A predictive model for the development of chronic obstructive pulmonary disease
- Authors:
- Published online on: August 5, 2015 https://doi.org/10.3892/br.2015.503
- Pages: 853-863
Abstract
Introduction
Chronic obstructive pulmonary disease (COPD) is characterized by progressive airflow limitation, driven by an abnormal inflammatory response of the airways to inhaled particles and fumes (1). The disease is predicted to become the third most common cause of mortality and the fifth cause of disability in the world by 2020 (2). COPD represents a significant burden for the health care systems worldwide (3).
COPD is also causing an increasing problem in China. A survey conducted in 2007 of 20,245 participants in seven regions of China indicated that the prevalence of COPD in adults aged ≥40 years was 8.2% (4). However, numerous patients with COPD remain undiagnosed until the more advanced stages of the disease. A study by Professor Nanshan Zhong (5), the Chief of the Chinese Medicine Association, showed that the diagnosis was established only in 31% of the COPD patients. A number of population-based studies revealed that the disease was also under-diagnosed in other countries (6–8). In a study of Spanish patients (9), only 25% of smokers with COPD were previously known regarding the diagnosis. Additionally, <50% of patients with severe or extremely severe airflow obstruction were diagnosed (10). COPD is usually diagnosed in the later stage when significant lung function has already been lost, being asymptomatic in the early phase, and sometimes patients are not diagnosed until they are hospitalized for an acute exacerbation (11). However, the airway limitation is much more reversible in early COPD, as early detection and timely treatment can slow the destruction of lung function. Therefore, a predictive model for COPD development that could have a clinical utility is required. Previous studies (12,13) of COPD predictors identified certain risk factors, including age, smoking, forced expiratory volume in 1 sec (FEV1), low body weight and poor performance status, but a single determinant was not reliable to estimate the probability of COPD development, therefore, a full predictive model must be developed using comprehensive indicators.
In addition, the natural history of the development of the disease in smokers is highly variable, as only a minority of smokers (20%) appear to present airflow limitation, suggesting that besides smoking, COPD is partially genetically determined (14,15). Genes were evidenced to be associated with familial aggregation of COPD (16), and certain other twin studies have also indicated a genetic contribution to clinically relevant parameters on pulmonary function (17). Genome-wide association studies (GWAS) have identified certain susceptibility loci, but these are few in the Asian population (18,19). Consequently, we hypothesized that the abovementioned risk factors in combination with genetic markers would aid the prediction of COPD development prior to its onset.
The aim of the present study was to set up a predictive model for COPD development in a Chinese population. First, the candidate genes for the susceptibility to COPD were identified among 97 single-nucleotide polymorphisms (SNPs) of 46 genes. Second, a mathematical formula based on the clinical and demographic data recorded combined with SNP markers was produced.
Materials and methods
Part I
Study population of SNP identificationA total of 331 unrelated adult patients with COPD were recruited from the Department of Pulmonary Medicine of Shanghai Ruijin Hospital (Shanghai, China) between January 2012 and November 2013. COPD was diagnosed according to the criteria established by the National Heart, Lung and Blood Institute/World Health Organization Global Initiative for COPD (GOLD) (20). The entry criteria were as follows: Presence of relentlessly progressive symptoms, such as cough, productive sputum or breathlessness; age, ≥40 years; airflow limitation as indicated by FEV1/forced vital capacity (FVC) ≤70%; FEV1 reversibility following the inhalation of salbutamol <12% of the pre-bronchodilator FEV1 (MS-Body Diffusion; Jaeger GmbH, Würzburg, Germany); and no evidence of hereditary diseases or other respiratory diseases.
A total of 213 control healthy smokers were selected from a pool of healthy subjects who visited the General Health Checkup Center of Shanghai Ruijin Hospital in the same period. The enrollment criteria for the controls were as follows: Age ≥40 years, smoker, no known disease, no history of any disease and lung function was measured at baseline following the American Thoracic Society/European Respiratory Society standard procedure to confirm no evidence of airflow obstruction. All the cases and control subjects were Chinese. The study protocol was approved by the Medical Ethics Committee of Shanghai Ruijin Hospital and all the participants provided written informed consent.
