Passive smoking and type 2 diabetes among never-smoking women: The Japan Public Health Center-based Prospective Study

Trends of type 2 diabetes with pulmonary tuberculosis patients,2013-2022, and changes after the coronavirus disease 2019 (COVID-19) pandemic

BACKGROUND

To describe the trends of Type 2 Diabetes with Pulmonary Tuberculosis (T2DM-TB) patients from 2013 to 2022 and to investigate the impact of COVID-19 lockdown on glycemic control and associated factors in T2DM-TB.

METHODS

In this population-based study of the First Affiliated Yijishan Hospital of Wannan Medical College in China, we described the 10-year trends of patients diagnosed with T2DM-TB. We included patients diagnosed with TB, T2DM-TB and T2DM-TB patients for comparative analysis, aged 15 years or older. Data were missing, and both multidrug-resistant (MDR) TB patients and non-T2DM patients were excluded from our study.

RESULTS

We pooled Type 2 Diabetes (T2DM) and Tuberculosis (TB) data from The First Affiliated Yijishan Hospital of Wannan Medical College in China, gathered between January 1, 2013, and December 31, 2022. The data included 14,227 T2DM patients, 6130 TB patients, and 982 T2DM-TB patients. During the past 10 years, the number of inpatients with TB decreased, while the number of patients with T2DM and T2DM-TB increased year by year. To rule out any influence factors, we analyzed the ratio of the three groups. The ratio of TB/T2DM decreased year by year (p < 0.05), while the ratio of TB-T2DM/TB increasing year by year (p = 0.008). During the COVID-19 epidemic period, there was no significant change in the ratio of TB-T2DM/T2DM (p = 0.156). There was no significant change in the proportion of male patients with TB and TB-T2DM (p = 0.325; p = 0.190), but the proportion of male patients with T2DM showed an increasing trend (p < 0.001). The average age of TB patients over the past 10 years was 54.5 ± 18.4 years and showed an increasing trend year by year (p < 0.001). However, there was no significant change in the age of T2DM or TB-T2DM patients (p = 0.064; p = 0.241). Patients data for the first (2013-2017) and the last (2018-2022) five years were compared. We found that the number of T2DM and TB-T2DM in the last five years was significantly higher than in the first five years, but the number of TB was significantly lower than in the first five years. There is a significant statistical difference in the proportion of TB/T2DM and TB-T2DM/TB, which is similar to the previous results. The average age (56.0 ± 17.6 years) of TB patients in the last five years is significantly higher than in the first five years (53.1 ± 18.9) (p < 0.001). The number of male patients with T2DM in the last five years is higher than that in the first five years, with significant difference (p < 0.001).

CONCLUSION

The trends of T2DM-TB among hospitalized TB patients have increased significantly over the past 10 years, which may be related to the increase in the number of T2DM cases. The COVID-19 pandemic has been effective in controlling the transmission of TB, but it has been detrimental to the control of T2DM. Male patients with T2DM and elderly TB patients are the key populations for future prevention and control efforts.

Global trends in deaths and disability-adjusted life years of diabetes attributable to second-hand smoke and the association with smoke-free policies

OBJECTIVES

The diabetic burden attributable to second-hand smoke (SHS) is a global public health challenge. We sought to explore the diabetic burden attributable to SHS by age, sex, and socioeconomic status during 1990-2019 and to evaluate the health benefit of smoke-free policies on this burden.

STUDY DESIGN

Cross-sectional study.

METHODS

The diabetic burden attributable to SHS was extracted from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 dataset. Country-level smoke-free policies were obtained from the World Health Organization Global Health Observatory. The deaths or disability-adjusted life years (DALYs) were quantified, and the average annual percentage changes were calculated. Hierarchical linear mixed models were applied to evaluate the health effects.

RESULTS

From 1990 to 2019, the absolute number of global deaths and DALYs of diabetes attributable to SHS has doubled, and the age-standardised rate has significantly increased. The disease burden was higher in females than in males and increased with increasing age. The SHS-related diabetic burden varied across regions and countries. Age-standardised death or DALY rates first increased and then decreased with increased Socio-demographic Index (SDI), peaking in the 0.60-0.70 range. In low to low-middle, and middle to high-middle SDI countries, SHS-related diabetic deaths and DALYs were significantly lower in countries with more than 3 smoke-free public places than in countries with 0-2 smoke-free public places.

