Air pollution and diabetes association: Modification by type 2 diabetes genetic risk score

Long-Term Exposure to Ambient Air Pollution and Metabolic Syndrome and Its Components

Ambient air pollution is a serious public health issue worldwide. A growing number of studies has highlighted the negative effects of air pollution on metabolic syndrome (MetS) and its components, including abdominal obesity, disorders of lipid metabolism, elevated blood pressure, and impaired fasting blood glucose. This review provides a brief overview of epidemiological and genetic interaction studies of the links between chronic exposure to ambient air pollution and MetS and its components, as well as plausible mechanisms underlying these relationships. The cumulative evidence suggests that long-term exposure to air pollution, especially particulate matter, increases the risk of MetS and its components. These associations can be partly modified by baseline characteristics, lifestyle, and health conditions. Gene-by-air-pollution interaction studies, limited to candidate genes in the past, have recently been conducted at an expanded genome-wide level. However, more such studies are needed to comprehensively understand the genetics involved in the association between air pollution and MetS. Mechanistic evidence suggests potential biological pathways, including inflammation, oxidative stress, and endothelial dysfunction.

Methylation Risk Score Modelling in Endometriosis: Evidence for Non-Genetic DNA Methylation Effects in a Case-Control Study

Endometriosis is a chronic gynaecological disease characterised by endometrial-like tissue found external to the uterus. While several studies have reported strong evidence of a genetic contribution to the disease, studies on the environmental impact on endometriosis are limited. DNA methylation (DNAm) can be influenced by genetic and environmental factors and serves as a useful biological marker of the effects of genetic and environmental exposures on complex diseases. This study aims to develop a methylation risk score (MRS) for endometriosis to increase the power to detect DNAm signals associated with the disease and enhance our understanding of the pathogenesis of the disease. Endometrial methylation and genotype data from 318 controls and 590 cases were analysed. MRSs were developed using several different models. MRS performances were evaluated by splitting samples into training and test sets based on independent cohort institutions, and the area under the receiver-operator curve (AUC) was calculated. The maximum AUC obtained from the best-performing MRS is 0.6748, derived from 746 DNAm sites. The classification performance of MRS and polygenic risk score (PRS) combined was consistently higher than PRS alone. This study demonstrates that there are DNAm signals independent of common genetic variants associated with endometriosis.

Ambient air fine particulate matter (PM10 and PM2.5) and risk of type 2 diabetes mellitus and mechanisms of effects: a global systematic review and meta-analysis

Type 2 diabetes causes early mortality worldwide. Air pollution's relationship with T2DM has been studied. The association between them is unclear because of inconsistent outcomes. Studies on this topic have been published since 2019, but not thoroughly evaluated. We conducted a systematic review and meta-analysis using relevant data. The study protocol was registered in PROSPIRO and conducted according to MOOSE guidelines. In total, 4510 manuscripts were found. After screening, 46 studies were assessed using the OHAT tool. This meta-analysis evaluated fine particles with T2DM using OR and HR effect estimates. Evaluation of publication bias was conducted by Egger's test, Begg's test, and funnel plot analysis. A sensitivity analysis was conducted to evaluate the influence of several studies on the total estimations. Results show a significant association between PM2.5 and PM10 exposure and T2DM. Long-term exposure to fine air particles may increase the prevalence and incidence of T2DM. Fine air pollution increases the chance of developing T2DM mainly via systemic inflammation, oxidative stress, and endoplasmic reticulum stress.

The role of sex hormone binding globulin levels in the association of surgical and natural premature menopause with incident type 2 diabetes

OBJECTIVE

To examine associations of surgical and natural menopause before the age of 40 years with the risk of type 2 diabetes (T2D) in women.

METHODS

A total of 273,331 women from the United Kingdom were recruited between 2006 and 2010 in the UK Biobank (UKB) study, and 146,343 women aged 40 to 69 years who were postmenopausal at baseline were included in the analysis. Surgical menopause and natural premature menopause were defined as bilateral oophorectomy before the age of 40 and menopause before the age of 40 without oophorectomy, respectively. Multivariable Cox regression models were used to estimate the hazard ratios (HRs) and 95 % confidence intervals (CIs) for the association between premature menopause and the incidence of T2D.

RESULTS

During a median follow-up of 10.4 years, 47 women with surgical premature menopause, 244 women with natural premature menopause, and 4724 women without premature menopause developed T2D. Compared with women without premature menopause, both surgical premature menopause (adjusted HR = 1.46, 95 % CI: 1.09-1.95; P = 0.01) and natural premature menopause (adjusted HR = 1.20, 95 % CI: 1.06-1.37; P < 0.01) were associated with higher risks of incident T2D in the multivariable-adjusted models. Additionally, we observed a significant interaction between levels of sex hormone binding globulin (SHBG) (Pinteraction < 0.01) and the effects of premature menopause on incident T2D. The association between premature menopause and T2D risk appeared to be stronger in women with higher SHBG levels. Furthermore, a joint association was detected between premature menopause and the genetic risk score (GRS) of T2D, with a higher score indicating a higher risk of developingT2D. The highest risk of T2D was observed with higher T2D GRS and surgical premature menopause (adjusted HR = 2.61, 95 % CI: 1.65-4.12; P < 0.01).

CONCLUSIONS

Surgical menopause and natural menopause before the age of 40 years were associated with an increased risk of T2D among postmenopausal women. The findings also suggest potential interactions of premature menopause with SHBG levels, with the association appearing to be stronger in higher SHBG levels, as well as a joint association between menopause status and genetic risk factors on T2D incidence.

Air Pollution in Cardio-Oncology and Unraveling the Environmental Nexus: JACC: CardioOncology State-of-the-Art Review

Although recent advancements in cancer therapies have extended the lifespan of patients with cancer, they have also introduced new challenges, including chronic health issues such as cardiovascular disease arising from pre-existing risk factors or cancer therapies. Consequently, cardiovascular disease has become a leading cause of non-cancer-related death among cancer patients, driving the rapid evolution of the cardio-oncology field. Environmental factors, particularly air pollution, significantly contribute to deaths associated with cardiovascular disease and specific cancers, such as lung cancer. Despite these statistics, the health impact of air pollution in the context of cardio-oncology has been largely overlooked in patient care and research. Notably, the impact of air pollution varies widely across geographic areas and among individuals, leading to diverse exposure consequences. This review aims to consolidate epidemiologic and preclinical evidence linking air pollution to cardio-oncology while also exploring associated health disparities and environmental justice issues.