DNA extraction and genotypingAccording to the results of previous GWAS, 97 candidate SNPs were chosen for genotyping (Table I). Their minor allele frequencies were >0.05 in the Chinese patients. A peripheral blood sample was obtained from each participant and DNA was isolated using QuickGene DNA Whole Blood kit (Fujifilm Life Science, Tokyo, Japan). Any sample with a DNA concentration <10 ng/µl was excluded and required another sample. The Mass-Array™ Technology platform of Sequenom, Inc., (San Diego, CA, USA) was used to perform genotyping. For quality control, two independent investigators interpreted the results and a random selection of 10% of all the samples was re-tested. Each of the SNPs in the control group was analyzed for the Hardy-Weinberg equilibrium (HWE), and SNPs were excluded from the analysis if they were out of HWE (P≤0.05). The χ2 test and unconditional logistic method were applied to compare the allele frequencies between the two groups, and logistic analysis was adjusted for age, gender and smoking. Frequencies were compared, respectively, using a P cut-off of 0.05 and the Bonferroni correction method for multiple testing in order to identify several SNPs in susceptibility to COPD. P<0.05 was considered to indicate a statistically significant difference.
Part II
Study population of predictive model-buildingIn total, 331 COPD patients and 351 control subjects were recruited from the Department of Pulmonary Medicine between January 2012 and December 2013. All the patients met the diagnostic criteria of GOLD and were ≥40 years. The control subjects were present with no evidence of airflow obstruction, aged ≥40 years, and were smokers or non-smokers. They had no hereditary diseases or other respiratory diseases.
SNP genotypingA peripheral blood sample was obtained from each participant and DNA was isolated using the same methods, as previously described. The SNPs identified in the susceptibility to COPD in part I were genotyped.
Documentation of dataIn addition to the SNP genotyping, demographic data, body mass index, history of respiratory infection in childhood, low birth weight (<2,500 g), environmental pollution (their place of residence and work environment), smoking history, family history of lung disease, and spirometry of these 682 subjects were recorded. The case group was defined as 1, the control group as 0; similarly, 1=male, 0=female; 1=respiratory infection in childhood, 0=no infection; 1=history of low birth weight, 0=non low birth weight; 1=environmental pollution, 0=no exposure; 1=smoking history, 0=non smoking; and 1=family history of lung disease, 0=no known family history. These risk factors were identified in association to COPD based on our previous epidemiology study (21). Genotyping results were also recorded using 0 or 1.
Predictive model-building methodsThe predictive model was constructed by means of logistic regression with a stepwise model-building approach, using an entry and exit criterion of P≤0.05. The variables included genetic polymorphisms verified according to the results of genotyping and clinical data of each participant recorded above. The goodness of fit, namely how closely the prediction reflected observed events, was determined by the Hosmer-Lemeshow test.
Statistical analysisData analyses were performed with the Statistical Package for the Social Science version 20.0 (SPSS, Inc., Chicago, IL, USA) and P<0.05 was considered to indicate a statistically significant difference. The two-sided Student's t-test was used for checking the significant differences in the clinical data between the cases and control subjects. The relative risk of the allelic gene was estimated as an odds ratio with a 95% confidence interval.
Results
Part I
Study population characteristicsThe study population characteristics are described in Table II. They were matched for gender and age. FEV1 predictive and FEV1/FVC of the case group decreased significantly compared to the control group (P<0.05).
Univariate analysis of each genotypeEight SNPs with a deviation from HWE in the controls were removed from the association analysis; rs361525, rs1042713, rs34829399, rs2853677, rs2571445, rs8192288, rs2066960 and rs2230054. Thirteen SNPs (rs1130866, rs56155294, rs10498230, rs2035901, rs3091244, rs511898, rs2869967, rs7583463, rs2276109, rs737693, rs9904270, rs4934 and rs6830970) were also eliminated for missing data of genotyping in ≥10% of samples. Finally, 76 of the 97 SNPs were included in the association analysis. The allele frequencies and the genotype distributions for these SNPs were compared between the patients and control healthy smokers. Several allelic genes of seven SNPs were found to be more frequent in the COPD patients compared to the control subjects. These were human hedgehog interacting protein (HHIP) (rs2353397 C allele) (P<0.0001), TNF-α (rs1800629 G allele) (P=0.0060), TGF-β1 (rs2241712 A allele) (P=0.0498), CRP (rs1205 C allele) (P=0.0030), IL-13 (rs20541 T allele) (P=0.0280), AGER (rs2070600 G allele) (P=0.0130) and PPT2 (rs10947233 G allele) (P=0.0060). These seven SNPs tended to be associated with COPD. Among these seven SNPs, following Bonferroni correction, rs2353397 (P<0.0001) was most strongly associated with the susceptibility to COPD (Table III).
