Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D's Real-Time Capabilities and Cross-Hospital Transferability.

Background: Postoperative ileus (POI) after colorectal surgery leads to increased morbidity, costs, and hospital stays. Identifying POI risk for early intervention is important for improving surgical outcomes especially given the increasing trend towards early discharge after surgery. While existing studies have assessed POI risk with regression models, the role of deep learning's remains unexplored. Methods: We assessed the performance and transferability (brutal force/instance/parameter transfer) of Gated Recurrent Unit with Decay (GRU-D), a longitudinal deep learning architecture, for real-time risk assessment of POI among 7,349 colorectal surgeries performed across three hospital sites operated by Mayo Clinic with two electronic health records (EHR) systems. The results were compared with atemporal models on a panel of benchmark metrics. Results: GRU-D exhibits robust transferability across different EHR systems and hospital sites, showing enhanced performance by integrating new measurements, even amid the extreme sparsity of real-world longitudinal data. On average, for labs, vitals, and assisted living status, 72.2%, 26.9%, and 49.3% respectively lack measurements within 24 hours after surgery. Over the follow-up period with 4-hour intervals, 98.7%, 84%, and 95.8% of data points are missing, respectively. A maximum of 5% decrease in AUROC was observed in brutal-force transfer between different EHR systems with non-overlapping surgery date frames. Multi-source instance transfer witnessed the best performance, with a maximum of 2.6% improvement in AUROC over local learning. The significant benefit, however, lies in the reduction of variance (a maximum of 86% decrease). The GRU-D model's performance mainly depends on the prediction task's difficulty, especially the case prevalence rate. Whereas the impact of training data and transfer strategy is less crucial, underscoring the challenge of effectively leveraging transfer learning for rare outcomes. While atemporal Logit models show notably superior performance at certain pre-surgical points, their performance fluctuate significantly and generally underperform GRU-D in post-surgical hours. Conclusion: GRU-D demonstrated robust transferability across EHR systems and hospital sites with highly sparse real-world EHR data. Further research on built-in explainability for meaningful intervention would be highly valuable for its integration into clinical practice.

GRU-D-Weibull: A novel real-time individualized endpoint prediction.

BACKGROUND: In the era of healthcare digital transformation, using electronic health record (EHR) data to generate various endpoint estimates for active monitoring is highly desirable in chronic disease management. However, traditional predictive modeling strategies leveraging well-curated data sets can have limited real-world implementation potential due to various data quality issues in EHR data. METHODS: We propose a novel predictive modeling approach, GRU-D-Weibull, which models Weibull distribution leveraging gated recurrent units with decay (GRU-D), for real-time individualized endpoint prediction and population level risk management using EHR data. EXPERIMENTS: We systematically evaluated the performance and showcased the real-world implementability of the proposed approach through individual level endpoint prediction using a cohort of patients with chronic kidney disease stage 4 (CKD4). A total of 536 features including ICD/CPT codes, medications, lab tests, vital measurements, and demographics were retrieved for 6879 CKD4 patients. The performance metrics including C-index, L1-loss, Parkes' error, and predicted survival probability at time of event were compared between GRU-D-Weibull and other alternative approaches including accelerated failure time model (AFT), XGBoost based AFT (XGB(AFT)), random survival forest (RSF), and Nnet-survival. Both in-process and post-process calibrations were experimented on GRU-D-Weibull generated survival probabilities. RESULTS: GRU-D-Weibull demonstrated C-index of ~0.7 at index date, which increased to ~0.77 at 4.3 years of follow-up, comparable to that of RSF. GRU-D-Weibull achieved absolute L1-loss of ~1.1 years (sd≈0.95) at CKD4 index date, and a minimum of ~0.45 year (sd≈0.3) at 4 years of follow-up, comparing to second-ranked RSF of ~1.4 years (sd≈1.1) at index date and ~0.64 years (sd≈0.26) at 4 years. Both significantly outperform competing approaches. GRU-D-Weibull constrained predicted survival probability at time of event to smaller and more fixed range than competing models throughout follow-up. Significant correlations were observed between prediction error and missing proportions of all major categories of input features at index date (Corr ~0.1 to ~0.3), which faded away within 1 year after index date as more data became available. Through post training recalibration, we achieved a close alignment between the predicted and observed survival probabilities across multiple prediction horizons at different time points during follow-up. CONCLUSION: GRU-D-Weibull shows advantages over competing methods in handling missingness commonly encountered in EHR data and providing both probability and point estimates for diverse prediction horizons during follow-up. The experiment highlights the potential of GRU-D-Weibull as a suitable candidate for individualized endpoint risk management, utilizing real-time clinical data to generate various endpoint estimates for monitoring. Additional research is warranted to evaluate the influence of different data quality aspects on prediction performance. Furthermore, collaboration with clinicians is essential to explore the integration of this approach into clinical workflows and evaluate its effects on decision-making processes and patient outcomes.

