Association between smoking and glycemic control in diabetic patients: Results from the Risk Evaluation of cAncers in Chinese diabeTic Individuals: A lONgitudinal (REACTION) study

Low blood glucose and fatigue accumulation at peak hours of occupational trauma in secondary industry workers

Objectives. The incidence of occupational traumatic injuries caused by human error has been reported to occur at 11:00 and 8-9 h after commencing work. Impaired attention is closely related to the incidence of these accidents. Therefore, this study aimed to clarify the changes in blood glucose, fatigue and stress response hormone levels over time among workers in a secondary industry. Methods. The blood glucose and subjective fatigue levels of 26 male secondary-industry workers were measured on workdays. In addition, the cortisol and dehydroepiandrosterone levels in saliva were measured on one workday and one holiday. Results. Blood glucose levels at 11:00 and 17:30 on the workday were significantly lower than those at 09:30. Moreover, hypoglycemia was observed in some participants. A significant increase in subjective fatigue levels was observed during the workday. However, no significant differences in salivary cortisol levels were observed between the workday and the holiday at any time point. Conclusions. Blood glucose levels decreased and subjective fatigue levels increased at the time points that occupational accidents were reported to occur most frequently during work. These factors may contribute to human errors due to impaired attentional function.

Metabolic Changes Following Smoking Cessation in Patients with Type 2 Diabetes Mellitus

BACKGROUND

Smoking cessation is crucial for reducing complications of type 2 diabetes mellitus (T2DM), but associated weight gain can worsen glycemic control, discouraging quitting attempts. Varenicline, a partial agonist of α4β2 nicotinic receptors, aids smoking cessation. This study examines the effects of varenicline on body weight and metabolic parameters in patients with T2DM and prediabetes.

METHODS

Fifty-three patients were enrolled, of which 32 successfully quit smoking after a three-month course of varenicline and were examined after an additional month with no medication. Measurements taken at baseline, 2.5 months, and 4 months included body weight, blood pressure, resting metabolic rate (RMR), glycated hemoglobin (HbA1c), fasting glucose, blood lipids, C-reactive protein (CRP), appetite-related hormones, and physical activity.

RESULTS

Post-treatment, there were no significant changes in body weight, blood pressure, RMR, or glycemic control. Total (CHOL) and low-density lipoprotein (LDL-C) cholesterol decreased significantly at 4 months of the study (from 168 to 156 mg/dL, p = 0.013, and from 96 to 83 mg/dL, p = 0.013, respectively). Leptin levels increased (from 11 to 13.8 ng/dL, p = 0.004), as did glucagon-like peptide-1 (GLP-1) levels (from 39.6 to 45.8 pM, p = 0.016) at 4 months of follow-up. The percentage of participants who reported moderate-intensity activity increased from 28% to 56%, while those reporting high-intensity activity increased from 19% to 22%, respectively (p = 0.039).

CONCLUSIONS

Our study showed that smoking cessation with varenicline in smokers with T2DM and prediabetes led to significant improvements in lipid profile, significant increase in plasma leptin and GLP-1 levels, and increased physical activity, without significant weight gain. Thus, smoking cessation without weight gain or deteriorated glycemic control is feasible for these smokers, with added benefits to lipid profiles, GLP-1 regulation, and physical activity.

The association between smoking exposure and endothelial function evaluated using flow-mediated dilation values: a meta-analysis

BACKGROUND

Tobacco use is recognized as a major cause of cardiovascular disease, which is associated with endothelial dysfunction. Endothelial function is evaluated using flow-mediated dilation (FMD), which is a noninvasive method. This meta-analysis aimed to investigate the association between smoking exposure and endothelial function evaluated using FMD values.

METHODS

We searched the PubMed, Embase, Web of Science, and Cochrane Library databases for cohort studies of smokers or passive smokers that used FMD to assess endothelial function. The primary outcome of the study was the change in the rate of FMD. The risk of bias was evaluated using the Cochrane Collaboration tool and Newcastle-Ottawa Scale. Further, the weighted mean difference was used to analyze the continuous data.

RESULTS

Overall, 14 of 1426 articles were included in this study. The results of these articles indicated that smoking is a major cause of endothelial dysfunction and altered FMD; a pooled effect size of - 3.15 was obtained with a 95% confidence interval of (- 3.84, - 2.46). Notably, pregnancy status, Asian ethnicity, or health status did not affect heterogeneity.

CONCLUSIONS

We found that smoking has a significant negative impact on FMD, and measures such as medication or education for smoking cessation may improve endothelial function and reduce the risk of cardiovascular disease.

