---
title: "Using a Genetic Inflammatory Index and Prakriti Stratification to Predict Inflammaging Risk"
id: "frontiers-in-immunology-17-predicting-susceptibility-to-inflammaging-using-a-genetic-inflammatory-index"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-17-predicting-susceptibility-to-inflammaging-using-a-genetic-inflammatory-index"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1854940"
published_at: "2026-07-29T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Using a Genetic Inflammatory Index and Prakriti Stratification to Predict Inflammaging Risk
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-17-predicting-susceptibility-to-inflammaging-using-a-genetic-inflammatory-index
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1854940)
- **Published At:** 2026-07-29T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The source article title indicates an investigation of predicting susceptibility to **inflammaging** using a **genetic inflammatory index** combined with **Prakriti**-based phenotypic stratification. The work was published in Frontiers in Immunology. - The title implies an integrative approach linking genomic markers of inflammation with an Ayurvedic-type phenotypic classification (Prakriti) to identify individuals at higher risk of age-related chronic inflammation. - No specific methods, cohort characteristics, gene panels, index construction, statistical analyses, effect sizes, or results were reported in the provided source text. Details about validation, predictive performance, or clinical recommendations were not available in the source material. - The approach described would be relevant to clinicians and researchers interested in biomarkers of aging immunity, personalized risk stratification, and potential implications for infection risk or chronic inflammatory disease in older adults. - Because the supplied source body lacked the article content, the precise conclusions, limitations, and next steps reported by the authors are unknown and are explicitly not reported here.
## Clinical Analysis & Structured Key Points
Frontiers | Predicting susceptibility to inflammaging using a genetic inflammatory index through Prakriti-based phenotypic stratification 0) genetic predisposition, while Kapha dominant constitutions are hypothesized to possess an anti-inflammatory (GII HYPOTHESIS AND THEORY article Front. Immunol. , 29 July 2026 Sec. Systems Immunology Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1854940 Published in Frontiers in Immunology Systems Immunology 7 impact factor 11.3 citescore Part of a Research Topic Epigenetic Clocks and Biomarkers of Aging Submission open 4442 views 2 articles Editor & Reviewers Edited by L X Li Xiangwei Reviewed by P G Parvathy G Nair L R Liu Rundong Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Table 1 Proposed representative mapping between doshas and cytokines for calculating prakriti genetic vector . View in article Table 2 Theoretical worked examples of GII calculation for extreme constitutional types. View in article HYPOTHESIS AND THEORY article Front. Immunol. , 29 July 2026 Sec. Systems Immunology Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1854940 Predicting susceptibility to inflammaging using a genetic inflammatory index through Prakriti-based phenotypic stratification N B Neha Bera 1 A G Agaje Gowri S 1 N S Nousheen Syed 2 V G Varadhi Govinda 3 A K A. Karteek Rao 3 J J Jahnavi Jeeru 4 K K Krishna Kurthkoti 1 K N Kamatham Narayanaswamy 2 S R Surendar R. Jakka 5 * A K Akhouri Kishore Raghawan 6 * P N Pratibha Nair 7 * +3 more B O Baswanth Oruganti 2,8 * 1. School of Biosciences, Chanakya University, Bengaluru, Karnataka, India 2. Department of Chemistry, SRM University-AP, Amaravati, Andhra Pradesh, India 3. Department of Organic Chemistry, Gayatri Vidya Parishad College for Degree and PG Courses (A), Visakhapatnam, Andhra Pradesh, India 4. Department of Chemistry, GITAM Institute of Science, GITAM (deemed to be University), Visakhapatnam, Andhra Pradesh, India 5. Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bengaluru, Karnataka, India 6. Dana-Farber Cancer Institute, Boston, MA, United States 7. Department of Kayachikitsa, VPSV Ayurveda College, Kottakkal, Kerala, India 8. Department of Mathematics and Mathematical Statistics, Umea˚ University, Umea, Sweden See more Article metrics View details Abstract Inflammaging is chronic low-grade inflammation arising from a progressive imbalance between pro-inflammatory and anti-inflammatory immune networks. The rate of inflammaging exhibits marked interindividual variation owing to differences in the pace of biological aging. Within the two-hit theory of inflammaging, such variability involves both genetic and epigenetic components. Stratification models derived from quantification of the genetic component allow classification of individuals according to their baseline genetic inflammatory tone. Such phenotypic stratification could facilitate informed decision-making in prioritizing medical care and vaccination strategies during public healthcare emergencies, including epidemics and pandemics. To this end, this paper develops a novel conceptual framework that predicts susceptibility to inflammaging based on an individual’s unique constitutional phenotype, termed Prakriti. Based on the Prakriti-based phenotypic stratification rooted in Ayurveda (Indian traditional medicine) and emerging ayurgenomics evidence suggesting genotype-phenotype correlations, we hypothesize that Prakriti constitutes a phenotypic biomarker of genetic inflammatory tone. Specifically, we introduce a mathematical model that quantifies the baseline inflammatory tone as the Genetic Inflammatory Index (GII), a measure of constitutional inflammatory architecture derived from cytokine single nucleotide polymorphism (SNP) scores. Based on the sign of GII, we hypothesize that Vata and Pitta dominant constitutions carry a pro-inflammatory (GII > 0) genetic predisposition, while Kapha dominant constitutions are hypothesized to possess an anti-inflammatory (GII < 0) genetic resilience. Accordingly, within age-matched cohorts exposed to broadly comparable environmental conditions, Vata and Pitta Prakriti individuals are predicted to exhibit greater epigenetic age acceleration than Kapha Prakriti individuals. Finally, we outline experimental strategies to test these hypotheses using epigenetic clocks in Prakriti-stratified cohort studies. 1 Introduction During the COVID-19 pandemic, one issue that garnered substantial interest was identifying risk factors for progression of the COVID-19 disease to critical stages that involves hyperinflammatory immune response against the infection, termed as cytokine storm. While aging and age-related chronic diseases such as type 2 diabetes (T2D), obesity, hypertension, atherosclerosis, cancer, cardiovascular and autoimmune diseases were identified as some key risk factors ( 1 , 2 ), it has become clear that the underlying mechanism that interconnects such chronic diseases with COVID-19 pathogenesis is a process known as inflammaging ( 3 – 7 ). Inflammaging is an increased chronic low-grade inflammatory state of the innate immune system that occurs with age, primarily characterized by an imbalance between production of pro-inflammatory and anti-inflammatory cytokines by the innate immune system. Counteracting such imbalance facilitates longevity in centenarians, which has been associated not with the absence of pro-inflammatory cytokines but with a dynamic balance between elevated IL-6, TNF- α , and IFN- α and compensatory increases in anti-inflammatory mediators such as IL-19 ( 8 ). The term inflammaging was first coined by Claudio Franceschi et al. ( 4 ). Inflammaging constitutes a threshold in the pro-inflammatory state associated with pathological aging, the rate of reaching which is governed by two factors: the absence of robust gene alleles or presence of frail gene alleles, and persistent cumulative exposure to inflammatory stimuli over time ( 3 , 4 ). Consequently, individuals possessing frail gene alleles or those exposed to a high inflammatory burden are expected to exhibit increased susceptibility to pathological aging and age-related chronic diseases, as illustrated in Figure 1 . Inflammaging can therefore be regarded as a manifestation of accelerated aging that reduces lifespan. The emerging field of geroscience accordingly aims to counteract age-related diseases collectively, by identifying and targeting fundamental mechanisms of inflammaging rather than addressing individual diseases in isolation. Figure 1 The inflammaging threshold model proposed by Franceschi et al. ( 4 ). The rate at which an individual reaches the pro-inflammatory threshold — determined by genetic makeup and