Multimodal Imaging and Visual Evoked Potentials Reveal Key Structural and Functional Features That Distinguish Symptomatic From Presymptomatic Huntington's Disease Brain
Correspondence Address: Source of Support: None, Conflict of Interest: None DOI: 10.4103/0028-3886.329528
Source of Support: None, Conflict of Interest: None
Keywords: Atrophy, chorea, diffusivity, Huntington's disease, visual evoked potentials
Huntington's disease (HD) is an autosomal, dominant, inherited, neurodegenerative disorder caused by the expansion of CAG trinucleotide repeats on the Huntingtin gene (HTT) with a mean age at onset of around 40 years. HTT containing >39 CAG repeats produces a mutant Huntingtin protein (mHtt) with a polyglutamine (Qn) tag that aggregates and causes neuronal degeneration. HD is characterized by progressive involuntary movements, predominantly chorea, cognitive decline, and psychiatric/behavioral abnormalities,,,,, that are correlated to and determined by the CAG repeat length.,, Striatum is the earliest region affected in the HD brain.
The regional brain atrophy and neurochemical alterations in the HD brain occur before the clinical onset of the disease and have been characterized in several multicenter investigations.,,,,,,, Structural magnetic resonance imaging (MRI) studies show a widespread progressive volumetric loss in cortical gray matter, cerebral white matter, and subcortical regions.,, Additionally, demyelination and axonal degeneration lead to increased diffusivity in HD brain, as visualized through diffusion analysis.,, Also, visual evoked potential (VEP) studies investigating the structural integrity and functional processing of the visual cortex have described an association between delayed visual latencies and white matter degeneration in HD patients. Furthermore, longitudinal quantification of neurochemical changes in the HD brain using magnetic resonance (MR) spectroscopy and glucose hypometabolism using fluorodeoxyglucose F 18 (18F-FDG) positron emission tomography (PET) help in understanding neuronal degeneration in presymptomatic stages of the disease and their association with early cognitive and psychiatric abnormalities.,
Currently, matched analysis involving all the parameters listed above comparing HD patients and control participants within the same study cohort is lacking, and such studies could provide biological insights into key alterations associated with progression of HD from a presymptomatic to a clinically significant state. Besides, research findings on HD patients in the Indian population are limited because of low prevalence of the disease, lack of HD awareness among the patients, or poor follow-up.
To address the above gaps in knowledge, a comprehensive multimodal imaging study was carried out on a cohort of HD patients and control participants with additional exploratory analyses comparing either P-HD to controls or to S-HD patients, respectively.
This study was approved by the ethics committee of the institution. Every participant provided informed written and video consent to participate in the study. Data were collected in a de-identified manner.
Twenty participants who were either HD gene mutation carriers (CAG > 39) and their siblings or participants with involuntary movements had consulted the neurology division. Polymerase chain reaction (PCR)–based genetic test for exon 1 of HTT gene was carried out (www.lalpathlabs.com) to confirm the presence of the mutant (pathogenic) allele containing CAG repeats. Components of Unified Huntington's Disease Rating Scale (UHDRS), namely, motor assessment, and the Total Functional Capacity (TFC) score were determined by a senior neurologist. To distinguish the S-HD from the P-HD patients, we assessed chorea in seven different body regions (face, bucco-oro-lingual region, trunk, right and left arms, right and left legs) to derive a Chorea Sum Score or Total Chorea Score (TCS),, which is a simplified form of UHDRS–Total Motor Score (TMS). The neurologically healthy control participants included three genetic controls and nine age and gender-matched controls. [Figure 1]a describes the genetic tree for the P-HD, S-HD, and genetic controls.
