SynthesisBMC cancer2025
Assessing the relationship between cardiometabolic diseases and the risk of developing aggressive prostate cancer: a systematic review and meta-analysis.
Synthesis in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundProstate cancer is the most prevalent cancer among men within the U.S. and globally, with rising incidence, including advanced-staged disease. Risk factors for aggressive prostate cancer are not well defined. This systematic review and meta-analysis provide an overview of the relationship between cardiometabolic diseases (diabetes, dyslipidemia, obesity, and hypertension) and aggressive prostate cancer.
methodsAggressive prostate cancer was defined as disease that has spread or is at high risk of spreading: high-risk or very high-risk localized (T3-T4, Grade Group 4-5), node-positive (N1), or metastatic (M1). Using PRISMA guidelines, a total of 4,830 publications revealed 25 cohort studies of over 974,000 men. Following the systematic review of these prospective studies of men with prostate cancer, R was utilized to run a random effects model, yielding hazard ratios with 95% confidence intervals and generating forest plots with measures of heterogeneity.
resultsExamination of these studies revealed that a positive association exists. Diabetes was associated with a significantly increased risk of aggressive prostate cancer (HR = 1.18; 95% CI: 1.07-1.30; p = 0.0008). Obesity also showed a significant association (HR = 1.15; 95% CI: 1.06-1.24; p = 0.0006), as did hypertension, though to a lesser degree (HR = 1.07; 95% CI: 1.00-1.14; p = 0.04). Dyslipidemia was not significantly associated with aggressive prostate cancer (HR = 1.03; 95% CI: 0.98-1.03; p = 0.26). DISCUSSION: Three of the four cardiometabolic disease components (diabetes, obesity and hypertension) were shown to have statistical significance and offered intriguing evidence on their potential associations with aggressive prostate cancer. Dyslipidemia's association was not statistically significant, which could be attributed to variations in methods of assessment and differing mechanistic effects. High heterogeneity and limited study availability remain key limitations.
conclusionIf such associations between cardiometabolic diseases and prostate cancer aggressiveness are shown to be cause and effect, such controllable and treatable conditions can allow oncologists to work alongside primary care physicians to improve patient outcomes and reduce the incidence of aggressive disease. Through the promotion of lifestyle modifications, tighter cardiometabolic control, and targeted interventions, public health efforts might improve prostate cancer outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.