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OBJECTIVE: The main goal of the present research is to explore the potential link of body mass index (BMI) with different survival metrics in breast cancer patients. Our aim is to offer the latest and most thorough meta-analysis, assessing the strength and reliability of the connection that BMI has with prognostic indicators in this disease. PATIENTS AND METHODS: As of January 2024, we conducted a systematic literature search across PubMed, Embase, Web of Science, and the Cochrane Library databases. Our search aimed to identify studies examining BMI as an exposure factor, with breast cancer patients constituting the study population, and utilizing adjusted hazard ratio (HR) as the data type of interest. RESULTS: The evidence synthesis incorporated a total of 61 eligible articles involving 201,006 patients. Being underweight posed a risk factor for overall survival (OS) in breast cancer patients compared to normal weight (HR 1.15, 95% CI 0.98-1.35; P = 0.08). Overweight or obesity, in comparison to normal weight, was a risk factor for OS (HR 1.18, 95% CI 1.14-1.23; P < 0.00001), disease-free survival (DFS) (HR 1.11, 95% CI 1.08-1.13; P < 0.00001), relapse-free survival (RFS) (HR 1.14, 95% CI 1.06-1.22; P = 0.03), and breast cancer-specific survival (BCSS) (HR 1.18, 95% CI 1.11-1.26; P < 0.00001), but not for progression-free survival (PFS) (HR 0.91, 95% CI 0.76-1.10; P = 0.33). Notably, in subgroup analyses, overweight patients achieved prolonged PFS (HR 0.80, 95% CI 0.64-0.99; P = 0.04), and compared to the obese population, the overweight cohort exhibited a significant difference in OS (HR 1.11, 95% CI 1.05-1.16; P < 0.00001) and DFS (HR 1.06, 95% CI 1.03-1.10; P = 0.0004), with a considerably stronger association. Furthermore, compared to HER- patients, HER + patients exhibited a greater predictive value for OS (HR 1.23, 95% CI 1.10-1.37; P = 0.0004), RFS (HR 1.30, 95% CI 1.03-1.64; P < 0.00001), and DFS (HR 1.10, 95% CI 1.03-1.17; P = 0.003). CONCLUSIONS: The results of our meta-analysis reveal a notable association between BMI and various survival measures in breast cancer prognosis. These findings provide a solid basis for predicting breast cancer outcomes and implementing more effective therapeutic approaches.
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BACKGROUND: Brazil is exceptionally abundant in medicinal plant resources and has a rich ethnopharmacological history. Brazilian Pharmacopoeia (BP) acts as a national standard that regulates drug quality and has six published editions. Recent genomic approaches have led to a resurgence of interest in herbal drugs. The genomic data of plants has been used for pharmaceutical applications, protecting natural resources, and efficiently regulating the market. However, there are few genomic databases specifically for medicinal plants, and the establishment of a database that focuses on the herbs contained in the BP is urgently required. METHODS: The medicinal plant species included in each edition of the BP were analyzed to understand the evolution of the Brazilian herbal drugs. The data of 82 plants in the BP were collected and categorized into four sections: DNA barcodes, super-barcodes, genomes, and sequencing data. A typical web server architecture pattern was used to build the database and website. Furthermore, the cp-Gs of the Aloe genus in the database were analyzed as an illustration. RESULTS: A new database, the Brazilian Pharmacopoeia Genomic Database (BPGD) was constructed and is now publicly accessible. A BLAST server for species identification and sequence searching with the internal transcribed spacer 2 (ITS2), the intergenic region (psbA-trnH), and the chloroplast genome (cp-G) of Brazilian medicinal plants was also embedded in the BPGD. The database has 753 ITS2 of 76 species, 553 psbA-trnH and 190 genomes (whole genome and chloroplast genome) of 57 species. In addition, it contains 37 genome sequence data sets of 24 species and 616 transcriptome sequence data sets of 34 species and also includes 187 cp-Gs representing 57 medicinal species in the BP. Analyses of the six cp-Gs of three Aloe species identified the variable regions in the cp-Gs. These can be used to identify species and understand the intraspecific relationships. CONCLUSIONS: This study presents the first genomic database of medicinal plants listed in the latest BP. It serves as an efficient platform to obtain and analyze genomic data, accelerate studies regarding Brazilian medicinal plants and facilitate the rational development on their market regulation.