Nutritional Biomarkers and Heart Rate Variability in Patients with Subacute Stroke
Department of Rehabilitation Medicine, Kyung Hee University Hospital at Gangdong, Seoul 05278, Republic of Korea
*
Author to whom correspondence should be addressed.
Nutrients 2022, 14(24), 5320; https://doi.org/10.3390/nu14245320
Submission received: 7 November 2022
/
Revised: 12 December 2022
/
Accepted: 13 December 2022
/
Published: 15 December 2022
(This article belongs to the Special Issue Nutrition in Post-stroke Subjects during Rehabilitation)
Abstract
:Malnutrition and autonomic dysfunction are associated with poor outcomes, mortality, and psychological problems after stroke. Relevant laboratory biomarkers include serum albumin, prealbumin, and transferrin. Heart rate variability (HRV), a noninvasive measurement, can objectively measure autonomic nervous system (ANS) function. The relationship between HRV and nutritional biomarkers in stroke patients has not been studied. This study aimed to examine the relationship between nutritional biomarkers and HRV parameters in stroke patients. We retrospectively recruited 426 patients with subacute stroke who were examined for nutritional biomarkers, such as serum albumin, prealbumin, and transferrin, and underwent 24 h ambulatory Holter electrocardiography. Patients were divided into groups according to their nutritional biomarker status. Differences in HRV parameters between nutritional biomarker-deficient and normal groups were assessed. Pearson’s correlation and multiple regression analyses were used to verify the relationship between HRV parameters and nutritional biomarkers. HRV parameters were significantly lower in the nutritional biomarker-deficient groups. In addition, there was a significant association between HRV parameters and nutritional biomarkers. Serum albumin, prealbumin, and transferrin levels were associated with ANS function, as measured by HRV, and their deficiency may be a predictive factor for the severity of ANS dysfunction in stroke patients.
1. Introduction
Malnutrition is a major problem in stroke patients. Although malnutrition in stroke patients is underdiagnosed and undertreated, its prevalence at admission is predicted to range from 6.1% to 62% [1]. Poor nutritional status leads to poor prognoses, especially after a stroke and has been linked to worse functional outcomes, longer hospital stays, and increased mortality risk [2,3,4]. Malnutrition assessment is complex and requires a multidisciplinary approach. Relevant laboratory biomarkers that are easy to measure and widely used include serum albumin, prealbumin, and transferrin [5]. Several biomarkers have been identified to evaluate malnutrition. When blood albumin levels are low, muscle mass diminishes [6,7]. Hypoalbuminemia is used to predict the mortality of older individuals living in the community, nursing homes, or hospitals. Hypoalbuminemia is affected by inflammation and is characterized by high levels of TNF-α and IL-6. Prealbumin is produced by the liver and is partially catabolized by the kidneys. Low levels of serum prealbumin are correlated with malnutrition [8]. Serum prealbumin levels are positively associated with decreased muscle mass in the elderly. During states of protein deficiency, the liver creates less prealbumin [6]. When prealbumin levels decrease, they may be utilized to determine the severity of involutional lean body mass processes by discriminating between hepatic and muscle abnormalities caused by protein restriction [9]. Transferrin may also function as a nutritional status indicator [5]. Transferrin is regarded as a useful indicator of nutritional health [10]. Serum transferrin levels decrease in the presence of severe malnutrition. However, this marker is unable to reliably measure mild malnutrition in the elderly [11].
The heart rate variability (HRV) has been proven to reflect the autonomic nervous system (ANS) activity, which regulates almost all vascular, visceral, and metabolic activities [12,13]. HRV abnormalities indicate autonomic imbalance and are associated with poor cardiovascular prognoses [14]. Low HRV is an independent risk factor for cardiovascular mortality [15,16]. Several studies on stroke patients have shown significant evidence that HRV parameters may act as biomarkers for incident stroke and a variety of post-stroke outcomes, including functional prognosis and motor function [17,18]. Abnormalities in frequency-domain HRV parameters, including low frequency (LF) and high frequency (HF), are associated with post-stroke infection, activities of daily living, and functional outcomes [17]. In addition, time-domain HRV parameters, such as root mean square difference between successive R-R intervals (rMSSD) and standard deviation of all normal beat interval (SDNN), are associated with stroke severity, motor impairment, mortality, and functional outcome [17,19].
HRV is a noninvasive measurement that can objectively measure ANS function. A decrease in HRV indicates ANS dysfunction. In particularly, the LF and LF/HF ratios reflect sympathetic tone, and HF reflects parasympathetic tone [17,20]. The relationship between HRV and nutritional biomarkers in stroke patients has not been studied. This study aimed to examine the relationship between nutritional biomarkers and HRV parameters in patients with stroke.
2. Materials and Methods
2.1. Subjects
Subacute stroke patients hospitalized at the Kyung Hee University Hospital in Gangdong from September 2010 to April 2022 were included in this retrospective study. Among the patients, we recruited those who were examined for nutritional biomarkers such as serum albumin, prealbumin, and transferrin, and underwent 24 h ambulatory Holter electrocardiography. Patients with first-onset stroke and those who underwent testing within three months of onset were included. Patients with a history of diseases, such as liver, inflammatory bowel, thyroid, and metabolic disease and nephrotic syndrome, that could affect albumin, prealbumin, and transferrin levels were excluded. To investigate whether there was a difference in HRV parameters according to albumin, prealbumin, and transferrin deficiency, patients were divided into deficiency and normal groups. The study was performed according to a protocol authorized by the Institutional Review Board of Kyung Hee University Hospital in Gangdong, Korea (IRB approval number: 2022-10-048).