For all patients, gender, age at onset and clinical diagnosis, and disease duration were recorded. For the P-HD patients, years to the onset of symptoms was calculated using the formula, Estimated years to onset = 21.54 + exp (9.556 -0.146 × CAG repeats) − current age.,
All the analyses primarily focused on comparing HD patients (n = 17) versus controls (n = 12). In addition, we specifically looked for differences between the controls (n = 12) and P-HD (n = 6) versus S-HD (n = 11), and in each case, only the significant changes (Bonferroni corrected P values <0.05) were reported. Different numbers of HD patients contributed data for each assay as indicated in the text and figures. In contrast, the 12 control participants contributed data to all the analyses except 18F-FDG PET.
Visual Evoked Potential acquisition
The latency of the pattern reversal VEP waveforms was measured using Neuropack X1 ME-2300 (Nihon Kohden, Tokyo, Japan) with one channel montage. The international 10/20 system was used for electrode placements with active electrode Oz over the occipital cortex, reference electrode Fz on the midfrontal region, and the ground electrode Cz on the vertex. The electrode impedances were maintained below 10 kΩ to reduce unwanted noise signals. At a distance of 1 m, a 21-inch LCD (liquid crystal display) monitor with black and white reversing checkerboard with a brightness of 80 cd/m2, 1° visual angle, and 2 Hz flashing frequency was presented monocularly to patients with the unstimulated eye closed with a cotton pad. The participants were instructed to fixate on the white dot at the center of the monitor, and 100 responses were averaged to give a waveform. Two such VEPs were recorded for the right and left eye, respectively, and the values were averaged. The pattern reversal VEP waveform has three phases: two negative peaks (N75 and N145) and a positive peak (P100). The positive peak with a mean latency of 100 ms (P100) was considered as a normal VEP response. VEP data were measured in 13 HD patients.
MRI acquisition and analysis
MRI was performed using a 1.5 T Siemens AERA (Siemens, Munich, Germany) whole-body MR system with a 16-channel head coil. The quality of the MRI images was checked by a senior radiographer and a senior radiologist, during data acquisition, and the scans that had movement artifacts were repeated as needed. The acquisition parameters of the axial T1-weighted sequences that were used for registration are as follows: TR = 610 ms; TE = 9.7 ms; flip angle = 90°; voxel size = 0.6 × 0.6 × 5 mm3; 5 mm slice thickness with 24 slices and 1.5 mm slice gap with a field-of-view (FOV) of 230 mm, FOV phase = 84.4%, base resolution = 384, and phase resolution = 70%.
Caudate atrophy is a standard radiological feature of HD. The axially placed FLAIR (fluid-attenuated inversion recovery) slices were acquired with TR = 8,500 ms; TE = 86 ms; flip angle = 150°; voxel size = 0.7 × 0.7 × 5 mm3; 5 mm slice thickness with 24 slices and 1.5 mm slice gap with FOV read = 230 mm, FOV phase = 84.4%, base resolution = 320, and phase resolution = 70%. Caudate atrophy was quantified by comparing the ratios of intercaudate distance (CC) with the internal standards such as frontal horn width (FH) or inner table width (IT; as described in https://radiopaedia.org) [Supp. Figure 3]a. The ratios were calculated on the axial plane FLAIR sequences of HD patients (n = 17) and controls (n = 12) using RadiAnt DICOM Viewer.
T1-weighted sagittal three-dimensional (3D) magnetization-prepared rapid gradient echo (MP-RAGE) sequence was obtained with TR = 2,000 ms; TE = 2.7 ms; flip angle = 8°; voxel size = 0.9 × 0.9 × 1 mm3 with 1 mm slice thickness, no interslice gap and 230 mm FOV, FOV phase = 100%, base resolution = 256, phase resolution = 96%, and number of slices = 160. The subcortical volumes were measured using the default pipeline in the FreeSurfer Version 5.3.0 (https://surfer.nmr.mgh.harvard.edu/). A neuroanatomical label was automatically assigned to each voxel in MRI volume. The accuracy of segmentation was visually inspected for all the participants. Volumes corresponding to each of the left and right hemisphere regions were summed and normalized with the respective intracranial volumes (ICV) using the formula: Vnorm = (Vraw/VICV) × Vmean ICV, where Vnorm denotes normalized volume, Vraw represents raw volume, and Vmean ICV represents mean ICV volume. Volumetric analysis data were collected for 13 HD patients.
