METHODS: A literature review examined how far such frameworks exist, with a view to identifying conducive factors - and crucial gaps. This extensive review of key factors across 22 countries and 5 regions revealed a wide variety of attitudes, approaches, provisions and conditions, and permitted the construction of a comprehensive overview of the current status of PM. Based on seven key pillars identified from the literature review and expert panels, the data was quantified, and on the basis of further analysis, an index was developed to allow comparison country by country and region by region.
RESULTS: The results show that United States of America is leading according to overall outcome whereas Kenya scored the least in the overall outcome.
CONCLUSIONS: Still, common approaches exist that could help accelerate take-up of opportunities even in the less prosperous parts of the world.
METHODS: In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.
RESULTS: The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.
CONCLUSIONS: We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.
MATERIALS AND METHOD: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.
RESULTS: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.
CONCLUSIONS: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.
METHODS: The case-control portion of the study was conducted in nine UK centers with men ages 50-69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.
RESULTS: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal.
CONCLUSIONS: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk.
IMPACT: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.
OBJECTIVE: To investigate clinical laboratory markers of SARS-CoV-2 and PASC.
DESIGN: Propensity score-weighted linear regression models were fitted to evaluate differences in mean laboratory measures by prior infection and PASC index (≥12 vs. 0). (ClinicalTrials.gov: NCT05172024).
SETTING: 83 enrolling sites.
PARTICIPANTS: RECOVER-Adult cohort participants with or without SARS-CoV-2 infection with a study visit and laboratory measures 6 months after the index date (or at enrollment if >6 months after the index date). Participants were excluded if the 6-month visit occurred within 30 days of reinfection.
MEASUREMENTS: Participants completed questionnaires and standard clinical laboratory tests.
RESULTS: Among 10 094 participants, 8746 had prior SARS-CoV-2 infection, 1348 were uninfected, 1880 had a PASC index of 12 or higher, and 3351 had a PASC index of zero. After propensity score adjustment, participants with prior infection had a lower mean platelet count (265.9 × 109 cells/L [95% CI, 264.5 to 267.4 × 109 cells/L]) than participants without known prior infection (275.2 × 109 cells/L [CI, 268.5 to 282.0 × 109 cells/L]), as well as higher mean hemoglobin A1c (HbA1c) level (5.58% [CI, 5.56% to 5.60%] vs. 5.46% [CI, 5.40% to 5.51%]) and urinary albumin-creatinine ratio (81.9 mg/g [CI, 67.5 to 96.2 mg/g] vs. 43.0 mg/g [CI, 25.4 to 60.6 mg/g]), although differences were of modest clinical significance. The difference in HbA1c levels was attenuated after participants with preexisting diabetes were excluded. Among participants with prior infection, no meaningful differences in mean laboratory values were found between those with a PASC index of 12 or higher and those with a PASC index of zero.
LIMITATION: Whether differences in laboratory markers represent consequences of or risk factors for SARS-CoV-2 infection could not be determined.
CONCLUSION: Overall, no evidence was found that any of the 25 routine clinical laboratory values assessed in this study could serve as a clinically useful biomarker of PASC.
PRIMARY FUNDING SOURCE: National Institutes of Health.
METHOD: Data collected for an individual participant data meta-analysis of Patient Health Questionnaire-9 (PHQ-9) diagnostic accuracy were analysed and binomial generalised linear mixed models were fit.
RESULTS: A total of 17 158 participants (2287 with major depression) from 57 primary studies were analysed. Among fully structured interviews, odds of major depression were higher for the MINI compared with the Composite International Diagnostic Interview (CIDI) (odds ratio (OR) = 2.10; 95% CI = 1.15-3.87). Compared with semi-structured interviews, fully structured interviews (MINI excluded) were non-significantly more likely to classify participants with low-level depressive symptoms (PHQ-9 scores ≤6) as having major depression (OR = 3.13; 95% CI = 0.98-10.00), similarly likely for moderate-level symptoms (PHQ-9 scores 7-15) (OR = 0.96; 95% CI = 0.56-1.66) and significantly less likely for high-level symptoms (PHQ-9 scores ≥16) (OR = 0.50; 95% CI = 0.26-0.97).
CONCLUSIONS: The MINI may identify more people as depressed than the CIDI, and semi-structured and fully structured interviews may not be interchangeable methods, but these results should be replicated.Declaration of interestDrs Jetté and Patten declare that they received a grant, outside the submitted work, from the Hotchkiss Brain Institute, which was jointly funded by the Institute and Pfizer. Pfizer was the original sponsor of the development of the PHQ-9, which is now in the public domain. Dr Chan is a steering committee member or consultant of Astra Zeneca, Bayer, Lilly, MSD and Pfizer. She has received sponsorships and honorarium for giving lectures and providing consultancy and her affiliated institution has received research grants from these companies. Dr Hegerl declares that within the past 3 years, he was an advisory board member for Lundbeck, Servier and Otsuka Pharma; a consultant for Bayer Pharma; and a speaker for Medice Arzneimittel, Novartis, and Roche Pharma, all outside the submitted work. Dr Inagaki declares that he has received grants from Novartis Pharma, lecture fees from Pfizer, Mochida, Shionogi, Sumitomo Dainippon Pharma, Daiichi-Sankyo, Meiji Seika and Takeda, and royalties from Nippon Hyoron Sha, Nanzando, Seiwa Shoten, Igaku-shoin and Technomics, all outside of the submitted work. Dr Yamada reports personal fees from Meiji Seika Pharma Co., Ltd., MSD K.K., Asahi Kasei Pharma Corporation, Seishin Shobo, Seiwa Shoten Co., Ltd., Igaku-shoin Ltd., Chugai Igakusha and Sentan Igakusha, all outside the submitted work. All other authors declare no competing interests. No funder had any role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.