Effect of Calorie on Cognitive Function among Traumatic Brain Injury (TBI) Patients: A Pilot Study

 

Mohd Ibrahim Abdullah1, Aryati Ahmad1*, Noor Aini Mohd Yusoff1, Sharifah Wajihah Wafa Syed Saadun Tarek Wafa1, Ahmad Zubaidi Abdul Latif2, Nujaimin Udin3,

Kartini Abdul Karim4

1Faculty of Health Science, Universiti Sultan Zainal Abidin (UniSZA), Gong Badak Campus, 21300 Kuala Terengganu, Terenganu, Malaysia.

2Faculty of Medicine, Universiti Sultan Zainal Abidin (UniSZA), Medical Campus, 20400 Kuala Terengganu, Terengganu, Malaysia.

3Neurosurgery Department, Hospital Sultanah Nur Zahirah (HSNZ), Ministry of Health Malaysia, Jalan Sultan Mahmud, 20400 Kuala Terengganu, Terengganu, Malaysia.

4Dietetic and Food Service Department, Hospital Sultanah Nur Zahirah (HSNZ), Ministry of Health Malaysia, Jalan Sultan Mahmud, 20400 Kuala Terengganu, Terengganu, Malaysia.

*Corresponding Author E-mail: aryatiahmad@unisza.edu.my

 

ABSTRACT:

Cognitive recovery is a major concern to traumatic brain injury (TBI) patients and their caregivers as patients need to resume normal life after hospital discharge. A recent discovery showed that low calorie was beneficial for cognitive function with lower mortality rate and produced better clinical outcomes in critically-ill and medical intensive care unit patients. However, the effect of low calorie on cognitive function among TBI patients has not been determined yet. Hence this study was conducted to provide preliminary evidence of the relation between calorie and cognitive function and to determine calorie adequacy for optimal cognitive recovery among them. This pilot study involved ten patients recruited from Hospital Sultanah Nur Zahirah, Kuala Terengganu. The patients underwent a series of nutritional assessments including 24-hours diet recall combined with food diary; neuropsychology test [Montreal Cognitive Assessment (MoCA)], and eye-open-eye-close paradigm of electroencephalography (EEG) for two to six days. Socio-demography, nutritional and neuropsychology data were analyzed using SPSS version 22 with the percentage of calorie intake (%CI) was set as the independent variable; whilst duration to finish trail-making subtest (FT), and the total score (TS) of MoCA were the dependent variables. EEG data were analyzed using Fast-Fourier Transform (FFT) and power ratio (PR) which is the ratio of slow to fast frequency brain band was calculated. Then the comparison of %CI, FT, TS, and PR were done between day-1 and discharge-day (early or follow-up visit in EEG). Six male and four female patients with mild TBI, median age 24.0 years (IQR = 9.5) were included in the study. Scatter dot plot between %CI to FT and TS showed that the graph increased at the beginning before it became a plateau at 70%. The %CI had significant negative correlation with FT (r = -0.717; p = 0.000) but positive correlation with TS (r = 0.789; p = 0.000). Results revealed that cognitive function was improved at low %CI, approximately below CI of 70%. Improvement in neuropsychology test results was strengthened by the significant differences of median EEG power ratio between follow-up and first visit (p = 0.000). In this pilot study, it appears that cognitive function showed improvement with low calorie intake among TBI patients.

 

KEYWORDS: calorie adequacy, calorie intake, cognitive function, traumatic brain injury, Montreal Cognitive Assessment.

 

INTRODUCTION:

Traumatic brain injury (TBI) is a common medical event among trauma patients that results from various incidents involving brain1,2. TBI is defined as damage to the brain resulting from external mechanical force3. The incidence of TBI worldwide is rising and become a major cause of morbidity and mortality4. To date, no available statistic of TBI cases in Malaysia has been published. However, TBI that was included in the head and neck injury group accounted for 90% of major trauma cases in Malaysia5.

 

Several common etiologies of TBI include falls, road injuries, interpersonal violence, and industrial6-8. Surprisingly, each of etiologies increases by years such as the total number of motor vehicle accidents (MVA) in Malaysia9 had increased more than 60% from 279,711 cases in 2002 to 449,040 cases in 2011 and MVA contributed to approximately 76% to major trauma cases5. In the future, blasts may become a leading cause of TBI, especially in the conflict area10.

