Skip to main content

Advertisement

Log in

Relationship between cognitive function and phase angle measured with a bioelectrical impedance system

  • Research Paper
  • Published:
European Geriatric Medicine Aims and scope Submit manuscript

Key summary points

AbstractSection Aim

Investigate the relationship between cognitive function and phase angle, a measure of muscle quality.

AbstractSection Findings

Phase angle consistently decreased with worsening general cognition in individuals with Alzheimer's dementia or amnesic mild cognitive impairment in men.

AbstractSection Message

Our study strengthened the findings of the very few previous studies indicating that low muscle quality is associated with poor cognitive function.

Abstract

Purpose

The purpose of this study was to investigate the relationship between cognitive function and phase angle (PhA), an indicator of muscle quality.

Methods

This cross-sectional study enrolled outpatients who visited a memory clinic at the Nagoya University hospital from January 2016 to June 2022. We enrolled 153 participants with body composition measurements. Inclusion criteria were a Mini-Mental State Examination score of 20–30 and a clinical diagnosis of Alzheimer’s dementia (AD) or amnesic mild cognitive impairment (aMCI). The background characteristics of the participants were compared according to AD and aMCI. Next, linear regression analysis was performed with PhA as the objective variable. In addition, logistic regression analysis was performed for AD diagnosis.

Results

PhA was lower in the AD group (P = 0.009). In linear regression analysis, PhA consistently decreased with worsening ADAS score. In logistic regression analysis, high PhA was associated with absence of AD. Gender-specific analyses showed these associations existed only in men.

Conclusions

Our study of patients with AD and aMCI found that PhA decreased with worsening of cognitive function. Compared with aMCI, AD was associated with significantly lower PhA. Our results strengthen the limited evidence in the literature showing that low muscle quality is associated with poor cognitive function.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Delmonico MJ et al (2009) Longitudinal study of muscle strength, quality, and adipose tissue infiltration. Am J Clin Nutr 90(6):1579–1585

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Cesari M et al (2009) Skeletal muscle and mortality results from the InCHIANTI study. J Gerontol A Biol Sci Med Sci 64(3):377–384

    Article  PubMed  Google Scholar 

  3. Newman AB et al (2006) Strength, but not muscle mass, is associated with mortality in the health, aging and body composition study cohort. J Gerontol A Biol Sci Med Sci 61(1):72–77

    Article  PubMed  Google Scholar 

  4. Avesani CM et al (2023) Muscle fat infiltration in chronic kidney disease: a marker related to muscle quality, muscle strength and sarcopenia. J Nephrol 36(3):895–910

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Lim JY, Frontera WR (2022) Single skeletal muscle fiber mechanical properties: a muscle quality biomarker of human aging. Eur J Appl Physiol 122(6):1383–1395

    Article  PubMed  CAS  Google Scholar 

  6. Yamada M et al (2017) Differential characteristics of skeletal muscle in community-dwelling older adults. J Am Med Dir Assoc 18(9):807.e9-807.e16

    Article  PubMed  Google Scholar 

  7. Sergi G et al (2016) Imaging of sarcopenia. Eur J Radiol 85(8):1519–1524

    Article  PubMed  Google Scholar 

  8. Mirón Mombiela R et al (2020) Ultrasound biomarkers for sarcopenia: what can we tell so far? Semin Musculoskelet Radiol 24(2):181–193

    Article  PubMed  Google Scholar 

  9. Norman K et al (2012) Bioelectrical phase angle and impedance vector analysis–clinical relevance and applicability of impedance parameters. Clin Nutr 31(6):854–861

    Article  PubMed  Google Scholar 

  10. Bosy-Westphal A et al (2006) Phase angle from bioelectrical impedance analysis: population reference values by age, sex, and body mass index. JPEN J Parenter Enteral Nutr 30(4):309–316

