Sources of particulate-matter air pollution and its oxidative potential in Europe.

Particulate matter is a component of ambient air pollution that has been linked to millions of annual premature deaths globally1-3. Assessments of the chronic and acute effects of particulate matter on human health tend to be based on mass concentration, with particle size and composition also thought to play a part4. Oxidative potential has been suggested to be one of the many possible drivers of the acute health effects of particulate matter, but the link remains uncertain5-8. Studies investigating the particulate-matter components that manifest an oxidative activity have yielded conflicting results7. In consequence, there is still much to be learned about the sources of particulate matter that may control the oxidative potential concentration7. Here we use field observations and air-quality modelling to quantify the major primary and secondary sources of particulate matter and of oxidative potential in Europe. We find that secondary inorganic components, crustal material and secondary biogenic organic aerosols control the mass concentration of particulate matter. By contrast, oxidative potential concentration is associated mostly with anthropogenic sources, in particular with fine-mode secondary organic aerosols largely from residential biomass burning and coarse-mode metals from vehicular non-exhaust emissions. Our results suggest that mitigation strategies aimed at reducing the mass concentrations of particulate matter alone may not reduce the oxidative potential concentration. If the oxidative potential can be linked to major health impacts, it may be more effective to control specific sources of particulate matter rather than overall particulate mass.

Longitudinal metabolite and protein trajectories prior to diabetes mellitus diagnosis in Danish blood donors: a nested case-control study.

AIMS/HYPOTHESIS: Metabolic risk factors and plasma biomarkers for diabetes have previously been shown to change prior to a clinical diabetes diagnosis. However, these markers only cover a small subset of molecular biomarkers linked to the disease. In this study, we aimed to profile a more comprehensive set of molecular biomarkers and explore their temporal association with incident diabetes. METHODS: We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose. RESULTS: We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone. CONCLUSIONS/INTERPRETATION: Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models.

Exploring TNFi drug-levels and anti-drug antibodies during tapering among patients with inflammatory arthritis: secondary analyses from the randomised BIODOPT trial.

To evaluate tumour necrosis factor inhibitor (TNFi) drug-levels and presence of anti-drug antibodies (ADAb) in patients with inflammatory arthritis who taper TNFi compared to TNFi continuation. Patients with rheumatoid arthritis, psoriatic arthritis, or axial spondyloarthritis on stable TNFi dose and in low disease activity ≥ 12 months were randomised (2:1) to disease activity-guided tapering or control. Blood samples at baseline, 12- and 18-months were evaluated for TNFi drug-levels and ADAb. In total, 129 patients were randomised to tapering (n = 88) or control (n = 41). Between baseline and month 18, a significant shift in TNFi drug-levels were observed in the tapering group resulting in fewer patients with high drug-levels (change: - 14% [95% CI - 27 to - 1%]) and more with low drug-levels (change: 18% [95% CI 5-31%]). Disease activity was equivalent between groups at 18 months, mean difference: RA - 0.06 (95% CI - 0.44 to 0.33), PsA 0.03 (95% CI - 0.36 to 0.42), and axSpA 0.16 (- 0.17 to 0.49), equivalence margins ± 0.5 disease activity points. ADAb were detected in eight patients, all from the tapering group. TNFi drug-level category or ADAb were not predictive for achieving successful tapering at 18 months. TNFi drug-levels decreased during tapering which indicate adherence to the tapering algorithm. Despite the difference in TNFi drug-levels at 18 months, disease activity remained equivalent, and only few tapering patients had detectable ADAb. These data do not support using TNFi drug-level and/or ADAb to guide the tapering decision but future research with larger trials is needed.Trial registration: EudraCT: 2017-001970-41, December 21, 2017.

A large cohort study of the effects of Lewis, ABO, 13 other blood groups, and secretor status on COVID-19 susceptibility, severity, and long COVID-19.