Diffusion data were acquired using two-dimensional single-shot echo-planar imaging sequence with TR = 4,500 ms and TE = 83 ms. A bipolar diffusion scheme was used, and 40 diffusion sampling directions were acquired with a b-value of 1,000 s/mm2 along with six b0 images. Slice thickness = 5 mm; number of slices = 28; slice gap = 1.5 mm; FOV = 230 mm; voxel size = 1.5 × 1.5 × 5 mm3, and acquisition matrix = 174 × 192. Diffusion data were preprocessed and analyzed using DSI Studio (May 22, 2020, build, http://dsi-studio.labsolver.org/). The b-table was checked by an automatic quality control routine to ensure its accuracy. Motion artifacts and eddy current distortions were checked through neighboring diffusion-weighed image correlation. The diffusion data were reconstructed at the Montreal Neurological Institute space using q-space diffeomorphic reconstruction (QSDR) to obtain the spin distribution function. A diffusion sampling length ratio of 1.25 was used with output resolution set at 2 mm isotropic. The restricted diffusion was quantified using restricted diffusion imaging. The effect of covariates gender and age was removed from the diffusion measures using a multiple regression model, and regions of interest (ROIs) were created using the FreeSurfer segmentation Atlas More Details in the DSI Studio. For each patient, mean values were obtained for fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AxD), and radial diffusivity (RD) from the whole brain and different regions. Values from the left and right hemispheres were averaged during statistical analysis. Diffusion data from one HD patient were excluded due to inconsistency in image dimension, resulting in data from 16 HD patients.
MR spectroscopic imaging
1H MR spectroscopic imaging data were acquired using PRESS (Point RESolved Spectroscopy) sequence with phase-encoding gradients in two directions, outer volume saturation and VAPOR (Variable power radiofrequency pulses with optimized relaxation) water suppression. TR = 1,690 ms, TE = 135 ms, spectral bandwidth = 1,000 Hz, spectra points = 1,024, averages = 4, voxel size = 10 × 10 × 15 mm3, acquisition time = 7 min 35 s. FOV of 160 mm and volume of interest of 80 mm were positioned at the level of the lateral ventricles close to the magnet isocenter and almost in the middle of the brain avoiding the skull. Postprocessing steps included automatic phase correction, residual water removal, coil combination, apodization with a 1-Hz exponential filter, and zero-filling to 4,096 points. Metabolite maps were generated by fitting the processed data with LCModel from 1.1 ppm and 3.9 ppm with a basis set simulated using GAMMA. This basis set included the spectra from 10 metabolites: taurine, creatine, phosphocreatine, glycerophosphocholine, phosphocholine, guanine, N-acetyl aspartate, N-acetyl aspartyl glutamate, lactate, and inositol. MR spectroscopic imaging data were obtained for nine HD patients.
18F-FDG PET imaging
All 18F-FDG PET/CT brain images were acquired in 3D mode using a bismuth germanium oxide–based PET/CT scanner (Discovery510, GE Healthcare, Milwaukee, WI, USA) after 75 min of 8.0 mCi tracer injection. Blinded analysis was performed by a nuclear medicine radiologist. The images were reconstructed using CortexID suite (ADW 4.4, GE Healthcare, Milwaukee, WI, USA), and 3D stereotactic surface projections (3D-SSP) were performed and compared with a database containing mean and standard deviation (SD) of 18F-FDG metabolism calculated for each pixel (in 3D, x, y, and z) in a region from the age-matched healthy controls. Each participant's data were normalized to the cerebellum as a reference. The Z-scores were calculated by comparing pixels from a region in the patient with the corresponding pixels from the corresponding region in the control database (C), using the formula Z-score = (CMean (x, y, z) − HD (x, y, z))/CSD (x, y, z), where CMean and CSD represent the mean and SD of 18F-FDG metabolism derived from the healthy controls in the database. HD represents normalized 18F-FDG metabolism in HD patients in the study cohort. Considering the magnitude of subtraction, a positive Z-score represents hypometabolism and a negative Z-score described hypermetabolism. Z-score values greater than 2 were considered significant. Fused PET/CT images were obtained in axial, coronal, and sagittal planes. 18F-FDG PET data were obtained from 14 HD patients.
