Spatial vulnerability of fine particulate matter relative to the prevalence of diabetes in the United States

Assessment of Disparities in Diabetes Mortality in Adults in US Rural vs Nonrural Counties, 1999-2018

IMPORTANCE

US rural vs nonrural populations have striking disparities in diabetes care. Whether rurality contributes to disparities in diabetes mortality is unknown.

OBJECTIVE

To examine rates and trends in diabetes mortality based on county urbanization.

DESIGN, SETTING, AND PARTICIPANTS

In this observational, cross-sectional study, the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database was searched from January 1, 1999, to December 31, 2018, for diabetes as a multiple cause and the underlying cause of death among residents aged 25 years or older in US counties. County urbanization was categorized as metro, medium-small, and rural. Weighted multiple linear regression models and jackknife resampling, with a 3-segment time component, were used. The models included exposures with up to 3-way interactions and were age standardized to the 2009-2010 population. The analyses were conducted from July 1, 2020, to February 1, 2022.

EXPOSURES

County urbanization (metro, medium-small, or rural), gender (men or women), age group (25-54, 55-74, or ≥75 years), and region (Midwest, Northeast, South, or West).

MAIN OUTCOMES AND MEASURES

Annual diabetes mortality rate per 100 000 people.

RESULTS

From 1999-2018, based on 4 022 238 309 person-years, diabetes was a multiple cause of death for 4 735 849 adults aged 25 years or older. As a multiple cause, diabetes mortality rates in 2017-2018 vs 1999-2000 were highest and unchanged in rural counties (157.2 [95% CI, 150.7-163.7] vs 154.1 [95% CI, 148.2-160.1]; P = .49) but lower in medium-small counties (123.6 [95% CI, 119.6-127.6] vs 133.6 [95% CI, 128.4-138.8]; P = .003) and urban counties (92.9 [95% CI, 90.5-95.3] vs 109.7 [95% CI, 105.2-114.1]; P < .001). In 2017-2018 vs 1999-2000, mortality rates were higher in rural men (+18.2; 95% CI, 14.3-22.1) but lower in rural women (-14.0; 95% CI, -17.7 to -10.3) (P < .001 for both). In the 25- to 54-year age group, mortality rates in 2017-2018 vs 1999-2000 showed a greater increase in rural counties (+9.4; 95% CI, 8.6-10.2) compared with medium-small counties (+4.5; 95% CI, 4.0-5.0) and metro counties (+0.9; 95% CI, 0.4-1.4) (P < .001 for all). Of all regions and urbanization levels, the mortality rate in 2017-2018 vs 1999-2000 was higher only in the rural South (+13.8; 95% CI, 7.6-20.0; P < .001).

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, US rural counties had the highest overall diabetes mortality rate. The determinants of persistent rural disparities, in particular for rural men and for adults in the rural South, require investigation.

PM2.5 exposure as a risk factor for type 2 diabetes mellitus in the Mexico City metropolitan area

BACKGROUND

Exposure to air pollution is the main risk factor for morbidity and mortality in the world. Exposure to particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) is associated with cardiovascular and respiratory conditions, as well as with lung cancer, and there is evidence to suggest that it is also associated with type II diabetes (DM). The Mexico City Metropolitan Area (MCMA) is home to more than 20 million people, where PM2.5 levels exceed national and international standards every day. Likewise, DM represents a growing public health problem with prevalence around 12%. In this study, the objective was to evaluate the association between exposure to PM2.5 and DM in adults living in the MCMA.

METHODS

Data from the 2006 or 2012 National Health and Nutrition Surveys (ENSANUT) were used to identify subjects with DM and year of diagnosis. We estimated PM2.5 exposure at a residence level, based on information from the air quality monitoring system (monitors), as well as satellite measurements (satellite). We analyzed the relationship through a cross-sectional approach and as a case - control study.

RESULTS

For every 10 μg/m3 increase of PM2.5 we found an OR = 3.09 (95% CI 1.17-8.15) in the 2012 sample. These results were not conclusive for the 2006 data or for the case - control approach.

