Publications by authors named "Parichoy Pal Choudhury"

10 Publications

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Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries.

Int J Epidemiol 2021 Mar 23. Epub 2021 Mar 23.

Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

Background: Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk.

Methods: Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds.

Results: Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases.

Conclusion: Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.
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http://dx.doi.org/10.1093/ije/dyab036DOI Listing
March 2021

Comparative validation of the BOADICEA and Tyrer-Cuzick breast cancer risk models incorporating classical risk factors and polygenic risk in a population-based prospective cohort of women of European ancestry.

Breast Cancer Res 2021 02 15;23(1):22. Epub 2021 Feb 15.

Division of Cancer Epidemiology and Genetics, National Cancer Institute of Health, 9609 Medical Center Drive 7E-342, Rockville, MD, 20850, USA.

Background: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and the Tyrer-Cuzick breast cancer risk prediction models are commonly used in clinical practice and have recently been extended to include polygenic risk scores (PRS). In addition, BOADICEA has also been extended to include reproductive and lifestyle factors, which were already part of Tyrer-Cuzick model. We conducted a comparative prospective validation of these models after incorporating the recently developed 313-variant PRS.

Methods: Calibration and discrimination of 5-year absolute risk was assessed in a nested case-control sample of 1337 women of European ancestry (619 incident breast cancer cases) aged 23-75 years from the Generations Study.

Results: The extended BOADICEA model with reproductive/lifestyle factors and PRS was well calibrated across risk deciles; expected-to-observed ratio (E/O) at the highest risk decile :0.97 (95 % CI 0.51 - 1.86) for women younger than 50 years and 1.09 (0.66 - 1.80) for women 50 years or older. Adding reproductive/lifestyle factors and PRS to the BOADICEA model improved discrimination modestly in younger women (area under the curve (AUC) 69.7 % vs. 69.1%) and substantially in older women (AUC 64.6 % vs. 56.8%). The Tyrer-Cuzick model with PRS showed evidence of overestimation at the highest risk decile: E/O = 1.54(0.81 - 2.92) for younger and 1.73 (1.03 - 2.90) for older women.

Conclusion: The extended BOADICEA model identified women in a European-ancestry population at elevated breast cancer risk more accurately than the Tyrer-Cuzick model with PRS. With the increasing availability of PRS, these analyses can inform choice of risk models incorporating PRS for risk stratified breast cancer prevention among women of European ancestry.
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http://dx.doi.org/10.1186/s13058-021-01399-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7885342PMC
February 2021

Combined Utility of 25 Disease and Risk Factor Polygenic Risk Scores for Stratifying Risk of All-Cause Mortality.

Am J Hum Genet 2020 09 5;107(3):418-431. Epub 2020 Aug 5.

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. Electronic address:

While genome-wide association studies have identified susceptibility variants for numerous traits, their combined utility for predicting broad measures of health, such as mortality, remains poorly understood. We used data from the UK Biobank to combine polygenic risk scores (PRS) for 13 diseases and 12 mortality risk factors into sex-specific composite PRS (cPRS). These cPRS were moderately associated with all-cause mortality in independent data within the UK Biobank: the estimated hazard ratios per standard deviation were 1.10 (95% confidence interval: 1.05, 1.16) and 1.15 (1.10, 1.19) for women and men, respectively. Differences in life expectancy between the top and bottom 5% of the cPRS were estimated to be 4.79 (1.76, 7.81) years and 6.75 (4.16, 9.35) years for women and men, respectively. These associations were substantially attenuated after adjusting for non-genetic mortality risk factors measured at study entry (i.e., middle age for most participants). The cPRS may be useful in counseling younger individuals at higher genetic risk of mortality on modification of non-genetic factors.
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http://dx.doi.org/10.1016/j.ajhg.2020.07.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477009PMC
September 2020

Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers.

Nat Commun 2020 07 3;11(1):3353. Epub 2020 Jul 3.

Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK.

Genome-wide association studies (GWAS) have led to the identification of hundreds of susceptibility loci across cancers, but the impact of further studies remains uncertain. Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) and underlying effect-size distribution. All cancers show a high degree of polygenicity, involving at a minimum of thousands of loci. We project that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores (PRS), compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that PRS have potential for risk stratification for cancers of breast, colon and prostate, but less so for others because of modest heritability and lower incidence.
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http://dx.doi.org/10.1038/s41467-020-16483-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7335068PMC
July 2020

Combined Associations of a Polygenic Risk Score and Classical Risk Factors With Breast Cancer Risk.

J Natl Cancer Inst 2021 03;113(3):329-337

Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.

We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.
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http://dx.doi.org/10.1093/jnci/djaa056DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7936056PMC
March 2021

Evaluating Discrimination of a Lung Cancer Risk Prediction Model Using Partial Risk-Score in a Two-Phase Study.

Cancer Epidemiol Biomarkers Prev 2020 06 10;29(6):1196-1203. Epub 2020 Apr 10.

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

Background: Independent validation of risk prediction models in prospective cohorts is required for risk-stratified cancer prevention. Such studies often have a two-phase design, where information on expensive biomarkers are ascertained in a nested substudy of the original cohort.