TRIAL REGISTRATION

The meta-analysis was registered with PROSPERO on April 5th, 2023 (CRD42023414654).

Exploring the self-efficacy of patients with diabetes: its role as a predictor of diabetes management and well-being

Background

Self-efficacy is a popular psychological concept that refers to an individual's perception or belief in his ability to perform specific actions. This study aimed to assess the predictive value of self-efficacy, measured using the Self-Efficacy for Managing Chronic Disease 6-Item Scale (SEM6S) questionnaire, for diabetes management and overall well-being in patients with diabetes.

Subject and methods

An anonymous online cross-sectional study was conducted to evaluate the self-efficacy of diabetic patients in the Asser region of Saudi Arabia. The participants were requested to upload their most recent glycated hemoglobin A1C (HbA1C) measurements taken in the last three months, which helped in the accurate categorization of their diabetes as either controlled or uncontrolled. We used the valid Arabic version of the SEM6S and WHO-5 well-being questionnaires to assess patient self-efficacy and well-being.

Results

A cohort of 342 patients was enrolled in the study, 67.25% were married, their mean age was 43.17 ± 17.61 years, and 52.69% had university-level or higher education. Among the participants, 46.0% exhibited well-being, while 24.9% reported poor well-being, including 9.4% who were identified as experiencing depression. The mean scores of self-efficacy and well-being were significantly higher among those with controlled diabetes versus uncontrolled diabetes (40.86 ± 13.26 vs. 36.48 ± 13.26) and (67.35 ± 21.22 vs. 60.93 ± 25.05), respectively. The predictors of glycemic control were self-efficacy [Odds ratio (OR)=1.03 (95%CI, 1.01-1.06, P=0.002], having other chronic diseases [OR=3.25 (95%CI), P<0.001], having type 1 diabetes [OR=7.16, 95%CI, P=0.005], being Saudi [OR=7.67, (95%CI, P=0.027], working in a public sector [OR=0.15, (95%CI, 0.05-0.44), P=0.005], being unemployed [OR=0.19, (95%CI, 0.06-0.59), P=0.005], being a smoker [OR=0.44, 95%CI, 0.19-0.98, P=0.048], and duration of diabetes between 6-10 years [OR= 0.33, 95%CI, 0.11-0.95), P=0.043] or more than 10 years OR=0.32, 95%CI, 0.12-0.86), P=0.026]. The main determinants of well-being were having self-efficacy [OR=1.07 (95%CI, 1.04-1.09), P = 0.0001], having public health insurance [OR=4.36 (95%CI, P=0.015], and education level (read and write) [OR=0.13 (95%CI,.02-.70), P=0.021].

Conclusions

The study reveals that non-modifiable and modifiable factors, including self-efficacy, play a crucial role in diabetes control. The study recommends providing targeted educational interventions, using different social media platforms, psychosocial support programs, and inclusive healthcare policies to improve diabetes control and mental well-being among diabetic patients.

Scans per day as predictors of optimal glycemic control in people with type 1 diabetes mellitus using flash glucose monitoring: what number of scans per day should raise a red flag?

AIMS

This study aimed to determine the minimum frequency of flash glucose monitoring (FGM) scans necessary for optimal glycemic control in patients with type 1 diabetes (T1D).

METHODS

Data were collected from 692 patients (47.5% female, with a median age of 47.4 years) who used FGM systems daily and recorded their clinical variables and device data.

RESULTS

Logistic regression models showed that performing more than 12 scans per day was associated with improved T1D control (OR = 4.22, p < 0.001) and a reduction in HbA1c (7.6 vs 7.0%, 60-53 mmol/mol p < 0.001). However, those performing less than 6 scans showed no improvement in HbA1c (7.9 vs 7.8%, 63-61 mmol/mol p = 0.514). Thirteen daily scans were determined as the optimal cutoff point for predicting optimal glycemic control using a maximally selected rank algorithm. Significant reductions were observed in mean glucose (< 0.001), coefficient of variation (< 0.001), HbA1c (< 0.001), and an increase in TIR (< 0.001) in patients who performed more than 12 daily scans.

CONCLUSIONS

The results suggest that a higher frequency of daily scans by T1D patients using FGM systems leads to improved chronic glycemic control. The minimum recommended frequency for optimal control is 13 scans per day, and more than 6 daily scans are needed to improve HbA1c.

