Sex difference in the associations among risk factors with gastroesophageal reflux disease in a large Taiwanese population study.

BACKGROUND: Gastroesophageal reflux disease (GERD) is a common global health issue. Previous studies have revealed a higher prevalence of GERD in females than in males, however few studies have investigated sex differences in the risk factors associated with GERD. Therefore, the aim of this population-based study was to examine sex differences in the risk factors for GERD in a large cohort of over 120,000 Taiwanese participants. METHODS: We enrolled 121,583 participants (male: 43,698; female: 77,885; mean age 49.9 ± 11.0 years) from the Taiwan Biobank. The presence of GERD was ascertained using self-reported questionnaires. Sex differences in the risk factors associated with GERD were examined using multivariable logistic regression analysis. RESULTS: The overall prevalence of GERD was 13.7%, including 13.0% in the male participants and 14.1% in the female participants (p < 0.001). Multivariable analysis showed that older age, hypertension, smoking history, alcohol history, low fasting glucose, and low uric acid were significantly associated with GERD in the male participants. In the female participants, older age, diabetes, hypertension, smoking history, alcohol history, low systolic blood pressure, low fasting glucose, high hemoglobin, high total cholesterol, low high-density lipoprotein cholesterol (HDL-C), low low-density lipoprotein cholesterol, and low uric acid were significantly associated with GERD. Significant interactions were found between sex and age (p < 0.001), diabetes (p < 0.001), smoking history (p < 0.001), fasting glucose (p = 0.002), triglycerides (p = 0.001), HDL-C (p = 0.001), and estimated glomerular filtration rate (p = 0.002) on GERD. CONCLUSIONS: Our results showed a higher prevalence of GERD among females compared to males. Furthermore, sex differences were identified in the risk factors associated with GERD, and older age, diabetes, smoking history, and low HDL-C were more closely related to GERD in females than in males.

Identification of hot regions in protein-protein interactions by sequential pattern mining.

BACKGROUND: Identification of protein interacting sites is an important task in computational molecular biology. As more and more protein sequences are deposited without available structural information, it is strongly desirable to predict protein binding regions by their sequences alone. This paper presents a pattern mining approach to tackle this problem. It is observed that a functional region of protein structures usually consists of several peptide segments linked with large wildcard regions. Thus, the proposed mining technology considers large irregular gaps when growing patterns, in order to find the residues that are simultaneously conserved but largely separated on the sequences. A derived pattern is called a cluster-like pattern since the discovered conserved residues are always grouped into several blocks, which each corresponds to a local conserved region on the protein sequence. RESULTS: The experiments conducted in this work demonstrate that the derived long patterns automatically discover the important residues that form one or several hot regions of protein-protein interactions. The methodology is evaluated by conducting experiments on the web server MAGIIC-PRO based on a well known benchmark containing 220 protein chains from 72 distinct complexes. Among the tested 218 proteins, there are 900 sequential blocks discovered, 4.25 blocks per protein chain on average. About 92% of the derived blocks are observed to be clustered in space with at least one of the other blocks, and about 66% of the blocks are found to be near the interface of protein-protein interactions. It is summarized that for about 83% of the tested proteins, at least two interacting blocks can be discovered by this approach. CONCLUSION: This work aims to demonstrate that the important residues associated with the interface of protein-protein interactions may be automatically discovered by sequential pattern mining. The detected regions possess high conservation and thus are considered as the computational hot regions. This information would be useful to characterizing protein sequences, predicting protein function, finding potential partners, and facilitating protein docking for drug discovery.