabetes Care
2016
;
39
:
1879
1888
181.
SEARCH Study Group
.
SEARCH for Diabetes in Youth: a multicenter study of the prevalence, incidence and classification of diabetes mellitus in youth
.
Control Clin Trials
2004
;
25
:
458
471
182.
Pihoker
C
,
Gilliam
LK
,
Ellard
S
, et al.;
SEARCH for Diabetes in Youth Study Group
.
Prevalence, characteristics and clinical diagnosis of maturity onset diabetes of the young due to mutations in HNF1A, HNF4A, and glucokinase: results from the SEARCH for Diabetes in Youth
.
J Clin Endocrinol Metab
2013
;
98
:
4055
4062
183.
Draznin
B
(Ed.).
Atypical Diabetes: Pathophysiology, Clinical Presentations, and Treatment Options
.
Arlington, VA
,
American Diabetes Association
,
2018
184.
Exeter Diabetes
.
MODY Probability Calculator
.
185.
Urbanová
J
,
Rypáčková
B
,
Procházková
Z
, et al
.
Positivity for islet cell autoantibodies in patients with monogenic diabetes is associated with later diabetes onset and higher HbA1c level
.
Diabet Med
2014
;
31
:
466
471
186.
Naylor
RN
,
John
PM
,
Winn
AN
, et al
.
Cost-effectiveness of MODY genetic testing: translating genomic advances into practical health applications
.
Diabetes Care
2014
;
37
:
202
209
187.
Shields
BM
,
Shepherd
M
,
Hudson
M
, et al.;
UNITED study team
.
Population-based assessment of a biomarker-based screening pathway to aid diagnosis of monogenic diabetes in young-onset patients
.
Diabetes Care
2017
;
40
:
1017
1025
188.
Hattersley
A
,
Bruining
J
,
Shield
J
,
Njolstad
P
,
Donaghue
KC
.
The diagnosis and management of monogenic diabetes in children and adolescents
.
Pediatr Diabetes
2009
;
10
(
Suppl. 12
):
33
42
189.
Rubio-Cabezas
O
,
Hattersley
AT
,
Njølstad
PR
, et al.;
International Society for Pediatric and Adolescent Diabetes
.
ISPAD clinical practice consensus guidelines 2014. The diagnosis and management of monogenic diabetes in children and adolescents
.
Pediatr Diabetes
2014
;
15
(
Suppl. 20
):
47
64
190.
Steele
AM
,
Shields
BM
,
Shepherd
M
,
Ellard
S
,
Hattersley
AT
,
Pearson
ER
.
Increased all-cause and cardiovascular mortality in monogenic diabetes as a result of mutations in the HNF1A gene
.
Diabet Med
2010
;
27
:
157
161
191.
Anık
A
,
Çatlı
G
,
Abacı
A
,
Böber
E
.
Maturity-onset diabetes of the young (MODY): an update
.
J Pediatr Endocrinol Metab
2015
;
28
:
251
263
192.
Greeley
SAW
,
Naylor
RN
,
Philipson
LH
,
Bell
GI
.
Neonatal diabetes: an expanding list of genes allows for improved diagnosis and treatment
.
Curr Diab Rep
2011
;
11
:
519
532
193.
Le Bodic
L
,
Bignon
JD
,
Raguénès
O
, et al
.
The hereditary pancreatitis gene maps to long arm of chromosome 7
.
Hum Mol Genet
1996
;
5
:
549
554
194.
Hardt
PD
,
Brendel
MD
,
Kloer
HU
,
Bretzel
RG
.
Is pancreatic diabetes (type 3c diabetes) underdiagnosed and misdiagnosed?
Diabetes Care
2008
;
31
(
Suppl. 2
):
S165
S169
195.
Woodmansey
C
,
McGovern
AP
,
McCullough
KA
, et al
.
Incidence, demographics, and clinical characteristics of diabetes of the exocrine pancreas (type 3c): a retrospective cohort study
.
Diabetes Care
2017
;
40
:
1486
1493
196.
Duggan
SN
,
Ewald
N
,
Kelleher
L
,
Griffin
O
,
Gibney
J
,
Conlon
KC
.
The nutritional management of type 3c (pancreatogenic) diabetes in chronic pancreatitis
.
