Abstract
Objective
Studies suggest that smoking may be a risk factor for the development of microvascular complications such as diabetic peripheral neuropathy (DPN). The objective of this study was to assess the relationship between smoking and DPN in persons with type 1 or type 2 diabetes.
Research Design and Methods
A systematic review of the PubMed, Embase, and Cochrane clinical trials databases was conducted for the period from January 1966 to November 2014 for cohort, cross-sectional and case–control studies that assessed the relationship between smoking and DPN. Separate meta-analyses for prospective cohort studies and case–control or cross-sectional studies were performed using random effects models.
Results
Thirty-eight studies (10 prospective cohort and 28 cross-sectional) were included. The prospective cohort studies included 5558 participants without DPN at baseline. During follow-up ranging from 2 to 10 years, 1550 cases of DPN occurred. The pooled unadjusted odds ratio (OR) of developing DPN associated with smoking was 1.26 (95 % CI 0.86–1.85; I 2 = 74 %; evidence grade: low strength). Stratified analyses of the prospective studies revealed that studies of higher quality and with better levels of adjustment and longer follow-up showed a significant positive association between smoking and DPN, with less heterogeneity. The cross-sectional studies included 27,594 participants. The pooled OR of DPN associated with smoking was 1.42 (95 % CI 1.21–1.65; I 2 = 65 %; evidence grade: low strength). There was no evidence of publication bias.
Conclusions
Smoking may be associated with an increased risk of DPN in persons with diabetes. Further studies are needed to test whether this association is causal and whether smoking cessation reduces the risk of DPN in adults with diabetes.
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Diabetic peripheral neuropathy (DPN), also known as distal symmetrical polyneuropathy or sensorimotor neuropathy, is part of a wider spectrum of microvascular complications of diabetes that includes ulcer/amputation, erectile dysfunction, and autonomic dysfunction. DPN is the most common of these, affecting approximately 30 % of persons with diabetes 1–3. Symptoms include numbness, tingling, or a burning sensation in the legs and hands, typically in a “stocking and glove” distribution 1. Ultimately, muscle weakness, loss of reflexes, and foot deformities can result, leading to end clinical sequelae of ulcers, potential infection, and amputation for some patients with poorly controlled disease.
The pathogenesis of DPN involves a complex interaction between metabolic and vascular factors 1 4. Hyperglycemia, the most commonly described factor, leads to nerve cell damage through several mechanisms, including oxidative stress and polyol accumulation 3. Reduced nerve perfusion, endoneurial hypoxia, and endothelial dysfunction also contribute to neuropathy development 1.
Previous studies have investigated potential risk factors for DPN, including hypertension, microalbuminuria, dyslipidemia, and, of particular interest, cigarette smoking 5–7. There appears to be an increased likelihood of neuropathy in people with diabetes who smoke, although prior studies investigating this relationship included only a small number of participants 7.
In order to better assess the relationship between smoking and diabetic neuropathy, we conducted a systematic review and meta-analysis of cross-sectional, case–control, prospective, and retrospective cohort studies.
RESEARCH DESIGN AND METHODS
Search Strategy and Selection Criteria
We conducted a search of the PubMed (January 1966 to November 2014), Embase (January 1980 to November 2014), and Cochrane clinical trials (to November 2014) databases, and we also searched the references of the relevant retrieved articles. Studies that assessed the effect of cigarette smoking on the risk of peripheral neuropathy among patients with type 1 or type 2 diabetes were included (population of interest). Only participants with diabetes at baseline were included, as we were interested primarily in the effect of smoking on diabetic complications. The exposure of interest was cigarette smoking. In order to be considered for inclusion in the systematic review, studies had to include a control or comparison group of participants with diabetes who did not smoke. The outcome of interest was DPN.
Cohort studies as well as cross-sectional and case–control studies were included based on our search results. For cohort studies, we included studies with at least 1 year of follow-up, as we assumed a latency period of at least 1 year for smoking to influence the development of diabetic neuropathy. We considered studies published in all languages and did not restrict our search to published studies.
We used a combination of three search themes: 1) diabetes, 2) smoking, and 3) neuropathy. The full electronic search is available in the online Appendix 1.
