Prevalence of admission plasma glucose in 'diabetes' or 'at risk' ranges in hospital emergencies with no prior diagnosis of diabetes by gender, age and ethnicity.

AIMS: To establish the prevalence of admission plasma glucose in 'diabetes' and 'at risk' ranges in emergency hospital admissions with no prior diagnosis of diabetes; characteristics of people with hyperglycaemia; and factors influencing glucose measurement. METHODS: Electronic patient records for 113 097 hospital admissions over 1 year from 2014 to 2015 included 43 201 emergencies with glucose available for 31 927 (74%) admissions, comprising 22 045 people. Data are presented for 18 965 people with no prior diagnosis of diabetes and glucose available on first attendance. RESULTS: Three quarters (14 214) were White Europeans aged 62 (43-78) years, median (IQ range); 12% (2241) South Asians 46 (32-64) years; 9% (1726) Unknown/Other ethnicities 43 (29-61) years; and 4% (784) Afro-Caribbeans 49 (33-63) years, P < .001. Overall, 5% (1003) had glucose in the 'diabetes' range (≥11.1 mmol/L) higher at 8% (175) for South Asians; 16% (3042) were 'at risk' (7.8-11.0 mmol/L), that is 17% (2379) White Europeans, 15% (338) South Asians, 14% (236) Unknown/Others and 11% (89) Afro-Caribbeans, P < .001. The prevalence for South Asians aged <30 years was 2.1% and 5.2%, respectively, 2.6% and 8.6% for Afro-Caribbeans <30 years, and 2.0% and 8.4% for White Europeans <40 years. Glucose increased with age and was more often in the 'diabetes' range for South Asians than White Europeans with South Asian men particularly affected. One third of all emergency admissions were for <24 hours with 58% of these having glucose measured compared to 82% with duration >24 hours. CONCLUSIONS: Hyperglycaemia was evident in 21% of adults admitted as an emergency; various aspects related to follow-up and initial testing, age and ethnicity need to be considered by professional bodies addressing undiagnosed diabetes in hospital admissions.

Prevalence of undiagnosed type 2 diabetes in patients admitted with acute coronary syndrome: the utility of easily reproducible screening methods.

BACKGROUND: Despite the recognition of the importance of diagnosing dysglycaemia in patients with acute coronary syndrome (ACS) there remains a lack of consensus on the best screening modality. Our primary aims were to determine the prevalence of undiagnosed dysglycaemia and to compare the OGTT and HbA1c criteria for diagnosis of T2DM in patients admitted to hospital with ACS at baseline and at 3-months. We also aimed to investigate the role of a screening algorithm and a predictor score to define glucose tolerance in this population. METHODS: A prospective study in which patients admitted with ACS to two UK teaching hospitals were assessed at baseline and 3 months follow-up. RESULTS: The prevalence of diabetes at baseline was 20% and 16% based on OGTT and HbA1c criteria respectively. Forty three (43) % of the patients with T2DM based on OGTT would have been missed by the HbA1c criteria at baseline. Our screening algorithm identified 87% of patients with T2DM diagnosed with OGTT. Diabetes Predictor score had better sensitivity (>80%) and negative predictive value (>90%) compared to HbA1c criteria. Two thirds of participants with IGS and a third with T2DM changed their glycaemic status at 3 months. CONCLUSIONS: Only 48% of the patients admitted with ACS had normo-glycaemia based on OGTT. OGTT and HbA1c identified two different populations of patients with dysglycaemia with the HbA1c criteria missing almost half the patients with T2DM based on OGTT. Compared to HbA1c criteria our diabetes algorithm and diabetes predictor score had a better correlation with OGTT criteria.

Can HbA1c detect undiagnosed diabetes in acute medical hospital admissions?

OBJECTIVE: To study hyperglycaemia in acute medical admissions to Irish regional hospital. RESEARCH DESIGN AND METHODS: From 2005 to 2007, 2061 white Caucasians, aged >18 years, were admitted by 1/7 physicians. Those with diabetes symptoms/complications but no previous record of hyperglycaemia (n=390), underwent OGTT with concurrent HbA1c in representative subgroup (n=148). Comparable data were obtained for 108 primary care patients at risk of diabetes. RESULTS: Diabetes was diagnosed immediately by routine practice in 1% (22/2061) [aged 36 (26-61) years (median IQ range)/55% (12/22) male] with pre-existing diabetes/dysglycaemia present in 19% (390/2061) [69 (58-80) years/60% (235/390) male]. Possible diabetes symptoms/complications were identified in 19% [70 (59-79) years/57% (223/390) male] with their HbA1c similar to primary care patients [54 (46-61) years], 5.7 (5.3-6.0)%/39 (34-42)mmol/mol (n=148) vs 5.7 (5.4-6.1)%/39 (36-43)mmol/mol, p=0.35, but lower than those diagnosed on admission, 10.2 (7.4-13.3)%/88 (57-122)mmol/mol, p<0.001. Their fasting plasma glucose (FPG) was similar to primary care patients, 5.2 (4.8-5.7) vs 5.2 (4.8-5.9) mmol/L, p=0.65, but 2hPG higher, 9.0 (7.3-11.4) vs 5.5 (4.4-7.5), p<0.001. HbA1c identified diabetes in 10% (15/148) with 14 confirmed on OGTT but overall 32% (48/148) were in diabetic range on OGTT. The specificity of HbA1c in 2061 admissions was similar to primary care, 99% vs 96%, p=0.20, but sensitivity lower, 38% vs 93%, p<0.001 (63% on FPG/23% on 2hPG, p=0.037, in those with possible symptoms/complications). CONCLUSION: HbA1c can play a diagnostic role in acute medicine as it diagnosed another 2% of admissions with diabetes but the discrepancy in sensitivity shows that it does not reflect transient/acute hyperglycaemia resulting from the acute medical event.