Part II
Predictive model for COPDThe clinical data of the 331 COPD patients and 351 control subjects recruited for the second part of the study were recorded. Clinical variables recorded for the logistic regression model are presented in Table IV. The genotype of the seven SNPs was also recorded. Genetic variables that achieved significance in univariate analysis were defined as follows: CT=1 0, TT=0 0, CC=0 1 (rs2353397); GA=1 0, AA=0 0, GG=0 1 (rs2070600); GT=1 0, TT=0 0, GG=0 1 (rs10947233); GA=1 0, AA=0 0, GG=0 1 (rs1800629); AG=1 0, GG=0 0, AA=0 1 (rs2241712); CT=1 0, TT=0 0, CC=0 1 (rs1205); and TC=1 0, CC=0 0, TT=0 1 (rs20541). The different genotypes combined with the clinical data of the two groups were entered in the multivariate analysis, which was performed using the logistic regression model. Finally, the model was established using the following formula: (P-value for each variable in Table IV) COPD = 1/[1 + exp (−2.4933–1.2197 gender + 1.1842 respiratory infection in early life + 2.4350 low birth weight + 1.8524 smoking − 1.1978 rs2070600 + 2.0270 rs10947233 + 1.1913 rs10947233 + 0.6468 rs1800629 + 0.5272 rs2241712 + 0.4024 rs1205)] (when the value is >0.5). For example, if the value calculated using the formula above is >0.5 for an individual, it can be speculated that the patient is more likely to develop COPD prior to becoming symptomatic.
Validation of the modelThe Hosmer-Lemeshow test showed no significant deviation between the observed and predicted events, suggesting an excellent goodness of fit. Table V shows the results of the test (χ2=3.948, P=0.862). Data of gender, history of early life respiratory infection, low birth weight, smoking and SNPs identified by logistic regression of 30 COPD patients and 20 healthy controls were entered into the formula, and the values calculated were compared to the observed status. In total, 25 patients obtained values >0.5, and 17 healthy controls had values <0.5 (Table VI). The sensitivity was 83%, specificity was 85%, false negative was 16%, false positive was 15% and Youden index was 0.68.
Discussion
In the present case-control study of 682 participants whose pulmonary function spanned a broad spectrum, a predictive model for development of COPD with a modest sensitivity and specificity was constructed by incorporating demographic, clinical and genetic information, and the statistical model fitted well with the set of observations by the Hosmer-Lemeshow test. The study suggests that the mathematic formula may serve as a helpful tool to identify persons at risk for COPD prior to the onset of symptoms.
Screening for early disease is extremely important, as current medication can only relieve symptoms of COPD, and it has little effect on the delay of its natural progression. Only the person at risk is prospectively identified. Therefore, whether preventive measures can be taken to provide important opportunities for curbing the progressive nature of the disease requires confirmation. Early detection of COPD and intervention for smoking cessation is suggested to delay lung function decline, to reduce the burden of symptoms and to improve the patient quality of life (22,23). However, initially there are no evident symptoms, which becomes a barrier to detection. Therefore, determining how COPD can be detected in the early phase or prior to its onset is required. Given the low diagnostic rate in early phase, the risk assessment for development appears to be valuable. The accurate prediction of the course of airway inflammation in healthy smokers or non-smokers remains a significant challenge.
