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Hsu YC, Chen HL, Cheng CF, Chattopadhyay A, Chen PS, Lin CC, Chiang HY, Liu TY, Huang CH, Kuo CC, Chuang EY, Lu TP, Tsai FJ. The largest genome-wide association study for breast cancer in Taiwanese Han population. Breast Cancer Res Treat 2024; 203:291-306. [PMID: 37851288 DOI: 10.1007/s10549-023-07133-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/23/2023] [Indexed: 10/19/2023]
Abstract
PURPOSE Breast cancer is a molecularly heterogeneous disease, and multiple genetic variants contribute to its development and prognosis. Most of previous genome-wide association studies (GWASs) and polygenic risk scores (PRSs) analyses focused on studying breast cancers of Caucasian populations, which may not be applicable to other population. Therefore, we conducted the largest breast cancer cohort of Taiwanese population to fill in the knowledge gap. METHODS A total of 152,534 Participants recruited by China Medical University Hospital between 2003 and 2019 were filtered by several patient selection criteria and GWAS quality control steps, resulting in the inclusion of 2496 cases and 9984 controls for this study. We then conducted GWAS for all breast cancers and PRS analyses for all breast cancers and the four breast cancer subtypes, including luminal A, luminal B, basal-like, and HER2-enriched. RESULTS The GWAS analyses identified 113 SNPs, 50 of which were novel. The PRS models for all breast cancers and the luminal A subtype showed positively correlated trends between the PRS and the risk of developing breast cancer. The odds ratios (95% confidence intervals) for the groups with the highest PRS in all breast cancers and the luminal A subtype were 5.33 (3.79-7.66) and 3.55 (2.13-6.14), respectively. CONCLUSION In summary, we explored the association of genetic variants with breast cancer in the largest Taiwanese cohort and developed two PRS models that can predict the risk of developing any breast cancer and the luminal A subtype in Taiwanese women.
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Affiliation(s)
- Yu-Ching Hsu
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei, Taiwan
- Bioinformatics Program, Institute of Statistical Science, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan
- Institute of Health Data Analytics and Statistics, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Hung-Lin Chen
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
| | - Chi-Fung Cheng
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
| | - Amrita Chattopadhyay
- Center for Translational Genomic Research, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Pei-Shan Chen
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
| | - Che-Chen Lin
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
| | - Hsiu-Yin Chiang
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
| | - Ting-Yuan Liu
- Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Chi-Hao Huang
- Division of Breast Surgery, Department of Surgery, China Medical University Hospital, Taichung, Taiwan
| | - Chin-Chi Kuo
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Eric Y Chuang
- Bioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Tzu-Pin Lu
- Institute of Health Data Analytics and Statistics, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
| | - Fuu-Jen Tsai
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
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Kao PY, Yeh HC, Hsia YF, Hsiao YL, Wang JS, Chang DR, Chang SN, Chiang HY, Kuo CC. Paradoxical mortality of high estimated glomerular filtration rate reversed by 24-h urine creatinine excretion rate adjustment: sarcopenia matters. J Cachexia Sarcopenia Muscle 2022; 13:1704-1716. [PMID: 35253387 PMCID: PMC9178165 DOI: 10.1002/jcsm.12951] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 01/21/2022] [Accepted: 02/01/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Muscle wasting may explain the paradoxical mortality of patients with high estimated glomerular filtration rates (eGFRs) derived from equation methods. However, empirical evidence and solutions remain insufficient. METHODS In this retrospective cohort study, we compared the performance of equation methods for predicting all-cause mortality; we used 24-h creatinine clearance (24-h CrCl), equation-based eGFRs, and a new eGFR estimating equation weighting for population 24-h urine creatinine excretion rate (U-CER). From 2003 to 2018, we identified 4986 patients whose data constituted the first 24-h CrCl measurement data in the Clinical Research Data Repository of China Medical University Hospital and were followed up for at least 5 years after careful exclusion. Three GFR estimation equations [the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), Modification of Diet in Renal Disease (MDRD) Study, and Taiwanese MDRD], 24-h CrCl, and 24-h U-CER-adjusted eGFR were used. RESULTS A high correlation was observed among the eGFR levels derived from the equation methods (0.995-1.000); however, the correlation decreased to 0.895-0.914 when equation