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Cortical thickness, neurocognitive, and body composition effects of fasting during Ramadan
Muhammad Iqbal1, Abdullah Abu Jamea2, Miguel Alonso-Alonso3, Khalid A Al-Regaiey1, Shahid Bashir4
1 Departments of Physiology, Faculty of Medicine, King Saud University, Riyadh, Saudi Arabia
2 Departments of Radiology, Faculty of Medicine, King Saud University, Riyadh, Saudi Arabia
3 Center for the Study of Nutrition Medicine, Beth Israel Deaconess Medical Center, Laboratory of Bariatric and Nutritional Neuroscience, Harvard Medical School, Boston, Massachusetts, USA
4 Center for the Study of Nutrition Medicine, Beth Israel Deaconess Medical Center, Laboratory of Bariatric and Nutritional Neuroscience, Harvard Medical School, Boston, Massachusetts, USA; Neuroscience Center, King Fahad Specialist Hospital Dammam, Dammam, Saudi Arabia
| Date of Submission | 19-Nov-2018 |
| Date of Decision | 26-Dec-2018 |
| Date of Acceptance | 24-Feb-2019 |
| Date of Web Publication | 25-Jun-2019 |
Correspondence Address:
Dr. Shahid Bashir
King Fahad Specialist Hospital Dammam, Dammam
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jrms.JRMS_783_18
Background: We aimed to study the anatomical, physiological, and cognitive function of healthy individuals practicing fasting during the month of Ramadan. Measurements were taken 1 week before and 2 weeks after Ramadan fasting. Materials and Methods: Twelve healthy male individuals (mean age ± standard error of the mean: 34.3 ± 2.9 years; body mass index: 26.26 ± 1.4 kg/m2) were assessed for various parameters before and after Ramadan fasting. All the tests were performed in the morning. Body composition characteristics were assessed by bioelectrical impedance analysis using a commercially available body composition analyzer. For neurocognitive analysis, participants underwent the stop signal task (SST), pattern recognition memory task (PRM), and spatial working memory strategy (SWM) from the Cambridge Neuropsychological Test Automated Battery. T1-weighted, 1 mm-thick magnetic resonance images were also acquired. Results: Anthropometric analysis showed a significant decrease in body weight, fat-free mass (FFM), trunk FFM, and trunk predicted muscle mass, while the other body composition parameters did not exhibit any changes. The stop signal reaction time (SSRT) latency (ms) (P > 0.05) and PRM did not show any significant difference before and after fasting. SWM task (P < 0.05) improved significantly after fasting. Cortical thickness data of the whole brain were not significantly different after fasting at any brain location. There was a significant correlation between the left amygdala and the SWM strategy (r2 = 0.518) and between fat and brain segmentation volume (r2 = 0.375). Conclusion: Our pilot data suggest that Ramadan fasting leads to weight loss and FFM reductions and improve cognitive function.
Keywords: Body composition, cognitive ability, neuroimaging, Ramadan fasting
How to cite this article:
Iqbal M, Jamea AA, Alonso-Alonso M, Al-Regaiey KA, Bashir S. Cortical thickness, neurocognitive, and body composition effects of fasting during Ramadan. J Res Med Sci 2019;24:50 |
How to cite this URL:
Iqbal M, Jamea AA, Alonso-Alonso M, Al-Regaiey KA, Bashir S. Cortical thickness, neurocognitive, and body composition effects of fasting during Ramadan. J Res Med Sci [serial online] 2019 [cited 2023 Mar 24];24:50. Available from: https://www.jmsjournal.net/text.asp?2019/24/1/50/261205 |
| Introduction | |  |
Fasting during Ramadan is an obligatory practice for Muslims worldwide. There are approximately 2 billion Muslims and hundreds of millions fast every year to abstain from eating, drinking, conjugal relationships, and smoking from sunrise until sunset as a sign of restraint and introspection in the 9th month of the lunar Islamic year for 29–30 days.[1] Depending on the season, that Ramadan coincides with and the location, the duration of fasting may vary between approximately 10 and 20 h. Fasting affects the circadian rhythm and the biorhythm of nutrient consumption, which results in changes in physiological, cognitive, behavioral, and metabolic functions, as well as sleep patterns.[2],[3],[4] The reported effects of fasting on body composition are inconsistent. Some studies have suggested an increase in body weight while others have reported a reduction, however, there have been no effects of fasting on both body weight and composition.[2],[5] The variability in body weight has been attributed to the economic conditions of the society/country, age, sex, lifestyle, dietary intake, and a number of hours spent fasting.[6],[7]
