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Chang KC, Huang SY, Tsai WH, Liu HW, Liu JS, Wu CL, Kuo KL. Dissecting the risk factors for hyperuricemia in vegetarians in Taiwan. J Chin Med Assoc 2024; 87:393-399. [PMID: 38380911 DOI: 10.1097/jcma.0000000000001074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Vegetarian diets have been shown to lower the risks of hyperuricemia and gout. Little is known about the risk factors of hyperuricemia in vegetarians. METHODS This community-based retrospective case-control study was conducted to establish prediction models for hyperuricemia. From September 5, 2005, to December 31, 2016, 7331 adult vegetarians were recruited at Taipei Tzu Chi Hospital. Hyperuricemia was defined as a serum uric acid concentration greater than 7 mg/dL. RESULTS There were 593 (8.1%) vegetarians with hyperuricemia and 6738 (91.9%) without hyperuricemia. We stepwise built up three models for predicting hyperuricemia in vegetarians. The full model (model 3) has the highest area under the receiver operating characteristic curve (AUROC, 85.52%). Additionally, the AUROC of model 3 is 77.97% and 84.85% in vegetarians with or without prior gout history, respectively. Moreover, male gender, hyperlipidemia, body mass index, and serum albumin are independent risk factors for hyperuricemia in vegetarians. In contrast, estimated glomerular filtration rate and proteinuria are independently associated with lower risks of hyperuricemia in vegetarians. CONCLUSION Our study revealed that risk factors for hyperuricemia, which includes clinical characteristics, account for more than 85% of discriminatory performance in Taiwanese vegetarians. This model may be helpful for monitoring and preventing hyperuricemia in the population.
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Affiliation(s)
- Kai-Chieh Chang
- Division of Nephrology, Department of Internal Medicine, Changhua Christian Hospital, Changhua, Taiwan, ROC
| | - Sin-Yi Huang
- School of Medicine, Tzu Chi University, Hualien, Taiwan, ROC
- Department of Family Medicine, MacKay Memorial Hospital, Taipei, Taiwan, ROC
| | - Wen-Hsin Tsai
- School of Medicine, Tzu Chi University, Hualien, Taiwan, ROC
- Department of Pediatrics, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taiwan, ROC
| | - Hao-Wen Liu
- Tai-Yang Otorhinolaryngology Clinic, New Taipei, Taiwan, ROC
| | - Jia-Sin Liu
- Division of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan, ROC
| | - Chia-Lin Wu
- Division of Nephrology, Department of Internal Medicine, Changhua Christian Hospital, Changhua, Taiwan, ROC
- School of Medicine, Chung-Shan Medical University, Taichung, Taiwan, ROC
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan, ROC
| | - Ko-Lin Kuo
- School of Medicine, Tzu Chi University, Hualien, Taiwan, ROC
- Division of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan, ROC
- School of Post-Baccalaureate Chinese Medicine, Tzu Chi University, Hualien, Taiwan, ROC
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2
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Abudureyimu P, Pang Y, Huang L, Luo Q, Zhang X, Xu Y, Jiang L, Mohemaiti P. A predictive model for hyperuricemia among type 2 diabetes mellitus patients in Urumqi, China. BMC Public Health 2023; 23:1740. [PMID: 37679683 PMCID: PMC10483783 DOI: 10.1186/s12889-023-16669-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 08/31/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND Patients with type 2 diabetes Mellitus (T2DM) are more likely to suffer from a higher uric acid level in blood-hyperuricemia (HUA). There are no conclusive studies done to predict HUA among T2DM patients. Therefore, this study aims to explore the risk factors of HUA among T2DM patients and finally suggest a model to help with its prediction. METHOD In this retrospective research, all the date were collected between March 2017 and October 2019 in the Medical Laboratory Center of the First Affiliated Hospital of Xinjiang Medical University. The information included sociodemographic factors, blood routine index, thyroid function indicators and serum biochemical markers. The least absolute shrinkage and selection operator (LASSO) and multivariate binary logistic regression were performed to screen the risk factors of HUA among T2DM patients in blood tests, and the nomogram