Yun‐Chul Hong

BACKGROUND: Assessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic instrumental variables to assess causality, may help to identify the metabolic drivers of lung cancer. METHODS AND FINDINGS: We identified genetic instruments for potential metabolic risk factors and evaluated these in relation to risk using 29,266 lung cancer cases (including 11,273 adenocarcinomas, 7,426 squamous cell and 2,664 small cell cases) and 56,450 controls. The MR risk analysis suggested a causal effect of body mass index (BMI) on lung cancer risk for two of the three major histological subtypes, with evidence of a risk increase for squamous cell carcinoma (odds ratio (OR) [95% confidence interval (CI)] = 1.20 [1.01-1.43] and for small cell lung cancer (OR [95%CI] = 1.52 [1.15-2.00]) for each standard deviation (SD) increase in BMI [4.6 kg/m2]), but not for adenocarcinoma (OR [95%CI] = 0.93 [0.79-1.08]) (Pheterogeneity = 4.3x10-3). Additional analysis using a genetic instrument for BMI showed that each SD increase in BMI increased cigarette consumption by 1.27 cigarettes per day (P = 2.1x10-3), providing novel evidence that a genetic susceptibility to obesity influences smoking patterns. There was also evidence that low-density lipoprotein cholesterol was inversely associated with lung cancer overall risk (OR [95%CI] = 0.90 [0.84-0.97] per SD of 38 mg/dl), while fasting insulin was positively associated (OR [95%CI] = 1.63 [1.25-2.13] per SD of 44.4 pmol/l). Sensitivity analyses including a weighted-median approach and MR-Egger test did not detect other pleiotropic effects biasing the main results. CONCLUSIONS: Our results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma. The latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior.

BACKGROUND: Although the effect of air pollution on various diseases has been extensively investigated, few studies have examined its effect on depression. OBJECTIVES: We investigated the effect of air pollution on symptoms of depression in an elderly population. METHODS: We enrolled 537 participants in the study who regularly visited a community center for the elderly located in Seoul, Korea. The Korean version of the Geriatric Depression Scale-Short Form (SGDS-K) was used to evaluate depressive symptomatology during a 3-year follow-up study. We associated ambient air pollutants with SGDS-K results using generalized estimating equations (GEE). We also conducted a factor analysis with items on the SGDS-K to determine which symptoms were associated with air pollution. RESULTS: SGDS-K scores were positively associated with interquartile range (IQR) increases in the 3-day moving average concentration of particulate matter with an aerodynamic diameter ≤ 10 μm (PM10) [17.0% increase in SGDS-K score, 95% confidence interval (CI): 4.9%, 30.5%], the 0-7 day moving average of nitrogen dioxide [NO2; 32.8% (95% CI: 12.6%, 56.6%)], and the 3-day moving average of ozone [O3; 43.7% (95% CI: 11.5%, 85.2%)]. For these three pollutants, factor analysis showed that air pollution was more strongly associated with emotional symptoms such as feeling happy and satisfied than with somatic or affective symptoms. CONCLUSIONS: Our study suggests that increases in PM10, NO2, and O3 may increase depressive symptoms among the elderly. Of the symptoms evaluated, ambient air pollution was most strongly associated with emotional symptoms.

The relationship between stroke and air pollution has not been adequately studied. We conducted a time-series study to examine the evidence of an association between air pollutants and stroke over 4 years (January 1995-December 1998) in Seoul, Korea. We used a generalized additive model to regress daily stroke death counts for each pollutant, controlling for seasonal and long-term trends and meteorologic influences, such as temperature, relative humidity, and barometric pressure. We observed an estimated increase of 1.5% [95% confidence interval (CI), 1.3-1.8%] and 2.9% (95% CI, 0.3-5.5%) in stroke mortality for each interquartile range increase in particulate matter < 10 microm aerodynamic diameter (PM(10)) and ozone concentrations in the same day. Stroke mortality also increased 3.1% (95% CI, 1.1-5.1%) for nitrogen dioxide, 2.9% (95% CI, 0.8-5.0%) for sulfur dioxide, and 4.1% (95% CI, 1.1-7.2%) for carbon monoxide in a 2-day lag for each interquartile range increase in single-pollutant models. When we examined the associations among PM(10) levels stratified by the level of gaseous pollutants and vice versa, we found that these pollutants are interactive with respect to their effects on the risk of stroke mortality. We also observed that the effects of PM(10) on stroke mortality differ significantly in subgroups by age and sex. We conclude that PM(10) and gaseous pollutants are significant risk factors for acute stroke death and that the elderly and women are more susceptible to the effect of particulate pollutants.