2.2. Nutritional Biomarkers
2.3. Heart Rate Variability
The HRV was evaluated using 24 h ambulatory Holter electrocardiography with three channels (GE Healthcare, Milwaukee, WI, USA). Prior to the recording, each patient was placed in a supine position for at least 10 min in a silent environment. The digitized data were recorded at a sampling frequency of 128 Hz. The R-R interval, interval between consecutive heartbeats, and QRS complex were measured by a single examiner using the same electromyography equipment. Time-domain features included SDNN in milliseconds, mean of five-minute standard deviations of intervals (ASDNN), standard deviation of five-minute mean R-R intervals (SDANN), percentage of intervals that were more than 50 ms different from the prior interval (pNN50), and rMSSD. The frequency-domain features included very low frequency (VLF; 0.003–0.04 Hz), HF (0.15–0.40 Hz), LF (0.04–0.15 Hz), and the LF/HF ratio utilizing standard fast Fourier transformation.
2.4. Statistical Analysis
SPSS version 20.0 for Windows (IBM Corp., Armonk, NY, USA) was used to conduct statistical analyses. The Kolmogorov–Smirnov test was performed to determine the normality of the data distribution, and Levene’s test was used to check the homogeneity of variance. To compare HRV parameters between subgroups, an independent t-test was conducted. The correlations between serum albumin, prealbumin, and transferrin levels and HRV parameters were examined using Pearson’s correlation coefficient. The influence of serum albumin, prealbumin, and transferrin levels on HRV parameters was examined using multiple linear regression analysis with stepwise adjustment for age, sex, type of stroke, comorbidities, medication, MMSE (Mini-Mental State Examination), MBI (modified Barthel index), and BMI (body mass index). In all statistical tests, a p-value < 0.05 was regarded as statistically significant.
3. Results
3.1. Baseline Characteristics
A total of 426 patients were recruited with a mean age of 67.03 ± 13.03 years. The study included 197 men and 229 women. The classification according to stroke type included 278 and 148 patients with ischemic stroke, respectively. Baseline characteristics, comorbidities, medications, nutritional biomarkers, and HRV parameters are shown in Table 1.
3.2. Comparison of the HRV Parameters among Subgroups Categorized by Serum Albumin Level
In the frequency domain of HRV parameters, the VLF, LF, HF, and LF/HF ratio were significantly lower in the albumin-deficient group. In the time domain, the albumin-deficient group showed significantly lower values for SDNN, ASDNN, and rMSSD. Although the SDANN and pNN50 values were lower in the albumin-deficient group, the difference was not statistically significant (Table 2).
3.3. Comparison of the HRV Parameters among Subgroups Categorized by Serum Prealbumin Level
In the frequency domain of the HRV parameters, the VLF, LF, HF, and LF/HF ratio were significantly lower in the prealbumin-deficient group. In the time domain, SDNN and ASDNN had significantly lower values in the prealbumin-deficient group. The prealbumin-deficient group showed lower values for SDANN, rMSSD, and pNN50; however, the difference was not statistically significant (Table 3).
3.4. Comparison of the HRV Parameters among Subgroups Categorized by Serum Transferrin Level
In the frequency domain of the HRV parameters, the VLF, LF, HF, and LF/HF ratio were significantly lower in the transferrin-deficient group. In the time domain, SDNN, SDANN, ASDNN, rMSSD, and pNN50 showed lower values in the transferrin-deficient group, but the differences were not statistically significant (Table 4).
3.5. Correlation of Serum Albumin, Prealbumin, Transferrin Level with HRV Parameters
A statistically significant correlation was reported between serum albumin levels and all frequency domains of the HRV parameters (VLF: r = 0.312; LF: r = −0.223; HF: r = 0.218; LF/HF ratio: r = 0.256). In the time domain, there was a statistically significant correlation with SDNN (r = 0.124), SDANN (r = 0.099), ASDNN (r = 0.116), and rMSSD (r = 0.122), but not with pNN50. There was a statistically significant correlation between serum prealbumin levels and all frequency domains of the HRV parameters (VLF: r = 0.313; LF: r = 0.226; HF: r = 0.257; LF/HF ratio: r = 0.257). In the time domain, there was a significant correlation with SDNN (r = 0.127), SDANN (r = 0.116), ASDNN (r = 0.122), and rMSSD (r = 0.127), but not with pNN50. There was a statistically significant correlation between serum transferrin levels and all frequency domains of the HRV parameters (VLF: r = 0.307; LF: r = 0.219; HF: r = 0.254; LF/HF ratio: r = 0.254). In the time domain, there was a significant correlation with SDNN (r = 0.126), SDANN (r = 0.122), ASDNN (r = 0.121), and rMSSD (r = 0.121), but not with pNN50 (Table 5).