was used to perform and visualise the predictive model. The receiver operator characteristic (ROC) curve, internal validation, and clinical decision curve analysis (DCA) were applied to evaluate the prediction performance of the model. RESULTS We total collected the clinical date of 841 T2DM patients, whose age vary from 19-86. In this study, the overall prevalence of HUA in T2DM patients was 12.6%. According to the result of LASSO-logistic regression analysis, sex, ethnicity, serum albumin (ALB), serum cystatin C (CysC), serum inorganic phosphorus (IPHOS), alkaline phosphatase (ALP), serum bicarbonate (CO2) and high-density lipoprotein (HDLC) were included in the HUA risk prediction model. The nomogram confirmed that the prediction model fits well (χ2 = 5.4952, P = 0.704) and the calibration curve indicates the model had a good calibration. ROC analysis indicates that the predictive model shows the best discrimination ability (AUC = 0.827; 95% CI: 0.78-0.874) whose specificity is 0.885, and sensitivity is 0.602. CONCLUSION Our study reveals that there were 8 variables that can be considered as independent risk factors for HUA among T2DM patients. In light of our findings, a predictive model was developed and clinical advice was given on its use.
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Affiliation(s)
- Palizhati Abudureyimu
- Medical Laboratory Center, First Affiliated Hospital of Xinjiang Medical University, No.137, Liyushan South Road, Xinshi District, Urumqi, 830001, China
| | - Yuesheng Pang
- Xinjiang Uygur Autonomous Region, Xinjiang Medical University, No.567, North Shangde Road, Shuimogou District, Urumqi, 830017, China
| | - Lirun Huang
- Xinjiang Uygur Autonomous Region, Xinjiang Medical University, No.567, North Shangde Road, Shuimogou District, Urumqi, 830017, China
| | - Qianqian Luo
- Xinjiang Uygur Autonomous Region, Xinjiang Medical University, No.567, North Shangde Road, Shuimogou District, Urumqi, 830017, China
| | - Xiaozheng Zhang
- Xinjiang Uygur Autonomous Region, Xinjiang Medical University, No.567, North Shangde Road, Shuimogou District, Urumqi, 830017, China
| | - Yifan Xu
- Xinjiang Uygur Autonomous Region, Xinjiang Medical University, No.567, North Shangde Road, Shuimogou District, Urumqi, 830017, China
| | - Liang Jiang
- Xinjiang Uygur Autonomous Region, Xinjiang Medical University, No.567, North Shangde Road, Shuimogou District, Urumqi, 830017, China
| | - Patamu Mohemaiti
- Xinjiang Uygur Autonomous Region, Xinjiang Medical University, No.567, North Shangde Road, Shuimogou District, Urumqi, 830017, China.
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Nutritional Biomarkers and Heart Rate Variability in Patients with Subacute Stroke. Nutrients 2022; 14:nu14245320. [PMID: 36558479 PMCID: PMC9784051 DOI: 10.3390/nu14245320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Malnutrition and autonomic dysfunction are associated with poor outcomes, mortality, and psychological problems after stroke. Relevant laboratory biomarkers include serum albumin, prealbumin, and transferrin. Heart rate variability (HRV), a noninvasive measurement, can objectively measure autonomic nervous system (ANS) function. The relationship between HRV and nutritional biomarkers in stroke patients has not been studied. This study aimed to examine the relationship between nutritional biomarkers and HRV parameters in stroke patients. We retrospectively recruited 426 patients with subacute stroke who were examined for nutritional biomarkers, such as serum albumin, prealbumin, and transferrin, and underwent 24 h ambulatory Holter electrocardiography. Patients were divided into groups according to their nutritional biomarker status. Differences in HRV parameters between nutritional biomarker-deficient and normal groups were assessed. Pearson's correlation and multiple regression analyses were used to verify the relationship between HRV parameters and nutritional biomarkers. HRV parameters were significantly lower in the nutritional biomarker-deficient groups. In addition, there was a significant association between HRV parameters and nutritional biomarkers. Serum albumin, prealbumin, and transferrin levels were associated with ANS function, as measured by HRV, and their deficiency may be a predictive factor for the severity of ANS dysfunction in stroke patients.