Eur J Clin Nutr
2017
;
71
:
3
8
197.
Makuc
J
.
Management of pancreatogenic diabetes: challenges and solutions
.
Diabetes Metab Syndr Obes
2016
;
9
:
311
315
198.
Andersen
DK
,
Korc
M
,
Petersen
GM
, et al
.
Diabetes, pancreatogenic diabetes, and pancreatic cancer
.
Diabetes
2017
;
66
:
1103
1110
199.
Petrov
MS
,
Basina
M
.
Diagnosis of endocrine disease: diagnosing and classifying diabetes in diseases of the exocrine pancreas
.
Eur J Endocrinol
2021
;
184
:
R151
R163
200.
Bellin
MD
,
Gelrud
A
,
Arreaza-Rubin
G
, et al
.
Total pancreatectomy with islet autotransplantation: summary of an NIDDK workshop
.
Ann Surg
2015
;
261
:
21
29
201.
Anazawa
T
,
Okajima
H
,
Masui
T
,
Uemoto
S
.
Current state and future evolution of pancreatic islet transplantation
.
Ann Gastroenterol Surg
2018
;
3
:
34
42
202.
Quartuccio
M
,
Hall
E
,
Singh
V
, et al
.
Glycemic predictors of insulin independence after total pancreatectomy with islet autotransplantation
.
J Clin Endocrinol Metab
2017
;
102
:
801
809
203.
Huvinen
E
,
Koivusalo
SB
,
Meinilä
J
,
Valkama
A
,
Tiitinen
A
,
Rönö
K
, et al
.
Effects of a lifestyle intervention during pregnancy and first postpartum year: findings from the RADIEL study
.
J Clin Endocrinol Metab
2018
;
103
:
1669
1677
204.
Feig
DS
,
Hwee
J
,
Shah
BR
,
Booth
GL
,
Bierman
AS
,
Lipscombe
LL
.
Trends in incidence of diabetes in pregnancy and serious perinatal outcomes: a large, population-based study in Ontario, Canada, 1996-2010
.
Diabetes Care
2014
;
37
:
1590
1596
205.
Peng
TY
,
Ehrlich
SF
,
Crites
Y
, et al
.
Trends and racial and ethnic disparities in the prevalence of pregestational type 1 and type 2 diabetes in Northern California: 1996-2014
.
Am J Obstet Gynecol
2017
;
216
:
177.e1
177.e8
206.
Jovanovič
L
,
Liang
Y
,
Weng
W
,
Hamilton
M
,
Chen
L
,
Wintfeld
N
.
Trends in the incidence of diabetes, its clinical sequelae, and associated costs in pregnancy
.
Diabetes Metab Res Rev
2015
;
31
:
707
716
207.
Poltavskiy
E
,
Kim
DJ
,
Bang
H
.
Comparison of screening scores for diabetes and prediabetes
.
Diabetes Res Clin Pract
2016
;
118
:
146
153
208.
Mission
JF
,
Catov
J
,
Deihl
TE
,
Feghali
M
,
Scifres
C
.
Early pregnancy diabetes screening and diagnosis: prevalence, rates of abnormal test results, and associated factors
.
Obstet Gynecol
2017
;
130
:
1136
1142
209.
Cho
NH
,
Shaw
JE
,
Karuranga
S
, et al
.
IDF Diabetes Atlas: global estimates of diabetes prevalence for 2017 and projections for 2045
.
Diabetes Res Clin Pract
2018
;
138
:
271
281
210.
Britton
LE
,
Hussey
JM
,
Crandell
JL
,
Berry
DC
,
Brooks
JL
,
Bryant
AG
.
Racial/ethnic disparities in diabetes diagnosis and glycemic control among women of reproductive age
.
J Womens Health (Larchmt)
2018
;
27
:
1271
1277
211.
Robbins
C
,
Boulet
SL
,
Morgan
I
, et al
.
Disparities in preconception health indicators—Behavioral Risk Factor Surveillance System, 2013–2015, and Pregnancy Risk Assessment Monitoring System, 2013–2014
.
MMWR Surveill Summ
2018
;
67
:
1
16
212.
Yuen
L
,
Wong
VW
,
Simmons
D
.