Methods: We propose a simple approach for evaluating model discrimination that accounts for incomplete follow-up and gains efficiency by using data from all individuals in the cohort irrespective of whether they were sampled in the substudy. For evaluating the AUC, we estimated probabilities of risk-scores for cases being larger than those in controls conditional on partial risk-scores, computed using partial covariate information. The proposed method was compared with an inverse probability weighted (IPW) approach that used information only from the subjects in the substudy. We evaluated age-stratified AUC of a model including questionnaire-based risk factors and inflammation biomarkers to predict 10-year risk of lung cancer using data from the Prostate, Lung, Colorectal, and Ovarian Cancer (1993-2009) trial (30,297 ever-smokers, 1,253 patients with lung cancer).

Results: For estimating age-stratified AUC of the combined lung cancer risk model, the proposed method was 3.8 to 5.3 times more efficient compared with the IPW approach across the different age groups. Extensive simulation studies also demonstrated substantial efficiency gain compared with the IPW approach.

Conclusions: Incorporating information from all individuals in a two-phase cohort study can substantially improve precision of discrimination measures of lung cancer risk models.

Impact: Novel, simple, and practically useful methods are proposed for evaluating risk models, a critical step toward risk-stratified cancer prevention.
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http://dx.doi.org/10.1158/1055-9965.EPI-19-1574DOI Listing
June 2020

iCARE: An R package to build, validate and apply absolute risk models.

PLoS One 2020 5;15(2):e0228198. Epub 2020 Feb 5.

Department of Biostatistics, The Johns Hopkins University, Baltimore, MD, United States of America.

This report describes an R package, called the Individualized Coherent Absolute Risk Estimator (iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individual's risk of developing disease during a specified time interval based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: a model for relative risk, an age-specific disease incidence rate and the distribution of risk factors for the population of interest. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228198PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001949PMC
April 2020

Toward Risk-Stratified Breast Cancer Screening: Considerations for Changes in Screening Guidelines.

JAMA Oncol 2020 Jan;6(1):31-33

Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.

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http://dx.doi.org/10.1001/jamaoncol.2019.3820DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170848PMC
January 2020

Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification.

J Natl Cancer Inst 2020 03;112(3):278-285

Johns Hopkins University, Baltimore, MD.

Background: External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification.

Methods: Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35-74 years. Risk projections in a target population of US white non-Hispanic women age 50-70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).

Results: The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years.

Conclusions: iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.
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http://dx.doi.org/10.1093/jnci/djz113DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073933PMC
March 2020

The Association of Vitamin D Deficiency and Incident Frailty in Older Women: The Role of Cardiometabolic Diseases.

J Am Geriatr Soc 2017 Mar 23;65(3):619-624. Epub 2016 Dec 23.

Department of Medicine, Johns Hopkins University, Baltimore, Maryland.

Objectives: Evidence suggests vitamin D deficiency is associated with developing frailty. However, cardiometabolic factors are related to both conditions and may confound and/or mediate the vitamin D-frailty association. We aimed to determine the association of vitamin D concentration with incidence of frailty, and the role of cardiometabolic diseases (cardiovascular disease, diabetes, hyperlipidemia, hypertension) in this relationship.

Design: Prospective longitudinal cohort study (7 visits from 1994-2008).

Setting: Baltimore, Maryland.

Participants: Three hundred sixty-nine women from the Women's Health and Aging Study II aged 70-79 years, free of frailty at baseline.

Measurements: Serum circulating 25-hydroxyvitamin D (25[OH]D) concentration was assessed at baseline and categorized as: <10; 10-19.9; 20-29.9; and ≥30 ng/mL. Frailty incidence was determined based on presence of three or more criteria: weight loss, low physical activity, exhaustion, weakness, and slowness. Cardiometabolic diseases were ascertained at baseline. Analyses included Cox regression models adjusted for key covariates.

Results: Incidence rate of frailty was 32.2 per 1,000 person-years in participants with 25(OH)D < 10 ng/mL, compared to 12.9 per 1,000 person-years in those with 25(OH)D ≥ 30 ng/mL (mean follow-up = 8.5 ± 3.7 years). In cumulative incidence analyses, those with lower 25(OH)D exhibited higher frailty incidence, though differences were non-significant (P = .057). In regression models adjusted for demographics, smoking, and season, 25(OH)D < 10 ng/mL (vs ≥30 ng/mL) was associated with nearly three-times greater frailty incidence (hazard ratio (HR) = 2.77, 95% CI = 1.14, 6.71, P = .02). After adjusting for BMI, the relationship of 25(OH)D < 10 ng/mL (vs ≥30 ng/mL) with incident frailty persisted, but was attenuated after further accounting for cardiometabolic diseases (HR = 2.29, 95% CI = 0.92, 5.69, P = .07).

Conclusion: Low serum vitamin D concentration is associated with incident frailty in older women; interestingly, the relationship is no longer significant after accounting for the presence of cardiometabolic diseases. Future studies should explore mechanisms to explain this relationship.
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http://dx.doi.org/10.1111/jgs.14677DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5357177PMC
March 2017
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