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4
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Lee MJ, Khang AR, Kang YH, Yun MS, Yi D. Synergistic Interaction between Hyperuricemia and Abdominal Obesity as a Risk Factor for Metabolic Syndrome Components in Korean Population. Diabetes Metab J 2022; 46:756-766. [PMID: 35045592 PMCID: PMC9532181 DOI: 10.4093/dmj.2021.0166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/26/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND The present study investigated the role of synergistic interaction between hyperuricemia and abdominal obesity as a risk factor for the components of metabolic syndrome. METHODS We performed a cross-sectional study using the data of 16,094 individuals from the seventh Korean National Health and Nutrition Examination Survey (2016 to 2018). The adjusted odds ratios of metabolic syndrome and its components were analyzed by multivariate logistic regression analysis. The presence of synergistic interaction between hyperuricemia and abdominal obesity was evaluated by calculating the additive scales-the relative excess risk due to interaction, attributable proportion due to interaction, and synergy index (SI). RESULTS There was a synergistic interaction between hyperuricemia and abdominal obesity in hypertriglyceridemia (men: SI, 1.39; 95% confidence interval [CI], 1.01 to 1.98; women: SI, 1.61; 95% CI, 1.02 to 2.69), and low high-density lipoprotein cholesterol (HDL-C) (men: SI, 2.03; 95% CI, 1.41 to 2.91; women: SI, 1.70; 95% CI, 1.05 to 2.95). There was no significant synergistic interaction between hyperuricemia and abdominal obesity for the risk of high blood pressure (men: SI, 1.22; 95% CI, 0.85 to 1.77; women: SI, 1.53; 95% CI, 0.79 to 2.97), and hyperglycemia (men: SI, 1.03; 95% CI, 0.72 to 1.47; women: SI, 1.39; 95% CI, 0.75 to 2.57). CONCLUSION Hyperuricemia and abdominal obesity synergistically increased the risk of hypertriglyceridemia and low HDL-C in both sexes.
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Affiliation(s)
- Min Jin Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea
- Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Ah Reum Khang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea
- Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Yang Ho Kang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea
- Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Mi Sook Yun
- Division of Biostatistics, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Dongwon Yi
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea
- Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
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5
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Pluimakers VG, van Santen SS, Fiocco M, Bakker MCE, van der Lelij AJ, van den Heuvel-Eibrink MM, Neggers SJCMM. Can biomarkers be used to improve diagnosis and prediction of metabolic syndrome in childhood cancer survivors? A systematic review. Obes Rev 2021; 22:e13312. [PMID: 34258851 PMCID: PMC8596408 DOI: 10.1111/obr.13312] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/11/2021] [Accepted: 06/09/2021] [Indexed: 12/26/2022]
Abstract
Childhood cancer survivors (CCS) are at increased risk to develop metabolic syndrome (MetS), diabetes, and cardiovascular disease. Common criteria underestimate adiposity and possibly underdiagnose MetS, particularly after abdominal radiotherapy. A systematic literature review and meta-analysis on the diagnostic and predictive value of nine newer MetS related biomarkers (adiponectin, leptin, uric acid, hsCRP, TNF-alpha, IL-1, IL-6, apolipoprotein B (apoB), and lipoprotein(a) [lp(a)]) in survivors and adult non-cancer survivors was performed by searching PubMed and Embase. Evidence was summarized with GRADE after risk of bias evaluation (QUADAS-2/QUIPS). Eligible studies on promising biomarkers were pooled. We identified 175 general population and five CCS studies. In the general population, valuable predictive biomarkers are uric acid, adiponectin, hsCRP and apoB (high level of evidence), and leptin (moderate level of evidence). Valuable diagnostic biomarkers are hsCRP, adiponectin, uric acid, and leptin (low, low, moderate, and high level of evidence, respectively). Meta-analysis showed OR for hyperuricemia of 2.94 (age-/sex-adjusted), OR per unit uric acid increase of 1.086 (unadjusted), and AUC for hsCRP of 0.71 (unadjusted). Uric acid, adiponectin, hsCRP, leptin, and apoB can be alternative biomarkers in the screening setting for MetS in survivors, to enhance early identification of those at high risk of subsequent complications.