Study Selection
An initial screening of retrieved citations was performed based on titles and abstracts; each citation was screened by two coauthors (CC, MJC, FE or KJS). The inclusion criteria for this first screening were as follows: population with diabetes (type 1 or type 2), neuropathy as one recorded outcome (not necessarily the primary outcome), and identification as prospective, cohort, or cross-sectional study. We included studies even if they did not mention smoking exposure in the title or abstract. Exclusion criteria included gestational diabetes, animal studies, and non-original study design (such as reviews, editorials, or case reports/case series). A second screening was then performed based on full-text review of retained citations. The exclusion criteria were the same as those for the first screen, with the addition of the following: 1) smoking–neuropathy relationship was not assessed and/or data did not allow calculating it manually, 2) peripheral neuropathy was not one of the outcomes, or 3) persons without diabetes were included. Two reviewers (CC, MJC, FE, or KJS) independently reviewed the articles, and any disagreement was resolved by consensus.
Data Extraction and Quality Assessment
Two authors independently extracted the data from selected studies. To evaluate the risk of bias in individual studies and to assess overall quality, we considered several criteria based on the Newcastle-Ottawa scale 8. For cohort studies, the Newcastle-Ottawa scale has three categories: 1) selection (representativeness of the exposed cohort, selection of the non-exposed cohort, ascertainment of exposure, and demonstration that outcome of interest was not present at start of study) (0–4 points); 2) comparability (comparability of cohorts on the basis of design or analyses) (0–2 points); and 3) outcome (assessment of outcome, was follow-up long enough for outcomes to occur, adequacy of follow-up of cohorts) (0–3 points). We used a modified version of the Newcastle-Ottawa scale for case–control studies in order to evaluate the quality of cross-sectional studies. In the modified version, we deleted the question on selection of controls (in the “selection” category, yielding a maximum of 3 points) and the questions on methods of ascertainment for cases and controls and non-response rate (in the “exposure” section, yielding a maximum of 2 points). We reported the score for each subcategory in the extraction form. We defined the quality of studies as good if they had the maximum scores for selection and exposure and at least one point for comparability. Other studies were considered suboptimal, and were classified as moderate quality if they had at least one point for each Newcastle-Ottawa scale category, and low quality if one or more categories had no points. Two authors also independently evaluated the strength of the body of evidence separately for each meta-analysis, using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) working group criteria 9. The following domains were evaluated: consistency, directness, precision, dose–response association, and residual confounding. The strength of evidence was considered as “high” if there was high confidence that the evidence reflected the true effect; “moderate” if there was moderate confidence that the evidence reflected the true effect, and it was possible that further research would change the estimate; “low” if there was low confidence that the evidence reflected the true effect, and further research was likely to change the estimate; and “insufficient” if evidence was unavailable. Studies reported risk ratios (RRs), odds ratios (ORs), or absolute numbers in describing the relationship between smoking and DPN. As most prospective and cross-sectional studies reported ORs, and not all studies provided information to convert OR into RR, we used ORs in our meta-analyses. For studies that provided neither OR nor RR, we calculated unadjusted ORs and confidence intervals (CIs) manually.
Data Synthesis and Analysis
We pooled our results using the DerSimonian and Laird random effects model 10, since we expected to observe heterogeneity among studies. Anticipated sources of heterogeneity included study population (persons with type 1 versus type 2 diabetes), definition of smoking, and definition of neuropathy, and were defined a priori. We explored other sources of heterogeneity for three variables that were added post hoc: level of adjustment, mean duration of follow-up (for prospective studies only), and level of quality assessed with the Newcastle-Ottawa scale 8. We then performed stratified analyses to assess/explore potential sources of heterogeneity linked to a priori and post hoc variables. In parallel, we performed univariate meta-regression analyses to quantify potential source of heterogeneity. We performed separate meta-analyses stratified by type of design. To assess heterogeneity, the Q-statistic and I 2 statistic were calculated 11 12. The possibility of publication bias was assessed using the Begg test and visual inspection of the funnel plot 13 14. Stata software (Version13; StataCorp LP, College Station, TX, USA) was used for statistical analysis.
RESULTS
Study Selection
In terms of study selection, the initial search included 2006 citations from the PubMed, Embase and Cochrane clinical trials databases. After excluding duplicates, 1554 unique citations were available (see Fig. 1). After the first screening, 126 citations were considered for further review. In a second screening, 88 studies were excluded based on full-text review. Agreement between reviewers at this stage was good, with a Kappa of 0.78. Reasons for exclusion in this phase included the lack of an estimate (or numbers to allow manual calculation) of the smoking–neuropathy relationship (n = 54), an outcome other than peripheral neuropathy (n = 30), and inclusion of participants without diabetes (n = 4). Ultimately, 38 studies were selected for inclusion in the systematic review, and we performed separate meta-analyses for the ten prospective studies 5 15–23 and 28 cross-sectional studies 6 7 24–49.
