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Active Smoking and the Risk of Type 2 Diabetes: A Systematic Review and Meta-analysis

Active Smoking and the Risk of Type 2 Diabetes: A Systematic Review and Meta-analysis Abstract Context Observational studies have suggested an association between active smoking and the incidence of type 2 diabetes. Objective To conduct a systematic review with meta-analysis of studies assessing the association between active smoking and incidence of type 2 diabetes. Data Sources A search of MEDLINE (1966 to May 2007) and EMBASE (1980 to May 2007) databases was supplemented by manual searches of bibliographies of key retrieved articles, reviews of abstracts from scientific meetings, and contact with experts. Study Selection Studies were included if they reported risk of impaired fasting glucose, impaired glucose tolerance, or type 2 diabetes in relationship to smoking status at baseline; had a cohort design; and excluded persons with diabetes at baseline. Data Extraction and Data Synthesis Two authors independently extracted the data, including the presence or absence of active smoking at baseline, the risk of diabetes, methods used to detect diabetes, and key criteria of study quality. Relative risks (RRs) were pooled using a random-effects model. Associations were tested in subgroups representing different patient characteristics and study quality criteria. Results The search yielded 25 prospective cohort studies (N = 1.2 million participants) that reported 45 844 incident cases of diabetes during a study follow-up period ranging from 5 to 30 years. Of the 25 studies, 24 reported adjusted RRs greater than 1 (range for all studies, 0.82-3.74). The pooled adjusted RR was 1.44 (95% confidence interval [CI], 1.31-1.58). Results were consistent and statistically significant in all subgroups. The risk of diabetes was greater for heavy smokers (≥20 cigarettes/day; RR, 1.61; 95% CI, 1.43-1.80) than for lighter smokers (RR,1.29; 95% CI, 1.13-1.48) and lower for former smokers (RR, 1.23; 95% CI, 1.14-1.33) compared with active smokers, consistent with a dose-response phenomenon. Conclusion Active smoking is associated with an increased risk of type 2 diabetes. Future research should attempt to establish whether this association is causal and to clarify its mechanisms. Smoking is the leading cause of avoidable death globally.1 Every year about 4 million people die because of smoking and it is estimated that tobacco causes about 8.8% of deaths worldwide.2 The magnitude of this public health challenge is growing, and estimates suggest that as many as 10 million people may die from smoking-related causes in 2025.3 The prevalence of diabetes is also expected to have a major increase by the year 2025,4 a concerning trend given that diabetes imposes a significant public health burden and large demands on health care systems.5 A number of primary studies have assessed the association between smoking and incidence of glucose abnormalities, suggesting that active smoking could be independently associated with glucose intolerance, impaired fasting glucose, and type 2 diabetes; smoking may therefore be a modifiable risk factor for type 2 diabetes. Some of these studies have been summarized in qualitative reviews.6-8 However, to our knowledge, the quality of existing studies has not been systematically assessed and the clinical features of these studies have not been fully assessed to further characterize this potential association and its determinants. We therefore conducted a systematic review and meta-analysis of prospective cohort studies describing the association between active smoking and the incidence of diabetes or other glucose metabolism irregularities. Methods Search Strategy We conducted a systematic literature search of MEDLINE (1966 to May 2007) and EMBASE (1980 to May 2007) for studies describing the association between active smoking (in contrast to passive or secondhand smoking) and impaired fasting glucose, impaired glucose tolerance, or type 2 diabetes. In addition, we searched the reference lists of all identified relevant publications, reviewed abstracts of selected scientific meetings (the Society for Research on Nicotine and Tobacco and the American Diabetes Association meetings) and contacted experts in smoking cessation and diabetes. We considered articles published in any language. We used a literature searching approach described by Egger et al9 for identifying observational studies and studies of prognosis. Three search themes were combined using the Boolean operator “and.” The first theme, glucose metabolism irregularity, combined exploded versions of Medical Subject Headings (MeSH) diabetes mellitus, type 2 or diabetes mellitus or prediabetic state or metabolic syndrome X or glucose intolerance or hyperglycemia or glucose metabolism disorders or insulin resistance or glucose tolerance test or text words insulin sensitivity or impaired fasting glucose or impaired glucose tolerance or IGT or IFG. The second theme, smoking, combined exploded versions of MeSH terms smoking or smoking cessation or smoke inhalation injury or tobacco, smokeless or tobacco use cessation or tobacco use disorder or tobacco or nicotine or text words nicotine dependence or tobacco dependence or smoking dependence or cigarette*. The third theme, studies with a prospective design, combined exploded versions of MeSH terms incidence or cohort studies or follow-up studies or prognosis or early diagnosis or survival analysis or text words course or predict* or prognos* . Because we focused on original studies and observational cohort studies, we excluded other design types using the Boolean operator “not”: meta-analysis (MeSH term) or review (publication type) or case-control studies (MeSH term). No previous meta-analyses were identified. Selection Criteria Two reviewers (C.W. and P.B.) identified articles eligible for further review by performing an initial screen of identified abstracts or titles. Articles were considered for inclusion in the systematic review if they reported data from an original study (ie, no review articles) and reported the incidence of impaired fasting glucose, glucose intolerance, or type 2 diabetes in active cigarette smokers. We used broad inclusion criteria for studies, including all spectra of glucose abnormality (from impaired fasting glucose to diabetes type 2) and smoking status. The observed agreement between reviewers for eligibility of articles on this first screening was 94.6%, corresponding to modest agreement (κ = 0.40). Articles were retained when either of the 2 reviewers believed that it should be retained. The second screening was based on full-text review. To be included, studies had to be cohort studies (prospective cohort or historical cohort) with an adult population (≥16 years) exposed to active cigarette smoking and a comparison group of nonsmokers. One of the outcomes had to be the incidence of impaired glucose tolerance, impaired fasting glucose, or type 2 diabetes. Exclusion criteria were studies that included participants with diabetes at the beginning of the study or that used an inappropriate comparison group (a comparison group that was not nonsmokers or former smokers). The agreement between reviewers for eligibility of articles was 96.0%, with a κ of 0.86. Any disagreement was resolved by consensus. Data Extraction The key exposure variable was the presence or absence of active smoking at baseline, and the preferred reference group was “never smokers.” The majority of studies (n = 18) defined a group of former smokers but 7 studies dichotomized the exposure variable (smokers vs nonsmokers) without mentioning whether the nonsmoking group included former smokers. We considered this heterogeneous group as nonsmokers in the pooled analysis and performed a sensitivity analysis that only included studies with a reference group defined as strictly never smokers. The outcome variable of interest was defined as the presence or absence of type 2 diabetes, impaired fasting glucose, glucose intolerance, or a combination of these. The definitions and diagnostic procedures used to define this outcome varied somewhat across studies because of the different