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Affiliation(s)
| | - Selveta S van Santen
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands.,Department of Medicine, Endocrinology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Marta Fiocco
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands.,Medical Statistics, Department of Biomedical Data Science, Leiden UMC, Leiden, Netherlands.,Mathematical Institute, Leiden University, Leiden, Netherlands
| | - Marie-Christine E Bakker
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands.,Department of Medicine, University Medical Center Utrecht, Netherlands
| | - Aart J van der Lelij
- Department of Medicine, Endocrinology, Erasmus Medical Center, Rotterdam, Netherlands
| | | | - Sebastian J C M M Neggers
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands.,Department of Medicine, Endocrinology, Erasmus Medical Center, Rotterdam, Netherlands
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Ahn J, Koh J, Kim D, Kim G, Hur KY, Seo SW, Kim K, Kim JH, Yang JH, Jin SM. Mean and visit-to-visit variability of glycemia and left ventricular diastolic dysfunction: A longitudinal analysis of 3025 adults with serial echocardiography. Metabolism 2021; 116:154451. [PMID: 33248066 DOI: 10.1016/j.metabol.2020.154451] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 10/27/2020] [Accepted: 11/23/2020] [Indexed: 11/30/2022]
Abstract
OBJECTIVE We aimed to determine the mean glucose thresholds to increase the risk of left ventricular diastolic dysfunction (LVDD) and whether visit-to-visit variability of fasting plasma glucose (FPG) and glycated hemoglobin (A1C) could independently increase the risk in a cohort with serial echocardiography. METHODS This was a 3.5-year (range, 0.5-8.3) retrospective longitudinal cohort study of 3025 adults (age, 55.15 ± 7.6 years; without diabetes, n = 2755) with LV ejection fraction > 50% by serial echocardiography between 2006 and 2016. Mean, standard of deviation (SD) and coefficient of variation (CV) of FPG and A1C obtained from three consecutive measurements preceding the first echocardiography. The definition of LVDD in this study was primarily based on early peak mitral inflow velocity and early diastolic mitral annulus motion velocity. RESULTS LVDD developed in 611/3025 subjects (20.2%). Cox proportional hazard models showed increased adjusted hazard ratios (HRs) for incident LVDD in the highest quartile of FPG-mean (HR 1.76, 95% confidence interval [CI]; 1.36-2.30), FPG-SD (HR 1.63, 95% CI; 1.27-2.09), FPG-CV (HR 1.47, 95% CI; 1.15-1.89), and A1C-mean (HR 1.83, 95% CI; 1.41-2.38) versus the lowest quartile, which was consistent even in subjects without diabetes. Mean glucose thresholds for the increased risk were below the lower limits for pre-diabetes. CONCLUSIONS In terms of mean glycemia, LVDD may be initiated in the earliest diabetic continuum, and such changes could be measurable within several years. Visit-to-visit variability of FPG, but not that of A1C, predicted accelerated development of LVDD.