Ethnic disparities in gestational diabetes
.
Curr Diab Rep
2018
;
18
:
68
213.
Wahabi
HA
,
Fayed
A
,
Esmaeil
S
, et al
.
Systematic review and meta-analysis of the effectiveness of pre-pregnancy care for women with diabetes for improving maternal and perinatal outcomes
.
PLoS One
2020
;
15
:
e0237571
214.
Zhu
WW
,
Yang
HX
,
Wei
YM
, et al
.
Evaluation of the value of fasting plasma glucose in the first prenatal visit to diagnose gestational diabetes mellitus in China
.
Diabetes Care
2013
;
36
:
586
590
215.
Hughes
RCE
,
Moore
MP
,
Gullam
JE
,
Mohamed
K
,
Rowan
J
.
An early pregnancy HbA1c ≥5.9% (41 mmol/mol) is optimal for detecting diabetes and identifies women at increased risk of adverse pregnancy outcomes
.
Diabetes Care
2014
;
37
:
2953
2959
216.
Mañé
L
,
Flores-Le Roux
JA
,
Gómez
N
, et al
.
Association of first-trimester HbA1c levels with adverse pregnancy outcomes in different ethnic groups
.
Diabetes Res Clin Pract
2019
;
150
:
202
210
217.
Boe
B
,
Barbour
LA
,
Allshouse
AA
,
Heyborne
KD
.
Universal early pregnancy glycosylated hemoglobin A1c as an adjunct to Carpenter-Coustan screening: an observational cohort study
.
Am J Obstet Gynecol MFM
2019
;
1
:
24
32
218.
Immanuel
J
,
Simmons
D
.
Screening and treatment for early-onset gestational diabetes mellitus: a systematic review and meta-analysis
.
Curr Diab Rep
2017
;
17
:
115
219.
Yefet
E
,
Jeda
E
,
Tzur
A
,
Nachum
Z
.
Markers for undiagnosed type 2 diabetes mellitus during pregnancy—a population-based retrospective cohort study
.
J Diabetes
2020
;
12
:
205
214
220.
Kattini
R
,
Hummelen
R
,
Kelly
L
.
Early gestational diabetes mellitus screening with glycated hemoglobin: a systematic review
.
J Obstet Gynaecol Can
2020
;
42
:
1379
1384
221.
Chen
L
,
Pocobelli
G
,
Yu
O
, et al
.
Early pregnancy hemoglobin A1c and pregnancy outcomes: a population-based study
.
Am J Perinatol
2019
;
36
:
1045
1053
222.
Osmundson
SS
,
Zhao
BS
,
Kunz
L
, et al
.
First trimester hemoglobin A1c prediction of gestational diabetes
.
Am J Perinatol
2016
;
33
:
977
982
223.
McIntyre
HD
,
Sacks
DA
,
Barbour
LA
, et al
.
Issues with the diagnosis and classification of hyperglycemia in early pregnancy
.
Diabetes Care
2016
;
39
:
53
54
224.
Cavagnolli
G
,
Pimentel
AL
,
Freitas
PAC
,
Gross
JL
,
Camargo
JL
.
Factors affecting A1C in non-diabetic individuals: review and meta-analysis
.
Clin Chim Acta
2015
;
445
:
107
114
225.
Buchanan
TA
,
Xiang
A
,
Kjos
SL
,
Watanabe
R
.
What is gestational diabetes?
Diabetes Care
2007
;
30
(
Suppl. 2
):
S105
S111
226.
Noctor
E
,
Crowe
C
,
Carmody
LA
, et al.;
ATLANTIC-DIP investigators
.
Abnormal glucose tolerance post-gestational diabetes mellitus as defined by the International Association of Diabetes and Pregnancy Study Groups criteria
.
Eur J Endocrinol
2016
;
175
:
287
297
227.
Kim
C
,
Newton
KM
,
Knopp
RH
.
Gestational diabetes and the incidence of type 2 diabetes: a systematic review
.
Diabetes Care
2002
;
25
:
1862
1868
228.
Ratner
RE
,
Christophi
CA
,
Metzger
BE
, et al.;
Diabetes Prevention Program Research Group
.