countries and periods in which the studies were performed. The American Diabetes Association and the World Health Organization now share identical diagnostic criteria for type 2 diabetes10 but definitions have changed over time. Moreover, the prevalence of diabetes can change as a function of diagnostic criteria used.11 The criteria used in the studies retrieved included the World Health Organization 1985 criteria12 (fasting glucose threshold ≥140 mg/dL; to convert glucose to mmol/L, multiply by 0.0555), the World Health Organization 1999 criteria13 or the American Diabetes Association 199714 criteria (fasting glucose threshold ≥126 mg/dL), or other criteria (fasting glucose threshold ≥110 mg/dL or ≥120 mg/dL). In all analyzed studies, participants were screened for diabetes at baseline and excluded if found to have diabetes. However, baseline diabetes screening methods also varied across studies and could be a biological screening (blood tests or urine analysis), patient report, or physician report. The methods used to screen for glucose metabolism irregularities in the follow-up periods varied across studies and included a biological screening, participant's report with or without confirmation (by physician, registry, or subsequent biological testing), or physician's report with or without confirmation (by registry or biological testing). We then extracted any reported relative risks (RRs), hazard ratios, odds ratios (ORs), or incidence density ratios for the risk of developing diabetes or other glucose metabolism irregularities for active smokers compared with nonsmokers. Both unadjusted and adjusted values were extracted for these measures of risk. If available, we extracted any reported risks of diabetes for former smokers compared with never smokers and for heavy smokers and lighter smokers compared with never smokers. We also extracted information on key indicators of study quality, using the Meta-analysis of Observational Studies in Epidemiology (MOOSE) standards15 for reporting of meta-analyses of observational studies. We considered inclusion of consecutive participants in a cohort (all participants presenting with study inclusion criteria during a specific period should be included in a cohort), follow-up duration (the duration should be long enough to allow for a latent period), blinding study personnel evaluating key outcomes to exposure status, and statistical adjustment for the main confounding factors of interest (sex, socioeconomic level, physical activity, age, obesity, diet, ethnicity, increased waist circumference, alcohol consumption, heredity, hypercholesterolemia, blood pressure, fasting blood glucose, comorbidities, and use of antihypertensive drugs). Statistical Analysis The RRs were used as the common measure of association across studies. To do this, the hazard ratios and incidence density ratios were directly considered as RRs. The ORs were transformed into RRs using the formula RR = OR/[(1-Po)+(Po × OR)], in which Po is the incidence of the outcome of interest in the nonexposed group.16 This method of transformation has some limitations and can underestimate the variance of the RRs derived from the ORs.17,18 We therefore performed a sensitivity analysis that excluded the 5 studies in which this transformation was performed. We also compared the results applying the Miettinen test-based approach19 for calculating the variance of the lnRR (variance lnRR = variance lnOR × [lnRR/lnOR]). Meta-analysis was performed using Stata version 9.1 (StataCorp, College Station, Texas). We used the “metan” command in Stata to pool the lnRR across studies using the DerSimonian and Laird random-effects model.20 Forest plots were used to visually assess the RR estimates and corresponding 95% confidence intervals (CIs) across studies. Analyses were stratified by study quality criteria and by participant characteristics. To assess for heterogeneity of RRs across studies, the Cochrane Q statistic (significance level of P ≤ .10) and the I2 statistic were calculated.21,22 Meta-regression and sensitivity analysis were performed to assess the effects of selected study quality and clinical factors on diabetes risk. The possibility of publication bias was assessed using the Begg test and visual inspection of a funnel plot.23,24 We also performed the Duval and Tweedie nonparametric “trim and fill” procedure to further assess the possible effect of publication bias in our meta-analysis.21 This method considers the possibility of hypothetical “missing” studies that might exist, imputes their RRs, and recalculates a pooled RR that incorporates the hypothetical missing studies as though they actually existed. Results Literature Search The search strategy retrieved 2246 unique citations: 1340 from MEDLINE and 906 from EMBASE. Of these, 2098 citations were excluded after the first screening based on abstracts or titles, leaving 148 articles for full-text review (Figure 1). Hand searching of the bibliographic references of these articles identified 1 additional article, for a total of 149 articles for full-text review. On this review, 124 articles were excluded for the reasons listed in Figure 1, leaving 25 studies for final inclusion in the systematic review and meta-analysis. Two supplementary studies were identified that had been published only as abstracts from conference proceedings of scientific meetings.25,26 They were not included in the pooled analyses because of lack of details on key study variables and because of study quality. Risks were given for subgroups of smokers (heavy and lighter smokers) and were higher for smokers vs nonsmokers. However, there were not sufficient data to calculate a mean risk for smokers and therefore they could not be included with other studies in sensitivity analyses. The review identified 2 articles that were based on overlapping data from the Nurses' Health study27,28 and 2 articles that were based on overlapping data from the Monitoring Trends and Determinants of Cardiovascular Disease (MONICA) Augsburg cohort study.29,30 We avoided duplicate inclusion of data by selecting only the more complete article from each cohort. Study Characteristics Characteristics of the 25 selected studies are shown in Table 1.27,29,31-53 All were prospective cohort studies. All studies reported the incidence of diabetes as an outcome of interest except 1 study53 that reported the incidence of a composite outcome (diabetes and/or impaired fasting glucose). The association between smoking and diabetes was the primary outcome of interest for 16 studies, whereas it was a secondary question in 9 studies. Diabetes was screened with biological measures in 11 studies, was reported by patients or physicians in 11 studies, and was determined by other methods (hospital medical registries, insurance registries) in 3 studies. Regarding cut-point definitions for diabetes, 6 studies used a fasting glucose threshold of 140 mg/dL or higher, 8 studies used a threshold of 126 mg/dL or higher, 1 study used a threshold of 120 mg/dL or higher, 2 studies used a threshold of 110 mg/dL or higher, and 8 studies did not explicitly mention the criteria that they used. Screening for diabetic participants at baseline was performed using a biological screening for 14 studies and by asking patients or physicians for the other 11. The selected studies were published between 1992 and 2006, and the number of participants per study ranged from 630 to 709 827, for a total of 1.2 million participants across studies (45 844 incident cases of diabetes). Seven studies were conducted in the United States, 7 in Japan, 6 in Scandinavian countries, 3 in the United Kingdom, 1 in Germany, and 1 in Israel. Eleven studies involved men only, 1 study involved only women, and the other 13 studies included both men and women. Mean body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of participants ranged from 22.3 to 28.4 and mean age at baseline varied from 16 to 60.7 years. The percentage of smokers ranged from 9% to 67% and the pooled percentage of smokers was 35%. The cohort study quality criteria of blinding to ascertain outcome and recruiting consecutive patients were not explicitly specified in any of the studies that we selected. Follow-up