CONCLUSIONS

More attention should be paid to females and the elderly, who bear a heavy SHS-related diabetic burden. Banning smoking in public places was associated with reduced burden of SHS-attributable diabetes, especially in low to middle social development countries.

Health effects associated with exposure to secondhand smoke: a Burden of Proof study

Despite a gradual decline in smoking rates over time, exposure to secondhand smoke (SHS) continues to cause harm to nonsmokers, who are disproportionately children and women living in low- and middle-income countries. We comprehensively reviewed the literature published by July 2022 concerning the adverse impacts of SHS exposure on nine health outcomes. Following, we quantified each exposure-response association accounting for various sources of uncertainty and evaluated the strength of the evidence supporting our analyses using the Burden of Proof Risk Function methodology. We found all nine health outcomes to be associated with SHS exposure. We conservatively estimated that SHS increases the risk of ischemic heart disease, stroke, type 2 diabetes and lung cancer by at least around 8%, 5%, 1% and 1%, respectively, with the evidence supporting these harmful associations rated as weak (two stars). The evidence supporting the harmful associations between SHS and otitis media, asthma, lower respiratory infections, breast cancer and chronic obstructive pulmonary disease was weaker (one star). Despite the weak underlying evidence for these associations, our results reinforce the harmful effects of SHS on health and the need to prioritize advancing efforts to reduce active and passive smoking through a combination of public health policies and education initiatives.

Effect of passive smoking exposure on risk of type 2 diabetes: a systematic review and meta-analysis of prospective cohort studies

Objective

The effect of passive smoking exposure on the risk of type 2 diabetes has not been systematically studied. A meta-analysis was conducted to assess the association between passive smoking exposure and the risk of diabetes.

Methods

We searched three major databases up to 31 October 2022 to identify relevant prospective cohort studies on the association between passive smoking and the risk of type 2 diabetes. The pooled relative risk (RR) and 95% confidence interval (CI) for the association between passive smoking exposure and the risk of type 2 diabetes were analyzed using a fixed-effect model.

Results

Ten prospective cohort studies were included in this meta-analysis, with a total of 251,620 participants involved. The pooled RR showed a significantly positive association between nonsmokers exposed to passive smoking and type 2 diabetes as compared to non-smokers who were not exposed to passive smoking [RR = 1.27; 95% CI (1.19, 1.36); p < 0.001]. Sensitivity analysis indicated that the pooled RR was not substantially affected by any of the individual studies.

Conclusion

Exposure to passive smoking increases the risk of type 2 diabetes. This study may have a positive effect on the prevention of type 2 diabetes.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023372532.

The impact of prolonged exposure to air pollution on the incidence of chronic non-communicable disease based on a cohort in Tianjin

Evidence on the associations of prolonged ambient pollutants exposure with chronic non-communicable diseases among middle-aged and elderly residents is still limited. This prospective cohort study intends to investigate the long-term effects of ambient pollution on hypertension and diabetes incidence among relatively older residents in China. Individual particulate matter exposure levels were estimated by satellite-based model. Individual gaseous pollutants exposure levels were estimated by Inverse Distance Weighted model. A Cox regression model was employed to assess the risks of hypertension and diabetes morbidity linked to air pollutants exposures. The cross-product term of ambient pollutants exposure and covariates was further added into the regression model to test whether covariates would modify these air pollution-morbidity associations. During the period from 2014 to 2018, a total of 97,982 subjects completed follow-up. 12,371 incidents of hypertension and 2034 of diabetes occurred. In the multi-covariates model, the hazard ratios (HR) and 95% confidence interval (CI) were 1.49 (1.45-1.52), 1.28 (1.26-1.30), 1.17 (1.15-1.18), 1.21 (1.17-1.25) and 1.33 (1.31-1.35) for hypertension morbidity per 10 μg/m³ increment in PM₁, PM_2.5, PM₁₀, NO₂ and SO₂, respectively. For diabetes onsets, the HR (95% CI) were 1.17 (1.11-1.23), 1.09 (1.04-1.13), 1.06 (1.02-1.09), 1.02 (0.95-1.10), and 1.24 (1.19-1.29), respectively. In addition, for hypertension analyses, the effect estimates were more pronounced in the participants with age <60 years old, BMI ≥24 kg/m², and frequent alcohol drinking. These findings provided the evidence on elevated risks of morbidity of hypertension and diabetes associated with prolonged ambient pollutants exposure at relatively high levels.