Smoking and incidence of diabetic peripheral neuropathy in prospective cohort studies
The main individual characteristics of the prospective studies are shown in Table 1. Together, they comprised 5558 participants: three studies in individuals with type 2 diabetes, six studies with type 1 diabetes, and one study that included both participants with type 1 and type 2 diabetes. Participants were from different settings including inpatient, outpatient and the community; mean age of participants ranged from 25 to 66 years old and mean diabetes duration ranged from 0 to 17 years. All studies excluded participants with neuropathy at baseline and participants were followed for 2 to 10 years. Peripheral neuropathy screening was done by neurological history and examination in most studies 5 17–20 23, by electromyography to measure nerve conduction velocities in one study 15, by measure of vibration perception using biothesiometry in one study 22, and by monofilament examinations in two studies 16 21. The definition of smoking exposure varied among studies; six studies compared ever smokers (i.e. current and former smokers) to never smokers, one study compared current to nonsmokers (i.e. former and never smokers), and three studies did not clearly specify the smoking comparison groups. Most studies provided ORs; two provided RRs, and one provided numbers of smokers and nonsmokers and of participants in each category who developed peripheral neuropathy. All but one study performed multivariable-adjusted analyses; five controlled for at least A1C and diabetes duration, and four adjusted for either A1C or diabetes duration and several other confounders (see Online Appendix 2). The quality of studies varied. Most were considered to be of good quality, with maximum points for selection and exposure criteria on the Newcastle-Ottawa scale 5 17–20 22; however, three were classified as moderate quality 16 21 23, and one as low quality 15, largely due to the risk of selection bias and poorly defined outcomes 15 16 21 23.
With regard to the incidence of DPN, seven studies showed a positive association with smoking and three showed a negative association; the OR ranged from 0.22 to 10.16. When we pooled the data using a random effects model, the pooled OR was 1.26 (95 % CI 0.86–1.85; Fig. 2). The strength of evidence was considered low according to GRADE criteria (see Online Appendix 3). There was evidence of high heterogeneity across studies, as suggested by the I-squared statistic (I 2 = 74 %). Visual inspection of the funnel plot (Online Appendix 4) and Begg’s test (p value = 0.72) did not suggest publication bias (i.e. no evidence of small negative unpublished studies), but showed a cluster of medium to large negative studies. Attempting to correct for eventual small unpublished negative studies using the “trim and fill” method in Stata 50 did not significantly change the results (OR 1.26, 95 % CI 0.86–1.83). In stratified analyses, studies of higher quality and with better levels of adjustment and longer follow-up showed a stronger positive association between smoking and DPN (Table 2). Studies including persons with type 1 diabetes showed increased risk of DPN for smokers than non-smokers, whereas studies in individuals with type 2 diabetes showed no statistically significant association.
