Can sodium and potassium measured in timed voids be used as reference instruments for validating self-report instruments? Results from a urine calibration study.

BACKGROUND: Sodium and potassium measured in 24-h urine collections are often used as reference measurements to validate self-reported dietary intake instruments. OBJECTIVES: To evaluate whether collection and analysis of a limited number of urine voids at specified times during the day ("timed voids") can provide alternative reference measurements, and to identify their optimal number and timing. METHODS: We used data from a urine calibration study among 441 adults aged 18-39 y. Participants collected each urine void in a separate container for 24 h and recorded the collection time. For the same day, they reported dietary intake using a 24-h recall. Urinary sodium and potassium were analyzed in a 24-h composite sample and in 4 timed voids (morning, afternoon, evening, and overnight). Linear regression models were used to develop equations predicting log-transformed 24-h urinary sodium or potassium levels using each of the 4 single timed voids, 6 pairs, and 4 triples. The equations also included age, sex, race, BMI (kg/m2), and log creatinine. Optimal combinations minimizing the mean squared prediction error were selected, and the observed and predicted 24-h levels were then used as reference measures to estimate the group bias and attenuation factors of the 24-h dietary recall. These estimates were compared. RESULTS: Optimal combinations found were as follows: single voids-evening; paired voids-afternoon + overnight (sodium) and morning + evening (potassium); and triple voids-morning + evening + overnight (sodium) and morning + afternoon + evening (potassium). Predicted 24-h urinary levels estimated 24-h recall group biases and attenuation factors without apparent bias, but with less precision than observed 24-h urinary levels. To recover lost precision, it was estimated that sample sizes need to be increased by ∼2.6-2.7 times for a single void, 1.7-2.1 times for paired voids, and 1.5-1.6 times for triple voids. CONCLUSIONS: Our results provide the basis for further development of new reference biomarkers based on timed voids. CLINICAL TRIAL REGISTRY: clinicaltrials.gov as NCT01631240.

Urinary Sucrose and Fructose From Spot Urine May Be Used as a Predictive Biomarker of Total Sugar Intake-Findings From a Controlled Feeding Study.

BACKGROUND: Recently, we confirmed 24-h urinary sucrose plus fructose (24 uSF) as a predictive biomarker of total sugar intake. However, the collection of 24-h urine samples has limited feasibility in population studies. OBJECTIVE: We investigated the utility of the urinary sucrose plus fructose (uSF) biomarker measured in spot urine as a measure of 24 uSF biomarker and total sugar intake. METHODS: Hundred participants, 18-70 y of age, from the Phoenix Metropolitan Area completed a 15-d feeding study. For 2 of the 8 collected 24-h urine samples, each spot urine sample was collected in a separate container. We considered 4 timed voids of the day [morning (AM) void: first void 08:30-12:30; afternoon (PM) void: first void 12:31-17:30; evening (EVE) void: first void 17:31-12:00; and next-day (ND) void: first void 04:00-12:00]. We investigated the performance of uSF from 1 void, and uSF combined from 2 and 3 voids as a measure of 24 uSF and sugar intake. RESULTS: The biomarker averaged from PM/EVE void strongly correlated with 24 uSF (partial r = 0.75). The 24 uSF predicted from the PM/EVE combination was significantly associated with observed sugar intake and was selected for building the calibrated biomarker equation (marginal R2 = 0.36). Spot urine-based calibrated biomarker, ie, biomarker-estimated sugar intake was moderately correlated with the 15-d mean-observed sugar intake (r = 0.50). CONCLUSIONS: uSF measured from a PM and EVE void may be used to generate biomarker-based sugar intake estimate when collecting 24-h urine samples is not feasible, pending external validation.

Evaluating a Model of Added Sugar Intake Based on Amino Acid Carbon Isotope Ratios in a Controlled Feeding Study of U.S. Adults.