cumulative exposure to inflammatory stimuli — predicts susceptibility to chronic age-related diseases and dictates lifespan. Individuals with frail gene alleles and high inflammatory exposure reach the threshold earlier (accelerated aging), while those with robust alleles and low exposure age more slowly. The plots were generated using hypothetical data following the inflammaging threshold model by Franceshi et al. ( 4 ). Understanding and predicting interindividual variation in susceptibility to chronic age-related diseases is a central theme of P4 medicine — predictive, preventative, personalized, and participatory medicine ( 9 , 10 ). P4 medicine rests on the premise that each person’s health is unique and that uniform approaches to prevention and treatment are inherently inadequate. Two prominent frameworks within P4 medicine are pharmacogenomics ( 11 , 12 ) and ayurgenomics ( 13 – 15 ). Pharmacogenomics quantifies interindividual variation in drug responses as a function of genetic polymorphism in drug-metabolizing and transporting enzymes. Ayurgenomics, in contrast, is primarily concerned with predicting susceptibility to chronic age-related diseases as a function of phenotypic variation — termed Prakriti in Ayurveda — by correlating it with genotypic variation ( 13 – 15 ). Ayurveda is a system of personalized traditional medicine practiced in India for over 3,500 years that classifies individuals into primarily three Prakriti types — Vata, Pitta, and Kapha — characterized by specific phenotypic attributes known as Gurvadi Gunas. These attributes act as foundational parameters that manifest as observable variations in metabolism (Agni) and immune resilience (Ojas). The present hypothesis paper integrates insights from inflammaging, ayurgenomics, and Ayurveda to develop a conceptual framework for predicting susceptibility to inflammaging through a genotype-Prakriti correlation in inflammation. By proposing a mathematical model quantifying cumulative single nucleotide polymorphisms (SNPs) in genes associated with pro-inflammatory and anti-inflammatory cytokines, we derive a Genetic Inflammatory Index (GII) that correlates interindividual variations in baseline genetic inflammatory tone with differences in Prakriti in Ayurveda. Specifically, we hypothesize that distinct Prakriti types exhibit systematic differences in baseline inflammatory tone, with Vata and Pitta individuals being more susceptible to inflammaging than Kapha, which can be validated in terms of differences in epigenetic age acceleration. The paper is organized as follows. Section 2 discusses the causes and stimuli that drive inflammaging, the two key mechanisms of inflammaging: epigenetic and immunometabolic reprogramming, and epigenetic clocks that quantify these changes. Section 3 presents the concept of Prakriti in Ayurveda, and discusses the Ayurvedic perspective on aging. Section 4 integrates these insights into a set of novel hypotheses connecting Prakriti and inflammaging, and Section 5 outlines experimental strategies to test the hypotheses, and the final section presents an integrated discussion, and identifies limitations of the proposed model. 2 Causes, mechanisms and biomarkers of inflammaging 2.1 Inflammatory stimuli and processes Stimuli that drive inflammaging can be broadly classified into three categories: self (endogenous), quasi-self, and non-self (exogenous), all of which are interconnected in a complex interplay between two mechanisms: epigenetic and immunometabolic reprogramming, as illustrated in Figure 2 . Some key processes that contribute to or regulate inflammaging include oxidative stress ( 16 – 18 ), cellular senescence ( 19 , 20 ), immunosenescence ( 21 , 22 ), metaflammation ( 23 – 26 ), and trained immunity ( 27 – 30 ), many of which span more than one category. However, in the following discussion, for the sake of brevity of presentation, we categorize each process under only one of the classes and limit our discussion to those processes that have been extensively studied in the context of inflammaging. Figure 2 Stimuli, processes, and mechanisms of inflammaging. Self, non-self, and quasi-self stimuli contribute to inflammaging through induction of cellular senescence, immunosenescence, trained immunity, and metaflammation. Self-stimuli include cellular garbage such as damaged DNA, protein aggregates, and reactive oxygen species (ROS); non-self stimuli include pathogens, vaccines, and environmental pollutants; whereas quasi-self stimuli arise from metabolic perturbations associated with nutrient excess causing adipocyte hypertrophy and inflamed adipose tissue. These processes are regulated by two central mechanisms, namely epigenetic and immunometabolic reprogramming. 