Determinants of poor glycaemic control and proteinuria in patients with type 2 diabetes: a retrospective analysis of general practice records in Ireland

BACKGROUND

Analysis of general practice records can address the information gap on the epidemiology of type 2 diabetes (T2DM) in Ireland, informing practice and the development of interventions in primary care. The aim of this study was to identify patients with poor glycaemic control, risk factors for complications and evidence of end organ damage in a large multi-practice study and to profile their characteristics.

METHODS

Patients with T2DM were identified using disease coding in Health One practice management software in 41 general practices. Patients' demographics and clinical data were extracted. Rates of poor glycaemic control (glycated haemoglobin > 58 mmol/mol) and albumin creatinine ratio > 3 mg/mmol were calculated. A multilevel logistic regression analysis using both patient and practice variables was conducted.

RESULTS

Data was collected from 3188 patients of whom 29% (95% CI 28 to 31%) had poor glycaemic control, which was associated with younger age, higher BMI and higher total cholesterol. Only 42% of patients (n = 1332) had albumin creatinine ratio measured with 42% (95% CI 40 to 45%) of these having values > 3 mg/mmol. Older age groups, men, those with hypertension, eGFR < 60 ml/min/1.73m2 and poor glycaemic control were most associated with higher values of albumin creatinine ratio.

CONCLUSIONS

Analysing this large multi-practice dataset gives important information on the prevalence and characteristics of diabetic patients who are most at risk of poor outcomes. It highlights that recording of some data could be improved.

Association between initial in-hospital heart rate and glycemic control in patients with acute ischemic stroke and diabetes mellitus

BACKGROUND

A high resting heart rate (HR) has been associated with an increased risk of diabetes mellitus. This study explored the association between initial in-hospital HR and glycemic control in patients with acute ischemic stroke (AIS) and diabetes mellitus.

METHODS

We analyzed data from 4,715 patients with AIS and type 2 diabetes mellitus enrolled in the Chang Gung Research Database between January 2010 and September 2018. The study outcome was unfavorable glycemic control, defined as glycated hemoglobin (HbA1c) ≥ 7%. In statistical analyses, the mean initial in-hospital HR was used as both a continuous and categorical variable. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression analysis. The associations between the HR subgroups and HbA1c levels were analyzed using a generalized linear model.

RESULTS

Compared with the reference group (HR < 60 bpm), the adjusted ORs for unfavorable glycemic control were 1.093 (95% CI 0.786-1.519) for an HR of 60-69 bpm, 1.370 (95% CI 0.991-1.892) for an HR of 70-79 bpm, and 1.608 (95% CI 1.145-2.257) for an HR of ≥ 80 bpm. Even after adjusting for possible confounders, the HbA1c levels after admission and discharge among diabetic stroke patients increased significantly in the subgroups with higher HRs (p < 0.001).

CONCLUSIONS

High initial in-hospital HR is associated with unfavorable glycemic control in patients with AIS and diabetes mellitus, particularly in those with an HR of ≥ 80 bpm, compared with those with an HR of < 60 bpm.

Association between smoking and glycemic control in men with newly diagnosed type 2 diabetes: a retrospective matched cohort study

BACKGROUND

Longitudinal data on the association between smoking and glycemic control in men with newly diagnosed type 2 diabetes (T2DM) is scarce. Therefore, this study aimed to examine the extent of the association between smoking and glycemic control in this population.

METHODS

The retrospective cohort study identified 3044 eligible men with T2DM in a medical centre in Taiwan between 2002 and 2017. Smokers (n = 757) were matched 1:1 with non-smokers using propensity score-matching. All of them were followed for one year. Glycated haemoglobin (HbA1c) levels were measured at 0, 3, 6, 9, and 12 months after enrolment. Generalised estimating equations were used to assess smoking status-by-time interaction to determine the difference in HbA1c reduction between the two cohorts. All analyses were performed in 2020.

RESULTS

The estimated maximal difference in HbA1c reduction between smokers and non-smokers was 0.33% (95% CI, 0.05-0.62%) at 3 months of follow-up. For patients with body mass index (BMI) <25 kg/m², the difference in HbA1c reduction between smokers and non-smokers was much larger (0.74%, 95% CI, 0.35-1.14%) than in those with a higher BMI.

CONCLUSIONS

Our findings show that smoking was independently associated with unfavourable glycemic control among men with newly diagnosed T2DM, and such a detrimental association could be stronger in men with a lower BMI.