In the multiple regression analysis, serum albumin levels (R2 = 0.174) measured in VLF (β = 0.005, p < 0.001), LF/HF ratio (β = 0.32, p = 0.001), LF (β = 0.005, p = 0.002) and HF (β = 0.010, p = 0.005) were significant predictors. Serum prealbumin levels (R2 = 0.176) measured in the VLF (β = 0.003, p < 0.001), LF/HF ratio (β = 0.207, p = 0.001), LF (β = 0.003, p = 0.002), and HF (β = 0.006, p = 0.005) were significant predictors. Serum transferrin levels (R2 = 0.170) measured in VLF (β = 0.089, p < 0.001), LF/HF ratio (β = 0.485, p = 0.001), LF (β = 0.081, p = 0.002), and HF (β = 0.171, p = 0.005) were significant predictors (Table 6).
4. Discussion
To the best of our knowledge, this is the first study to evaluate the correlation between nutritional biomarkers and HRV measures that indicate ANS function in patients with subacute stroke. In this study, serum albumin, prealbumin, and transfer levels, which are used as nutritional biomarkers to evaluate nutritional status, were correlated with HRV parameters measured by 24 h ambulatory Holter electrocardiography. The albumin-deficient and prealbumin-deficient groups had lower HRV parameters in all frequency domains and in some time domains as compared to the normal group. The transferrin-deficient group had lower HRV parameters in all frequency domains as com-pared to the normal group. These findings suggest that these nutritional biomarkers are clinically significant for predicting ANS function, as evaluated using HRV.
Since acute stroke often generates changes in cardiovascular interactions, post-stroke blood pressure and heart rate upon admission might be possible indicators of individuals at risk of poor prognosis [24,25]. However, it is difficult to measure the effect on the ANS simply by measuring blood pressure and heart rate. Complex data are contained within the heart rhythm, showing the indicative of several overlapping systems. ANS dysfunction is prevalent in patients with stroke [26,27]. ANS dysfunction is characterized by a decrease in HRV, an increase in sympathetic tone as measured by LF and the LF/HF ratio, and a decrease in parasympathetic tone as measured by HF [17]. They are caused by injury to the central autonomic network, namely, in the frontoparietal cortex and brainstem, or disruption of autonomic routes from the hypothalamus to the midbrain, pons, and spinal cord [28]. The most prevalent clinical disorders are anomalies in heart rate and blood pressure control, which represent cardiovascular autonomic dysfunction, and sweating reflect alterations in the vasomotor and sudomotor regulatory systems [27]. Moreover, autonomic dysfunction after stroke affects the respiratory and sexual systems [29]. In the early period of stroke, cardiovascular autonomic dysfunction, which is mostly due to increased sympathetic activity, is most apparent; however, other autonomic problems, such as excessive sweating, are chronic or even permanent [27]. In addition to the well-known sympathetic malfunction, parasympathetic nervous system anomalies may also contribute to autonomic dysfunction following stroke [26,30]. The use of quantitative analytical techniques to identify autonomic dysfunction reliably is crucial. In addition, quantitative data serves as the basis for the sequential treatment of a variety of autonomic diseases associated with stroke [28].
Several studies have reported an association between malnutrition and autonomic dysfunctions. Serum albumin, prealbumin and transferrin have been reported to be related to body mass index and hydration status [31,32,33]. Low body mass index and fluid hydration status were independently associated with elevated sympathetic indices such as LF and the LF/HF ratio [34,35]. Other studies have also reported an association between serum albumin, prealbumin, and body fat mass [36,37]. rMSSD is an indicator of the parasympathetic activity index and has a negative correlation with the hip-to-waist ratio and body fat mass [38]. An increase in lean body mass and body weight is associated with improved survival in hemodialysis patients, whereas a decrease in body weight is associated with increased mortality [39]. This is due to the fact that the negative impact of malnutrition partially balances out the alleged dangers of a higher body mass index until the accumulated cardiovascular risks outweigh its preventive effect [40]. ANS dysfunction may begin with hyperactivity of sympathetic tone and a decrease in parasympathetic tone, resulting in sympathetic withdrawal during stressful situations, but typically manifests as overall decrease in HRV [34]. In the multiple linear regression analysis of this study, the decrease in VLF reflecting thermal control of vasomotor tone, the decrease in LF/HF ratio and LF reflecting sympathetic tone, the decrease in LF, and the decrease in HF reflecting parasympathetic tone, were significantly associated with low nutritional [34,41]. A poor nutritional status occurs due to the interaction of multiple factors in stroke patients and results in severe morbidity and mortality. Our findings reported a correlation between malnutrition and autonomic dysfunction, possibly leading to a negative prognosis in individuals with subacute stroke.
This study had several limitations. First, this was a retrospective, cross-sectional study. Second, patients with ischemic and hemorrhagic stroke were recruited. Finally, it is not known whether malnutrition persisted long before onset. Large-scale prospective studies are needed in the future.
5. Conclusions
In conclusion, a poor nutritional status, as measured by nutritional biomarkers, such as serum albumin, prealbumin, and transferrin levels, was associated with ANS dysfunction, as measured by HRV parameters. The ANS dysfunction may be an evidence of a correlation between poor nutritional status and stroke.