2.1.1 Self stimuli and related processes 2.1.1.1 Cellular garbage, oxidative stress, and cellular senescence Cellular garbage refers to a wide range of cellular components including damaged DNA, misfolded proteins, dysfunctional mitochondria or organelles, and other cellular components that are no longer functioning properly. These stressors constitute damage-associated molecular patterns (DAMPs) that are recognized by pattern recognition receptors (PRRs), such as Toll-like receptor (TLRs) and NOD-like receptors (NLRs), of the innate immune cells. The accumulation of cellular garbage stimulates the production of reactive oxygen species (ROS) ( 19 , 20 ), including peroxides and free radicals, which contribute to cellular damage and may eventually cause cell death. Oxidative stress occurs when there is an imbalance between the production of ROS and the body’s antioxidant defense mechanisms in neutralizing them to prevent cellular damage. Autophagy and mitophagy are essential self-repair mechanisms that clear cellular garbage and maintain cellular homeostasis. With aging, the production of cellular garbage increases, while the efficiency of these mechanisms decreases. Furthermore, the expression of antioxidant enzymes, such as glutathione peroxidase, superoxide dismutase and catalase, diminishes with age ( 17 ). This cumulative dysfunction leads to impaired clearance of cellular garbage and sustains oxidative stress, which triggers the activation of the NLR family pyrin containing domain 3 (NLRP3) inflammasome ( 17 , 31 ). Upon activation, the NLRP3 inflammasome promotes the activation of caspase-1, resulting in the maturation and release of pro-inflammatory cytokines, including IL-1 β and IL-18 ( 31 ), which exacerbate tissue damage. Oxidative stress also triggers cellular senescence, a state in which cells are no longer able to divide and function normally. Senescent cells exhibit a unique secretory phenotype (SASP) ( 32 , 33 ), which promotes inflammation through the production of pro-inflammatory cytokines such as IL-6, IL-1 β , and TNF- α , chemokines, growth factors, and proteases ( 32 , 33 ). 2.1.1.2 Immunosenescence Immunosenescence is a remodeling of the functions of the immune system with age, leading to ineffective or delayed responses against pathogens, and reduced response to vaccination in older individuals ( 21 , 22 , 34 ). While inflammaging involves increased pro-inflammatory activity of the innate immune cells, such as monocytes and macrophages, immunosenescence is characterized by a decrease in the effector functions of these cells, such as phagocytosis, free-radical production, chemotaxis, and antigen-presenting capacity ( 21 , 22 , 34 , 35 ). Although an initial anti-inflammatory burst is triggered in response to the pro-inflammatory activity, it is often not sustained or is inefficient in older individuals to completely resolve the inflammation. Overall, inflammaging and immunosenescence collectively sustain an increased baseline threshold level of inflammation in the elderly to ensure that innate immune system remains alert against any serious pathogen challenge. It was suggested that immunosenescence might be a consequence of the energetic costs associated with maintenance of high intensities in both pro-inflammatory activity and in effector functions in older individuals ( 22 ). As a result, effector functions are compromised at the cost of maintaining high innate immune cell alertness. Such a remodeling or adaptation in innate immune functions might be essential to cope with other age-associated changes in the body to ensure long-term survival ( 22 , 34 ). 