Tobacco smoking and risks of more than 470 diseases in China: a prospective cohort study

Chan, Ka Hung; Xiao, Dingquan; Avery, Daniel; Bennett, Derrick; Boxall, Ruth; Burgess, Sushila; Chan, Peter Ka Hung; Chen, Yiping; Clarke, Johnathan; Du, Huaidong; Mohamed, Ahmed Edris; Fry, Hannah; Gilbert, Simon; Hill, Mike; Im, Becky Pek Kei; Iona, Andri; Kakkoura, Maria G.; Kartsonaki, Christiana; Lam, Hoyin; Lin, Kuang; Mazidi, Mohsen; Millwood, Iona Y.; Morris, Sam; Nie, Qunhua; Pozarickij, Alfred; Ryder, Paul; Said, Saredo; Schmidt, Dan; Sherliker, Paul; Stevens, Rebecca; Turnbull, Iain; Wang, Lin; Wright, Neil; Yang, Ling; Yang, Xiaoming; Pang, Yong; Han, Xiao; Hou, Can; Xia, Qingmei; Liu, Chao; Pei, Pei; Yu, Canqing; Chen, Junshi; Chen, Zhengming; Clarke, Robert; Collins, Rory; Guo, Yu; Li, Liming; Wang, Chen; Lv, Jun; Pető, Richárd; Walters, Robin; Chen, Naying; Liu, Duo; Tang, Zhenzhu; Chen, Ningyu; Jiang, Qilian; Lan, Jian; Li, Mingqiang; Liu, Yun; Meng, Fanwen; Meng, Jia; Pan, Rong; Qin, Yulu; Wang, Ping; Wang, Sisi; Wei, Liuping; Zhou, Liyuan; Dong, Caixia; Ge, Pengfei; Ren, Xiaolan; Li, Zhongxiao; Mao, Enke; Wang, Tao; Zhang, Hui; Zhang, Xi; Chen, Jinyan; Hu, Xiaoyun; Wang, Xiaohuan; Guo, Zhendong; Li, Huimei; Li, Yilei; Wang, Min; Wu, Shukuan; Yan, Shichun; Zou, Mingyuan; Zhou, Xue; Guo, Ziyan; Kang, Qi; Li, Yanjie; Yu, Bo; Xu, Qinai; Chang, Liang; Fan, Lei; Shi, Feng; Zhang, Ding; Zhou, Gang; Gao, Yulian; He, Tianyou; He, Pan; Hu, Chen; Sun, Huarong; Zhang, Xukui; Chen, Biyun; Fu, Zhongxi; Huang, Yuelong; Liu, Huilin; Xu, Qi; Yin, Li; Long, Huajun; Xu, Xin; Zhang, Hao; Zhang, Libo; Su, Jian; Tao, Ran; Wu, Ming-Hsun; Yang, Jie; Zhou, Jinyi; Zhou, Yonglin; Hu, Yihe; Hua, Yujie; Jin, Jianrong; Liu, Fang; Liu, Jingchao; Lü, Yan; Ma, Liangcai; Tang, Aiyu; Zhang, Jun; Cheng, Liang; Du, Ranran; Gao, Ruqin; Li, Feifei; Li, Shanpeng; Liu, Yongmei; Feng, Na; Pang, Zengchang; Sun, Xiaohui; Tian, Xiaocao; Wang, Shaojie; Zhai, Yaoming; Zhang, Hua; Hou, Wei; Lv, Silu; Wang, Junzheng; Wu, Xianping; Zhang, Ningmei; Zhou, Weiwei; Chen, Xiaofang; Li, Jianguo; Liu, Jiaqiu; Luo, Guojin; Sun, Qiang; Zhong, Xunfu; Gong, Weiwei; Hu, Ruying; Wang, Hao; Wang, Meng; Yu, Min; Chen, Lingli; Gu, Qijun; Pan, Dongxia; Wang, Chunmei; Xie, Kaixu; Zhang, Xiaoyi

doi:10.1016/s2468-2667(22)00227-4

Cited by 61 publications

(31 citation statements)

