Body mass index trajectories and prostate cancer risk: Results from the EPICAP study

Lavalette, Céline; Duverger, E Cordina; Artaud, Fanny; Rébillard, Xavier; Lamy, Pierre; Trétarre, Brigitte; Cénée, Sylvie; Ménégaux, Florence

doi:10.1002/cam4.3241

Cited by 21 publications

(24 citation statements)

References 39 publications

(46 reference statements)

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“…As expected, aging was the most potent factor in our model. Higher BMI was associated with increased risk of PC, which is consistent with a previous Korean prediction model [15] and other epidemiological studies [46,47]. As found in previous studies, persons with hypertension showed a higher risk of PC [28], and DM was inversely associated with PC incidence [26,27].…”

Section: Predictor Variables For Pc Risksupporting

confidence: 90%

Personalized 5-Year Prostate Cancer Risk Prediction Model in Korea Based on Nationwide Representative Data

Yeo

Shin

Lee

et al. 2021

JPM

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Prostate cancer is the fourth most common cause of cancer in men in Korea, and there has been a rapid increase in cases. In the present study, we constructed a risk prediction model for prostate cancer using representative data from Korea. Participants who completed health examinations in 2009, based on the Korean National Health Insurance database, were eligible for the present study. The crude and adjusted risks were explored with backward selection using the Cox proportional hazards model to identify possible risk variables. Risk scores were assigned based on the adjusted hazard ratios, and the standardized points for each risk factor were proportional to the β-coefficient. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability was assessed by plotting the mean predicted probability against the mean observed probability of prostate cancer. Among the candidate predictors, age, smoking intensity, body mass index, regular exercise, presence of type 2 diabetes mellitus, and hypertension were included. Our risk prediction model showed good discrimination (c-statistic: 0.826, 95% confidence interval: 0.821–0.832). The relationship between model predictions and actual prostate cancer development showed good correlation in the calibration plot. Our prediction model for individualized prostate cancer risk in Korean men showed good performance. Using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding prostate cancer screening.

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Section: Predictor Variables For Pc Risksupporting

confidence: 90%

Personalized 5-Year Prostate Cancer Risk Prediction Model in Korea Based on Nationwide Representative Data

Yeo

Shin

Lee

et al. 2021

JPM

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“…Of the 47 studies that included both males and females, 18 studies reported trajectories only stratified by sex. The most common study design used was a cohort study ( n = 55), three studies were case‐control designs 27,30,35 with measures of recalled body size from different age periods and one study was a randomized control trial 25 . Table in the Appendix outlines more detailed characteristics of the included studies.…”

Section: Resultsmentioning

confidence: 99%

“…Of these 22 studies with growth trajectories beginning in childhood, five studies 20,22,38,41,51 had measures until young adulthood (19–23 years old), 12 studies 16,30–32,36,42–50 had measures until midadulthood (38–50 years old), and five studies 5,14,17,18,46 had measures until older adulthood (60–80 years old). The remaining studies ( n = 37) 15,19,21–29,33–35,37,39,40,52–72 did not include any childhood measures, and only included measures >18 years of age. Of these 37 studies that only reported growth measures in adulthood, six studies 26,61–64,70 only included measures during the older adulthood period of life (≥60 years of age).…”

Section: Resultsmentioning

confidence: 99%

Group‐based trajectory modeling of body mass index and body size over the life course: A scoping review

Rubeis

Andreacchi

Sharpe

et al. 2020

Obesity Science & Practice

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Background Group‐based trajectory modeling has been applied to identify distinct trajectories of growth across the life course. Objectives of this study were to describe the methodological approaches for group‐based modeling of growth across the life course and to summarize outcomes across studies. Methods A scoping review with a systematic search of Medline, EMBASE, CINAL, and Web of Science was conducted. Studies that used a group‐based procedure to identify trajectories on any statistical software were included. Data were extracted on trajectory methodology, measures of growth, and association with outcomes. Results A total of 59 studies were included, and most were published from 2013 to 2020. Body mass index was the most common measure of growth (n = 43). The median number of identified trajectories was 4 (range: 2–9). PROC TRAJ in SAS was used by 33 studies, other procedures used include TRAJ in STATA, lcmm in R, and Mplus. Most studies evaluated associations between growth trajectories and chronic disease outcomes (n = 22). Conclusions Group‐based trajectory modeling of growth in adults is emerging in epidemiologic research, with four distinct trajectories observed somewhat consistently from all studies. Understanding life course growth trajectories may provide further insight for population health interventions.