Smoking and diabetes interplay: A comprehensive review and joint statement

Evidence shows that smoking increases the risk of pre-diabetes and diabetes in the general population. Among persons with diabetes, smoking has been found to increase the risk of all-cause mortality and aggravate chronic diabetic complications and glycemic control. The current paper, which is a joint position statement by the French-Speaking Society on Tobacco (Société Francophone de Tabacologie) and the French-Speaking Society of Diabetes (Société Francophone du Diabète), summarizes the data available on the association between smoking and diabetes and on the impact of smoking and smoking cessation among individuals with type 1, type 2, and gestational diabetes mellitus. It also provides evidence-based information about the pharmacological and behavioral strategies for smoking cessation in these patients.

The Effect of Smoking Cessation on Body Weight and Other Metabolic Parameters with Focus on People with Type 2 Diabetes Mellitus

Smokers with diabetes mellitus substantially lower their risks of microvascular and macrovascular diabetic complications, in particular cardiovascular disease, by quitting smoking. However, subsequent post-smoking-cessation weight gain may attenuate some of the beneficial effects of smoking cessation and discourage attempts to quit. Weight gain can temporarily exacerbate diabetes and deteriorate glycemic control and metabolic profile. The molecular mechanisms by which quitting smoking leads to weight gain are largely associated with the removal of nicotine's effects on the central nervous system. This review addresses mechanisms of post-smoking-cessation weight gain, by reviewing the effects of nicotine on appetite, food intake, eating behaviour, energy expenditure, fat oxidation and appetite-regulating peptides. We also highlight correlations between post-cessation weight gain and risk of type 2 diabetes, consequences of weight gain in people with type 2 diabetes and the role of pharmacotherapies, which combine treatment of nicotine addiction and promotion of weight control.

Glycemic control and its influencing factors in type 2 diabetes patients in Anhui, China

Objective

To investigate the status of glycemic control and analyze its influencing factors in patients with type 2 diabetes (T2D) in Anhui, China.

Methods

1,715 T2D patients aged 18-75 years old were selected from 4 counties or districts in Anhui Province in 2018, using a convenience sampling method. All patients have undergone a questionnaire survey, physical examination, and a glycosylated hemoglobin (HbA1c) test. According to the 2022 American Diabetes Association criteria, HbA1c was used to evaluate the glycemic control status of patients, and HbA1c < 7.0% was defined as good glycemic control. The influencing factors of glycemic control were analyzed by multivariate unconditional logistic regression.

Results

The prevalence of good glycemic control among people with T2D in the Anhui Province was low (22.97%). On univariate analysis, gender, education level, occupation, region, smoking, drinking, waist circumference and disease duration (all P < 0.05) were significantly associated with glycemic control. The factors associated with pool glycemic control were female gender [OR = 0.67, 95%CI (0.52, 0.86), P = 0.001], higher level of education [OR = 0.47, 95%CI (0.27, 0.83), P = 0.001], living in rural areas [OR = 1.77, 95%CI (1.39, 2.26), P < 0.001], central obesity [OR = 1.58, 95%CI (1.19, 2.09), P = 0.001] and longer duration of disease [OR = 2.66, 95%CI (1.91, 3.69), P < 0.001].

Conclusions

The prevalence of good glycemic control in people with T2D in Anhui Province was relatively low, and gender, region, education level, central obesity and course of the disease were influencing factors. The publicity and education on the importance of glycemic control should be further strengthened in T2D patients, and targeted intervention measures should be carried out for risk groups.

Association between metabolic phenotype and urinary albumin-creatinine ratio in Chinese community adults: A cross-sectional study

BACKGROUND

Urinary albumin-creatinine ratio (UACR) is a sensitive marker of kidney injury. This study analyzed the prevalence of different metabolic phenotypes and investigated their relationship with UACR in Chinese community adults.

METHODS

This study involved 33 303 participants over 40 years old from seven centers across China. They were stratified into six groups according to their body mass index (BMI) and metabolic status: metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), metabolically healthy obesity (MHO), metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and metabolically unhealthy obesity (MUO). Increased albuminuria was defined as a UACR ≥30 mg/g.

RESULTS

The percentages of MHNW, MHOW, MHO, MUNW, MUOW, and MUO were 27.6%, 15.9%, 4.1%, 19.8%, 22.5%, and 9.6%, respectively. Multiple logistic regression analysis showed that the MHO group (odds ratio [OR] 1.205; 95% CI, 1.081-1.343), MUNW group (OR 1.232; 95% CI, 1.021-1.486), MUOW group (OR 1.447; 95% CI, 1.303-1.607), and MUO group (OR 1.912; 95% CI, 1.680-2.176) were at higher risk of increased albuminuria compared to the MHNW group. Subgroup analysis indicated that the risk of increased albuminuria was further elevated among regular smokers in men aged 40 to 55 years old with abdominal obesity.