methods were compared with the 24-h CrCl or 24-h U-CER-adjusted equation-based eGFR. In the Bland-Altman plots, the average discrepancy between the equation methods and the 24-h CrCl method was close to zero (maximal bias range: 5.12 for the Taiwanese MDRD equation vs. 24-h CrCl), but the range in limit of agreement was wide, from ±43.7 mL/min/1.73 m2 for the CKD-EPI equation to ±54.3 mL/min/1.73 m2 for the Taiwanese MDRD equation. A J-shaped dose-response relationship was observed between all equation-based eGFRs and all-cause mortality. Only 24-h CrCl exhibited a non-linear negative dose-response relationship with all-cause mortality. After adjustment for 24-h U-CER in the statistical model, the paradoxical increase in mortality risk for an eGFR of >90 mL/min/1.73 m2 returned to null. When 24-h U-CER was used directly to correct eGFR, the monotonic non-linear negative relationship with all-cause mortality was almost identical to that of 24-h CrCl. CONCLUSIONS The 24-h U-CER-adjusted eGFR and 24-h CrCl are viable options for informing mortality risk. The 24-h U-CER adjustment method can be practically implemented to eGFR-based care and effectively mitigate the inherent confounding biases from individual's muscle mass amount due to both sex and racial differences.
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Affiliation(s)
- Pei-Yu Kao
- Division of Chest Surgery, Department of Surgery, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
| | - Hung-Chieh Yeh
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.,AKI-CARE (Acute Kidney Injury Clinical Advancement, Research and Education) Center, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.,Big Data Center, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
| | - Ying-Fang Hsia
- Big Data Center, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
| | - Ya-Luan Hsiao
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jie-Sian Wang
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.,AKI-CARE (Acute Kidney Injury Clinical Advancement, Research and Education) Center, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
| | - David Ray Chang
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.,AKI-CARE (Acute Kidney Injury Clinical Advancement, Research and Education) Center, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
| | - Shih-Ni Chang
- Big Data Center, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
| | - Hsiu-Yin Chiang
- Big Data Center, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
| | - Chin-Chi Kuo
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.,AKI-CARE (Acute Kidney Injury Clinical Advancement, Research and Education) Center, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.,Big Data Center, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
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Liu Q, Bian G, Chen X, Han J, Chen Y, Wang M, Yang F. Application of a six sigma model to evaluate the analytical performance of urinary biochemical analytes and design a risk-based statistical quality control strategy for these assays: A multicenter study. J Clin Lab Anal 2021; 35:e24059. [PMID: 34652033 PMCID: PMC8605169 DOI: 10.1002/jcla.24059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/15/2021] [Accepted: 10/04/2021] [Indexed: 11/13/2022] Open
Abstract
Background The six sigma model has been widely used in clinical laboratory quality management. In this study, we first applied the six sigma model to (a) evaluate the analytical performance of urinary biochemical analytes across five laboratories, (b) design risk‐based statistical quality control (SQC) strategies, and (c) formulate improvement measures for each of the analytes when needed. Methods Internal quality control (IQC) and external quality assessment (EQA) data for urinary biochemical analytes were collected from five laboratories, and the sigma value of each analyte was calculated based on coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts for these urinary biochemical analytes were then generated. Risk‐based SQC strategies and improvement measures were formulated for each laboratory according to the flowchart of Westgard sigma rules, including run sizes and the quality goal index (QGI). Results Sigma values of urinary biochemical analytes were significantly different at different quality control levels. Although identical detection platforms with matching reagents were used, differences in these analytes were also observed between laboratories. Risk‐based SQC strategies for urinary biochemical analytes were formulated based on the flowchart of Westgard sigma rules, including run size and analytical performance. Appropriate improvement measures were implemented for urinary biochemical analytes with analytical performance lower than six sigma according to the QGI calculation. Conclusions In multilocation laboratory systems, a six sigma model is an excellent quality management tool and can quantitatively evaluate analytical performance and guide risk‐based SQC strategy development and improvement measure implementation.
