Metabolic encephalopathy is a diffuse brain dysfunction resulting from systemic metabolic disturbances rather than structural brain lesions.1 It encompasses a wide range of reversible and potentially treatable neurological impairments caused by metabolic derangements, including hepatic failure, uremia, hypoxia, electrolyte imbalances, and sepsis. These conditions can impair normal brain function by disrupting neurotransmission, cellular metabolism, and neuronal activity, leading to altered mental status, confusion, and even coma in severe cases.2 Electroencephalography (EEG) plays a pivotal role in the evaluation and management of metabolic encephalopathy by providing real-time insights into brain electrical activity.3 EEG is a non-invasive, widely used diagnostic tool that can detect functional changes in the brain even before clinical symptoms manifest. In metabolic encephalopathy, the EEG often reveals generalized slowing, triphasic waves, and diffuse background abnormalities, which help in both diagnosis and monitoring of disease progression.4 The pathophysiology underlying the EEG alterations in metabolic encephalopathy involves disruption of cortical and subcortical neuronal function due to the accumulation of neurotoxic metabolites.5 For example, in hepatic encephalopathy, elevated ammonia levels impair astrocytic regulation of neurotransmitter balance, leading to generalized brain dysfunction. Similarly, in uremic encephalopathy, retention of nitrogenous waste products and oxidative stress contribute to neurotoxicity and altered EEG patterns.6 Clinically, metabolic encephalopathy presents with a spectrum of symptoms, ranging from mild cognitive disturbances to profound coma. Early identification through EEG can be crucial for timely intervention, as it often reflects the severity of the metabolic disturbance and the brain’s functional response. Specific EEG patterns, such as triphasic waves, can be highly suggestive of certain metabolic conditions like hepatic encephalopathy, whereas diffuse slowing may indicate more generalized metabolic derangement.7 EEG findings also provide significant prognostic value in patients with metabolic encephalopathy. Studies have demonstrated that patients with severe EEG abnormalities, such as burst suppression patterns or non-reactive tracings, often have poorer neurological outcomes. Therefore, EEG not only aids in diagnosis but also serves as a valuable tool for monitoring therapeutic response and predicting recovery.8 Differentiating metabolic encephalopathy from structural brain lesions is a key diagnostic challenge in clinical neurology. While structural lesions typically produce focal EEG abnormalities, metabolic encephalopathy results in more generalized patterns of dysfunction. This distinction is essential for guiding appropriate treatment strategies, as metabolic encephalopathy is often reversible with prompt correction of the underlying disorder.9

Study Setting and Design This study was conducted in the Department of Medicine at Gandhi Medical College and its associated Hamidia Hospital, Bhopal, Madhya Pradesh. The study was carried out over a period of 18 months and followed a prospective observational study design. Ethical Considerations Ethical clearance was obtained from the institutional ethical and scientific committee before the study commenced. Written informed consent was obtained from all participants before their enrollment in the study. Patient Enrollment and Data Collection Patients who met the inclusion criteria were enrolled in the study. A detailed medical history was recorded, and a thorough clinical examination was conducted for all participants. Baseline investigations included Complete Blood Count (CBC), platelet count, liver function test (LFT), renal function test (RFT), prothrombin time/international normalized ratio (PT/INR), random blood sugar (RBS), HbA1c, serum electrolytes, urine routine and quantification, and arterial blood gas (ABG) analysis. Imaging studies such as ultrasound (USG) of the whole abdomen, computed tomography (CT) scan of the head, and magnetic resonance imaging (MRI) of the brain were performed. Patients with abnormal findings in any of these tests underwent electroencephalography (EEG) for further evaluation. Sample Size Calculation The sample size was determined based on statistical calculations considering an expected prevalence of 10%, a confidence level of 95%, and an acceptable margin of error of 8%. Using these parameters, the estimated sample size was calculated to be 50 patients. Inclusion Criteria Patients fulfilling the following criteria were included in the study: • Age > 18 years and willing to participate. • Diagnosed with hepatic encephalopathy. • Diagnosed with uremic encephalopathy. • Diagnosed with hypoxic encephalopathy. • Diagnosed with CO₂ narcosis leading to encephalopathy. • Diagnosed with dyselectrolytemia leading to encephalopathy. • Diagnosed with hypoglycemic or hyperglycemic encephalopathy. • Patients admitted with septic encephalopathy. Exclusion Criteria Patients meeting any of the following criteria were excluded from the study: • Age < 18 years or unwilling to participate. • Patients admitted with head injury. • Patients admitted with poisoning. • Patients admitted with stroke. Statistical Analysis All collected data were systematically recorded and analyzed. Quantitative variables were expressed as mean and standard deviation (SD), while qualitative data were represented as numbers and percentages. Statistical tests were applied as appropriate using IBM SPSS version 27. A p-value of <0.05 was considered statistically significant.

