Second, the study did not consider the phases of kidney cancer or its pathologic type. Thirdly, an accurately measured increase in lower body fat mass is now recognized as an independent indicator of metabolic health Therefore, the identification of distinct fat distribution phenotypes using relevant measurements, such as hip circumference, could provide better insight into the relationship between adiposity and cancer risk; however, we were unable to investigate the impact of these measurements on KC risk in our analyses because the NHIS data did not include any measurement for lower body fat mass.

Future study on the significance of lower body fat mass in obesity-related cancer would give greater precision to our understanding of the clinical implications of metabolic health in obese populations.

Despite these limitations, our study has strengths in that we used a large nationwide cohort and explained the effects of dynamic metabolic health on the incidence of kidney cancer in obese adults.

Our methodology revealed the implication of metabolic unhealthiness on kidney cancer risk and therefore suggested that being metabolically healthy should be prioritized to lower the kidney cancer risk in obese patients.

Our findings identified metabolic unhealthiness as a risk factor for kidney cancer risk in individuals with obesity. Furthermore, our results suggest that the dynamic metabolic health status should be considered as significantly affecting the kidney cancer risk.

do , approval number: NHIS The studies involving human participants were reviewed and approved by Hallym Sacred Heart Hospital Institutional Review Board IRB.

The ethics committee waived the requirement of written informed consent for participation. Conceptualization, YC and CJ; methodology, Y-JK; software, Y-JK; validation, YC and Y-JK; formal analysis, YC and Y-JK; investigation, YC; resources, YC; data curation, CJ; writing—original draft preparation, YC; writing—review and editing, HK, J-YP, WL, Y-JK, and CJ; visualization, Y-JK; supervision, YC.

All authors have read and agreed to the published version of the manuscript. This research was supported by the Hallym University Research Fund HURF The authors thank Editage for the English language review. We would like to thank the Korean National Health Insurance Service and all the participants of the study and health check-up.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. BMI, body mass index; BP, blood pressure; FPG, fasting plasma glucose; ICD, International Classification of Diseases; IRB, Hallym Sacred Heart Hospital Institutional Review Board; MHNO, metabolically healthy non-obese; MHO, metabolically healthy obesity; MHO, metabolically healthy obesity; MUNO, metabolically unhealthy non-obese; MUO, metabolically unhealthy obesity; NHIS-HEALS, Korean National Health Insurance Service-National Health Screening Cohort; RCC, renal cell cancer.

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Institute of Diabetes Research and Metabolic Diseases IDM , the Helmholtz Center, Munich, Germany. Department of Internal Medicine IV, Division of Endocrinology, Diabetology and Nephrology, University Hospital of Tübingen, Tübingen, Germany. German Center for Diabetes Research DZD , Neuherberg, Germany.

Norbert Stefan, Andreas L. Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany. Institute of Nutritional Science, University of Potsdam, Potsdam, Germany.

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Correspondence to Norbert Stefan. Nature Reviews Endocrinology thanks J. Wilding, M. Zheng and the other, anonymous, reviewer s for their contribution to the peer review of this work.

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Abstract Obesity and impaired metabolic health are established risk factors for the non-communicable diseases NCDs type 2 diabetes mellitus, cardiovascular disease, neurodegenerative diseases, cancer and nonalcoholic fatty liver disease, otherwise known as metabolic associated fatty liver disease MAFLD.

Key points Obesity, particularly severe obesity, is a strong and independent determinant of severe coronavirus disease COVID ; novel studies also suggest that visceral obesity increases the risk of complications.

Long COVID: major findings, mechanisms and recommendations Article 13 January Muscle abnormalities worsen after post-exertional malaise in long COVID Article Open access 04 January Long COVID manifests with T cell dysregulation, inflammation and an uncoordinated adaptive immune response to SARS-CoV-2 Article Open access 11 January Introduction As of 20 December , more than 75 million people have been infected with severe acute respiratory syndrome coronavirus 2 SARS-CoV-2 and more than 1.

Table 1 Summary table of the independent association of obesity with COVID severity Full size table. Full size image.

Cardiometabolic damage To clarify why some patients experience severe COVID, it is important to better understand viral—host interactions and, more specifically, which human tissues and organs are most strongly affected by SARS-CoV-2 infection.

Long-term effects of the pandemic Data are just emerging regarding the effect of the COVID pandemic on the prevalence and incidence of obesity and cardiometabolic diseases during a long-term follow-up Fig. Treating obesity in the COVID pandemic Considering the strong effects of overweight, obesity and impaired metabolic health on the course of COVID, achieving reductions in adipose tissue mass and improvements in metabolic health is crucial.

Treatment of patients with metabolically unhealthy obesity and COVID Supportive and intensive care treatment of patients with COVID is well established 8. Treatment of people with metabolically unhealthy obesity without COVID Owing to the striking resemblance between the mechanisms by which obesity and impaired metabolic health affect the risk of cardiometabolic diseases and severe COVID refs 16 , 22 , 40 , 41 , 65 , 66 , preventive measures taken to reduce the risk of cardiometabolic diseases could also decrease the risk of severe COVID Conclusions Based on data from large studies that investigated relationships of comorbidities with the course of COVID using multivariate adjustment, obesity emerged as a strong and independent determinant of increased risk of morbidity and mortality in patients infected with SARS-CoV References World Health Organization.

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Medicine 96 , 45 Li, X. Download references. Joungyoun Kim's research was supported by New Faculty Research Grant from Yonsei University College of Nursing.

Joungyoun Kim received a research grant by New Faculty Research Grant from Yonsei University College of Nursing. Department of Family Medicine, Chungbuk National University Hospital, 1-Soonwhan-ro, Seowon-gu, Cheongju, , Republic of Korea. Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yonsei-ro, Seodaemun-gu, Seoul, , Republic of Korea.

College of Nursing, Mo-Im Kim Nursing Research Institute, Yonsei University, , Yonsei-ro, Seodaemun-gu, Seoul, , Republic of Korea. Department of Artificial Intelligence, University of Seoul, Seoulsiripdae-ro, Dongdaemun-gu, Seoul, , Republic of Korea.

Department of Family Medicine, Chungbuk National University College of Medicine, 1 Chungdae-ro, Seowon-gu, Cheongju, , Chungbuk, Republic of Korea. You can also search for this author in PubMed Google Scholar. All authors made substantial contributions to the conception or design of the work.

All the authors made substantial contributions to the acquisition, analysis, or interpretation of data for the work. All authors contributed to the manuscript drafting or critically revised it for important intellectual content and reviewed and approved the final manuscript.

and H. are the guarantors of this work and, as such, had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Reprints and permissions. Kim, YS. Effect of metabolic health and obesity on all-cause death and CVD incidence in Korean adults: a retrospective cohort study. Sci Rep 13 , Download citation. Received : 20 May Accepted : 26 December Published : 12 January Anyone you share the following link with will be able to read this content:.

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Download PDF. Subjects Cardiology Endocrinology Risk factors. This article has been updated. Abstract This study aimed to investigate the risk of all-cause mortality and incidence of CVD according to metabolic health and body mass index BMI in Korean adults.

Introduction Cardiovascular diseases CVDs , primarily ischemic heart disease IHD and stroke, are the leading cause of mortality and a significant contributor to disability. For example, when you're walking or jogging on a treadmill or outside, speed up for 30 to 60 seconds, and then slow to your usual pace; repeat the cycle for eight to 12 minutes.

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Led by researchers at NYU Grossman School of Medicine, the work is the first to evaluate the effects of eTRF on glycemia and inflammation independent of weight loss. VIDEO: Dr. For their study, the researchers compared eTRF 80 percent of calories consumed before PM to a usual feeding pattern 50 percent of calories consumed after PM among 10 participants with prediabetes and obesity.

The patients were randomized to eTRF or usual feeding patterns for the first seven days and were changed over to the alternative arm for the next seven days. Patients wore continuous glucose blood sugar monitors throughout the study. Alemán, MD, PhD , assistant professor in the Department of Medicine at NYU Langone.

The time in range was similar between the eTRF and usual feeding pattern group.