The effect of glibenclamide on insulin secretion at normal glucose concentrations.

AIMS/HYPOTHESIS: The aim of this study was to investigate the incremental and proportional effect of a sulfonylurea on insulin secretion rates at low, elevated and high blood glucose, using parallel groups with ascending or descending glucose steps to minimise potential biases of a single stepped clamp order. METHODS: Following 14 days on placebo or glibenclamide (2.5 mg) tablets twice daily, separated by 14 days washout, 19 type 2 diabetic patients had ascending or descending three-step hyperinsulinaemic glucose clamps at 4, 8 and 12 mmol/l. C-peptide secretion was estimated by two-compartment C-peptide deconvolution. RESULTS: Patients in the ascending glucose steps group (n = 10) had mean (SD) age of 60.3 (6.5) years, BMI of 29.8 (4.9) kg/m(2) and fasting glucose on diet alone of 10.6 (2.9) mmol/l; while those in the descending glucose steps group (n = 9) had mean age of 58.2 (8.0) years, BMI of 30.5 (5.4) kg/m(2) and fasting glucose on diet alone of 9.8 (2.2) mmol/l. The geometric means (95% CI) of C-peptide secretion rates on placebo for glucose at 4.0, 8.0 and 12.0 mmol/l were 63 (46, 86), 143 (105, 195) and 205 (149, 281) pmol/min, respectively. On glibenclamide, this increased by 140 (99, 181), 126 (85, 167) and 158 (117, 199) pmol/min, respectively (p < 0.001 vs placebo). The absolute increment was significant (p < 0.001) and independent of clamp glucose concentration (p = 0.54). The proportional increase was greater at 4 mmol/l: 2.8-fold (2.4, 3.2), compared with 1.8-fold (1.5, 2.0) and 1.7-fold (1.4, 1.9) at 8 and 12 mmol/l, respectively (p < 0.001). CONCLUSIONS/INTERPRETATION: At low-normal glucose, glibenclamide exerted a disproportionate effect on insulin secretion. This study highlights the risks of hypoglycaemia when aiming for tight glucose control on this agent.

Comparison of IFCC-calibrated HbA(1c) from laboratory and point of care testing systems.

OBJECTIVE: WHO, IDF and ADA recommend HbA(1c) ≥6.5% (48 mmol/mol) for diagnosis of diabetes with pre-diabetes 6.0% (42 mmol/mol) [WHO] or 5.7% (39 mmol/mol) [ADA] to 6.4% (47 mmol/mol). We have compared HbA(1c) from several methods for research relating glycaemic markers. RESEARCH DESIGN AND METHODS: HbA1c was measured in EDTA blood from 128 patients with diabetes on IE HPLC analysers (Bio-Rad Variant II NU, Menarini HA8160 and Tosoh G8), point of care systems, POCT, (A1cNow+ disposable cartridges and DCA 2000(®)+ analyser), affinity chromatography (Primus Ultra2) and the IFCC secondary reference method (Menarini HA8160 calibrated using IFCC SRM protocol). RESULTS: Median (IQ range) on IFCC SRM was 7.5% (6.8-8.4) (58(51-68) mmol/mol) HbA(1c) with minimum 5.3%(34 mmol/mol)/maximum 11.9%(107 mmol/mol). There were positive offsets between IFCC SRM and Bio-Rad Variant II NU, mean difference (1SD), +0.33%(0.17) (+3.6(1.9) mmol/mol), r(2)=0.984, p<0.001 and Tosoh G8, +0.22%(0.20) (2.4(2.2) mmol/mol), r(2)=0.976, p<0.001 with a very small negative difference -0.04%(0.11) (-0.4(1.2) mmol/mol), r(2)=0.992, p<0.001 for Menarini HA8160. POCT methods were less precise with negative offsets for DCA 2000(®)+ analyser -0.13%(0.28) (-1.4(3.1) mmol/mol), r(2)=0.955, p<0.001 and A1cNow+ cartridges -0.70%(0.67) (-7.7(7.3) mmol/mol), r(2)=0.699, p<0.001 (n=113). Positive biases for Tosoh and Bio-Rad (compared with IFCC SRM) have been eliminated by subsequent revision of calibration. CONCLUSIONS: Small differences observed between IFCC-calibrated and NGSP certified methods across a wide HbA(1c) range were confirmed by quality control and external quality assurance. As these offsets affect estimates of diabetes prevalence, the analyser (and calibrator) employed should be considered when evaluating diagnostic data.