Real-time risk prediction of colorectal surgery-related post-surgical complications using GRU-D model.

BACKGROUND: Post-surgical complications (PSCs) have been an increasing concern for hospitals in light of Medicare penalties for 30-day readmissions. PSCs have become a target for quality improvement and benchmark for the healthcare system. Over half (60 %) of the deep or organ space surgical site infections are discovered after discharge, leading to a readmission. There has thus been a push to develop risk prediction models for targeted preventive interventions for PSCs. OBJECTIVE: We experiment several Gated Recurrent Unit with Decay (GRU-D) based deep learning architectures with various feature sampling schemes in predicting the risk of colorectal PSCs and compare with atemporal logistic regression models (logit). METHOD: We used electronic health record (EHR) data of 3,535 colorectal surgical patients involved in the national surgical quality improvement program (NSQIP) between 2006 and 2018. Single layer, stacked layer, and multimodal GRU-D models with sigmoid activation were used to develop risk prediction models. Area Under the Receiver Operating Characteristic curve (AUROC) was calculated by comparing predicted probability of developing complications versus truly observed PSCs (NSQIP adjudicated) within 30 days after surgery. We set up cross-validation and an independent held-out dataset for testing model performance consistency. RESULTS AND CONCLUSION: The primary contribution of our study is the formulation of a novel real-time PSC risk prediction task using GRU-D with demonstrated clinical utility. GRU-D outperforms the logit model in predicting wound and organ space infection and shows improved performance as additional data points become available. Logit model outperforms GRU-D before surgery for superficial infection and bleeding. For the same sampling scheme, there is no obvious advantage of complex architectures (stacked, multimodal) over single layer GRU-D. Obtaining more data points closer to the occurrence of PSCs is more important than using a more frequent sampling scheme in training GRU-D models. The fourth predicted risk quartile by single layer GRU-D contains 63 %, 59 %, and 66 % organ space infection cases, at 4 h before, 72 h after, and 168 h after surgery, respectively, suggesting its potential application as a bedside risk assessment tool.

Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.

PURPOSE: The advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer research and patient care by using the Minimal Common Oncology Data Elements (mCODE), which is a community-driven effort to define a minimal set of data elements for cancer research and practice. Specifically, we aim to assess the alignment of NLP-extracted data elements with mCODE and review existing NLP methodologies for extracting said data elements. METHODS: Published literature studies were searched to retrieve cancer-related NLP articles that were written in English and published between January 2010 and September 2020 from main literature databases. After the retrieval, articles with EHRs as the data source were manually identified. A charting form was developed for relevant study analysis and used to categorize data including four main topics: metadata, EHR data and targeted cancer types, NLP methodology, and oncology data elements and standards. RESULTS: A total of 123 publications were selected finally and included in our analysis. We found that cancer research and patient care require some data elements beyond mCODE as expected. Transparency and reproductivity are not sufficient in NLP methods, and inconsistency in NLP evaluation exists. CONCLUSION: We conducted a comprehensive review of cancer NLP for research and patient care using EHRs data. Issues and barriers for wide adoption of cancer NLP were identified and discussed.

Assessment of Data Quality Variability across Two EHR Systems through a Case Study of Post-Surgical Complications.

Translation of predictive modeling algorithms into routine clinical care workflows faces challenges in the form of varying data quality-related issues caused by the heterogeneity of electronic health record (EHR) systems. To better understand these issues, we retrospectively assessed and compared the variability of data produced from two different EHR systems. We considered three dimensions of data quality in the context of EHR-based predictive modeling for three distinct translational stages: model development (data completeness), model deployment (data variability), and model implementation (data timeliness). The case study was conducted based on predicting post-surgical complications using both structured and unstructured data. Our study discovered a consistent level of data completeness, a high syntactic, and moderate-high semantic variability across two EHR systems, for which the quality of data is context-specific and closely related to the documentation workflow and the functionality of individual EHR systems.