CONCLUSIONS

Among Chinese community adults, increased albuminuria was associated with increased BMI whether metabolism was normal or not, and those with abnormal metabolism were at greater risk of increased albuminuria than those with normal metabolism. These findings suggest that overweight or obesity or metabolic abnormalities are risk factors for chronic kidney disease.

Smoking is Associated With Increased Risk of Not Achieving Glycemic Target, Increased Glycemic Variability, and Increased Risk of Hypoglycemia for People With Type 1 Diabetes

BACKGROUND

The prevalence of smoking and diabetes is increasing in many developing countries. The aim of this study was to investigate the association of smoking with inadequate glycemic control and glycemic variability with continuous glucose monitoring (CGM) data in people with type 1 diabetes.

METHODS

Forty-nine smokers and 320 nonsmokers were obtained from the Novo Nordisk Onset 5 trial. After 16 weeks of treatment with continuous subcutaneous insulin infusion, risk of not achieving glycemic target and glycemic variability from six CGM measures was investigated. Analyzes were carried out with logistic regression models (glycemic target) and general linear models (glycemic variability). Finally, CGM median profiles were examined for the identification of daily glucose excursions.

RESULTS

A 4.7-fold (95% confidence interval: 1.5-15.4) increased risk of not achieving glycemic target was observed for smokers compared with nonsmokers. Increased time in hyperglycemia, decreased time in range, increased time in hypoglycemia (very low interstitial glucose), and increased fluctuation were observed for smokers compared with nonsmokers from CGM measures. CGM measures of coefficient of variation and time in hypoglycemia were not statistically significantly different. Examination of CGM median profiles revealed that risk of morning hypoglycemia is increased for smokers.

CONCLUSIONS

In conclusion, smoking is associated with inadequate glycemic control and increased glycemic variability for people with type 1 diabetes with especially risk of morning hypoglycemia. It is important for clinicians to know that if the patient has type 1 diabetes and is smoking, a preemptive action to treat high glycated hemoglobin levels should not necessarily be treatment intensification due to the risk of hypoglycemia.

Effect of Smoking on Coronary Artery Plaques in Type 2 Diabetes Mellitus: Evaluation With Coronary Computed Tomography Angiography

BACKGROUND

The effect of smoking on coronary artery plaques examined by coronary computed tomography angiography (CCTA) in type 2 diabetes mellitus (DM) patients is not fully understood. This study explored the effect of smoking on coronary artery plaques by comparing the characteristics of plaques between diabetes patients with and without a smoking history and among those with different smoking durations.

MATERIALS AND METHODS

In total, 1058 DM patients found to have coronary plaques on CCTA were categorized into the smoker (n=448) and nonsmoker groups (n=610). Smokers were stratified by smoking duration [≤20 years (n=115), 20~40 years (n=233) and >40 years (n=100)]. The plaque types, luminal stenosis [obstructive (<50%) or nonobstructive (≥50%) stenosis], segment involvement score (SIS), and segment stenosis score (SSS) of the CCTA data were compared among groups.

RESULTS

Compared to nonsmokers, smokers demonstrated increased odds ratios (ORs) of any noncalcified plaques (OR=1.423; P=0.014), obstructive plaques (OR=1.884; P<0.001), multivessel disease (OR=1.491; P=0.020), SIS≥4 (OR=1.662; P<0.001), and SSS≥7 (OR=1.562; P=0.001). Compared to diabetes patients with a smoking duration ≤20 years, those with a smoking duration of 20~40 years and >40 years had higher OR of any mixed plaques (OR=2.623 and 3.052, respectively; Ps<0.001), obstructive plaques (OR=2.004 and 2.098; P=0.003 and 0.008, respectively), multivessel disease (OR=3.171 and 3.784; P<0.001 and P=0.001, respectively), and SSS≥7 (OR=1.605 and 1.950; P=0.044 and 0.020, respectively). Diabetes with a smoking duration >40 years had a higher OR of SIS≥4 (OR=1.916, P=0.034).

CONCLUSION

Smoking is independently associated with the presence of noncalcified, obstructive, and more extensive coronary artery plaques in diabetes patients, and a longer smoking duration is significantly associated with a higher risk of mixed, obstructive, and more extensive plaques.