 

Cognitive impairment is a frequent sequel post-TBI. As survivors will return to normal life, later on, cognitive recovery is highly crucial for them. With poor cognitive recovery, the patient might suffer some difficulty to do functional routine activities and may face a decrease in academic performance, or reduced working productivity. Cognitive impairment can persist for up to two years. Its recovery rate is influenced by various factors such as age, access and response to treatment, severity, condition co-occurring, medical complication, gender, and pre-existing environment11. Nutrition plays a major role in the healing and recovery process. Diet is an important modifier of brain plasticity with a significant impact on central nervous system health and disease12. Overall previous evidence indicates that diet can be used to improve molecular mechanisms of neural repair after brain surgery13.

 

Cognition and plasticity of the brain have been shown to be affected by calorie intake and the frequency of food consumption13. Recently, researchers discovered that calorie was important for brain plasticity, cognitive recovery and reducing neuronal damage in animal and human study14. Although the effect of calorie restriction on cognitive function among TBI patients was not studied yet, scientists found that restriction of calorie among critically ill and medical ICU patients exhibited lower mortality rates and better clinical outcomes if compared to standard calorie as recommended15-17. Therefore, this pilot study was conducted to examine the relation of calorie to cognitive recovery among TBI patients and to determine calorie adequacy for optimal cognitive recovery.

 

MATERIAL AND METHODS:

This pilot research was a prospective cross-sectional study. Ten patients or approximately 17% of project sample size were recruited from surgical ward Hospital Sultanah Nur Zahirah, Kuala Terengganu after diagnosed and referred by the neurosurgeons18,19. Patients age between 20-50 years20, both genders; conscious and hospitalized, mild (miTBI) and moderate TBI (moTBI) patients with the ability to follow command were included in the study.

 

In contrast, patients who were on parenteral nutrition, or with history of neurology or psychiatry problem, or history of taking drugs or alcohol abuse, or suffer any other serious diseases or multiple injuries, or on medication that affect consciousness, or previously completed or withdrawn from this study were excluded. In addition, patients with any other conditions that can interfere with data recording such as bad-tempered person and with dominant finger/hand problem were also excluded from the study.

 

After written informed consent obtained, a short introduction was given to the patients regarding the study. The patients then were interviewed for information on their level of education, duration of post-injury, and daily life event including occupation. Data recording was taken daily unless stated otherwise for a duration of two to six days or longer, i.e. the total day patients’ calorie intake (CI) might achieve their calorie requirement (CR). All patients underwent a series of nutritional assessments, neuropsychology, and EEG test.

 

Basic anthropometry including height, body weight (and knee height, tricep skinfold [TSF] and mid-upper arm circumference [MUAC] for the non-ambulated patient) were measured twice (day-1 and discharge-day). Clinical assessments including GCS score, etiology of injury, pre-existing illness, blood pressure were retrieved from the Hospital Information System (HIS). CR was calculated from established formula Harris-Benedict equation21 and Mifflin St Jeor22. CI was collected based on 24-hour diet recall with a food diary and/or through case note for enteral feeding and the percentage of CI (%CI) was calculated.

 

Patients were required to undertake neuropsychology test namely Montreal Cognitive Assessment23 (MoCA) which assessed the element of attention, processing speed, and memory at every visit. Duration to finish the trail-making subtest (TS) and the total score (TS) were recorded. The patients then underwent an EEG test in a dedicated lab after applying net on head according to the guidelines for standard electrode positions by The 10–20 International System of Electrode Placement. EEG was recorded using an eye-open-eye-close paradigm for approximately 30 minutes. Technically, with a bandpass filter between 0.3-30 Hz, EEG was digitized continuously at a sampling rate of 250 Hz and baseline was corrected from 0-100 ms.

 

Socio-demography, nutritional and neuropsychology data were analyzed using SPSS (Version 22) with %CI set as the independent variable; whilst FT and TS were set as dependent variables. Basic descriptive analysis and scatter dot graph was done. Spearman correlation was carried out to determine the relation of %CI to FT and TS. Meanwhile, EEG data were analyzed using Fast-Fourier Transform (FFT) and later power ratio (PR) was calculated by comparing slow to fast EEG band during the early (baseline) and follow-up visit. A comparison from day-1 to discharge-day (early or follow-up visit for EEG) was done through the Wilcoxon Signed Rank Test.