Prevention of diabetes in women with a history of gestational diabetes: effects of metformin and lifestyle interventions
.
J Clin Endocrinol Metab
2008
;
93
:
4774
4779
229.
Aroda
VR
,
Christophi
CA
,
Edelstein
SL
, et al.;
Diabetes Prevention Program Research Group
.
The effect of lifestyle intervention and metformin on preventing or delaying diabetes among women with and without gestational diabetes: the Diabetes Prevention Program outcomes study 10-year follow-up
.
J Clin Endocrinol Metab
2015
;
100
:
1646
1653
230.
Wang
C
,
Wei
Y
,
Zhang
X
, et al
.
A randomized clinical trial of exercise during pregnancy to prevent gestational diabetes mellitus and improve pregnancy outcome in overweight and obese pregnant women
.
Am J Obstet Gynecol
2017
;
216
:
340
351
231.
Metzger
BE
,
Lowe
LP
,
Dyer
AR
, et al.;
HAPO Study Cooperative Research Group
.
Hyperglycemia and adverse pregnancy outcomes
.
N Engl J Med
2008
;
358
:
1991
2002
232.
O’Sullivan
JB
,
Mahan
CM
.
Criteria for the oral glucose tolerance test in pregnancy
.
Diabetes
1964
;
13
:
278
285
233.
Sacks
DA
,
Hadden
DR
,
Maresh
M
, et al.;
HAPO Study Cooperative Research Group
.
Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study
.
Diabetes Care
2012
;
35
:
526
528
234.
Brown
FM
,
Wyckoff
J
.
Application of one-step IADPSG versus two-step diagnostic criteria for gestational diabetes in the real world: impact on health services, clinical care, and outcomes
.
Curr Diab Rep
2017
;
17
:
85
235.
Lowe
WL
Jr
,
Scholtens
DM
,
Lowe
LP
, et al.;
HAPO Follow-up Study Cooperative Research Group
.
Association of gestational diabetes with maternal disorders of glucose metabolism and childhood adiposity
.
JAMA
2018
;
320
:
1005
1016
236.
Lowe
WL
Jr
,
Scholtens
DM
,
Kuang
A
, et al.;
HAPO Follow-up Study Cooperative Research Group
.
Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS): maternal gestational diabetes mellitus and childhood glucose metabolism
.
Diabetes Care
2019
;
42
:
372
380
237.
Landon
MB
,
Spong
CY
,
Thom
E
, et al.;
Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network
.
A multicenter, randomized trial of treatment for mild gestational diabetes
.
N Engl J Med
2009
;
361
:
1339
1348
238.
Crowther
CA
,
Hiller
JE
,
Moss
JR
,
McPhee
AJ
,
Jeffries
WS
;
Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) Trial Group
.
Effect of treatment of gestational diabetes mellitus on pregnancy outcomes
.
N Engl J Med
2005
;
352
:
2477
2486
239.
Hillier
TA
,
Pedula
KL
,
Ogasawara
KK
, et al
.
A pragmatic, randomized clinical trial of gestational diabetes screening
.
N Engl J Med
2021
;
384
:
895
904
240.
Coustan
DR
,
Dyer
AR
,
Metzger
BE
.
One-step or 2-step testing for gestational diabetes: which is better?
Am J Obstet Gynecol
2021
;
225
:
634
644
241.
Cowie
CC
,
Casagrande
SS
,
Menke
A
, et al
, Eds.
Diabetes in America
. 3rd ed.
National Institute of Diabetes and Digestive and Kidney Diseases
,
2018
.
242.
Scholtens
DM
,
Kuang
A
,
Lowe
LP
, et al.;
HAPO Follow-up Study Cooperative Research Group
;
HAPO Follow-Up Study Cooperative Research Group
.
Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS): maternal glycemia and childhood glucose metabolism
.
Diabetes Care
2019
;
42
:
381
392
243.
Josefson
JL
,
Scholtens
DM
,
Kuang
A
, et al.;
HAPO Follow-up Study Cooperative Research Group
.
Newborn adiposity and cord blood C-peptide as mediators of the maternal metabolic environment and childhood adiposity
.
Diabetes Care
2021
;
44
:
1194
1202
244.
Tam
WH
,
Ma
RCW
,
Ozaki
R
, et al
.
