Association between smoking and glycemic control in diabetic patients: <scp>R</scp>esults from the <scp>R</scp>isk <scp>E</scp>valuation of c<scp>A</scp>ncers in <scp>C</scp>hinese diabe<scp>T</scp>ic <scp>I</scp>ndividuals: <scp>A</scp> l<scp>ON</scp>gitudinal (<scp>REACTION</scp>) study

Peng, Kui; Chen, Gang; Liu, Chao; Mu, Yiming; Ye, Zhen; Shi, Lixin; Zhao, Jiajun; Chen, Lulu; Li, Qiang; Yang, Tao; Yan, Li; Wan, Qin; Wu, Shengli; Wang, Guixia; Luo, Zuojie; Tang, Xulei; Huo, Yanan; Gao, Zhengnan; Su, Qing; Wang, Youmin; Qin, Guijun; Deng, Hongkui; Yu, Xuefeng; Shen, Feixia; Chen, Li; Zhao, Liebin; Xu, Yu; Xu, Min; Chen, Yuhong; Lu, Jieli; Lin, Lin; Du, Rui; Dai, Meng; Li, Mian; Wang, Tiange; Zhao, Zhiyun; Zhang, Di; Bi, Yufang; Li, Donghui; Wang, Weiqing; Ning, Guang

doi:10.1111/1753-0407.12625

Cited by 39 publications

(32 citation statements)

References 35 publications

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“…On the other hand, the odds of poor glycemic control was four times higher among smokers compared to nonsmokers. This complies with previous study that reported current smokers had an increased risk of poor glycemic control [60]. As well as Willi C et al reported that the risk of diabetes is shown to be higher by 45% in smokers than among nonsmokers [61].…”

Section: Discussionsupporting

confidence: 92%

Challenges and factors associated with poor glycemic control among type 2 diabetes mellitus patients at Nekemte referral hospital, western Ethiopia: cross-sectional study

Fekadu¹,

Bula²,

Bayisa³

2019

Preprint

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Background: Diabetes is increasing at an alarming rate throughout the world and about 80% of diabetics’ lives in developing countries. Similar to the rest of sub Saharan African countries, Ethiopia is experiencing significant burden of diabetes with increased prevalence, complications and mortality as well as life threatening disabilities. Reasons for poor glycemic control in type 2 diabetes are complex and multivariable. Hence, this study was 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 on type 2 diabetic patients attending diabetic clinic of Nekemte Referral Hospital (NRH) from February 1 to April 30, 2018. Fasting blood glucose of last three clinic visits were obtained and the mean fasting blood glucose measurements was used to determine the level of glycemic control. Analysis included both descriptive and inferential statistics with SPSS version 20.0. Predictor variable with P< 0.05 was considered statistically significant. Result: Out of the total 228 included type 2 diabetes mellitus (DM) patients, 51.8% were males. The mean age of participants was 43±12.4 years and 154(67.5%) were found not following their general dietary program correctly. Nearly one third, 73(32%) of participants 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 range of 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), smoking (AOR = 4.51, 95 % CI 0.00-0.50, p=0.022) were independent predictors of poor glycemic control up on a multivariable logistic regression analysis. Conclusion: Nearly two third 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 as well as barriers of glycemic control should be explored with further research.

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Section: Discussionsupporting

confidence: 92%

Challenges and factors associated with poor glycemic control among type 2 diabetes mellitus patients at Nekemte referral hospital, western Ethiopia: cross-sectional study

Fekadu¹,

Bula²,

Bayisa³

2019

Preprint

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“…In another cohort study of 10,551 men and 15,297 Chinese women with DM, smoking was associated with an increased risk OR of 1.49 in men and 1.56 in women for poor glycemic control (defined as HbA1c ≥ 7.0%), particularly in elderly patients [84]. The relationship is dose-dependent and independent of traditional confounding factors, including sociodemographic and lifestyle factors.…”

Section: Smoking and Glycemic Controlmentioning

confidence: 99%

Smoking and diabetes: dangerous liaisons and confusing relationships

Campagna

Alamo

Pino

et al. 2019

Diabetol Metab Syndr

138

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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.

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“…We used the GATK HaplotypeCaller (v4.0.4.0) to call variants per sample and produced an intermediate file in GVCF format and consolidated GVCF files from 10,588 samples into one GVCF file using the GATK CombineGVCFs (v4.0.4.0). When we combined the GVCF files, the low-complexity regions (LCRs, covering 2% of the genome and identified by the mDust program) 63 were ignored. Based on the combined GVCF file, the joint call was performed using the GATK GenotypeGVCFs (v4.0.4.0) with filter of the GATK Variant Filtration (v4.0.4.0).…”

Section: Methodsmentioning

confidence: 99%

“…The measurement and collection of phenotype information for all individuals are described previously. 14 , 16 , 63 Before genotype-phenotype association analyses, all variants were subjected to a series of quality control with criteria: (1) median depth > 8; (2) within LCRs (< 7 single base repeat units); (3) homozygous variants (AF ≥ 0.90); (4) homozygous variants (AF ≥ 0.2); (5) genotyping rate ≥ 90%; (6) HWE > 0.000001. 4,764,593 SNPs passed the quality control.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

Self Cite

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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.

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

Abstract: Active smoking is a modifiable risk factor for poor glycemic control in Chinese diabetic patients.

Cited by 39 publications

References 35 publications

Challenges and factors associated with poor glycemic control among type 2 diabetes mellitus patients at Nekemte referral hospital, western Ethiopia: cross-sectional study

Challenges and factors associated with poor glycemic control among type 2 diabetes mellitus patients at Nekemte referral hospital, western Ethiopia: cross-sectional study

Smoking and diabetes: dangerous liaisons and confusing relationships

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

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