ranged from 5 to 30 years; average (mean or median) follow-up duration was given for only 9 studies. The frequency of follow-up was at least once per year for 9 studies, once every 2 years for 4 studies, and at baseline and the end of the study for the remaining 12 studies. The proportion of patients with complete follow-up to the end of the study was given for 17 studies and ranged from 69.2% to 99.7%. Adjusted RRs could be determined for all studies, either as reported or by conversion from ORs (Table 2). Most risk measures were adjusted for age (22 studies) and BMI (22 studies); fewer were adjusted for physical activity (13 studies), alcohol consumption (14 studies), heredity (10 studies), education (6 studies), diet (2 studies), or waist circumference (3 studies). Risk of Diabetes for Smokers Compared With Nonsmokers Quiz Ref IDAmong the 25 selected studies, all but one35 found an association between active smoking and an increased risk of diabetes, although not all were statistically significant. Three studies reported unadjusted RRs43,52,53; the pooled crude RR estimate from these studies was 1.89 (95% CI, 1.58-2.27). All 25 studies provided adjusted risks expressed as RRs, hazard ratios, incidence density ratios, or ORs, and the derived fully adjusted RRs ranged from 0.82 to 3.74. Active smokers had an increased risk of developing type 2 diabetes compared with nonsmokers, with a pooled RR of 1.44 (95% CI, 1.31-1.58) (Figure 2). A sensitivity analysis that excluded all studies for which the OR to RR conversion was used had a similar result, with a pooled RR of 1.44 (95% CI, 1.30-1.59). Using the test-based approach by Miettinen19 to calculate the variance resulted in essentially identical results (RR, 1.44 [95% CI, 1.31-1.58]). In a sensitivity analysis that included only the 18 studies in which the comparison was defined as strictly nonsmokers (without former smokers), the pooled RR was 1.45 (95% CI, 1.31-1.62). There was evidence of statistical heterogeneity of RRs across studies (Q statistic, 98.08; P < .001; I2, 75.5%). These measurements of heterogeneity were likely driven by the extremely large overall number of participants in our analysis (>1 million). The point estimates of the RRs were consistently greater than 1 in all but 1 study, and study subgroups were more homogeneous. Stratified Analyses To explore the study heterogeneity, we performed stratified analyses across a number of key study characteristics and clinical factors (Table 3). The finding of increased diabetes risk in smokers was consistently found in all of the stratified analyses. Study quality characteristics did not seem to markedly influence the results, although studies that met more quality criteria tended to report a slightly stronger association of smoking with diabetes incidence. For example, stronger associations between smoking and diabetes incidence were found in studies that were adjusted to 8 or more confounding factors, if smoking and incidence of diabetes was the primary outcome, and if a biological screening for diabetes was performed at baseline (Table 3). The characteristics of participants included in the primary studies also seemed to be associated with the results. For example, studies reported a stronger association between smoking and diabetes incidence if they included older participants (mean age of participants, ≥50 years) or when the participants tended to be overweight or obese (mean BMI of participants, ≥25) (Table 3). In studies that included both men and women, the pooled risk was similar in both sexes (pooled RR of 1.28 [95% CI, 1.12-1.45] for men and 1.25 [95% CI, 1.06-1.65] for women). The stratified analyses shown in Table 3 suggest a dose-response relationship between smoking and diabetes. Quiz Ref IDThe association between smoking and diabetes was stronger for heavy smokers (≥20 cigarettes/day; RR, 1.61 [95% CI, 1.43-1.80]) compared with lighter smokers (RR, 1.29 [95% CI, 1.13-1.48]). The association also was weaker for former smokers (RR, 1.23 [95% CI, 1.14-1.33]) than it was for active smokers. The association between smoking and diabetes was slightly stronger if there was a biological screening for diabetes during follow-up (RR, 1.49 [95% CI, 1.35-1.63]) compared with cases reported by patient or physician (RR, 1.39 [95% CI, 1.20-1.62]). The association between smoking and diabetes was also stronger for the 6 studies in which a glucose threshold of 140 mg/dL or higher was used (RR, 1.63 [95% CI, 1.33-1.99]) compared with 8 studies in which a glucose threshold of 126 mg/dL or higher was used (RR, 1.47 [95% CI, 1.30-1.65]). In a sensitivity analysis of the 24 studies that reported only on the incidence of diabetes (excluding the 1 study that evaluated the risk of impaired glucose tolerance53), the overall pooled result did not change (RR, 1.43 [95% CI, 1.30-1.57]). Publication Bias Visual inspection of the Begg funnel plot revealed asymmetry (P < .001) (Figure 3A). This raises the possibility of publication bias, although the Begg test was not statisitically significant (z = 1.45; P = .15). Because of this, we undertook a sensitivity analysis using the trim and fill method,54 which conservatively imputes hypothetical negative unpublished studies to mirror the positive studies that cause funnel plot asymmetry. The imputed studies produce a symmetrical funnel plot (Figure 3B). The pooled analysis incorporating the hypothetical studies continued to show a statistically significant association between smoking and diabetes (RR, 1.32 [95% CI, 1.21-1.44]; P < .001). Comment There is an extensive body of literature reporting on the association between active cigarette smoking and the incidence of diabetes. The 25 studies that we identified report RRs that, while somewhat variable in magnitude, indicate a positive association in all but 1 study. Furthermore, the association persists and remains statistically significant across a number of stratified analyses exploring clinical and study quality factors, and also persists in sensitivity analyses performed to assess the potential effect of varying diabetes outcome definitions and hypothetical unpublished studies. Given this consistency, we conclude that the relevant question should no longer be whether this association exists, but rather whether this established association is causal. Observational primary studies cannot prove causality. However, the studies in this review do meet several of the Hill criteria55 for causation. First, there is an appropriate temporal relationship: the cigarette smoking preceded diabetes incidence in all studies. Second, the findings are consistent with a dose-response relationship, with stronger associations for heavy smokers relative to lighter smokers and for active smokers relative to former smokers. However, an observed dose-response relationship can arise from the intensity of clustering with other diabetes risk factors such as lack of physical activity and unhealthy diet. Quiz Ref IDThird, there is theoretical biological plausibility for causality in that smoking may lead to insulin resistance or inadequate compensatory insulin secretion responses according to several56-61 but not all62 studies. Smoking also has a clinically significant effect on both oral and intravenous glucose tolerance tests that could influence diabetes detection.60,63-65 This could be due to a direct effect of nicotine or other components of cigarette smoke on beta cells of the pancreas, as suggested by the association of cigarette smoking with chronic pancreatitis and pancreatic cancer.66 Fourth, there is consistency of this association across 24 studies, as shown by the forest plot (Figure 2). Fifth, the strength of the association with diabetes is not negligible in the context of tobacco research. Conversely, there are also possible noncausal explanations for this association. Smoking is often associated with other unhealthy behaviors that favor weight gain and/or diabetes, such as lack of physical activity, poor fruit and vegetable intake, and high alcohol intake.67,68 Furthermore, this clustering of behaviors is more prevalent in individuals of lower socioeconomic status.69,70 Some of these factors were considered and adjusted for in the studies