Effects of endocrine disrupting chemicals and their interactions with genetic risk scores on cardiometabolic traits

Ubiquitous non-persistent endocrine disrupting chemicals (EDCs) have inconsistent associations with cardiometabolic traits. Additionally, large-scale genome-wide association studies (GWASs) have yielded many genetic risk variants for cardiometabolic traits and diseases. This study aimed to investigate the associations between a wide range of EDC exposures (parabens, bisphenols, and phthalates) and 14 cardiometabolic traits and whether these are moderated by their respective genetic risk scores (GRSs). Data were from 1074 participants aged 18 years or older of the Lifelines Cohort Study, a large population-based biobank. GRSs for 14 cardiometabolic traits were calculated based on genome-wide significant common variants from recent GWASs. The concentrations of 15 EDCs in 24-hour urine were measured by isotope dilution liquid chromatography tandem mass spectrometry technology. The main effects of trait-specific GRSs and each of the EDC exposures and their interaction effects on the 14 cardiometabolic traits were examined in multiple linear regression. The present study confirmed significant main effects for all GRSs on their corresponding cardiometabolic trait. Regarding the main effects of EDC exposures, 26 out of 280 EDC-trait tests were significant with explained variances ranging from 0.43 % (MMP- estimated glomerular filtration rate (eGFR)) to 2.37 % (PrP-waist-hip ratio adjusted body mass index (WHRadjBMI)). We confirmed the association of MiBP and MBzP with WHRadjBMI and body mass index (BMI), and showed that parabens, bisphenol F, and many other phthalate metabolites significantly contributed to the variance of WHRadjBMI, BMI, high-density lipoprotein (HDL), eGFR, fasting glucose (FG), and diastolic blood pressure (DBP). Only one association between BMI and bisphenol F was nominally significantly moderated by the GRS explaining 0.36 % of the variance. However, it did not survive multiple testing correction. We showed that non-persistent EDC exposures exerted effects on BMI, WHRadjBMI, HDL, eGFR, FG, and DBP. However no evidence for a modulating role of GRSs was found.

Effects of residential greenness and genetic predisposition on hemoglobin A1c and type 2 diabetes: Gene-environment interaction analysis from a nationwide study

BACKGROUND

Current evidence on the relations of residential greenness with glucose homeostasis and type 2 diabetes (T2D) remained largely uncertain. Most importantly, no prior studies have investigated whether genetic predisposition modifies the above associations.

METHODS

We leveraged data from the UK Biobank prospective cohort study, with participants enrolled between 2006 and 2010. Residential greenness was assessed by using the Normalized Difference Vegetation Index, and the weighting T2D-specific genetic risk score (GRS) was constructed based on previously published genome-wide association studies. Linear regression models and logistic regression models were used to investigate associations of residential greenness with glycated hemoglobin (HbA_1c) and T2D prevalence, respectively. Interaction models explored whether genetic predisposition modifies greenness-HbA_1c/T2D associations.

RESULTS

Among 315,146 individuals (mean [SD] age, 56.59 [8.09] years), each one-unit increase in residential greenness was associated with reduction in HbA_1c (β: -0.87, 95% CI: -1.16 to -0.58) and a 12% decrease in odds of T2D (OR: 0.88, 95% CI: 0.79 to 0.98), respectively. Additionally, interaction analyses further demonstrated that residential greenness and genetic risk had cumulative effects on HbA_1c and T2D. Compared with individuals who were exposed to low greenness and had high GRS, participants with low GRS and high greenness had a significant decline in HbA_1c (β: -2.96, 95% CI: -3.10 to -2.82, P for interaction = 0.04) and T2D (OR: 0.47, 95% CI: 0.45 to 0.50, P for interaction = 0.09).

CONCLUSIONS

We add novel evidence that residential greenness has protective effects on glucose metabolism and T2D, and those beneficial effects can be amplified by low genetic risk. Our findings may facilitate the improvement of the living environment and the development of prevention strategies by considering genetic susceptibility to T2D.

KCNQ1 rs2237892 polymorphism modify the association between short-term ambient particulate matter exposure and fasting blood glucose: A family-based study

BACKGROUND

The association between particulate matter and fasting blood glucose (FBG) has shown conflicting results. Genome-wide association studies have shown that KCNQ1 rs2237892 polymorphism is associated with the risk of diabetes. Whether KCNQ1 rs2237892 polymorphism might modify the association between particulate matter and FBG is still uncertain.

METHODS

Data collected from a family-based cohort study in Northern China, were used to perform the analysis. A generalized additive Gaussian model was used to examine the short-term effects of air pollutants on FBG. We further conducted interaction analyses by including a cross-product term of air pollutants by rs2237892 within KCNQ1 gene.

RESULTS

A total of 4418 participants were included in the study. In the single pollutant model, the FBG level increased 0.0031 mmol/L with per 10 μg/m³ elevation in fine particular matter (PM_2.5) for lag 0 day. After additional adjustments for nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), similar results were observed for lag 0-2 days. As for particulate matter with particle size below 10 μm (PM₁₀), the significant association between the daily average concentration of the pollutant and FBG level was observed for lag 0-3 days. Additionally, rs2237892 in KCNQ1 gene modified the association between PM and FBG level. The higher risk of FBG levels associated with elevations in PM₁₀ and PM_2.5 were more evident as the number of risk allele C increased. Individuals with a CC genotype had the highest risk of elevation in FBG levels.

CONCLUSION

Short-term exposures to PM_2.5 and PM₁₀ were associated with higher FBG levels. Additionally, rs2237892 in KCNQ1 gene might modify the association between the air pollutants and FBG levels.

Association of physical activity and air pollution exposure with the risk of type 2 diabetes: a large population-based prospective cohort study

BACKGROUND

The interplay between physical activity (PA) and air pollution in relation to type 2 diabetes (T2D) remains largely unknown. Based on a large population-based cohort study, this study aimed to examine whether the benefits of PA with respect to the risk of T2D are moderated by exposure to air pollution.