Sociodemographic Characteristics The study included 50 patients diagnosed with metabolic encephalopathy. The majority of the patients (40%) were in the 41–50 years age group, followed by 32% in the 31–40 years category. Only 10% were aged 21–30 years, while 18% were older than 50 years. The male-to-female ratio was 1.63:1, with 62% being male and 38% female, indicating a male predominance, possibly due to a higher prevalence of risk factors such as liver disease, chronic kidney disease, and alcohol use in men. Table 1: Sociodemographic Characteristics of Patients Parameter Category Frequency Percentage (%) Age (years) 21–30 5 10.0 31–40 16 32.0 41–50 20 40.0 >50 9 18.0 Gender Male 31 62.0 Female 19 38.0 Laboratory Parameters Laboratory investigations revealed a broad spectrum of metabolic disturbances. The mean total bilirubin was 3.734 ± 4.2946 mg/dL (range: 0.6–25.7 mg/dL), suggesting hepatic dysfunction in a subset of patients. SGOT and SGPT levels were mildly elevated (53.54 ± 19.08 IU/L and 45.24 ± 17.58 IU/L, respectively). The mean prothrombin time (PT) was 21.14 ± 4.72 seconds, with an INR of 1.3246 ± 0.49187, indicating possible coagulopathy. Renal function tests showed a wide range of urea (26–224 mg/dL) and creatinine (0.8–8.4 mg/dL) levels, reflecting variable degrees of kidney dysfunction. Electrolyte imbalances were evident, with sodium ranging from 112 to 160 mEq/L (mean: 135.02 ± 7.18) and potassium from 3.0 to 5.2 mmol/L (mean: 3.97 ± 0.67). These metabolic derangements underscored the heterogeneous nature of metabolic encephalopathy. Neuroimaging and EEG Findings MRI could be done in 1 patient only and MRI changes were suggestive of metabolic encephalopathy in that patient (2%), with the remaining 98% of patients could not be taken for MRI because of hemodynamic instability, which restricted radiological correlation among patients with positive EEG findings. EEG abnormalities were observed in 84% of cases, with generalized slow activity being the most frequent pattern (46%), followed by triphasic waves (20%). Other notable abnormalities included paroxysmal delta activity (8%) and alpha coma with burst suppression (2%), a severe encephalopathic pattern. These findings demonstrated the utility of EEG in assessing disease severity. Etiology and Severity of Metabolic Encephalopathy The most common cause was hepatic encephalopathy (40%), followed by uremic encephalopathy (26%) and septic encephalopathy (20%). Less frequent causes included hypoglycemic, hypercapnic/hypoxic, hypernatremic, and hyponatremic encephalopathy (2–4% each). Association Between EEG Results and Etiology of Encephalopathy There was a significant association (p < 0.001) between EEG patterns and the type of metabolic encephalopathy. Generalized slow activity was the most common abnormality across all types, while triphasic waves were frequently observed in uremic encephalopathy (7 cases). Alpha coma with burst suppression was noted in a case of hypercapnic/hypoxic encephalopathy. These findings emphasized the potential role of EEG in differentiating metabolic encephalopathy subtypes. Table 1: Showing distribution of patients as per the EEG results among etiologies of Encephalopathy Etiology of Encephalopathy Total P value HE HHE HyE HypoE HypoNE SE UE EEG Abnormal-Alpha Coma with Burst Suppression Pattern 0 1 0 0 0 0 0 1 <0.001 Abnormal-Generalized Slow Activity 10 1 0 1 0 6 5 23 Abnormal-Generalized Slow Activity with Epileptiform Sharp Wave Bursts 0 0 0 0 1 0 0 1 Abnormal-Generalized Slow Activity with Paroxysmal Delta Activity 2 0 0 1 0 1 0 4 Abnormal-Generalized Slow Activity with Triphasic Waves 1 0 1 0 1 0 7 10 Abnormal-Progressive Generalized Slow Activity 0 0 0 0 0 0 1 1 Abnormal-Progressive Generalized Slow Activity with Right Temporal Focal Epileptiform Discharges 0 0 0 0 0 1 0 1 Abnormal-Progressive Generalized Slow Activity with Firda 1 0 0 0 0 0 0 1 Normal 6 0 0 0 0 2 0 8 Total 20 2 1 2 2 10 13 50 HE: Hepatic Encephalopathy, HHE: Hypercapnic / Hypoxic Encephalopathy, HyE: Hypernatremic Encephalopathy; HypoE: Hypoglycemic Encephalopathy; HypoNE: Hyponatremic Encephalopathy, SE: Septic Encephalopathy, UE: Uremic Encephalopathy

