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A study of mental health status and its influencing factors in normal weight obesity population
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  • Published: 16 January 2026

A study of mental health status and its influencing factors in normal weight obesity population

  • Ying Che1 na1,
  • Guoliang Jia2 na1,
  • Jiayu Gao3 na1,
  • Honghai He1,
  • Ying Liang2 &
  • …
  • Peng Wang1 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Diseases
  • Health care
  • Health occupations
  • Medical research
  • Risk factors

Abstract

Normal weight obesity (NWO) is a subtype of obesity characterized by a normal weight but a high body fat percentage. However, its research in terms of metabolic health, particularly its relationship with mental health remains understudied. This study aims to explore the relationship between NWO and mental health status to provide more reliable information for future scientific research and clinical practice. This study recruited healthy people who received health checkups at a tertiary hospital in Beijing. General information of the participants was collected through a self-administered questionnaire. The mental health status of participants were assessed using the Symptom Checklist-90 (SCL-90) and the Stress Self-Assessment Questionnaire-53 (SSQ-53). False Discovery Rate (FDR) correction was applied using the Benjamini–Hochberg procedure implemented in R software. Pearson or Spearman correlation coefficients were used for correlation analysis. The A-test validation method is also employed for correlation calculations. The physiological status was assessed by collecting hematological indices. Binary logistic regression analysis assessed influencing factors associated with psychiatric symptoms. 1181 healthy participants were included, with 824 (69.8%) having abnormal body composition and 357 (30.2%) normal. The somatization factor scores in the NWO group were substantially higher than those in the normal group (FDR-adjusted P-value < 0.10). Compared with the normal group, the NWO group had more serious psychological stress abnormalities (FDR-adjusted P-value < 0.10). Results of correlation analysis indicate there might be a correlation between psychiatric symptoms and stress levels in NWO participants. Stress-related hematological indices were significantly different from those of the normal group. Middle/older age and NWO are independent factors associated with higher somatization symptoms cross-sectionally (P-value < 0.05). The NWO participants suffered higher stress levels and were highly associated with psychiatric symptoms, especially somatization. There might be a correlation between psychiatric symptoms and stress levels in NWO participants. Middle/older age and NWO are associated with higher somatization symptoms cross-sectionally.

Data availability

The data that support the findings of this study are available from Peking University Third Hospital but restrictions apply to the availability of those data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding authors upon reasonable request and with permission of Peking University Third Hospital.

Abbreviations

NWO:

Normal weight obesity

SCL-90:

Symptom Checklist-90

SSQ-53:

Stress Self-Assessment Questionnaire-53

References

  1. Schetz, M. et al. Obesity in the critically ill: a narrative review. Intensive Care Med. 45, 757–769 (2019).

    Google Scholar 

  2. Dai, H. et al. The global burden of disease attributable to high body mass index in 195 countries and territories, 1990–2017: an analysis of the global burden of disease study. PLoS Med. 17, e1003198 (2020).

    Google Scholar 

  3. Vos, T. et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet 396, 1204–1222 (2020).

    Google Scholar 

  4. World Health Organization. World Obesity Day 2022—Accelerating action to stop obesity. World Health Organization Network (2022). https://www.who.int/news/item/04-03-2022-world-obesity-day-2022-accelerating-action-to-stop-obesity (2024).

  5. Byker Shanks, C., Bruening, M. & Yaroch, A. L. BMI or not to BMI? Debating the value of body mass index as a measure of health in adults. Int. J. Behav. Nutr. Phys. Act. 22, 23 (2025).

    Google Scholar 

  6. Kalra, S., Arora, S. & Kapoor, N. Hidden obesity. JPMA J. Pak. Med. Assoc. 73, 937–938 (2023).

    Google Scholar 

  7. Osadnik, K. et al. Metabolic syndrome is associated with similar long-term prognosis in those living with and without obesity: an analysis of 45 615 patients from the nationwide LIPIDOGRAM 2004–2015 studies. Eur. J. Prev. Cardiol. 30, 1195–1204 (2023).

    Google Scholar 

  8. Lee, W. Body fatness charts based on BMI and waist circumference. Obesity 24, 245–249 (2016).

    Google Scholar 

  9. Oliveros, E., Somers, V. K., Sochor, O., Goel, K. & Lopez-Jimenez, F. The concept of normal weight obesity. Prog. Cardiovasc. Dis. 56, 426–433 (2014).

    Google Scholar 

  10. Schulze, M. B. & Stefan, N. Metabolically healthy obesity: from epidemiology and mechanisms to clinical implications. Nat. Rev. Endocrinol. 20, 633–646 (2024).

    Google Scholar 

  11. Mohammadian Khonsari, N. et al. Association of normal weight obesity phenotype with inflammatory markers: a systematic review and meta-analysis. Front. Immunol. 14, 1044178 (2023).