included in our review, but the extent to which these potential intervening factors were controlled for in the individual studies was generally limited. The lack of adjustment for socioeconomic status (only 6 studies adjusted for socioeconomic status or education), diet (only 2 studies), physical activity (only 13 studies), and alcohol consumption (only 14 studies) could contribute to a noncausal association between smoking and diabetes. Smokers tend to be thinner than nonsmokers or former smokers, and several studies have shown that smokers' BMI is lower.71-73Quiz Ref IDHowever, there is evidence that smokers (especially heavy smokers) tend to have higher BMIs than lighter smokers and even some nonsmokers.74 In addition to a clustering of risky behaviors, this finding could be due to the weight cycling phenomenon. Smokers tend to gain weight when they quit smoking; the stronger the dependence, the greater the risk of relapse.75,76 Therefore, heavy smokers may need several attempts before they definitively quit smoking, and they gain weight during these attempts that they never completely manage to lose when they relapse. Quiz Ref IDFurthermore, with a normal BMI, smokers tend to have a greater risk of abdominal fat accumulation compared with nonsmokers.62,77-79 The mechanism is not well elucidated but because smoking has an anti-estrogenic effect,80,81 it could be related to a hormonal imbalance that could lead to central obesity. Obesity and weight gain are strong risk factors for developing type 2 diabetes82,83 and several studies also show that abdominal obesity is associated with the development of type 2 diabetes.84,85 Limitations of this meta-analysis must be considered. First, the quality of individual studies was not always optimal, as shown by the general lack of information on blinding and recruiting of consecutive patients for all studies. Second, conversion of ORs to RRs15 could have underestimated the variance of the RRs derived from ORs. However, a sensitivity analysis that excluded the affected studies and use of the Miettinen test-based approach to calculate variance of the lnRR had only an extremely small effect on the results. Third, there is heterogeneity of RRs across studies, corresponding in part to heterogeneity in study definitions. However, stratified analyses showed pooled RRs consistently greater than 1 across a number of clinical factors. Fourth, the funnel plot analysis showed some asymmetry suggesting the possibility of publication bias. The trim and fill sensitivity analysis did not change the general result (although the strength of the association was slightly attenuated), suggesting that the association is not an artifact of unpublished negative studies. Nevertheless, that possibility is not fully excluded by this method. Considering the consistent finding of increased diabetes incidence associated with active cigarette smoking across a large number of studies, we believe that there is no need for further cohort studies to test this hypothesis. However, there is a need for studies that include detailed measurement and adjustment for potential confounding factors such as socioeconomic status, education, and exercise with a goal of establishing whether the association with smoking is causal. We recommend that future studies focus on plausible causal mechanisms or mediating factors such as obesity, lack of physical activity, dietary habits, and stress levels. Back to top Article Information Corresponding Author: Carole Willi, MD, Department of Ambulatory Care and Community Medicine, 44 Bugnon Ave, 1011 Lausanne, Switzerland (carole.willi@hospvd.ch). Author Contributions: Dr Willi had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Willi, Ghali, Cornuz. Acquisition of data: Willi, Bodenmann. Analysis and interpretation of data: Willi, Ghali, Faris, Cornuz. Drafting of the manuscript: Willi, Bodenmann. Critical revision of the manuscript for important intellectual content: Ghali, Faris, Cornuz. Statistical analysis: Willi, Ghali, Faris. Obtained funding: Cornuz. Administrative, technical, or material support: Bodenmann, Ghali. Study supervision: Ghali, Cornuz. Financial Disclosures: None reported. Funding/Support: Dr Ghali is supported by a Senior Scholar Award from the Alberta Heritage Foundation for Medical Research and by a Canada Research Chair in Health Services Research. Role of the Sponsor: The agencies that provide Dr Ghali with funding support did not have any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Additional Contributions: We thank Finlay McAlister, MD, MSc, University of Alberta, for his helpful comments on an earlier version of the manuscript. The authors also thank Anne Parrical, DESS (Diplôme d’Etudes Supérieures Spécialisées), medical library scientist at the University Hospital of Lausanne, for her guidance in formulating our literature searching strategy. These individuals did not receive any compensation for their contribution to the study. References 1. Annual smoking-attributable mortality, years of potential life lost, and productivity losses—United States, 1997-2001. MMWR Morb Mortal Wkly Rep. 2005;54(25):625-62815988406Google Scholar 2. The World Health Report. Reducing Risks, Promoting Healthy Life. 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Effects of smoking on the incidence of non-insulin-dependent diabetes mellitus: replication and extension in a Japanese cohort of male employees. Am J Epidemiol. 1997;145(2):103-1099006306Google ScholarCrossref 35. Njølstad I, Arnesen E, Lund-Larsen PG. Sex differences in risk factors for clinical diabetes mellitus in a general population: a 12-year follow-up of the Finnmark Study. Am J Epidemiol. 1998;147(1):49-589440398Google ScholarCrossref 36. Sugimori H, Miyakawa M, Yoshida K. et al. Health risk assessment for diabetes mellitus based on longitudinal analysis of MHTS database. J Med Syst. 1998;22(1):27-329554107Google ScholarCrossref 37. Uchimoto S, Tsumura K, Hayashi T. et al. Impact of cigarette smoking on the incidence of type 2 diabetes mellitus in middle-aged Japanese men: the Osaka Health Survey. Diabet Med. 1999;16(11):951-95510588526Google ScholarCrossref 38. Strandberg TE, Salomaa V, Strandberg TE, Salomaa V. Factors related to the development of diabetes during a 20-year follow-up: a prospective study in a homogeneous group of middle-aged men. Nutr Metab Cardiovasc Dis. 2000;10(5):239-24611213532Google Scholar 39. Nakanishi N, Nakamura K, Matsuo Y, Suzuki K, Tatara K. Cigarette smoking and risk for impaired fasting glucose and type 2 diabetes in middle-aged Japanese men. Ann Intern Med. 2000;133(3):183-19110906832Google ScholarCrossref 40. Manson JE, Ajani UA, Liu S. et al. A prospective study of cigarette smoking and the incidence of diabetes mellitus among US male physicians. Am J Med. 2000;109(7):538-54211063954Google ScholarCrossref 41. Will JC, Galuska DA, Ford ES. et al. Cigarette smoking and diabetes mellitus: evidence of a positive association from a large prospective cohort study. Int J Epidemiol. 2001;30(3):540-54611416080Google ScholarCrossref 42. Wannamethee SG, Shaper AG, Perry IJ. et al. Smoking as a modifiable risk factor for type 2 diabetes in middle-aged men. Diabetes Care. 2001;24(9):1590-159511522704Google ScholarCrossref 43. Montgomery SM, Ekbom A. Smoking during pregnancy and diabetes mellitus in a British longitudinal birth cohort. BMJ. 2002;324(7328):26-2711777801Google ScholarCrossref 44. Sawada SS, Lee IM, Muto T, Matuszaki K, Blair SN. Cardiorespiratory fitness and the incidence of type 2 diabetes: prospective study of Japanese men. Diabetes Care. 2003;26(10):2918-292214514602Google ScholarCrossref 45. Sairenchi T, Iso H, Nishimura A. et al. Cigarette smoking and risk of type 2 diabetes mellitus among middle-aged and elderly Japanese men and women. Am J Epidemiol. 2004;160(2):158-16215234937Google ScholarCrossref 46. Carlsson S, Midthjell K, Grill V. Smoking is associated with an increased risk of type 2 diabetes but a decreased risk of autoimmune diabetes in adults: an 11-year follow-up of incidence of diabetes in the Nord-Trondelag study. Diabetologia. 