METHODS

UK Biobank participants (n = 359,153) without diabetes at baseline were included. Information on PA was obtained using the International Physical Activity Questionnaire short form. Exposure to air pollution, including PM_2.5, PM_coarse (PM_2.5-10), PM₁₀, and NO_2, was estimated from land use regression models. Cox regression models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (95% CIs).

RESULTS

During a median of 8.9 years of follow-up, 13,706 T2D events were recorded. Compared with a low PA level, the HRs for the risk of T2D among individuals with moderate and high PA were 0.82 (95% CI, 0.79-0.86) and 0.73 (95% CI, 0.70-0.77), respectively. Compared with low levels of air pollution, the HRs for risk of T2D for high levels of air pollution (PM_2.5, PM_coarse, PM₁₀, and NO₂) were 1.19 (1.14-1.24), 1.06 (1.02-1.11), 1.13 (1.08-1.18), and 1.19 (1.14-1.24), respectively. There was no effect modification of the associations between PA and T2D by air pollution (all P-interactions > 0.05). The inverse associations between PA and T2D in each air pollution stratum were generally consistent (all P for trend < 0.05).

CONCLUSION

A higher PA and lower air pollution level were independently associated with a lower risk of T2D. The beneficial effects of PA on T2D generally remained stable among participants exposed to different levels of air pollution. Further studies are needed to replicate our findings in moderately and severely polluted areas.

Evaluation of associations between estimates of particulate matter exposure and new onset type 2 diabetes in the REGARDS cohort

BACKGROUND

Studies of PM_2.5 and type 2 diabetes employ differing methods for exposure assignment, which could explain inconsistencies in this growing literature. We hypothesized associations between PM_2.5 and new onset type 2 diabetes would differ by PM_2.5 exposure data source, duration, and community type.

METHODS

We identified participants of the US-based REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort who were free of diabetes at baseline (2003-2007); were geocoded at their residence; and had follow-up diabetes information. We assigned PM_2.5 exposure estimates to participants for periods of 1 year prior to baseline using three data sources, and 2 years prior to baseline for two of these data sources. We evaluated adjusted odds of new onset diabetes per 5 µg/m³ increases in PM_2.5 using generalized estimating equations with a binomial distribution and logit link, stratified by community type.

RESULTS

Among 11,208 participants, 1,409 (12.6%) had diabetes at follow-up. We observed no associations between PM_2.5 and diabetes in higher and lower density urban communities, but within suburban/small town and rural communities, increases of 5 µg/m³ PM_2.5 for 2 years (Downscaler model) were associated with diabetes (OR [95% CI] = 1.65 [1.09, 2.51], 1.56 [1.03, 2.36], respectively). Associations were consistent in direction and magnitude for all three PM_2.5 sources evaluated.

SIGNIFICANCE

1- and 2-year durations of PM_2.5 exposure estimates were associated with higher odds of incident diabetes in suburban/small town and rural communities, regardless of exposure data source. Associations within urban communities might be obfuscated by place-based confounding.

Integrative analysis to explore the biological association between environmental skin diseases and ambient particulate matter

Although numerous experimental studies have suggested a significant association between ambient particulate matter (PM) and respiratory damage, the etiological relationship between ambient PM and environmental skin diseases is not clearly understood. Here, we aimed to explore the association between PM and skin diseases through biological big data analysis. Differential gene expression profiles associated with PM and environmental skin diseases were retrieved from public genome databases. The co-expression among them was analyzed using a text-mining-based network analysis software. Activation/inhibition patterns from RNA-sequencing data performed with PM_2.5-treated normal human epidermal keratinocytes (NHEK) were overlapped to select key regulators of the analyzed pathways. We explored the adverse effects of PM on the skin and attempted to elucidate their relationships using public genome data. We found that changes in upstream regulators and inflammatory signaling networks mediated by MMP-1, MMP-9, PLAU, S100A9, IL-6, and S100A8 were predicted as the key pathways underlying PM-induced skin diseases. Our integrative approach using a literature-based co-expression analysis and experimental validation not only improves the reliability of prediction but also provides assistance to clarify underlying mechanisms of ambient PM-induced dermal toxicity that can be applied to screen the relationship between other chemicals and adverse effects.

Genome-Wide Interaction Study of Late-Onset Asthma With Seven Environmental Factors Using a Structured Linear Mixed Model in Europeans

Asthma is among the most common chronic diseases worldwide, creating a substantial healthcare burden. In late-onset asthma, there are wide global differences in asthma prevalence and low genetic heritability. It has been suggested as evidence for genetic susceptibility to asthma triggered by exposure to multiple environmental factors. Very few genome-wide interaction studies have identified gene-environment (G×E) interaction loci for asthma in adults. We evaluated genetic loci for late-onset asthma showing G×E interactions with multiple environmental factors, including alcohol intake, body mass index, insomnia, physical activity, mental status, sedentary behavior, and socioeconomic status. In gene-by-single environment interactions, we found no genome-wide significant single-nucleotide polymorphisms. However, in the gene-by-multi-environment interaction study, we identified three novel and genome-wide significant single-nucleotide polymorphisms: rs117996675, rs345749, and rs17704680. Bayes factor analysis suggested that for rs117996675 and rs17704680, body mass index is the most relevant environmental factor; for rs345749, insomnia and alcohol intake frequency are the most relevant factors in the G×E interactions of late-onset asthma. Functional annotations implicate the role of these three novel loci in regulating the immune system. In addition, the annotation for rs117996675 supports the body mass index as the most relevant environmental factor, as evidenced by the Bayes factor value. Our findings help to understand the role of the immune system in asthma and the role of environmental factors in late-onset asthma through G×E interactions. Ultimately, the enhanced understanding of asthma would contribute to better precision treatment depending on personal genetic and environmental information.

