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Metabolic disorders, particularly metabolic syndrome (MetS), represent a growing global health burden characterised by obesity, insulin resistance, hypertension, and dyslipidemia. Ayurveda, through its foundational concepts of Agni (digestive and metabolic fire) and Ama (toxic, undigested metabolic residue), provides a holistic framework to understand and manage such conditions. This narrative review integrates classical Ayurvedic perspectives with contemporary biomedical research to explore the applied aspects of Ama in metabolic disorders. Relevant classical texts, including Charaka Samhita, Sushruta Samhita, and Ashtanga Hridaya, alongside modern scientific databases such as PubMed and Google Scholar, were reviewed. Findings reveal that Ama, formed due to impaired Agni, shares pathophysiological similarities with modern concepts such as oxidative stress, gut dysbiosis, and systemic inflammation. The review highlights that chronic Agnimandya (weak digestion) parallels metabolic inefficiency, while Ama-induced srotorodha (obstruction) corresponds to cellular and vascular dysfunction observed in MetS. Ayurvedic management approaches—Deepana, Pachana, Langhana, and Shodhana—align with modern metabolic interventions like calorie restriction and detoxification. Integrating these traditional principles with biomedical understanding may enhance preventive and therapeutic outcomes for metabolic disorders. This review thus bridges ancient Ayurvedic insights with modern metabolic science, offering a unified model of digestion, metabolism, and health restoration.
Keywords:
Ama, Agni, Metabolic Syndrome, Inflammation
Cite Article:
"“Applied Aspects of Ama in Metabolic Disorders: A Narrative Review”", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.11, Issue 1, page no.a561-a566, January-2026, Available :http://www.ijrti.org/papers/IJRTI2601081.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator