Application of the new ADA criteria for the diagnosis of diabetes to population studies in sub‐Saharan Africa

Levitt, Naomi; Unwin, Nigel; Bradshaw, D.; Kitange, H. M.; Mbanya, Jean Claude; Mollentze, W.F.; Omar, M. A. K.; Motala, Ayesha A.; Joubert, Gina; Masuki, Gabriel; Machibya, Harun

doi:10.1046/j.1464-5491.2000.00264.x

Cited by 41 publications

(26 citation statements)

References 19 publications

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“…Similarly, in the Guía Study (GS), they found a 2.8% difference between the 2 criteria [18]. As shown in Asian population in DECODA study [24] or African population in another study [25], the difference varied when different populations were studied. We also found a significant portion of subjects (4.2%) with undiagnosed DM (IPH) in this population.…”

Section: Discussionmentioning

confidence: 86%

“…A study on Japanese-American men also demonstrated that 66.1% (341/516) of subjects were not identified by FPG tests only [19]. Compared with data from Asian populations, rates of IPH from western countries were lower but still high enough [16][17][18]25], 29.0% (154/531) in the SHS [16], 19.7% (27/137) in the GS [18], and 41.1% (125/304) in the Third National Health and Nutrition Examination Survey (NHANES III) [17].…”

Section: Discussionmentioning

confidence: 95%

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The performance of risk scores and hemoglobin A1c to find undiagnosed diabetes with isolated postload hyperglycemia

Lin

Shih

et al. 2011

Endocr J

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Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 95%

The performance of risk scores and hemoglobin A1c to find undiagnosed diabetes with isolated postload hyperglycemia

Lin

Shih

et al. 2011

Endocr J

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“…and increased basal hepatic glucose output, whereas subjects with isolated post-load hyperglycaemia have decreased peripheral insulin sensitivity. 8,12,15 There is evidence that such metabolic differences may be relevant from a clinical standpoint: patients with post-load hyperglycaemia have worse cardiovascular risk profiles 10,24 and increased death risks.…”

Section: Discussionmentioning

confidence: 99%

WHO and ADA criteria for the diagnosis of diabetes mellitus in relation to body mass index. Insulin sensitivity and secretion in resulting subcategories of glucose tolerance

et al. 2002

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OBJECTIVE:To determine the influence of body mass index (BMI) on agreement between the American Diabetes Association (ADA) and the new World Health Organization diagnostic criteria for the diagnosis of diabetes mellitus and to investigate the metabolic profile of the resulting subcategories. DESIGN: Cross-sectional study SUBJECTS: A total of 3018 subjects with no previous history of diabetes and fasting glucose < 7.8 mmol=l, with a wide range of BMIs. MEASUREMENTS: (1) Prevalence of impaired glucose regulation (IGR) and diabetes (DM) according to ADA and WHO diagnostic criteria; (2) basal and post-load insulin sensitivity and secretion, calculated on the basis of data derived from an oral glucose tolerance test (OGTT). RESULTS: The diagnosis according to the two classifications was concordant in 2490 subjects, discordant in 528 (452 were identified as impaired glucose tolerance (IGT) and 76 as DM only by means of OGTT). The disagreement increased with increasing BMI, being as high as 25.3% in subjects with BMI ! 35 kg=m 2 . Subjects with isolated fasting hyperglycaemia were mainly characterised by reduced insulin sensitivity and secretion in the basal state, but normal first-phase insulin secretion and moderately reduced insulin sensitivity after glucose challenge. Subjects with isolated 2 h hyperglycaemia were mainly characterised by normal basal insulin secretion and by a marked insulin resistance associated with a blunted first-phase insulin secretion after the glucose load. CONCLUSIONS:The disagreement between ADA and WHO classifications is particularly relevant in obesity, making OGTT mandatory in these subjects. Different pathogenic mechanisms are involved in isolated fasting or post-load hyperglycaemia, possibly related to a different site of insulin resistance (hepatic vs peripheral), and=or to a different disregulation of insulin secretion (basal vs post-load). A correct identification of the underlying mechanism(s) is the rationale for future studies to detect the effectiveness of different pharmacological or behavioural approaches.

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“…Examples include psychology variables [7,8]; tumour and cell image data [3,9,10]; heart rate [11]; ECG record [12]; and laboratory data [13]. Therefore, a reasonable categorization with domain knowledge is one possible approach to categorizing variables, but this is appropriate for experts (e.g.…”

Section: Introductionmentioning

confidence: 99%

Appropriate medical data categorization for data mining classification techniques

Liao

Lee

2002

Medical Informatics and the Internet in Medicine

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Some data mining (DM) methods, or software tools, require normalized data, others rely on categorized data, and some can accommodate multiple data scales. Each DM technique has a specific background theory; therefore, different results are expected when applying multiple methods. The purpose of this study is to find the data format appropriate for each DM classification technique for wider applications, and efficiently to obtain trustworthy results. Considering the nature of medical data, categorical variables are sometimes useful for making decisions and can make it easier to extrapolate knowledge. In this study, three mathematical data categorization methods (Fusinter, minimum description length principle [MDLPC] and Chi-merge) were applied to accommodate five data mining classification techniques (statistics discriminant analysis, supervised classification with Neural Networks, Decision trees, Genetic supervised clustering and Bayesian classification [probability neural networks; PNN]) using a heart disease database with four types of data (continuous data, binary data, nominal data, and ordinal data). Compared with original or normalized data, data categorized by the MDLPC categorization method was found to perform better in most of the DM classification techniques used in this study. Categorical data is good for most DM classification techniques (e.g. classification of disease and non-disease groups) and is relatively easy to use for extracting medical knowledge.

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Application of the new ADA criteria for the diagnosis of diabetes to population studies in sub‐Saharan Africa

Cited by 41 publications

References 19 publications

The performance of risk scores and hemoglobin A1c to find undiagnosed diabetes with isolated postload hyperglycemia

The performance of risk scores and hemoglobin A1c to find undiagnosed diabetes with isolated postload hyperglycemia

WHO and ADA criteria for the diagnosis of diabetes mellitus in relation to body mass index. Insulin sensitivity and secretion in resulting subcategories of glucose tolerance

Appropriate medical data categorization for data mining classification techniques

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