Study Population
UK Biobank, as a large population-based, nationwide, and open-access prospective study, recruited over 500,000 individuals in conjunction with 22 assessment centres across the UK. Through self-completed touch-screen questionnaires, computer-assisted interviews, physical and functional measurements, and samples of blood, urine, and saliva, it successfully collected a large variety of health-related information, consisting of sociodemographic characteristics, diseases phenotypic, lifestyle, and genetic variants.
- UK Biobank is operating under the approval of North-West Multicentre Research Ethics Committee to ensure its ethical robustness.
- All participants provided their consent for regular blood, urine, and saliva sampling and more accurate data on their lifestyles.
- The dataset also promised to be anonymized to protect the privacy of participants.
Assessment of Outcomes
All patients were diagnosed in accordance with the criteria of the International Classification of Diseases, 10th revision (ICD-10). The ICD-10 codes of all-cause dementia included G30, F01, G20. The ICD-10 codes of Alzheimer’s dementia, vascular dementia, and MCI were set as G30, F01, and F06.7 respectively.
Air Pollution Score (APS)
The UK Biobank Study adopted a land-use regression model based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) project to estimate the annual average concentrations of PM2.5, PM10, PM2.5−10, NO2, and NO. Participants’ ambient air pollution concentrations were then assigned according to their residential coordinates in the 100 m × 100 m grid cells. The exposure levels of five air pollutants that mentioned above were all collected in 2010.
In order to further assess the combined exposure of five different ambient air pollutants, an Air Pollution Score (APS) was calculated by summarizing the concentrations of PM2.5, PM10, PM2.5−10, NO2, and NO, weighted by the multivariable-adjusted risk estimates (β coefficients) on cognitive impairment in the current study.
Evaluation of Genetic Risk
The APOE status recorded in the genetic database of UK Biobank were fully utilized to evaluate the genetic risk for cognitive impairment among participants. The population was divided into three groups of low, intermediate, and high genetic risk of cognitive impairment based on their APOE gene carrying status.
Healthy Lifestyle Score (HLS)
A Healthy Lifestyle Score (HLS) was generated based on 7 variables: physical activity, body mass index (BMI), alcohol consumption, smoke status, waist-to-hip ratio (WHR), sedentary time (hours/day), and vegetable and fruit intake (servings/day).
Participants scored 1 point for each of these health-related behaviours once they met the criteria mentioned above. The HLS ranged from 0 to 7 theoretically. After the scoring was completed, participants were split up into three groups as unfavourable (0, 1), intermediate (2, 3), and favourable (≥ 4) in accordance with their HLSs.
Measurements of Other Potential Covariates
Research team collected age, sex, ethnicity, Townsend deprivation index (TDI), blood pressure level, employment status, education background, income bracket, and history of hypertension, diabetes, cardiovascular disease (CVD), coronary artery disease (CAD), and stroke as potential modification factors.
Statistical Analysis
The follow-up time was measured from the recruitment date to the first diagnosis of any form of cognitive impairment or dementia, lost to follow-up, death, or end of the current follow-up, whichever came first. Cox proportional hazards models were used to evaluate the hazard ratio (HR) and 95% confidence interval (CI) for the incident cognitive impairment and dementia related to single air pollutants and the APS. Other potential confounders were adjusted in those models.
Sensitivity analyses were performed to test the robustness of the outcomes. The restricted cubic spline analysis was used to examine whether there was a dose-response relationship between single air pollutants or the APS with the incidence of cognitive impairment and dementia. Stratified analysis on potential confounders was conducted to further explore the possible relevance of genetic predisposition, sociodemographic characteristics, lifestyle, and prevalent disease with air pollution-induced cognitive impairment.
Levin’s formula was used to estimate the proportion of patients that could be prevented if the risk factor was eliminated. The weighted PAF adjusted for the correlation was calculated to further account for the existence of multiple risk factors.
All statistical analyses were performed with SAS software. All P-values were based on the two-sided test, and P-values < 0.05 were considered statistically significant. The figures in the current article were generated with R software, GraphPad Prism software (version 9.4.1), and Adobe Illustrator software.
SDGs, Targets, and Indicators
SDGs, Targets, and Indicators Identified in the Article:
- SDG 3: Good Health and Well-being
- Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being
- Indicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease
- SDG 11: Sustainable Cities and Communities
- Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management
- Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted)
- SDG 13: Climate Action
- Target 13.2: Integrate climate change measures into national policies, strategies and planning
- Indicator 13.2.1: Number of countries that have communicated the strengthening of institutional, systemic and individual capacity-building to implement adaptation, mitigation and technology transfer, and development actions
Detailed Explanations:
1. SDG 3: Good Health and Well-being is addressed in the article as it discusses the impact of air pollution on cognitive impairment and dementia. The article highlights the need to prevent non-communicable diseases and promote mental health and well-being, which aligns with Target 3.4 of SDG 3.
2. Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being is relevant to the article’s content. The article focuses on the association between air pollution and cognitive impairment, which is a non-communicable disease. By identifying this association, the article contributes to the efforts of reducing premature mortality from non-communicable diseases.
3. Indicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease can be used to measure progress towards Target 3.4. Although the article does not directly mention this indicator, it indirectly contributes to the understanding of the impact of air pollution on cognitive impairment, which is a non-communicable disease.
4. SDG 11: Sustainable Cities and Communities is connected to the issues discussed in the article as it emphasizes the importance of air quality in cities. The article discusses the adverse effects of air pollution on cognitive health, highlighting the need to address air quality in urban areas. This aligns with Target 11.6 of SDG 11.
5. Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management is relevant to the article’s content. The article focuses on the impact of air pollution on cognitive health, emphasizing the need to improve air quality in cities to reduce its adverse effects.
6. Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted) can be used to measure progress towards Target 11.6. Although the article does not specifically mention this indicator, it provides insights into the association between air pollution and cognitive impairment, which is influenced by the levels of fine particulate matter in the air.
7. SDG 13: Climate Action is connected to the issues discussed in the article as it highlights the impact of air pollution, a major contributor to climate change, on cognitive health. The article emphasizes the need to integrate climate change measures into policies and strategies to address the adverse effects of air pollution on cognitive impairment.
8. Target 13.2: Integrate climate change measures into national policies, strategies and planning is relevant to the article’s content. The article emphasizes the importance of considering air pollution and its impact on cognitive health in national policies and strategies related to climate change.
9. Indicator 13.2.1: Number of countries that have communicated the strengthening of institutional, systemic and individual capacity-building to implement adaptation, mitigation and technology transfer, and development actions can be used to measure progress towards Target 13.2. Although the article does not directly mention this indicator, it highlights the need to strengthen institutional capacity to address the adverse effects of air pollution on cognitive health.
Table: SDGs, Targets, and Indicators
| SDGs | Targets | Indicators |
|---|---|---|
| SDG 3: Good Health and Well-being | Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being | Indicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease |
| SDG 11: Sustainable Cities and Communities | Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management | Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted) |
| SDG 13: Climate Action | Target 13.2: Integrate climate change measures into national policies, strategies and planning | Indicator 13.2.1: Number of countries that have communicated the strengthening of institutional, systemic and individual capacity-building to implement adaptation, mitigation and technology transfer, and development actions |
Behold! This splendid article springs forth from the wellspring of knowledge, shaped by a wondrous proprietary AI technology that delved into a vast ocean of data, illuminating the path towards the Sustainable Development Goals. Remember that all rights are reserved by SDG Investors LLC, empowering us to champion progress together.
Source: bmcpublichealth.biomedcentral.com
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