Poultry consumption and prostate cancer risk: a meta-analysis

Poultry Consumption and Human Health: How Much Is Really Known? A Systematically Searched Scoping Review and Research Perspective

This scoping review was conducted to systematically search and chronicle scientific literature pertinent to poultry intake and human health. The protocol (uploaded to Open Science Framework, https://osf.io/2k7bj/) was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews guidelines. Articles with observational and experimental research, narrative and systematic reviews, and meta-analyses were included. Among 13,141 articles identified, 525 met inclusion criteria. Among these 525 articles, 212 focused on cancer morbidity and mortality; 41 on cardiovascular disease (CVD) morbidity and mortality; 52 on CVD risk factors; 32 on type 2 diabetes mellitus (T2DM) morbidity and mortality; 33 on T2DM risk factors; and 42 on body weight and body composition. An "Other" category (181 articles) included nutrient status, psychological well-being/mental health, cognition, microbiome, chronic kidney disease, nonalcoholic fatty liver disease, skin disorders, and fertility, among others. Among the 525 included articles, 366 were observational, 64 were experimental, and 76 were reviews and meta-analyses. Eighty-three percent of articles focused on adults or older adults. A paucity of research exists to support poultry as health-promoting foods, with most research only indirectly assessing poultry intake compared with other foods of interest (e.g., red meats or plant-based protein foods). No randomized controlled trials and only 1% of OBS assessed the influence of processed poultry intake on human health. In the future, the relative health effects of consuming poultry will be compared with a widening array of traditional and new protein-rich food products, necessitating the need for research to assess poultry as foods of choice. Science and health professionals, the poultry industry, and the public will benefit from new observational and experimental research to address cutting-edge scientific, public policy, and consumer topics pertinent to poultry intake and human health.

Modifiable risk factors for prostate cancer in low- and lower-middle-income countries: a systematic review and meta-analysis

BACKGROUND

Most of the epidemiological data on prostate cancer risk factors come from high-income countries (HIC). Reducing exposure to prostate cancer modifiable risk factors may significantly lower PCa morbidity and mortality in LIC and MIC. The objective of this study was to summarize the evidence on modifiable risk factors (RFs) for PCa in LIC and lower-middle-income countries (LMIC).

METHODS

We conducted a systematic search on MEDLINE, EMBASE, and Global Health databases. We selected case-control and cohort studies from 2010 onwards that studied modifiable RFs for PCa in LIC and LMIC with a population of 30 million or more, as defined by the World Bank in January 2021. Risk of bias was assessed by the Ottawa-Newcastle tool. Individual study estimates were pooled when estimates were available for at least two studies.

RESULTS

5740 studies were initially identified; 16 studies met inclusion criteria. All were case-control studies except one retrospective cohort study. Higher fat intake was associated with a higher risk of PCa incidence with an odds ratio (OR) of 3.13 (95% CI 1.33-7.33). Higher vegetable intake (OR 0.48, 95% CI 0.24-0.97) and tea consumption (OR 0.51, 95% CI 0.32-0.83) were associated with a lower risk for PCa. There was no association between fruits, fish, and chicken consumption and risk of PCa. Alcohol consumption, smoking, red meat intake, and a BMI ≥ 25-30 kg/m² showed a trend towards an increased risk, although these were not statistically significant.

CONCLUSIONS

In LIC and LMIC, high fat intake was associated with higher risk of PCa while a diet rich in vegetables and tea intake was associated with a lower risk. Future prospective studies will be important to elucidate whether other modifiable risk factors for PCa specific to LIC and LMIC can be identified to inform impactful and cost-effective preventive strategies in these countries.

Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression

Cancer diagnosis using machine learning algorithms is one of the main topics of research in computer-based medical science. Prostate cancer is considered one of the reasons that are leading to deaths worldwide. Data analysis of gene expression from microarray using machine learning and soft computing algorithms is a useful tool for detecting prostate cancer in medical diagnosis. Even though traditional machine learning methods have been successfully applied for detecting prostate cancer, the large number of attributes with a small sample size of microarray data is still a challenge that limits their ability for effective medical diagnosis. Selecting a subset of relevant features from all features and choosing an appropriate machine learning method can exploit the information of microarray data to improve the accuracy rate of detection. In this paper, we propose to use a correlation feature selection (CFS) method with random committee (RC) ensemble learning to detect prostate cancer from microarray data of gene expression. A set of experiments are conducted on a public benchmark dataset using 10-fold cross-validation technique to evaluate the proposed approach. The experimental results revealed that the proposed approach attains 95.098% accuracy, which is higher than related work methods on the same dataset.