Impact of polluting fuels for cooking on diabetes mellitus and glucose metabolism in south urban China

We hypothesized that exposure to polluting fuels for cooking was associated with abnormality of glucose metabolism and diabetes mellitus (DM) in south urban China. 3414 residents were surveyed in 14 urban areas of Guangdong Province in 2018. We recorded polluting fuels for cooking exposure, different DM status (DM, prediabetes), fasting blood glucose (FBG), oral glucose tolerance test (OGTT), glycated hemoglobin (HbA_1c ), and other covariates by using a structured questionnaire. We conducted logistic regression model and multivariate linear regression model based on propensity-score method (inverse probability of weighting) to examine the effect of polluting fuels for cooking exposure on DM and glucose metabolism. Exposure to polluting fuels for cooking was associated with DM (odds ratio: 2.57, 95% confidence interval: 1.71 to 3.86) and prediabetes (odds ratio: 1.98, 95% confidence interval: 1.52 to 2.58) in both the adjusted and unadjusted models (all p < 0.05). Exposure to polluting fuels for cooking was significantly associated with an increase of FBG (β: 0.30 mmol/L, 95% confidence interval: 0.22 to 0.38 mmol/L). Sensitivity analysis showed that the results were not substantially changed. There was an increased risk of DM, prediabetes and high levels of FBG, OGTT, and HbA_1c among participants aged ≥ 40 years with exposure to polluting fuels for cooking. We demonstrated that exposure to polluting fuels for cooking was associated with higher levels of FBG, which contributed to the increased risk of DM and prediabetes in middle-aged elderly Chinese population living in urban areas.

Obesity and the relation between joint exposure to ambient air pollutants and incident type 2 diabetes: A cohort study in UK Biobank

BACKGROUND

Air pollution has been related to incidence of type 2 diabetes (T2D). We assessed the joint association of various air pollutants with the risk of T2D and examined potential modification by obesity status and genetic susceptibility on the relationship.

METHODS AND FINDINGS

A total of 449,006 participants from UK Biobank free of T2D at baseline were included. Of all the study population, 90.9% were white and 45.7% were male. The participants had a mean age of 56.6 (SD 8.1) years old and a mean body mass index (BMI) of 27.4 (SD 4.8) kg/m2. Ambient air pollutants, including particulate matter (PM) with diameters ≤2.5 μm (PM2.5), between 2.5 μm and 10 μm (PM2.5-10), nitrogen dioxide (NO2), and nitric oxide (NO) were measured. An air pollution score was created to assess the joint exposure to the 4 air pollutants. During a median of 11 years follow-up, we documented 18,239 incident T2D cases. The air pollution score was significantly associated with a higher risk of T2D. Compared to the lowest quintile of air pollution score, the hazard ratio (HR) (95% confidence interval [CI]) for T2D was 1.05 (0.99 to 1.10, p = 0.11), 1.06 (1.00 to 1.11, p = 0.051), 1.09 (1.03 to 1.15, p = 0.002), and 1.12 (1.06 to 1.19, p < 0.001) for the second to fifth quintile, respectively, after adjustment for sociodemographic characteristics, lifestyle factors, genetic factors, and other covariates. In addition, we found a significant interaction between the air pollution score and obesity status on the risk of T2D (p-interaction < 0.001). The observed association was more pronounced among overweight and obese participants than in the normal-weight people. Genetic risk score (GRS) for T2D or obesity did not modify the relationship between air pollution and risk of T2D. Key study limitations include unavailable data on other potential T2D-related air pollutants and single-time measurement on air pollutants.

CONCLUSIONS

We found that various air pollutants PM2.5, PM2.5-10, NO2, and NO, individually or jointly, were associated with an increased risk of T2D in the population. The stratified analyses indicate that such associations were more strongly associated with T2D risk among those with higher adiposity.

Longitudinal associations between ambient air pollution and insulin sensitivity: results from the KORA cohort study

BACKGROUND

Impaired insulin sensitivity could be an intermediate step that links exposure to air pollution to the development of type 2 diabetes. However, longitudinal associations of air pollution with insulin sensitivity remain unclear. Our study investigated the associations of long-term air pollution exposure with the degree and rate of change of insulin sensitivity.

METHODS

In this longitudinal study, we analysed data from the Cooperative Health Research in the Region of Augsburg (KORA) cohort from Augsburg, Germany, which recruited participants aged 25-74 years in the survey between 1999 and 2001 (KORA S4), with two follow-up examinations in 2006-08 (KORA F4) and 2013-14 (KORA FF4). Serum concentrations of fasting insulin and glucose, and homoeostasis model assessment of insulin resistance (HOMA-IR, a surrogate measure of insulin sensitivity) and β-cell function (HOMA-B, a surrogate marker for fasting insulin secretion) were assessed at up to three visits between 1999 and 2014. Annual average air pollutant concentrations at the residence were estimated by land-use regression models. We examined the associations of air pollution with repeatedly assessed biomarker levels using mixed-effects models, and we assessed the associations with the annual rate of change in biomarkers using quantile regression models.

FINDINGS

Among 9620 observations from 4261 participants in the KORA cohort, we included 6008 (62·5%) observations from 3297 (77·4%) participants in our analyses. Per IQR increment in annual average air pollutant concentrations, HOMA-IR significantly increased by 2·5% (95% CI 0·3 to 4·7) for coarse particulate matter, by 3·1% (0·9 to 5·3) for PM_2·5, by 3·6% (1·0 to 6·3) for PM_{2·5absorbance}, and by 3·2% (0·6 to 5·8) for nitrogen dioxide, and borderline significantly increased by 2·2% (-0·1 to 4·5) for ozone, whereas it did not significantly increase for the whole range of ultrafine particles. Similar positive associations in slightly smaller magnitude were observed for HOMA-B and fasting insulin levels. In addition, air pollutant concentrations were positively associated with the annual rate of change in HOMA-IR, HOMA-B, and fasting insulin. Neither the level nor the rate of change of fasting glucose were associated with air pollution exposure.

INTERPRETATION

Our study indicates that long-term air pollution exposure could contribute to the development of insulin resistance, which is one of the key factors in the pathogenesis of type 2 diabetes.

FUNDING

German Federal Ministry of Education and Research.