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Affiliation(s)
- Jiyeon Ahn
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Division of Endocrinology and Metabolism, Department of Internal Medicine, Hanyang University College of Medicine, Myongji Hospital, Goyang, Republic of Korea
| | - Janghyun Koh
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Darae Kim
- Division of Cardiology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Gyuri Kim
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyu Yeon Hur
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Kyunga Kim
- Statistics and Data Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Jae Hyeon Kim
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jeong Hoon Yang
- Division of Cardiology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Department of Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Sang-Man Jin
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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Mena-Sánchez G, Babio N, Becerra-Tomás N, Martínez-González MÁ, Díaz-López A, Corella D, Zomeño MD, Romaguera D, Vioque J, Alonso-Gómez ÁM, Wärnberg J, Martínez JA, Serra-Majem L, Estruch R, Bernal R, Lapetra J, Pintó X, Tur JA, Lopez-Miranda J, Cano-Ibáñez N, Gaforio JJ, Matía-Martín P, Daimiel L, Caro JLL, Vidal J, Vázquez C, Ros E, Arellano AG, Palau A, Fernández-Carrión R, Pérez-Vega KA, Morey M, de la Hera MG, Vaquero-Luna J, Carmona-González FJ, Abete I, Álvarez-Pérez J, Casas R, Fernández-García JC, Santos-Lozano JM, Corbella E, Sureda A, Ruiz-Canela M, Barragán R, Goday A, Martín M, Altozano Rodado MC, Toledo E, Fitó M, Salas-Salvadó J. Association between dairy product consumption and hyperuricemia in an elderly population with metabolic syndrome. Nutr Metab Cardiovasc Dis 2020; 30:214-222. [PMID: 31791636 DOI: 10.1016/j.numecd.2019.09.023] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 09/19/2019] [Accepted: 09/23/2019] [Indexed: 12/16/2022]
Abstract
BACKGROUND AND AIMS The prevalence of hyperuricemia has increased substantially in recent decades. It has been suggested that it is an independent risk factor for weight gain, hypertension, hypertriglyceridemia, metabolic syndrome (MetS), and cardiovascular disease. Results from epidemiological studies conducted in different study populations have suggested that high consumption of dairy products is associated with a lower risk of developing hyperuricemia. However, this association is still unclear. The aim of the present study is to explore the association of the consumption of total dairy products and their subtypes with the risk of hyperuricemia in an elderly Mediterranean population with MetS. METHODS AND RESULTS Baseline cross-sectional analyses were conducted on 6329 men/women (mean age 65 years) with overweight/obesity and MetS from the PREDIMED-Plus cohort. Dairy consumption was assessed using a food frequency questionnaire. Multivariable-adjusted Cox regressions were fitted to analyze the association of quartiles of consumption of total dairy products and their subtypes with the prevalence of hyperuricemia. Participants in the upper quartile of the consumption of total dairy products (multiadjusted prevalence ratio (PR) = 0.84; 95% CI: 0.75-0.94; P-trend 0.02), low-fat dairy products (PR = 0.79; 95% CI: 0.70-0.89; P-trend <0.001), total milk (PR = 0.81; 95% CI: 0.73-0.90; P-trend<0.001), low-fat milk (PR = 0.80; 95% CI: 0.72-0.89; P-trend<0.001, respectively), low-fat yogurt (PR = 0.89; 95% CI: 0.80-0.98; P-trend 0.051), and cheese (PR = 0.86; 95% CI: 0.77-0.96; P-trend 0.003) presented a lower prevalence of hyperuricemia. Whole-fat dairy, fermented dairy, and yogurt consumption were not associated with hyperuricemia. CONCLUSIONS High consumption of total dairy products, total milk, low-fat dairy products, low-fat milk, low-fat yogurt, and cheese is associated with a lower risk of hyperuricemia.