Smoking and prevalence of diabetic peripheral neuropathy in cross-sectional studies
The primary individual characteristics of the cross-sectional studies are shown in Table 3. They included a total of 27,594 participants; 21 studies included persons with type 2 diabetes, three studies with type 1 diabetes, and four studies with both type 1 and type 2 diabetes. The mean age of the participants ranged from 19 to 68 years, and the mean duration of diabetes encompassed 0 to 20 years. There was high heterogeneity in the definition of exposure: seven studies compared current smokers to nonsmokers (i.e. former and never smokers), four studies compared ever smokers (i.e. current and former smokers) to never smokers, six studies compared current vs. never smokers, two studies compared smokers of 30 or more pack-years to smokers of less than 30 pack-years, one study compared smokers of < 20 pack-years to never smokers, and eight studies did not specify the comparison groups. The majority of studies expressed the estimate in OR; two used RR and nine used numbers or proportions, allowing us to manually calculate unadjusted OR and 95 % CI. Seven studies controlled for at least A1C and diabetes duration, one adjusted for either A1C or diabetes duration, four adjusted for some confounders but not A1C and diabetes duration, and 16 did not adjust for potential confounders (see Online Appendix ). Based on the Newcastle-Ottawa scale, ten studies were rated as moderate quality 6 7 24 31 33 34 36 37 49 and 18 as low quality 25–30 35 38–48, largely because of selection bias, lack of adjustment for confounders, or poorly defined exposure and/or outcome. The majority of studies showed increased odds of neuropathy for smokers compared with non-smokers, and ORs ranged from 0.68 to 8.20. The pooled OR using a random effects model was 1.42 (95 % CI 1.21–1.65; see Fig. 3). The strength of evidence was considered low according to GRADE criteria (see Online Appendix 3). There was evidence of some heterogeneity among studies (I 2 = 65 %, and there was no evidence of publication bias, as suggested by both visual inspection of the funnel plots (Online Appendix 6) and Begg’s test (p value = 0.17). In stratified analyses, studies with higher levels of adjustment, those that included participants with type 1diabetes, and those comparing ever vs. never smokers showed a higher and stronger association between smoking and DPN. (Table 2)
CONCLUSIONS
In summary, we found a positive association between smoking and the prevalence and incidence of DPN. The ten prospective studies (5558 participants) showed no significant association between smoking and diabetic neuropathy, with low evidence strength. However, the studies were heterogeneous, and stratified analyses did show a significant trend toward less heterogeneity when stratified by quality and longer follow-up. Prospective studies comparing ever-smokers (current and former smokers) with never smokers as well as those including participants with type 1 diabetes showed a stronger positive association between smoking and DPN. These studies were also of higher quality, however, and may not have necessarily reflected a real effect modification. In addition, the 28 cross-sectional studies with a total of 27,594 participants showed a moderate association between smoking and DPN, with low evidence strength. There was substantial heterogeneity among the cross-sectional meta-analyses. However, in stratified analyses, studies with higher levels of adjustment and of higher quality showed a stronger positive association between smoking and DPN, with less heterogeneity.
In persons without diabetes, cigarette smoking has been positively associated with increased levels of A1C, a surrogate for metabolic control, which reflects average glycemia over the previous 2 or 3 months 51. A previous meta-analysis showed a 44 % increased risk of developing type 2 diabetes for smokers compared with nonsmokers 52. Among persons with diabetes, prior studies have suggested that smoking is also associated with insulin resistance 53 and higher insulin needs 54 55, and thus poor metabolic control 56–61. As microvascular complications in individuals with type 1 or type 2 diabetes are highly linked to metabolic control 62 63, A1C probably acts as a mediator in the relationship between smoking and DPN. However, the fact that the association remains positive after adjusting for A1C suggests that hyperglycemia may not entirely mediate this relationship. Furthermore, smoking is associated with oxidative stress, systemic inflammation, and endothelial dysfunction independent of diabetes 64–66, and it may increase the risk of nerve damage through these pathways in parallel with metabolic factors. Smoking may also have direct toxic effects, and may induce DPN via hypoxemia and microvascular insufficiency. Similar to what occurs with larger vessels (coronary arteries), smaller arteries, including the vasa nervorum, can be damaged by smoking, which in turn leads to the development and progression of DPN. Smoking has been found to be a causal variable in other microvascular complications such as retinopathy and nephropathy, and similar mechanisms might occur for DPN to damage those target organs 67. Finally, confounding factors could also contribute to the association between smoking and DPN. Smokers may have poorer adherence to recommended self-care compared with nonsmokers 68. Smokers also tend to accumulate unhealthy behaviors, including alcohol abuse, lack of physical activity, and diets rich in fat and poor in fruits and vegetables 69. However, while these factors may contribute to diabetes complications through poorer diabetes control, they do not entirely explain the association that remains after adjusting for diabetes control.
Our study has several strengths. We retrieved and pooled a substantial number of studies assessing the association between smoking and DPN. Contrary to other microvascular complications such as nephropathy or retinopathy, few studies to date had shown a clear positive association between smoking and DPN. Indeed, few studies have been directly designed to measure the impact of smoking on DPN. The complex multifactorial pathogenesis of DPN makes it difficult to measure the effect of smoking on this unique outcome. Many prospective studies and some cross-sectional studies included in our meta-analysis provided adjusted estimates that permitted controlling for some potential confounders and exploring mediating factors.