Improvement in Cardiometabolic Risk Factors During Smoking Cessation Treatment in Patients with Type 2 Diabetes: A Retrospective Cohort Study

PURPOSE

Smoking cessation reduces morbidity and mortality of cardiovascular diseases. The purpose of this study was to evaluate the effect during smoking cessation treatment on glycemic control and cardiometabolic risk factors, including blood pressure and lipid levels, in patients with type 2 diabetes.

PATIENTS AND METHODS

This retrospective cohort study included patients with type 2 diabetes who participated in a smoking cessation program, which comprised health education and medication prescription at an outpatient clinic in combination with a 3-month follow-up by phone. Data on patient background characteristics, cardiometabolic factors, smoking status, body weight, and body mass index before and after the program were collected for analysis.

RESULTS

The analysis included 241 participants with an average age of 58.6 ± 10.6 years. The rate of successful cessation at three months was 34.0%. Compared with the baseline levels, there were significant decreases in the levels of fasting plasma glucose (10.0 ± 46.8 mg/dL, P = 0.001), HbA1c (0.3 ± 1.4%, P = 0.004), systolic blood pressure (4.6 ± 17.5 mmHg, P < 0.001), diastolic blood pressure (2.9 ± 11.3 mmHg, P < 0.001), and total cholesterol (7.9 ± 42.8 mg/dL, P = 0.020) after participation in the smoking cessation program while there was no significant difference in body weight (0.1 ± 1.2 kg, P = 0.444). After adjustment for covariates, the decreases in HbA1c and total cholesterol levels were significantly better in younger participants and higher baseline nicotine dependence scores were associated with decreases in the levels of blood pressure, fasting plasma glucose, and triglycerides. However, the decrease in smoking amount was not associated with the changes in cardiometabolic factors.

CONCLUSION

Participation in a smoking cessation program was associated with improvements in glycemic control and cardiometabolic risk factors in patients with type 2 diabetes. The observed improvements were associated with participation in the program but not with the decrease in smoking amount.

Cardiometabolic Morbidity and Mortality with Smoking Cessation, Review of Recommendations for People with Diabetes and Obesity

PURPOSE OF REVIEW

Obesity is closely linked with the pathogenesis of type 2 diabetes (T2DM) and cardiovascular disease (CVD), and whilst smoking cessation is associated with weight gain, there are concerns that this weight gain may offset the benefit of CVD risk reduction especially in those with considerable post-cessation weight gain. The aim of this narrative review is to evaluate recent evidence on smoking cessation and cardiometabolic outcomes and discuss limitations of current knowledge and studies.

RECENT FINDINGS

Nicotine is a key player in modulating energy balance by influencing lipid storage in adipose tissue by affecting lipolysis, energy input by modulating appetite and energy output by increasing sympathetic drive and thermogenesis. It also increases insulin resistance and promotes abdominal obesity. The CVD risk and mortality associated with cigarette smoking potentiate the CVD risks in patients with diabetes. Evidence supports the benefit of quitting cigarette smoking regardless of any subsequent weight gain. Data suggests that the cardiometabolic risk is limited to the first few years and that cardiovascular health and mortality benefit of smoking cessation outweighs the harm related to weight gain. This weight gain can be limited by nicotine replacement of which e-cigarettes (vaping) are increasingly popular if it is not an alternative to cigarette smoking. However, long-term health data on e-cigarettes is needed prior to formal recommendation for its use in smoking cessation. The recommendation for cessation of cigarette smoking is justified for those at high risk of weight gain and diabetes. However, for most benefit, consideration should be given for personalized weight management to limit weight gain. Awareness of a 'lean paradox' by which lower weight is associated with increased CVD risk may help to improve motivation and insight into the bias of smoking, health and body composition otherwise known to epidemiologists as the 'obesity paradox'.

Dietary patterns and glycaemic control among Qatari adults with type 2 diabetes

OBJECTIVE

To assess the association between dietary patterns and glycaemic control among Qatari adults with type 2 diabetes (T2DM).

DESIGN

Cross-sectional analysis using data from the Qatar Biobank Study. Poor glycaemic control was defined as HbA1c ≥7·0 %. Dietary patterns were constructed using factor analysis based on habitual food intake assessed by a FFQ. Medication use was based on self-report. Multivariable logistic regression was used to assess the association.

SETTING

Qatar.

PARTICIPANTS

Adults aged ≥18 years (n 1000) with known diabetes.