The mean, SD, and the range were calculated for each of the data types. For all the analyses, mean ± SD is presented for each of the comparison groups. Statistical significance was assessed using nonparametric, Mann–Whitney U test primarily comparing data from HD patients (n = 17) against the controls (n = 12). In addition, one-way analysis of variance was used in the exploratory analysis comparing the three groups, controls (n = 12), P-HD (n = 6), and S-HD (n = 11), and Bonferroni corrected P values (<0.05) were reported after correcting for family-wise errors involving multiple comparisons. Significant P values for each comparison are indicated in the figures. Spearman rank correlation was used to correlate TCS with the age of onset, CAG repeat, and diffusion indices, in S-HD patients. The volumes and diffusion indices of HD patients were compared with the values obtained from the control participants. Z-scores of the 18F-FDG PET analysis were obtained from the 3D-SSP, CortexID suite (ADW 4.4, GE Healthcare, Milwaukee, WI, USA).
The demographics and clinical parameters of HD patients (stratified by P-HD and S-HD) and controls are given in [Table 1]. Pedigree data for the HD patients and genetically related controls along with information on the CAG repeats are shown in [Figure 1]a and [Supp. Figure 1]a.
Of the 20 participants examined, 17 had a mutant HTT gene with 48.76 ± 5.78 (mean ± SD) CAG repeats and were labeled as HD patients. The remaining three individuals did not show CAG expansion (CAG repeats: 15.66 ± 2.89), were neurologically healthy siblings of HD patients, and were considered as genetic controls. Among the 17 HD patients with CAG expansion, 11 patients experienced prominent choreatic movements before they visited the clinic and had 49.82 ± 6.7 CAG repeats, a total chorea score (TCS) of 17.27 ± 4.27, and a TFC score of 5.18 ± 2.04 (refer [Supp. Table 1] for a list of additional TMS scores). These individuals were stratified as symptomatic HD patients or S-HD. The remaining six HD patients had 46.83 ± 3.19 CAG repeats but no movement abnormalities in the seven regions examined (TCS = 0). These individuals were considered presymptomatic or P-HD. These P-HD patients presented subtle signs of HD that included mild slowing of finger taps (refer [Supp. Table 1]) but did not exhibit gross clinical symptoms or changes in functional capacity (TFC = 13).
Consistent with previous studies,, within the S-HD group, a strong inverse correlation was observed between CAG repeat length and age at onset of HD symptoms (Spearman rho = −0.926, P = 0.00004, [Figure 1]b). As shown in [Supp. Figure 2], S-HD patients with CAG > 50 had an early onset (23.8 ± 3.77 years) of clinical signs, whereas those with CAG repeats between 40 and 50 had delayed onset (38.17 ± 2.71 years) of clinical symptoms. In addition, TCS in S-HD patients was also positively correlated to age at onset (Spearman rho = 0.659, P = 0.027, [Supp. Figure 1]b) and hence also negatively correlated to the CAG repeat length (Spearman rho = −0.619, P = 0.042, [Supp. Figure 1]c). These findings reveal a strong correlation between the number of CAG repeats and the manifestation of clinical symptoms with patient age.
Having characterized the study population, an integrative approach consisting of genetic analysis, clinical assessment, electrophysiological studies, MRI, MR spectroscopy, and PET analyses were carried out on these participants to determine changes in macrostructural, microstructural, and functional parameters associated with HD [Figure 1]c.