Our study has several limitations, including the relatively small number of prospective studies and the heterogeneity among studies. Stratified analyses allowed us to address the source of heterogeneity, but given the limited number of prospective studies and the post hoc nature of some of these analyses, we cannot draw firm conclusions regarding the stratified analyses. For example, studies including participants with type 1 diabetes were of higher quality, rendering it difficult to conclude that the association between smoking and DPN was significant only among persons with type 1 but not type 2 diabetes. Another limitation is that the cross-sectional studies were of medium to poor quality. Some did not adjust for the main confounders, some did not assess the outcome clinically, and the smoking exposure was highly variable among studies. Finally, we cannot prove that the association we observed is causal, since all of the studies identified were observational in nature.
Few studies have prospectively assessed the impact of smoking cessation on the control of diabetes and diabetes complications. We identified only one study that prospectively assessed the impact of smoking cessation on DPN 70. Among 193 participants newly diagnosed with type 2 diabetes and microalbuminuria, 62 % had quit smoking at 12 months. In this population, the prevalence of DPN decreased significantly more in participants who quit smoking than those who continued (p < 0.04), but no absolute numbers were given. This was also the case for microalbuminuria, peripheral vascular disease, blood pressure, and dyslipidemia. This unique study of suboptimal quality suggests that the effect of smoking on DPN might be reversible, but more research is needed to assess the effect of smoking cessation on diabetes control and microvascular complications.
In conclusion, smoking may be associated with an increased risk of developing DPN. This is an important finding, as this exposure is a modifiable behavior to be targeted in clinical practice based on diabetes guideline recommendations 71. Future research should be focused on evaluating the impact of smoking cessation on improvement of diabetic neuropathy, and on helping to establish a causal link between exposure and outcome.
References
Cameron NE, Eaton SE, Cotter MA, Tesfaye S. Vascular factors and metabolic interactions in the pathogenesis of diabetic neuropathy. Diabetologia. 2001;44:1973–1988.
Tesfaye S, Stevens LK, Stephenson JM, et al. Prevalence of diabetic peripheral neuropathy and its relation to glycaemic control and potential risk factors: the EURODIAB IDDM Complications Study. Diabetologia. 1996;39:1377–1384.
Vinik AI, Park TS, Stansberry KB, Pittenger GL. Diabetic neuropathies. Diabetologia. 2000;43:957–973.
Vincent AM, Calabek B, Roberts L, Feldman EL. Biology of diabetic neuropathy. Handbook of clinical neurology. 2013;115:591–606.
Tesfaye S, Chaturvedi N, Eaton SE, et al. Vascular risk factors and diabetic neuropathy. N Engl J Med. 2005;352:341–350.
Maser RE, Steenkiste AR, Dorman JS, et al. Epidemiological correlates of diabetic neuropathy. Report from Pittsburgh Epidemiology of Diabetes Complications Study. Diabetes. 1989;38:1456–1461.
Mitchell BD, Hawthorne VM, Vinik AI. Cigarette smoking and neuropathy in diabetic patients. Diabetes Care. 1990;13:434–7.
The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hospital Research Institute. (Accessed March 29, 2015, at http://www.ohri.ca/programs/clinical_epidemiology/oxford.htm
Berkman ND, Lohr KN, Ansari M, et al. Grading the Strength of a Body of Evidence When Assessing Health Care Interventions for the Effective Health Care Program of the Agency for Healthcare Research and Quality: An Update. Rockville (MD): Methods Guide for Effectiveness and Comparative Effectiveness Reviews; 2008.
DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177–188.
Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–1558.
Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–560.
Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088–1101.
Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–634.
Lehtinen JM, Niskanen L, Hyvonen K, Siitonen O, Uusitupa M. Nerve function and its determinants in patients with newly-diagnosed type 2 (non-insulin-dependent) diabetes mellitus and in control subjects–a 5-year follow-up. Diabetologia. 1993;36:68–72.
Adler AI, Boyko EJ, Ahroni JH, Stensel V, Forsberg RC, Smith DG. Risk factors for diabetic peripheral sensory neuropathy. Results of the Seattle Prospective Diabetic Foot Study. Diabetes Care. 1997;20:1162–7.
Forrest KY, Maser RE, Pambianco G, Becker DJ, Orchard TJ. Hypertension as a risk factor for diabetic neuropathy: a prospective study. Diabetes. 1997;46:665–670.
