Dataset Information

HbA1c, fasting and 2 h plasma glucose in current, ex- and never-smokers: a meta-analysis.

ABSTRACT: The relationships between smoking and glycaemic variables have not been well explored. We compared HbA1c, fasting plasma glucose (FPG) and 2 h plasma glucose (2H-PG) in current, ex- and never-smokers.This meta-analysis used individual data from 16,886 men and 18,539 women without known diabetes in 12 DETECT-2 consortium studies and in the French Data from an Epidemiological Study on the Insulin Resistance Syndrome (DESIR) and Telecom studies. Means of three glycaemic variables in current, ex- and never-smokers were modelled by linear regression, with study as a random factor. The I (2) statistic was used to evaluate heterogeneity among studies.HbA1c was 0.10% (95% CI 0.08, 0.12) (1.1 mmol/mol [0.9, 1.3]) higher in current smokers and 0.03% (0.01, 0.05) (0.3 mmol/mol [0.1, 0.5]) higher in ex-smokers, compared with never-smokers. For FPG, there was no significant difference between current and never-smokers (-0.004 mmol/l [-0.03, 0.02]) but FPG was higher in ex-smokers (0.12 mmol/l [0.09, 0.14]). In comparison with never-smokers, 2H-PG was lower (-0.44 mmol/l [-0.52, -0.37]) in current smokers, with no difference for ex-smokers (0.02 mmol/l [-0.06, 0.09]). There was a large and unexplained heterogeneity among studies, with I (2) always above 50%; I (2) was little changed after stratification by sex and adjustment for age and BMI. In this study population, current smokers had a prevalence of diabetes that was 1.30% higher as screened by HbA1c and 0.52% lower as screened by 2H-PG, in comparison with never-smokers.Across this heterogeneous group of studies, current smokers had a higher HbA1c and lower 2H-PG than never-smokers. This will affect the chances of smokers being diagnosed with diabetes.

SUBMITTER: Soulimane S

PROVIDER: S-EPMC4240946 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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HbA1c, fasting and 2 h plasma glucose in current, ex- and never-smokers: a meta-analysis.

Soulimane Soraya S Simon Dominique D Herman William H WH Lange Celine C Lee Crystal M Y CM Colagiuri Stephen S Shaw Jonathan E JE Zimmet Paul Z PZ Magliano Dianna D Ferreira Sandra R G SR Dong Yanghu Y Zhang Lei L Jorgensen Torben T Tuomilehto Jaakko J Mohan Viswanathan V Christensen Dirk L DL Kaduka Lydia L Dekker Jacqueline M JM Nijpels Giel G Stehouwer Coen D A CD Lantieri Olivier O Fujimoto Wilfred Y WY Leonetti Donna L DL McNeely Marguerite J MJ Borch-Johnsen Knut K Boyko Edward J EJ Vistisen Dorte D Balkau Beverley B

Diabetologia 20130925 1

<h4>Aims/hypothesis</h4>The relationships between smoking and glycaemic variables have not been well explored. We compared HbA1c, fasting plasma glucose (FPG) and 2 h plasma glucose (2H-PG) in current, ex- and never-smokers.<h4>Methods</h4>This meta-analysis used individual data from 16,886 men and 18,539 women without known diabetes in 12 DETECT-2 consortium studies and in the French Data from an Epidemiological Study on the Insulin Resistance Syndrome (DESIR) and Telecom studies. Means of thre ...[more]

PMID: 24065153

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Project description:Background DNA methylation (DNAm) performs excellently in the discrimination of current and former smokers from never smokers, where AUCs > 0.9 are regularly reported using a single CpG site (cg05575921; AHRR). However, there is a paucity of DNAm models which attempt to distinguish current, former and never smokers as individual classes. Derivation of a robust DNAm model that accurately distinguishes between current, former and never smokers would be particularly valuable to epidemiological research (as a more accurate smoking definition vs. self-report) and could potentially translate to clinical settings. Therefore, we appraise 4 DNAm models of ternary smoking status (that is, current, former and never smokers): methylation at cg05575921 (AHRR model), weighted scores from 13 CpGs created by Maas et al. (Maas model), weighted scores from a LASSO model of candidate smoking CpGs from the literature (candidate CpG LASSO model), and weighted scores from a LASSO model supplied with genome-wide 450K data (agnostic LASSO model). Discrimination is assessed by AUC, whilst classification accuracy is assessed by accuracy and kappa, derived from confusion matrices. Results We find that DNAm can classify ternary smoking status with reasonable accuracy, including when applied to external data. Ternary classification using only DNAm far exceeds the classification accuracy of simply assigning all classes as the most prevalent class (63.7% vs. 36.4%). Further, we develop a DNAm classifier which performs well in discriminating current from former smokers (agnostic LASSO model AUC in external validation data: 0.744). Finally, across our DNAm models, we show evidence of enrichment for biological pathways and human phenotype ontologies relevant to smoking, such as haemostasis, molybdenum cofactor synthesis, body fatness and social behaviours, providing evidence of the generalisability of our classifiers. Conclusions Our findings suggest that DNAm can classify ternary smoking status with close to 65% accuracy. Both the ternary smoking status classifiers and current versus former smoking status classifiers address the present lack of former smoker classification in epigenetic literature; essential if DNAm classifiers are to adequately relate to real-world populations. To improve performance further, additional focus on improving discrimination of current from former smokers is necessary. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-021-01191-6.

Dataset Information

HbA1c, fasting and 2 h plasma glucose in current, ex- and never-smokers: a meta-analysis.

Publications

HbA1c, fasting and 2 h plasma glucose in current, ex- and never-smokers: a meta-analysis.

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