2004;47(11):1953-195615558231Google ScholarCrossref 47. Eliasson M, Asplund K, Nasic S, Rodu B. Influence of smoking and snus on the prevalence and incidence of type 2 diabetes amongst men: the northern Sweden MONICA study. J Intern Med. 2004;256(2):101-11015257722Google ScholarCrossref 48. Lyssenko V, Almgren P, Anevski D. et al. Predictors of and longitudinal changes in insulin sensitivity and secretion preceding onset of type 2 diabetes. Diabetes. 2005;54(1):166-17415616025Google ScholarCrossref 49. Patja K, Jousilahti P, Hu G. et al. Effects of smoking, obesity and physical activity on the risk of type 2 diabetes in middle-aged Finnish men and women. J Intern Med. 2005;258(4):356-36216164575Google ScholarCrossref 50. Waki K, Noda M, Sasaki S. et al. Alcohol consumption and other risk factors for self-reported diabetes among middle-aged Japanese: a population-based prospective study in the JPHC study cohort I. Diabet Med. 2005;22(3):323-331[published correction appears in Diabet Med . 2005;22(6):818]15717882Google ScholarCrossref 51. Tenenbaum A, Fisman EZ, Adler Y, Motro M, Boyko V, Behar S. Smoking and development of type 2 diabetes in patients with decreased functional capacity. Int J Cardiol. 2005;104(3):275-28116186056Google ScholarCrossref 52. Foy CG, Bell RA, Farmer DF, Goff DC Jr, Wagenknecht LE. Smoking and incidence of diabetes among US adults: findings from the Insulin Resistance Atherosclerosis Study. Diabetes Care. 2005;28(10):2501-250716186287Google ScholarCrossref 53. Houston TK, Person SD, Pletcher MJ, Liu K, Iribarren C, Kiefe CI. Active and passive smoking and development of glucose intolerance among young adults in a prospective cohort: CARDIA study. BMJ. 2006;332(7549):1064-106916603565Google ScholarCrossref 54. Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455-46310877304Google ScholarCrossref 55. Hill AB. The environment and disease: association or causation? Proc R Soc Med. 1965;58:295-30014283879Google Scholar 56. Attvall S, Fowelin J, Lager I, Von Schenck H, Smith U. Smoking induces insulin resistance–a potential link with the insulin resistance syndrome. J Intern Med. 1993;233(4):327-3328463765Google ScholarCrossref 57. Facchini FS, Hollenbeck CB, Jeppesen J, Chen YD, Reaven GM. Insulin resistance and cigarette smoking. Lancet. 1992;339(8802):1128-11301349365Google ScholarCrossref 58. Frati AC, Iniestra F, Ariza CR. Acute effect of cigarette smoking on glucose tolerance and other cardiovascular risk factors. Diabetes Care. 1996;19(2):112-1188718429Google ScholarCrossref 59. Schwartz GG, Il'yasova D, Ivanova A. Urinary cadmium, impaired fasting glucose, and diabetes in the NHANES III. Diabetes Care. 2003;26(2):468-47012547882Google ScholarCrossref 60. Janzon L, Berntorp K, Hanson M, Lindell SE, Trell E. Glucose tolerance and smoking: a population study of oral and intravenous glucose tolerance tests in middle-aged men. Diabetologia. 1983;25(2):86-886354814Google ScholarCrossref 61. Eliasson M, Asplund K, Evrin PE, Lundblad D. Relationship of cigarette smoking and snuff dipping to plasma fibrinogen, fibrinolytic variables and serum insulin: the Northern Sweden MONICA Study. Atherosclerosis. 1995;113(1):41-537755654Google ScholarCrossref 62. Wareham NJ, Ness EM, Byrne CD, Cox BD, Day NE, Hales CN. Cigarette smoking is not associated with hyperinsulinemia: evidence against a causal relationship between smoking and insulin resistance. Metabolism. 1996;45(12):1551-15568969291Google ScholarCrossref 63. Sandberg H, Roman L, Zavodnick J, Kupers N. The effect of smoking on serum somatotropin, immunoreactive insulin and blood glucose levels of young adult males. J Pharmacol Exp Ther. 1973;184(3):787-7914687238Google Scholar 64. Cryer PE, Haymond MW, Santiago JV, Shah SD. Norepinephrine and epinephrine release and adrenergic mediation of smoking-associated hemodynamic and metabolic events. N Engl J Med. 1976;295(11):573-577950972Google ScholarCrossref 65. Godsland IF, Wynn V, Walton C, Stevenson JC. Insulin resistance and cigarette smoking. Lancet. 1992;339(8809):1619-16201351597Google ScholarCrossref 66. Talamini G, Bassi C, Falconi M. et al. Alcohol and smoking as risk factors in chronic pancreatitis and pancreatic cancer. Dig Dis Sci. 1999;44(7):1303-131110489910Google ScholarCrossref 67. Chiolero A, Wietlisbach V, Ruffieux C, Paccaud F, Cornuz J. Clustering of risk behaviors with cigarette consumption: a population-based survey. Prev Med. 2006;42(5):348-35316504277Google ScholarCrossref 68. van Dam RM, Rimm EB, Willett WC. et al. Dietary patterns and risk for type 2 diabetes mellitus in US men. Ann Intern Med. 2002;136(3):201-20911827496Google ScholarCrossref 69. Healton CG, Vallone D, McCausland KL, Xiao H, Green MP. Smoking, obesity, and their co-occurrence in the United States: cross sectional analysis. BMJ. 2006;333(7557):25-2616698804Google ScholarCrossref 70. Wild SH, Byrne CD. ABC of obesity: risk factors for diabetes and coronary heart disease. BMJ. 2006;333(7576):1009-101117095784Google ScholarCrossref 71. Albanes D, Jones DY, Micozzi MS, Mattson ME. Associations between smoking and body weight in the US population: analysis of NHANES II. Am J Public Health. 1987;77(4):439-4443493709Google ScholarCrossref 72. Smoking wastes a good Parisienne. JAMA. 1989;262(9):1185-11862761057Google Scholar 73. Gordon T, Kannel WB, Dawber TR, McGee D. Changes associated with quitting cigarette smoking: the Framingham Study. Am Heart J. 1975;90(3):322-3281163424Google ScholarCrossref 74. Chiolero A, Jacot-Sadowski I, Faeh D, Paccaud F, Cornuz J. Association of cigarettes smoked daily with obesity in a general adult population. Obesity (Silver Spring ). 2007;15(5):1311-1318Google ScholarCrossref 75. Augustson EM, Wanke KL, Rogers S. et al. Predictors of sustained smoking cessation: a prospective analysis of chronic smokers from the Alpha-Tocopherol Beta-Carotene Cancer Prevention Study [published online ahead of print June 28, 2007]. Am J Public Healthdoi:10.2105/AJPH.2005.084137Google Scholar 76. Ockene JK, Emmons KM, Mermelstein RJ. et al. Relapse and maintenance issues for smoking cessation. Health Psychol. 2000;19(1):(suppl) 17-3110709945Google ScholarCrossref 77. Simon JA, Seeley DG, Lipschutz RC. et al. The relation of smoking to waist-to-hip ratio and diabetes mellitus among elderly women. Prev Med. 1997;26(5 pt 1):639-6449327471Google ScholarCrossref 78. Seidell JC. Time trends in obesity: an epidemiological perspective. Horm Metab Res. 1997;29(4):155-1589178022Google ScholarCrossref 79. Shimokata H, Muller DC, Andres R. Studies in the distribution of body fat, III: effects of cigarette smoking. JAMA. 1989;261(8):1169-11732915440Google ScholarCrossref 80. Tankó LB, Christiansen C. An update on the antiestrogenic effect of smoking: a literature review with implications for researchers and practitioners. Menopause. 2004;11(1):104-10914716190Google ScholarCrossref 81. Windham GC, Mitchell P, Anderson M. et al. Cigarette smoking and effects on hormone function in premenopausal women. Environ Health Perspect. 2005;113(10):1285-129016203235Google ScholarCrossref 82. Colditz GA, Willett WC, Stampfer MJ. et al. Weight as a risk factor for clinical diabetes in women. Am J Epidemiol. 1990;132(3):501-5132389754Google Scholar 83. Colditz GA, Willett WC, Rotnitzky A, Manson JE. Weight gain as a risk factor for clinical diabetes mellitus in women. Ann Intern Med. 1995;122(7):481-4867872581Google ScholarCrossref 84. Ohlson LO, Larsson B, Svardsudd K. et al. The influence of body fat distribution on the incidence of diabetes mellitus: 13.5 years of follow-up of the participants in the study of men born in 1913. Diabetes. 1985;34(10):1055-10584043554Google ScholarCrossref 85. Lundgren H, Bengtsson C, Blohme G, Lapidus L, Sjostrom L. Adiposity and adipose tissue distribution in relation to incidence of diabetes in women: results from a prospective population study in Gothenburg, Sweden. Int J Obes. 1989;13(4):413-4232793297Google Scholar http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA American Medical Association

Active Smoking and the Risk of Type 2 Diabetes: A Systematic Review and Meta-analysis

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American Medical Association
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Copyright © 2007 American Medical Association. All Rights Reserved.