First, we explored conduction in the optic pathway using VEP. [Figure 2]a shows a representative pattern reversal VEP waveform for N75, P100, and N145, in controls and HD patients. Interestingly, there was a significant increase (P = 0.005) in mean P100 latency in HD patients (115.45 ± 16.31) compared with the controls (103.32 ± 10.38). In addition, interestingly, we also observed a significant increase (P < 0.01) in mean P100 latency in S-HD compared with P-HD patients [Figure 2]b.
We next sought to determine the structural changes in the HD brain. [Figure 3]a illustrates bilateral atrophy of caudate and putamen, marked in dotted line, in representative control, P-HD, and S-HD participants. Bilateral caudate atrophy was quantified by comparing CC with the internal brain standards (FH and IT). In HD patients compared with controls, the average CC was significantly increased (HD: 1.79 ± 0.39, controls: 1.02 ± 0.16, P < 0.00001). Accordingly, there was a significant decrease in the FH/CC ratio (HD: 1.74 ± 0.48 vs. controls: 3.06 ± 0.37, P < 0.00001). Furthermore, this also resulted in a significant increase in the CC/IT ratio in HD patients (HD: 0.17 ± 0.04, controls: 0.09 ± 0.01, P < 0.00001). Furthermore, within the HD group, significant difference in ratios were observed in S-HD (FH/CC: 1.51 ± 0.18, P < 0.01 and CC/IT: 0.19 ± 0.02, P < 0.0001) versus P-HD (FH/CC: 2.14 ± 0.61 and CC/IT: 0.14 ± 0.03, [Figure 3]b). In turn, the caudate atrophy resulted in an enlargement of frontal horns of the lateral ventricles in S-HD compared with P-HD [Figure 3]a and [Supp. Figure 3]b.
Additionally, to estimate the volumetric loss in cortical and subcortical regions, an automated brain segmentation analysis was performed using FreeSurfer in 3D MP-RAGE, and volumes for 19 ROIs were determined after normalization to the mean intracranial volume (ICV). [Table 2] shows the changes in volume in the whole brain and subcortical regions that were significant between the HD patients (n = 13) and controls (n = 12). Consistent with earlier findings,, we found a significant reduction (P < 0.05) in the gray matter (cortical and subcortical) and cerebral white matter in HD patients compared with controls. The analysis further revealed significant reduction (P < 0.05) in the volumes of caudate, putamen, accumbens, thalamus, and hippocampus, as well as corpus callosum and cerebellum white matter, in S-HD compared with P-HD [Figure 3]c and [Supp. Figure 3]b.
To understand the microstructural alterations, diffusion imaging of the whole brain was performed. The whole-brain diffusion analysis of HD patients showed a significant decrease in FA (P < 0.00001) and an increase in MD (P = 0.0001), AxD (P = 0.001) and RD (P = 0.0001), compared with the controls [Figure 4]a, see [Supp. Figure 4] for representative diffusion maps). Similar to our earlier observation with the volumetric loss, diffusion data when examined in a regional context also showed a significant (P < 0.05) decrease in FA and increase in MD, AxD, and RD in cerebral white matter and subcortical regions of HD patients compared with controls [Figure 4]b, P values for region-specific comparisons are given in [Supp. Table 2]). Also, a significant increase (P ≤ 0.05) in diffusivity (MD, AxD, and RD) was observed in caudate, putamen, pallidum, and hippocampus, as well as in cerebellum (cortex and white matter), in S-HD compared with P-HD [Supp. Figure 5].