BACKGROUND: Previous studies have reported Blood type O to confer a lower risk of SARS-CoV-2 infection, while secretor status and other blood groups have been suspected to have a similar effect as well. STUDY DESIGN AND METHODS: To determine whether any other blood groups influence testing positive for SARS-CoV-2, COVID-19 severity, or prolonged COVID-19, we used a large cohort of 650,156 Danish blood donors with varying available data for secretor status and blood groups ABO, Rh, Colton, Duffy, Diego, Dombrock, Kell, Kidd, Knops, Lewis, Lutheran, MNS, P1PK, Vel, and Yt. Of these, 36,068 tested positive for SARS-CoV-2 whereas 614,088 tested negative between 2020-02-17 and 2021-08-04. Associations between infection and blood groups were assessed using logistic regression models with sex and age as covariates. RESULTS: The Lewis blood group antigen Lea displayed strongly reduced SARS-CoV-2 susceptibility OR 0.85 CI[0.79-0.93] p < .001. Compared to blood type O, the blood types B, A, and AB were found more susceptible toward infection with ORs 1.1 CI[1.06-1.14] p < .001, 1.17 CI[1.14-1.2] p < .001, and 1.2 CI[1.14-1.26] p < .001, respectively. No susceptibility associations were found for the other 13 blood groups investigated. There was no association between any blood groups and COVID-19 hospitalization or long COVID-19. No secretor status associations were found. DISCUSSION: This study uncovers a new association to reduced SARS-CoV-2 susceptibility for Lewis type Lea and confirms the previous link to blood group O. The new association to Lea could be explained by a link between mucosal microbiome and SARS-CoV-2.

Genetic prediction of 33 blood group phenotypes using an existing genotype dataset.

BACKGROUND: Accurate blood type data are essential for blood bank management, but due to costs, few of 43 blood group systems are routinely determined in Danish blood banks. However, a more comprehensive dataset of blood types is useful in scenarios such as rare blood type allocation. We aimed to investigate the viability and accuracy of predicting blood types by leveraging an existing dataset of imputed genotypes for two cohorts of approximately 90,000 each (Danish Blood Donor Study and Copenhagen Biobank) and present a more comprehensive overview of blood types for our Danish donor cohort. STUDY DESIGN AND METHODS: Blood types were predicted from genome array data using known variant determinants. Prediction accuracy was confirmed by comparing with preexisting serological blood types. The Vel blood group was used to test the viability of using genetic prediction to narrow down the list of candidate donors with rare blood types. RESULTS: Predicted phenotypes showed a high balanced accuracy >99.5% in most cases: A, B, C/c, Coa /Cob , Doa /Dob , E/e, Jka /Jkb , Kna /Knb , Kpa /Kpb , M/N, S/s, Sda , Se, and Yta /Ytb , while some performed slightly worse: Fya /Fyb , K/k, Lua /Lub , and Vel ~99%-98% and CW and P1 ~96%. Genetic prediction identified 70 potential Vel negatives in our cohort, 64 of whom were confirmed correct using polymerase chain reaction (negative predictive value: 91.5%). DISCUSSION: High genetic prediction accuracy in most blood groups demonstrated the viability of generating blood types using preexisting genotype data at no cost and successfully narrowed the pool of potential individuals with the rare Vel-negative phenotype from 180,000 to 70.

Multiomics analysis of rheumatoid arthritis yields sequence variants that have large effects on risk of the seropositive subset.

OBJECTIVES: To find causal genes for rheumatoid arthritis (RA) and its seropositive (RF and/or ACPA positive) and seronegative subsets. METHODS: We performed a genome-wide association study (GWAS) of 31 313 RA cases (68% seropositive) and ~1 million controls from Northwestern Europe. We searched for causal genes outside the HLA-locus through effect on coding, mRNA expression in several tissues and/or levels of plasma proteins (SomaScan) and did network analysis (Qiagen). RESULTS: We found 25 sequence variants for RA overall, 33 for seropositive and 2 for seronegative RA, altogether 37 sequence variants at 34 non-HLA loci, of which 15 are novel. Genomic, transcriptomic and proteomic analysis of these yielded 25 causal genes in seropositive RA and additional two overall. Most encode proteins in the network of interferon-alpha/beta and IL-12/23 that signal through the JAK/STAT-pathway. Highlighting those with largest effect on seropositive RA, a rare missense variant in STAT4 (rs140675301-A) that is independent of reported non-coding STAT4-variants, increases the risk of seropositive RA 2.27-fold (p=2.1×10-9), more than the rs2476601-A missense variant in PTPN22 (OR=1.59, p=1.3×10-160). STAT4 rs140675301-A replaces hydrophilic glutamic acid with hydrophobic valine (Glu128Val) in a conserved, surface-exposed loop. A stop-mutation (rs76428106-C) in FLT3 increases seropositive RA risk (OR=1.35, p=6.6×10-11). Independent missense variants in TYK2 (rs34536443-C, rs12720356-C, rs35018800-A, latter two novel) associate with decreased risk of seropositive RA (ORs=0.63-0.87, p=10-9-10-27) and decreased plasma levels of interferon-alpha/beta receptor 1 that signals through TYK2/JAK1/STAT4. CONCLUSION: Sequence variants pointing to causal genes in the JAK/STAT pathway have largest effect on seropositive RA, while associations with seronegative RA remain scarce.