Previous studies suggest that amino acid carbon stable isotope ratios (CIRAAs) may serve as biomarkers of added sugar (AS) intake, but this has not been tested in a demographically diverse population. We conducted a 15-day feeding study of U.S. adults, recruited across sex, age, and BMI groups. Participants consumed personalized diets that resembled habitual intake, assessed using two consecutive 7-day food records. We measured serum (n = 99) CIRAAs collected at the end of the feeding period and determined correlations with diet. We used forward selection to model AS intake using participant characteristics and 15 CIRAAs. This model was internally validated using bootstrap optimism correction. Median (25th, 75th percentile) AS intake was 65.2 g/day (44.7, 81.4) and 9.5% (7.2%, 12.4%) of energy. The CIR of alanine had the highest, although modest, correlation with AS intake (r = 0.32, p = 0.001). Serum CIRAAs were more highly correlated with animal food intakes, especially the ratio of animal to total protein. The AS model included sex, body weight and 6 CIRAAs. This model had modest explanatory power (multiple R2 = 0.38), and the optimism-corrected R2 was lower (R2 = 0.15). Further investigations in populations with wider ranges of AS intake are warranted.

Establishing 24-Hour Urinary Sucrose Plus Fructose as a Predictive Biomarker for Total Sugars Intake.

BACKGROUND: Twenty-four-hour urinary sucrose and fructose (24uSF) has been studied as a biomarker of total sugars intake in two feeding studies conducted in the United Kingdom (UK) and Arizona (AZ). We compare the biomarker performance in these populations, testing whether it meets the criteria for a predictive biomarker. METHODS: The UK and AZ feeding studies included 13 and 98 participants, respectively, aged 18 to 70 years, consuming their usual diet under controlled conditions. Linear mixed models relating 24uSF to total sugars and personal characteristics were developed in each study and compared. The AZ calibrated biomarker equation was applied to generate biomarker-estimated total sugars intake in UK participants. Stability of the model across AZ study subpopulations was also examined. RESULTS: Model coefficients were similar between the two studies [e.g., log(total sugars): UK 0.99, AZ 1.03, P = 0.67], as was the ratio of calibrated biomarker person-specific bias to between-person variance (UK 0.32, AZ 0.25, P = 0.68). The AZ equation estimated UK log(total sugar intakes) with mean squared prediction error of 0.27, similar to the AZ study estimate (0.28). Within the AZ study, the regression coefficients of log(total sugars) were similar across age, gender, and body mass index subpopulations. CONCLUSIONS: Similar model coefficients in the two studies and good prediction of UK sugar intakes by the AZ equation suggest that 24uSF meets the criteria for a predictive biomarker. Testing the biomarker performance in other populations is advisable. IMPACT: Applications of the 24uSF biomarker will enable improved assessment of the role of sugars intake in risk of chronic disease, including cancer. See related commentary by Prentice, p. 1151.

Investigating the performance of 24-h urinary sucrose and fructose as a biomarker of total sugars intake in US participants - a controlled feeding study.

BACKGROUND: Developing approaches for the objective assessment of sugars intake in population research is crucial for generating reliable disease risk estimates, and evidence-based dietary guidelines. Twenty-four-hour urinary sucrose and fructose (24uSF) was developed as a predictive biomarker of total sugars intake based on 3 UK feeding studies, yet its performance as a biomarker of total sugars among US participants is unknown. OBJECTIVES: To investigate the performance of 24uSF as a biomarker of sugars intake among US participants, and to characterize its use. METHODS: Ninety-eight participants, aged 18-70 y, consumed their usual diet under controlled conditions of a feeding study for 15 d, and collected 8 nonconsecutive 24-h urines measured for sucrose and fructose. RESULTS: A linear mixed model regressing log 24uSF biomarker on log total sugars intake along with other covariates explained 56% of the biomarker variance. Total sugars intake was the strongest predictor in the model (Marginal R2 = 0.52; P <0.0001), followed by sex (P = 0.0002) and log age (P = 0.002). The equation was then inverted to solve for total sugars intake, thus generating a calibrated biomarker equation. Calibration of the biomarker produced mean biomarker-based log total sugars of 4.79 (SD = 0.59), which was similar to the observed log 15-d mean total sugars intake of 4.69 (0.35). The correlation between calibrated biomarker and usual total sugars intake was 0.59 for the calibrated biomarker based on a single biomarker measurement, and 0.76 based on 4 biomarker repeats spaced far apart. CONCLUSIONS: In this controlled feeding study, total sugars intake was the main determinant of 24uSF confirming its utility as a biomarker of total sugars in this population. Next steps will include validation of stability assumptions of the biomarker calibration equation proposed here, which will allow its use as an instrument for dietary validation and measurement error correction in diet-disease association studies.

Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial.

