The importance of social components of health has been emphasized both in epidemiology and public health. This paper highlights the significant impact of social components on health outcomes in a novel way. Introducing the concept of sociomarkers, which are measurable indicators of social conditions in which a patient is embedded, we employed a machine learning approach that uses both biomarkers and sociomarkers to identify asthma patients at risk of a hospital revisit after an initial visit with an accuracy of 66%. The analysis has been performed over an integrated dataset consisting of individual-level patient information such as gender, race, insurance type, and age, along with ZIP code-level sociomarkers such as poverty level, blight prevalence, and housing quality. Using this uniquely integrated database, we then compare the traditional biomarker-based risk model and the sociomarker-based risk model. A biomarker-based predictive model yields an accuracy of 65% and the sociomarker-based model predicts with an accuracy of 61%. Without knowing specific symptom-related features, the sociomarker-based model can correctly predict two out of three patients at risk. We systematically show that sociomarkers play an important role in predicting health outcomes at the individual level in pediatric asthma cases. Additionally, by merging multiple data sources with detailed neighborhood-level data, we directly measure the importance of residential conditions for predicting individual health outcomes.

Association analysis using logistic regression analysis showed that +82466C>T and haplotypes 1(CC) and 2(CT) were associated with the development of asthma (p=0.01-0.04). The frequency of PPARG-ht2 was significantly lower in the patients with asthma compared to the normal controls in codominant and dominant models (p=0.01, p(corr)=0.03 and p=0.02, p(corr)=0.03, respectively). Conversely, the frequency of PPARG-ht1 was significantly higher in the patients with asthma compared to the normal controls in the codominant model [p=0.04, OR: 1.27 (1.01-1.6)]. In addition, the rare allele frequency of +82466C>T was significantly lower in patients with asthma in comparison to normal controls in the codominant model (OR: 0.78, p=0.04). Thus, polymorphism of the PPARG gene may be linked to an increased risk of asthma development.

Colony-stimulating factor 1 receptor (CSF1R) is expressed in monocytes/macrophages and dendritic cells. These cells play important roles in the innate immune response, which is regarded as an important aspect of asthma development. Genetic alterations in the CSF1R gene may contribute to the development of asthma. We investigated whether CSF1R gene polymorphisms were associated with the risk of asthma. Through direct DNA sequencing of the CSF1R gene, we identified 28 single nucleotide polymorphisms (SNPs) and genotyped them in 303 normal controls and 498 asthmatic patients. Expression of CSF1R protein and mRNA were measured on CD14-positive monocytes and neutrophils in peripheral blood of asthmatic patients using flow cytometry and real-time PCR. Among the 28 polymorphisms, two intronic polymorphism (+20511C>T and +22693T>C) were associated with the risk of asthma by logistic regression analysis. The frequencies of the minor allele at CSF1R +20511C>T and +22693T>C were higher in asthmatic subjects than in normal controls (4.6 vs. 7.7%, p = 0.001 in co-dominant and dominant models; 16.4 vs. 25.8%, p = 0.0006 in a recessive model). CSF1R mRNA levels in neutrophils of the asthmatic patients having the +22693CC allele were higher than in those having the +22693TT allele (p = 0.026). Asthmatic patients with the +22693CC allele also showed significantly higher CSF1R expression on CD14-positive monocytes and neutrophils than did those with the +22693TT allele (p = 0.045 and p = 0.044). The +20511C>T SNP had no association with CSF1R mRNA or protein expression. In conclusion, the minor allele at CSF1R +22693T>C may have a susceptibility effect in the development of asthma, via increased CSF1R protein and mRNA expression in inflammatory cells.Electronic supplementary materialThe online version of this article (doi:10.1007/s00439-010-0850-3) contains supplementary material, which is available to authorized users.