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Fasting Glucose Variability and the Risk of Dementia in Individuals with Diabetes: A Nationwide Cohort Study
Da Young Lee, Jaeyoung Kim, Sanghyun Park, So Young Park, Ji Hee Yu, Ji A Seo, Nam Hoon Kim, Hye Jin Yoo, Sin Gon Kim, Kyung Mook Choi, Sei Hyun Baik, Kyungdo Han, Nan Hee Kim
Diabetes Metab J. 2022;46(6):923-935.   Published online May 24, 2022
DOI: https://doi.org/10.4093/dmj.2021.0346
  • 6,104 View
  • 259 Download
  • 7 Web of Science
  • 8 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
We investigated whether fasting glucose (FG) variability could predict the risk of dementia.
Methods
This cohort study analyzed data from Koreans with diabetes after at least three health examinations by the Korean National Health Insurance Corporation between 2005 and 2010, which included at least one examination between 2009 and 2010. A total of 769,554 individuals were included, excluding those aged <40 years and those with dementia. FG variability was measured using the variability independent of the mean (FG-VIM). The incidence of dementia was defined by the International Classification of Diseases 10th Revision codes and prescription of anti-dementia medication and was subdivided into Alzheimer’s disease (AD) and vascular dementia (VD).
Results
During the 6.9-year follow-up, 54,837, 41,032, and 6,892 cases of all-cause dementia, AD, and VD, respectively, were identified. Cox proportional regression analyses showed that as the FG-VIM quartile increased, the risk of dementia serially increased after adjustment for metabolic factors, income status, and diabetes-related characteristics, including the mean FG. Participants in FG-VIM quartile 4 showed a 18%, 19%, and 17% higher risk for all-cause dementia, AD, and VD, respectively, than those in quartile 1; this particularly included non-obese patients with a longer duration of diabetes, high FG levels, dyslipidemia, and those taking glucose-lowering medications. Conversely, the baseline FG status and dementia showed a U-shaped association.
Conclusion
Increased FG variability over 5 years can predict the risk of dementia in individuals with diabetes in Korea. This finding was more pronounced in patients with less favorable metabolic profiles.

Citations

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  • Fasting glucose variability and risk of dementia in Parkinson’s disease: a 9-year longitudinal follow-up study of a nationwide cohort
    Sung Hoon Kang, Yunjin Choi, Su Jin Chung, Seok-Joo Moon, Chi Kyung Kim, Ji Hyun Kim, Kyungmi Oh, Joon Shik Yoon, Sang Won Seo, Geum Joon Cho, Seong-Beom Koh
    Frontiers in Aging Neuroscience.2024;[Epub]     CrossRef
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    Vishal Chavda, Dhananjay Yadav, Snehal Patel, Minseok Song
    Brain Sciences.2024; 14(3): 284.     CrossRef
  • The relationship between diabetes and the dementia risk: a meta-analysis
    Fang Cao, Fushuang Yang, Jian Li, Wei Guo, Chongheng Zhang, Fa Gao, Xinxin Sun, Yi Zhou, Wenfeng Zhang
    Diabetology & Metabolic Syndrome.2024;[Epub]     CrossRef
  • Effect of glucose variability on the mortality of adults aged 75 years and over during the first year of the COVID-19 pandemic
    Miguel A. Salinero-Fort, F. Javier San Andrés-Rebollo, Juan Cárdenas-Valladolid, José Mostaza, Carlos Lahoz, Fernando Rodriguez-Artalejo, Paloma Gómez-Campelo, Pilar Vich-Pérez, Rodrigo Jiménez-García, José M. de-Miguel-Yanes, Javier Maroto-Rodriguez, Bel
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  • The Association of Glucose Variability and Dementia Incidence in Latinx Adults with Type 2 Diabetes: A Retrospective Study
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    Clinical Nursing Research.2023; 32(2): 249.     CrossRef
  • The effects of long-term cumulative HbA1c exposure on the development and onset time of dementia in the patients with type 2 diabetes mellitus: Hospital based retrospective study (2005–2021)
    Sunyoung Cho, Choon Ok Kim, Bong-soo Cha, Eosu Kim, Chung Mo Nam, Min-Gul Kim, Min Soo Park
    Diabetes Research and Clinical Practice.2023; 201: 110721.     CrossRef
  • Physiological Mechanisms Inherent to Diabetes Involved in the Development of Dementia: Alzheimer’s Disease
    Himan Mohamed-Mohamed, Victoria García-Morales, Encarnación María Sánchez Lara, Anabel González-Acedo, Teresa Pardo-Moreno, María Isabel Tovar-Gálvez, Lucía Melguizo-Rodríguez, Juan José Ramos-Rodríguez
    Neurology International.2023; 15(4): 1253.     CrossRef
  • Cumulative effect of impaired fasting glucose on the risk of dementia in middle-aged and elderly people: a nationwide cohort study
    Jin Yu, Kyu-Na Lee, Hun-Sung Kim, Kyungdo Han, Seung-Hwan Lee
    Scientific Reports.2023;[Epub]     CrossRef
Clinical Complications
Incidence and Risk Factors for Dementia in Type 2 Diabetes Mellitus: A Nationwide Population-Based Study in Korea
Ji Hee Yu, Kyungdo Han, Sanghyun Park, Hanna Cho, Da Young Lee, Jin-Wook Kim, Ji A Seo, Sin Gon Kim, Sei Hyun Baik, Yong Gyu Park, Kyung Mook Choi, Seon Mee Kim, Nan Hee Kim
Diabetes Metab J. 2020;44(1):113-124.   Published online November 12, 2019
DOI: https://doi.org/10.4093/dmj.2018.0216
  • 8,446 View
  • 206 Download
  • 33 Web of Science
  • 32 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   
Background