    Google Scholar 

  12. Duque, A. P. et al. Increased cardiometabolic risk and autonomic function alterations in normal weight obesity. Circulation. 143, (2021).

  13. Huang, J. et al. Body composition and biochemical characteristics of normal weight obesity in Japanese young women with different physical activities. In Proceedings IEEE International Conference on Bioinformatics and Biomedicine (ed. Zheng, H.) 1480–1483 (IEEE, 2018).

  14. Kosmala, W. et al. Left ventricular function impairment in patients with Normal-Weight obesity: contribution of abdominal fat Deposition, profibrotic State, reduced insulin Sensitivity, and Proinflammatory activation. Circ. Cardiovasc. Imaging. 5, 349–356 (2012).

    Google Scholar 

  15. Rakhmat, I. I. et al. Cardiometabolic risk factors in adults with normal weight obesity: a systematic review and meta-analysis. Clin. Obes. 12, e12523 (2022).

    Google Scholar 

  16. Coelho, C. C. N. D. S. et al. Incidence of metabolic syndrome in adults with healthy weight, normal weight obesity, and overweight/obesity. Nutrition 85, 111134 (2021).

    Google Scholar 

  17. Romero-Corral, A. et al. Normal weight obesity: a risk factor for cardiometabolic dysregulation and cardiovascular mortality. Eur. Heart J. 31, 737–746 (2010).

    Google Scholar 

  18. Park, S. C., Kato, T. A., Lee, J. H. & Yu, S. H. Exploring the bidirectional relationship between depression and obesity. Endocrinol. Metab. Clin. N. Am. 54, 193–206 (2025).

    Google Scholar 

  19. Henriques, J., Afreixo, V., Unterrainer, H. & Senra, H. Psychological mediators of the association between obesity and symptoms of depression, anxiety, and stress. Neuropsychobiology 84, 26–37 (2025).

    Google Scholar 

  20. Kiliaan, A. J., Arnoldussen, I. A. C. & Gustafson, D. R. Adipokines: a link between obesity and dementia? Lancet Neurol. 13, 913–923 (2014).

    Google Scholar 

  21. Casanova, F. et al. Higher adiposity and mental health: causal inference using Mendelian randomization. Hum. Mol. Genet. 30, 2371–2382 (2021).

    Google Scholar 

  22. Panda, S. S., Nayak, A., Shah, S. & Aich, P. A systematic review on the association between obesity and mood disorders and the role of gut microbiota. Metabolites 13, 488 (2023).

    Google Scholar 

  23. Agustí, A. et al. Interplay between the gut-brain axis, obesity and cognitive function. Front. Neurosci. 12, 155 (2018).

    Google Scholar 

  24. Moazzami, K. et al. Independent and joint association of obesity and metabolic syndrome with depression and inflammation. Health Psychol. Off J. Div. Health Psychol. Am. Psychol. Assoc. 38, 586–595 (2019).

    Google Scholar 

  25. Edirappuli, S. D., Venkatesh, A. & Zaman, R. The effect of nutrition on mental health: a focus on inflammatory mechanisms. Psychiatr. Danub. 32, 114–120 (2020).

    Google Scholar 

  26. Gray, G. A. Contemporary diagnosis and management of obesity. Newtown Handb. Health Care CO. 41, 65–72 (1998).

    Google Scholar 

  27. Gharehbaghi, A. Deep Learning in time Series Analysis (CRC, 2023). https://doi.org/10.1201/9780429321252

  28. Chan, K. L. et al. Palmitoleate reverses high fat-induced proinflammatory macrophage polarization via AMP-activated protein kinase (AMPK). J. Biol. Chem. 290, 16979–16988 (2015).

    Google Scholar 

  29. Capuron, L., Lasselin, J. & Castanon, N. Role of adiposity-driven inflammation in depressive morbidity. Neuropsychopharmacology 42, 115–128 (2017).

    Google Scholar 

  30. Stimson, R. H. & Walker, B. R. The role and regulation of 11β-hydroxysteroid dehydrogenase type 1 in obesity and the metabolic syndrome. Hmbci 15, 37–48 (2013).

    Google Scholar 

  31. Davison, M. L. et al. Development of a brief, multidimensional, self-report instrument for treatment outcomes assessment in psychiatric settings: preliminary findings. Assessment 4, 259–276 (1997).

    Google Scholar 

  32. Derogatis, L. R. & Cleary, P. A. Confirmation of the dimensional structure of the scl-90: a study in construct validation. J. Clin. Psychol. 33, 981–989 (1977).

    Google Scholar 

  33. Cannavale, C. N. et al. Systemic inflammation mediates the negative relationship between visceral adiposity and cognitive control. Int. J. Psychophysiol. Off J. Int. Organ. Psychophysiol. 165, 68–75 (2021).