Hyperhidrosis and human leucocyte antigens in the Danish Blood Donor Study.

Familial clustering of the skin disease primary hyperhidrosis suggests a genetic component to the disease. The human leucocyte antigen (HLA) is implicated in a range of diseases, including many comorbidities to hyperhidrosis. No study has investigated whether the HLA genes are involved in the pathogenesis of hyperhidrosis. We, therefore, compared HLA alleles in individuals with and without hyperhidrosis in this study of 65 000 blood donors. In this retrospective cohort study, we retrieved information on individuals with and without hyperhidrosis using self-reported questionnaires, the Danish National Patient Registry and the Danish National Prescription Registry on participants recruited to the Danish Blood Donor Study between 2010 and 2019. Association tests using logistic regression were conducted for each HLA allele corrected for sex, age, body mass index, smoking and principal components. Overall, 145 of 65 795 (0.2%) participants had hospital diagnosed hyperhidrosis. Similarly, 1379 of 15 530 (8.9%) participants had moderate-severe self-reported hyperhidrosis, of whom 447 (2.9%) had severe self-reported hyperhidrosis. Altogether, 28 participants had both hospital diagnosed and moderate-severe self-reported hyperhidrosis. Severe self-reported hyperhidrosis was associated with HLA-A*80:01 (adjusted odds ratio 26.97; 95% confidence interval 5.32-136.70; n = 7; P < .001). Moderate-severe self-reported hyperhidrosis and hospital diagnosed hyperhidrosis were not associated with any HLA. The association between hyperhidrosis and HLA-A*80:01 was based on a very small number of cases and not replicated in other patient subsets, and therefore likely a chance finding. Thus, this study suggests that genes other than the HLA are involved in the pathogenesis of hyperhidrosis.

Singlet Oxygen Seasonality in Aqueous PM10 is Driven by Biomass Burning and Anthropogenic Secondary Organic Aerosol.

The first excited state of molecular oxygen is singlet-state oxygen (1O2), formed by indirect photochemistry of chromophoric organic matter. To determine whether 1O2 can be a competitive atmospheric oxidant, we must first quantify its production in organic aerosols (OA). Here, we report the spatiotemporal distribution of 1O2 over a 1-year dataset of PM10 extracts at two locations in Switzerland, representing a rural and suburban site. Using a chemical probe technique, we measured 1O2 steady-state concentrations with a seasonality over an order of magnitude peaking in wintertime at 4.59 ± 0.01 × 10-13 M and with a quantum yield of up to 2%. Next, we identified biomass burning and anthropogenic secondary OA (SOA) as the drivers for 1O2 formation in the PM10 aqueous extracts using source apportionment data. Importantly, the quantity, the amount of brown carbon present in PM10, and the quality, the chemical composition of the brown carbon present, influence the concentration of 1O2 sensitized in each extract. Anthropogenic SOA in the extracts were 4 times more efficient in sensitizing 1O2 than primary biomass burning aerosols. Last, we developed an empirical fit to estimate 1O2 concentrations based on PM10 components, unlocking the ability to estimate 1O2 from existing source apportionment data. Overall, 1O2 is likely a competitive photo-oxidant in PM10 since 1O2 is sensitized by ubiquitous biomass burning OA and anthropogenic SOA.

Real-Time Source Apportionment of Organic Aerosols in Three European Cities.