Association of environmental tobacco smoke exposure with metabolic syndrome: A longitudinal Cohort Study of 71,055 never smokers

BACKGROUND AND AIMS

Effects of environmental tobacco smoke (ETS) exposure and a change in ETS exposure status on metabolic syndrome (MetS) remain unknown. Thus, the aim of this study was to evaluate the effect of ETS exposure on MetS in self-reported and cotinine-validated never smokers.

METHODS AND RESULTS

From a large longitudinal cohort study, 71,055 cotinine-validated never smokers without MetS at baseline were included. These participants were divided into four groups (no, new, former, and continuous ETS exposure groups) based on their ETS exposure status at baseline and follow-up. The association between ETS exposure and MetS was assessed using multivariable Cox hazard regression analyses. During a median follow-up of 33 months, 15.0 cases/10,000 person-years (PY) developed MetS. Incidence rates per 10,000 PY of MetS in no, new, former, and continuous ETS exposure groups were 14.0, 18.5, 16.5, and 19.0, respectively. In multivariable Cox hazard regression analyses, the new and continuous ETS exposure groups showed increased risk of MetS compared to the no ETS exposure group (hazard ratio [95% confidence interval]: 1.35 [1.16, 1.56], p-value < 0.001 for the new ETS exposure group and 1.19 [1.06, 1.34], p-value = 0.004 for the continuous ETS exposure group). However, the former ETS exposure group did not show an increased risk of MetS (0.96 [0.88, 1.05], p-value = 0.36).

CONCLUSION

This study showed that ETS exposure and changes in ETS exposure status over approximately three years could modify the risk of MetS, suggesting that avoidance of ETS may not increase the risk of incidence of MetS.

Impact of healthy lifestyle on the risk of type 2 diabetes mellitus in southwest China: A prospective cohort study

AIMS

To explore the influence of nine healthy lifestyle factors on the risk of type 2 diabetes mellitus in adults in Guizhou, China.

METHODS

Data were obtained from a large population-based prospective cohort study in Guizhou Province, China. A total of 7,319 participants aged ≥18 years without diabetes at baseline were included in this study and were followed up from 2016 to 2020. A healthy lifestyle score was calculated based on the number of healthy lifestyle factors.

RESULTS

During an average of 7.1 person-years of follow-up, 764 participants were diagnosed with type 2 diabetes mellitus. Compared with those of participants who scored 0-3 for a healthy lifestyle, the hazard ratios (95% confidence intervals) of those who scored 4, 5, 6, and ≥7 were 0.676 (0.523-0.874), 0.599 (0.464-0.773), 0.512 (0.390-0.673), and 0.393 (0.282-0.550), respectively, showing a gradual downward trend (P for trend <0.01). More importantly, they had lower fasting and 2 h post-load plasma glucose levels and fewer changes in plasma glucose levels during follow-up. If ≥7 healthy lifestyle factors were maintained, 33.8% of incident diabetes cases could have been prevented. Never smoking was the strongest protective factor against type 2 diabetes mellitus.

CONCLUSIONS

A healthy lifestyle can effectively decrease plasma glucose levels and reduce the incidence of type 2 diabetes mellitus in adults in Guizhou, China. In addition, not smoking may be an effective way to prevent type 2 diabetes mellitus.