    Article  PubMed  Google Scholar 

  11. Akamatsu Y et al (2022) Phase angle from bioelectrical impedance analysis is a useful indicator of muscle quality. J Cachexia Sarcopenia Muscle 13(1):180–189

    Article  MathSciNet  PubMed  Google Scholar 

  12. Barbosa-Silva MC et al (2005) Bioelectrical impedance analysis: population reference values for phase angle by age and sex. Am J Clin Nutr 82(1):49–52

    Article  PubMed  CAS  Google Scholar 

  13. Uemura K et al (2020) Predictivity of bioimpedance phase angle for incident disability in older adults. J Cachexia Sarcopenia Muscle 11(1):46–54

    Article  PubMed  Google Scholar 

  14. Di Vincenzo O et al (2021) Bioelectrical impedance analysis (BIA) -derived phase angle in sarcopenia: a systematic review. Clin Nutr 40(5):3052–3061

    Article  PubMed  Google Scholar 

  15. Lukaski HC, Kyle UG, Kondrup J (2017) Assessment of adult malnutrition and prognosis with bioelectrical impedance analysis: phase angle and impedance ratio. Curr Opin Clin Nutr Metab Care 20(5):330–339

    Article  PubMed  Google Scholar 

  16. Fujisawa C et al (2017) Physical function differences between the stages from normal cognition to moderate Alzheimer disease. J Am Med Dir Assoc 18(4):368.e9-368.e15

    Article  PubMed  Google Scholar 

  17. Sui SX et al (2020) Skeletal muscle health and cognitive function: a narrative review. Int J Mol Sci. https://doi.org/10.3390/ijms22010255

    Article  PubMed  PubMed Central  Google Scholar 

  18. van Dam R et al (2018) Lower cognitive function in older patients with lower muscle strength and muscle mass. Dement Geriatr Cogn Disord 45(3–4):243–250

    PubMed  Google Scholar 

  19. Buchman AS et al (2013) Association of brain pathology with the progression of frailty in older adults. Neurology 80(22):2055–2061

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Del Campo N et al (2016) Relationship of regional brain β-amyloid to gait speed. Neurology 86(1):36–43

    Article  PubMed  PubMed Central  Google Scholar 

  21. Liu C et al (2023) Does the regulation of skeletal muscle influence cognitive function? A scoping review of pre-clinical evidence. J Orthop Translat 38:76–83

    Article  PubMed  Google Scholar 

  22. Binder DK, Scharfman HE (2004) Brain-derived neurotrophic factor. Growth Factors 22(3):123–131

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Park H, Poo MM (2013) Neurotrophin regulation of neural circuit development and function. Nat Rev Neurosci 14(1):7–23

    Article  PubMed  CAS  Google Scholar 

  24. Valenzuela PL et al (2020) Obesity-associated poor muscle quality: prevalence and association with age, sex, and body mass index. BMC Musculoskelet Disord 21(1):200

    Article  PubMed  PubMed Central  Google Scholar 

  25. Shaw FE (2002) Falls in cognitive impairment and dementia. Clin Geriatr Med 18(2):159–173

    Article  PubMed  Google Scholar 

  26. Vassallo M et al (2004) The effect of changing practice on fall prevention in a rehabilitative hospital: the Hospital Injury Prevention Study. J Am Geriatr Soc 52(3):335–339

    Article  PubMed  Google Scholar 

  27. Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3):189–98

    Article  PubMed  CAS  Google Scholar 

  28. Mohs RC, Cohen L (1988) Alzheimer’s disease assessment scale (ADAS). Psychopharmacol Bull 24(4):627–628

    PubMed  CAS  Google Scholar 

  29. Yesavage JA et al (1982) Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res 17(1):37–49

    Article  PubMed  Google Scholar 

  30. McKhann GM et al (2011) The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7(3):263–269

    Article  PubMed  PubMed Central  Google Scholar 

  31. Petersen RC et al (2001) Current concepts in mild cognitive impairment. Arch Neurol 58(12):1985–1992

    Article  PubMed  CAS  Google Scholar 

  32. Sale A et al (2023) Long-term beneficial impact of the randomised trial ‘Train the Brain’, a motor/cognitive intervention in mild cognitive impairment people: effects at the 14-month follow-up. Age Ageing. https://doi.org/10.1093/ageing/afad067