97% of the urban population in the EU in 2019 were exposed to an annual fine particulate matter level higher than the World Health Organization (WHO) guidelines (5 µg/m3). Organic aerosol (OA) is one of the major air pollutants, and the knowledge of its sources is crucial for designing cost-effective mitigation strategies. Positive matrix factorization (PMF) on aerosol mass spectrometer (AMS) or aerosol chemical speciation monitor (ACSM) data is the most common method for source apportionment (SA) analysis on ambient OA. However, conventional PMF requires extensive human labor, preventing the implementation of SA for routine monitoring applications. This study proposes the source finder real-time (SoFi RT, Datalystica Ltd.) approach for efficient retrieval of OA sources. The results generated by SoFi RT agree remarkably well with the conventional rolling PMF results regarding factor profiles, time series, diurnal patterns, and yearly relative contributions of OA factor on three year-long ACSM data sets collected in Athens, Paris, and Zurich. Although the initialization of SoFi RT requires a priori knowledge of OA sources (i.e., the approximate number of factors and relevant factor profiles) for the sampling site, this technique minimizes user interactions. Eventually, it could provide up-to-date trustable information on timescales useful to policymakers and air quality modelers.

Nontarget Screening Exhibits a Seasonal Cycle of PM2.5 Organic Aerosol Composition in Beijing.

The molecular composition of atmospheric particulate matter (PM) in the urban environment is complex, and it remains a challenge to identify its sources and formation pathways. Here, we report the seasonal variation of the molecular composition of organic aerosols (OA), based on 172 PM2.5 filter samples collected in Beijing, China, from February 2018 to March 2019. We applied a hierarchical cluster analysis (HCA) on a large nontarget-screening data set and found a strong seasonal difference in the OA chemical composition. Molecular fingerprints of the major compound clusters exhibit a unique molecular pattern in the Van Krevelen-space. We found that summer OA in Beijing features a higher degree of oxidation and a higher proportion of organosulfates (OSs) in comparison to OA during wintertime, which exhibits a high contribution from (nitro-)aromatic compounds. OSs appeared with a high intensity in summer-haze conditions, indicating the importance of anthropogenic enhancement of secondary OA in summer Beijing. Furthermore, we quantified the contribution of the four main compound clusters to total OA using surrogate standards. With this approach, we are able to explain a small fraction of the OA (∼11-14%) monitored by the Time-of-Flight Aerosol Chemical Speciation Monitor (ToF-ACSM). However, we observe a strong correlation between the sum of the quantified clusters and OA measured by the ToF-ACSM, indicating that the identified clusters represent the major variability of OA seasonal cycles. This study highlights the potential of using nontarget screening in combination with HCA for gaining a better understanding of the molecular composition and the origin of OA in the urban environment.

The health-related quality of life in hyperhidrosis and co-morbidities.

PURPOSE: Hyperhidrosis has been associated with a reduced health-related quality of life (HRQoL). The role of common confounding factors of this association such as stress and socioeconomic status, however, remain largely unexplored, and may affect the management strategy for hyperhidrosis. Therefore, the study objective was to compare the HRQoL in individuals with and without hyperhidrosis while adjusting for confounders. METHODS: In this retrospective cohort study, data on the HRQoL measured by the short-form-12 questionnaire and self-reported hyperhidrosis were collected from the Danish Blood Donor Study-cohort. Data on international classification of disease-10 codes and redeemed prescriptions were collected from nationwide registries. Linear regression investigated the association between hyperhidrosis and HRQoL. RESULTS: Total 2794 (9.1%) of 30,808 blood donors had self-reported hyperhidrosis and 284 (0.2%) of 122,225 had hospital diagnosed hyperhidrosis. Self-reported hyperhidrosis was associated with a reduced mental HRQoL (adjusted beta coefficient - 1.10; 95% confidence interval - 1.37, - 0.82; p < 0.001) and physical HRQoL (adjusted beta coefficient - 0.90; 95% confidence interval - 1.09, - 0.70; p < 0.001). Hospital diagnosed hyperhidrosis was associated with a reduced mental HRQoL (adjusted beta coefficient - 0.91; 95% confidence interval - 1.82, - 0.04; p = 0.049). CONCLUSION: Hyperhidrosis is associated with a reduced HRQoL, independently of confounders or mode of diagnosis. This supports an approach primarily targeting hyperhidrosis.

Genetic insight into sick sinus syndrome.