In our study, the majority of patients with metabolic encephalopathy (ME) were within the 41–50 years age group (40%), followed by the 31–40 years group (32%). This middle-aged predominance aligns with findings from Koul et al. 10, where the mean age of patients with hepatic encephalopathy was 48.34 years. The vulnerability in this age group likely reflects the natural history of metabolic disorders such as liver failure, diabetes, and renal dysfunction, which often manifest clinically in middle age. In contrast, Kadivar et al.11 reported minimal EEG abnormalities in neonates with metabolic disorders, emphasizing that EEG patterns and etiologies vary significantly across the lifespan. Similarly, Wang et al.12 noted that encephalopathy in pediatric populations is more commonly associated with infections and immune responses, leading to distinct sequelae such as developmental delay. These findings suggest that metabolic encephalopathy in middle-aged adults results from cumulative metabolic insults, whereas pediatric cases arise from distinct etiopathogenetic mechanisms. Our study also observed a male predominance (62%), consistent with Koul et al.10, who reported that 84% of hepatic encephalopathy patients were male. This sex-based disparity may be due to a higher prevalence of risk factors such as chronic liver disease and alcohol abuse in men. Sutter et al. 13 further associated delta activity and triphasic waves on EEG with alcohol use and liver failure. However, Demir et al.14 did not find significant sex-based differences in EEG abnormalities, suggesting that while the prevalence of encephalopathy is higher in males, the EEG characteristics remain similar between sexes. These findings emphasize the need for clinicians to consider gendered epidemiology when evaluating EEG manifestations of metabolic encephalopathy. Our study's laboratory parameters reflected varying degrees of hepatic and renal dysfunction among ME patients. The mean total bilirubin level was 3.73 mg/dL, reaching as high as 25.7 mg/dL, indicating hepatic involvement in a subset of patients. Mild elevation in SGOT and SGPT levels further supported this finding. Similar patterns were reported by Koul et al.10, who found a strong correlation between worsening liver function and poor EEG grades. Additionally, the elevated mean creatinine level (2.77 mg/dL) and urea (87.7 mg/dL) in our cohort indicated renal failure as a significant contributor. These findings are consistent with Demir et al.14, who reported that renal failure commonly presents with diffuse EEG slowing and altered baseline rhythms. The convergence of hepatic and renal dysfunction underscores the multifactorial pathogenesis of ME in metabolically compromised states. Electrolyte imbalances were a key feature in our study, with sodium levels ranging from severe hyponatremia (112 mEq/L) to hypernatremia (160 mEq/L). The mean serum sodium was 135.02 ± 7.18 mEq/L, suggesting that both extremes contributed to altered sensorium. These findings are consistent with Demir et al.14, who found that hyponatremia and hypernatremia were major contributors to EEG deterioration and consciousness levels. Additionally, the mean hemoglobin level (9.33 g/dL) and platelet count (113,800) suggest a systemic inflammatory or chronic disease backdrop. Coagulopathy, indicated by an elevated INR (mean 1.32) and prolonged PT, was observed in a subset of patients, likely linked to liver dysfunction or sepsis. Wabulya et al. 15 highlighted that patients with white matter lesions often presented with polymorphic delta activity (PDA) on