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Affiliation(s)
- Guillermo Mena-Sánchez
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, IISPV, Hospital Universitari Sant Joan de Reus, Spain; CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, IISPV, Hospital Universitari Sant Joan de Reus, Spain; CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Nerea Becerra-Tomás
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, IISPV, Hospital Universitari Sant Joan de Reus, Spain; CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Miguel Á Martínez-González
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Navarra, IDISNA, Pamplona, Spain; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Andrés Díaz-López
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, IISPV, Hospital Universitari Sant Joan de Reus, Spain; CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Dolores Corella
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Maria D Zomeño
- Cardiovascular Risk and Nutrition research group (CARIN), Hospital del Mar Research Institute (IMIM), Barcelona, Spain; Blanquerna, School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain
| | - Dora Romaguera
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Clinical Epidemiology and Public Health Department, Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain
| | - Jesús Vioque
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Miguel Hernandez University, ISABIAL-FISABIO, Alicante, Spain
| | - Ángel M Alonso-Gómez
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Cardiology, Organización Sanitaria Integrada (OSI) ARABA, University Hospital Araba, Vitoria-Gasteiz, Spain
| | - Julia Wärnberg
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Nursing, School of Health Sciences, University of Málaga-IBIMA, Málaga, Spain
| | - José A Martínez
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; University of Navarra, Department of Nutrition, Food Science and Physiology, IDISNA, Pamplona, Spain; Institute of Biomedicine (IBIOMED), University of León, León, Spain
| | - Luís Serra-Majem
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; University of Las Palmas de Gran Canaria, Research Institute of Biomedical and Health Sciences (IUIBS), Preventive Medicine Service, Centro Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canarian Health Service, Las Palmas, Spain
| | - Ramon Estruch
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Internal Medicine, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Rosa Bernal
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Virgen de la Victoria Hospital, Department of Endocrinology, University of Málaga, Málaga, Spain
| | - José Lapetra
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Sevilla, Spain
| | - Xavier Pintó
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge-IDIBELL, Hospitalet de Llobregat, Barcelona, Spain
| | - Josep A Tur
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, Palma de Mallorca, Spain
| | - José Lopez-Miranda
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain
| | - Naomi Cano-Ibáñez
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine, University of Granada, Granada, Spain
| | - Jose J Gaforio
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Departamento de Ciencias de la Salud, Centro de Estudios Avanzados en Olivar y Aceites de Oliva, Universidad de Jaén, Jaén, Spain
| | - Pilar Matía-Martín
- Department of Endocrinology and Nutrition, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Lidia Daimiel
- Nutritional Genomics and Epigenomics Group, IMDEA Food, CEI UAM + CSIC, Madrid, Spain
| | - José L Llisterri Caro
- Institute of Biomedicine (IBIOMED), University of León, León, Spain; CIBER Diabetes y enfermedades Metabólicos (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Josep Vidal
- Departament of Endocrinology, IDIBAPS, Hospital Clínic, University of Barcelona, Barcelona, Spain
| | - Clotilde Vázquez
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Endocrinology, Fundación Jiménez-Díaz, Madrid, Spain
| | - Emili Ros
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Lipid Clinic, Department of Endocrinology and Nutrition, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
| | - Ana Garcia Arellano
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Navarra, IDISNA, Pamplona, Spain
| | - Antoni Palau
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, IISPV, Hospital Universitari Sant Joan de Reus, Spain
| | - Rebeca Fernández-Carrión
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Karla A Pérez-Vega
- Cardiovascular Risk and Nutrition research group (CARIN), Hospital del Mar Research Institute (IMIM), Barcelona, Spain
| | - Marga Morey
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Clinical Epidemiology and Public Health Department, Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain
| | - Manoli García de la Hera
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Miguel Hernandez University, ISABIAL-FISABIO, Alicante, Spain
| | - Jessica Vaquero-Luna
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Cardiology, Organización Sanitaria Integrada (OSI) ARABA, University Hospital Araba, Vitoria-Gasteiz, Spain
| | - Francisco J Carmona-González
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Unidad Gestión Clínica de Torrequebrada, Distrito Atención Primaria Costa del Sol, Servicio Andaluz de Salud, Spain
| | - Itziar Abete
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; University of Navarra, Department of Nutrition, Food Science and Physiology, IDISNA, Pamplona, Spain