Importance: Low-dose computed tomography lung cancer screening is most effective when applied to high-risk individuals. Objectives: To develop and validate a risk prediction model that incorporates low-dose computed tomography screening results. Design, Setting, and Participants: A logistic regression risk model was developed in National Lung Screening Trial (NLST) Lung Screening Study (LSS) data and was validated in NLST American College of Radiology Imaging Network (ACRIN) data. The NLST was a randomized clinical trial that recruited participants between August 2002 and April 2004, with follow-up to December 31, 2009. This secondary analysis of data from the NLST took place between August 10, 2013, and November 1, 2018. Included were LSS (n = 14 576) and ACRIN (n = 7653) participants who had 3 screens, adequate follow-up, and complete predictor information. Main Outcomes and Measures: Incident lung cancers occurring 1 to 4 years after the third screen (202 LSS and 96 ACRIN). Predictors included scores from the validated PLCOm2012 risk model and Lung CT Screening Reporting & Data System (Lung-RADS) screening results. Results: Overall, the mean (SD) age of 22 229 participants was 61.3 (5.0) years, 59.3% were male, and 90.9% were of non-Hispanic white race/ethnicity. During follow-up, 298 lung cancers were diagnosed in 22 229 individuals (1.3%). Eight result combinations were pooled into 4 groups based on similar associations. Adjusted for PLCOm2012 risks, compared with participants with 3 negative screens, participants with 1 positive screen and last negative had an odds ratio (OR) of 1.93 (95% CI, 1.34-2.76), and participants with 2 positive screens with last negative or 2 negative screens with last positive had an OR of 2.66 (95% CI, 1.60-4.43); when 2 or more screens were positive with last positive, the OR was 8.97 (95% CI, 5.76-13.97). In ACRIN validation data, the model that included PLCOm2012 scores and screening results (PLCO2012results) demonstrated significantly greater discrimination (area under the curve, 0.761; 95% CI, 0.716-0.799) than when screening results were excluded (PLCOm2012) (area under the curve, 0.687; 95% CI, 0.645-0.728) (P < .001). In ACRIN validation data, PLCO2012results demonstrated good calibration. Individuals who had initial negative scans but elevated PLCOm2012 six-year risks of at least 2.6% did not have risks decline below the 1.5% screening eligibility criterion when subsequent screens were negative. Conclusions and Relevance: According to this analysis, some individuals with elevated risk scores who have negative initial screens remain at elevated risks, warranting annual screening. Positive screens seem to increase baseline risk scores and may identify high-risk individuals for continued screening and enrollment into clinical trials. Trial Registration: ClinicalTrials.gov Identifier: NCT00047385.

Factors Associated With Small Aggressive Non-Small Cell Lung Cancers in the National Lung Screening Trial: A Validation Study.

BACKGROUND: A small proportion of non-small cell lung cancers (NSCLCs) have been observed to spread to distant lymph nodes (N3) or metastasize (M1) or both, while the primary tumor is small (≤3 cm, T1). These small aggressive NSCLCs (SA-NSLSC) are important as they are clinically significant, may identify unique biologic pathways, and warrant aggressive follow-up and treatment. This study identifies factors associated with SA-NSCLC and attempts to validate a previous finding that women with a family history of lung cancer are at particularly elevated risk of SA-NSCLC. METHODS: This study used a case-case design within the National Cancer Institute's National Lung Screening Trial (NLST) cohort. Case patients and "control" patients were selected based on TNM staging parameters. Case patients (n = 64) had T1 NSCLCs that were N3 or M1 or both, while "control" patients (n = 206) had T2 or T3, N0 to N2, and M0 NSCLCs. Univariate and multivariable logistic regression were used to identify factors associated with SA-NSCLC. RESULTS: In bootstrap bias-corrected multivariable logistic regression models, small aggressive adenocarcinomas were associated with a positive history of emphysema (odds ratio [OR] = 5.15, 95% confidence interval [CI] = 1.63 to 23.00) and the interaction of female sex and a positive family history of lung cancer (OR = 6.55, 95% CI = 1.06 to 50.80). CONCLUSIONS: Emphysema may play a role in early lung cancer progression. Females with a family history of lung cancer are at increased risk of having small aggressive lung adenocarcinomas. These results validate previous findings and encourage research on the role of female hormones interacting with family history and genetic factors in lung carcinogenesis and progression.