Sands ML, Shetterly SM, Franklin GM, Hamman RF. Incidence of distal symmetric (sensory) neuropathy in NIDDM. The San Luis Valley Diabetes Study. Diabetes Care. 1997;20:322–9.
Christen WG, Manson JE, Bubes V, Glynn RJ. Risk factors for progression of distal symmetric polyneuropathy in type 1 diabetes mellitus. Sorbinil Retinopathy Trial Research Group. Am J Epidemiol. 1999;150:1142–1151.
Sibal L, Law HN, Gebbie J, Home P. Cardiovascular risk factors predicting the development of distal symmetrical polyneuropathy in people with type 1 diabetes: A 9-year follow-up study. Ann N Y Acad Sci. 2006;1084:304–318.
Gerrits EG, Lutgers HL, Kleefstra N, et al. Skin autofluorescence: a tool to identify type 2 diabetic patients at risk for developing microvascular complications. Diabetes Care. 2008;31:517–521.
Elliott J, Tesfaye S, Chaturvedi N, et al. Large-fiber dysfunction in diabetic peripheral neuropathy is predicted by cardiovascular risk factors. Diabetes Care. 2009;32:1896–1900.
Uruska A, Araszkiewicz A, Uruski P, Zozulinska-Ziolkiewicz D. Higher risk of microvascular complications in smokers with type 1 diabetes despite intensive insulin therapy. Microvasc Res. 2014;92:79–84.
Franklin GM, Shetterly SM, Cohen JA, Baxter J, Hamman RF. Risk factors for distal symmetric neuropathy in NIDDM. The San Luis Valley Diabetes Study. Diabetes Care. 1994;17:1172–7.
Gregory R, Tattersall RB, Allison SP. Peripheral neuropathy as a presenting feature of type 2 diabetes: a case-controlled study. Diabet Med. 1994;11:407–9.
Matsumoto T, Ohashi Y, Yamada N, Kikuchi M. Hyperglycemia as a major determinant of distal polyneuropathy independent of age and diabetes duration in patients with recently diagnosed diabetes. Diabetes Res Clin Pract. 1994;26:109–113.
Zafra Mezcua JA, Mendez Segovia JC, Novalbos Ruiz JP, Costa Alonso MJ, Failde Martinez I. Chronic complications in patients with type 2 diabetes mellitus cared for at a health center. Aten Primaria. 2000;25:529–535.
Barbosa AP, Medina JL, Ramos EP, Barros HP. Prevalence and risk factors of clinical diabetic polyneuropathy in a Portuguese primary health care population. Diabetes Metab. 2001;27:496–502.
Gomez-Viera N, Soto-Lavastida A, Rosello-Silva H, Gomez de Molina-Iglesias M. [Risk factors involved in symmetrical distal diabetic neuropathy]. Rev Neurol. 2001;32:806–812.
Tapp RJ, Shaw JE, de Courten MP, Dunstan DW, Welborn TA, Zimmet PZ. Foot complications in Type 2 diabetes: an Australian population-based study. Diabet Med. 2003;20:105–113.
Boru UT, Alp R, Sargin H, et al. Prevalence of peripheral neuropathy in type 2 diabetic patients attending a diabetes center in Turkey. Endocr J. 2004;51:563–7.
Tamer A, Yildiz S, Yildiz N, et al. The prevalence of neuropathy and relationship with risk factors in diabetic patients: a single-center experience. Medical principles and practice : international journal of the Kuwait University, Health Science Centre. 2006;15:190–4.
Al-Mahroos F, Al-Roomi K. Diabetic neuropathy, foot ulceration, peripheral vascular disease and potential risk factors among patients with diabetes in Bahrain: a nationwide primary care diabetes clinic-based study. Annals of Saudi medicine. 2007;27:25–31.
Cho HC. The Association between Serum GGT Concentration and Diabetic Peripheral Polyneuropathy in Type 2 Diabetic Patients. Korean diabetes journal. 2010;34:111–8.
Jianbo L, Yuche C, Ming S, et al. Association of homocysteine with peripheral neuropathy in Chinese patients with type 2 diabetes. Diabetes Res Clin Pract. 2011;93:38–42.
Spallone V, Morganti R, D’Amato C, et al. Clinical correlates of painful diabetic neuropathy and relationship of neuropathic pain with sensorimotor and autonomic nerve function. European Journal of Pain. 2011;15:153–160.