Author Contributions
Conceptualization: E.J.P. and S.D.Y.; Data curation: E.J.P.; Formal analysis: E.J.P.; Methodology: E.J.P. and S.D.Y.; Supervision: S.D.Y.; Writing—original draft: E.J.P.; Writing—review and editing: S.D.Y. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study protocol was approved by the Institutional Review Board (IRB) of Kyung Hee Univer-sity Hospital at Gangdong, Korea (IRB approval number: 2022-10-048).
Informed Consent Statement
The need for written informed consent was waived due to the retrospective study design.
Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
Acknowledgments
We would like to thank the members of the Department of Rehabilitation Medicine at Kyung Hee University Medical Center for their support and technical expertise.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Foley, N.C.; Salter, K.L.; Robertson, J.; Teasell, R.W.; Woodbury, M.G. Which reported estimate of the prevalence of malnutrition after stroke is valid? Stroke 2009, 40, e66–e74. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gomes, F.; Emery, P.W.; Weekes, C.E. Risk of malnutrition is an independent predictor of mortality, length of hospital stay, and hospitalization costs in stroke patients. J. Stroke Cerebrovasc. Dis. 2016, 25, 799–806. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tang, H.; Gong, F.; Guo, H.; Dai, Z.; Wang, J.; Liu, B.; Li, T.; Tang, X.; Dong, J.; Pan, S.; et al. Malnutrition and risk of mortality in ischemic stroke patients treated with intravenous thrombolysis. Front. Aging Neurosci. 2022, 14, 834973. [Google Scholar] [CrossRef] [PubMed]
- Huppertz, V.; Guida, S.; Holdoway, A.; Strilciuc, S.; Baijens, L.; Schols, J.; van Helvoort, A.; Lansink, M.; Muresanu, D.F. Impaired nutritional condition after stroke from the hyperacute to the chronic phase: A systematic review and meta-analysis. Front. Neurol. 2021, 12, 780080. [Google Scholar] [CrossRef]
- Keller, U. Nutritional laboratory markers in malnutrition. J. Clin. Med. 2019, 8, 775. [Google Scholar] [CrossRef] [Green Version]
- Papadopoulou, S.K.; Voulgaridou, G.; Kondyli, F.S.; Drakaki, M.; Sianidou, K.; Andrianopoulou, R.; Rodopaios, N.; Pritsa, A. Nutritional and nutrition-related biomarkers as prognostic factors of sarcopenia, and their role in disease progression. Diseases 2022, 10, 42. [Google Scholar] [CrossRef]
- Sawada, S.; Ozaki, H.; Natsume, T.; Nakano, D.; Deng, P.; Yoshihara, T.; Osawa, T.; Kobayashi, H.; Machida, S.; Naito, H. Serum albumin levels as a predictive biomarker for low-load resistance training programs’ effects on muscle thickness in the community-dwelling elderly Japanese population: Interventional study result. BMC Geriatr. 2021, 21, 464. [Google Scholar] [CrossRef]
- Smith, S.H. Using albumin and prealbumin to assess nutritional status. Nursing 2017, 47, 65–66. [Google Scholar] [CrossRef]
- Ingenbleek, Y. Plasma transthyretin as a biomarker of sarcopenia in elderly subjects. Nutrients 2019, 11, 895. [Google Scholar] [CrossRef] [Green Version]
- Takemoto, E.; Yoo, J.; Blizzard, S.R.; Shannon, J.; Marshall, L.M. Preoperative prealbumin and transferring: Relation to 30-day risk of complication in elective spine surgical patients. Medicine 2019, 98, e14741. [Google Scholar] [CrossRef]
- Sergi, G.; Coin, A.; Enzi, G.; Volpato, S.; Inelmen, E.M.; Buttarello, M.; Peloso, M.; Mulone, S.; Marin, S.; Bonometto, P. Role of visceral proteins in detecting malnutrition in the elderly. Eur. J. Clin. Nutr. 2006, 60, 203–209. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ben Mrad, I.; Ben Mrad, M.; Besbes, B.; Zairi, I.; Ben Kahla, N.; Kamoun, S.; Mzoughi, K.; Kraiem, S. Heart rate variability as an indicator of autonomic nervous system disturbance in Behcet’s disease. Int. J. Gen. Med. 2021, 14, 4877–4886. [Google Scholar] [CrossRef]
- Shaffer, F.; Ginsberg, J.P. An overview of heart rate variability metrics and norms. Front. Public Health 2017, 5, 258. [Google Scholar] [CrossRef] [Green Version]
- Goldenberg, I.; Goldkorn, R.; Shlomo, N.; Einhorn, M.; Levitan, J.; Kuperstein, R.; Klempfner, R.; Johnson, B. Heart rate variability for risk assessment of myocardial ischemia in patients without known coronary artery disease: The HRV-detect (heart rate variability for the detection of myocardial ischemia) study. J. Am. Heart Assoc. 2019, 8, e014540. [Google Scholar] [CrossRef] [PubMed]