To gain insights into the effect of neuronal degeneration on the neurochemical changes and glucose metabolism in the HD brain, we performed MR spectroscopic imaging for the neuronal integrity marker tNAA (sum of N-acetyl aspartate and N-acetyl aspartyl glutamate) and the demyelination marker tCholine (sum of phosphocholine and choline), as well as 18F-FDG PET analysis for glucose. Ratios of tNAA and tCho levels to total creatine (tCr) are represented in [Figure 5]a and [Figure 5]b. Interestingly, tNAA was significantly reduced and tCho levels were significantly elevated in HD patients compared with controls (refer to [Supp. Figure 6]a for representative spectra and [Supp. Figure 6]b for the results of Mann–Whitney U test). Furthermore, glucose metabolism was also reduced bilaterally in caudate (average Z-score 3.92 ± 1.35) in HD patients [Figure 5]c.
We have used advanced in vivo neuroimaging to study the structural and functional aspects of the HD brain. In HD, the disease onset is clinically determined based on movement abnormalities, commonly chorea. These involuntary movements result from the degeneration of medium spiny neurons of the striatum (which includes caudate, putamen, and accumbens) and pallidum. These neurons predominantly regulate voluntary movements and also enable learning, memory, reward, and motivation. In our study, the brain morphometric analysis of HD patients showed marked atrophy in these regions both in the presymptomatic and symptomatic stages of the disease. We estimated the proximity to clinical diagnosis in P-HD patients to be around 10 years before the commencement of involuntary movements. Furthermore, an increase in the extent of atrophy from P-HD to S-HD suggests gradual degeneration associated with the progression of HD.
Interestingly, HD shows remarkable variability in the age at onset, severity, and trajectory of progression. This variability has been correlated with the length of the CAG repeat expansion, which differs between patients. Recent studies have also implicated a role for certain genetic modifiers in disease variability. Consistent with this notion, our study revealed differences in the number of CAG repeats between the early age-at-onset and late age-at-onset HD groups. Furthermore, we could trace the autosomal dominant inheritance pattern in two to four generations prior to the participants studied, and the inheritance pattern showed complete penetrance.
Notwithstanding the variability in age at onset and disease severity, the type of neuropathological changes in HD patients remains more or less similar except for their magnitude. In neurodegenerative diseases such as HD, aberrant cellular signaling cascades lead to neuronal cell death that ultimately results in tissue atrophy. These signaling cascades alter the brain neurochemistry and metabolism, several years prior to the onset of clinical symptoms. We have used functional imaging techniques such as PET using radiotracers and proton MR spectroscopic imaging to capture a few of these early metabolic changes in the HD brain. We observed reduced consumption of glucose, lower levels of NAA, and elevated levels of choline in presymptomatic and symptomatic brains, with the former showing no clinical symptoms for HD. Glucose metabolism is pivotal for the functioning of energy-intensive neuronal activity,, and NAA is a marker for neuronal viability and density. Choline, which is released during neuronal death, is a marker for cellular membrane turnover and increased demyelination. Notably, without temporal imaging data, it is difficult to determine whether these neurochemical changes represent the cause or consequence of neuronal degeneration.
Initiation of neuronal degeneration is identified by alterations in gray and white matter integrity, demyelination, and axonal damage. These microstructural alterations increase the Brownian motion More Details of water molecules and lead to increased diffusivity and decreased anisotropy., Consistent with this premise, in our diffusion analysis, we observed increase in MD, AxD, and RD and decrease in FA in the subcortical regions of HD brain. Interestingly enough, these differences were also observed between S-HD and P-HD brains. Once validated in a larger cohort, these underlying structural changes could be targeted to develop imaging markers to monitor the onset of HD symptoms in patients.
Furthermore, it is well documented that axonal demyelination could lead to dysfunctions in the optic pathway conduction from the retina to the occipital cortex. This is usually observed as prolonged latencies in VEP in HD patients. In our data, however, despite observing moderate changes in tNAA and tCho levels between P-HD and controls, their P100 latencies were comparable. This could either be due to the low number of samples in the P-HD group or due to the absence of dysfunction in optic pathway conduction in the presymptomatic stages of HD. Additional work needs to be done to address each of the above possibilities.
