Skip to main content
SpringerLink
Log in

Increased Stroke Risk in Metabolically Abnormal Normal Weight: a 10-Year Follow-up of 102,037 Participants in China

  • Original Article
  • Published:
Translational Stroke Research Aims and scope Submit manuscript

Abstract

The purpose of this study was to investigate the risks of stroke in subjects with metabolically abnormal normal weight (MANW) in China. We recruited 102,037 participants from the Zhejiang Metabolic Syndrome Cohort and the Kailuan cohort. The mean years of follow-up were 9.9 years. General obesity was defined by body mass index (BMI) ≥ 28, overweight by BMI < 28 and ≥ 24, and normal weight by BMI < 24 and ≥ 18.5. Metabolic abnormality was defined as two or more abnormal components (elevated triglycerides, low high-density lipoprotein cholesterol, elevated systolic blood pressure or diastolic blood pressure, or use of antihypertensive drug therapy, elevated fasting plasma glucose, or antidiabetic treatment). A multiple Cox regression model was used to calculate hazard ratio (HR) and its 95% confidence interval (CI), adjusted by potential confounding factors. Overall HR of the risks in two cohorts was calculated by a meta-analysis. Compared with the subjects who were metabolically normal with normal weight (MNNW), the pooled HR for stroke in MANW subjects was 1.82 (95% CI, 1.59–2.07). The risks of stroke in MANW subjects were significantly lower than that in subjects with metabolically abnormal obesity (MAO), but higher than that in those with metabolically normal obesity (MNO) (P < 0.05). These associations remained in the subtypes of cerebral infarction and cerebral hemorrhage. In normal-weight subjects, the HR for stroke was significantly positively correlated with the number of abnormal metabolic components (Ptrend < 0.001). In brief, metabolic abnormality increased the risk of stroke irrespective of obesity status. MANW individuals showed a greater risk of stroke, and this risk was positively correlated with the number of abnormal metabolic components.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author and investigating coordinators on reasonable request.

Abbreviations

MANW:

metabolically abnormal but normal weight

MNO:

metabolically normal obesity

MAO:

metabolically abnormal obesity

HR:

hazard risk

T2DM:

type 2 diabetes mellitus

CHD:

coronary heart disease

WC:

waist circumference

TG:

biochemical values, including triglycerides

HDL-C:

high-density lipoprotein cholesterol

FPG:

fasting plasma glucose

SBP:

systolic blood pressure

DBP:

diastolic blood pressure

TC:

total cholesterol

LDL-C:

low-density lipoprotein cholesterol

References

  1. Collaboration, N.C.D.R.F. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016;387(10026):1377–96.

    Article  Google Scholar 

  2. Aune D, Sen A, Norat T, Janszky I, Romundstad P, Tonstad S, et al. Body mass index, abdominal fatness, and heart failure incidence and mortality: a systematic review and dose-response meta-analysis of prospective studies. Circulation. 2016;133(7):639–49.

    Article  Google Scholar 

  3. Zheng W, McLerran DF, Rolland B, Zhang X, Inoue M, Matsuo K, et al. Association between body-mass index and risk of death in more than 1 million Asians. N Engl J Med. 2011;364(8):719–29.

    Article  CAS  Google Scholar 

  4. Schulze MB. Metabolic health in normal-weight and obese individuals. Diabetologia. 2019;62(4):558–66.

    Article  Google Scholar 

  5. Primeau V, Coderre L, Karelis AD, Brochu M, Lavoie ME, Messier V, et al. Characterizing the profile of obese patients who are metabolically healthy. Int J Obes. 2011;35(7):971–81.

    Article  CAS  Google Scholar 

  6. Zheng R, Yang M, Bao Y, Li H, Shan Z, Zhang B, et al. Prevalence and determinants of metabolic health in subjects with obesity in Chinese population. Int J Environ Res Public Health. 2015;12(11):13662–77.

    Article  Google Scholar 

  7. Ding C, Chan ZL, Magkos F. Lean, but not healthy: the “metabolically obese, normal-weight” phenotype. Curr Opin Clin Nutr Metab Care. 2016;19(6):408–17.

    Article  CAS  Google Scholar 

  8. Wang B, et al. Prevalence of metabolically healthy obese and metabolically obese but normal weight in adults worldwide: a meta-analysis. Horm Metab Res. 2015;47(11):839–45.

    Article  CAS  Google Scholar 

  9. Stefan N, Schick F, Haring HU. Causes, characteristics, and consequences of metabolically unhealthy normal weight in humans. Cell Metab. 2017;26(2):292–300.

