Association of Metabolically Healthy Overweight Phenotype With Abnormalities of Glucose Levels and Blood Pressure Among Chinese Adults
Open accessAbstract
Importance: Whether the metabolically healthy overweight (MHO) phenotype is resistant to metabolic abnormalities remains unknown. Objective: To evaluate the association of MHO with glucose level abnormalities and high blood pressure (BP) in Chinese adults. Design, Setting, and Participants: This prospective cohort study was conducted from January 1, 2013, to October 31, 2018, in the Health Management Center at Ren Ji Hospital, Shanghai, China, using data from 55 155 recruited Chinese adults. Body weight, fasting blood glucose (FBG) level, hemoglobin A1c (HbA1c) level, and BP were measured annually. Exposures: Metabolically healthy overweight was defined as a body mass index (calculated as weight in kilograms divided by height in meters squared) of 24.0 in 2013 (baseline) and 2014, no history of metabolic diseases, and normal FBG level, HbA1c level, BP, lipid profile, serum uric acid level, and liver ultrasonographic findings at baseline; the remaining participants were defined as being metabolically healthy normal. Main Outcomes and Measures: Glucose level abnormality was confirmed if the FBG level was 101 mg/dL or greater or the HbA1c level was 5.7%, and high BP was confirmed if the systolic BP was 130 mm Hg or higher or the diastolic BP was 80 mm Hg or higher at least twice during the subsequent 4 years of follow-up. Results: A total of 3204 metabolically healthy Chinese adults (mean [SD] age, 39.8 [10.9] years; 1940 women [60.5%]) were included in the study. The prevalence of MHO was 7.0%. A total of 146 incident cases of glucose level abnormality and 220 cases of high BP during 4 years of follow-up were identified. Compared with the metabolically healthy normal group, the MHO group had a higher risk of glucose level abnormality (adjusted hazard ratio, 2.36; 95% CI, 1.52-3.64) and high BP (adjusted hazard ratio, 1.73; 95% CI, 1.18-2.53) after adjusting for several potential confounders. Conclusions and Relevance: Metabolically healthy overweight may be associated with a high future risk of glucose abnormality and high BP in Chinese adults. If the individuals are confirmed with MHO, early interventions, including diet and exercise, should be recommended to decrease the risk of developing abnormalities of glucose and BP.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Given existing evidence that even non-MetS obese individuals face elevated cardiovascular risk, these false positives may represent a clinically meaningful subgroup who could benefit from earlier lifestyle or medical interventions.
“Given existing evidence that even non-MetS obese individuals face elevated cardiovascular risk [43–45], these so-called “false positives” may represent a clinically meaningful subgroup who could benefit from earlier lifestyle or medical interventions.”
From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [45]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Alberti, K. G. M. M., Eckel, R. H., Grundy, S. M., Zimmet, P. Z., Cleeman, J. I., Donato, K. A., Fruchart, J.-C., James, W. P. T., Loria, C. M., & Smith, S. C. (2009) · Circulation · doi:10.1161/circulationaha.109.192644 · Cited by 14,860 · see citing works
Diagnosis and Classification of Diabetes Mellitus
American Diabetes Association (2010) · Diabetes Care · doi:10.2337/dc11-s062 · Cited by 14,019 · see citing works