AIMS: The aim of this study was to use human genetics to investigate the pathogenesis of sick sinus syndrome (SSS) and the role of risk factors in its development. METHODS AND RESULTS: We performed a genome-wide association study of 6469 SSS cases and 1 000 187 controls from deCODE genetics, the Copenhagen Hospital Biobank, UK Biobank, and the HUNT study. Variants at six loci associated with SSS, a reported missense variant in MYH6, known atrial fibrillation (AF)/electrocardiogram variants at PITX2, ZFHX3, TTN/CCDC141, and SCN10A and a low-frequency (MAF = 1.1-1.8%) missense variant, p.Gly62Cys in KRT8 encoding the intermediate filament protein keratin 8. A full genotypic model best described the p.Gly62Cys association (P = 1.6 × 10-20), with an odds ratio (OR) of 1.44 for heterozygotes and a disproportionally large OR of 13.99 for homozygotes. All the SSS variants increased the risk of pacemaker implantation. Their association with AF varied and p.Gly62Cys was the only variant not associating with any other arrhythmia or cardiovascular disease. We tested 17 exposure phenotypes in polygenic score (PGS) and Mendelian randomization analyses. Only two associated with the risk of SSS in Mendelian randomization, AF, and lower heart rate, suggesting causality. Powerful PGS analyses provided convincing evidence against causal associations for body mass index, cholesterol, triglycerides, and type 2 diabetes (P > 0.05). CONCLUSION: We report the associations of variants at six loci with SSS, including a missense variant in KRT8 that confers high risk in homozygotes and points to a mechanism specific to SSS development. Mendelian randomization supports a causal role for AF in the development of SSS.

Genetic insight into sick sinus syndrome.

AIMS: The aim of this study was to use human genetics to investigate the pathogenesis of sick sinus syndrome (SSS) and the role of risk factors in its development. METHODS AND RESULTS: We performed a genome-wide association study of 6469 SSS cases and 1 000 187 controls from deCODE genetics, the Copenhagen Hospital Biobank, UK Biobank, and the HUNT study. Variants at six loci associated with SSS, a reported missense variant in MYH6, known atrial fibrillation (AF)/electrocardiogram variants at PITX2, ZFHX3, TTN/CCDC141, and SCN10A and a low-frequency (MAF = 1.1-1.8%) missense variant, p.Gly62Cys in KRT8 encoding the intermediate filament protein keratin 8. A full genotypic model best described the p.Gly62Cys association (P = 1.6 × 10-20), with an odds ratio (OR) of 1.44 for heterozygotes and a disproportionally large OR of 13.99 for homozygotes. All the SSS variants increased the risk of pacemaker implantation. Their association with AF varied and p.Gly62Cys was the only variant not associating with any other arrhythmia or cardiovascular disease. We tested 17 exposure phenotypes in polygenic score (PGS) and Mendelian randomization analyses. Only two associated with the risk of SSS in Mendelian randomization, AF, and lower heart rate, suggesting causality. Powerful PGS analyses provided convincing evidence against causal associations for body mass index, cholesterol, triglycerides, and type 2 diabetes (P > 0.05). CONCLUSION: We report the associations of variants at six loci with SSS, including a missense variant in KRT8 that confers high risk in homozygotes and points to a mechanism specific to SSS development. Mendelian randomization supports a causal role for AF in the development of SSS.

The impact of health-related quality of life and depressive symptoms on blood donor career-Results from the Danish blood donor study.

BACKGROUND: Blood donors report better health-related quality of life (HRQL) than non-donors. Likewise, donors reporting good health are less likely to stop donating and have a higher donation frequency. This is evidence of the healthy donor effect (HDE). This study is the first to investigate the impact of HRQL and depressive symptoms on subsequent donor career. STUDY DESIGN AND METHODS: Prospective cohort study includes 102,065 participants from the Danish Blood Donor Study applying the 12-item short-form health survey (SF-12) measuring a mental (MCS) and a physical component score (PCS) and the Major Depression Inventory (MDI). Poisson and Cox regression models were used to assess the effect of SF-12 and MDI scores on donation frequency and donor cessation. Higher MCS/PCS scores indicate good HRQL, while higher MDI score indicates higher experience of depressive symptoms. RESULTS: For both sexes, MCS was positively correlated with donation frequency for up to 5 years, and similarly for PCS among women. A negative correlation between MDI score and donation frequency in the year following assessment was observed only among men. No correlation was observed among women. An increase in both MCS and PCS was associated with a lower risk of donation cessation in both sexes, while an increase in MDI score was only associated with an increased risk of donation cessation in men. CONCLUSION: MCS, PCS, and MDI score affect donor career. Thus, adjusting for donation frequency may reduce HDE-bias in donor health research. However, because of the small effect sizes, other ways of quantifying HDE may be beneficial.

Is reducing new particle formation a plausible solution to mitigate particulate air pollution in Beijing and other Chinese megacities?

