RESUMEN

BACKGROUND: Multiple sclerosis risk has been shown to have seasonal variations that are more pronounced in higher latitudes. However, this phenomenon has not been adequately studied near the Equator. OBJECTIVE: To explore the risk of multiple sclerosis associated with month, season of birth, and sunlight exposure variables in Colombia. METHODS: In this case-control study, 668 multiple sclerosis cases were matched to 2672 controls by sex and age. Association of multiple sclerosis with each month/season of birth and sunlight exposure variables was estimated with multilevel mixed-effects logistic regression and ecological regression models, respectively. Seasonality in the births of multiple sclerosis was assessed with a non-parametric seasonality test. RESULTS: We found a higher probability of multiple sclerosis in September (0.25; 95% confidence interval (CI) = 0.21-0.31) and lower in March (0.15; 95% CI = 0.10-0.18), which turned non-significant after a multiple comparisons test. Sunlight exposure variables had no significant effect on the risk of MS, and the tests of seasonality in the births of MS did not show significant results. CONCLUSION: Our results show no seasonality in the risk of multiple sclerosis near the Equator, supporting the hypothesis that this phenomenon is latitude dependent.

Asunto(s)

RESUMEN

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID's tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan-Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

RESUMEN

Background: Early diagnosis and treatment of multiple sclerosis (MS) is crucial to avoid future disability. The factors that influence diagnostic delay in low prevalence settings have been poorly studied.Objectives: To evaluate the factors associated with a delayed diagnosis of MS after the symptomatic onset.Methods: Clinical records of confirmed MS patients were reviewed. Diagnostic delay was calculated by subtracting the date of onset from the date of diagnosis and categorized as early and delayed, when below and above than 1 year. Logistic regression was performed to evaluate the likelihood of a delayed diagnosis according to age at first symptom, gender, type of the first symptom, progressive vs relapsing onset, diagnostic criteria prevailing at the time of symptom onset, comorbidities, and family history of MS.Results: Data of 525 (95.6%) from a cohort of 549 patients were analyzed. About 69.1% were women. The mean age was 43.2 years. About 86.3% had relapsing-remitting MS. The mean overall diagnostic delay was 3.07 years. About 45.7% of the patients had a delayed diagnosis, and it was dependent on the symptom and the diagnostic criteria prevailing at the onset. Multivariate logistic regression showed onset during the Schumacher (OR = 10.03 [95%CI 1.30-77.1], p = 0.027) and Poser (OR = 4.26 [95%CI 1.25-15.15], p = 0.021) years were associated with delayed MS diagnosis.Conclusions: MS onset before the McDonald diagnostic criteria era is associated with delayed diagnosis.

Asunto(s)