Diabetes mellitus is associated with an increased risk of dementia. We aimed to comprehensively analyze the incidence and risk factors for dementia and young-onset dementia (YOD) in diabetic patients in Korea using the National Health Insurance Service data.

Methods

Between January 1, 2009 and December 31, 2012, a total of 1,917,702 participants with diabetes were included and followed until the date of dementia diagnosis or until December 31, 2015. We evaluated the incidence and risk factors for all dementia, Alzheimer's disease (AD), and vascular dementia (VaD) by Cox proportional hazards analyses. We also compared the impact of risk factors on the occurrence of YOD and late-onset dementia (LOD).

Results

During an average of 5.1 years of follow-up, the incidence of all types of dementia, AD, or VaD was 9.5, 6.8, and 1.3/1,000 person-years, respectively, in participants with diabetes. YOD comprised 4.8% of all dementia occurrence, and the ratio of AD/VaD was 2.1 for YOD compared with 5.5 for LOD. Current smokers and subjects with lower income, plasma glucose levels, body mass index (BMI), and subjects with hypertension, dyslipidemia, vascular complications, depression, and insulin treatment developed dementia more frequently. Vascular risk factors such as smoking, hypertension, and previous cardiovascular diseases were more strongly associated with the development of VaD than AD. Low BMI and a history of stroke or depression had a stronger influence on the development of YOD than LOD.

Conclusion

The optimal management of modifiable risk factors may be important for preventing dementia in subjects with diabetes mellitus.