    Google Scholar 

  34. McInnis, C. M. et al. Measures of adiposity predict interleukin-6 responses to repeated psychosocial stress. Brain Behav. Immun. 42, 33–40 (2014).

    Google Scholar 

  35. Savastano, S., Di Somma, C., Barrea, L. & Colao, A. The complex relationship between obesity and the somatropic axis: the long and winding road. Growth Horm. IGF Res. Off J. Growth Horm. Res. Soc. Int. IGF Res. Soc. 24, 221–226 (2014).

    Google Scholar 

  36. Kumar, R., Rizvi, M. R. & Saraswat, S. Obesity and stress: A contingent paralysis. Int. J. Prev. Med. 13, 95 (2022).

    Google Scholar 

  37. Vreeken, D. et al. Impact of white adipose tissue on brain structure, perfusion, and cognitive function in patients with severe obesity: the BARICO study. Neurology 100, e703–e718 (2023).

    Google Scholar 

  38. Ihle, A. et al. The relation of obesity to performance in verbal abilities, processing speed, and cognitive flexibility in old age: the role of cognitive reserve. Dement. Geriatr. Cogn. Disord. 42, 117–126 (2016).

    Google Scholar 

  39. Johnston, J. N. et al. Inflammation, stress and depression: an exploration of ketamine’s therapeutic profile. Drug Discovery Today. 28, 103518 (2023).

    Google Scholar 

  40. Majd, M., Saunders, E. F. H. & Engeland, C. G. Inflammation and the dimensions of depression: a review. Front. Neuroendocrinol. 56, 100800 (2020).

    Google Scholar 

  41. Bhatt, S. et al. Targeting inflammation: a potential approach for the treatment of depression. Metab. Brain Dis. 38, 45–59 (2023).

    Google Scholar 

  42. Waloszek, J. M. et al. Early physiological markers of cardiovascular risk in community based adolescents with a depressive disorder. J. Affect. Disord. 175, 403–410 (2015).

    Google Scholar 

  43. Olive, L. S. et al. Depression, stress and vascular function from childhood to adolescence: a longitudinal investigation. Gen. Hosp. Psychiatry. 62, 6–12 (2020).

    Google Scholar 

  44. Elkiran, O. et al. The association between intima media thickness, central obesity and diastolic blood pressure in obese and owerweight children: a cross-sectional school-based study. Int. J. Cardiol. 165, 528–532 (2013).

    Google Scholar 

  45. Xu, Z. et al. Observational and genetic analyses of the bidirectional relationship between depression and hypertension. J. Affect. Disord. 348, 62–69 (2024).

    Google Scholar 

  46. Bharti, V. et al. A systematic review and meta-analysis of lipid metabolomic signatures of major depressive disorder. J. Psychiatr. Res. 139, 197–205 (2021).

    Google Scholar 

  47. Bot, M. et al. Metabolomics profile in depression: a pooled analysis of 230 metabolic markers in 5283 cases with depression and 10,145 controls. Biol. Psychiatry. 87, 409–418 (2020).

    Google Scholar 

  48. Cepeda, M. S., Kern, D. M., Blacketer, C. & Drevets, W. C. Low levels of cholesterol and the cholesterol type are not associated with depression: results of a cross-sectional NHANES study. J. Clin. Lipidol. 14, 515–521 (2020).

    Google Scholar 

  49. Zhang, C. et al. Neural correlates of the association between depression and high density lipoprotein cholesterol change. J. Psychiatr. Res. 130, 9–18 (2020).

    Google Scholar 

  50. Lambrinakou, S. et al. Correlations between nutrition habits, anxiety and metabolic parameters in Greek healthy adults. Adv. Exp. Med. Biol. 987, 23–34 (2017).

    Google Scholar 

  51. Liu, X. B. & Hou, X. J. Correlation of blood uric acid level with the degree of anxiety as well as the neurotransmitter and cytokine secretion in young and middle-aged patients with anxiety disorder. J. Hainan Med. Univ. 25, 62–65 (2019).

    Google Scholar 

  52. Luo, G. et al. Prevalence of overweight and obesity in patients with major depressive disorder with anxiety: mediating role of thyroid hormones and metabolic parameters. J. Affect. Disord. 335, 298–304 (2023).

    Google Scholar 

  53. Medici, M. et al. Thyroid function within the normal range and the risk of depression: a population-based cohort study. J. Clin. Endocrinol. Metab. 99, 1213–1219 (2014).

    Google Scholar 

  54. Dandona, L. et al. Nations within a nation: variations in epidemiological transition across the States of India, 1990–2016 in the global burden of disease study. Lancet 390, 2437–2460 (2017).