Wang W, Balamurugan A, Biddle J, Rollins KM. Diabetic neuropathy status and the concerns in underserved rural communities: challenges and opportunities for diabetes educators. Diabetes Educ. 2011;37:536–548.
Abougalambou SS, Abougalambou AS. Explorative study on diabetes neuropathy among type II diabetic patients in Universiti Sains Malaysia Hospital. Diabetes & metabolic syndrome. 2012;6:167–172.
Ji N, Zhang N, Ren ZJ, et al. Risk factors and pain status due to diabetic neuropathy in chronic long-term diabetic patients in a Chinese urban population. Chin Med J (Engl). 2012;125:4190–6.
Katulanda P, Ranasinghe P, Jayawardena R, Constantine GR, Sheriff MH, Matthews DR. The prevalence, patterns and predictors of diabetic peripheral neuropathy in a developing country. Diabetology & metabolic syndrome. 2012;4:21.
Rasul S, Ilhan A, Wagner L, Luger A, Kautzky-Willer A. Diabetic polyneuropathy relates to bone metabolism and markers of bone turnover in elderly patients with type 2 diabetes: greater effects in male patients. Gender medicine. 2012;9:187–196.
Eleftheriadou I, Grigoropoulou P, Mourouzis I, et al. Serum osteoprotegerin levels are associated with peripheral neuropathy in patients with type 2 diabetes. Diabetologia. 2013;56:S496.
Molina M, Gonzalez R, Folgado J, et al. Correlation between plasma concentrations of homocysteine and diabetic polyneuropathy evaluated with the Semmes-Weinstein monofilament test in patients with type 2 diabetes mellitus. Med Clin (Barc). 2013;141:382–6.
Aubert CE, Michel PL, Gillery P, et al. Association of peripheral neuropathy with circulating advanced glycation end products, soluble receptor for advanced glycation end products and other risk factors in patients with type 2 diabetes. Diabetes Metab Res Rev. 2014;30:679–685.
Bener A, Al-Laftah F, Al-Hamaq AO, Daghash M, Abdullatef WK. A study of diabetes complications in an endogamous population: an emerging public health burden. Diabetes & metabolic syndrome. 2014;8:108–114.
Brownrigg JR, de Lusignan S, McGovern A, et al. Peripheral neuropathy and the risk of cardiovascular events in type 2 diabetes mellitus. Heart. 2014;100:1837–43.
Hu Y, Liu F, Shen J, et al. Association between serum cystatin C and diabetic peripheral neuropathy: a cross-sectional study of a Chinese type 2 diabetic population. Eur J Endocrinol. 2014;171:641–8.
Jaiswal M, Divers J, Isom S, et al. Prevalence and clinical correlates of diabetic peripheral neuropathy among youth with type 1 diabetes: Search for diabetes in youth cohort study. Diabetes. 2014;63:A148–A149.
Wang DD, Bakhotmah BA, Hu FB, Ali Alzahrani H. Prevalence and correlates of diabetic peripheral neuropathy in a Saudi Arabic population: A cross-sectional study. PloS one 2014;9
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;56:455–463.
Clair C, Bitton A, Meigs JB, Rigotti NA. Relationships of cotinine and self-reported cigarette smoking with hemoglobin A1c in the U.S.: results from the National Health and Nutrition Examination Survey, 1999–2008. Diabetes Care. 2011;34:2250–5.
Willi C, Bodenmann P, Ghali WA, Faris PD, Cornuz J. Active smoking and the risk of type 2 diabetes: a systematic review and meta-analysis. JAMA. 2007;298:2654–64.
Targher G, Alberiche M, Zenere MB, Bonadonna RC, Muggeo M, Bonora E. Cigarette smoking and insulin resistance in patients with noninsulin-dependent diabetes mellitus. J Clin Endocrinol Metab. 1997;82:3619–24.
Madsbad S, McNair P, Christensen MS, et al. Influence of smoking on insulin requirement and metabolic status in diabetes mellitus. Diabetes Care. 1980;3:41–3.
Bott U, Jorgens V, Grusser M, Bender R, Muhlhauser I, Berger M. Predictors of glycaemic control in type 1 diabetic patients after participation in an intensified treatment and teaching programme. Diabet Med. 1994;11:362–371.