Early genetic aberrations in patients with sporadic colorectal cancer.

Chromosome instability (CIN) is widely observed in both sporadic and hereditary colorectal cancer (CRC). Defects in APC and WNT signaling are primarily associated with CIN in hereditary CRC, but the genetic causes for CIN in sporadic CRC remain elusive. Using high-density SNP array and exome data from The Cancer Genome Atlas (TCGA), we characterized loss of heterozygosity (LOH) and copy number variation (CNV) in the peripheral blood, normal colon, and corresponding tumor tissue in 15 CRC patients with proficient mismatch repair (MMR) and 24 CRC patients with deficient MMR. We found a high frequency of 18q LOH in tumors and arm-specific enrichment of genetic aberrations on 18q in the normal colon (primarily copy neutral LOH) and blood (primarily copy gain). These aberrations were specific to the sporadic, pMMR CRC. Though in tumor samples genetic aberrations were observed for genes commonly mutated in hereditary CRC (eg, APC, CTNNB1, SMAD4, BRAF), none of them showed LOH or CNV in the normal colon or blood. DCC located on 18q21.1 topped the list of genes with genetic aberrations in the tumor. In an independent cohort of 13 patients subjected to Whole Genome Sequencing (WGS), we found LOH and CNV on 18q in adenomatous polyp and tumor tissues. Our data suggests that patients with sporadic CRC may have genetic aberrations preferentially enriched on 18q in their blood, normal colon epithelium, and non-malignant polyp lesions that may prove useful as a clinical marker for sporadic CRC detection and risk assessment.

A Novel Energy-Efficient Approach for Human Activity Recognition.

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.

Peripheral Neutrophil to Lymphocyte Ratio Improves Prognostication in Colon Cancer.

BACKGROUND: We studied the role of peripheral neutrophil to lymphocyte ratio (NLR) on survival outcomes in colon and rectal cancer to determine if its inclusion improved prognostication within existing staging systems. PATIENTS AND METHODS: Disease-free (DFS) and overall survival (OS) hazard ratios (HRs) of pretreatment NLR were calculated for 2536 patients with stage I to III colon or rectal cancer and adjusted for age, positive/total number of nodes, T stage, and grade. The association of NLR with clinicopathologic features and survival was evaluated and compared with the American Joint Committee on cancer (AJCC) TNM staging and Memorial Sloan Kettering Cancer Center (MSKCC) models. RESULTS: High NLR was significantly associated with worse DFS (HR, 1.36; 95% confidence interval [CI], 1.08-1.70; P = .009) and OS (HR, 1.65; 95% CI, 1.29-2.10; P < .0005) in all stages for patients with colon, but not rectal, cancer. High NLR was significantly associated with site-specific worse prognosis, which was stronger in the left versus right colon; an inverse relationship with grade was found. The impact of high NLR on DFS and OS occurred early, with the majority of deaths within 2 years following surgery. Adjusted HRs for 5-year and 2-year outcomes in colon cancer per each additional 2-unit increase in NLR were 1.15 (95% CI, 1.08-1.23) and 1.20 (95% CI, 1.10-1.30), respectively. The addition of NLR enhanced the prognostic utility of TNM (TNM alone vs. TNM + NLR: concordance index, 0.60 vs. 0.68), and MSKCC (MSKCC alone vs. MSKCC + NLR: concordance index, 0.71 vs. 0.73) models for colon cancer patients. CONCLUSION: NLR is an independent prognostic variable for nonmetastatic colon cancer that enhances existing clinical staging systems.

Comparison of Three Information Sources for Smoking Information in Electronic Health Records.