- Fang, S.C.; Wu, Y.L.; Tsai, P.S. Heart rate variability and risk of all-cause death and cardiovascular events in patients with cardiovascular disease: A meta-analysis of cohort studies. Biol. Res. Nurs. 2020, 22, 45–56. [Google Scholar] [CrossRef]
- Sessa, F.; Anna, V.; Messina, G.; Cibelli, G.; Monda, V.; Marsala, G.; Ruberto, M.; Biondi, A.; Cascio, O.; Bertozzi, G.; et al. Heart rate variability as predictive factor for sudden cardiac death. Aging 2018, 10, 166–177. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lees, T.; Shad-Kaneez, F.; Simpson, A.M.; Nassif, N.T.; Lin, Y.; Lal, S. Heart rate variability as a biomarker for predicting stroke, post-stroke complications and functionality. Biomark. Insights 2018, 13, 1177271918786931. [Google Scholar] [CrossRef] [Green Version]
- Li, C.; Meng, X.; Pan, Y.; Li, Z.; Wang, M.; Wang, Y. The association between heart rate variability and 90-day prognosis in patients with transient ischemic attack and minor stroke. Front. Neurol. 2021, 12, 636474. [Google Scholar] [CrossRef]
- Kuzemczak, M.; Białek-Ławniczak, P.; Torzyńska, K.; Janowska-Kulińska, A.; Miechowicz, I.; Kramer, L.; Moczko, J.; Siminiak, T. Comparison of baseline heart rate variability in stable ischemic heart disease patients with and without stroke in long-term observation. J. Stroke Cerebrovasc. Dis. 2016, 25, 2526–2534. [Google Scholar] [CrossRef]
- Grassi, G.; Quarti-Trevano, F.; Seravalle, G.; Arenare, F.; Volpe, M.; Furiani, S.; Dell’Oro, R.; Mancia, G. Early sympathetic activation in the initial clinical stages of chronic renal failure. Hypertension 2011, 57, 846–851. [Google Scholar] [CrossRef]
- Akirov, A.; Masri-Iraqi, H.; Atamna, A.; Shimon, I. Low albumin levels are associated with mortality risk in hospitalized patients. Am. J. Med. 2017, 130, e1411–e1465. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, W.; Pan, Y.; Tang, X.; Hao, G.; Xie, Y.; Ma, S.; Luo, J.; Guo, D.; Ding, F. Serum prealbumin and its changes over time are associated with mortality in acute kidney injury. Sci. Rep. 2017, 7, 41493. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Roche, M.; Law, T.Y.; Kurowicki, J.; Sodhi, N.; Rosas, S.; Elson, L.; Summers, S.; Sabeh, K.; Mont, M.A. Albumin, prealbumin, and transferrin may be predictive of wound complications following total knee arthroplasty. J. Knee Surg. 2018, 31, 946–951. [Google Scholar] [CrossRef] [PubMed]
- Lee, K.J.; Kim, B.J.; Han, M.K.; Kim, J.T.; Choi, K.H.; Shin, D.I.; Cha, J.K.; Kim, D.H.; Kim, D.E.; Ryu, W.S.; et al. Effect of heart rate on 1-year outcome for patients with acute ischemic stroke. J. Am. Heart Assoc. 2022, 11, e025861. [Google Scholar] [CrossRef] [PubMed]
- Maida, C.; Tuttolomondo, A.; Di Raimondo, D.; Daidone, M.; Pinto, A. Management of blood pressure and heart rate in patients with acute stroke. Curr. Pharm. Des. 2017, 23, 4583–4597. [Google Scholar] [CrossRef]
- Zhao, M.; Guan, L.; Wang, Y. The association of autonomic nervous system function with ischemic stroke, and treatment strategies. Front. Neurol. 2019, 10, 1411. [Google Scholar] [CrossRef]
- Xiong, L.; Tian, G.; Leung, H.; Soo, Y.O.Y.; Chen, X.; Ip, V.H.L.; Mok, V.C.T.; Chu, W.C.W.; Wong, K.S.; Leung, T.W.H. Autonomic dysfunction predicts clinical outcomes after acute ischemic stroke: A prospective observational study. Stroke 2018, 49, 215–218. [Google Scholar] [CrossRef]
- Korpelainen, J.T.; Sotaniemi, K.A.; Myllylä, V.V. Autonomic nervous system disorders in stroke. Clin. Auton. Res. 1999, 9, 325–333. [Google Scholar] [CrossRef]
- Mo, J.; Huang, L.; Peng, J.; Ocak, U.; Zhang, J.; Zhang, J.H. Autonomic disturbances in acute cerebrovascular disease. Neurosci. Bull. 2019, 35, 133–144. [Google Scholar] [CrossRef]
- Guan, L.; Collet, J.P.; Mazowita, G.; Claydon, V.E. Autonomic nervous system and stress to predict secondary ischemic events after transient ischemic attack or minor stroke: Possible implications of heart rate variability. Front. Neurol. 2018, 9, 90. [Google Scholar] [CrossRef]
- Lee, Y.B.; Jun, J.E.; Lee, S.E.; Ahn, J.; Kim, G.; Jee, J.H.; Bae, J.C.; Jin, S.M.; Kim, J.H. Utility of serum albumin for predicting incident metabolic syndrome according to hyperuricemia. Diabetes Metab. J. 2018, 42, 529–537. [Google Scholar] [CrossRef] [PubMed]