EEG, further supporting the role of biochemical disturbances in influencing EEG and consciousness in ME. Only 4% of patients in our cohort had ABG findings suggestive of CO₂ narcosis or hypoxia, indicating that respiratory failure was not a dominant contributor to encephalopathy in this population. This contrasts with hepatic or sepsis-related encephalopathy, where hypoxia and altered ventilation often exacerbate neurological dysfunction. Stewart et al. 13 highlighted that cerebral hypoperfusion and rising delta power in EEG correlated with advancing hepatic encephalopathy stages. However, the absence of significant hypoxic findings in our study suggests that metabolic or biochemical factors were the primary drivers of encephalopathy rather than ventilatory compromise. MRI could be done in 1 patient only and MRI changes were suggestive of metabolic encephalopathy in that patient (2%), with the remaining 98% of patients could not be taken for MRI because of hemodynamic instability, which restricted radiological correlation among patients with positive EEG findings. Demir et al. 14 concluded that EEG findings correlate better with disease severity than MRI in ME. Wabulya et al. 15 reported that while EEG abnormalities were prevalent in over 96% of encephalopathic cases, MRI findings were often nonspecific, reinforcing the superior diagnostic yield of electrophysiology over imaging in diffuse metabolic insults. Conversely, Tomkins et al. 16 reported a case of metabolic encephalopathy due to diabetic ketoacidosis (DKA) with bilateral temporal lobe MRI changes, suggesting that while MRI findings may emerge in select cases, they are not the primary diagnostic modality for ME. Ultrasonography (USG) revealed chronic liver disease (CLD) in 42% and chronic kidney disease (CKD) in 20% of patients, with only 2% showing normal sonographic findings. These results indicate that USG was more sensitive than MRI in identifying systemic contributors to encephalopathy. Sutter et al. 13 found that triphasic waves on EEG were strongly associated with liver failure and multi-organ dysfunction, underscoring the clinical relevance of identifying CLD via imaging. Demir et al. 14 similarly identified hepatic and renal failure as leading etiologies in ME, supporting the notion that systemic dysfunction is the primary driver of encephalopathy. EEG abnormalities were observed in 84% of our ME patients, with generalized slow activity being the most common pattern (46%), followed by triphasic waves (20%). These findings highlight the critical role of EEG in assessing both the presence and severity of encephalopathy. Demir et al. 14 reported similar patterns, noting that reduced alpha activity, triphasic waves, and delta bursts were common in hepatic and renal dysfunction, correlating closely with consciousness deterioration. Sutter et al. 13 emphasized the prognostic significance of specific EEG patterns, linking triphasic waves with higher mortality risk (OR 4.5). One patient in our cohort exhibited an alpha coma pattern with burst suppression, a rare but severe EEG finding associated with poor outcomes, as described by Koutroumanidis et al. 17 in cases of COVID-19-related encephalopathy. Hepatic encephalopathy was the most prevalent form of ME in our study (40%), followed by uremic (26%) and septic (20%) encephalopathy. This distribution aligns with previous literature where liver and renal dysfunction are primary contributors to metabolic brain injury. Koul et al. 10 found that severe EEG abnormalities correlated with higher hepatic encephalopathy grades, reinforcing the prognostic value of EEG in disease severity assessment. Our findings support the growing consensus that EEG is indispensable in evaluating encephalopathy across a spectrum of metabolic derangements, particularly when biochemical and imaging markers may not fully capture clinical deterioration.

