Commentary: Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored 1,2 . FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10–11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.

In Mendelian randomization (MR) studies, where genetic variants are used as proxy measures for an exposure trait of interest, obtaining adequate statistical power is frequently a concern due to the small amount of variation in a phenotypic trait that is typically explained by genetic variants. A range of power estimates based on simulations and specific parameters for two-stage least squares (2SLS) MR analyses based on continuous variables has previously been published. However there are presently no specific equations or software tools one can implement for calculating power of a given MR study. Using asymptotic theory, we show that in the case of continuous variables and a single instrument, for example a single-nucleotide polymorphism (SNP) or multiple SNP predictor, statistical power for a fixed sample size is a function of two parameters: the proportion of variation in the exposure variable explained by the genetic predictor and the true causal association between the exposure and outcome variable. We demonstrate that power for 2SLS MR can be derived using the non-centrality parameter (NCP) of the statistical test that is employed to test whether the 2SLS regression coefficient is zero. We show that the previously published power estimates from simulations can be represented theoretically using this NCP-based approach, with similar estimates observed when the simulation-based estimates are compared with our NCP-based approach. General equations for calculating statistical power for 2SLS MR using the NCP are provided in this note, and we implement the calculations in a web-based application.

Epidemic of Cardiometabolic Disease in Developing Nations: A Threat to Global Prosperity According to the International Diabetes Federation in the year 2011, diabetes mellitus (DM) affects at least 366 million people worldwide, and that number is expected to reach 566 million by the year 2030. Over 99% of all diabetes cases represent type 2 DM with most of these projected to occur in low- to middle-income countries. Technology innovations, globalization with its free movement of food and services, seismic shifts in agrarian practices, and nutritional transition to freely available high-caloric diets have irrevocably altered energy expenditures during work and leisure. These and other factors are helping to foster the continued epidemiological transition occurring across the globe. Scientific effort over the last few decades has focused primarily on components of urbanization such as inactivity and dietary factors. More recent observations have provided additional links between exposure to environmental factors in air/water and propensity to chronic diseases (1). This issue is of importance given the extraordinary confluence of high levels of airborne and water pollutants in urbanized environments. Multiple studies in China, India, and other rapidly urbanizing economies demonstrate a steep gradient in urban–rural prevalence. This review will summarize recent evidence on how outdoor air pollution may represent an underappreciated yet critical linkage between urbanization and the emergence of cardiometabolic diseases, with a focus on type 2 DM. We define cardiometabolic disease as the confluence of cardiovascular disease and type 2 DM in recognition of the fact that the milieu of diabetes fundamentally alters the pathophysiology of coronary, cerebrovascular, and peripheral arterial disease. Thus, alteration in susceptibility to DM automatically increases the likelihood of cardiovascular disease. Indoor air pollution is not discussed owing to the paucity of data. It should be noted that our current understanding of air pollution–mediated cardiometabolic disease is derived from outdoor air pollution studies, with there being no good reasons to believe that the dose-response relationship to indoor air pollution will be any different. An understanding of potentially reversible environmental factors responsible for this rapid burgeoning of cardiometabolic disorders among developing nations is crucial in order to devise a societal response that is proportionate and adequate (2). In this review, the association between air pollution and type 2 DM is discussed unless this distinction cannot be made in the cited study (typically health registry data sets). Exposure to Environmental Toxins and Metabolic Disease Epidemiologic studies that have attempted to investigate environmental factors that accentuate risk for development of cardiometabolic disorders have uncovered a number of factors other than traditional suspects related to diet and exercise. These variables include factors such as stress (mental and emotional), cultural and socioeconomic variables, chronic low-grade infection, and environmental pollutants (Fig. 1). In many instances, these factors are strongly correlated, rendering isolation of cause and effect difficult. FIG. 1. A model for development of cardiometabolic disease highlighting importance of gene–environment interactions. (A high-quality digital representation of this figure is available in the online issue.) The plausibility that environmental exposures are linked to metabolic disease is exemplified by persistent organic pollutants, toxins that have consistently shown to associate with insulin resistance (IR) and type 2 DM. Prospective cohort studies of subjects exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin or other persistent organic pollutants in occupational and other settings have reported increased risk of DM and IR (1,3). Air pollution in Asia, Latin America, and Africa is a significant public health burden, especially given the often extraordinarily high concentrations of pollutants (e.g., particulate matter), high population density, and pervasive nature of air pollution. Table 1 lists the top countries for particulate matter (PM) air pollution in the world, all of which have rapidly urbanized populations based on a World Health Organization (WHO) database that reviewed pollution data in >1,100 cities in 91 countries. The mean annual average for the top 10 countries is roughly fivefold higher than the U.S. National Ambient Air Quality Standard of 15 μg/m3 for PM 176 ng/mL associated with a fourfold excess risk. Addition to SP-D levels to traditional risk factors improved c-statistic (from 0.76 to 0.78) and net reclassification across all levels of risk (52). In the Dallas Heart Study, increasing levels of SP-B was associated with other traditional cardiac risk factors and higher levels of inflammatory biomarkers. In multivariable analyses after adjusting for risk factors, SP-B remained associated with aortic plaque in smokers (odds ratio 1.87, fourth versus first quartiles; P < 0.0001) (53). How does one reconcile increases in plasma SP levels in population studies to increase susceptibility? Increased levels of surfactants in plasma seen in smoking and lung inflammation have been hypothesized to indicate translocation from the lung to the circulation with lung damage. Surfactants A and D are assembled as large multimeric units composed of lectin-containing globular domains and a collagenous domain. In the presence of DAMPs, they may exert proinflammatory effects by binding to CD47 (thrombospondin receptor) (48). It is also highly possible that increased levels may indicate oxidatively modified forms of surfactant that are not functional. Surfactants are often assembled as multimers and are well-known to undergo oxidative modification to oligomeric forms. Current assays for SPs do not distinguish between these various forms. Future Directions A growing body of evidence has implicated inflammatory responses to diet and environmental factors as a key mechanism that help explain the emerging epidemic in diabetes and cardiovascular disease. Both genetic and environmental factors undoubtedly play a role, although the role of the physical and social environment in determining susceptibility appears to be critical. Nontraditional factors such as air pollution that are pervasive in the urban environment may provide low-level synergism with other dominant factors in accelerating propensity for type 2 DM. Emerging data from both experimental and epidemiologic studies are beginning to provide insights into this association. There are a number of areas that would benefit from further studies and enable additional insights into the mechanisms by which environmental signals modulate susceptibility to metabolic disease. The effects of air-pollution exposure on β-cell function, counterregulatory hormones such as glucagon, and effects on insulinotropic mechanisms deserve further study. The effects of air pollution on hypothalamic mechanisms of appetite and satiety are areas of emerging interest, as it is entirely possible that air pollutants may modulate inflammation in key brain homeostatic centers. In addition, the effects on central autonomic control of peripheral inflammation may represent additional pathways by which environmental triggers may play an important role in determining peripheral inflammation. The societal costs of this link, if indeed true, are staggering given the ubiquitous nature of air pollution and the economic costs of obesity/DM-related complications. Given the already established nature of the links between air pollution and cardiovascular disease and regulations already in place, at least in countries like the U.S. and Europe, these additional links, if they can be established in additional large cohorts, would provide persuasive rationale for limiting exposure to air pollution.