Citations

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  • Unlocking the Protective Potential of Upper Respiratory Infection Treatment Histories against Alzheimer’s Disease: A Korean Adult Population Study
    Ho Suk Kang, Ji Hee Kim, Joo-Hee Kim, Woo Jin Bang, Hyo Geun Choi, Nan Young Kim, Ha Young Park, Mi Jung Kwon
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  • Hepatopancreatic metabolic disorders and their implications in the development of Alzheimer's disease and vascular dementia
    Francisco I. Pinheiro, Irami Araújo-Filho, Amália C.M. do Rego, Eduardo P. de Azevedo, Ricardo N. Cobucci, Fausto P. Guzen
    Ageing Research Reviews.2024; 96: 102250.     CrossRef
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    Juhyun Song
    Biomedicine & Pharmacotherapy.2023; 162: 114647.     CrossRef
  • The effects of long-term cumulative HbA1c exposure on the development and onset time of dementia in the patients with type 2 diabetes mellitus: Hospital based retrospective study (2005–2021)
    Sunyoung Cho, Choon Ok Kim, Bong-soo Cha, Eosu Kim, Chung Mo Nam, Min-Gul Kim, Min Soo Park
    Diabetes Research and Clinical Practice.2023; 201: 110721.     CrossRef
  • Association of triglyceride/high-density lipoprotein cholesterol ratio with severe complications of COVID-19
    Yoonkyung Chang, Jimin Jeon, Tae-Jin Song, Jinkwon Kim
    Heliyon.2023; 9(6): e17428.     CrossRef
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    Wenhui Lei, Yiwen Cheng, Jie Gao, Xia Liu, Li Shao, Qingming Kong, Nengneng Zheng, Zongxin Ling, Weiming Hu
    Frontiers in Cellular and Infection Microbiology.2023;[Epub]     CrossRef
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    Jiayi Feng, Cuihong Huang, Lei Liang, Chuang Li, Xiaojie Wang, Jianping Ma, Xinhui Guan, Bin Jiang, Shaofen Huang, Pei Qin
    Journal of the American Medical Directors Association.2023; 24(9): 1363.     CrossRef
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    Hee-Cheol Kim, Ho-Jun Lee, Yang-Tae Kim, Byeong-Churl Jang, Asirvatham Alwin Robert
    Journal of Diabetes Research.2023; 2023: 1.     CrossRef
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    Gratianne Rabiller, Zachary Ip, Shahram Zarrabian, Hongxia Zhang, Yoshimichi Sato, Azadeh Yazdan-Shahmorad, Jialing Liu
    Aging and disease.2023;[Epub]     CrossRef
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    Mingyang Sun, Zhongyuan Lu, Wan-Ming Chen, Szu-Yuan Wu, Jiaqiang Zhang
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    Moo-Seok Park, Jimin Jeon, Tae-Jin Song, Jinkwon Kim
    Journal of Diabetes and its Complications.2022; 36(2): 108107.     CrossRef
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    Jinkwon Kim, Hyung Jun Kim, Jimin Jeon, Tae-Jin Song
    Journal of Hypertension.2022; 40(2): 374.     CrossRef
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    Mei Wu, Fan Mei, Kaiyan Hu, Liyuan Feng, Zhe Wang, Qianqian Gao, Fei Chen, Li Zhao, Xiaohui Li, Bin Ma
    Acta Diabetologica.2022; 59(4): 443.     CrossRef
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    Yeng Yan Chow, Milou Verdonschot, Claire T. McEvoy, Geeske Peeters
    Diabetes Research and Clinical Practice.2022; 185: 109227.     CrossRef
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    Rahnuma Ahmad, Kona Chowdhury, Santosh Kumar, Mohammed Irfan, Govindool Reddy, Farhana Akter, Dilshad Jahan, Mainul Haque
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    Hee Kyung Kim, Juhyun Song
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    Athanasia Athanasaki, Konstantinos Melanis, Ioanna Tsantzali, Maria Ioanna Stefanou, Sofia Ntymenou, Sotirios G. Paraskevas, Theodosis Kalamatianos, Eleni Boutati, Vaia Lambadiari, Konstantinos I. Voumvourakis, George Stranjalis, Sotirios Giannopoulos, Ge
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    Maturitas.2022; 162: 58.     CrossRef
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    Vijay Kumar, So-Hyeon Kim, Kausik Bishayee
    International Journal of Molecular Sciences.2022; 23(17): 9540.     CrossRef
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    Yun Kyung Cho, Chang Hee Jung
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    You-Bin Lee, Min Young Kim, Kyungdo Han, Bongsung Kim, Jiyun Park, Gyuri Kim, Kyu Yeon Hur, Jae Hyeon Kim, Sang-Man Jin
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    Alzheimer's Research & Therapy.2021;[Epub]     CrossRef
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    Yebeen Ysabelle Boo, Otto-Emil Jutila, Meghan A. Cupp, Logan Manikam, Sung-Il Cho
    Aging Clinical and Experimental Research.2021; 33(9): 2573.     CrossRef
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    Yura Lee, Chi C. Cho
    Archives of Gerontology and Geriatrics.2021; 95: 104424.     CrossRef
  • Cumulative Exposure to Metabolic Syndrome Components and the Risk of Dementia: A Nationwide Population-Based Study
    Yunjung Cho, Kyungdo Han, Da Hye Kim, Yong-Moon Park, Kun-Ho Yoon, Mee Kyoung Kim, Seung-Hwan Lee
    Endocrinology and Metabolism.2021; 36(2): 424.     CrossRef
  • Cardiovascular risks of periodontitis and oral hygiene indicators in patients with diabetes mellitus
    Tae-Jin Song, Jimin Jeon, Jinkwon Kim
    Diabetes & Metabolism.2021; 47(6): 101252.     CrossRef
  • Association Between Diabetic Retinopathy and Cognitive Impairment: A Systematic Review and Meta-Analysis
    Dihe Cheng, Xue Zhao, Shuo Yang, Guixia Wang, Guang Ning
    Frontiers in Aging Neuroscience.2021;[Epub]     CrossRef
  • Improving Cognition with Nutraceuticals Targeting TGF-β1 Signaling
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  • Letter: Hypoglycemia and Dementia Risk in Older Patients with Type 2 Diabetes Mellitus: A Propensity-Score Matched Analysis of a Population-Based Cohort Study (Diabetes Metab J 2020;44:125–33)
    Jin Hwa Kim
    Diabetes & Metabolism Journal.2020; 44(2): 356.     CrossRef
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Reviews
Obesity and Metabolic Syndrome
Contributing Factors to Diabetic Brain Injury and Cognitive Decline
Nirmal Verma, Florin Despa
Diabetes Metab J. 2019;43(5):560-567.   Published online October 24, 2019
DOI: https://doi.org/10.4093/dmj.2019.0153
  • 5,278 View
  • 100 Download
  • 8 Web of Science
  • 9 Crossref
AbstractAbstract PDFPubReader   

