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Comparison of EQ-5D-3L and metabolic components between patients with hyperhidrosis and the general population: a propensity score matching analysis

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Abstract

Purpose

It is important to understand the characteristics of patients with hyperhidrosis, which are different from the general population, for treating hyperhidrosis. Sympathetic overactivity, which might play an important role in hyperhidrosis, can contribute to metabolic diseases and the decreased quality of life (QoL). We compared the metabolic components and health-related QoL between patients with hyperhidrosis and the general population.

Methods

We conducted a case-control study and compared the characteristics of the patients (N = 196) with hyperhidrosis and propensity score-matched controls (N = 196) selected from the Korean National Health and Nutrition Examination Survey. Metabolic components and EQ-5D-3L (EQ-5D) index were compared using a two-way mixed analysis of covariance after adjusting for confounders.

Results

Patients with hyperhidrosis had significantly higher waist circumference (estimated mean values ± SD for patients and the control group, 85.5  ±  10.8 cm vs 81.3  ±  10.3 cm, p < 0.001), blood pressure (SBP, 121.1  ±  16.9 vs 111.7  ±  10.3, p < 0.001 AND DBP, 77.5  ±  12.8 vs 73.6  ±  8.6, p < 0.001, respectively), fasting glucose (97.1  ±  11.3 vs 91.5  ±  9.2, p < 0.001), and the number of components of metabolic syndrome (1.4  ±  1.3 vs 1.0  ±  1.2, p = 0.002), and significantly lower estimated glomerular filtration rate (144.3  ±  53.2 vs 158.3  ±  55.7, p = 0.002) and EQ-5D values (estimated mean values (standard error) for patients and the control group, 0.92 (0.01) vs 0.97 (0.01), p < 0.001) compared to the control group after adjustment.

Conclusion

The patients with hyperhidrosis had more central obesity and unfavorable metabolic parameters and a lower EQ-5D index compared with the general population, emphasizing clinical importance of hyperhidrosis to be cured in aspect of metabolic components as well as patients’ QOL.

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Acknowledgements

This study was supported by a faculty research grant from Yonsei University College of Medicine (6-2019-0074). This work was supported by the Technology Innovation Program (20002781, A Platform for Prediction and Management of Health Risk Based on Personal Big Data and Lifelogging) funded by the Ministry of Trade, Industry & Energy (MOTIE, South Korea).

Funding

This research received no external funding.

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Author notes

  1. Yea-Chan Lee and Young Kyung You have contributed equally to this work.

Authors and Affiliations

  1. Department of Family Medicine, Yonsei University College of Medicine, 50-1, Yonsei-ro, Seodaemun-gu, Seoul, Korea

    Yea-Chan Lee, Young Kyung You & Ji-Won Lee

  2. Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 06273, Republic of Korea

    Young Kyung You & Ji-Won Lee

  3. Department of Family Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyoung-gi, Korea

    Yea-Chan Lee & Jun Hyuk Lee

  4. Department of Thoracic and Cardiovascular Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, Korea

    Duk Hwan Moon & Sungsoo Lee

  5. Biostatistics Collaboration Unit, Department of Research Affairs , Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemoon-gu, Seoul, 03722, Republic of Korea

    Soyoung Jeon & Hye Sun Lee

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  1. Yea-Chan Lee
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Correspondence to Hye Sun Lee or Ji-Won Lee.

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This study followed the ethical guidelines by the 1975 Declaration of Helsinki. All procedures of the study were approved by the Institutional Review Board of Gangnam Severance Hospital (IRB #3-2020-0224).

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All data from this retrospective observational study were obtained through an anonymized dataset.

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Lee, YC., You, Y.K., Lee, J.H. et al. Comparison of EQ-5D-3L and metabolic components between patients with hyperhidrosis and the general population: a propensity score matching analysis. Qual Life Res 30, 2591–2599 (2021). https://doi.org/10.1007/s11136-021-02856-8

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