CONCLUSIONS

Our results add to the evidence linking PM2.5 exposure to DM in Mexican adults. Studies in low- and middle-income countries, where PM2.5 atmospheric concentrations exceed WHO standards, are required to strengthen the evidence.

Spatiotemporal trends and influence factors of global diabetes prevalence in recent years

Diabetes is one of the most widespread global epidemics and has become the main component of the global disease burden. Based on data regarding the prevalence of diabetes in 203 countries and territories from 2013 to 2017, we employed the Bayesian space-time model to investigate the spatiotemporal trends in the global diabetes prevalence. The factors influencing the diabetes prevalence were assessed by the Bayesian LASSO regression model. We identified 77 (37.9%) hotspots with a higher diabetes prevalence than the global average, 10 (0.4%) warm spots with global average level and 116 (57.1%) cold spots with lower level than global average. Of the 203 countries and territories, 68 (33.5%), including 31 hotspots, 5 warm spots and 32 cold spots, exhibited an increasing trend. Of these, 60 experienced an annual increase of more than 0.25%, and 8 showed an increasing trend. Three populous countries, namely China, the USA and Mexico, exhibited a high prevalence and an increasing trend simultaneously. Three socioeconomic factors, body mass index (BMI), urbanization rate (UR) and gross domestic product per capita (GDP-PC), and PM_2.5 pollution were found to significantly influence the prevalence of diabetes. BMI was the strongest factor; for every 1% increase in BMI, the prevalence of diabetes increased by 2.371% (95% confidence interval (95% CI): 0.957%, 3.890%) in 2013 and by 3.045% (95% CI: 1.803%, 4.397%) in 2015 and 2017. PM_2.5 pollution could be a risk factor, and its influencing magnitude gradually increased as well. With an annual PM_2.5 concentrations increase of 1.0% in a country, the prevalence of diabetes increased by 0.196% (95% CI: 0.020%, 0.356%). The UR, on the other hand, was found to be inversely associated with the prevalence of diabetes; with each UR increase of 1%, the prevalence of diabetes decreased by 0.006% (95% CI: 0.001%, 0.011%).

The Association Between PM2.5 and Ozone and the Prevalence of Diabetes Mellitus in the United States, 2002 to 2008

OBJECTIVE

To examine the association between air pollution and diabetes prevalence in the United States, 2002 to 2008.

METHODS

Annual average particulate matter (PM2.5) and ozone concentrations were calculated using daily county-level data from the CDC's Tracking Network. Individual-level outcome and covariate data were obtained from the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System for 862,519 individuals. We used Poisson regression analyses to examine associations between each air pollutant (per 10-unit increase) with diabetes, including regional sub-analyses. Analyses were adjusted for year, age, sex, race, ethnicity, education, income, smoking status, body mass index, exercise, and asthma.

RESULTS

Positive associations between each pollutant and diabetes were found (PM2.5: prevalence ratio [PR] = 1.10; 95% confidence interval [CI] = 1.03, 1.17; ozone: PR = 1.06; 95% CI = 1.03, 1.09). There was limited evidence of effect modification by region.

CONCLUSIONS

Interventions to reduce ambient air pollution may help alleviate the diabetes burden in the US.

Ambient and Traffic-Related Air Pollution Exposures as Novel Risk Factors for Metabolic Dysfunction and Type 2 Diabetes

PURPOSE OF REVIEW

Diabetes mellitus is a top contributor to the global burden of mortality and disability in adults. There has also been a slow, but steady rise in prediabetes and type 2 diabetes in youth. The current review summarizes recent findings regarding the impact of increased exposure to air pollutants on the type 2 diabetes epidemic.

RECENT FINDINGS

Human and animal studies provide strong evidence that exposure to ambient and traffic-related air pollutants such as particulate matter (PM), nitrogen dioxide (NO2), and nitrogen oxides (NOx) play an important role in metabolic dysfunction and type 2 diabetes etiology. This work is supported by recent findings that have observed similar effect sizes for increased exposure to air pollutants on clinical measures of risk for type 2 diabetes in children and adults. Further, studies indicate that these effects may be more pronounced among individuals with existing risk factors, including obesity and prediabetes.