The link of diabetes with co-occurring disorders in the brain involves complex and multifactorial pathways. Genetically engineered rodents that express familial Alzheimer's disease-associated mutant forms of amyloid precursor protein and presenilin 1 (PSEN1) genes provided invaluable insights into the mechanisms and consequences of amyloid deposition in the brain. Adding diabetes factors (obesity, insulin impairment) to these animal models to predict success in translation to clinic have proven useful at some extent only. Here, we focus on contributing factors to diabetic brain injury with the aim of identifying appropriate animal models that can be used to mechanistically dissect the pathophysiology of diabetes-associated cognitive dysfunction and how diabetes medications may influence the development and progression of cognitive decline in humans with diabetes.

Citations

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  • The association between renal accumulation of pancreatic amyloid-forming amylin and renal hypoxia
    Nirmal Verma, Florin Despa
    Frontiers in Endocrinology.2023;[Epub]     CrossRef
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    Sweta Priyadarshini Pradhan, Pratap Kumar Sahu, Anindita Behera
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  • Nicotinamide Prevents Diabetic Brain Inflammation via NAD+-Dependent Deacetylation Mechanisms
    Jeimy Katherine Torres-Méndez, Julia Niño-Narvión, Patricia Martinez-Santos, Elena María Goretti Diarte-Añazco, Karen Alejandra Méndez-Lara, Tania Vázquez del Olmo, Noemi Rotllan, Maria Teresa Julián, Núria Alonso, Didac Mauricio, Mercedes Camacho, Juan P
    Nutrients.2023; 15(14): 3083.     CrossRef
  • Ipriflavone as a non‐steroidal glucocorticoid receptor antagonist ameliorates diabetic cognitive impairment in mice
    Ruifang Nie, Jian Lu, Rui Xu, Juanzhen Yang, Xingyi Shen, Xingnan Ouyang, Danyang Zhu, Yujie Huang, Tong Zhao, Xuejian Zhao, Yin Lu, Minyi Qian, Jiaying Wang, Xu Shen
    Aging Cell.2022;[Epub]     CrossRef
  • Effects of linagliptin vs glimepiride on cognitive performance in type 2 diabetes: results of the randomised double-blind, active-controlled CAROLINA-COGNITION study
    Geert Jan Biessels, Chloë Verhagen, Jolien Janssen, Esther van den Berg, Gudrun Wallenstein, Bernard Zinman, Mark A. Espeland, Odd Erik Johansen
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  • The Association Between Second-Line Oral Antihyperglycemic Medication on Types of Dementia in Type 2 Diabetes: A Nationwide Real-World Longitudinal Study
    Won Jun Kim, Jung Hyun Noh, Kyungdo Han, Cheol-Young Park
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    Jinni Meng, Yafei Zhu, Huixia Ma, Xiaobo Wang, Qipeng Zhao
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    Jin Hwa Kim
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    Athanasia Papazafiropoulou, Chris Koros, Andreas Melidonis , Stavros Antonopoulos
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Others
Mitochondrial Toxins and Healthy Lifestyle Meet at the Crossroad of Hormesis
Yu-Mi Lee, Duk-Hee Lee
Diabetes Metab J. 2019;43(5):568-577.   Published online October 24, 2019
DOI: https://doi.org/10.4093/dmj.2019.0143
  • 6,305 View
  • 94 Download
  • 12 Web of Science
  • 12 Crossref
AbstractAbstract PDFPubReader   