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Affiliation(s)
- Qian Liu
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, China
| | - Guangrong Bian
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, China
| | - Xinkuan Chen
- Department of Laboratory Medicine, Xuzhou Medical University Affiliated Hospital of Lianyungang, Lianyungang, China
| | - Jingjing Han
- Department of Laboratory Medicine, Wuxi Branch of Ruijin Hospital, Wuxi, China
| | - Ying Chen
- Department of Laboratory Medicine, Nantong Hospital of Traditional Chinese Medicine, Nantong, China
| | - Menglin Wang
- Department of Laboratory Medicine, Suqian First Hospital, Suqian, China
| | - Fumeng Yang
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, China
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Chiang HY, Lin KTR, Hsiao YL, Huang HC, Chang SN, Hung CH, Chang Y, Wang YC, Kuo CC. Association Between Preoperative Blood Glucose Level and Hospital Length of Stay for Patients Undergoing Appendectomy or Laparoscopic Cholecystectomy. Diabetes Care 2021; 44:107-115. [PMID: 33177174 PMCID: PMC7783940 DOI: 10.2337/dc19-0963] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 10/17/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To evaluate the effect of preoperative blood glucose (POBG) level on hospital length of stay (LOS) in patients undergoing appendectomy or laparoscopic cholecystectomy. RESEARCH DESIGN AND METHODS We conducted a retrospective cohort study of patients aged ≥18 years who had undergone appendectomy or laparoscopic cholecystectomy procedures between 2005 and 2016 at a tertiary medical center in Taiwan. The association between POBG level and LOS was evaluated using a multivariable quasi-Poisson regression with robust variance. Multiple imputations were performed to replace missing values. RESULTS We included 8,291 patients; 4,025 patients underwent appendectomy (appendectomy group) and 4,266 underwent laparoscopic cholecystectomy (laparoscopic cholecystectomy group). In the appendectomy group, patients with POBG levels of ≥123 mg/dL (adjusted relative risk [aRR] 1.19; 95% CI 1.06-1.33) had a 19% higher risk of having a LOS of >3 days than did those with POBG levels of <106 mg/dL. In the laparoscopic cholecystectomy group, patients with POBG levels of ≥128 mg/dL also had a significantly higher risk of having a LOS of >3 days (aRR 1.17; 95% CI 1.07-1.29) than did those with POBG levels of <102 mg/dL. A positive dose-response curve between POBG and an adjusted risk of a LOS of >3 days was observed, although the curve starts to flatten at a POBG level of ∼130 mg/dL. CONCLUSIONS We demonstrated that a higher POBG level was significantly associated with a prolonged LOS for patients undergoing appendectomy or laparoscopic cholecystectomy. The optimal POBG level may be lower than that commonly perceived.
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Affiliation(s)
- Hsiu-Yin Chiang
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
| | - Kuan-Ting Robin Lin
- College of Medicine, China Medical University, Taichung, Taiwan.,Department of Neurosurgery, Hualien Tzu Chi Hospital, Hualien, Taiwan
| | - Ya-Luan Hsiao
- Big Data Center, China Medical University Hospital, Taichung, Taiwan.,Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | - Han-Chun Huang
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
| | - Shih-Ni Chang
- Big Data Center, China Medical University Hospital, Taichung, Taiwan.,PhD Program for Cancer Biology and Drug Discovery, College of Medicine, China Medical University, Taichung, Taiwan
| | - Chien-Hui Hung
- Department of Medical Quality, China Medical University Hospital, Taichung, Taiwan
| | - Ying Chang
- Department of Nursing, China Medical University Hospital and College of Nursing, Taichung, Taiwan
| | - Yu-Chun Wang
- Department of Medical Quality, China Medical University Hospital, Taichung, Taiwan .,Department of Acute Care Surgery, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
| | - Chin-Chi Kuo
- Big Data Center, China Medical University Hospital, Taichung, Taiwan .,College of Medicine, China Medical University, Taichung, Taiwan.,Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
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Scurt FG, Menne JJ, Korda A, Haller H, Chatzikyrkou C. Effect of gender on transition of normo- to microalbuminuria under angiotensin receptor blocker therapy in diabetes. J Diabetes 2020; 12:856-859. [PMID: 32755046 DOI: 10.1111/1753-0407.13102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 07/24/2020] [Accepted: 07/31/2020] [Indexed: 11/30/2022] Open
Abstract
Highlights In normoalbuminuric diabetic patients at low cardiovascular risk, the risk of transition from normo- to microalbuminuria is lower in women, despite the nonprotective effects of the angiotensin receptor blocker olmesartan. Additional methods of assessment of albuminuria in clinical studies (eg, measurements of albumin and creatinine excretion rate) should be implemented or the actually accepted higher urine albumin creatinine ratio (UACR) cutoff values for microalbuminuria in women reconsidered.