Exposure to urban particulate matter and its association with human health risks

Human health and environmental risks are increasing following air pollution associated with vehicular and industrial emissions in which particulate matter is a constituent. The purpose of this review was to assess studies on the health effects and mortality induced by particles published for the last 15 years. The literature survey indicated the existence of strong positive associations between fine and ultrafine particles' exposure and cardiovascular, hypertension, obesity and type 2 diabetes mellitus, cancer health risks, and mortality. Its exposure is also associated with increased odds of hypertensive and diabetes disorders of pregnancy and premature deaths. The ever increasing hospital admission and mortality due to heart failure, diabetes, hypertension, and cancer could be due to long-term exposure to particles in different countries. Therefore, its effect should be communicated for legal and scientific actions to minimize emissions mainly from traffic sources.

Majid NL, Abd. Hamid HA, Rodzlan Hasani WS, Mohd Yussoff MFB, Aris HTB, Ab. Rahman EB, M. Rashid ZB. Association between long-term exposure to ambient air pollution and prevalence of diabetes mellitus among Malaysian adults

BACKGROUND

Malaysia has the highest rate of diabetes mellitus (DM) in the Southeast Asian region, and has ongoing air pollution and periodic haze exposure.

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.

Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes

Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.

Ambient air pollution and diabetes: A systematic review and meta-analysis

BACKGROUND

Air pollutants are suggested to be related to type 2 diabetes (T2D). Since several high quality papers on air pollutants and T2D have been published beyond the last reviews, an extended systematic review is highly warranted. We review epidemiological studies to quantify the association between air pollutants and T2D, and to answer if diabetes patients are more vulnerable to air pollutants.

METHODS

We systematically reviewed the databases of PubMed and Web of Science based on the guidelines of the Preferred Reporting Items for Systematic review and Meta-analysis (PRISMA). We calculated odds ratios (OR) or hazard ratios (HR) and their 95% confidence intervals (CI) to assess the strength of the associations between air pollutants [e.g., particulate matter with diameter ≤ 2.5 μm (PM_2.5), particulate matter with diameter ≤ 10 μm (PM₁₀), and nitrogen dioxide (NO₂)] and T2D. We evaluated the quality and risk of bias of the included studies and graded the credibility of the pooled evidence using several recommended tools. We also performed sensitivity analysis, meta-regression analysis, and publication bias test.

RESULTS

Out of 716 articles identified, 86 were used for this review and meta-analysis. Meta-analyses showed significant associations of PM_2.5 with T2D incidence (11 studies; HR = 1.10, 95% CI = 1.04-1.17 per 10 μg/m³ increment; I² = 74.4%) and prevalence (11 studies; OR = 1.08; 95% CI = 1.04-1.12 per 10 μg/m³ increment; I² = 84.3%), of PM₁₀ with T2D prevalence (6 studies; OR = 1.10; 95% CI = 1.03-1.17 per 10 μg/m³ increment; I² = 89.5%) and incidence (6 studies; HR = 1.11; 95% CI = 1.00-1.22 per μg/m³ increment; I² = 70.6%), and of NO₂ with T2D prevalence (11 studies; OR = 1.07; 95% CI = 1.04-1.11 per 10 μg/m³ increment; I² = 91.1%). The majority of studies on glucose-homoeostasis markers also showed increased risks with higher air pollutants levels, but the studies were too heterogeneous for meta-analysis. Overall, patients with diabetes might be more vulnerable to PM.

CONCLUSIONS

Recent publications strengthened the evidence for adverse effects of ambient air pollutants exposure (especially for PM) on T2D and that diabetic patients might be more vulnerable to air pollutants exposure.

Methodological challenges in constructing DNA methylation risk scores

Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring GRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses, and 4) to predict individual risks of disease or treatment success. Most GRS approaches can directly be transferred to methylation data. However, since methylation data is more sensitive to confounding, e.g. by age and tissue, it is more complex to find appropriate external weights. In this review, we will outline the adaption of current GRS approaches to methylation data and highlight occurring challenges.

Role of Air Pollution and rs10830963 Polymorphism on the Incidence of Type 2 Diabetes: Tehran Cardiometabolic Genetic Study

Diabetes mellitus (DM) is considered one of the leading health issues that are egregiously threatening human life throughout the world. Several epidemiological studies have examined the relationship of a particular matter < 10 μm (PM10) exposure and with type 2 diabetes mellitus (T2DM) prevalence and incidence. Accordingly, the current study is a study investigating the independent influence of air pollution (AP) and rs10830963 on the incidence of T2DM. A total number of 2428 adults over 20 years of age participated in a prospective cohort (TCGS) during a 9-year follow-up phase. The concentration of AP was measured, and the obtained values were considered the mean level in three previous years since the exposure concentration took the people living in that location. The COX regression model was employed to determine the influence of AP and rs10830963 on the incidence of T2DM in adjustment with covariate factors. Among the 392 T2DM, 230 cases (58.7%) were female diabetics, and 162 (41.3%) were male diabetics. According to the multivariable-adjusted model, exposure to PM10 (per 10 μm/m3), associated with the risk of T2DM, although just a borderline (p = 0.07) was found in the multivariable model (HR; 1.50, 95% CI; 1-2.32). The rs10830963 was directly associated with the incidence of diabetes, and the GG genotype increased the T2DM rate by 113% (more than two times) (HR; 2.134, 95% CI; 1.42-3.21, p ≤ 0.001) and GC increased it by 65% (HR; 1.65, 95% CI; 1.24-2.21, p ≤ 0.001). Long-term exposure to PM10 was associated with an increased risk of diabetes. Thus, it is suggested that the individuals with variant rs10830963 genotypes fall within a group susceptible to an increased risk of T2DM arising from AP.

Effects of long-term exposure to air pollution on the incidence of type 2 diabetes mellitus: a meta-analysis of cohort studies

This meta-analysis aimed to comprehensively assess the effects of long-term air pollution exposure on the risk of type 2 diabetes mellitus (T2DM). Studies were selected from three electronic databases. Random- or fixed-effect model was used to obtain the pooled hazard ratios (HRs) and corresponding 95% confidential intervals (CIs). Stratified analyses by regions of the studies and length of follow-up were conducted to assess the effects in different subgroups. Sensitivity analyses by omitted studies one by one, as well as adjusting certain confounding factors, were also conducted. The search resulted in 1878 studies, among which 16 studies with 18 cohorts were included. The incidence of T2DM was significantly associated with 10 μg/m³ increase of PM_2.5 (overall HR = 1.11, 95% CI: 1.03, 1.19) and PM₁₀ (overall HR = 1.12, 95% CI: 1.01, 1.23) exposure. Stratified analyses confirmed that PM_2.5 was significantly associated with increased T2DM incidence in American countries but not European countries. The results in the long follow-up subgroup also confirmed that exposure of PM_2.5 and PM₁₀ was associated with increased T2DM incidence. Interestingly, educational level and gender could potentially affect the impacts of PM₁₀ and PM_2.5 on T2DM incidence. The findings show long-term exposure to PM_2.5, and PM₁₀ could significantly increase the incidence of T2DM, especially in cohorts with long follow-up time.