| | - Jacqueline Álvarez-Pérez
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; University of Las Palmas de Gran Canaria, Research Institute of Biomedical and Health Sciences (IUIBS), Preventive Medicine Service, Centro Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canarian Health Service, Las Palmas, Spain
| | - Rosa Casas
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Internal Medicine, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - José C Fernández-García
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Virgen de la Victoria Hospital, Department of Endocrinology, University of Málaga, Málaga, Spain
| | - José M Santos-Lozano
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Sevilla, Spain
| | - Emili Corbella
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge-IDIBELL, Hospitalet de Llobregat, Barcelona, Spain
| | - Antoni Sureda
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, Palma de Mallorca, Spain
| | - Miguel Ruiz-Canela
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Navarra, IDISNA, Pamplona, Spain
| | - Rocio Barragán
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Albert Goday
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Cardiovascular Risk and Nutrition research group (CARIN), Hospital del Mar Research Institute (IMIM), Barcelona, Spain; Department of Medicine, Universitat Autonoma de Barcelona, Endocrinology Unit, Hospital del Mar, Barcelona, Spain
| | - Marian Martín
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Clinical Epidemiology and Public Health Department, Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain
| | - María C Altozano Rodado
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Miguel Hernandez University, ISABIAL-FISABIO, Alicante, Spain
| | - Estefanía Toledo
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Navarra, IDISNA, Pamplona, Spain
| | - Montse Fitó
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Cardiovascular Risk and Nutrition research group (CARIN), Hospital del Mar Research Institute (IMIM), Barcelona, Spain
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, IISPV, Hospital Universitari Sant Joan de Reus, Spain; CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
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8
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Evaluation of serum uric acid levels in patients with rosacea. Arch Dermatol Res 2020; 312:447-451. [PMID: 32020322 DOI: 10.1007/s00403-020-02033-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 12/19/2019] [Accepted: 01/18/2020] [Indexed: 10/25/2022]
Abstract
Rosacea is an inflammatory skin disease with a chronic course. Although the pathogenesis of rosacea is not completely understood, it is regarded as an inflammatory process. The aim of the present study was to evaluate uric acid (UA) levels in patients with rosacea and to detect the correlation of UA levels with disease activity. A total of 61 patients with rosacea and 64 sex- and age-matched controls were included in the study. Demographic characteristics, medical history, and dermatological examination of the patient and control groups were recorded. Concentrations of serum UA and C-reactive protein (CRP) were evaluated and compared in both groups. This study included 61 patients with rosacea (39 females, 22 males, median age = 30 years) and 64 age- and sex-matched controls. Metabolic syndrome was significantly more common in patients with rosacea than in the control group. Patients with rosacea had significantly higher body mass index (BMI) values compared with those of controls. Serum UA and CRP values were significantly higher in the rosacea group than values in the control group. There was no statistically significant correlation between serum UA level and clinical rosacea severity. This study suggests that rosacea is not only a skin-related disease but also an inflammatory disease that can be related to higher uric acid levels, BMI values, and metabolic syndrome. It may be recommended that clinicians pay careful attention to the clinical follow-up of these patients to avoid missed associated comorbidities.
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9
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Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Delling FN, Djousse L, Elkind MSV, Ferguson JF, Fornage M, Khan SS, Kissela BM, Knutson KL, Kwan TW, Lackland DT, Lewis TT, Lichtman JH, Longenecker CT, Loop MS, Lutsey PL, Martin SS, Matsushita K, Moran AE, Mussolino ME, Perak AM, Rosamond WD, Roth GA, Sampson UKA, Satou GM, Schroeder EB, Shah SH, Shay CM, Spartano NL, Stokes A, Tirschwell DL, VanWagner LB, Tsao CW. Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation 2020; 141:e139-e596. [PMID: 31992061 DOI: 10.1161/cir.0000000000000757] [Citation(s) in RCA: 4861] [Impact Index Per Article: 1215.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals. RESULTS Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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10
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Kim JH. Letter: Increased Serum Angiopoietin-Like 6 Ahead of Metabolic Syndrome in a Prospective Cohort Study ( Diabetes Metab J 2019;43:521-9). Diabetes Metab J 2019; 43:727-728. [PMID: 31694083 PMCID: PMC6834845 DOI: 10.4093/dmj.2019.0172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Affiliation(s)
- Jin Hwa Kim
- Department of Endocrinology and Metabolism, Chosun University Hospital, Chosun University College of Medicine, Gwangju, Korea.
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