Alterations in the circadian rhythm influence cognitive function during fasting. The existing data on the effect of fasting on cognitive function vary. Some studies have suggested a decrease in subjective alertness, psychomotor performance, and memory, while others have reported mixed responses depending on the time of the day, with better performance in the morning and poorer performance in the evening.[8],[9] Other factors that affect cognition include food composition, particularly macronutrients, as well as the time of ingestion. Different types of food have different effects that result in either improved memory function or learning ability or impaired reaction time.[10] Hypoglycemia and accumulated sleep loss can also impair certain cognitive functions.[11]
Neuroimaging and other related techniques serve as important tools for understanding the brain systems linked to different aspects of brain functions such as cognition and behavior. A recent study that used diffusion tensor imaging suggested that microstructural changes occur following 3 weeks of fasting.[12]
In the literature, different brain locations associated with hunger and satiety have been identified. In a study that used positron emission tomography, hunger was shown to be associated with an increase in neuronal activity in the hypothalamus, thalamus, basal ganglia, temporal cortex, cerebellum, insula, anterior cingulate, and orbitofrontal cortex; and satiety was found to be related to increased neuronal activity in prefrontal cortex.[13],[14]
Although previous studies have focused on the effect of Ramadan fasting on body composition, cognitive function, and neuroimaging, the cumulative effects of these parameters in any one setting have not previously been studied. Therefore, in this study, we aimed to assess the effect of 2 weeks of fasting on body composition, cognitive function, and neuroanatomical changes in healthy individuals.
| Materials and Methods | | |
Subjects
Twelve healthy volunteers (mean age ± standard error of the mean [SEM]: 34.3 ± 2.9 years) took part in the present study. The individuals were nonsmokers and did not have a history of heart problems or diabetes. The individuals were enrolled in the study after receiving a verbal explanation regarding the research procedures and providing written informed consent. The study was conducted in 2014 after approval by the Local Ethics Committee of the College of Medicine, King Saud University, Riyadh, Saudi Arabia. Assessments were carried out at two different time points: 1 week before Ramadan and 2 weeks following Ramadan fasting. All the tests were performed in the morning. Ramadan started on June 28th and ended on July 27th and the approximate duration of fasting was just over a period of 16 h in Riyadh city. All the participants were residents of Riyadh and completed the study period, and they were their own controls.
Anthropometric measurements
Body composition was analyzed by bioelectrical impedance analysis using a commercially available Body Composition Analyzer (Type BC-418 MA, TANITA Corporation, Tokyo, Japan). The bioimpedance analyzer uses eight points of tactile electrodes with contacts at the hands and feet. The technique uses multiple frequencies to measure body mass index (BMI), total body water, fat, and fat-free mass (FFM) in total and segmental distributions in the trunk and limbs. The participants were asked to wipe out sole of the feet by a wet tissue and the stand over the electrodes of the machine and the data were recorded.
Cognitive function evaluate
Neuropsychological functions were assessed using the Cambridge Neuropsychological Test Automated Battery (CANTAB, http://www.cambridgecognition.com/cantab) research suite software (version 6. 0.37, Cambridge Cognition, Cambridge, UK). The responses were recorded with a touch-sensitive screen monitor. Each participant was given training trial to familiarize with the test before the actual test was taken. The entire battery required 25–30 min to complete the tests.
Stop signal task
The stop signal task (SST) is a task designed to measure response inhibitions and the response inhibition reaction time (impulse control). In this test, an arrow pointing left or right is shown on the screen. First, the participants were instructed to press on the corresponding (left/right) button on a press pad as soon as they see the direction of the arrow. In the next phase of the task, the participants were instructed to press on the corresponding button like before, but should not press the button when they hear a beep (the stop signal). The stop signal reaction time (SSRT) was calculated by subtracting the stop signal delay from the median go reaction time (the reaction time on trials with no stop signal). SSRT was selected as the main outcome measure.
Pattern recognition memory
The participant was presented with a series of 12 visual patterns at a time, displayed in the center of the screen. These patterns were designed not verbally labeled easily. In the recognition phase, the participant was required to choose between a pattern they had already seen and a novel pattern. In this phase, the test patterns were presented in the reverse order of the original order of presentation. The events were recorded as the number of correct responses, expressed as a percentage, with higher values indicating a better pattern of recognition memory (PRM Percentage correct measures).