Type-2 Diabetes Mellitus (T2DM): Spatial-temporal Patterns of Incidence, Mortality and Attributable Risk Factors from 1990 to 2019 among 21 World Regions

PURPOSE

Type-2 diabetes Mellitus (T2DM) is one of the leading causes of death and disability worldwide. This study examines temporal patterns of the global, regional, and national burden of T2DM in the last three decades.

DATA AND METHODS

The estimates of age, sex and location-wise incident cases, deaths, prevalent cases, and disability-adjusted-life-years (DALYs) and risk factors for 21 regions and 204 countries are retrieved from the Global Burden of Disease 2019 study from 1990 to 2019. Socio-demographic index (SDI) is used as the indicator of the development status of countries, and quadratic regression is employed to examine the relationship between country-level age-standardized rates and SDI.

RESULTS

Globally, incident cases of T2DM more than doubled from 8.4 million[95% uncertainty interval, 7.8-9.1 million] in 1990 to 21.7 million[20.0-23.5 million] in 2019, and deaths more than doubled from 606,407[573,069-637,508] to 1.5 million[1.4-1.6 million] between 1990 and 2019. Global T2DM prevalence increased from 148.4 million[135.5-162.6 million] in 1990 to 437.9 million[402.0-477.0 million] in 2019. In 2019, global age-standardized prevalence rate stood at 5282.8/100,000[4853.6-5752.1], varying from 2174.5/100,000[1924.3-2470.5] in Mongolia to 19876.8/100,000[18211.1-21795.3] in American Samoa. SDI exhibited inverted-U shaped relationship with country-level age-standardised rates. Globally, high body-mass-index (51.9%), ambient particulate matter pollution (13.6%), smoking (9.9%) and secondhand smoke (8.7%) were the major contributing risk factors towards T2DM DALYs in 2019.

CONCLUSION

With ubiquitously rising prevalent cases globally, particularly in low and middle-income countries and regions, T2DM requires immediate attention and targeted policy response worldwide centered on lifestyle interventions (e.g., physical activity, smoking, diet, and obesity), air pollution control and cost-effective timely treatment.

Risk Factors and Wellness Measures Associated with Prediabetes and Newly Diagnosed Type 2 Diabetes Mellitus in Hispanic Adults

Background: To characterize the associations of clinical risk factors, lifestyle factors, and wellness measures with prediabetes and new type 2 diabetes mellitus (T2DM) diagnosis in Hispanic adults and guide primary prevention. Methods: Sangre Por Salud Biobank enrolled 3733 Hispanic adults from Phoenix, AZ, United States, from 2013 to 2018. This analysis included participants with euglycemia, prediabetes, or new T2DM diagnosis (i.e., no prior T2DM diagnosis) at enrollment. Participants completed a baseline questionnaire on cardiometabolic risk factors and wellness measures and provided biometric measurements. The associations of factors and measures with odds (95% confidence interval) of prediabetes and new T2DM diagnosis were analyzed in logistic regression models. Results: Among 3299 participants with euglycemia (n = 1301), prediabetes (n = 1718), and new T2DM diagnosis (n = 280) at enrollment, 72% were women (n = 2376/3299). In adjusted models, most cardiometabolic risk factors were positively associated with prediabetes and new T2DM diagnosis, with stronger associations for new T2DM diagnosis. Obesity (body mass index ≥30 kg/m² vs. lower) was associated with higher odds of new T2DM diagnosis (3.14 [2.30-4.28]; P < 0.01) than prediabetes versus euglycemia (1.96 [1.66-2.32]; P < 0.01) and Interaction (P = 0.01). Similarly, waist circumference, family history of diabetes, and average systolic and diastolic blood pressure were associated with higher odds of new T2DM diagnosis versus euglycemia than prediabetes versus euglycemia. Using stepwise logistic regression modeling, a parsimonious model of age, family history of diabetes, waist circumference, diastolic blood pressure, passive tobacco exposure, and self-rated general health were associated with new T2DM diagnosis versus euglycemia. Conclusions: In Hispanic adults, modifiable cardiometabolic and lifestyle factors were associated with prediabetes and new T2DM diagnosis. Personalized interventions targeting these factors and measures could guide T2DM primary prevention efforts among Hispanic adults.