Thus far, certain studies have focused on identifying tools to diagnose COPD in its earliest stage, but to be exact, the patients had already presented more or less airway limitation at the time. These tools are not able to play a sufficient role in identifying the healthy subjects at high risk. For instance, as reviewed by Grouse (24), in the study of Bai among Chinese patients, low-dose computed tomography lung scanning diagnosed early COPD when only ~10% of the lung function was affected. Ley-Zaporozhan and Kauczor (25) made an early diagnosis by measuring the airway diameter and wall thickness. Fain et al (26) demonstrated presymptomatic detection of degraded pulmonary function in smokers using diffusion-weighted 3He magnetic resonance imaging. These studies have provided information, but a single variable appears to be rather weak to predict the probability of COPD development. A predictive model is required to estimate the risk prior to onset of the disease. The present model possibly aids to calculate the estimation.
Certain previous studies regarding prediction in the fields of COPD may be taken as examples, but they do not refer to the pathogenesis. Schembri et al (27) created a model to evaluate the risk of hospitalization and mortality in COPD patients. Castaldi et al (28) set up predictive models for FEV1 and the presence of severe COPD in α-1-antitrypsin deficiency, as this information could be used to inform treatment and monitoring decisions. Bacteria play a leading role in acute exacerbations of COPD. A simple prediction model developed by Lode et al (29) based on certain factors can identify patients at low risk for exacerbations with gram-negative enteric bacilli and Pseudomonas aeruginosa. To the best of our knowledge, a model for COPD development in Chinese patients has not been generated except for the present study.
The present mathematical formula aids in the comprehension of the risk of an individual for whether they smoke or not, as the model includes genetic data summarized from genotyping 76 SNPs in addition to demographic and clinical information. Genetic polymorphisms must be taken into consideration, as COPD is a result of an interaction of genetics and environment. The present case-control study verified that the rs2353397 C allele (HHIP), rs1800629 G allele (TNF-α), rs2241712 A allele (TGF-β1), rs1205 C allele (CRP), rs20541 T allele (IL-13), rs2070600 G allele (AGER) and rs10947233 G allele (PPT2) were the risk allelic genes for COPD in a Chinese population. The HHIP gene encodes a glycoprotein that is a critical regulator of the hedgehog signaling pathway. The pathway has been indicated in development, repair and cancer in multiple tissues (30). Several gene studies regarding TNF-α SNPs also identified that its promoter polymorphism was associated with chronic bronchitis or the extent of emphysematous changes, among which two were carried out in the Caucasian population (31,32) and two in the Japanese population (33,34). The TGF-β1 SNPs has been explored in the study by Su et al (35), which revealed that more COPD patients carried the −800A allele and fewer carried the −509T allele, but there were only 84 COPD and 97 controls who participated in the study. The IL-13 SNPs, rs2066960, rs20541 and rs1295685, were associated with the COPD risk and a lower baseline lung function in Caucasian patients based on the study by Beghé et al (36). The same SNPs as Beghé et al were chosen to analyze, but the present results only showed that rs20541 may be of significance in susceptibility in the Chinese population. Sunyer et al (37) assessed the association between CRP SNP (rs1205) and lung function, and identified that the TT homozygote in the CRP gene was associated with improved lung function. The present results identified that the TT genotype protects patients against COPD, which is similar to the study by Sunyer et al, as COPD is characterized by airflow limitation according to lung function. Based on these findings, further research is required to improve the understanding of the gene function in the pathogenesis of COPD. In all the predictive genetic variants that reached the levels of significance in the univariate analysis, five SNPs (rs2070600, rs10947233, rs1800629, rs2241712 and rs1205) were retained through the stepwise variable selection procedure and were incorporated into the final predictive model.
The present study had certain limitations. First, with a larger study sample size, the mathematical formula would have improved the prediction accuracy. Second, further validation in a much larger population is required. Third, although 97 SNPs were selected for the study of genetic susceptibility, further GWAS are required in the Chinese population in order to identify more associated loci, as it is likely that more genetic risk factors would enter the final model.
In conclusion, the present study has established a predictive model for COPD development in a Chinese population, but there remains room for improvement in predictive accuracy. Larger sample sizes for model development and validation will allow for the production of more powerful risk prediction tools.
Acknowledgements
The authors acknowledge the 11th Chinese National Five-Year Development Plan for support of the present study.
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