Atmospheric gas-to-particle conversion is a crucial or even dominant contributor to haze formation in Chinese megacities in terms of aerosol number, surface area and mass. Based on our comprehensive observations in Beijing during 15 January 2018-31 March 2019, we are able to show that 80-90% of the aerosol mass (PM2.5) was formed via atmospheric reactions during the haze days and over 65% of the number concentration of haze particles resulted from new particle formation (NPF). Furthermore, the haze formation was faster when the subsequent growth of newly formed particles was enhanced. Our findings suggest that in practice almost all present-day haze episodes originate from NPF, mainly since the direct emission of primary particles in Beijing has considerably decreased during recent years. We also show that reducing the subsequent growth rate of freshly formed particles by a factor of 3-5 would delay the buildup of haze episodes by 1-3 days. Actually, this delay would decrease the length of each haze episode, so that the number of annual haze days could be approximately halved. Such improvement in air quality can be achieved with targeted reduction of gas-phase precursors for NPF, mainly dimethyl amine and ammonia, and further reductions of SO2 emissions. Furthermore, reduction of anthropogenic organic and inorganic precursor emissions would slow down the growth rate of newly-formed particles and consequently reduce the haze formation.

Epidemiology of Hyperhidrosis in Danish Blood Donors.

The risk factors and disease implications of hyper-hidrosis are unknown. The objectives of this retrospective cohort study were to estimate the prevalence of hyperhidrosis and to compare demographic, life-style, and socioeconomic parameters in blood donors with and without self-reported or hospital-diagnosed hyperhidrosis. The study included blood donors from the Danish Blood Donor Study for the period 2010-2019. Registry data were collected from Statistics Denmark. Overall, 2,794 of 30,808 blood donors (9.07%; 95% confidence interval (95% CI) 8.75-9.40) had self- reported hyperhidrosis and 284 of 122,225 (0.23%; 95% CI 0.21-0.26) had hospital-diagnosed hyperhidrosis. Self-reported hyperhidrosis was associated with smoking (odds ratio (OR) 1.17; 95% CI 1.05-1.31), overweight (OR 1.72; 95% CI 1.58-1.87), "unemployed" (OR 1.60; 95% CI 1.24-2.08), "short education" (OR 0.76; 95% CI 0.64-0.90), and lower income (beta-coefficient -26,121; 95% CI -37,931, -14,311). Hospital-diagnosed hyperhidrosis did not differ from controls. Thus, self-reported hyperhidrosis was associated with potential hyperhidrosis risk factors (smoking, overweight) and disease implications (unemployment, low education level and income).

High secondary aerosol contribution to particulate pollution during haze events in China.

Rapid industrialization and urbanization in developing countries has led to an increase in air pollution, along a similar trajectory to that previously experienced by the developed nations. In China, particulate pollution is a serious environmental problem that is influencing air quality, regional and global climates, and human health. In response to the extremely severe and persistent haze pollution experienced by about 800 million people during the first quarter of 2013 (refs 4, 5), the Chinese State Council announced its aim to reduce concentrations of PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 micrometres) by up to 25 per cent relative to 2012 levels by 2017 (ref. 6). Such efforts however require elucidation of the factors governing the abundance and composition of PM2.5, which remain poorly constrained in China. Here we combine a comprehensive set of novel and state-of-the-art offline analytical approaches and statistical techniques to investigate the chemical nature and sources of particulate matter at urban locations in Beijing, Shanghai, Guangzhou and Xi'an during January 2013. We find that the severe haze pollution event was driven to a large extent by secondary aerosol formation, which contributed 30-77 per cent and 44-71 per cent (average for all four cities) of PM2.5 and of organic aerosol, respectively. On average, the contribution of secondary organic aerosol (SOA) and secondary inorganic aerosol (SIA) are found to be of similar importance (SOA/SIA ratios range from 0.6 to 1.4). Our results suggest that, in addition to mitigating primary particulate emissions, reducing the emissions of secondary aerosol precursors from, for example, fossil fuel combustion and biomass burning is likely to be important for controlling China's PM2.5 levels and for reducing the environmental, economic and health impacts resulting from particulate pollution.

Chemical characteristics and sources of water-soluble organic aerosol in southwest suburb of Beijing.