Mitochondrial function is crucial for the maintenance of cellular homeostasis under physiological and stress conditions. Thus, chronic exposure to environmental chemicals that affect mitochondrial function can have harmful effects on humans. We argue that the concept of hormesis should be revisited to explain the non-linear responses to mitochondrial toxins at a low-dose range and develop practical methods to protect humans from the negative effects of mitochondrial toxins. Of the most concern to humans are lipophilic chemical mixtures and heavy metals, owing to their physical properties. Even though these chemicals tend to demonstrate no safe level in humans, a non-linear dose-response has been also observed. Stress response activation, i.e., hormesis, can explain this non-linearity. Recently, hormesis has reemerged as a unifying concept because diverse stressors can induce similar stress responses. Besides potentially harmful environmental chemicals, healthy lifestyle interventions such as exercise, calorie restriction (especially glucose), cognitive stimulation, and phytochemical intake also activate stress responses. This conceptual link can lead to the development of practical methods that counterbalance the harm of mitochondrial toxins. Unlike chemical hormesis with its safety issues, the activation of stress responses via lifestyle modification can be safely used to combat the negative effects of mitochondrial toxins.

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Original Article
Clinical Complications
Hypoglycemia and Dementia Risk in Older Patients with Type 2 Diabetes Mellitus: A Propensity-Score Matched Analysis of a Population-Based Cohort Study
Young-Gun Kim, Dong Gyu Park, So Young Moon, Ja Young Jeon, Hae Jin Kim, Dae Jung Kim, Kwan-Woo Lee, Seung Jin Han
Diabetes Metab J. 2020;44(1):125-133.   Published online October 23, 2019
DOI: https://doi.org/10.4093/dmj.2018.0260
  • 6,829 View
  • 211 Download
  • 28 Web of Science
  • 29 Crossref
AbstractAbstract PDFPubReader   
Background

Type 2 diabetes mellitus (T2DM) is associated with an increased risk for dementia. The effects of hypoglycemia on dementia are controversial. Thus, we evaluated whether hypoglycemia increases the risk for dementia in senior patients with T2DM.

Methods

We used the Korean National Health Insurance Service Senior cohort, which includes >10% of the entire senior population of South Korea. In total, 5,966 patients who had ever experienced at least one episode of hypoglycemia were matched with those who had not, using propensity score matching. The risk of dementia was assessed through a survival analysis of matched pairs.

Results

Patients with underlying hypoglycemic events had an increased risk for all-cause dementia, Alzheimer's dementia (AD), and vascular dementia (VaD) compared with those who had not experienced a hypoglycemic event (hazard ratio [HR], 1.254; 95% confidence interval [CI], 1.166 to 1.349; P<0.001 for all-cause dementia; HR, 1.264; 95% CI, 1.162 to 1.375; P<0.001 for AD; HR, 1.286; 95% CI, 1.110 to 1.490; P<0.001 for VaD). According to number of hypoglycemic episodes, the HRs of dementia were 1.170, 1.201, and 1.358 in patients with one hypoglycemic episode, two or three episodes, and more than three episodes, respectively. In the subgroup analysis, hypoglycemia was associated with an increased risk for dementia in both sexes with or without T2DM microvascular or macrovascular complications.

Conclusion

Our findings suggest that patients with a history of hypoglycemia have a higher risk for dementia. This trend was similar for AD and VaD, the two most important subtypes of dementia.

Citations

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  • Potential risk factors for mild cognitive impairment among patients with type 2 diabetes experiencing hypoglycemia
    Ruonan Gao, Menglan Zhan, Sujie Ke, Kejun Wu, Guanlian He, Liqin Qi, Xiaoying Liu, Xiaohong Liu, Lijing Wang, Libin Liu
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  • Response: Hypoglycemia and Dementia Risk in Older Patients with Type 2 Diabetes Mellitus: A Propensity-Score Matched Analysis of a Population-Based Cohort Study (Diabetes Metab J 2020;44:125–33)
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