    Google Scholar 

  55. Feng, X. Advances in the study of stress hyperglycemia and insulin antagonism. Foreign Med. Pediatr. Fascicle. 21, 195–197 (1994).

    Google Scholar 

  56. Passos, A. F. F., Santos, A. C., Coelho, A. S. G. & Cominetti, C. Associations between normal-weight obesity and disturbances in the lipid profile of young adults. Arq. Bras. Cardiol. 120, 9 (2023).

    Google Scholar 

  57. Abelson, J. L. Hypothalamic–pituitary–adrenal axis activity in panic disorder: 24-hour secretion of Corticotropin and cortisol. Arch. Gen. Psychiatry. 53, 323 (1996).

    Google Scholar 

  58. Thakore, J. H., Barnes, C., Joyce, J., Medbak, S. & Dinan, T. G. Effects of antidepressant treatment on corticotropin-induced cortisol responses in patients with melancholic depression. Psychiatry Res. 73, 27–32 (1997).

    Google Scholar 

  59. Claydon, E., Austin, A. & Smith, M. V. Linking obesity prevention and mental health promotion to address health disparities. Community Ment. Health J. 52, 395–398 (2016).

    Google Scholar 

  60. Sharma, R. N., Casas, R. N., Crawford, N. M. & Mills, L. N. Geographic distribution of California mental health professionals in relation to sociodemographic characteristics. Cultur Divers. Ethnic Minor. Psychol. 23, 595–600 (2017).

    Google Scholar 

  61. Ohlsson, B. & Manjer, J. Sociodemographic and lifestyle factors in relation to overweight defined by BMI and ‘normal-weight obesity’. J. Obes. 2020, 2070297 (2020).

  62. Liang, W., Zhou, B., Miao, Z., Liu, X. & Liu, S. Abnormality in peripheral and brain iron contents and the relationship with grey matter volumes in major depressive disorder. Nutrients. 16, 2073 (2024).

  63. Milaneschi, Y. et al. The association between low vitamin D and depressive disorders. Mol. Psychiatry. 19, 444–451 (2014).

    Google Scholar 

  64. Sternberg, Z. et al. Serum Hepcidin levels, iron dyshomeostasis and cognitive loss in alzheimer’s disease. Aging Dis. 8, 215–227 (2017).

    Google Scholar 

  65. Zuo, L. J. et al. Serotonergic dysfunctions and abnormal iron metabolism: relevant to mental fatigue of Parkinson disease. Sci. Rep. 6, 19 (2016).

    Google Scholar 

  66. Dogan-Sander, E. et al. Inflammation and the association of vitamin D and depressive symptomatology. Nutrients 13, 1972 (2021).

    Google Scholar 

  67. Grudet, C. et al. Vitamin D and inflammation in major depressive disorder. J. Affect. Disord. 267, 33–41 (2020).

    Google Scholar 

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Acknowledgements

We would like to thank the support of Peking University Third Hospital.

Funding

This work was supported by the National Science and Technology Innovation 2030, Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant No. 2024ZD0524300, 2024ZD0524301). This work was supported by grants funded by the Peking University Third Hospital Nursing Seed Fund (BYSYHL2023009).

Author information

Author notes

  1. Ying Che, Guoliang Jia and Jiayu Gao contributed equally to this work.

Authors and Affiliations

  1. Medical Examination Center, Peking University Third Hospital, North Garden Road & 49, Beijing, 100191, China

    Ying Che, Honghai He & Peng Wang

  2. Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), North Garden Road & 51, Beijing, 100191, China

    Guoliang Jia & Ying Liang

  3. School of Chemical Engineering and Pharmaceutics, Henan University of Science and Technology, Luoyang, China

    Jiayu Gao

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  1. Ying Che
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Contributions

Y.C. collected the relevant data, designed the study, analyzed the data, and wrote the manuscript. G.J. wrote the manuscript. J.G. edited the manuscript. H.H. analyzed the data. Y.L. designed the study, wrote the manuscript. P.W. provided financial support. The author(s) read and approved the final manuscript.

Corresponding authors

Correspondence to Ying Liang or Peng Wang.

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Competing interests

The authors declare no competing interests.

Ethical approval

This study was conducted in accordance with the Declaration of Helsinki. The protocols involving human participants were reviewed and approved by the Institutional Ethics Committee of Peking University Third Hospital (project: M2023789).

Consent to participate

After being fully informed goals and methods of the study, all participants signed an informed consent form and consented to the use of the current health survey data, relevant physical exam results, and hematological results.

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Che, Y., Jia, G., Gao, J. et al. A study of mental health status and its influencing factors in normal weight obesity population. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35897-5

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  • Received: 28 February 2025

  • Accepted: 08 January 2026

  • Published: 16 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-35897-5

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Keywords

  • Normal weight obesity
  • Mental health
  • Psychiatric symptom
  • Influencing factor
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