 

Ethical approval of this study was obtained from the Medical Research and Ethics Committee (MREC), Ministry of Health Malaysia (NMMR-16-1925-32387).

 

RESULT:

Ten patients (six males and four females) diagnosed with miTBI due to MVA were enrolled in the study with a median age of 24.0 years (IQR=9.5). Patients had a median length of stay of 3.0 days (IQR=1.8) with median weight 57.0 kg (IQR=9.3); median height of 160.5 cm (IQR=16.3) and median calorie requirement of 2400 kcal/day (IQR=350) (Table 1).

 

Table 1: Basic characteristics of the patients (n=10)

Variables

 

Sex, in frequency

·     Male

·     Female

 

6

4

Occupation, in frequency

·     Self-employed

·     Student

·     Government servant

·     Private sector

·     Housewife

 

3

2

2

2

1

Type of TBI, in frequency

·     miTBI

·     moTBI

 

10

0

Etiology, in frequency

·      MVA

 

10

Age, in years (median [IQR])

24.0 (9.5)

LOS, in days (median [IQR])

3.0 (1.8)

Weight in kg (median [IQR])

57.0 (9.3)

Height, in cm (median [IQR])

160.5 (16.3)

BMI, in kg/m2 (median [IQR])

22.1 (6.4)

Calorie requirement, in kcal/day (mean [IQR])

2400 (350)

miTBI – mild TBI; moTBI – moderate TBI,

MVA – motor vehicle accident; LOS – length of stay

BMI – body mass index

 

FT and TS in relation to %CI was plotted through scatter dot graph as shown in Fig 1 and Fig 2. It was clearly shown that cognitive function was improved by increasing %CI. However, the rate of cognitive recovery was faster at lower calorie (approximately below 70%). However, at CI of 70%, cognitive function started to plateau. There was a significant negative correlation between %CI and FT (r = -0.717; p = 0.000 whilst positive correlation was found between %CI and TS (r = 0.789; p = 0.000).

 

 

Fig 1: Scatter dot graph on the relation between %CI and FT.

 

 

Fig 2: Scatter dot graph on the relation between %CI and TS.

 

The PR based on the EEG test among patients during the baseline and follow-up visits were shown in Table 2. The value of PR at follow-up visit became smaller from the baseline. The median power ratio was significantly different (p = 0.000) indicating the improvement of cognitive function.

 

Table 2: EEG power ratio value and it's mean (± SD) of all patients (Pt. 1 to Pt. 10) during baseline and follow-up visit.

Visit

Pt. 1

Pt. 2

Pt. 3

Pt. 4

Pt. 5

Pt. 6

Pt. 7

Pt. 8

Pt. 9

Pt. 10

Median (IQR)

First visit

2.11

2.25

2.14

1.98

2.03

2.14

2.16

2.21

1.87

1.86

2.08 (0.14)

Follow-up visit

0.54

0.62

0.55

0.33

0.26

0.32

0.82

1.60

1.49

1.15

0.77 (0.49)

 

DISCUSSION:

Adequate nutrition support is crucial to prevent malnutrition. The benefit of achieving the caloric target to prevent malnutrition in terms of outcome improvement has been addressed in several studies24,25 and the consensus has been included in major guidelines on nutritional therapy26,27.

 

Delayed, persistent or drastic underfeeding of TBI patients may result in negative outcomes such as impaired organ function; poor wound healing, and altered immunological status. At the same time, prolonged or excessive overfeeding can lead to deleterious effects including hyperglycemia, refeeding-like syndrome with electrolyte imbalance, hepatic steatosis, pulmonary compromise with difficulty weaning from the ventilator, and even obesity in long-term28.