Evaluation of the 24-Hour Recall as a Reference Instrument for Calibrating Other Self-Report Instruments in Nutritional Cohort Studies: Evidence From the Validation Studies Pooling Project.

Calibrating dietary self-report instruments is recommended as a way to adjust for measurement error when estimating diet-disease associations. Because biomarkers available for calibration are limited, most investigators use self-reports (e.g., 24-hour recalls (24HRs)) as the reference instrument. We evaluated the performance of 24HRs as reference instruments for calibrating food frequency questionnaires (FFQs), using data from the Validation Studies Pooling Project, comprising 5 large validation studies using recovery biomarkers. Using 24HRs as reference instruments, we estimated attenuation factors, correlations with truth, and calibration equations for FFQ-reported intakes of energy and for protein, potassium, and sodium and their densities, and we compared them with values derived using biomarkers. Based on 24HRs, FFQ attenuation factors were substantially overestimated for energy and sodium intakes, less for protein and potassium, and minimally for nutrient densities. FFQ correlations with truth, based on 24HRs, were substantially overestimated for all dietary components. Calibration equations did not capture dependencies on body mass index. We also compared predicted bias in estimated relative risks adjusted using 24HRs as reference instruments with bias when making no adjustment. In disease models with energy and 1 or more nutrient intakes, predicted bias in estimated nutrient relative risks was reduced on average, but bias in the energy risk coefficient was unchanged.

Application of a New Statistical Model for Measurement Error to the Evaluation of Dietary Self-report Instruments.

Most statistical methods that adjust analyses for dietary measurement error treat an individual's usual intake as a fixed quantity. However, usual intake, if defined as average intake over a few months, varies over time. We describe a model that accounts for such variation and for the proximity of biomarker measurements to self-reports within the framework of a meta-analysis, and apply it to the analysis of data on energy, protein, potassium, and sodium from a set of five large validation studies of dietary self-report instruments using recovery biomarkers as reference instruments. We show that this time-varying usual intake model fits the data better than the fixed usual intake assumption. Using this model, we estimated attenuation factors and correlations with true longer-term usual intake for single and multiple 24-hour dietary recalls (24HRs) and food frequency questionnaires (FFQs) and compared them with those obtained under the "fixed" method. Compared with the fixed method, the estimates using the time-varying model showed slightly larger values of the attenuation factor and correlation coefficient for FFQs and smaller values for 24HRs. In some cases, the difference between the fixed method estimate and the new estimate for multiple 24HRs was substantial. With the new method, while four 24HRs had higher estimated correlations with truth than a single FFQ for absolute intakes of protein, potassium, and sodium, for densities the correlations were approximately equal. Accounting for the time element in dietary validation is potentially important, and points toward the need for longer-term validation studies.

Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for potassium and sodium intake.

We pooled data from 5 large validation studies (1999-2009) of dietary self-report instruments that used recovery biomarkers as referents, to assess food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). Here we report on total potassium and sodium intakes, their densities, and their ratio. Results were similar by sex but were heterogeneous across studies. For potassium, potassium density, sodium, sodium density, and sodium:potassium ratio, average correlation coefficients for the correlation of reported intake with true intake on the FFQs were 0.37, 0.47, 0.16, 0.32, and 0.49, respectively. For the same nutrients measured with a single 24HR, they were 0.47, 0.46, 0.32, 0.31, and 0.46, respectively, rising to 0.56, 0.53, 0.41, 0.38, and 0.60 for the average of three 24HRs. Average underreporting was 5%-6% with an FFQ and 0%-4% with a single 24HR for potassium but was 28%-39% and 4%-13%, respectively, for sodium. Higher body mass index was related to underreporting of sodium. Calibration equations for true intake that included personal characteristics provided improved prediction, except for sodium density. In summary, self-reports capture potassium intake quite well but sodium intake less well. Using densities improves the measurement of potassium and sodium on an FFQ. Sodium:potassium ratio is measured much better than sodium itself on both FFQs and 24HRs.

Evaluation of the lung cancer risks at which to screen ever- and never-smokers: screening rules applied to the PLCO and NLST cohorts.