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0098-7484
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1538-3598
DOI
10.1001/jama.298.22.2654
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Abstract

Abstract Context Observational studies have suggested an association between active smoking and the incidence of type 2 diabetes. Objective To conduct a systematic review with meta-analysis of studies assessing the association between active smoking and incidence of type 2 diabetes. Data Sources A search of MEDLINE (1966 to May 2007) and EMBASE (1980 to May 2007) databases was supplemented by manual searches of bibliographies of key retrieved articles, reviews of abstracts from scientific meetings, and contact with experts. Study Selection Studies were included if they reported risk of impaired fasting glucose, impaired glucose tolerance, or type 2 diabetes in relationship to smoking status at baseline; had a cohort design; and excluded persons with diabetes at baseline. Data Extraction and Data Synthesis Two authors independently extracted the data, including the presence or absence of active smoking at baseline, the risk of diabetes, methods used to detect diabetes, and key criteria of study quality. Relative risks (RRs) were pooled using a random-effects model. Associations were tested in subgroups representing different patient characteristics and study quality criteria. Results The search yielded 25 prospective cohort studies (N = 1.2 million participants) that reported 45 844 incident cases of diabetes during a study follow-up period ranging from 5 to 30 years. Of the 25 studies, 24 reported adjusted RRs greater than 1 (range for all studies, 0.82-3.74). The pooled adjusted RR was 1.44 (95% confidence interval [CI], 1.31-1.58). Results were consistent and statistically significant in all subgroups. The risk of diabetes was greater for heavy smokers (≥20 cigarettes/day; RR, 1.61; 95% CI, 1.43-1.80) than for lighter smokers (RR,1.29; 95% CI, 1.13-1.48) and lower for former smokers (RR, 1.23; 95% CI, 1.14-1.33) compared with active smokers, consistent with a dose-response phenomenon. Conclusion Active smoking is associated with an increased risk of type 2 diabetes. Future research should attempt to establish whether this association is causal and to clarify its mechanisms. Smoking is the leading cause of avoidable death globally.1 Every year about 4 million people die because of smoking and it is estimated that tobacco causes about 8.8% of deaths worldwide.2 The magnitude of this public health challenge is growing, and estimates suggest that as many as 10 million people may die from smoking-related causes in 2025.3 The prevalence of diabetes is also expected to have a major increase by the year 2025,4 a concerning trend given that diabetes imposes a significant public health burden and large demands on health care systems.5 A number of primary studies have assessed the association between smoking and incidence of glucose abnormalities, suggesting that active smoking could be independently associated with glucose intolerance, impaired fasting glucose, and type 2 diabetes; smoking may therefore be a modifiable risk factor for type 2 diabetes. Some of these studies have been summarized in qualitative reviews.6-8 However, to our knowledge, the quality of existing studies has not been systematically assessed and the clinical features of these studies have not been fully assessed to further characterize this potential association and its determinants. We therefore conducted a systematic review and meta-analysis of prospective cohort studies describing the association between active smoking and the incidence of diabetes or other glucose metabolism irregularities. Methods Search Strategy We conducted a systematic literature search of MEDLINE (1966 to May 2007) and EMBASE (1980 to May 2007) for studies describing the association between active smoking (in contrast to passive or secondhand smoking) and impaired fasting glucose, impaired glucose tolerance, or type 2 diabetes. In addition, we searched the reference lists of all identified relevant publications, reviewed abstracts of selected scientific meetings (the Society for Research on Nicotine and Tobacco and the American Diabetes Association meetings) and contacted experts in smoking cessation and diabetes. We considered articles published in any language. We used a literature searching approach described by Egger et al9 for identifying observational studies and studies of prognosis. Three search themes were combined using the Boolean operator “and.” The first theme, glucose metabolism irregularity, combined exploded versions of Medical Subject Headings (MeSH) diabetes mellitus, type 2 or diabetes mellitus or prediabetic state or metabolic syndrome X or glucose intolerance or hyperglycemia or glucose metabolism disorders or insulin resistance or glucose tolerance test or text words insulin sensitivity or impaired fasting glucose or impaired glucose tolerance or IGT or IFG. The second theme, smoking, combined exploded versions of MeSH terms smoking or smoking cessation or smoke inhalation injury or tobacco, smokeless or tobacco use cessation or tobacco use disorder or tobacco or nicotine or text words nicotine dependence or tobacco dependence or smoking dependence or cigarette*. The third theme, studies with a prospective design, combined exploded versions of MeSH terms incidence or cohort studies or follow-up studies or prognosis or early diagnosis or survival analysis or text words course or predict* or prognos* . Because we focused on original studies and observational cohort studies, we excluded other design types using the Boolean operator “not”: meta-analysis (MeSH term) or review (publication type) or case-control studies (MeSH term). No previous meta-analyses were identified. Selection Criteria Two reviewers (C.W. and P.B.) identified articles eligible for further review by performing an initial screen of identified abstracts or titles. Articles were considered for inclusion in the systematic review if they reported data from an original study (ie, no review articles) and reported the incidence of impaired fasting glucose, glucose intolerance, or type 2 diabetes in active cigarette smokers. We used broad inclusion criteria for studies, including all spectra of glucose abnormality (from impaired fasting glucose to diabetes type 2) and smoking status. The observed agreement between reviewers for eligibility of articles on this first screening was 94.6%, corresponding to modest agreement (κ = 0.40). Articles were retained when either of the 2 reviewers believed that it should be retained. The second screening was based on full-text review. To be included, studies had to be cohort studies (prospective cohort or historical cohort) with an adult population (≥16 years) exposed to active cigarette smoking and a comparison group of nonsmokers. One of the outcomes had to be the incidence of impaired glucose tolerance, impaired fasting glucose, or type 2 diabetes. Exclusion criteria were studies that included participants with diabetes at the beginning of the study or that used an inappropriate comparison group (a comparison group that was not nonsmokers or former smokers). The agreement between reviewers for eligibility of articles was 96.0%, with a κ of 0.86. Any disagreement was resolved by consensus. Data Extraction The key exposure variable was the presence or absence of active smoking at baseline, and the preferred reference group was “never smokers.” The majority of studies (n = 18) defined a group of former smokers but 7 studies dichotomized the exposure variable (smokers vs nonsmokers) without mentioning whether the nonsmoking group included former smokers. We considered this heterogeneous group as nonsmokers in the pooled analysis and performed a sensitivity analysis that only included studies with a reference group defined as strictly never smokers. The outcome variable of interest was defined as the presence or absence of type 2 diabetes, impaired fasting glucose, glucose intolerance, or a combination of these. The definitions and diagnostic procedures used to define this outcome varied somewhat across studies because of the different countries and periods in which the studies were performed. The American Diabetes Association and the World Health Organization now share identical diagnostic criteria for type 2 diabetes10 but definitions have changed over time. Moreover, the prevalence of diabetes can change as a function of diagnostic criteria used.11 The criteria used in the studies retrieved included the World Health Organization 1985 criteria12 (fasting glucose threshold ≥140 mg/dL; to convert glucose to mmol/L, multiply by 0.0555), the World Health Organization 1999 criteria13 or the American Diabetes Association 199714 criteria (fasting glucose threshold ≥126 mg/dL), or other criteria (fasting glucose threshold ≥110 mg/dL or ≥120 mg/dL). In all analyzed studies, participants were screened for diabetes at baseline and excluded if found to have diabetes. However, baseline diabetes screening methods also varied across studies and could be a biological screening (blood tests or urine analysis), patient report, or physician report. The methods used to screen for glucose metabolism irregularities in the follow-up periods varied across studies and included a biological screening, participant's report with or without confirmation (by physician, registry, or subsequent biological testing), or physician's report with or without confirmation (by registry or biological testing). We then extracted any reported relative risks (RRs), hazard ratios, odds ratios (ORs), or incidence density ratios for the risk of developing diabetes or other glucose metabolism irregularities for active smokers compared with nonsmokers. Both unadjusted and adjusted values were extracted for these measures of risk. If available, we extracted any reported risks of diabetes for former smokers compared with never smokers and for heavy smokers and lighter smokers compared with never