Spatial working memory
Task
Spatial working memory (SWM) assesses the ability of participants to retain and update spatial information in working memory. This test is used to measure the heuristic strategy of participants. In this test, number of boxes was shown on the screen. The participant was told to search for tokens by opening the boxes by touching them. He was also advised not to open the box that had already shown a token. A between-search error was recorded when participants touched the box where a token had already been found, and this score was used as the measure for SWM (SWM-between errors). Once the token was found, the participant restarted the search using the sequence previously used. This task provided an indication of the participant's ability to use heuristic strategy. The evaluated events were SWM strategy (refers to number of times, the participant started a new search by touching a different box).
Image acquisition
A Siemens Magnetom Verio 3T magnetic resonance imaging (MRI) clinical scanner (Siemens AG, Healthcare Sector, Erlangen, Germany) and 12-channel phased-array head coil were used to acquire the following: (1) T1-weighted 3D magnetization prepared rapid gradient-echo imaging (TR = 1600 ms, TE = 2.19 ms, inversion time = 900 ms, flip angle = 9°, acquisition plane = sagittal, voxel size = 1 mm × 1 mm × 1 mm, FOV = 256 mm, acquired matrix = 256 × 256, and acceleration factor [iPAT] =2) and (2) fluid-attenuated inversion recovery (TR = 9000 ms, TE = 128 ms, inversion time = 2500 ms, flip angle = 150°, acquisition plane = axial, slice thickness = 5 mm, FOV = 220 mm, acquired matrix = 256 × 196, and acceleration factor [iPAT] =2).
We applied an independent component analysis to the resting state functional MRI (fMRI) series in 12-matched healthy controls. Imaging was performed on a Siemens Magnetom Verio 3T MRI. The resting state functional scans consisted of 404 volumes (repetition time = 1400 ms; echo time = 30 ms; flip angle = 65°; 64 axial slices; 64 × 64 matrix).
Data analysis was carried out using MELODIC from FMRIB's Software Library (FSL version 6.0; FSL, http://www.fmrib.ox.ac.uk/fsl) to identify the large-scale patterns of temporal signal intensity coherence. The detailed preprocessing procedure has been described previously.[15],[16] Components of interest were selected by visual inspection based on previous studies.[17],[18] The voxel-wise comparison of the resting functional connectivity in the between-subject analysis was carried out using a regression technique referred to as the “dual-regression” approach, as previously described.[15],[16],[17]
Statistical analysis
SPSS software (version 19.0; SPSS Inc., Chicago, IL, USA) was used for statistical analysis. We used paired t-tests for the comparison of quantitative data between the Ramadan (fasting) and non-Ramadan (nonfasting) groups when the assumptions of normality were satisfied; otherwise, nonparametric tests were used. Two-tailed statistics (P < 0.05) were used as the level of statistical significance for all parameters.
| Results | | |
Body composition analysis
Twelve male volunteers aged 34.33 ± 2.9 (mean ± SEM) years participated in the study. [Table 1] and [Table 2] show the anthropometric analysis of different body composition parameters. There were statistically significant decreases in weight (P = 0.019), total body water (P = 0.025), FFM (P = 0.021), prefasting, and 1-week post-fasting. In the segmental analysis, trunk muscle mass (P = 0.017) and FFM (P = 0.018) were also decreased significantly. However, there were no significant differences among the other body characteristics. | Table 1: Anthropometric analysis of body characteristics pre-Ramadan and 2-week post-Ramadan
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 | Table 2: Segmental body composition analysis pre-Ramadan and 2-week post-Ramadan
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Neuropsychological assessments
Cognitive analysis
The assessment of cognitive parameters is described in [Table 3]. The analysis of these parameters by CANTAB showed that the SWM task (P = 0.025) significantly improved after fasting [Table 3]. Other cognitive parameters such as SST and PRM did not show any statistically significant change in these parameters before and 2 weeks' post-Ramadan fasting [Table 3]. | Table 3: Analysis of cognitive parameters pre-Ramadan and 2-week post-Ramadan
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Magnetic resonance imaging
The neuroimaging analysis of the brain by MRI showed no significant differences in the major brain areas before and after fasting. There was a significant correlation between the left amygdala and SWM strategy [r2 = 0.518, [Figure 1], and between fat and the brain segmentation volume [r2 = 0.375, [Figure 2]. | Figure 1: Correlation analysis between the left amygdala and spatial working memory strategy
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 | Figure 2: Correlation analysis between cortical thickness and fat percentage
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| Discussion | | |