BACKGROUND: Lung cancer risks at which individuals should be screened with computed tomography (CT) for lung cancer are undecided. This study's objectives are to identify a risk threshold for selecting individuals for screening, to compare its efficiency with the U.S. Preventive Services Task Force (USPSTF) criteria for identifying screenees, and to determine whether never-smokers should be screened. Lung cancer risks are compared between smokers aged 55-64 and ≥ 65-80 y. METHODS AND FINDINGS: Applying the PLCO(m2012) model, a model based on 6-y lung cancer incidence, we identified the risk threshold above which National Lung Screening Trial (NLST, n = 53,452) CT arm lung cancer mortality rates were consistently lower than rates in the chest X-ray (CXR) arm. We evaluated the USPSTF and PLCO(m2012) risk criteria in intervention arm (CXR) smokers (n = 37,327) of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). The numbers of smokers selected for screening, and the sensitivities, specificities, and positive predictive values (PPVs) for identifying lung cancers were assessed. A modified model (PLCOall2014) evaluated risks in never-smokers. At PLCO(m2012) risk ≥ 0.0151, the 65th percentile of risk, the NLST CT arm mortality rates are consistently below the CXR arm's rates. The number needed to screen to prevent one lung cancer death in the 65th to 100th percentile risk group is 255 (95% CI 143 to 1,184), and in the 30th to <65th percentile risk group is 963 (95% CI 291 to -754); the number needed to screen could not be estimated in the <30th percentile risk group because of absence of lung cancer deaths. When applied to PLCO intervention arm smokers, compared to the USPSTF criteria, the PLCO(m2012) risk ≥ 0.0151 threshold selected 8.8% fewer individuals for screening (p<0.001) but identified 12.4% more lung cancers (sensitivity 80.1% [95% CI 76.8%-83.0%] versus 71.2% [95% CI 67.6%-74.6%], p<0.001), had fewer false-positives (specificity 66.2% [95% CI 65.7%-66.7%] versus 62.7% [95% CI 62.2%-63.1%], p<0.001), and had higher PPV (4.2% [95% CI 3.9%-4.6%] versus 3.4% [95% CI 3.1%-3.7%], p<0.001). In total, 26% of individuals selected for screening based on USPSTF criteria had risks below the threshold PLCO(m2012) risk ≥ 0.0151. Of PLCO former smokers with quit time >15 y, 8.5% had PLCO(m2012) risk ≥ 0.0151. None of 65,711 PLCO never-smokers had PLCO(m2012) risk ≥ 0.0151. Risks and lung cancers were significantly greater in PLCO smokers aged ≥ 65-80 y than in those aged 55-64 y. This study omitted cost-effectiveness analysis. CONCLUSIONS: The USPSTF criteria for CT screening include some low-risk individuals and exclude some high-risk individuals. Use of the PLCO(m2012) risk ≥ 0.0151 criterion can improve screening efficiency. Currently, never-smokers should not be screened. Smokers aged ≥ 65-80 y are a high-risk group who may benefit from screening. Please see later in the article for the Editors' Summary.

Interval lung cancers not detected on screening chest X-rays: How are they different?

BACKGROUND: The Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial provides us an opportunity to describe interval lung cancers not detected by screening chest X-ray (CXR) compared to screen-detected cancers. METHODS: Participants were screened for lung cancer with CXR at baseline and annually for two (never smokers) or three (ever smokers) more years. Screen-detected cancers were those with a positive CXR and diagnosed within 12 months. Putative interval cancers were those with a negative CXR screen but with a diagnosis of lung cancer within 12 months. Potential interval cancers were re-reviewed to determine whether lung cancer was missed and probably present during the initial interpretation or whether the lesion was a "true interval" cancer. RESULTS: 77,445 participants were randomized to the intervention arm with 70,633 screened. Of 5227 positive screens from any screening round, 299 resulted in screen-detected lung cancers; 151 had potential interval cancers with 127 CXR available for re-review. Cancer was probably present in 45/127 (35.4%) at time of screening; 82 (64.6%) were "true interval" cancers. Compared to screen-detected cancers, true interval cancers were more common among males, persons with <12 years education and those with a history of smoking. True interval lung cancers were more often small cell, 28.1% vs. 7.4%, and less often adenocarcinoma, 25.6% vs. 56.2% (p<0.001), more advanced stage IV (30.5% vs. 16.6%, p<0.02), and less likely to be in the right upper lobe, 17.1% vs. 36.1% (p<0.02). CONCLUSION: True interval lung cancers differ from CXR-screen-detected cancers with regard to demographic variables, stage, cell type and location. ClinicalTrials.gov number: NCT00002540.

Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake.

We pooled data from 5 large validation studies of dietary self-report instruments that used recovery biomarkers as references to clarify the measurement properties of food frequency questionnaires (FFQs) and 24-hour recalls. The studies were conducted in widely differing US adult populations from 1999 to 2009. We report on total energy, protein, and protein density intakes. Results were similar across sexes, but there was heterogeneity across studies. Using a FFQ, the average correlation coefficients for reported versus true intakes for energy, protein, and protein density were 0.21, 0.29, and 0.41, respectively. Using a single 24-hour recall, the coefficients were 0.26, 0.40, and 0.36, respectively, for the same nutrients and rose to 0.31, 0.49, and 0.46 when three 24-hour recalls were averaged. The average rate of under-reporting of energy intake was 28% with a FFQ and 15% with a single 24-hour recall, but the percentages were lower for protein. Personal characteristics related to under-reporting were body mass index, educational level, and age. Calibration equations for true intake that included personal characteristics provided improved prediction. This project establishes that FFQs have stronger correlations with truth for protein density than for absolute protein intake, that the use of multiple 24-hour recalls substantially increases the correlations when compared with a single 24-hour recall, and that body mass index strongly predicts under-reporting of energy and protein intakes.

Diagnostic evaluation following a positive lung screening chest radiograph in the Prostate, Lung, Colorectal, Ovarian (PLCO) Cancer Screening Trial.

Lung cancer is the major cause of cancer mortality. One of the aims of the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) was to determine if annual screening chest radiographs reduce lung cancer mortality. We enrolled 154,900 individuals, aged 55-74 years; 77,445 were randomized to the intervention arm and received an annual chest radiograph for 3 or 4 years. Participants with a positive screen underwent diagnostic evaluation under guidance of their primary physician. Methods of diagnosis or exclusion of cancer, interval from screen to diagnosis, and factors predicting diagnostic testing were evaluated. One or more positive screens occurred in 17% of participants. Positive screens resulted in biopsy in 3%, with 54% positive for cancer. Biopsy likelihood was associated with a mass, smoking, age, and family history of lung cancer. Diagnostic testing stopped after a chest radiograph or computed tomography/magnetic resonance imaging in over half. After a second or subsequent positive screen, evaluation stopped after comparison to prior radiographs in over half. Of 308 screen-detected cancers, the diagnosis was established by thoracotomy/thoracoscopy in 47.7%, needle biopsy in 27.6%, bronchoscopy in 20.1% and mediastinoscopy in 2.9%. Eighty-four percent of screen-detected lung cancers were diagnosed within 6 months. Diagnostic evaluations following a positive screen were conducted in a timely fashion. Lung cancer was diagnosed by tissue biopsy or cytology in all cases. Lung cancer was excluded during evaluation of positive screening examinations by clinical or radiographic evaluation in all but 1.4% who required a tissue biopsy.

Selection criteria for lung-cancer screening.

BACKGROUND: The National Lung Screening Trial (NLST) used risk factors for lung cancer (e.g., ≥30 pack-years of smoking and <15 years since quitting) as selection criteria for lung-cancer screening. Use of an accurate model that incorporates additional risk factors to select persons for screening may identify more persons who have lung cancer or in whom lung cancer will develop. METHODS: We modified the 2011 lung-cancer risk-prediction model from our Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to ensure applicability to NLST data; risk was the probability of a diagnosis of lung cancer during the 6-year study period. We developed and validated the model (PLCO(M2012)) with data from the 80,375 persons in the PLCO control and intervention groups who had ever smoked. Discrimination (area under the receiver-operating-characteristic curve [AUC]) and calibration were assessed. In the validation data set, 14,144 of 37,332 persons (37.9%) met NLST criteria. For comparison, 14,144 highest-risk persons were considered positive (eligible for screening) according to PLCO(M2012) criteria. We compared the accuracy of PLCO(M2012) criteria with NLST criteria to detect lung cancer. Cox models were used to evaluate whether the reduction in mortality among 53,202 persons undergoing low-dose computed tomographic screening in the NLST differed according to risk. RESULTS: The AUC was 0.803 in the development data set and 0.797 in the validation data set. As compared with NLST criteria, PLCO(M2012) criteria had improved sensitivity (83.0% vs. 71.1%, P<0.001) and positive predictive value (4.0% vs. 3.4%, P=0.01), without loss of specificity (62.9% and. 62.7%, respectively; P=0.54); 41.3% fewer lung cancers were missed. The NLST screening effect did not vary according to PLCO(M2012) risk (P=0.61 for interaction). CONCLUSIONS: The use of the PLCO(M2012) model was more sensitive than the NLST criteria for lung-cancer detection.