Whelton, P. K., Carey, R. M., Aronow, W. S., Casey, D. E., Collins, K. J., Dennison Himmelfarb, C., DePalma, S. M., Gidding, S., Jamerson, K. A., Jones, D. W., et al. (2018) · Hypertension · doi:10.1161/hyp.0000000000000065 · Cited by 7,391 · see citing works
Pearson, T. A., Mensah, G. A., Alexander, R. W., Anderson, J. L., Cannon, R. O., Criqui, M., Fadl, Y. Y., Fortmann, S. P., Hong, Y., Myers, G. L., et al. (2003) · Circulation · doi:10.1161/01.cir.0000052939.59093.45 · Cited by 6,401 · see citing works
Diagnosis and classification of diabetes mellitus
American Diabetes Association (2014) · Diabetes Care · doi:10.2337/dc14-s081 · Cited by 5,974 · see citing works
Flegal, K. M., Kit, B. K., Orpana, H., & Graubard, B. I. (2013) · JAMA · doi:10.1001/jama.2012.113905 · Cited by 3,998 · see citing works
PearsonT, A., MensahG, A., & AlexanderR, W. (2003) · Journal of Cardiopulmonary Rehabilitation · doi:10.1097/00008483-200305000-00016 · Cited by 2,937 · see citing works
Comment on the provisional report from the WHO consultation
(1999) · Diabetic Medicine · doi:10.1046/j.1464-5491.1999.00059.x · Cited by 1,820 · see citing works
Body Mass Index, Metabolic Syndrome, and Risk of Type 2 Diabetes or Cardiovascular Disease
Meigs, J. B., Wilson, P. W. F., Fox, C. S., Vasan, R. S., Nathan, D. M., SULLIVAN, L. M., & D’Agostino, R. B. (2006) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2006-0594 · Cited by 1,068 · see citing works
Identification and characterization of metabolically benign obesity in humans
Stefan, N. (2008) · Archives of Internal Medicine · doi:10.1001/archinte.168.15.1609 · Cited by 1,049 · see citing works
The Definition and Prevalence of Obesity and Metabolic Syndrome.
Engin, A. (2017) · Advances in Experimental Medicine and Biology · doi:10.1007/978-3-319-48382-5_1 · Cited by 978 · see citing works
Characterizing the profile of obese patients who are metabolically healthy
Primeau, V., Coderre, L., Karelis, A. D., Brochu, M., Lavoie, M.-È., Messier, V., SLADEK, R. M., & RABASA-LHORET, R. P. R. (2010) · International Journal of Obesity · doi:10.1038/ijo.2010.216 · Cited by 630 · see citing works
Ärnlöv, J., Ingelsson, E., Sundström, J., & Lind, L. (2009) · Circulation · doi:10.1161/circulationaha.109.887521 · Cited by 612 · see citing works
Caleyachetty, R., Thomas, G. N., Toulis, K. A., Mohammed, N., Gokhale, K. M., Balachandran, K., & Nirantharakumar, K. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.07.763 · Cited by 574 · see citing works
Metabolically Healthy Obesity and Risk of All-Cause and Cardiovascular Disease Mortality
Hamer, M., & Stamatakis, E. A. (2012) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2011-3475 · Cited by 565 · see citing works
Zhou, B. (2002) · PubMed · Cited by 526 · see citing works
Secular trends in the prevalence of general and abdominal obesity among Chinese adults, 1993–2009
Xi, B., Liang, Y., He, T., Reilly, K. H., Hu, Y., Wang, Q., Yan, Y., & Mi, J. (2011) · Obesity Reviews · doi:10.1111/j.1467-789x.2011.00944.x · Cited by 372 · see citing works
Mansur, R. B., Brietzke, E. M., & McIntyre, R. S. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.12.017 · Cited by 325 · see citing works
Lassale, C. M., Tzoulaki, I., Moons, K. G. M., Sweeting, M. J., Boer, J. M. A., Johnson, L., Huerta, J. M., Agnoli, C., Freisling, H., Weiderpass, E., et al. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx448 · Cited by 298 · see citing works