This study investigated physiological and neuropsychological alterations in a cohort of healthy individuals practicing Ramadan fasting. Changes in body composition have important implications for individual's health. Either these alterations lead to metabolic syndrome or body needs nutrients that are more essential. We have found that anthropometric parameters such as weight, TBW, FFM, trunk FFM, and trunk PMM are decreased 2 weeks after fasting [Table 1] and [Table 2]. There are mixed reports on the effects of Ramadan fasting on different body composition parameters. Some studies have demonstrated a reduction in body weight, while others have reported an increase in body weight, or fasting does not have any effect on body weight.[5],[6],[16],[19] There are various interpretations regarding weight reduction. In Ramadan, eating habits and meal times are changed. Calorie intake is increased in Ramadan, due to consumption of sugary foods and soft drinks, particularly during iftar times. However, there is an increase in physical activity such as mandatory night prayers “Terawih” which involve long period of standing and other religious activities. Thus, weight is reduced in Ramadan although calorie intake is increased. This reduction in weight loss is temporary and may be regained once the fasting period ends.[20]
Body composition analysis provides valuable information about muscle and body mass, which includes fat mass, FFM, and other body parameters. In our study, we have observed decrease in FFM, trunk FMM, and trunk PMM in all participants. There are variable findings about the effect of Ramadan fasting on body composition. Norouzy et al.[5] have found that Ramadan fasting has resulted in weight loss and FFM reduction in different age groups. However, the percent of body fat was lower only in male participants. A study by Karli et al.[21] had revealed that Ramadan fasting did not have significant changes in body weight, BMI, FFM, and percentage of body fat including other parameters. Contrary to this, Rohin et al.[22] found no significant effect on body composition in individual with different weight parameters. Another study by Sezen et al.[23] has found no effect on FFM in male volunteers, whereas visceral fat mass, BMI, and body fat mass were reduced. Our study group consists of participants of different age groups; therefore, it is very difficult to draw any conclusion. Further studies with larger sample size are required to draw any constructive conclusion. The inconsistencies reported in our study and others may be related to variables associated with these types of research settings. For example, the age, sex, physical activity, types of food consumed, variability of fasting times in different geographical regions, seasonal and climatic differences in the month of Ramadan, and socioeconomic status of the fasting individual may affect the outcomes of these studies.
The accumulation of excessive body fat is associated with various health problems, such as type 2 diabetes, cancer, and cardiovascular disease.[23] We observed a positive correlation between body fat and cortical brain segmentation volume [Figure 1]. An increase in visceral adipose tissue (VAT) has been associated with a decrease in gray matter volume.[24] Cortical thickness is an appropriate measure of alterations in gray matter. Reduced cortical thickness is associated with an increase in BMI and VAT.[25] An increase in BMI has been shown to be associated with regional alterations in brain structure in the left lateral occipital cortex and right ventromedial prefrontal cortex. Previous neuroimaging studies have highlighted the crucial role of hunger and satiety centers and brain locations in the hypothalamus, insula, amygdala, middle temporal cortex, thalamus, and medial prefrontal cortex in relation to emotional regulation deficits.[26],[27],[28],[29],[30]
In the limbic system, the hypothalamus, thalamus, and amygdala have major roles in processing emotions and memory. The left amygdala was correlated with cognitive function in our study.[30] The amygdala is a brain structure involved in emotion and higher cognitive functions such as working memory and executive control.[31],[32],[33] We found a positive correlation between the amygdala and working memory in our participants [Figure 1]. One limitation of the present study was its small sample size, which may have been inadequate to detect statistical differences for some parameters. Furthermore, the present study could have been improved by using two groups with measurements made before, during, and after the fasting period. Another limitation of this study was that the cortical thickness-based analysis might have affected the estimated cortical thickness statistics. Finally, total abstinence from not only food but also fluids, during fasting can effect serum osmolality and eventually alter the intracellular water content and quantitative values of structures of the brain. A direct measure of body water content, such as serum osmolality should be used in future studies for the measurement of dehydration effects.
| Conclusions | | |
Our pilot data suggest that Ramadan fasting leads to weight loss and FFM reductions and to improve cognitive function.
Acknowledgments
The authors would like to thank the study participants, without whose co-operation it would not have been possible to conduct this study. The authors are also grateful to the Researchers Support and Services Unit at King Saud University for their technical support.
Financial support and sponsorship
Work on this study was supported by grants from the Deanship of Scientific Research Group (IRG14-10), King Saud University, Saudi Arabia.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]
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