Assessing the risk of ovarian malignancy in asymptomatic women with abnormal CA 125 and transvaginal ultrasound scans in the prostate, lung, colorectal, and ovarian screening trial.

OBJECTIVE: To estimate the risk of ovarian malignancy among asymptomatic women with abnormal transvaginal ultrasound scans or CA 125 and to provide guidance to physicians managing these women. METHODS: A cohort of women from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial with abnormal ovarian results at the initial (T0) and subsequent (T1+) screens were analyzed to estimate which findings were associated with high risk of ovarian cancer. Cancer risks more than 10% were designated as high and risks of 3% or less were designated as low. RESULTS: For the T0 screen, two high-risk categories were identified: CA 125 of 70 or more with negative transvaginal ultrasound scan (positive predictive value [PPV] 15.9%, CI 14.7-17.7%); and positive for both CA 125 and transvaginal ultrasound scan (PPV 25.0%, CI 23.3-27.3%). For T1+ screens, three high-risk categories were identified: negative transvaginal ultrasound scan with change in CA 125 of 45 or more (PPV 29.0%, CI 28.3-30.3%); increase in size of cyst 6 cm or more with negative CA 125 (PPV 13.3%, CI 10.5-18.0%); and positive for both tests (PPV 42.9%, CI 40.0-46.0%). High-risk criteria for T0 provide a sensitivity of 60%, specificity of 96.2%, PPV of 19.7%, and a negative predictive value (NPV) of 99.3%. T1+ criteria yielded a sensitivity of 85.3%, specificity of 95.6%, PPV of 29.6%, and NPV of 99.7%. CONCLUSIONS: High-risk categories for predicting risk of cancer in women with abnormal CA 125, transvaginal ultrasound scan, or both at initial and subsequent screens have been identified. The large number of women in this study, the 4-year complete follow-up, and small number of invasive cancers in the low-risk categories provide guidance for clinical decisions regarding need for surgery in these women. LEVEL OF EVIDENCE: II.

Alcohol, genetics and risk of breast cancer in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.

We tested the hypothesis that genes involved in the alcohol oxidation pathway modify the association between alcohol intake and breast cancer. Subjects were women aged 55-74 at baseline from the screening arm of the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Incident breast cancers were identified through annual health surveys. Controls were frequency matched to cases by age and year of entry into the trial. A self-administered food frequency questionnaire queried frequency and usual serving size of beer, wine or wine coolers, and liquor. Three SNPs in genes in the alcohol metabolism pathway were genotyped: alcohol dehydrogenase 2, alcohol dehydrogenase 3, and CYP2E1. The study included 1,041 incident breast cancer cases and 1,070 controls. In comparison to non-drinkers, the intake of any alcohol significantly increased the risk of breast cancer, and this risk increased with each category of daily alcohol intake (OR 2.01, 95% CI 1.14, 3.53) for women who drank three or more standard drinks per day. Stratification by genotype revealed significant gene/environment interactions. For the ADH1B gene, there were statistically significant associations between all levels of alcohol intake and risk of breast cancer (all OR > 1.34 and all lower CI > 1.01), while for women with the GA or AA genotype, there were no significant associations between alcohol intake and risk of breast cancer. Alcohol intake, genes involved in alcohol metabolism and their interaction increase the risk of breast cancer in post-menopausal women. This information could be useful for primary care providers to personalize information about breast cancer risk reduction.

Longitudinal evaluation of CA-125 velocity and prediction of ovarian cancer.