OBJECTIVE: The primary aim was to compare independent and joint performance of retrieving smoking status through different sources, including narrative text processed by natural language processing (NLP), patient-provided information (PPI), and diagnosis codes (ie, International Classification of Diseases, Ninth Revision [ICD-9]). We also compared the performance of retrieving smoking strength information (ie, heavy/light smoker) from narrative text and PPI. MATERIALS AND METHODS: Our study leveraged an existing lung cancer cohort for smoking status, amount, and strength information, which was manually chart-reviewed. On the NLP side, smoking-related electronic medical record (EMR) data were retrieved first. A pattern-based smoking information extraction module was then implemented to extract smoking-related information. After that, heuristic rules were used to obtain smoking status-related information. Smoking information was also obtained from structured data sources based on diagnosis codes and PPI. Sensitivity, specificity, and accuracy were measured using patients with coverage (ie, the proportion of patients whose smoking status/strength can be effectively determined). RESULTS: NLP alone has the best overall performance for smoking status extraction (patient coverage: 0.88; sensitivity: 0.97; specificity: 0.70; accuracy: 0.88); combining PPI with NLP further improved patient coverage to 0.96. ICD-9 does not provide additional improvement to NLP and its combination with PPI. For smoking strength, combining NLP with PPI has slight improvement over NLP alone. CONCLUSION: These findings suggest that narrative text could serve as a more reliable and comprehensive source for obtaining smoking-related information than structured data sources. PPI, the readily available structured data, could be used as a complementary source for more comprehensive patient coverage.

Time Lapse to Colorectal Cancer: Telomere Dynamics Define the Malignant Potential of Polyps.

OBJECTIVE: Whereas few adenomas become cancer, most colorectal cancers arise from adenomas. Telomere length is a recognized biomarker in multiple cancers, and telomere maintenance mechanisms (TMM) are exploited by malignant cells. We sought to determine whether telomere length and TMM distinguish cancer-associated adenomas from those that are cancer-free. METHODS: Tissues were identified as cancer-adjacent polyp (CAP)-residual adenoma contiguous with cancer-and cancer-free polyp (CFP)-adenomas without malignancy. Telomere length, TMM, and expression were measured in 102 tissues including peripheral blood leukocytes (PBLs), normal colon epithelium, adenoma, and cancer (in CAP cases) from 31 patients. Telomere length was measured in a separate cohort of 342 PBL from CAP and CFP patients. RESULTS: The mean differences in telomere length between normal and adenoma were greater in CAP than in CFP cases, P=0.001; telomere length in PBL was 91.7 bp greater in CAP than in CFP, P=0.007. Each 100 bp telomere increase was associated with a 1.14 (1.04-1.26) increased odds of being a CAP, P=0.0063. The polyp tissue from CAP patients had shorter telomeres and higher Telomerase reverse transcriptase (hTERT) expression compared with polyps from CFP patients, P=0.05. There was a greater degree of alternative lengthening of telomere (ALT) level difference in CFP polyps than in CAP polyps. The polyp telomere lengths of aggressive CAPs were significantly different from the polyps of non-aggressive CAPs, P=0.01. CONCLUSIONS: Adenomas that progress to cancer exhibit distinct telomere length and TMM profiles. We report for the first time that PBL telomeres differ in patients with polyps that become malignant, and therefore may have clinical value in adenoma risk assessment and management.

Colorectal Cancer with Residual Polyp of Origin: A Model of Malignant Transformation.

The majority of colorectal cancers (CRCs) arise from adenomatous polyps. In this study, we sought to present the underrecognized CRC with the residual polyp of origin (CRC RPO+) as an entity to be utilized as a model to study colorectal carcinogenesis. We identified all subjects with biopsy-proven CRC RPO+ that were evaluated over 10 years at Mayo Clinic, Rochester, MN, and compared their clinical and pathologic characteristics to CRC without remnant polyps (CRC RPO-). Overall survival and disease-free survival overlap with an equivalent hazard ratio between CRC RPO+ and RPO- cases when age, stage, and grade are adjusted. The somatic genomic profile obtained by whole genome sequencing and the gene expression profiles by RNA-seq for CRC RPO+ tumors were compared with that of age -and gender-matched CRC RPO- evaluated by The Cancer Genome Atlas. CRC RPO+ cases were more commonly found with lower-grade, earlier-stage disease than CRC RPO-. However, within the same disease stage and grade, their clinical course is very similar to that of CRC RPO-. The mutation frequencies of commonly mutated genes in CRC are similar between CRC RPO+ and RPO- cases. Likewise, gene expression patterns are indistinguishable between the RPO+ and RPO- cases. We have confirmed that CRC RPO+ is clinically and biologically similar to CRC RPO- and may be utilized as a model of the adenoma to carcinoma transition.

Comprehensive nucleosome mapping of the human genome in cancer progression.