PM2.5 filter sampling and components measurement were conducted in autumn and winter from 2014 to 2015 at a suburban site (referred herein as "LLH site") located in the southwest of Beijing. The offline aerosol mass spectrometry (offline-AMS) analysis and positive matrix factorization (PMF) were applied for measurement and source apportionment of water-soluble organic aerosol (WSOA). Organic aerosol (OA) always dominated PM2.5 during the sampling period, especially in winter. WSOA pollution was serious during the polluted period both in autumn (31.1 µg/m3) and winter (31.9 µg/m3), while WSOA accounted for 54.4% of OA during the polluted period in autumn, much more than that (21.3%) in winter. The oxidation degree of WSOA at LLH site was at a high level (oxygen-to-carbon ratio, O/C=0.91) and secondary organic aerosol (SOA) contributed more mass ratio of WSOA than primary organic aerosol (POA) during the whole observation period. In winter, coal combustion OA (CCOA) was a stable source of OA and on average accounted for 25.1% of WSOA. In autumn, biomass burning OA (BBOA) from household combustion contributed 38.3% of WSOA during polluted period. In addition to oxygenated OA (OOA), aqueous-oxygenated OA (aq-OOA) was identified as an important factor of SOA. During heavy pollution period, the mass proportion of aq-OOA to WSOA increased significantly, implying the significant SOA formation through aqueous-phase process. The result of this study highlights the concentration on controlling the residential coal and biomass burning, as well as the research needs on aqueous chemistry in OA formation.

A Comprehensive Nontarget Analysis for the Molecular Reconstruction of Organic Aerosol Composition from Glacier Ice Cores.

Ice cores are climate archives suitable for the reconstruction of past atmospheric composition changes. Ice core analysis provides valuable insight into the chemical nature of aerosols and enables constraining emission inventories of primary emissions and of gas-phase precursors. Changes in the emissions of volatile organic compounds (VOCs) can affect formation rates and mechanisms as well as chemical composition of aerosols during the preindustrial era, key information for understanding aerosol climate effects. Here, we present an analytical method for the reconstruction of organic aerosol composition preserved in glacier ice cores. A solid-phase-extraction method, optimized toward oxidation products of biogenic VOCs, provides an enrichment factor of ∼200 and quantitative recovery for compounds of interest. We applied the preconcentration method on ice core samples from the high-alpine Fiescherhorn glacier (Swiss Alps), and used high-performance liquid chromatography coupled to high-resolution mass spectrometry as a sensitive detection method. We describe a nontarget analysis that screens for organic molecules in the ice core samples. We evaluate the atmospheric origin of the detected compounds in the ice by molecular-resolved comparison with airborne particulate matter samples from the nearby high-alpine research station Jungfraujoch. The presented method is able to shed light upon the history of the evolution of organic aerosol composition in the anthropocene, a research field in paleoclimatology with considerable potential.

Storage time of platelet concentrates and risk of a positive blood culture: a nationwide cohort study.

BACKGROUND: Concern of transfusion-transmitted bacterial infections has been the major hurdle to extend shelf life of platelet (PLT) concentrates. We aimed to investigate the association between storage time and risk of positive blood cultures at different times after transfusion. STUDY DESIGN AND METHODS: We performed a nationwide cohort study among PLT transfusion recipients in Denmark between 2010 and 2012, as recorded in the Scandinavian Donations and Transfusions (SCANDAT2) database. Linking with a nationwide database on blood cultures (MiBa), we compared the incidence of a positive blood culture among recipients of PLTs stored 6 to 7 days (old) to those receiving fresh PLTs (1-5 days), using Poisson regression models. We considered cumulative exposures in windows of 1, 3, 5, and 7 days. RESULTS: A total of 9776 patients received 66,101 PLT transfusions. The incidence rate ratio (IRR) of a positive blood culture the day after transfusion of at least one old PLT concentrate was 0.77 (95% confidence interval [CI], 0.54-1.09) compared to transfusion of fresh PLT concentrates. The incidence rate of a positive blood culture was lower the day after receiving one old compared to one fresh PLT concentrate (IRR, 0.57; 95% CI, 0.37-0.87). Three, 5, or 7 days after transfusion, storage time was not associated with the risk of a positive blood culture. CONCLUSION: Storage of buffy coat-derived PLT concentrates in PAS-C up to 7 days seems safe regarding the risk of a positive blood culture. If anything, transfusion of a single old PLT concentrate may decrease this risk the following day.