In utero exposure to maternal hyperglycemia increases childhood cardiometabolic risk in offspring
.
Diabetes Care
2017
;
40
:
679
686
245.
Landon
MB
,
Rice
MM
,
Varner
MW
, et al.;
Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal-Fetal Medicine Units (MFMU) Network
.
Mild gestational diabetes mellitus and long-term child health
.
Diabetes Care
2015
;
38
:
445
452
246.
Vandorsten
JP
,
Dodson
WC
,
Espeland
MA
, et al
.
NIH consensus development conference: diagnosing gestational diabetes mellitus
.
NIH Consens State Sci Statements
2013
;
29
:
1
31
247.
Committee on Practice Bulletins—Obstetrics
.
ACOG practice bulletin no. 190: gestational diabetes mellitus
.
Obstet Gynecol
2018
;
131
:
e49
e64
248.
Pillay
J
,
Donovan
L
,
Guitard
S
, et al
.
Screening for Gestational Diabetes Mellitus: A Systematic Review to Update the 2014 U.S. Preventive Services Task Force Recommendation
.
Rockville, MD, Agency for Healthcare Research and Quality (U.S. Preventive Services Task Force Evidence Syntheses, formerly Systematic Evidence Reviews), 2021. Accessed 8 September 2022. Available from http://www.ncbi.nlm.nih.gov/books/NBK573100/
249.
Khalafallah
A
,
Phuah
E
,
Al-Barazan
AM
, et al
.
Glycosylated haemoglobin for screening and diagnosis of gestational diabetes mellitus
.
BMJ Open
2016
;
6
:
e011059
250.
Horvath
K
,
Koch
K
,
Jeitler
K
,
Matyas
E
,
Bender
R
,
Bastian
H
, et al
.
Effects of treatment in women with gestational diabetes mellitus: systematic review and meta-analysis
.
BMJ
2010
;
340
:
c1395
251.
Carpenter
MW
,
Coustan
DR
.
Criteria for screening tests for gestational diabetes
.
Am J Obstet Gynecol
1982
;
144
:
768
773
252.
National Diabetes Data Group
.
Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance
.
Diabetes
1979
;
28
:
1039
1057
253.
Harper
LM
,
Mele
L
,
Landon
MB
, et al.;
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Maternal-Fetal Medicine Units (MFMU) Network
.
Carpenter-Coustan compared with national diabetes data group criteria for diagnosing gestational diabetes
.
Obstet Gynecol
2016
;
127
:
893
898
254.
Mo
X
,
Gai Tobe
R
,
Takahashi
Y
, et al
.
Economic evaluations of gestational diabetes mellitus screening: a systematic review
.
J Epidemiol
2021
;
31
:
220
230
255.
Wei
Y
,
Yang
H
,
Zhu
W
, et al
.
International Association of Diabetes and Pregnancy Study Group criteria is suitable for gestational diabetes mellitus diagnosis: further evidence from China
.
Chin Med J (Engl)
2014
;
127
:
3553
3556
256.
Feldman
RK
,
Tieu
RS
,
Yasumura
L
.
Gestational diabetes screening: the International Association of the Diabetes and Pregnancy Study Groups compared with Carpenter-Coustan Screening
.
Obstet Gynecol
2016
;
127
:
10
17
257.
Saccone
G
,
Khalifeh
A
,
Al-Kouatly
HB
,
Sendek
K
,
Berghella
V
.
Screening for gestational diabetes mellitus: one step versus two step approach. A meta-analysis of randomized trials
.
J Matern Fetal Neonatal Med
2020
;
33
:
1616
1624
258.
Ethridge
JK
Jr
,
Catalano
PM
,
Waters
TP
.
Perinatal outcomes associated with the diagnosis of gestational diabetes made by the international association of the diabetes and pregnancy study groups criteria
.
Obstet Gynecol
2014
;
124
:
571
578
259.
Mayo
K
,
Melamed
N
,
Vandenberghe
H
,
Berger
H
.
The impact of adoption of the international association of diabetes in pregnancy study group criteria for the screening and diagnosis of gestational diabetes
.
Am J Obstet Gynecol
2015
;
212
:
224.e1
224.e9
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.