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Affiliation(s)
- Florian G Scurt
- Clinic of Nephrology, Hypertension, Diabetes and Endocrinology, Health Campus Immunology, Infectiology and Inflammation, Otto-von Guericke University, Magdeburg, Germany
| | - Jan J Menne
- Nephrology Section, Hannover Medical School, Hannover, Germany
- Clinic of Nephrology, Angiology and Rheumatology, KRH Klinikum Siloah, Hannover, Germany
| | - Alexandra Korda
- LVR-Klinikum Düsseldorf, Heinrich, Heine, University Düsseldorf, Düsseldorf, Germany
| | - Hermann Haller
- Nephrology Section, Hannover Medical School, Hannover, Germany
| | - Christos Chatzikyrkou
- Clinic of Nephrology, Hypertension, Diabetes and Endocrinology, Health Campus Immunology, Infectiology and Inflammation, Otto-von Guericke University, Magdeburg, Germany
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Ting IW, Yeh HC, Huang HC, Chiang HY, Chu PL, Kuo CC. Joint Longitudinal Low Calcium High Phosphorus Trajectory Associates with Accelerated Progression, Acute Coronary Syndrome and Mortality in Chronic Kidney Disease. Sci Rep 2020; 10:9682. [PMID: 32541796 PMCID: PMC7296014 DOI: 10.1038/s41598-020-66577-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 05/13/2020] [Indexed: 12/16/2022] Open
Abstract
The effects of long-term disturbance of the mineral metabolism on patients with chronic kidney disease (CKD) are unclear. We investigated whether the longitudinal Ca-P (joint calcium and phosphorus) trajectories are associated with incident end-stage renal disease (ESRD), acute coronary syndrome (ACS), and all-cause mortality in patients with CKD. We conducted a prospective cohort study by using data from a 13-year multidisciplinary pre-ESRD care registry. The final study population consisted of 4,237 CKD patients aged 20–90 years with data gathered from 2003 to 2015. Individuals’ Ca-P trajectories were defined using group-based multi-trajectory modeling into three distinct patterns: reference, moderately abnormal, and severely abnormal. Times to ESRD, ACS, and death were analyzed using multiple Cox regression. Compared with those with a “reference” Ca-P trajectory, the adjusted hazard ratios (aHRs) (95% confidence interval [CI]) for incidental ESRD were 5.92 (4.71–7.44) and 15.20 (11.85–19.50) for “moderately abnormal” and “severely abnormal” Ca-P trajectories, respectively. The corresponding aHRs for ACS were 1.94 (1.49–2.52) and 3.18 (2.30–4.39), and for all-cause mortality, they were 1.88 (1.64–2.16) and 2.46 (2.05–2.96) for “moderately abnormal” and “severely abnormal” Ca-P trajectories, respectively. For outcomes of progression to ESRD, the detrimental effects of abnormal Ca-P trajectories were more substantial in patients with CKD stage 3 than those with CKD stage 4 or 5 (p-value for interaction < 0.001). Future studies should validate reliable longitudinal cut-offs of serum phosphorus and consider the “lowering phosphorus— the lower the better, the earlier the better” approach to phosphorus control in CKD.
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Affiliation(s)
- I-Wen Ting
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
| | - Hung-Chieh Yeh
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.,Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Han-Chun Huang
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Hsiu-Yin Chiang
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Pei-Lun Chu
- Department of Internal Medicine, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei, Taiwan.,School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei, Taiwan
| | - Chin-Chi Kuo
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan. .,Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
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