 

This study has shown that cognitive recovery was influenced by %CI. Lower CI exhibited faster cognitive rate recovery and vice-versa. Interestingly, approximately at CI of 70%, cognitive function started to slow and became a plateau. Thus, it is suggested that lower CI (below 70%) is better for cognitive recovery. As the effects of low calorie have not been studied yet among neurosurgical patients, hence we cannot compare with the previous related study. However, the beneficial effect of low calorie is consistent with the previous finding among critically-ill patients. Arabi et al (2010) in their randomized clinical trial (RCT) has shown that permissive underfeeding in critically ill patients was associated with lower mortality rates than those associated with target feeding. Meanwhile, lower CI among medical ICU was associated with better outcomes15,17. In the present study, even though calorie was low, it was sufficient for TBI patients to recover cognitively. Other clinical outcomes were not thoroughly investigated in this study but all patients can be discharged uneventfully within two to six days with median of LOS 3.0 (IQR=1.8) days and no significant median different of weight change from day one to day in which suggested that malnutrition might not occur, at least during acute and sub-acute duration. Calorie at 70% of CI was actually slightly higher if compared to basal energy expenditure.

 

Improvement of cognition from the MoCA test was strengthened by the EEG power ratio result. EEG result revealed that cognitive function improved during the follow-up visit compared to the early visit. This was shown as the power ratio of slow and fast EEG frequency bands were correlated negatively with cognitive performance29,30. However, the actual time point of significant improvement was unable to be determined as EEG recording was not taken daily but only twice throughout the hospital stay.

 

CONCLUSION:

Dietary intervention might be utilized as a non-invasive approach among TBI patients to improve their cognitive function in which low CI produces better cognitive recovery. To date, this study was the pioneer study on the effect of calorie towards cognitive in TBI patients and it combined a standard dietetic practice technique, well-known neuropsychology test and EEG which was newly utilized in the dietetic field. Hence the pilot study was expected to enable identify unforeseen problems such as ambiguous inclusion or exclusion criteria, smooth running methodology or misinterpret result. Although the sample size was apparently small, it was adequate for a pilot study and analyzed through the non-parametric test. Completion of study with bigger sample size; grouping patients into defined low and high calorie; and additional neuropsychology test might able to elaborate current findings.

 

ACKNOWLEDGEMENT:

All named authors made significant contributions to the study design, patients’ recruitment, data collection, data analysis, preparation and revision of the manuscript for submission. The authors would like to thank the Director General of Health Malaysia for his permission to publish this article. This research was funded by the Fundamental Research Grant Scheme (FRGS) by the Ministry of Education, Malaysia [FRGS/1/2016/SKK02/ UniSZA/02/1].

 

CONFLICT OF INTEREST:

The authors declare no conflict of interest.

 

REFERENCES:

1.      Mekhlef AK, Hameed IH, Khudhair ME. Prevalence of Physical Injuries on the Head, neck and Entire body in, Hilla, Iraq. Research Journal of Pharmacy and Technology. 2017 Oct 1;10(10): 3276-82.

2.      Reena VT, Gopichandran L, Sinha S, Muthuvenkatachalam S. Comparison of Glutamine Enriched Feed over normal Routine feed in Traumatic brain Injury Patients: A Randomized double blind Controlled Trial. Asian Journal of Nursing Education and Research. 2016 Oct 1;6(4): 485.

3.      Maas AI, Stocchetti N, Bullock R. Moderate and severe traumatic brain injury in adults. Lancet2008;7(8): 728-41.

4.      World Health Organization. The World health report: 2003: Shaping the Future. 2003.

5.      Jamaluddin SF, Wahab MA, Mohamed FL, Saiboon IM. National trauma database January to December 2007–second report. Ministry of Health Malaysia. 2009.

6.      Vos T, Abajobir AA, Abate KH, Abbafati C, Abbas KM, Abd-Allah F, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study. Lancet 2016. 2017;390(10100): 1211-59.

7.      Priya RS, Lavanya S. A Study to Evaluate the Effectiveness of Sensory Stimulation to Improve the Level of Consciousness among Traumatic Brain Injury Patients wit GCS 7 – 10 at Thanthai Perriyar Government Head Quarters Hospital, Erode. Int. J. Adv. Nur. Management. 2(4): Oct – Dec, 2014; 216-220

8.      Deiva K, Vijayalakshmi S, Suseela T. Effectiveness of Coma Stimulation Therapy on Traumatic Brain Injury patients in a Government hospital setup–A Feasibility Study. Asian Journal of Nursing Education and Research. 2017 Oct 1;7(4): 569-72.

9.      Ministry of Transport Malaysia. Transport Statistics Malaysia. 2011. 2011.

10.   Burgess P, Sullivent EE, Sasser SM, Wald MM, Ossmann E, Kapil V et al. Managing traumatic brain injury secondary to explosions. J Emerg Trauma Shock. 2010;3(2): 164.