- Jones, C.H.; Akbani, H.; Croft, D.C.; Worth, D.P. The relationship between serum albumin and hydration status in hemodialysis patients. J. Ren. Nutr. 2002, 12, 209–212. [Google Scholar] [CrossRef] [PubMed]
- Wang, H.H.; Liao, L.N.; Chang, C.W.; Chang, Y.C.; Wu, K.H.; Ko, J.L. The alteration of ferritin and transferrin saturation under body mass index and blood pressure in first-time and regular male blood donors in Taiwan. Medicine 2019, 98, e15854. [Google Scholar] [CrossRef]
- Wu, E.C.-H.; Huang, Y.-T.; Chang, Y.-M.; Chen, I.-L.; Yang, C.-L.; Leu, S.-C.; Su, H.-L.; Kao, J.-L.; Tsai, S.-C.; Jhen, R.-N.; et al. The association between nutritional markers and heart rate variability indices in patients undergoing chronic hemodialysis. J. Clin. Med. 2019, 8, 1700. [Google Scholar] [CrossRef] [Green Version]
- Ferrario, M.; Moissl, U.; Garzotto, F.; Cruz, D.N.; Clementi, A.; Brendolan, A.; Tetta, C.; Gatti, E.; Signorini, M.G.; Cerutti, S.; et al. Effects of fluid overload on heart rate variability in chronic kidney disease patients on hemodialysis. BMC Nephrol. 2014, 15, 26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Basolo, A.; Ando, T.; Chang, D.C.; Hollstein, T.; Krakoff, J.; Piaggi, P.; Votruba, S. Reduced albumin concentration predicts weight gain and higher ad libitum energy intake in humans. Front. Endocrinol. 2021, 12, 642568. [Google Scholar] [CrossRef]
- Matsuura, S.; Shirai, Y.; Kubo, M.; Nayama, C.; Okitsu, M.; Oiwa, Y.; Yasui, S.; Suzuki, Y.; Murata, T.; Ishikawa, E.; et al. Body fat mass is correlated with serum transthyretin levels in maintenance hemodialysis patients. J. Med. Investig. 2017, 64, 222–227. [Google Scholar] [CrossRef] [Green Version]
- Kim, J.A.; Park, Y.G.; Cho, K.H.; Hong, M.H.; Han, H.C.; Choi, Y.S.; Yoon, D. Heart rate variability and obesity indices: Emphasis on the response to noise and standing. J. Am. Board Fam. Pract. 2005, 18, 97–103. [Google Scholar] [CrossRef] [Green Version]
- Shenkin, A.; Cederblad, G.; Elia, M.; Isaksson, B. Laboratory assessment of protein-energy status. Clin. Chim. Acta 1996, 253, S5–S59. [Google Scholar] [CrossRef]
- Unger, R.H. Longevity, lipotoxicity and leptin: The adipocyte defense against feasting and famine. Biochimie 2005, 87, 57–64. [Google Scholar] [CrossRef]
- Kuo, G.; Chen, S.W.; Huang, J.Y.; Wu, C.Y.; Fu, C.M.; Chang, C.H.; Liu, S.H.; Chan, Y.H.; Wu, I.W.; Yang, H.Y. Short-term heart rate variability as a predictor of long-term survival in patients with chronic hemodialysis: A prospective cohort study. J. Formos. Med. Assoc. 2018, 117, 1058–1064. [Google Scholar] [CrossRef] [PubMed]
Table 1.
Clinical characteristics of patients.
| Characteristic | Value |
|---|---|
| Age (years) | 67.03 ± 13.03 |
| Sex | |
| Male | 197 (46.20) |
| Female | 229 (53.80) |
| Type of stroke | |
| Ischemic | 278 (65.30) |
| Hemorrhagic | 148 (34.70) |
| Comorbidities | |
| Hypertension | 260 (61.00) |
| Arrhythmia | 74 (17.40) |
| Diabetes mellitus | 158 (37.10) |
| Dyslipidemia | 325 (76.30) |
| Coronary artery disease | 22 (5.20) |
| Heart failure | 70 (16.40) |
| Medication | |
| Beta blocker | 87 (20.40) |
| Calcium channel blocker | 193 (45.30) |
| ACE-i/ARB | 199 (46.70) |
| Diuretics | 228 (53.50) |
| MMSE | 14.08 ± 8.25 |
| MBI | 39.35 ± 17.62 |
| BMI (kg/m2) | 21.66 ± 2.06 |
| Nutritional biomarkers | |
| Albumin (g/dL) | 2.99 ± 0.58 |
| Prealbumin (mg/dL) | 19.36 ± 3.72 |
| Transferrin (mg/dL) | 246.77 ± 99.62 |
| HRV parameter (Frequency domain) | |
| VLF (ms2) | 619.06 ± 223.28 |
| LF (ms2) | 421.44 ± 154.90 |
| HF (ms2) | 179.04 ± 71.17 |
| LF/HF ratio | 5.55 ± 2.66 |
| HRV parameter (Time domain) | |
| SDNN (ms) | 100.51 ± 28.52 |
| SDANN (ms) | 101.87 ± 29.10 |
| ASDNN (ms) | 50.03 ± 11.74 |
| rMSSD (ms) | 26.34 ± 6.23 |
| pNN50 (%) | 49.41 ± 23.60 |
Values are presented as the mean ± standard deviation or number (%). ACE-I, angiotensin-converting enzyme inhibitors; ARB, angiotensin II receptor blockers; MMSE, Mini-Mental State Examination; MBI, modified Barthel index; BMI, body mass index; HRV, heart rate variability; VLF, very low frequency; LF, low frequency; HF, high frequency; SDNN, standard deviation of intervals of all normal beats; SDANN, standard deviation of five-minute mean R-R interval; ASDNN, mean of five-minute standard deviations of intervals; rMSSD, root mean square of the difference of successive R-R intervals; pNN50, percentage of intervals that were more than 50 ms different from the previous interval.
