METHODS: Diabetes data were derived from the Malaysian National Health and Morbidity Surveys conducted in 2006, 2011 and 2015. The air pollution data (NOx, NO2, SO2, O3 and PM10) were obtained from the Department of Environment Malaysia. Using multiple logistic and linear regression models, the association between long-term exposure to these pollutants and prevalence of diabetes among Malaysian adults was evaluated.
RESULTS: The PM10 concentration decreased from 2006 to 2014, followed by an increase in 2015. Levels of NOx decreased while O3 increased annually. The air pollutant levels based on individual modelled air pollution exposure as measured by the nearest monitoring station were higher than the annual averages of the five pollutants present in the ambient air. The prevalence of overall diabetes increased from 11.4% in 2006 to 21.2% in 2015. The prevalence of known diabetes, underdiagnosed diabetes, overweight and obesity also increased over these years. There were significant positive effect estimates of known diabetes at 1.125 (95% CI, 1.042, 1.213) for PM10, 1.553 (95% CI, 1.328, 1.816) for O3, 1.271 (95% CI, 1.088, 1.486) for SO2, 1.124 (95% CI, 1.048, 1.207) for NO2, and 1.087 (95% CI, 1.024, 1.153) for NOx for NHMS 2006. The adjusted annual average levels of PM10 [1.187 (95% CI, 1.088, 1.294)], O3 [1.701 (95% CI, 1.387, 2.086)], NO2 [1.120 (95% CI, 1.026, 1.222)] and NOx [1.110 (95% CI, 1.028, 1.199)] increased significantly from NHMS 2006 to NHMS 2011 for overall diabetes. This was followed by a significant decreasing trend from NHMS 2011 to 2015 [0.911 for NO2, and 0.910 for NOx].
CONCLUSION: The findings of this study suggest that long-term exposure to O3 is an important associated factor of underdiagnosed DM risk in Malaysia. PM10, NO2 and NOx may have mixed effect estimates towards the risk of DM, and their roles should be further investigated with other interaction models. Policy and intervention measures should be taken to reduce air pollution in Malaysia.
METHODS: We searched relevant studies in electronic databases. When two or more observational studies reported the same outcome measures, we performed pooled analysis. All the analyses were performed on PBL using PCR. The odds ratio (OR) and its 95% confidence interval (CI) were used to assess the strength of association.
RESULTS: Seven studies (with 8 datasets) were included in this meta-analysis; 3 prospective studies, 3 retrospective studies and 1 study with a separate prospective and retrospective designs. The pooled analysis of 4 prospective studies (summary OR 1.01, 95% CI: 0.77-1.34, I (2):30%) and 4 retrospective studies (summary OR 1.65, 95% CI: 0.96-2.83, I (2):96%) showed no relationship between PBL telomere length and the CRC risk. A subgroup analysis of 2 prospective studies exclusively on females also showed no association between PBL telomere length and the CRC risk (summary OR, 1.17, 95% CI:0.72-1.91, I (2):57%).
CONCLUSION: The current analysis is insufficient to provide evidence on the relationship between PBL telomere length and the risk of CRC. Findings suggest that there may be a complex relationship between PBL telomere length and the CRC risk or discrepancy between genetics, age of patients and clinical studies. Future well powered, large prospective studies on the relationship between telomere length and the risk of CRC, and the investigations of the biologic mechanisms are recommended.