Air Pollution, Oxidative Stress, and Diabetes: a Life Course Epidemiologic Perspective

PURPOSE OF REVIEW

Ambient air pollution is strongly linked to cardiovascular and respiratory diseases. We summarize available published evidence regarding similar associations with diabetes across the life course.

RECENT FINDINGS

We performed a life-course survey of the recent literature, including prenatal, gestational, childhood/adolescence, and adult exposures to air pollution. Oxidative stress is identified as a key factor in both metabolic dysfunction and the effects of air pollution exposure, especially from fossil fuel combustion products, providing a plausible mechanism for air pollution-diabetes associations. The global burden of diabetes attributed to air pollution exposure is substantial, with a recent estimate that ambient fine particulate matter (PM_2.5) exposure contributes to more than 200,000 deaths from diabetes annually. There is a growing body of literature linking air pollution exposure during childhood and adulthood with diabetes etiology and related cardiometabolic biomarkers. A small number of studies found that exposure to air pollution during pregnancy is associated with elevated gestational diabetes risk among mothers. Studies examining prenatal air pollution exposure and diabetes risk among the offspring, as well as potential transgenerational effects of air pollution exposure, are very limited thus far. This review provides insight into how air pollutants affect diabetes and other metabolic dysfunction-related diseases across the different life stages.

Effects of gender, age and treatment duration on lipid profile and renal function indices in diabetic patients attending a teaching hospital in South-Western Nigeria

Background

Type 2 Diabetes Mellitus (T2DM) is associated with diabetic nephropathy and hyperlipidemia. Gender, age, medication adherence, lifestyle, culture and socioeconomic status could be sources of diversity in T2DM leading to differences in predisposition, development and clinical presentation.

Objectives

Therefore, this study aimed to investigate the influence of gender, age and treatment duration on kidney and lipid-related biochemical indices of T2DM patients attending Ekiti State University Teaching Hospital, Ado-Ekiti, Nigeria (EKSUTH).

Methods

Blood from diabetic patients and healthy subjects was analysed for fasting blood glucose (FBG), renal function parameters and lipid profile. Influence of age, gender and treatment duration on indices was assessed using standard baseline values.

Results

Dyslipidemia was pronounced among female diabetics while the incidence of diabetes was found to be higher in middle-age. The percentage of diabetics with high levels of FPG, urea, creatinine, cholesterol, triglyceride and low density lipoprotein-cholesterol after 9–10 years of treatment were lower compared with the percentage after 5–6 years of treatment.

Conclusion

Gender, age and treatment duration influenced clinical course of T2DM among patients presenting at EKSUTH but long term treatment appeared to improve T2DM among patients.

Associations of Exposure to Air Pollution with Insulin Resistance: A Systematic Review and Meta-Analysis

In this article, we review the available evidence and explore the association between air pollution and insulin resistance (IR) using meta-analytic techniques. Cohort studies published before January 2018 were selected through English-language literature searches in nine databases. Six cohort studies were included in our sample, which assessed air pollutants including PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm), NO₂(nitrogen dioxide), and PM10 (particulate matter with an aerodynamic diameter less than 10 μm). Percentage change in insulin or insulin resistance associated with air pollutants with corresponding 95% confidence interval (CI) was used to evaluate the risk. A pooled effect (percentage change) was observed, with a 1 μg/m³ increase in NO₂ associated with a significant 1.25% change (95% CI: 0.67, 1.84; I² = 0.00%, p = 0.07) in the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and a 0.60% change (95% CI: 0.17, 1.03; I² = 30.94%, p = 0.27) in insulin. Similar to the analysis of NO₂, a 1 μg/m³ increase in PM10 was associated with a significant 2.77% change (95% CI: 0.67, 4.87; I² = 94.98%, p < 0.0001) in HOMA-IR and a 2.75% change in insulin (95% CI: 0.45, 5.04; I² = 58.66%, p = 0.057). No significant associations were found between PM2.5 and insulin resistance biomarkers. We conclude that increased exposure to air pollution can lead to insulin resistance, further leading to diabetes and cardiometabolic diseases. Clinicians should consider the environmental exposure of patients when making screening and treatment decisions for them.

Airborne particulate matter biotoxicity estimated by chemometric analysis on bacterial luminescence data

In this work, PM₁₀ samples previously subjected to thorough chemical speciation and receptor modelling, have been investigated for their bio-toxicity using an inhibition test based on bacterial luminescence modulation when in contact with airborne particulate samples. The variation of light emission intensity from a luminescent bacteria strain, the Photobacterium phosphoreum, is proposed as an efficient proxy for the quantification of bio-toxic effects induced by airborne particulate matter. PM₁₀ samples characterized by definite levels of pollutants from the pertaining air shed were found to induce a decrease in the bacterial bioluminescence intensity, expressed as percentage of Inhibition Ratio (IR%). This behaviour suggests the decay of this energy-consuming activity because of a toxic effect. Cluster analysis on chemical composition and IR% data provides evidence of a statistically significant association between the adverse effects on living cells and the range of specific chemical species in PM₁₀.