Preanalytical, analytical, and computational factors affect homeostasis model assessment estimates.

OBJECTIVE: We investigated how beta-cell function and insulin sensitivity or resistance are affected by the type of blood sample collected or choice of insulin assay and homeostatis model assessment (HOMA) calculator (http://www.dtu.ox.ac.uk). RESEARCH DESIGN AND METHODS: Insulin was measured using 11 different assays in serum and 1 assay in heparinized plasma. Fasting subjects with normoglycemia (n = 12), pre-diabetes, i.e., impaired fasting glucose or impaired glucose tolerance (n = 18), or type 2 diabetes (n = 67) were recruited. Patients treated with insulin or those who were insulin antibody-positive were excluded. HOMA estimates were calculated using specific insulin (SI) or radioimmunoassay (RIA) calculators (version 2.2). RESULTS: All glucose values were within model (HOMA) limits but not all insulin results, as 4.3% were <20 pmol/l and 1% were >300 pmol/l. beta-Cell function derived from different insulin assays ranged from 67 to 122% (median) for those with normoglycemia (P = 0.026), from 89 to 138% for those with pre-diabetes (P = 0.990), and from 50 to 81% for those with type 2 diabetes (P < 0.0001). Furthermore, insulin resistance ranged from 0.8 to 2.0 (P = 0.0007), from 1.9 to 3.2 (P = 0.842), and from 1.5 to 2.9 (P < 0.0001), respectively. This twofold variation in HOMA estimates from the various insulin assays studied in serum may be significant metabolically. Insulin was 15% lower in heparinized plasma (used in the original HOMA study) compared with serum, which is now more commonly used. beta-Cell function differed by 11% and insulin resistance by 15% when estimates derived from specific insulin were calculated using the RIA rather than the SI calculator. CONCLUSIONS: To enable comparison of HOMA estimates among individuals and different research studies, preanalytical factors and calculator selection should be standardized with insulin assays traceable to an insulin reference method procedure.

Comparison of 11 human insulin assays: implications for clinical investigation and research.

BACKGROUND: The American Diabetes Association task force on standardization of insulin assays in 1996 showed wide variation in assay bias. Newer assays are specific for insulin, with several now available on automated immunoassay analyzers. METHODS: In 2004, we compared 11 commercially available insulin assays by analyzing 150 serum samples (99 fasting/51 postprandial) from study participants with various degrees of glucose intolerance (exclusions being type 1 diabetes, insulin treatment, or presence of insulin antibodies). All assays were calibrated against International Reference Preparation 66/304. One assay was not specific for insulin and another was an RIA; 10 assays used enzyme/chemiluminescent labels. Bland-Altman difference plots were modified to use the mean insulin from all assays on the x-axis as a common comparator. RESULTS: As in the 1996 study, insulin values from the different assays varied by a factor of 2, with the nonspecific assay ranking in the middle of the distribution. Spearman rank correlation coefficients, for ranking samples vs the mean, were 0.983-0.997. Both offsets and concentration-dependent differences were seen in the modified difference plots. Imprecision (mean CV) for automated assays (3%) was not significantly different from manual assays (5%). Similar values were obtained when one automated assay was run in laboratories in both the UK and the US. Results of 1 assay showed lower insulin concentrations in heparinized plasma than in serum. CONCLUSIONS: Assay performance must be considered before comparing insulin results. The 2-fold variation in insulin results may be related to specificity, manufacturers' calibration procedures or conversion factors.

UKPDS 60: risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine.

BACKGROUND AND PURPOSE: People with type 2 diabetes are at elevated risk of stroke compared with those without diabetes. Relative risks have been examined in earlier work, but there is no readily available method for predicting the absolute risk of stroke in a diabetic individual. We developed mathematical models to estimate the risk of a first stroke using data from 4549 newly diagnosed type 2 diabetic patients enrolled in the UK Prospective Diabetes Study. METHODS: During 30 700 person-years of follow-up, 188 first strokes (52 fatal) occurred. Model fitting was carried out by maximum likelihood estimation using the Newton-Raphson method. Diagnostic plots were used to compare survival probabilities calculated by the model with those calculated using nonparametric methods. RESULTS: Variables included in the final model were duration of diabetes, age, sex, smoking, systolic blood pressure, total cholesterol to high-density lipoprotein cholesterol ratio and presence of atrial fibrillation. Not included in the model were body mass index, hemoglobin A1c, ethnicity, and ex-smoking status. The use of the model is illustrated with a hypothetical study power calculation. CONCLUSIONS: This model forecasts the absolute risk of a first stroke in people with type 2 diabetes using variables readily available in routine clinical practice.