SUMMARY

Current epidemiological evidence suggests that increased air pollution exposure contributes to alterations in insulin signaling, glucose metabolism, and beta (β)-cell function. Future work is needed to identify the specific detrimental pollutants that alter glucose metabolism. Additionally, advanced tools and new areas of investigation present unique opportunities to study the underlying mechanisms, including intermediate pathways, that link increased air pollution exposure with type 2 diabetes onset.

Environmental Risk Factors for Developing Type 2 Diabetes Mellitus: A Systematic Review

Different elements of the environment have been posited to influence type 2 diabetes mellitus (T2DM). This systematic review summarizes evidence on the environmental determinants of T2DM identified in four databases. It proposes a theoretical framework illustrating the link between environment and T2DM, and briefly discusses some methodological challenges and potential solutions, and opportunities for future research. Walkability, air pollution, food and physical activity environment and roadways proximity were the most common environmental characteristics studied. Of the more than 200 reported and extracted relationships assessed in 60 studies, 82 showed significant association in the expected direction. In general, higher levels of walkability and green space were associated with lower T2DM risk, while increased levels of noise and air pollution were associated with greater risk. Current evidence is limited in terms of volume and study quality prohibiting causal inferences. However, the evidence suggests that environmental characteristics may influence T2DM prevention, and also provides a reasonable basis for further investigation with better quality data and longitudinal studies with policy-relevant environmental measures. This pursuit of better evidence is critical to support health-orientated urban design and city planning.

Considering spatial heterogeneity in the distributed lag non-linear model when analyzing spatiotemporal data

The distributed lag non-linear (DLNM) model has been frequently used in time series environmental health research. However, its functionality for assessing spatial heterogeneity is still restricted, especially in analyzing spatiotemporal data. This study proposed a solution to take a spatial function into account in the DLNM, and compared the influence with and without considering spatial heterogeneity in a case study. This research applied the DLNM to investigate non-linear lag effect up to 7 days in a case study about the spatiotemporal impact of fine particulate matter (PM_2.5) on preschool children's acute respiratory infection in 41 districts of northern Taiwan during 2005 to 2007. We applied two spatiotemporal methods to impute missing air pollutant data, and included the Markov random fields to analyze district boundary data in the DLNM. When analyzing the original data without a spatial function, the overall PM_2.5 effect accumulated from all lag-specific effects had a slight variation at smaller PM_2.5 measurements, but eventually decreased to relative risk significantly <1 when PM_2.5 increased. While analyzing spatiotemporal imputed data without a spatial function, the overall PM_2.5 effect did not decrease but increased in monotone as PM_2.5 increased over 20 μg/m³. After adding a spatial function in the DLNM, spatiotemporal imputed data conducted similar results compared with the overall effect from the original data. Moreover, the spatial function showed a clear and uneven pattern in Taipei, revealing that preschool children living in 31 districts of Taipei were vulnerable to acute respiratory infection. Our findings suggest the necessity of including a spatial function in the DLNM to make a spatiotemporal analysis available and to conduct more reliable and explainable research. This study also revealed the analytical impact if spatial heterogeneity is ignored.

Particulate matter (PM₁₀) induces metalloprotease activity and invasion in airway epithelial cells

Airborne particulate matter with an aerodynamic diameter ≤ 10 μm (PM10) is a risk factor for the development of lung diseases and cancer. The aim of this work was to identify alterations in airway epithelial (A549) cells induced by PM10 that could explain how subtoxic exposure (10 μg/cm(2)) promotes a more aggressive in vitro phenotype. Our results showed that cells exposed to PM10 from an industrial zone (IZ) and an urban commercial zone (CZ) induced an increase in protease activity and invasiveness; however, the cell mechanism is different, as only PM10 from CZ up-regulated the activity of metalloproteases MMP-2 and MMP-9 and disrupted E-cadherin/β-catenin expression after 48 h of exposure. These in vitro findings are relevant in terms of the mechanism action of PM10 in lung epithelial cells, which could be helpful in understanding the pathogenesis of some human illness associated with highly polluted cities.