smokers. We also extracted information on key indicators of study quality, using the Meta-analysis of Observational Studies in Epidemiology (MOOSE) standards15 for reporting of meta-analyses of observational studies. We considered inclusion of consecutive participants in a cohort (all participants presenting with study inclusion criteria during a specific period should be included in a cohort), follow-up duration (the duration should be long enough to allow for a latent period), blinding study personnel evaluating key outcomes to exposure status, and statistical adjustment for the main confounding factors of interest (sex, socioeconomic level, physical activity, age, obesity, diet, ethnicity, increased waist circumference, alcohol consumption, heredity, hypercholesterolemia, blood pressure, fasting blood glucose, comorbidities, and use of antihypertensive drugs). Statistical Analysis The RRs were used as the common measure of association across studies. To do this, the hazard ratios and incidence density ratios were directly considered as RRs. The ORs were transformed into RRs using the formula RR = OR/[(1-Po)+(Po × OR)], in which Po is the incidence of the outcome of interest in the nonexposed group.16 This method of transformation has some limitations and can underestimate the variance of the RRs derived from the ORs.17,18 We therefore performed a sensitivity analysis that excluded the 5 studies in which this transformation was performed. We also compared the results applying the Miettinen test-based approach19 for calculating the variance of the lnRR (variance lnRR = variance lnOR × [lnRR/lnOR]). Meta-analysis was performed using Stata version 9.1 (StataCorp, College Station, Texas). We used the “metan” command in Stata to pool the lnRR across studies using the DerSimonian and Laird random-effects model.20 Forest plots were used to visually assess the RR estimates and corresponding 95% confidence intervals (CIs) across studies. Analyses were stratified by study quality criteria and by participant characteristics. To assess for heterogeneity of RRs across studies, the Cochrane Q statistic (significance level of P ≤ .10) and the I2 statistic were calculated.21,22 Meta-regression and sensitivity analysis were performed to assess the effects of selected study quality and clinical factors on diabetes risk. The possibility of publication bias was assessed using the Begg test and visual inspection of a funnel plot.23,24 We also performed the Duval and Tweedie nonparametric “trim and fill” procedure to further assess the possible effect of publication bias in our meta-analysis.21 This method considers the possibility of hypothetical “missing” studies that might exist, imputes their RRs, and recalculates a pooled RR that incorporates the hypothetical missing studies as though they actually existed. Results Literature Search The search strategy retrieved 2246 unique citations: 1340 from MEDLINE and 906 from EMBASE. Of these, 2098 citations were excluded after the first screening based on abstracts or titles, leaving 148 articles for full-text review (Figure 1). Hand searching of the bibliographic references of these articles identified 1 additional article, for a total of 149 articles for full-text review. On this review, 124 articles were excluded for the reasons listed in Figure 1, leaving 25 studies for final inclusion in the systematic review and meta-analysis. Two supplementary studies were identified that had been published only as abstracts from conference proceedings of scientific meetings.25,26 They were not included in the pooled analyses because of lack of details on key study variables and because of study quality. Risks were given for subgroups of smokers (heavy and lighter smokers) and were higher for smokers vs nonsmokers. However, there were not sufficient data to calculate a mean risk for smokers and therefore they could not be included with other studies in sensitivity analyses. The review identified 2 articles that were based on overlapping data from the Nurses' Health study27,28 and 2 articles that were based on overlapping data from the Monitoring Trends and Determinants of Cardiovascular Disease (MONICA) Augsburg cohort study.29,30 We avoided duplicate inclusion of data by selecting only the more complete article from each cohort. Study Characteristics Characteristics of the 25 selected studies are shown in Table 1.27,29,31-53 All were prospective cohort studies. All studies reported the incidence of diabetes as an outcome of interest except 1 study53 that reported the incidence of a composite outcome (diabetes and/or impaired fasting glucose). The association between smoking and diabetes was the primary outcome of interest for 16 studies, whereas it was a secondary question in 9 studies. Diabetes was screened with biological measures in 11 studies, was reported by patients or physicians in 11 studies, and was determined by other methods (hospital medical registries, insurance registries) in 3 studies. Regarding cut-point definitions for diabetes, 6 studies used a fasting glucose threshold of 140 mg/dL or higher, 8 studies used a threshold of 126 mg/dL or higher, 1 study used a threshold of 120 mg/dL or higher, 2 studies used a threshold of 110 mg/dL or higher, and 8 studies did not explicitly mention the criteria that they used. Screening for diabetic participants at baseline was performed using a biological screening for 14 studies and by asking patients or physicians for the other 11. The selected studies were published between 1992 and 2006, and the number of participants per study ranged from 630 to 709 827, for a total of 1.2 million participants across studies (45 844 incident cases of diabetes). Seven studies were conducted in the United States, 7 in Japan, 6 in Scandinavian countries, 3 in the United Kingdom, 1 in Germany, and 1 in Israel. Eleven studies involved men only, 1 study involved only women, and the other 13 studies included both men and women. Mean body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of participants ranged from 22.3 to 28.4 and mean age at baseline varied from 16 to 60.7 years. The percentage of smokers ranged from 9% to 67% and the pooled percentage of smokers was 35%. The cohort study quality criteria of blinding to ascertain outcome and recruiting consecutive patients were not explicitly specified in any of the studies that we selected. Follow-up ranged from 5 to 30 years; average (mean or median) follow-up duration was given for only 9 studies. The frequency of follow-up was at least once per year for 9 studies, once every 2 years for 4 studies, and at baseline and the end of the study for the remaining 12 studies. The proportion of patients with complete follow-up to the end of the study was given for 17 studies and ranged from 69.2% to 99.7%. Adjusted RRs could be determined for all studies, either as reported or by conversion from ORs (Table 2). Most risk measures were adjusted for age (22 studies) and BMI (22 studies); fewer were adjusted for physical activity (13 studies), alcohol consumption (14 studies), heredity (10 studies), education (6 studies), diet (2 studies), or waist circumference (3 studies). Risk of Diabetes for Smokers Compared With Nonsmokers Quiz Ref IDAmong the 25 selected studies, all but one35 found an association between active smoking and an increased risk of diabetes, although not all were statistically significant. Three studies reported unadjusted RRs43,52,53; the pooled crude RR estimate from these studies was 1.89 (95% CI, 1.58-2.27). All 25 studies provided adjusted risks expressed as RRs, hazard ratios, incidence density ratios, or ORs, and the derived fully adjusted RRs ranged from 0.82 to 3.74. Active smokers had an increased risk of developing type 2 diabetes compared with nonsmokers, with a pooled RR of 1.44 (95% CI, 1.31-1.58) (Figure 2). A sensitivity analysis that excluded all studies for which the OR to RR conversion was used had a similar result, with a pooled RR of 1.44 (95% CI, 1.30-1.59). Using the test-based approach by Miettinen19 to calculate the variance resulted in essentially identical results (RR, 1.44 [95% CI, 1.31-1.58]). In a sensitivity analysis that included only the 18 studies in which the comparison was defined as strictly nonsmokers (without former smokers), the pooled RR was 1.45 (95% CI, 1.31-1.62). There was evidence of statistical heterogeneity of RRs across studies (Q statistic, 98.08; P < .001; I2, 75.5%). These measurements of heterogeneity were likely driven by the extremely large overall number of participants in our analysis (>1 million). The point estimates of the RRs were consistently greater than 1 in all but 1 study, and study subgroups were more homogeneous. Stratified Analyses To explore the study heterogeneity, we performed stratified analyses across a number of key study characteristics and clinical factors (Table 3). The finding of increased diabetes risk in smokers was consistently found in all of the stratified analyses. Study quality characteristics did not seem to markedly influence the results, although studies that met more quality criteria tended to report a slightly stronger association of smoking with diabetes incidence. For example, stronger associations between smoking and diabetes incidence were found in studies that were adjusted to 8 or more confounding factors, if smoking and incidence of diabetes was the primary outcome, and if a biological screening for diabetes was performed at baseline (Table 3). The characteristics of participants included in the primary studies also seemed to be associated with the results. For example, studies reported a stronger association between smoking and diabetes incidence if they included older participants (mean age of participants, ≥50 years) or when the participants tended to be overweight or obese (mean BMI of participants, ≥25) (Table 3). In studies that included both men and women, the pooled risk was similar in both sexes (pooled RR of 1.28 [95% CI, 1.12-1.45] for men and 1.25 [95% CI, 1.06-1.65] for women). The stratified