    Article  PubMed  Google Scholar 

  33. Mahoney FI, Barthel DW (1965) Functional evaluation: the barthel index. Md State Med J 14:61–65

    PubMed  CAS  Google Scholar 

  34. Rubenstein LZ et al (2001) Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). J Gerontol A Biol Sci Med Sci 56(6):M366–M372

    Article  PubMed  CAS  Google Scholar 

  35. Canon ME, Crimmins EM (2011) Sex differences in the association between muscle quality, inflammatory markers, and cognitive decline. J Nutr Health Aging 15(8):695–698

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Zhu X et al (2023) Screening efficacy of PhA and MNA-SF in different stages of sarcopenia in the older adults in community. BMC Geriatr 23(1):13

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Mattiello R et al (2020) Reference values for the phase angle of the electrical bioimpedance: systematic review and meta-analysis involving more than 250,000 subjects. Clin Nutr 39(5):1411–1417

    Article  PubMed  Google Scholar 

  38. Kajiyama S et al (2023) The impact of nutritional markers and dietary habits on the bioimpedance phase angle in older individuals. Nutrients. https://doi.org/10.3390/nu15163599

    Article  PubMed  PubMed Central  Google Scholar 

  39. Campa F et al (2023) New bioelectrical impedance vector references and phase angle centile curves in 4,367 adults: the need for an urgent update after 30 years. Clin Nutr 42(9):1749–1758

    Article  PubMed  Google Scholar 

  40. Bettcher BM, Kramer JH (2014) Longitudinal inflammation, cognitive decline, and Alzheimer’s disease: a mini-review. Clin Pharmacol Ther 96(4):464–469

    Article  PubMed  CAS  Google Scholar 

  41. Heringa SM et al (2014) Markers of low-grade inflammation and endothelial dysfunction are related to reduced information processing speed and executive functioning in an older population—the Hoorn Study. Psychoneuroendocrinology 40:108–118

    Article  PubMed  CAS  Google Scholar 

  42. Teunissen CE et al (2003) Inflammation markers in relation to cognition in a healthy aging population. J Neuroimmunol 134(1–2):142–150

    Article  PubMed  CAS  Google Scholar 

  43. Weaver JD et al (2002) Interleukin-6 and risk of cognitive decline: MacArthur studies of successful aging. Neurology 59(3):371–378

    Article  PubMed  CAS  Google Scholar 

  44. Schram MT et al (2007) Systemic markers of inflammation and cognitive decline in old age. J Am Geriatr Soc 55(5):708–716

    Article  PubMed  Google Scholar 

  45. Ershler WB, Keller ET (2000) Age-associated increased interleukin-6 gene expression, late-life diseases, and frailty. Annu Rev Med 51:245–270

    Article  PubMed  CAS  Google Scholar 

Download references

Funding

The study was partly supported by the Organized Registration for the Assessment of Dementia of the Nationwide General Consortium toward Effective Treatment in Japan.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hiroyuki Umegaki.

Ethics declarations

Conflict of interest

The authors have no direct conflicts of interest regarding the content of this manuscript.

Ethical approval

The study protocol was thoroughly reviewed before being approved by the Ethics Committee of Nagoya University Graduate School of Medicine.

Informed consent

All participants provided written informed consent prior to their participation.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yamada, Y., Watanabe, K., Fujisawa, C. et al. Relationship between cognitive function and phase angle measured with a bioelectrical impedance system. Eur Geriatr Med 15, 201–208 (2024). https://doi.org/10.1007/s41999-023-00894-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41999-023-00894-8

Keywords

Navigation