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“…We found a stronger correlation between FPG and T2D in the following groups of people: age < 45 years, BMI (18.5–24 kg/m 2 ), no hypertension, no smoking, and no drinking. Previous studies showed that age [ 28 ], obesity [ 29 ], hypertension [ 30 ], and smoking [ 31 ] are risk factors for T2D development. Consequently, the association between FPG and T2D may be weakened by the presence of these factors; however, drinking enhanced it, which might have been because the study population comprised light to moderate drinkers.…”

Section: Discussionmentioning

confidence: 99%

J-shape relationship between normal fasting plasma glucose and risk of type 2 diabetes in the general population: results from two cohort studies

Zheng

et al. 2023

J Transl Med

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Background Previous studies have reported that high fasting plasma glucose (FPG), even that within the normal range, is associated with the risk of type 2 diabetes (T2D). Nevertheless, these findings are limited to specific populations. Thus, studies in the general population are imperative. Methods This study included two cohorts comprising 204 640 individuals who underwent physical examinations at the Rich Healthcare Group present at 32 locations in 11 cities of China from 2010 to 2016 and 15 464 individuals who underwent physical tests at the Murakami Memorial Hospital in Japan. Cox regression, restricted cubic spline (RCS), Kaplan–Meier (KM) curves, and subgroup analysis were used to determine the relationship between FPG and T2D. Receiver operating characteristic (ROC) curves were used to evaluate the predictive power of FPG for T2D. Results The mean age of the 220 104 participants (204 640 Chinese and 15 464 Japanese participants) was 41.8 years (41.7 years for the Chinese and 43.7 years for the Japanese participants). During follow-up, 2611 individuals developed T2D (2238 Chinese and 373 Japanese participants). The RCS demonstrated a J-shaped relationship between FPG and T2D risk, with inflexion points of 4.5 and 5.2 for the Chinese and Japanese populations, respectively. Multivariate-adjusted hazard ratio (HR) was 7.75 for FPG and T2D risk after the inflexion point (7.3 for Chinese and 21.13 for Japanese participants). Conclusions In general Chinese and Japanese populations, the normal baseline FPG range showed a J-shaped relationship with the risk of T2D. Baseline FPG levels help identify individuals at high risk of T2D and may enable early primary prevention to improve their outcomes.

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Section: Discussionmentioning

confidence: 99%

J-shape relationship between normal fasting plasma glucose and risk of type 2 diabetes in the general population: results from two cohort studies

Zheng

et al. 2023

J Transl Med

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“…Smoking is associated with the occurrence of multiple diseases. Recent research indicated that smoking was associated with 470 diseases, such as lung cancer, COPD, acute myocardial infarction, ischemic stroke, and ischemic heart disease ( 82 ). Cigarette smoke contains more than 7,000 chemical substances, of which approximately 70 are carcinogens.…”

Section: Discussionmentioning

confidence: 99%

Prevalence and factors associated with multimorbidity in adults in Brazil, according to sex: a population-based cross-sectional survey

Pereira

Pedroso

Batista

et al. 2023

Front. Public Health

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IntroductionMultimorbidity, defined as the coexistence of two or more chronic diseases in the same individual, represents a significant health challenge. However, there is limited evidence on its prevalence and associated factors in developing countries, such as Brazil, especially stratified by sex. Thus, this study aims to estimate the prevalence and analyze the factors associated with multimorbidity in Brazilian adults according to sex.MethodsCross-sectional population-based household survey carried out with Brazilian adults aged 18 years or older. The sampling strategy consisted of a three-stage conglomerate plan. The three stages were performed through simple random sampling. Data were collected through individual interviews. Multimorbidity was classified based on a list of 14 self-reported chronic diseases/conditions. Poisson regression analysis was performed to estimate the magnitude of the association between sociodemographic and lifestyle factors with the prevalence of multimorbidity stratified by sex.ResultsA total of 88,531 individuals were included. In absolute terms, the prevalence of multimorbidity was 29.4%. The frequency in men and women was 22.7 and 35.4%, respectively. Overall, multimorbidity was more prevalent among women, the older people, residents of the South and Southeast regions, urban area residents, former smokers, current smokers, physically inactive, overweight, and obese adults. Individuals with complete high school/incomplete higher education had a lower prevalence of multimorbidity than those with higher educational level. The associations between education and multimorbidity differed between sexes. In men, multimorbidity was inversely associated with the strata of complete middle school/incomplete high school and complete high school/incomplete higher education, while in women, the association between these variables was not observed. Physical inactivity was positively associated with a higher prevalence of multimorbidity only in men. An inverse association was verified between the recommended fruit and vegetable consumption and multimorbidity for the total sample and both sexes.ConclusionOne in four adults had multimorbidity. Prevalence increased with increasing age, among women, and was associated with some lifestyles. Multimorbidity was significantly associated with educational level and physical inactivity only in men. The results suggest the need to adopt integrated strategies to reduce the magnitude of multimorbidity, specific by gender, including actions for health promotion, disease prevention, health surveillance and comprehensive health care in Brazil.