Table 2.
Comparison of the HRV parameters among subgroups categorized by serum albumin level.
| Albumin-Deficient Group (n = 321) | Normal Group (n = 105) | p-Value | |
|---|---|---|---|
| Frequency domain | |||
| VLF (ms2) | 543.89 ± 264.70 | 602.81 ± 230.75 | 0.029 * |
| LF(ms2) | 376.53 ± 180.21 | 424.84 ± 163.12 | 0.011 * |
| HF(ms2) | 147.72 ± 82.09 | 183.20 ± 71.82 | 0.003 * |
| LF/HF ratio | 4.13 ± 2.90 | 5.70 ± 2.58 | 0.003 * |
| Time domain | |||
| SDNN (ms) | 90.53 ± 31.02 | 99.32 ± 30.41 | 0.01 * |
| SDANN (ms) | 92.61 ± 34.12 | 99.32 ± 30.41 | 0.58 |
| ASDNN (ms) | 47.44 ± 13.27 | 50.29 ± 11.48 | 0.03 * |
| rMSSD (ms) | 24.90 ± 7.43 | 26.70 ± 6.69 | 0.02 * |
| pNN50 (%) | 45.75 ± 27.47 | 49.85 ± 23.00 | 0.13 |
Values are presented as the mean ± standard deviation. * p < 0.05. HRV, heart rate variability; VLF, very low frequency; LF, low frequency; HF, high frequency; SDNN, standard deviation of intervals of all normal beats; SDANN, standard deviation of five-minute mean R-R interval; ASDNN, mean of five-minute standard deviations of intervals; rMSSD, root mean square of the difference of successive R-R intervals; pNN50, percentage of intervals that were more than 50 ms different from the previous interval.
Table 3.
Comparison of the HRV parameters among subgroups categorized by serum prealbumin level.
| Prealbumin-Deficient Group (n = 237) | Normal Group (n = 189) | p-Value | |
|---|---|---|---|
| Frequency domain | |||
| VLF (ms2) | 585.99 ± 250.44 | 664.85 ± 260.34 | 0.002 * |
| LF(ms2) | 391.12 ± 175.31 | 430.32 ± 161.25 | 0.017 * |
| HF(ms2) | 159.54 ± 79.64 | 186.34 ± 70.81 | 0.003 * |
| LF/HF ratio | 4.98 ± 2.87 | 5.58 ± 2.62 | 0.026 * |
| Time domain | |||
| SDNN (ms) | 93.46 ± 30.39 | 99.85 ± 29.03 | 0.028 * |
| SDANN (ms) | 95.34 ± 32.05 | 99.51 ± 30.91 | 0.174 |
| ASDNN (ms) | 48.01 ± 12.56 | 50.85 ± 11.39 | 0.015 * |
| rMSSD (ms) | 25.85 ± 6.96 | 26.57 ± 6.87 | 0.286 |
| pNN50 (%) | 48.75 ± 25.97 | 48.91 ± 22.75 | 0.946 |
Values are presented as the mean ± standard deviation. * p < 0.05. HRV, heart rate variability; VLF, very low frequency; LF, low frequency; HF, high frequency; SDNN, standard deviation of intervals of all normal beats; SDANN, standard deviation of five-minute mean R-R interval; ASDNN, mean of five-minute standard deviations of intervals; rMSSD, root mean square of the difference of successive R-R intervals; pNN50, percentage of intervals that were more than 50 ms different from the previous interval.
Table 4.
Comparison of the HRV parameters among subgroups categorized by serum transferrin level.
| Transferrin-Deficient Group (n = 161) | Normal Group (n = 265) | p-Value | |
|---|---|---|---|
| Frequency domain | |||
| VLF (ms2) | 583.48 ± 244.77 | 706.20 ± 263.56 | 0.002 * |
| LF (ms2) | 405.84 ± 173.17 | 448.89 ± 171.33 | 0.013 * |
| HF (ms2) | 167.63 ± 77.57 | 185.69 ± 72.03 | 0.017 * |
| LF/HF ratio | 5.18 ± 2.83 | 5.85 ± 2.79 | 0.018 * |
| Time domain | |||
| SDNN (ms) | 96.26 ± 30.66 | 98.25 ± 28.31 | 0.504 |
| SDANN (ms) | 96.79 ± 31.62 | 99.79 ± 31.17 | 0.278 |
| ASDNN (ms) | 49.18 ± 12.48 | 50.26 ± 11.16 | 0.366 |
| rMSSD (ms) | 25.89 ± 6.95 | 26.85 ± 6.83 | 0.164 |
| pNN50 (%) | 48.51 ± 24.97 | 49.38 ± 22.96 | 0.719 |
Values are presented as the mean ± standard deviation. * p < 0.05. HRV, heart rate variability; VLF, very low frequency; LF, low frequency; HF, high frequency; SDNN, standard deviation of intervals of all normal beats; SDANN, standard deviation of five-minute mean R-R interval; ASDNN, mean of five-minute standard deviations of intervals; rMSSD, root mean square of the difference of successive R-R intervals; pNN50, percentage of intervals that were more than 50 ms different from the previous interval.