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“…Additionally, we performed subsequent analyses to explore the potential mediatory role of BMI, Lp(a) and testosterone in the associations between drug targets and prostate cancer risk. Elevated BMI has been found to associated with increased prostate cancer risk in multiple observational studies [24][25][26], whereas MR study identified evidence for an inversed association between them [66]. In addition, levels of testosterone were found to be strongly correlated with the risk of prostate cancer in both observational [28] and MR [29] studies.…”

Section: Discussionmentioning

confidence: 97%

“…The effect of LDL-c lowering drugs, LDLR expression and circulating LDL-c on advanced prostate cancer and early-onset prostate cancer was also investigated. Finally, we evaluated the genetically proxied effects of LDL-c lowering drugs on several traits previously implicated to play a role in prostate cancer risk (body mass index (BMI) [24][25][26], lipoprotein A (Lp(a)) [27] and testosterone [28,29]) to discern whether they may reside along the pathways between therapeutic targets and risk of prostate cancer.…”

Section: Introductionmentioning

confidence: 99%

Genetically proxied PCSK9 inhibition provides indication of lower prostate cancer risk: a Mendelian randomization study

Fang

Yarmolinsky

Bull

et al. 2022

Preprint

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Background Prostate cancer (PrCa) is the second most prevalent malignancy in men worldwide. Observational studies have linked the use of low-density lipoprotein cholesterol (LDL-c) lowering therapies with reduced risk of PrCa, which may potentially be attributable to confounding factors. In this study, we performed a drug target Mendelian randomization (MR) analysis to evaluate the association of genetically proxied inhibition of LDL-c lowering drug targets on risk of PrCa. Methods and Findings Single-nucleotide polymorphisms (SNPs) in and around HMGCR , NPC1L1 and PCSK9 genes associated with LDL-c (P<5×10-8) from the Global Lipids Genetics Consortium genome-wide association study (GWAS) (N=173,082) were used to proxy the therapeutic inhibition of these targets. Association estimates for the risk of total, advanced and early-onset PrCa were obtained from the PRACTICAL consortium. Replication was performed using genetic instruments from an LDL-c GWAS conducted on male UK Biobank participants of European ancestry (N=201,678), as well as instruments selected based on liver-derived gene expression and circulation plasma levels of targets. We also investigated whether putative mediators may play a role in findings for traits previously implicated in PrCa risk (i.e., lipoprotein a (Lp(a)), body mass index (BMI) and testosterone). Applying MR using the inverse-variance weighted approach accounting for genetic correlations between instruments provided strong evidence supporting an effect of genetically proxied inhibition of PCSK9 (equivalent to a standard deviation (SD) reduction in LDL-c) on lower risk of total PrCa (odds ratio (OR)=0.84, 95% confidence interval (CI)=0.74 to 0.96, P=7.86×10-3) and early-onset PrCa OR=0.70, 95% CI=0.52 to 0.95, P=0.021. Analyses using male-stratified instruments provided consistent results. In contrast, there was little evidence of an association of genetically proxied HMGCR (OR=0.83, 95% CI=0.67 to 1.03, P=0.093) or NPC1L1 (OR=1.27, 95% CI=0.87 to 1.87, P=0.218) inhibition on PrCa risk. Secondary analyses supported a genetically proxied effect of liver-specific PCSK9 expression (OR=0.90, 95% CI=0.86 to 0.95, P=5.50×10-5) and circulating plasma levels of PCSK9 (OR=0.93 per SD reduction in PCSK9, 95% CI=0.87 to 0.997, P=0.04) on PrCa risk. Colocalization using eCAVIAR identified evidence (colocalization posterior probability=0.103) of a shared genetic variant (rs553741) between liver-derived PCSK9 expression and PrCa risk. Moreover, genetically proxied PCSK9 inhibition was strongly associated with Lp(a) levels (Beta= -0.07, 95% CI= -0.10 to -0.03, P=1.44×10-4), but not BMI or testosterone, indicating a putative mediatory role of Lp(a). Conclusions Our study supports a strong association between genetically proxied inhibition of PCSK9 and a lower risk of total and early-onset PrCa. Further evidence from clinical studies is needed to confirm this finding as well as the putative mediatory role of Lp(a).

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Body mass index trajectories and prostate cancer risk: Results from the EPICAP study

Cited by 21 publications

References 39 publications

Personalized 5-Year Prostate Cancer Risk Prediction Model in Korea Based on Nationwide Representative Data

Personalized 5-Year Prostate Cancer Risk Prediction Model in Korea Based on Nationwide Representative Data

Group‐based trajectory modeling of body mass index and body size over the life course: A scoping review

Genetically proxied PCSK9 inhibition provides indication of lower prostate cancer risk: a Mendelian randomization study

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