Aims: To evaluate the diagnostic accuracy of haemoglobin A1c (HbA1c) in screening for impaired fasting glucose and Type 2 diabetes (T2DM).
Methods: We screened 3904 adults aged 45-70 (mean age 58.6 [standard deviation (SD) 6.9] years, mean body mass index (BMI) 29.9 [SD 4.7]kg/m(2)), with fasting plasma glucose (FPG) and HbA1c as part of a large diabetes prevention programme. We assessed the diagnostic accuracy of HbA1c for predicting impaired fasting glucose (IFG), (defined either as FPG 5.6-6.9 mmol/l, or 6.1-6.9 mmol/l), and T2DM (FPG ≥ 7.0 mmol/l).
Results: The prevalences of IFG were 13.8% (FPG 5.6-6.9 mmol/l) and 4.5% (FPG 6.1-6.9 mmol/l) and of T2DM was 2.1%. Using FPG 5.6-6.9 mmol/l as the IFG reference standard, HbA1c of 39-47 mmol/mol (5.7-6.4%) was 63% sensitive and 81% specific, and HbA1c 43-47 mmol/mol (6.1-6.4%) was 21% sensitive and 98% specific, in diagnosing IFG. HbA1c ≥ 48 mmol/mol (6.5%) was 61% sensitive and 99% specific in diagnosing T2DM. Having HbA1c 39-47 mmol/mol (5.7-6.4%), male sex, and body mass index >29.5 together increased the odds of IFG 6.5-fold (95% confidence interval (CI) 5.5-7.8) compared to the pre-test odds.
Conclusion: Defining 'pre-diabetes' at a lower HbA1c threshold of 39 mmol/mol (5.7%) instead of 47 mmol/mol (6.1%) increases its sensitivity in diagnosing IFG, but current American Diabetes Association definitions of 'pre-diabetes' based on HbA1c would fail to detect almost 40% of people currently classified as IFG. This has implications for current and future diabetes prevention programmes, for vascular risk management, and for clinical advice given to people with 'pre-diabetes' based on fasting glucose data.
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http://dx.doi.org/10.1016/j.diabres.2011.12.004 | DOI Listing |
J Med Internet Res
January 2025
Department of Internal Medicine, Hospital Clinic, Institut d'Investigacio Biomèdica August Pi i Sunyer, Barcelona, Spain.
Background: Enhancing self-management in health care through digital tools is a promising strategy to empower patients with type 2 diabetes (T2D) to improve self-care.
Objective: This study evaluates whether the Greenhabit (mobile health [mHealth]) behavioral treatment enhances T2D outcomes compared with standard care.
Methods: A 12-week, parallel, single-blind randomized controlled trial was conducted with 123 participants (62/123, 50%, female; mean age 58.
South Asia has high prevalence rates of type 2 diabetes (T2D). Until the 1990s, the prevalence of T2D within South Asia was low but much higher in the South Asian diaspora living abroad. Today, high prevalence rates of T2D are reported among those living in South Asia.
View Article and Find Full Text PDFJ Appl Oral Sci
January 2025
Universidade Federal Fluminense, Instituto de Saúde de Nova Friburgo, Departamento de Clínica Odontológica, Nova Friburgo, Rio de Janeiro, Brasil.
Aim: To evaluate the clinical effectiveness of ozonated sunflower oil (Oz) as an adjunctive of non-surgical periodontal therapy in patients with type 2 diabetes mellitus (DM2), on fibroblast cell viability and migration and the effectiveness of Oz on a Candida albicans (C. albicans) culture.
Methodology: In total, 32 sites in 16 DM2 with moderate to advanced periodontal disease with periodontal pocket depths ≥5mm were selected.
Rev Paul Pediatr
January 2025
Universidade Federal do Espírito Santo, Programa de Pós-Graduação Nutrição e Saúde, Vitória, ES, Brazil.
Objective: To assess the association between the combination of corporal adiposity (CA) and cardiorespiratory physical fitness (CRF) with cardiometabolic risk factors in children aged 7-10 years.
Methods: Cross-sectional observational study with a sample of 251 children registered in Family Health Units. Sociodemographic, lifestyle, anthropometric, biochemical, blood pressure, and CRF data were collected.
PLOS Digit Health
January 2025
Department of Mathematics & Statistics, York University, Toronto, Canada.
Chronic kidney disease (CKD) affects over 13% of the population, totaling more than 800 million individuals worldwide. Timely identification and intervention are crucial to delay CKD progression and improve patient outcomes. This research focuses on developing a predictive model to classify diabetic patients showing signs of kidney function impairment based on their CKD development risk.
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