analyses shown in Table 3 suggest a dose-response relationship between smoking and diabetes. Quiz Ref IDThe association between smoking and diabetes was stronger for heavy smokers (≥20 cigarettes/day; RR, 1.61 [95% CI, 1.43-1.80]) compared with lighter smokers (RR, 1.29 [95% CI, 1.13-1.48]). The association also was weaker for former smokers (RR, 1.23 [95% CI, 1.14-1.33]) than it was for active smokers. The association between smoking and diabetes was slightly stronger if there was a biological screening for diabetes during follow-up (RR, 1.49 [95% CI, 1.35-1.63]) compared with cases reported by patient or physician (RR, 1.39 [95% CI, 1.20-1.62]). The association between smoking and diabetes was also stronger for the 6 studies in which a glucose threshold of 140 mg/dL or higher was used (RR, 1.63 [95% CI, 1.33-1.99]) compared with 8 studies in which a glucose threshold of 126 mg/dL or higher was used (RR, 1.47 [95% CI, 1.30-1.65]). In a sensitivity analysis of the 24 studies that reported only on the incidence of diabetes (excluding the 1 study that evaluated the risk of impaired glucose tolerance53), the overall pooled result did not change (RR, 1.43 [95% CI, 1.30-1.57]). Publication Bias Visual inspection of the Begg funnel plot revealed asymmetry (P < .001) (Figure 3A). This raises the possibility of publication bias, although the Begg test was not statisitically significant (z = 1.45; P = .15). Because of this, we undertook a sensitivity analysis using the trim and fill method,54 which conservatively imputes hypothetical negative unpublished studies to mirror the positive studies that cause funnel plot asymmetry. The imputed studies produce a symmetrical funnel plot (Figure 3B). The pooled analysis incorporating the hypothetical studies continued to show a statistically significant association between smoking and diabetes (RR, 1.32 [95% CI, 1.21-1.44]; P < .001). Comment There is an extensive body of literature reporting on the association between active cigarette smoking and the incidence of diabetes. The 25 studies that we identified report RRs that, while somewhat variable in magnitude, indicate a positive association in all but 1 study. Furthermore, the association persists and remains statistically significant across a number of stratified analyses exploring clinical and study quality factors, and also persists in sensitivity analyses performed to assess the potential effect of varying diabetes outcome definitions and hypothetical unpublished studies. Given this consistency, we conclude that the relevant question should no longer be whether this association exists, but rather whether this established association is causal. Observational primary studies cannot prove causality. However, the studies in this review do meet several of the Hill criteria55 for causation. First, there is an appropriate temporal relationship: the cigarette smoking preceded diabetes incidence in all studies. Second, the findings are consistent with a dose-response relationship, with stronger associations for heavy smokers relative to lighter smokers and for active smokers relative to former smokers. However, an observed dose-response relationship can arise from the intensity of clustering with other diabetes risk factors such as lack of physical activity and unhealthy diet. Quiz Ref IDThird, there is theoretical biological plausibility for causality in that smoking may lead to insulin resistance or inadequate compensatory insulin secretion responses according to several56-61 but not all62 studies. Smoking also has a clinically significant effect on both oral and intravenous glucose tolerance tests that could influence diabetes detection.60,63-65 This could be due to a direct effect of nicotine or other components of cigarette smoke on beta cells of the pancreas, as suggested by the association of cigarette smoking with chronic pancreatitis and pancreatic cancer.66 Fourth, there is consistency of this association across 24 studies, as shown by the forest plot (Figure 2). Fifth, the strength of the association with diabetes is not negligible in the context of tobacco research. Conversely, there are also possible noncausal explanations for this association. Smoking is often associated with other unhealthy behaviors that favor weight gain and/or diabetes, such as lack of physical activity, poor fruit and vegetable intake, and high alcohol intake.67,68 Furthermore, this clustering of behaviors is more prevalent in individuals of lower socioeconomic status.69,70 Some of these factors were considered and adjusted for in the studies included in our review, but the extent to which these potential intervening factors were controlled for in the individual studies was generally limited. The lack of adjustment for socioeconomic status (only 6 studies adjusted for socioeconomic status or education), diet (only 2 studies), physical activity (only 13 studies), and alcohol consumption (only 14 studies) could contribute to a noncausal association between smoking and diabetes. Smokers tend to be thinner than nonsmokers or former smokers, and several studies have shown that smokers' BMI is lower.71-73Quiz Ref IDHowever, there is evidence that smokers (especially heavy smokers) tend to have higher BMIs than lighter smokers and even some nonsmokers.74 In addition to a clustering of risky behaviors, this finding could be due to the weight cycling phenomenon. Smokers tend to gain weight when they quit smoking; the stronger the dependence, the greater the risk of relapse.75,76 Therefore, heavy smokers may need several attempts before they definitively quit smoking, and they gain weight during these attempts that they never completely manage to lose when they relapse. Quiz Ref IDFurthermore, with a normal BMI, smokers tend to have a greater risk of abdominal fat accumulation compared with nonsmokers.62,77-79 The mechanism is not well elucidated but because smoking has an anti-estrogenic effect,80,81 it could be related to a hormonal imbalance that could lead to central obesity. Obesity and weight gain are strong risk factors for developing type 2 diabetes82,83 and several studies also show that abdominal obesity is associated with the development of type 2 diabetes.84,85 Limitations of this meta-analysis must be considered. First, the quality of individual studies was not always optimal, as shown by the general lack of information on blinding and recruiting of consecutive patients for all studies. Second, conversion of ORs to RRs15 could have underestimated the variance of the RRs derived from ORs. However, a sensitivity analysis that excluded the affected studies and use of the Miettinen test-based approach to calculate variance of the lnRR had only an extremely small effect on the results. Third, there is heterogeneity of RRs across studies, corresponding in part to heterogeneity in study definitions. However, stratified analyses showed pooled RRs consistently greater than 1 across a number of clinical factors. Fourth, the funnel plot analysis showed some asymmetry suggesting the possibility of publication bias. The trim and fill sensitivity analysis did not change the general result (although the strength of the association was slightly attenuated), suggesting that the association is not an artifact of unpublished negative studies. Nevertheless, that possibility is not fully excluded by this method. Considering the consistent finding of increased diabetes incidence associated with active cigarette smoking across a large number of studies, we believe that there is no need for further cohort studies to test this hypothesis. However, there is a need for studies that include detailed measurement and adjustment for potential confounding factors such as socioeconomic status, education, and exercise with a goal of establishing whether the association with smoking is causal. We recommend that future studies focus on plausible causal mechanisms or mediating factors such as obesity, lack of physical activity, dietary habits, and stress levels. Back to top Article Information Corresponding Author: Carole Willi, MD, Department of Ambulatory Care and Community Medicine, 44 Bugnon Ave, 1011 Lausanne, Switzerland (carole.willi@hospvd.ch). Author Contributions: Dr Willi had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Willi, Ghali, Cornuz. Acquisition of data: Willi, Bodenmann. Analysis and interpretation of data: Willi, Ghali, Faris, Cornuz. Drafting of the manuscript: Willi, Bodenmann. Critical revision of the manuscript for important intellectual content: Ghali, Faris, Cornuz. Statistical analysis: Willi, Ghali, Faris. Obtained funding: Cornuz. Administrative, technical, or material support: Bodenmann, Ghali. Study supervision: Ghali, Cornuz. Financial Disclosures: None reported. Funding/Support: Dr Ghali is supported by a Senior Scholar Award from the Alberta Heritage Foundation for Medical Research and by a Canada Research Chair in Health Services Research. Role of the Sponsor: The agencies that provide Dr Ghali with funding support did not have any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Additional Contributions: We thank Finlay McAlister, MD, MSc, University of Alberta, for his helpful comments on an earlier version of the manuscript. The authors also thank Anne Parrical, DESS (Diplôme d’Etudes Supérieures Spécialisées), medical library scientist at the University Hospital of Lausanne, for her guidance in formulating our literature searching strategy. These individuals did not receive any compensation for their contribution to the study. References 1. Annual smoking-attributable mortality, years of potential life lost, and productivity losses—United States, 1997-2001. MMWR Morb Mortal Wkly Rep. 2005;54(25):625-62815988406Google Scholar 2. The World Health Report. Reducing Risks, Promoting Healthy Life. 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Journal

JAMAAmerican Medical Association

Published: Dec 12, 2007

Keywords: smoking,diabetes mellitus,diabetes mellitus, type 2,follow-up,impaired fasting glucose,glucose intolerance

References