    Article  CAS  Google Scholar 

  10. Meigs JB, Wilson PWF, Fox CS, Vasan RS, Nathan DM, Sullivan LM, et al. Body mass index, metabolic syndrome, and risk of type 2 diabetes or cardiovascular disease. J Clin Endocrinol Metab. 2006;91(8):2906–12.

    Article  CAS  Google Scholar 

  11. Hamer M, Stamatakis E. Metabolically healthy obesity and risk of all-cause and cardiovascular disease mortality. J Clin Endocrinol Metab. 2012;97(7):2482–8.

    Article  CAS  Google Scholar 

  12. Aung K, Lorenzo C, Hinojosa MA, Haffner SM. Risk of developing diabetes and cardiovascular disease in metabolically unhealthy normal-weight and metabolically healthy obese individuals. J Clin Endocrinol Metab. 2014;99(2):462–8.

    Article  CAS  Google Scholar 

  13. Boonchaya-Anant P, Apovian CM. Metabolically healthy obesity-does it exist? Current Atherosclerosis Reports, 2014. 16(10).

  14. Bluher M. Are metabolically healthy obese individuals really healthy? Eur J Endocrinol. 2014;171(6):R209–19.

    Article  Google Scholar 

  15. Lassale C, Tzoulaki I, Moons KGM, Sweeting M, Boer J, Johnson L, et al. Separate and combined associations of obesity and metabolic health with coronary heart disease: a pan-European case-cohort analysis. Eur Heart J. 2018;39(5):397–406.

    Article  Google Scholar 

  16. Seo YG, Choi HC, Cho B. The relationship between metabolically obese non-obese weight and stroke: the Korea National Health and Nutrition Examination Survey. PLoS One. 2016;11(8).

  17. Haldar S, Chia SC, Henry CJ. Body composition in Asians and Caucasians: comparative analyses and influences on cardiometabolic outcomes. Adv Food Nutr Res. 2015;75:97–154.

    Article  Google Scholar 

  18. Consultation, W.E. Appropriate body-mass index in Asian populations and its implications for policy and intervention strategies. (vol 363, pg 157, 2004). Lancet. 2004;363(9412):902.

    Article  Google Scholar 

  19. He W, Li Q, Yang M, Jiao J, Ma X, Zhou Y, et al. Lower BMI cutoffs to define overweight and obesity in China. Obesity (Silver Spring). 2015;23(3):684–91.

    Article  Google Scholar 

  20. Xu YJ, et al. Association between the metabolically healthy obese phenotype and the risk of myocardial infarction: results from the Kailuan study. Eur J Endocrinol. 2018;179(6):343–52.

    Article  CAS  Google Scholar 

  21. Wu SL, et al. Intra-individual variability of high-sensitivity C-reactive protein in Chinese general population. Int J Cardiol. 2012;157(1):75–9.

    Article  CAS  Google Scholar 

  22. Wu S, Jin C, Li S, Zheng X, Zhang X, Cui L, et al. Aging, arterial stiffness, and blood pressure association in Chinese adults. Hypertension. 2019;73(4):893–9.

    Article  CAS  Google Scholar 

  23. Zhou BF, C. Cooperative Meta-Analysis Group of the Working Group on Obesity in China. Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults--study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci. 2002;15(1):83–96.

    Google Scholar 

  24. Alberti KGMM, Zimmet P, Shaw J. The metabolic syndrome - a new worldwide definition. Lancet. 2005;366(9491):1059–62.

    Article  Google Scholar 

  25. Report of the WHO Task Force on Stroke and other Cerebrovascular Disorders. Recommendations on stroke prevention, diagnosis, and therapy. Stroke. 1989;20(10):1407–31.

  26. Wang A, Dai L, Su Z, Chen S, Li J, Wu S, et al. Proteinuria and risk of stroke in patients with hypertension: the Kailuan cohort study. J Clin Hypertens (Greenwich). 2018;20(4):765–74.

    Article  Google Scholar 

  27. Liu M, Tang R, Wang J, He Y. Distribution of metabolic/obese phenotypes and association with diabetes: 5 years' cohort based on 22,276 elderly. Endocrine. 2018;62(1):107–15.

    Article  CAS  Google Scholar 

  28. Caleyachetty R, Thomas GN, Toulis KA, Mohammed N, Gokhale KM, Balachandran K, et al. Metabolically healthy obese and incident cardiovascular disease events among 3.5 million men and women. J Am Coll Cardiol. 2017;70(12):1429–37.