Altered chromatin structure is a hallmark of cancer, and inappropriate regulation of chromatin structure may represent the origin of transformation. Important studies have mapped human nucleosome distributions genome wide, but the role of chromatin structure in cancer progression has not been addressed. We developed a MNase-Transcription Start Site Sequence Capture method (mTSS-seq) to map the nucleosome distribution at human transcription start sites genome-wide in primary human lung and colon adenocarcinoma tissue. Here, we confirm that nucleosome redistribution is an early, widespread event in lung (LAC) and colon (CRC) adenocarcinoma. These altered nucleosome architectures are consistent between LAC and CRC patient samples indicating that they may serve as important early adenocarcinoma markers. We demonstrate that the nucleosome alterations are driven by the underlying DNA sequence and potentiate transcription factor binding. We conclude that DNA-directed nucleosome redistributions are widespread early in cancer progression. We have proposed an entirely new hierarchical model for chromatin-mediated genome regulation.

Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality.

OBJECTIVES: Drug repurposing, which finds new indications for existing drugs, has received great attention recently. The goal of our work is to assess the feasibility of using electronic health records (EHRs) and automated informatics methods to efficiently validate a recent drug repurposing association of metformin with reduced cancer mortality. METHODS: By linking two large EHRs from Vanderbilt University Medical Center and Mayo Clinic to their tumor registries, we constructed a cohort including 32,415 adults with a cancer diagnosis at Vanderbilt and 79,258 cancer patients at Mayo from 1995 to 2010. Using automated informatics methods, we further identified type 2 diabetes patients within the cancer cohort and determined their drug exposure information, as well as other covariates such as smoking status. We then estimated HRs for all-cause mortality and their associated 95% CIs using stratified Cox proportional hazard models. HRs were estimated according to metformin exposure, adjusted for age at diagnosis, sex, race, body mass index, tobacco use, insulin use, cancer type, and non-cancer Charlson comorbidity index. RESULTS: Among all Vanderbilt cancer patients, metformin was associated with a 22% decrease in overall mortality compared to other oral hypoglycemic medications (HR 0.78; 95% CI 0.69 to 0.88) and with a 39% decrease compared to type 2 diabetes patients on insulin only (HR 0.61; 95% CI 0.50 to 0.73). Diabetic patients on metformin also had a 23% improved survival compared with non-diabetic patients (HR 0.77; 95% CI 0.71 to 0.85). These associations were replicated using the Mayo Clinic EHR data. Many site-specific cancers including breast, colorectal, lung, and prostate demonstrated reduced mortality with metformin use in at least one EHR. CONCLUSIONS: EHR data suggested that the use of metformin was associated with decreased mortality after a cancer diagnosis compared with diabetic and non-diabetic cancer patients not on metformin, indicating its potential as a chemotherapeutic regimen. This study serves as a model for robust and inexpensive validation studies for drug repurposing signals using EHR data.

Genome-wide analysis of loss of heterozygosity in breast infiltrating ductal carcinoma distant normal tissue highlights arm specific enrichment and expansion across tumor stages.

Studies have shown concurrent loss of heterozygosity (LOH) in breast infiltrating ductal carcinoma (IDC) and adjacent or distant normal tissue. However, the overall extent of LOH in normal tissue and their significance to tumorigenesis remain unknown, as existing studies are largely based on selected microsatellite markers. Here we present the first autosome-wide study of LOH in IDC and distant normal tissue using informative loci deduced from SNP array-based and sequencing-based techniques. We show a consistently high LOH concurrence rate in IDC (mean = 24%) and distant normal tissue (m = 54%), suggesting for most patients (31/33) histologically normal tissue contains genomic instability that can be a potential marker of increased IDC risk. Concurrent LOH is more frequent in fragile site related genes like WWOX (9/31), NTRK2 (10/31), and FHIT (7/31) than traditional genetic markers like BRCA1 (0/23), BRCA2 (2/29) and TP53 (1/13). Analysis at arm level shows distant normal tissue has low level but non-random enrichment of LOH (topped by 8p and 16q) significantly correlated with matched IDC (Pearson r = 0.66, p = 3.5E-6) (topped by 8p, 11q, 13q, 16q, 17p, and 17q). The arm-specific LOH enrichment was independently observed in tumor samples from 548 IDC patients when stratified by tumor size based T stages. Fine LOH structure from sequencing data indicates LOH in low order tissues non-randomly overlap (∼67%) with LOH that usually has longer tract length (the length of genomic region affected by LOH) in high order tissues. The consistent observations from multiple datasets suggest progressive LOH in the development of IDC potentially through arm-specific pile up effect with discernible signature in normal tissue. Our finding also suggests that LOH detected in IDC by comparing to paired adjacent or distant normal tissue are more likely underestimated.