Our study highlights the clinical spectrum, diagnostic challenges, and neurophysiological patterns associated with metabolic encephalopathy. EEG emerged as a valuable, non-invasive tool for detecting and monitoring encephalopathy. The data highlight the importance of integrating clinical, biochemical, and electrophysiological findings for accurate diagnosis and management. The predominance of hepatic, uremic, and septic etiologies reflects the systemic burden of chronic diseases, and the grading of encephalopathy effectively stratified patients by severity. These insights contribute to a better understanding of the pathophysiology and presentation of metabolic encephalopathy in real-world hospital settings.

1. Bathla G, Hegde A. MRI and CT appearances in metabolic encephalopathies due to systemic diseases in adults. Clin Radiol. 2013;68(6):545–54. 2. Angel MJ, Young GB. Metabolic encephalopathies. Neurol Clin. 2011;29(4):837–82. 3. Rosenberg S, Périn B, Michel V, Debs R, Navarro V, Convers P. EEG in adults in the laboratory or at the patient’s bedside. Neurophysiol Clin Neurophysiol. 2015;45(1):19–37. 4. Niedermeyer E. 22. Metabolic Central Nervous System Disorders. Electroencephalogr Basic Princ Clin Appl Relat Fields. 1982;305. 5. Korein J, Sansaricq C, Kalmijn M, Honig J, Lange B. Maple syrup urine disease: clinical, EEG, and plasma amino acid correlations with a theoretical mechanism of acute neurotoxicity. Int J Neurosci. 1994;79(1–2):21–45. 6. Hamed SA. Neurologic conditions and disorders of uremic syndrome of chronic kidney disease: presentations, causes, and treatment strategies. Expert Rev Clin Pharmacol. 2019;12(1):61–90. 7. Saunders MG. EEG changes in metabolic disorders. Am J EEG Technol. 1968;8(2):41–58. 8. Muhlhofer W, Szaflarski JP. Prognostic value of EEG in patients after cardiac arrest—an updated review. Curr Neurol Neurosci Rep. 2018;18:1–15. 9. Awal MA, Lai MM, Azemi G, Boashash B, Colditz PB. EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: A structured review. Clin Neurophysiol. 2016;127(1):285–96. 10. Koul R, Maiwall R, Ramalingam A, Kumar S, Dhamija RM, Pamecha V, et al. Role of EEG in predicting outcome of hepatic encephalopathy patients. Neurodiagnostic J. 2020;60(4):272–88. 11. Kadivar M, Mosayebi Z, Badoo RS, Sangesari R, Attari SJ, Saeedi M, et al. Evaluation of the efficacy of amplitude-integrated electroencephalography in the screening of newborns with metabolic disorder admitted to the NICU. BMC Med Imaging. 2018;18:1–5. 12. Wang IJ, Lee PI, Huang LM, Chen CJ, Chen CL, Lee WT. The correlation between neurological evaluations and neurological outcome in acute encephalitis: a hospital-based study. Eur J Paediatr Neurol. 2007;11(2):63–9. 13. Sutter R, Stevens RD, Kaplan PW. Clinical and imaging correlates of EEG patterns in hospitalized patients with encephalopathy. J Neurol. 2013;260:1087–98. 14. Demir AB, Bora I, Kaygili E, Ocakoglu G. The assessment of basic features of electroencephalography in metabolic encephalopathies. J Neurol Res. 2014;4(4):101–9. 15. Wabulya A, Lesser RP, Llinas R, Kaplan PW. Electroencephalography and brain MRI patterns in encephalopathy. Clin EEG Neurosci. 2016;47(2):150–6. 16. Tomkins M, McCormack R, O’Connell K, Agha A, Merwick Á. Metabolic encephalopathy secondary to diabetic ketoacidosis: a case report. BMC Endocr Disord. 2019;19:1–8. 17. Koutroumanidis M, Gratwicke J, Sharma S, Whelan A, Tan SV, Glover G. Alpha coma EEG pattern in patients with severe COVID-19 related encephalopathy. Clin Neurophysiol. 2021;132(1):218–25.