@article {9156, title = {Pooled Cohort Probability Score for Subclinical Airflow Obstruction.}, journal = {Ann Am Thorac Soc}, volume = {19}, year = {2022}, month = {2022 08}, pages = {1294-1304}, abstract = {

Early detection of chronic obstructive pulmonary disease (COPD) is a public health priority. Airflow obstruction is the single most important risk factor for adverse COPD outcomes, but spirometry is not routinely recommended for screening. To describe the burden of subclinical airflow obstruction (SAO) and to develop a probability score for SAO to inform potential detection and prevention programs. Lung function and clinical data were harmonized and pooled across nine U.S. general population cohorts. Adults with respiratory symptoms, inhaler use, or prior diagnosis of COPD or asthma were excluded. A probability score for prevalent SAO (forced expiratory volume in 1 second/forced vital capacity < 0.70) was developed via hierarchical group-lasso regularization from clinical variables in strata of sex and smoking status, and its discriminative accuracy for SAO was assessed in the pooled cohort as well as in an external validation cohort (NHANES [National Health and Nutrition Examination Survey] 2011-2012). Incident hospitalizations and deaths due to COPD (respiratory events) were defined by adjudication or administrative criteria in four of nine cohorts. Of 33,546 participants (mean age 52 yr, 54\% female, 44\% non-Hispanic White), 4,424 (13.2\%) had prevalent SAO. The incidence of respiratory events ( = 14,024) was threefold higher in participants with SAO versus those without (152 vs. 39 events/10,000 person-years). The probability score, which was based on six commonly available variables (age, sex, race and/or ethnicity, body mass index, smoking status, and smoking pack-years) was well calibrated and showed excellent discrimination in both the testing sample (C-statistic, 0.81; 95\% confidence interval [CI], 0.80-0.82) and in NHANES (C-statistic, 0.83; 95\% CI, 0.80-0.86). Among participants with predicted probabilities ⩾ 15\%, 3.2 would need to undergo spirometry to detect one case of SAO. Adults with SAO demonstrate excess respiratory hospitalization and mortality. A probability score for SAO using commonly available clinical risk factors may be suitable for targeting screening and primary prevention strategies.

}, keywords = {Adult, Female, Forced Expiratory Volume, Humans, Lung, Male, Middle Aged, Nutrition Surveys, Pulmonary Disease, Chronic Obstructive, Risk Factors, Spirometry, Vital Capacity}, issn = {2325-6621}, doi = {10.1513/AnnalsATS.202109-1020OC}, author = {Bhatt, Surya P and Balte, Pallavi P and Schwartz, Joseph E and Jaeger, Byron C and Cassano, Patricia A and Chaves, Paulo H and Couper, David and Jacobs, David R and Kalhan, Ravi and Kaplan, Robert and Lloyd-Jones, Donald and Newman, Anne B and O{\textquoteright}Connor, George and Sanders, Jason L and Smith, Benjamin M and Sun, Yifei and Umans, Jason G and White, Wendy B and Yende, Sachin and Oelsner, Elizabeth C} } @article {9702, title = {Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.}, journal = {Sci Rep}, volume = {14}, year = {2024}, month = {2024 May 30}, pages = {12436}, abstract = {

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model{\textquoteright}s performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1\% (SBP) and 14.3\% to 17.4\% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1\% (SBP) and 4.7 to 5\% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3\% (SBP) and 5.7\% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

}, keywords = {Blood Pressure, Female, Genetic Predisposition to Disease, Genetic Risk Score, Genome-Wide Association Study, Humans, Hypertension, Machine Learning, Male, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenotype, Risk Factors}, issn = {2045-2322}, doi = {10.1038/s41598-024-62945-9}, author = {Hrytsenko, Yana and Shea, Benjamin and Elgart, Michael and Kurniansyah, Nuzulul and Lyons, Genevieve and Morrison, Alanna C and Carson, April P and Haring, Bernhard and Mitchell, Braxton D and Psaty, Bruce M and Jaeger, Byron C and Gu, C Charles and Kooperberg, Charles and Levy, Daniel and Lloyd-Jones, Donald and Choi, Eunhee and Brody, Jennifer A and Smith, Jennifer A and Rotter, Jerome I and Moll, Matthew and Fornage, Myriam and Simon, Noah and Castaldi, Peter and Casanova, Ramon and Chung, Ren-Hua and Kaplan, Robert and Loos, Ruth J F and Kardia, Sharon L R and Rich, Stephen S and Redline, Susan and Kelly, Tanika and O{\textquoteright}Connor, Timothy and Zhao, Wei and Kim, Wonji and Guo, Xiuqing and Ida Chen, Yii-Der and Sofer, Tamar} } @article {9902, title = {Low Lung Function Is Associated with High Population Attributable Fraction for Cardiovascular Mortality.}, journal = {Ann Am Thorac Soc}, volume = {22}, year = {2025}, month = {2025 Mar}, pages = {359-366}, abstract = {

Chronic lung diseases are associated with increased risk of mortality due to coronary heart disease (CHD). Nonetheless, the population attributable fraction (PAF) of lung function impairment relative to other established cardiovascular risk factors is unclear. To evaluate the PAF of low lung function for CHD mortality We harmonized and pooled lung function and clinical data across eight U.S. general population cohorts. Impaired lung function was defined as forced expiratory volume in 1 second (FEV) and/or forced vital capacity <= 95\% predicted on baseline spirometry. The association between CHD mortality and risk factors was assessed using cause-specific proportional hazards and Fine-Gray proportional subdistribution hazard models, treating non-CHD mortality as a competing risk. Models were adjusted for lung function as well as age, sex, race/ethnicity, educational attainment, body mass index, smoking status, pack-years of smoking, diabetes mellitus, high-density lipoprotein, and high low-density lipoprotein (>=130 mg/dl). PAF was calculated as the relative change in the average absolute risk of 10-year CHD mortality by elimination of lung function lower than 95\% predicted. Among 35,143 participants, 1,844 of 13,174 (14.0\%) deaths were due to CHD. Compared with percentage predicted FEV (FEVpp) > 95\%, the subdistribution adjusted hazard ratio for low FEVpp was 1.30 (95\% confidence interval, 1.18-1.44). The PAF for FEVpp <= 95\% was 12\%, ranking low FEV third on the list of PAF for CHD mortality, after hypertension and diabetes. Low FEVpp ranked second in the subgroup of active smokers (PAF 14\%), after hypertension. Low lung function, even in the range considered clinically normal, ranks high on the list of attributable risk factors for CHD mortality and should be considered in cardiovascular risk stratification.

}, keywords = {Adult, Aged, Coronary Disease, Female, Forced Expiratory Volume, Humans, Lung, Lung Diseases, Male, Middle Aged, Proportional Hazards Models, Risk Factors, Smoking, Spirometry, United States, Vital Capacity}, issn = {2325-6621}, doi = {10.1513/AnnalsATS.202407-715OC}, author = {Bhatt, Surya P and Wu, Chaoqi and Sun, Yifei and Balte, Pallavi P and Schwartz, Joseph E and Divo, Miguel J and Jaeger, Byron C and Chaves, Paulo H and Couper, David and Jacobs, David R and Lloyd-Jones, Donald and Kalhan, Ravi and Newman, Anne B and O{\textquoteright}Connor, George T and Umans, Jason G and White, Wendy B and Yende, Sachin and Oelsner, Elizabeth C} }