In addition, we also observed neuronal degeneration and subsequent atrophy in the brain stem, which is known to regulate several basic functions including cardiac function, respiration, and vasodilation. Notably, HD patients usually succumb to heart failure, pneumonia, and infection, none of which was examined in our patient cohort due to lack of enough follow-up time. In a subsequent study, it will be interesting to investigate these in greater detail.
Although the present study is from the Indian population, our results correlate with the cross-sectional and longitudinal studies on HD patients from other populations, elsewhere in the world. Our results correlate with the multicenter analysis such as the European PADDINGTON study, which proposed use of caudate atrophy, ventricular expansion, and diffusivity as sensitive biomarkers., Similar multicenter studies from PREDICT-HD and TRACK-HD constituting HD patients from the United States, Canada, Australia, and the United Kingdom have also validated volumetric analysis of subcortical and cortical regions as structural imaging markers in HD. An 18-month and 30-month IMAGE-HD study on premanifest HD and early-symptomatic HD patients revealed longitudinal caudate and putamen atrophy as a suitable biomarker for clinical trials., Together, these multicenter studies reconfirm our findings on HD patients.
Taken together, our study provides clinically relevant insights into structural, functional, and metabolic features of HD brain, albeit using a small sample size. Nevertheless, this is the first study in the Indian population to use multimodal imaging on P-HD and S-HD brains and to compare the findings with the controls brain examined under identical conditions. In addition to key changes between HD and control brain, our findings also reveal intriguing changes between P-HD and control brain revealing structural and molecular changes associated with CAG expansion. In addition, changes between S-HD and P-HD brains suggest key alterations that precede the manifestation of clinical HD symptoms, some of which could be further developed into imaging markers to monitor the progression of HD in individuals harboring CAG repeats. The development of such monitoring tools will in turn allow testing of therapeutic approaches that can delay the onset of clinical symptoms in these individuals.
We thank all the patients, their family members, and healthy individuals for consenting to carry out the study. We thank Prof. Arun Sreekumar from Baylor College of Medicine, Houston, Texas, USA, for valuable insights throughout the project and for reviewing the manuscript. We thank Mr. Ritul Kamal from the Department of Statistics, University of Lucknow, India, for assisting in statistical analysis. We thank all doctors, technical team, managers, and office executives of Neurology, Neurosurgery, Radiology, Phlebotomy, Biochemistry, Registration, and Information Systems departments of Sri Sathya Sai Institute of Higher Medical Sciences (SSSIHMS), Bengaluru, for their immense support throughout the project. We thank the Director and his office staff, SSSIHMS, Bengaluru. We thank the Sri Sathya Sai Institute of Higher Learning and SSSIHMS for providing support, funding, and facilities. We also thank Sri Sathya Sai Central Trust for providing Central Research Instruments Facility, SSSIHL, Puttaparthi, India.
This study was approved by the Ethics committee of Sri Sathya Sai Institute of Higher Learning (SSSIHL) and has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.
Consent to participate
Every participant provided informed written and video consent to participate in the study and use their anonymized medical and clinical data for research purposes and to publish the study findings.
Financial support and sponsorship
We acknowledge the support of the Science and Engineering Research Board, Department of Science and Technology (EMR/2017/005381); Basic Scientific Research, University Grants commission (F.25-1/2013-14(BSR)/7-164/2007(BSR)); Bioinformatics Infrastructure Facility, Department of Biotechnology (BT/BI/25/063/2012); Fund for Improvement of S and T Infrastructure, Department of Science and Technology (SR/FST/LSI-616/2014); Special Assistance Programme, University Grants Commission (F.3-19/2018/DRS-III (SAP-II)); and Basic Research in Modern Biology, Department of Biotechnology (BT/PR8226/BRB/10/1224/2013).
Conflicts of interest
There are no conflicts of interest.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2]