Mass homozygotes accumulation in the NCI-60 cancer cell lines as compared to HapMap Trios, and relation to fragile site location.

Runs of homozygosity (ROH) represents extended length of homozygotes on a long genomic distance. In oncology, it is known as loss of heterozygosity (LOH) if identified exclusively in cancer cell rather than in matched control cell. Studies have identified several genomic regions which show consistent ROH in different kinds of carcinoma. To query whether this consistency can be observed on broader spectrum, both in more cancer types and in wider genomic regions, we investigated ROH patterns in the National Cancer Institute 60 cancer cell line panel (NCI-60) and HapMap Caucasian healthy trio families. Using results from Affymetrix 500 K SNP arrays, we report a genome wide significant association of ROH regions between the NCI-60 and HapMap samples, with much a higher level of ROH (11 fold) in the cancer cell lines. Analysis shows that more severe ROH found in cancer cells appears to be the extension of existing ROH in healthy state. In the HapMap trios, the adult subgroup had a slightly but significantly higher level (1.02 fold) of ROH than did the young subgroup. For several ROH regions we observed the co-occurrence of fragile sites (FRAs). However, FRA on the genome wide level does not show a clear relationship with ROH regions.

TAQIB and I405V polymorphisms of CETP are moderately associated with obesity risk in the Chinese adult population.

Associations between the TAQIB and I405V polymorphisms and obesity risk were studied for a single locus as well as in combination. A total of 934 obese subjects and 924 normal controls were included in the study. TAQIB was associated with high-density lipoprotein (HDL) levels (P < 0.001), while I405V was associated with levels of low-density lipoprotein (P = 0.03) and total cholesterol (P = 0.007). Less common alleles of TAQIB and I405V were associated with decreased obesity risk and further drops in odds ratio (OR) were observed in carriers with rare homozygous alleles on both loci (OR = 0.659, P = 0.02). The TAQIB B2 allele was associated with reductions in both hip circumference (P = 0.034) and triceps skinfold thickness (TST) (P = 0.045), although this effect was completely abolished after controlling for HDL levels. The 405V variant was associated with reductions in hip circumference (P = 0.031), body fat composition (P = 0.039) and TST (P = 0.036); these effects were weakened (P < 0.1) after controlling for HDL levels. In conclusion, less common alleles of TAQIB and I405V appear to be modestly associated with obesity risk in an adult Chinese population. Adjustments for HDL levels completely (TAQIB) or partially (I405V) abolished the observed association.

Association of two CETP polymorphisms with HDL levels in the Chinese obese population.

The association of two cholesterol ester transfer protein (CETP) polymorphisms, D442G and TAQIB (B1-->B2), with high-density lipoprotein (HDL) levels in 932 Chinese obese individuals (BMI >or= 27) was investigated in comparison with normal controls (BMI

The functional variant rs1048990 in PSMA6 is associated with susceptibility to myocardial infarction in a Chinese population.

A recent case-control study reported that a functional single nucleotide polymorphism (SNP) in the proteasome subunit alpha type 6 gene (PSMA6) (rs1048990, C/G) was associated with susceptibility to myocardial infarction (MI) in the Japanese population. Replication studies have been performed in European and other Japanese study samples, but the results were not conclusive. The purpose of the present study was to determine whether this locus confers significant susceptibility to MI in a Chinese population. We conducted a case-control association study on a cohort of 1884 MI patients and 2643 unrelated controls from the Chinese population. Genotyping of the rs1048990 SNP was performed by the Allele-specific Real Time PCR method. We found that rs1048990 was significantly associated with MI (adjusted for age and sex, odds ratio 1.22, p=0.000005, allele frequency model; odds ratio 1.44, p=0.0000025; recessive model; odds ratio 1.56, p=0.00000048, additive model). A meta-analysis yielded a combined OR for MI of 1.15 (95% CI: 1.11-1.21) with an allele frequency model, 1.37 (95% CI: 1.23-1.51) with a recessive model and 1.44 (95% CI: 1.29-1.60) with an additive model. There was no relationship between rs1048990 and age, sex or other conventional cardiovascular risk factors. Our results indicate that the PSMA6 variant rs1048990 is a risk factor of myocardial infarction in the Chinese population.