11.   Koehler R, Wilhelm E, Shoulson I. Cognitive rehabilitation therapy for traumatic brain injury: evaluating the evidence: National Academies Press; 2012.

12.   Gomez-Pinilla F, Gomez AG. The influence of dietary factors in central nervous system plasticity and injury recovery. PM R 2011;3(6):S111-S6.

13.   Gomez-Pinilla F, Kostenkova KJ. The influence of diet and physical activity on brain repair and neurosurgical outcome. Surg Neurol. 2008;70(4):333.

14.   Murphy T, Dias GP, Thuret S. Effects of diet on brain plasticity in animal and human studies: mind the gap. Neural Plasticity. 2014;2014.

15.   Arabi YM, Tamim HM, Dhar GS, Al-Dawood A, Al-Sultan M, Sakkijha MH, et al. Permissive underfeeding and intensive insulin therapy in critically ill patients: a randomized controlled trial. Am J Clin Nutr. 2011;93(3): 569-77.

16.   Arabi YM, Haddad SH, Tamim HM, Rishu AH, Sakkijha MH, Kahoul SH, et al. Near‐target caloric intake in critically ill medical‐surgical patients is associated with adverse outcomes. JPEN. 2010;34(3):280-8.

17.   Krishnan JA, Parce PB, Martinez A, Diette GB, Brower RG. Caloric intake in medical ICU patients: consistency of care with guidelines and relationship to clinical outcomes. Chest. 2003;124(1):297-305.

18.   Connelly LM. Pilot studies. Medsurg Nursing. 2008;17(6):411.

19.   Julious SA. Sample size of 12 per group rule of thumb for a pilot study. Pharmaceutical Statistics. 2005;4(4):287-91.

20.   Burton-Danner CO, Gregory R. Jackson, Kerri J. Aging and feature search: The effect of search area, experimental aging research. An International Journal of Vevoted to the Scientific Study of the Aging Process. 2001;27(1):1-18.

21.   Harris JA, Benedict FG. A biometric study of human basal metabolism. Proceedings of the National Academy of Sciences of the United States of Amerika. 1918 No 4: 370.

22.   Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. The American Journal of Clinical Nutrition. 1990;51(2):241-7.

23.   Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695-9.

24.   Norman K, Pichard C, Lochs H, Pirlich MJCn. Prognostic impact of disease-related malnutrition. Clin Nutr. 2008;27(1):5-15.

25.   Goiburu M, Goiburu MJ, Bianco H, Díaz JR, Alderete F, Palacios M, et al. The impact of malnutrition on morbidity, mortality and length of hospital stay in trauma patients. Nutr Hosp. 2006;21(5):604-10.

26.   McClave SA, Taylor BE, Martindale RG, Warren MM, Johnson DR, Braunschweig C, et al. Guidelines for the provision and assessment of nutrition support therapy in the adult critically ill patient: Society of Critical Care Medicine (SCCM) and American Society for Parenteral and Enteral Nutrition (ASPEN). JPEN. 2016;40(2):159-211.

27.   Daradkeh G, Essa MM, Al-Adawi SS, Subash S, Mahmood L, Kumar PR. Nutritional status, assessment, requirements and adequacy of traumatic brain injury patients. Pakistan Journal of Biological Sciences. 2014;17(10):1089-97.

28.   Walker RN, Heuberger RA. Predictive equations for energy needs for the critically ill. Respiratory Care. 2009;54(4):509-21.

29.   Munia TT, Haider A, Schneider C, Romanick M, Fazel-Rezai RJ. A novel EEG based spectral analysis of persistent brain function alteration in athletes with concussion history. Scientific Report. 2017;7(1):17221.

30.   Chen C-C, Hsu C-Y, Chiu H-W, Hu C-J, Lee T-CJJotFMA. Frequency power and coherence of electroencephalography are correlated with the severity of Alzheimer's disease: A multicenter analysis in Taiwan. Journal of the Formosan Medical Association. 2015;114(8):729-35.

 

 

 

 

Received on 31.07.2019          Modified on 27.09.2019

Accepted on 29.11.2019         © RJPT All right reserved

Research J. Pharm. and Tech. 2020; 13(10):4545-4549.

DOI: 10.5958/0974-360X.2020.00801.X