Engin, A., & Engin, A. B. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-48382-5 · Cited by 212 · see citing works
Iliodromiti, S., Celis-Morales, C. A., Lyall, D. M., Anderson, J. J., Gray, S. R., Mackay, D., Nelson, S. M., Welsh, P. I., Pell, J. P., Gill, J. M. R., et al. (2018) · European Heart Journal · doi:10.1093/eurheartj/ehy057 · Cited by 210 · see citing works
Kong, X., Ma, Y., Chen, J., Luo, Q., Yu, X., Li, Y., Xu, J., Huang, S., Wang, L., Huang, W., et al. (2012) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfs491 · Cited by 202 · see citing works
All-Cause Mortality Risk of Metabolically Healthy Obese Individuals in NHANES III
Durward, C. M., Hartman, T. J., & Nickols‐Richardson, S. M. (2012) · Journal of Obesity · doi:10.1155/2012/460321 · Cited by 149 · see citing works
Insulin resistance syndrome, body mass index and the risk of ischemic heart disease
St-Pierre, A. (2005) · Canadian Medical Association Journal · doi:10.1503/cmaj.1040834 · Cited by 146 · see citing works
Incident Cardiovascular Disease Events in Metabolically Benign Obese Individuals
Ogorodnikova, A. D., Kim, M., McGinn, A. P., Muntner, P., Khan, U. I., & Wildman, R. P. (2011) · Obesity · doi:10.1038/oby.2011.243 · Cited by 146 · see citing works
The Prevalence of Metabolically Healthy and Unhealthy Obesity according to Different Criteria
Liu, C., Wang, C., Guan, S., Liu, H., Wu, X., Zhang, Z., Gu, X., Zhang, Y., Zhao, Y., Tse, L. A., et al. (2019) · Obesity Facts · doi:10.1159/000495852 · Cited by 99 · see citing works
Chrysant, S. G., & Chrysant, G. S. (2019) · Postgraduate Medicine · doi:10.1080/00325481.2019.1568019 · Cited by 70 · see citing works
Sookoian, S. C., & Pirola, C. J. (2019) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.15163 · Cited by 66 · see citing works
Radwan, H. M., Ballout, R. A., Hasan, H. A., Lessan, N. G., Karavetian, M., & Rizk, R. (2018) · Journal of Obesity · doi:10.1155/2018/2185942 · Cited by 61 · see citing works
Long-term metabolic risk for the metabolically healthy overweight/obese phenotype
Ycaza, A. E. E. D., Donegan, D. M., & Jensen, M. D. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.233 · Cited by 60 · see citing works
Metabolic Health Status and the Obesity Paradox in Older Adults
Cheng, F. W., Gao, X., Mitchell, D. C., Wood, C., Rolston, D. D. K., Still, C. D., & Jensen, G. L. (2016) · Journal of Nutrition in Gerontology and Geriatrics · doi:10.1080/21551197.2016.1199004 · Cited by 49 · see citing works
Self-citationIs metabolically healthy obesity a useful concept?
Beh, S. (2018) · Diabetic Medicine · doi:10.1111/dme.13869 · Cited by 16 · see citing works
Flagged first, then by citations
3 of the 35 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Metabolic Syndrome Study Cooperation Group of Chinese Diabetes Society, 2004, Suggestions about metabolic syndrome of Chinese Diabetes Society., Chin J Diabetes, 12, 156
- Zhou, 2002, Zhonghua Liu Xing Bing Xue Za Zhi, 23, 5
Cited by
Sorted by citations
Zhang, Q., Shen, X., Yuan, X., Huang, J., Zhu, Y., Zhu, T., Zhang, T., Wu, H., Wu, Q., Fan, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47553-5 · Cited by 66 · see citing works
All-cause mortality in metabolically healthy individuals was not predicted by overweight and obesity
Tian, Q., Wang, A., Zuo, Y., Chen, S., Hou, H., Wang, W., Wu, S., & Wang, Y. (2020) · JCI Insight · doi:10.1172/jci.insight.136982 · Cited by 29 · see citing works
Metabolically Healthy Obesity: Are Interventions Useful?