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“…1,069 controls, found that a rapidly decreasing odds ratio of lung cancer within the first 5 years of quitting; the odds ratio continued to decrease but at a slower rate in the subsequent years [39]. Similarly, a recent study based on CKB has reported that people who had stopped smoking had only small excess risks for overall mortality including lung cancer (HR = 1.06, 95% CI: 1.01‐1.11) and morbidity (HR = 1.05, 95% CI: 1.03‐1.08), with the risks approaching those among never‐smokers about 5‐10 years after quitting [44]. Besides, the aforementioned meta‐analysis also showed the risk of lung cancer decreases significantly with quit‐years, with the relative risk close to 1 after 7 years of abstinence [42].…”

Section: Discussionmentioning

confidence: 99%

Lung cancer risk score for ever and never smokers in China

Zhu

et al. 2023

Cancer Communications

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Background Most lung cancer risk prediction models were developed in European and North‐American cohorts of smokers aged ≥ 55 years, while less is known about risk profiles in Asia, especially for never smokers or individuals aged < 50 years. Hence, we aimed to develop and validate a lung cancer risk estimate tool for ever and never smokers across a wide age range. Methods Based on the China Kadoorie Biobank cohort, we first systematically selected the predictors and explored the nonlinear association of predictors with lung cancer risk using restricted cubic splines. Then, we separately developed risk prediction models to construct a lung cancer risk score (LCRS) in 159,715 ever smokers and 336,526 never smokers. The LCRS was further validated in an independent cohort over a median follow‐up of 13.6 years, consisting of 14,153 never smokers and 5,890 ever smokers. Results A total of 13 and 9 routinely available predictors were identified for ever and never smokers, respectively. Of these predictors, cigarettes per day and quit years showed nonlinear associations with lung cancer risk (Pnon‐linear < 0.001). The curve of lung cancer incidence increased rapidly above 20 cigarettes per day and then was relatively flat until approximately 30 cigarettes per day. We also observed that lung cancer risk declined sharply within the first 5 years of quitting, and then continued to decrease but at a slower rate in the subsequent years. The 6‐year area under the receiver operating curve for the ever and never smokers’ models were respectively 0.778 and 0.733 in the derivation cohort, and 0.774 and 0.759 in the validation cohort. In the validation cohort, the 10‐year cumulative incidence of lung cancer was 0.39% and 2.57% for ever smokers with low (< 166.2) and intermediate‐high LCRS (≥ 166.2), respectively. Never smokers with a high LCRS (≥ 21.2) had a higher 10‐year cumulative incidence rate than those with a low LCRS (< 21.2; 1.05% vs. 0.22%). An online risk evaluation tool (LCKEY; http://ccra.njmu.edu.cn/lckey/web) was developed to facilitate the use of LCRS. Conclusions The LCRS can be an effective risk assessment tool designed for ever and never smokers aged 30 to 80 years.

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Tobacco smoking and risks of more than 470 diseases in China: a prospective cohort study

Cited by 61 publications

References 50 publications

J-shape relationship between normal fasting plasma glucose and risk of type 2 diabetes in the general population: results from two cohort studies

J-shape relationship between normal fasting plasma glucose and risk of type 2 diabetes in the general population: results from two cohort studies

Prevalence and factors associated with multimorbidity in adults in Brazil, according to sex: a population-based cross-sectional survey

Lung cancer risk score for ever and never smokers in China

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