Associations between long-term exposure to air pollution, glycosylated hemoglobin, fasting blood glucose and diabetes mellitus in northern France

INTRODUCTION

A growing body of evidence suggests that long-term exposure to air pollutants like nitrogen oxides (NO_x) and particulate matter (PM) is associated with the prevalence and incidence of type 2 diabetes mellitus. Serum glucose and glycosylated hemoglobin (HbA1c) levels are biomarkers of glucose homeostasis. Data on the association between glucose homeostasis biomarkers and air pollution are scarce. HbA1c and fasting blood glucose (FBG) concentrations have been linked to PM and NO₂ exposure in Taiwan, where mean pollution levels are 3 to 7 times higher than the guideline maximum annual mean values of 40 μg/m³ (for NO₂) and 20 μg/m³ (for PM10) set by the World Health Organization (WHO). However, this association is not consistently reported at lower levels of pollution. The objective of the present study was to investigate the relationships between long-term exposure to air pollution at the place of residence, diabetes biomarkers, and prevalent diabetes in two cities with relatively low level of pollution.

METHODS

Data were recorded for 2895 adults (aged 40 to 65) having participated in the 2011-2013 ELISABET cross-sectional survey of the Lille and Dunkirk urban areas in northern France. Using multiple logistic and generalized linear regression models, we analyzed the associations between individual exposure to pollution on one hand and HbA1c, FBG and prevalent diabetes mellitus (DM) on the other. An atmospheric dispersion modelling system was used to assess annual exposure at the place of residence to coarse particulate matter (PM10), NO₂, and sulfur dioxide (SO₂).

RESULTS

The median pollutant levels were 21.96 μg/m³ for NO₂, 26.75 μg/m³ for PM10, and 3.07 μg/m³ for SO₂. A 2 μg/m³ increment in PM10 was associated with an HbA1c increment [95% confidence interval] of 0.044% [0.021; 0.067]. This association was still statistically significant after adjustment for the neighborhood's characteristics. A 5 μg/m³ increment in NO₂ was associated with an HbA1c increment of 0.031% [0.010; 0.053]. Associations between DM or FBG and air pollution did not achieve statistical significance.

CONCLUSION

Our study of a middle-aged, urban population evidenced an association between elevated HbA1c levels and long-term exposure to PM10 and NO₂ pollution levels that were relatively low but close to the WHO's guideline maximum values.

Long-term exposure to transportation noise and air pollution in relation to incident diabetes in the SAPALDIA study

Background

Epidemiological studies have inconsistently linked transportation noise and air pollution (AP) with diabetes risk. Most studies have considered single noise sources and/or AP, but none has investigated their mutually independent contributions to diabetes risk.

Methods

We investigated 2631 participants of the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA), without diabetes in 2002 and without change of residence between 2002 and 2011. Using questionnaire and biomarker data, incident diabetes cases were identified in 2011. Noise and AP exposures in 2001 were assigned to participants’ residences (annual average road, railway or aircraft noise level during day-evening-night (Lden), total night number of noise events, intermittency ratio (temporal variation as proportion of event-based noise level over total noise level) and nitrogen dioxide (NO₂) levels. We applied mixed Poisson regression to estimate the relative risk (RR) of diabetes and their 95% confidence intervals (CI) in mutually-adjusted models.

Results

Diabetes incidence was 4.2%. Median [interquartile range (IQR)] road, railway, aircraft noise and NO₂ were 54 (10) dB, 32 (11) dB, 30 (12) dB and 21 (15) μg/m³, respectively. Lden road and aircraft were associated with incident diabetes (respective RR: 1.35; 95% CI: 1.02–1.78 and 1.86; 95% CI: 0.96–3.59 per IQR) independently of Lden railway and NO₂ (which were not associated with diabetes risk) in mutually adjusted models. We observed stronger effects of Lden road among participants reporting poor sleep quality or sleeping with open windows.

Conclusions

Transportation noise may be more relevant than AP in the development of diabetes, potentially acting through noise-induced sleep disturbances.

Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies

BACKGROUND

Weighted genetic risk scores (GRS), defined as weighted sums of risk alleles of single nucleotide polymorphisms (SNPs), are statistically powerful for detection gene-environment (GxE) interactions. To assign weights, the gold standard is to use external weights from an independent study. However, appropriate external weights are not always available. In such situations and in the presence of predominant marginal genetic effects, we have shown in a previous study that GRS with internal weights from marginal genetic effects ("GRS-marginal-internal") are a powerful and reliable alternative to single SNP approaches or the use of unweighted GRS. However, this approach might not be appropriate for detecting predominant interactions, i.e. interactions showing an effect stronger than the marginal genetic effect.

METHODS

In this paper, we present a weighting approach for such predominant interactions ("GRS-interaction-training") in which parts of the data are used to estimate the weights from the interaction terms and the remaining data are used to determine the GRS. We conducted a simulation study for the detection of GxE interactions in which we evaluated power, type I error and sign-misspecification. We compared this new weighting approach to the GRS-marginal-internal approach and to GRS with external weights.

RESULTS

Our simulation study showed that in the absence of external weights and with predominant interaction effects, the highest power was reached with the GRS-interaction-training approach. If marginal genetic effects were predominant, the GRS-marginal-internal approach was more appropriate. Furthermore, the power to detect interactions reached by the GRS-interaction-training approach was only slightly lower than the power achieved by GRS with external weights. The power of the GRS-interaction-training approach was confirmed in a real data application to the Traffic, Asthma and Genetics (TAG) Study (N = 4465 observations).

CONCLUSION

When appropriate external weights are unavailable, we recommend to use internal weights from the study population itself to construct weighted GRS for GxE interaction studies. If the SNPs were chosen because a strong marginal genetic effect was hypothesized, GRS-marginal-internal should be used. If the SNPs were chosen because of their collective impact on the biological mechanisms mediating the environmental effect (hypothesis of predominant interactions) GRS-interaction-training should be applied.