OBJECTIVE: To determine whether CA-125 velocity is a statistically significant predictor of ovarian cancer and develop a classification rule to screen for ovarian cancer. METHODS: In the ovarian component of the PLCO cancer screening trial, 28,038 women aged 55-74 had at least two CA-125 screening tests. Ovarian cancer was diagnosed in 72 (0.26%) women. A multiple logistic regression model was developed to evaluate CA-125 velocity and other related covariates as predictors of ovarian cancer. Predictive accuracy was assessed by the concordance index and measures of discrimination and calibration while the fit of the model was assessed by the Hosmer and Lemeshow's goodness-of-fit χ(2)test. RESULTS: CA-125 velocity decreased as the number of CA-125 measurements increased but was unaffected by age at baseline screen and family history of ovarian cancer. The average velocity (19.749U/ml per month) of the cancer group was more than 500 times the average velocity (0.035U/ml per month) of the non-cancer group. CONCLUSION: Among six covariates used in the model, CA-125 velocity and time intervals between baseline and second to last screening test and between last two screening tests were statistically significant predictors of ovarian cancer. The chance of having ovarian cancer increased as velocity increased, and the chance decreased when the time intervals between baseline and the second to last screening test and between last two screening tests of an individual increased.

Lung cancer risk prediction: Prostate, Lung, Colorectal And Ovarian Cancer Screening Trial models and validation.

INTRODUCTION: Identification of individuals at high risk for lung cancer should be of value to individuals, patients, clinicians, and researchers. Existing prediction models have only modest capabilities to classify persons at risk accurately. METHODS: Prospective data from 70 962 control subjects in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) were used in models for the general population (model 1) and for a subcohort of ever-smokers (N = 38 254) (model 2). Both models included age, socioeconomic status (education), body mass index, family history of lung cancer, chronic obstructive pulmonary disease, recent chest x-ray, smoking status (never, former, or current), pack-years smoked, and smoking duration. Model 2 also included smoking quit-time (time in years since ever-smokers permanently quit smoking). External validation was performed with 44 223 PLCO intervention arm participants who completed a supplemental questionnaire and were subsequently followed. Known available risk factors were included in logistic regression models. Bootstrap optimism-corrected estimates of predictive performance were calculated (internal validation). Nonlinear relationships for age, pack-years smoked, smoking duration, and quit-time were modeled using restricted cubic splines. All reported P values are two-sided. RESULTS: During follow-up (median 9.2 years) of the control arm subjects, 1040 lung cancers occurred. During follow-up of the external validation sample (median 3.0 years), 213 lung cancers occurred. For models 1 and 2, bootstrap optimism-corrected receiver operator characteristic area under the curves were 0.857 and 0.805, and calibration slopes (model-predicted probabilities vs observed probabilities) were 0.987 and 0.979, respectively. In the external validation sample, models 1 and 2 had area under the curves of 0.841 and 0.784, respectively. These models had high discrimination in women, men, whites, and nonwhites. CONCLUSION: The PLCO lung cancer risk models demonstrate high discrimination and calibration.

Pathologic findings following false-positive screening tests for ovarian cancer in the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial.

OBJECTIVE: In the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO), ovarian cancer screening with transvaginal ultrasound (TVU) and CA-125 produced a large number of false-positive tests. We examined relationships between histopathologic diagnoses, false-positive test group, and participant and screening test characteristics. METHODS: The PLCO ovarian cancer screening arm included 39,105 women aged 55-74 years assigned to annual CA-125 and TVU. Histopathologic diagnoses from women with false-positive tests and subsequent surgery were reviewed in this analysis: all CA125+ (n=121); all CA125+/TVU+ (n=46); and a random sample of TVU+ (n=373). Demographic and ovarian cancer risk factor data were self-reported. Pathologic diagnoses were abstracted from surgical pathology reports. We compared participant characteristics and pathologic diagnoses by category of false-positive using Pearson χ2, Fisher's exact, or Wilcoxon-Mann-Whitney tests. RESULTS: Women with a false-positive TVU were younger (P<0.001), heavier (P<0.001), and reported a higher frequency of prior hysterectomy (P<0.001). Serous cystadenoma, the most common benign ovarian diagnosis, was more frequent among women with TVU+ compared to CA-125+ and CA-125+/TVU+ (P<0.001). Benign non-ovarian findings were commonly associated with all false-positives, although more frequently with CA-125+ than TVU+ or CA-125+/TVU+ groups (P=0.019). Non-ovarian cancers were diagnosed most frequently among CA-125+ (P<0.001). CONCLUSIONS: False-positive ovarian cancer screening tests were associated with a range of histopathologic diagnoses, some of which may be related to patient and screening test characteristics. Further research into the predictors of false-positive ovarian cancer screening tests may aid efforts to reduce false-positive results.