    Article  Google Scholar 

  29. Hansen L, Netterstrøm MK, Johansen NB, Rønn PF, Vistisen D, Husemoen LLN, et al. Metabolically healthy obesity and ischemic heart disease: a 10-year follow-up of the Inter99 study. J Clin Endocrinol Metab. 2017;102(6):1934–42.

    Article  Google Scholar 

  30. Calori G, Lattuada G, Piemonti L, Garancini MP, Ragogna F, Villa M, et al. Prevalence, metabolic features, and prognosis of metabolically healthy obese Italian individuals: the Cremona study. Diabetes Care. 2011;34(1):210–5.

    Article  Google Scholar 

  31. Zhang M, Wang B, Liu Y, Sun X, Luo X, Wang C, et al. Cumulative increased risk of incident type 2 diabetes mellitus with increasing triglyceride glucose index in normal-weight people: the Rural Chinese Cohort Study. Cardiovasc Diabetol. 2017;16(1):30.

    Article  Google Scholar 

  32. Eckel N, Li Y, Kuxhaus O, Stefan N, Hu FB, Schulze MB. Transition from metabolic healthy to unhealthy phenotypes and association with cardiovascular disease risk across BMI categories in 90 257 women (the Nurses’ Health Study): 30 year follow-up from a prospective cohort study. Lancet Diabetes Endocrinol. 2018;6(9):714–24.

    Article  Google Scholar 

  33. Hermans MP, Amoussou-Guenou KD, Bouenizabila E, Sadikot SS, Ahn SA, Rousseau MF. The normal-weight type 2 diabetes phenotype revisited. Diabetes Metab Syndr. 2016;10(2 Suppl 1):S82–8.

    Article  Google Scholar 

  34. Zheng QQ, et al. Prevalence and epidemiological determinants of metabolically obese but normal-weight in Chinese population. BMC Public Health. 2020;20(1):487.

    Article  Google Scholar 

Download references

Acknowledgements

We thank Professor Wei Zheng from the Vanderbilt University for his critical reviewing and suggestion. We also thank all the participants in the cohorts.

Funding

This work was supported by grants from the National Key Research and Development Program of China (2017YFC0907004), and Hangzhou Science and Technology Project (20171226Y27).

Author information

Author notes

  1. Yaohan Zhou, Xuhui Zhang and Liqun Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University, Hangzhou, 310058, Zhejiang, China

    Yaohan Zhou, Zhijian Li, Qiong Wu, Ziqi Jin, Di He & Yimin Zhu

  2. Hangzhou Center for Disease Control and Prevention, Hangzhou, 310051, Zhejiang, China

    Xuhui Zhang

  3. Putuo District People’s Hospital, Zhoushan, 316100, Zhejiang, China

    Liqun Zhang

  4. Department of Cardiology, Kailuan General Hospital, Hebei United University, Tangshan, China

    Shouhua Chen & Shouling Wu

  5. Department of Respiratory Diseases, Sir Run Run Shaw Hospital Affiliated to School of Medicine, Zhejiang University, Hangzhou, 310020, Zhejiang, China

    Yimin Zhu

Authors
  1. Yaohan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuhui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liqun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhijian Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qiong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ziqi Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shouhua Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Di He
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shouling Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yimin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Study design: Yimin Zhu and Shouling Wu. Data collecting: Xuhui Zhang, Liqun Zhang, Shouhua Chen, Ziqi Jin, Zhijian Li, Qiong Wu, and Di He. Data analysis and interpretation: Yimin Zhu and Shouling Wu. Drafting of the manuscript: Yimin Zhu and Xuhui Zhang. Statistical analysis: Yaohan Zhou, Qiong Wu, and Ziqi Jin.

Corresponding authors

Correspondence to Shouling Wu or Yimin Zhu.

Ethics declarations

Conflict of Interest

The authors declare that they have no competing interests.

Ethics Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Consent to Participate

The protocol was approved by the Ethics Committee of both Zhejiang University School of Medicine and Kailuan General Hospital. Written informed consent was obtained from all participants.

Consent for Publication

All the authors have agreed this publication.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 22 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Y., Zhang, X., Zhang, L. et al. Increased Stroke Risk in Metabolically Abnormal Normal Weight: a 10-Year Follow-up of 102,037 Participants in China. Transl. Stroke Res. 12, 725–734 (2021). https://doi.org/10.1007/s12975-020-00866-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12975-020-00866-1

Keywords

Search

Navigation