Exposure to Night-Time Traffic Noise, Melatonin-Regulating Gene Variants and Change in Glycemia in Adults

Traffic noise has been linked to diabetes, with limited understanding of its mechanisms. We hypothesize that night-time road traffic noise (RTN) may impair glucose homeostasis through circadian rhythm disturbances. We prospectively investigated the relationship between residential night-time RTN and subsequent eight-year change in glycosylated hemoglobin (ΔHbA1c) in 3350 participants of the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA), adjusting for diabetes risk factors and air pollution levels. Annual average RTN (Lnight) was assigned to participants in 2001 using validated Swiss noise models. HbA1c was measured in 2002 and 2011 using liquid chromatography. We applied mixed linear models to explore RTN–ΔHbA1c association and its modification by a genetic risk score of six common circadian-related MTNR1B variants (MGRS). A 10 dB difference in RTN was associated with a 0.02% (0.003–0.04%) increase in mean ΔHbA_1c in 2142 non-movers. RTN–ΔHbA1c association was modified by MGRS among diabetic participants (P_interaction = 0.001). A similar trend in non-diabetic participants was non-significant. Among the single variants, we observed strongest interactions with rs10830963, an acknowledged diabetes risk variant also implicated in melatonin profile dysregulation. Night-time RTN may impair glycemic control, especially in diabetic individuals, through circadian rhythm disturbances. Experimental sleep studies are needed to test whether noise control may help individuals to attain optimal glycemic levels.

Côte d'Ivoire Dual Burden of Disease (CoDuBu): Study Protocol to Investigate the Co-occurrence of Chronic Infections and Noncommunicable Diseases in Rural Settings of Epidemiological Transition

BACKGROUND

Individual-level concomitance of infectious diseases and noncommunicable diseases (NCDs) is poorly studied, despite the reality of this dual disease burden for many low- and middle-income countries (LMICs).

OBJECTIVE

This study protocol describes the implementation of a cohort and biobank aiming for a better understanding of interrelation of helminth and Plasmodium infections with NCD phenotypes like metabolic syndrome, hypertension, and diabetes.

METHODS

A baseline cross-sectional population-based survey was conducted over one year, in the Taabo health and demographic surveillance system (HDSS) in south-central Côte d'Ivoire. We randomly identified 1020 consenting participants aged ≥18 years in three communities (Taabo-Cité, Amani-Ménou, and Tokohiri) reflecting varying stages of epidemiological transition. Participants underwent health examinations consisting of NCD phenotyping (anthropometry, blood pressure, renal function, glycemia, and lipids) and infectious disease testing (infections with soil-transmitted helminths, schistosomes, and Plasmodium). Individuals identified to have elevated blood pressure, glucose, lipids, or with infections were referred to the central/national health center for diagnostic confirmation and treatment. Aliquots of urine, stool, and venous blood were stored in a biobank for future exposome/phenome research. In-person interviews on sociodemographic attributes, risk factors for infectious diseases and NCDs, medication, vaccinations, and health care were also conducted. Appropriate statistical techniques will be applied in exploring the concomitance of infectious diseases and NCDs and their determinants. Participants' consent for follow-up contact was obtained.

RESULTS

Key results from this baseline study, which will be published in peer-reviewed literature, will provide information on the prevalence and co-occurrence of infectious diseases, NCDs, and their risk factors. The Taabo HDSS consists of rural and somewhat more urbanized areas, allowing for comparative studies at different levels of epidemiological transition. An HDSS setting is ideal as a basis for longitudinal studies since their sustainable field work teams hold close contact with the local population.

CONCLUSIONS

The collaboration between research institutions, public health organizations, health care providers, and staff from the Taabo HDSS in this study assures that the synthesized evidence will feed into health policy towards integrated infectious disease-NCD management. The preparation of health systems for the dual burden of disease is pressing in low- and middle-income countries. The established biobank will strengthen the local research capacity and offer opportunities for biomarker studies to deepen the understanding of the cross-talk between infectious diseases and NCDs.

TRIAL REGISTRATION

International Standard Randomized Controlled Trials Number (ISRCTN): 87099939; http://www.isrctn.com/ISRCTN87099939 (Archived by WebCite at http://www.webcitation.org/6uLEs1EsX).

Health Multimedia: Lifestyle Recommendations Based on Diverse Observations

Managing health lays the core foundation to enabling quality life experiences. Modern multimedia research has enhanced the quality of experiences in fields such as entertainment, social media, and advertising; yet lags in the health domain. We are developing an approach to leverage multimedia systems for human health. Health is primarily a product of our everyday lifestyle actions, yet we have minimal health guidance on making everyday choices. Recommendations are the key to modern content consumption and decisions. Cybernetic navigation principles that integrate health media sources can power dynamic recommendations to dramatically improve our health decisions. Cybernetic components give real-time feedback on health status, while the navigational approach plots health trajectory. These two principles coalesce data to enable personalized, predictive, and precise health knowledge that can contextually disseminate the right actions to keep individuals on a path to wellness.

Predicting type 2 diabetes using genetic and environmental risk factors in a multi-ethnic Malaysian cohort

OBJECTIVE

Malaysia has a high and rising prevalence of type 2 diabetes (T2D). While environmental (non-genetic) risk factors for the disease are well established, the role of genetic variations and gene-environment interactions remain understudied in this population. This study aimed to estimate the relative contributions of environmental and genetic risk factors to T2D in Malaysia and also to assess evidence for gene-environment interactions that may explain additional risk variation.

STUDY DESIGN

This was a case-control study including 1604 Malays, 1654 Chinese and 1728 Indians from the Malaysian Cohort Project.

METHODS

The proportion of T2D risk variance explained by known genetic and environmental factors was assessed by fitting multivariable logistic regression models and evaluating McFadden's pseudo R² and the area under the receiver-operating characteristic curve (AUC). Models with and without the genetic risk score (GRS) were compared using the log likelihood ratio Chi-squared test and AUCs. Multiplicative interaction between genetic and environmental risk factors was assessed via logistic regression within and across ancestral groups. Interactions were assessed for the GRS and its 62 constituent variants.

RESULTS

The models including environmental risk factors only had pseudo R² values of 16.5-28.3% and AUC of 0.75-0.83. Incorporating a genetic score aggregating 62 T2D-associated risk variants significantly increased the model fit (likelihood ratio P-value of 2.50 × 10^-4-4.83 × 10^-12) and increased the pseudo R² by about 1-2% and AUC by 1-3%. None of the gene-environment interactions reached significance after multiple testing adjustment, either for the GRS or individual variants. For individual variants, 33 out of 310 tested associations showed nominal statistical significance with 0.001 < P < 0.05.

CONCLUSION

This study suggests that known genetic risk variants contribute a significant but small amount to overall T2D risk variation in Malaysian population groups. If gene-environment interactions involving common genetic variants exist, they are likely of small effect, requiring substantially larger samples for detection.