Table 5.
Correlation analysis of serum albumin, prealbumin, and transferrin levels with the HRV parameters.
Table 5.
Correlation analysis of serum albumin, prealbumin, and transferrin levels with the HRV parameters.
| Albumin | Prealbumin | Transferrin | |
|---|---|---|---|
| Frequency domain | |||
| VLF (ms2) | r = 0.314 | r = 0.313 | r = 0.307 |
| p = 0.003 * | p = 0.004 * | p = 0.009 * | |
| LF (ms2) | r = 0.223 | r = 0.226 | r = 0.219 |
| p = 0.003 * | p = 0.002 * | p = 0.003 * | |
| HF (ms2) | r = 0.218 | r = 0.257 | r = 0.254 |
| p = 0.005 * | p = 0.004 * | p = 0.005 * | |
| LF/HF ratio | r = 0.256 | r = 0.257 | r = 0.254 |
| p = 0.008 * | p = 0.007 * | p = 0.001 * | |
| Time domain | |||
| SDNN (ms) | r = 0.124 | r = 0.127 | r = 0.126 |
| p = 0.011 * | p = 0.009 * | p = 0.009 * | |
| SDANN (ms) | r = 0.099 | r = 0.116 | r = 0.122 |
| p = 0.041 * | p = 0.030 * | p = 0.034 * | |
| ASDNN (ms) | r = 0.116 | r = 0.122 | r = 0.121 |
| p = 0.017 * | p = 0.012 * | p = 0.012 * | |
| rMSSD (ms) | r = 0.122 | r = 0.127 | r = 0.121 |
| p = 0.011 * | p = 0.009 * | p = 0.012 * | |
| pNN50 (%) | r = 0.075 | r = 0.079 | r = 0.076 |
| p = 0.122 | p = 0.104 | p = 0.118 |
* p < 0.05. HRV, heart rate variability; VLF, very low frequency; LF, low frequency; HF, high frequency; SDNN, standard deviation of intervals of all normal beats; SDANN, standard deviation of five-minute mean R-R interval; ASDNN, mean of five-minute standard deviations of intervals; rMSSD, root mean square of the difference of successive R-R intervals; pNN50, percentage of intervals that were more than 50 ms different from the previous interval.
Table 6.
Multiple regression analysis between nutritional markers and HRV parameters.
| Standardized β | B | p-Value | Adjusted R2 | ||
|---|---|---|---|---|---|
| Albumin | Constant | 0.284 | 0.174 | ||
| VLF | 0.237 | 0.005 | <0.001 ** | ||
| LF/HF ratio | 0.157 | 0.032 | 0.001 * | ||
| LF | 0.139 | 0.005 | 0.002 * | ||
| HF | 0.128 | 0.010 | 0.005 * | ||
| Prealbumin | Constant | 11.040 | 0.176 | ||
| VLF | 0.236 | 0.003 | <0.001 ** | ||
| LF/HF ratio | 0.157 | 0.207 | 0.001 * | ||
| LF | 0.143 | 0.003 | 0.002 * | ||
| HF | 0.130 | 0.006 | 0.005 * | ||
| Transferrin | Constant | 40.408 | 0.170 | ||
| VLF | 0.230 | 0.089 | <0.001 ** | ||
| LF/HF ratio | 0.156 | 5.485 | 0.001 * | ||
| LF | 0.140 | 0.081 | 0.002 * | ||
| HF | 0.131 | 0.171 | 0.005 * |
Variables are based on their order of listing in multiple regression analysis. * p < 0.05, ** p < 0.001. HRV, heart rate variability; B, regression coefficient; VLF, very low frequency; LF, low frequency; HF, high frequency; SDNN, standard deviation of intervals of all normal beats; SDANN, standard deviation of five-minute mean R-R interval; ASDNN, mean of five-minute standard deviations of intervals; rMSSD, root mean square of the difference of successive R-R intervals; pNN50, percentage of intervals that were more than 50 ms different from the previous interval.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Park, E.J.; Yoo, S.D. Nutritional Biomarkers and Heart Rate Variability in Patients with Subacute Stroke. Nutrients 2022, 14, 5320. https://doi.org/10.3390/nu14245320
AMA Style
Park EJ, Yoo SD. Nutritional Biomarkers and Heart Rate Variability in Patients with Subacute Stroke. Nutrients. 2022; 14(24):5320. https://doi.org/10.3390/nu14245320
Chicago/Turabian StylePark, Eo Jin, and Seung Don Yoo. 2022. "Nutritional Biomarkers and Heart Rate Variability in Patients with Subacute Stroke" Nutrients 14, no. 24: 5320. https://doi.org/10.3390/nu14245320
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