RESULT

The mean age of the participants was 52·3 (sd 11·5) years. Overall, the prevalence of poor glycaemic control was 57·6 %, and 27·7 % of the participants were insulin users. Three dietary patterns were identified. The modern dietary pattern (high intake of fast food, croissants, white bread and cheese) was inversely associated with poor glycaemic control. The sd increments of the modern pattern had OR for poor glycaemic control of 0·86 (95 % CI 0·68, 1·08) in men and 0·76 (95 % CI 0·61, 0·95) in women. There was a significant interaction between the modern pattern and diabetes medication in men but not in women. In men without diabetes medication, the modern pattern was positively associated with poor glycaemic control with an OR of 2·35 (95 % CI 1·13, 4·87).

CONCLUSIONS

Male diabetes patients took medication to control diabetes but ate more unhealthy food. In men who were not taking diabetes medication, modern dietary pattern was associated with poor glycaemic control. Promoting healthy eating should be encouraged especially among those under diabetes medication.

The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals

Metabolic diseases are the most common and rapidly growing health issues worldwide. The massive population-based human genetics is crucial for the precise prevention and intervention of metabolic disorders. The China Metabolic Analytics Project (ChinaMAP) is based on cohort studies across diverse regions and ethnic groups with metabolic phenotypic data in China. Here, we describe the centralized analysis of the deep whole genome sequencing data and the genetic bases of metabolic traits in 10,588 individuals from the ChinaMAP. The frequency spectrum of variants, population structure, pathogenic variants and novel genomic characteristics were analyzed. The individual genetic evaluations of Mendelian diseases, nutrition and drug metabolism, and traits of blood glucose and BMI were integrated. Our study establishes a large-scale and deep resource for the genetics of East Asians and provides opportunities for novel genetic discoveries of metabolic characteristics and disorders.

Determinants of glycemic control: Phase 2 analysis from nationwide diabetes report of National Program for Prevention and Control of Diabetes (NPPCD-2018)

BACKGROUND

Diabetes is one of the leading causes of morbidity and mortality worldwide, especially among middle and low income nations. Many diabetic complications and comorbidities are attributable to poor glycemic control. The aim of this study was to update and extend the national diabetes reports on the status of comorbidities, diabetes care and complications in Iran. Moreover, we investigated the risk factors of poor glycemic control in the Iranian population.

METHODS

National database of 99,651 patients with diabetes who attended university-affiliated clinics between April 1, 2017 and February 30, 2018 was used to carry out a cross-sectional study. Stepwise backward selection logistic regression model was used to examine the associated factors of glycemic control.

RESULTS

In this study 73.0% and 56.5% of the enrolled population with diabetes, had hypertension and hyperlipidemia, respectively. The prevalence of patients who received education for nutrition therapy or diabetes self-management was 16.3% and 23.3% respectively. Poor glycemic control was associated with male gender (OR=1.06, p=0.001), obesity (OR=1.03, p=0.05), duration of diabetes (OR=1.018, p<0.001), smoking (OR=1.08, p=0.041), hypertension (OR=1.53, p<0.001), hyperlipidemia (OR=1.15, p<0.001), insulin therapy (OR=1.26, p<0.001) and combination of insulin and oral anti-diabetic agents compared to oral anti-diabetic agents alone (OR=2.36, p<0.001).

CONCLUSION

We demonstrated that the prevalence of diabetes comorbidities is high in Iranian population and that a great proportion of Iranian patients with diabetes had not reached the goal of glycemic control. Our findings provide a starting point from which to investigate the obstacles that prevent patients with diabetes from reaching metabolic targets.

Challenges And Factors Associated With Poor Glycemic Control Among Type 2 Diabetes Mellitus Patients At Nekemte Referral Hospital, Western Ethiopia

Background

Diabetes is increasing at an alarming rate throughout the world, and ~80% of diabetics live in developing countries. Similar to the rest of sub-Saharan African countries, Ethiopia is experiencing a significant burden of diabetes, with increased prevalence, complications, and mortality, as well as life threatening disabilities. Reasons for poor glycemic control among type 2 diabetes patients are complex and multivariable. Hence, this study aimed to identify challenges and factors associated with poor glycemic control among type 2 diabetes patients.

Method

A hospital-based cross-sectional study was conducted among type 2 diabetic patients attending the diabetic clinic of Nekemte Referral Hospital (NRH) from February 1 to April 30, 2018. Fasting blood glucose levels of the last three clinic visits were obtained and the mean fasting blood glucose measurement was used to determine the level of glycemic control. Analysis included both descriptive and inferential statistics with SPSS version 20.0. Predictor variable P<0.05 was considered statistically significant.