2.1.2 Quasi-self stimuli and related processes 2.1.2.1 Nutrients and metaflammation Metaflammation is a state of chronic low-grade inflammation driven by long-term nutrient excess or over-nutrition, observed commonly in endocrine-metabolic disorders such as obesity and T2D ( 24 , 25 , 36 , 37 ). Similar to senescent cells, nutrients present in nutrient-dense or high-fat diets act as DAMPs, and are recognized by TLRs of adipocytes, such as TLR4, leading to the production of pro-inflammatory cytokines such as IL-6 and TNF- α ( 23 , 24 ). Such increase in inflammatory responses upon ingestion of food is termed as post-prandial inflammation. Repeated cycles of post-prandial inflammation might cause adipocyte hypertrophy, which causes low responsiveness to insulin and increase in the release of free fatty acids into the blood. Moreover, hypertrophic adipocytes undergo apoptosis, which leads to the recruitment of immune cells, such as macrophages, to the adipose tissue (AT), which further exacerbates the pro-inflammatory environment of the AT. Thus, metaflammation can be considered as nutrient-excess driven accelerated aging, and metabolic disorders can be thought of as manifestations of metaflammation. 2.1.3 Non-self stimuli and related processes Non-self stimuli include pathogens, pollutants, allergens, and toxins, which possess specific epitopes or patterns, known as pathogen associated molecular patterns (PAMPs), that are recognized by PRRs, such as TLRs or NLRs, of innate immune cells. This recognition triggers acute inflammatory response, leading to production of a variety of cytokines and chemokines by innate immune cells. However, interindividual variability in acute inflammatory response can occur due to factors such as pre-existing chronic inflammatory conditions or involvement of trained immunity ( 38 , 39 ). 2.1.3.1 Interplay between acute and chronic inflammation When an initial acute inflammatory response is sufficient to eliminate a stressor, inflammation subsides due to the release of anti-inflammatory mediators without any involvement of the adaptive immune system. But more commonly, acute inflammation is terminated only after the activation of the adaptive immune system via antigen presentation by innate immune cells, such as macrophages and neutrophils, to naive T-cells. However, as discussed above, immunosenescence diminishes antigen presentation capacity and associated differentiation of naive Tcells into effector T-cells ( 21 , 22 ), thereby reducing the effectiveness of adaptive immune system in resolving inflammation. This eventually results in chronic inflammation contributing to the development of inflammaging and chronic age-related diseases ( 21 , 22 ). On the other hand, existing chronic inflammatory conditions can accentuate the acute inflammatory response against an invading pathogen, and transform it into a hyperinflammatory response leading to cytokine storm, as observed in severe COVID-19 disease ( 40 ). Thus, interplay between acute and chronic inflammation, and inefficiency of counteracting anti-inflammatory networks are key factors in predicting an individual’s susceptibility to age-related chronic diseases. 2.1.3.2 Trained immunity and immunobiography Trained immunity is a memory-like feature displayed by the innate immune system, resulting in a rapid and enhanced immune response upon a secondary infection ( 28 – 30 ). Innate immune memory can also manifest as a diminished immune response upon a secondary infection, referred to as innate immune tolerance ( 41 ). A unique aspect of these two processes is their heterologous nature, wherein enhanced or diminished responses are observed also against pathogens or stimuli that are unrelated to any previous insults. This cross protection is found to be independent of T- and B-lymphocytes of the adaptive arm. Rather, the protective effects are due to enhanced expression of PRRs of innate immune cells and associated increased production of pro-inflammatory cytokines due to epigenetic changes ( 41 ). The concept of trained immunity emerged from studies on non-specific protective effects offered by Bacille Calmette-Guerin (BCG) vaccine against unrelated pathogens ( 42 – 44 ). Specifically, BCG vaccination in healthy individuals was found to modulate the phenotype of circulating monocytes, resulting in enhanced pro-inflammatory cytokine production. Notably, a 4–7 fold increase in the production of IFN- γ , and a two-fold increase in TNF- α and IL-1 β levels were observed. Additionally, it was found that BCG vaccine amplifies pro-inflammatory activity of natural killer (NK) cells against Candida Albicans ( 42 ), through increased cytokine production and cytotoxic activity, even three months after vaccination,
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