Mathis, B. J., Tanaka, K., & Hiramatsu, Y. (2023) · Current Obesity Reports · doi:10.1007/s13679-023-00494-4 · Cited by 19 · see citing works
The Harm of Metabolically Healthy Obese and the Effect of Exercise on Their Health Promotion
Su, L., Pan, Y., & Chen, H. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.924649 · Cited by 12 · see citing works
Viticchi, G., Falsetti, L., Plutino, A., Bartolini, M., Buratti, L., & Silvestrini, M. (2020) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2020.116955 · Cited by 12 · see citing works
Metabolic Risk Profile and Graft Function Deterioration 2 Years After Kidney Transplant
Yan, J., Yang, X., Wang, J., Cai, H., Che, X., Ying, L., Zhang, T., Chen, Q., Xia, J., Gu, L., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.49538 · Cited by 11 · see citing works
Xu, R., Shen, P., Wu, C., Wan, Y., Fan, Z., & Gao, X. (2020) · Public Health Nutrition · doi:10.1017/s136898002000289x · Cited by 10 · see citing works
Self-citationZhang, X., Zhang, T., Wu, C., Zhou, Y., Chen, Z., & Xu, R. (2022) · Hypertension Research · doi:10.1038/s41440-022-01062-4 · Cited by 9 · see citing works
Self-citationVisceral Obesity and Lipid Profiles in Chinese Adults with Normal and High Body Mass Index
Lu, Y., Li, N., Kamishima, T., Jia, P., Zhou, D., Hind, K., Sutherland, K. L., & Cheng, X. (2022) · Diagnostics · doi:10.3390/diagnostics12102522 · Cited by 9 · see citing works
Min, J., Hu, X., Zhang, J.-Y., Zeng, T.-S., Wang, Y., Tian, S., Liu, G., Zhong, X.-Y., Qiu, K.-L., Peng, M.-M., et al. (2021) · Diabetes, Metabolic Syndrome and Obesity : Targets and Therapy · doi:10.2147/dmso.s313475 · Cited by 9 · see citing works
Shen, P., Zhou, Y., Song, A., Wan, Y. P., Fan, Z., & Xu, R. (2021) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2021.05.003 · Cited by 7 · see citing works
Self-citationHe, Y., Chen, Z., Tang, Z., Qin, Y., & Wang, F. (2025) · BMC Public Health · doi:10.1186/s12889-025-23858-y · Cited by 5 · see citing works
Biomarkers of Metabolic Adaptation to High Dietary Fats in a Mouse Model of Obesity Resistance
Milhem, F., Hamilton, L., Skates, E., et al. (2024) · Metabolites · doi:10.3390/metabo14010069 · Cited by 4 · see citing works
Tan, T., Zhou, Y., Wan, Y., Fan, Z., Xu, R., & Gao, X. (2021) · BMC Cardiovascular Disorders · doi:10.1186/s12872-021-02279-w · Cited by 2 · see citing works
Self-citationRamachandra, S. C., Prashant, A., Shivanna, A., Bhujanga, A. K., Meherunnisa, S., & Vishwanath, P. (2023) · International Journal of Health & Allied Sciences · doi:10.55691/2278-344x.1058 · Cited by 1 · see citing works
Comments on “Myocardial Tissue-Level Characteristics of Adults With Metabolically Healthy Obesity”
Zhou, Y., & Xu, R. (2023) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2023.05.022
Self-citationRani, C. A. L. J. A., Gowda, R. N., Muniswamy, V., Srinivas, A., Prashant, A., Vishwanath, P. M., & Ramachandra, S. C. (2025) · BMJ connections. · doi:10.1136/bmjccgg-2025-000034
Tan, T., Zhou, Y., Wan, Y., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16696629
Self-citation
- Authors
- Xu, R., Gao, X., Wan, Y., & Fan, Z.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Pennsylvania State University (US)
- Corresponding author
- Xu, Renying
- Fan, Zhuping
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2019-10-25
- Volume
- 2
- Issue
- 10
- Pages
- e1914025
- ISSN
- 2574-3805 (ISSN-L)
- PMC
- PMC6822082
- OpenAlex
- W2981802173
- Semantic Scholar
- 8d6636630f531496dd74ddebac25924702a3d413
- Cited by
- 18
- References
- 35
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 4 · 2020: 3
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Network Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition (17DZ2272000)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
- Keywords
- hypertension, fasting blood glucose, HbA1c, metabolic resistance, Chinese adults, overweight, metabolic disease risk, hypertension incidence, body mass index, serum uric acid, liver ultrasound, lipid profile, prospective cohort follow-up
- MeSH terms
- Adult, Blood Glucose, Blood Pressure Determination, Body Weight, China, Female, Glycated Hemoglobin, Humans, Hypertension, Male, Phenotype, Prospective Studies, Risk Factors, Body Mass Index, Incidence, Overweight
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