Results

Out of the total 228 included type 2 diabetes mellitus (DM) patients, 51.8% were males. The mean age of patients was 43±12.4 years and 154 (67.5%) were found to not be following their general dietary program correctly. Nearly one third, 73 (32%), of patients never attended diabetic education and 52 (22.8%) of the patients had greater than 10 years’ duration on treatment. The majority, 148 (64.9%), of patients had poor blood glucose control. Age 40–60 years (AOR=2.01, 95% CI=0.04–0.06, P=0.044), being illiterate (AOR=3.12, 95% CI=1.52–8.50, P=0.001), having informal education only (AOR=2.28, 95% CI=2.14–32.60, P=0.024), longer duration of diabetes treatment (>10 years) (AOR=3.94, 95% CI=1.51–27.83, P=0.012), inadequate physical exercise (AOR=3.19, 95% CI=1.05–19.84, P=0.019), and smoking (AOR=4.51, 95% CI=0.00–0.50, P=0.022) were independent predictors of poor glycemic control on multivariable logistic regression analysis.

Conclusion

Nearly two-thirds of patients had poorly controlled diabetes. Age, exercise, level of education, duration of the treatment, and smoking were significantly associated with poor glycemic control. Health facilities should provide continuous education, and barriers of glycemic control should be explored with further research.

Smoking in men and women with type 2 diabetes: A qualitative gender-sensitive exploration of barriers to smoking cessation among people with type 2 diabetes

Objectives

Despite growing evidence of the impact of smoking on diabetes complications, people with type 2 diabetes still smoke at high rates and little is known about the specific barriers to quit smoking in this group. The purpose of this article is to explore the perception of smoking, and motivation and barriers to quit among smokers with type 2 diabetes. This exploratory study will help designing a smoking cessation intervention tailored to the needs of people with type 2 diabetes. We hypothesize both that living with diabetes and gender may raise additional difficulties to quit smoking.

Setting

The qualitative exploratory research included ten in-depth semi-structured individual interviews and five focus groups conducted in an Ambulatory Care University Hospital in Switzerland. The thematic analysis was conducted with a gender-sensitive focus.

Participants

Both current and former smokers recruited from the ambulatory clinic and the community took part in the qualitative interviews. We restricted the analysis to 21 current smokers only.

Results

The sample included 12 men and 9 women with type 2 diabetes, having a mean age of 59.4 years, who had diabetes for an average of 9 years with a mean HbA1c of 7.4%. We found that harmful effects of tobacco were superficially understood, and participants used several patterns of thinking to minimize the risks. The relation between tobacco and diabetes was poorly known. Readiness to change was related to personal self-image and meaningful engagement in life and social relationships. Barriers could be organized into three groups: dependence, psychological habits and social barriers. Barriers were markedly shaped by gender and living with diabetes.

Conclusions

Results suggest that “one -fits-all” smoking cessation interventions do not satisfy the needs of type 2 diabetic smokers. Personalized smoking cessation interventions should be preferred and should pursue positive psychological outcomes addressing contextual factors.

Smoking and diabetes: dangerous liaisons and confusing relationships

The combined harmful effects of cigarette smoking and hyperglycemia can accelerate vascular damage in patients with diabetes who smoke, as is well known. Can smoking cause diabetes? What are the effects of smoking on macro and microvascular complications? Now growing evidence indicates that regular smokers are at risk of developing incident diabetes. Since the prevalence rates of smoking in patients with diabetes are relatively similar to those of the general population, it is essential to address the main modifiable risk factor of smoking to prevent the onset of diabetes and delay the development of its complications. Quitting smoking shows clear benefits in terms of reducing or slowing the risk of cardiovascular morbidity and mortality in people with diabetes. Does quitting smoking decrease the incidence of diabetes and its progression? What are the effects of quitting smoking on complications? The current evidence does not seem to unequivocally suggest a positive role for quitting in patients with diabetes. Quitting smoking has also been shown to have a negative impact on body weight, glycemic control and subsequent increased risk of new-onset diabetes. Moreover, its role on microvascular complications of the disease is unclear. What are the current smoking cessation treatments, and which ones are better for patients with diabetes? Stopping smoking may be of value for diabetes prevention and management of the disease and its macrovascular and microvascular complications. Unfortunately, achieving long-lasting abstinence is not easy and novel approaches for managing these patients are needed. This narrative review examines the evidence on the impact of smoking and smoking cessation in patients with diabetes and particularly in type 2 diabetes mellitus and its complications. In addition, management options and potential future directions will be discussed.