Melin EO, Thunander M, Svensson R, Landin-Olsson M, Thulesius HO. Depression, obesity, and smoking were independently associated with inadequate glycemic control in patients with type 1 diabetes. Eur J Endocrinol. 2013;168:861–9.
Gunton JE, Davies L, Wilmshurst E, Fulcher G, McElduff A. Cigarette smoking affects glycemic control in diabetes. Diabetes Care. 2002;25:796–7.
Kapoor D, Jones TH. Smoking and hormones in health and endocrine disorders. Eur J Endocrinol. 2005;152:491–9.
Nilsson PM, Gudbjornsdottir S, Eliasson B, Cederholm J. Smoking is associated with increased HbA1c values and microalbuminuria in patients with diabetes–data from the National Diabetes Register in Sweden. Diabetes Metab. 2004;30:261–8.
Sargeant LA, Khaw KT, Bingham S, et al. Cigarette smoking and glycaemia: the EPIC-Norfolk Study. European Prospective Investigation into Cancer. Int J Epidemiol. 2001;30:547–54.
Lundman BM, Asplund K, Norberg A. Smoking and metabolic control in patients with insulin-dependent diabetes mellitus. J Intern Med. 1990;227:101–6.
Fullerton B, Jeitler K, Seitz M, Horvath K, Berghold A, Siebenhofer A. Intensive glucose control versus conventional glucose control for type 1 diabetes mellitus. Cochrane Database Syst Rev. 2014;2:CD009122.
Hemmingsen B, Lund SS, Gluud C, et al. Targeting intensive glycaemic control versus targeting conventional glycaemic control for type 2 diabetes mellitus. Cochrane Database Syst Rev. 2013;11:CD008143.
Winkelmann BR, Boehm BO, Nauck M, et al. Cigarette smoking is independently associated with markers of endothelial dysfunction and hyperinsulinaemia in non-diabetic individuals with coronary artery disease. Curr Med Res Opin. 2001;17:132–141.
Burke A, Fitzgerald GA. Oxidative stress and smoking-induced vascular injury. Prog Cardiovasc Dis. 2003;46:79–90.
Benowitz NL. Cigarette smoking and cardiovascular disease: pathophysiology and implications for treatment. Prog Cardiovasc Dis. 2003;46:91–111.
Eliasson B. Cigarette smoking and diabetes. Prog Cardiovasc Dis. 2003;45:405–413.
Yu MK, Lyles CR, Bent-Shaw LA, Young BA. Sex disparities in diabetes process of care measures and self-care in high-risk patients. Journal of diabetes research. 2013;2013:575814.
Chiolero A, Wietlisbach V, Ruffieux C, Paccaud F, Cornuz J. Clustering of risk behaviors with cigarette consumption: A population-based survey. Prev Med. 2006;42:348–53.
Voulgari C, Katsilambros N, Tentolouris N. Smoking cessation predicts amelioration of microalbuminuria in newly diagnosed type 2 diabetes mellitus: a 1-year prospective study. Metabolism. 2011;60:1456–64.
Standards of medical care in diabetes--2014. Diabetes Care 2014;37 Suppl 1:S14-80
Acknowledgments
Portions of the data in this manuscript were presented as an oral presentation at the 35th Annual Meeting of the Society of General Internal Medicine (SGIM) in May 2012.
Author contributions
CC contributed to study conceptualization and design; research, extraction, and analysis of the data; and drafting of the manuscript. MJC and FE contributed to study conceptualization and design; research, extraction, and interpretation of the data; and drafting and review of the manuscript. KJS contributed to the research, extraction, and interpretation of the data and review and editing of the manuscript. NAR contributed to study conceptualization, interpretation of the data, and critical review of the manuscript. All authors read and approved the final manuscript.
Financial support
CC was supported by a grant from the Swiss National Science Foundation PBLAP3-127728/1 and by a grant from the SICPA foundation.
Conflict of interest
NR has been an unpaid consultant to Pfizer and Alere Wellbeing and receives royalties from UpToDate for chapters on smoking cessation. All other authors declare that they do not have a conflict of interest.
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Clair, C., Cohen, M.J., Eichler, F. et al. The Effect of Cigarette Smoking on Diabetic Peripheral Neuropathy: A Systematic Review and Meta-Analysis. J GEN INTERN MED 30, 1193–1203 (2015). https://doi.org/10.1007/s11606-015-3354-y
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DOI: https://doi.org/10.1007/s11606-015-3354-y