Predictive Biomarkers and Mortality Risk in the Geriatric Population Following COVID-19 Booster Vaccination: Insights from Assam, India

Abstract

This study explored how specific biomarkers can help predict disease severity and mortality risk among elderly individuals (?60 years) who received COVID-19 booster vaccinations in Assam, India. Using a cross-sectional, observational approach, the research examined the relationship between various biochemical, hematological, and inflammatory markers and post-vaccination outcomes. Key biomarkers—including C-reactive protein (CRP), interleukin-6 (IL-6), D-dimer, ferritin, lactate dehydrogenase (LDH), and the neutrophil-to-lymphocyte ratio (NLR)—were analyzed for their predictive potential. Statistical analyses revealed IL-6, D-dimer, and NLR as significant independent indicators of poor clinical outcomes. High IL-6 and D-dimer levels were linked to inflammation and clot formation, while an elevated NLR suggested immune imbalance after vaccination. The findings also indicated that vaccine type, age, and existing health conditions such as diabetes and hypertension influenced biomarker levels. Despite the protective effects of booster doses, older adults with pre-existing inflammation and comorbidities showed higher vulnerability to severe outcomes. Incorporating biomarker-based screening into vaccination programs could help identify high-risk individuals, guide timely interventions, and improve healthcare planning. This study offers valuable region-specific insights from Assam, enhancing global understanding of immune responses in the elderly and emphasizing the role of biomarkers in improving COVID-19 booster vaccination outcomes.

Keywords

Introduction

Figure 1. Mode of action of Covid-19 vaccine in elderly patients [7]

1.1 COVID-19 and the Geriatric Vulnerability

The global aging population has amplified concerns regarding the heightened vulnerability of older adults to COVID-19. Individuals aged 65 years and above are particularly susceptible due to immunosenescence, chronic comorbidities, and frequent use of immunosuppressive therapies, which impair both innate and adaptive immunity. Epidemiological data consistently show higher morbidity and mortality in this age group, especially among those with cardiovascular, metabolic, or respiratory disorders. Understanding the interplay between aging physiology and SARS-CoV-2 pathogenesis is critical for developing targeted preventive strategies, optimizing clinical care, and guiding vaccine prioritization in geriatric populations. Beyond physiological susceptibility, older adults face significant psychological challenges during the pandemic. A cross-sectional study in Egypt involving 500 participants revealed that elderly females (≥60 years) experienced the highest anxiety levels and poorest coping capacities, while elderly males showed diminished coping without significant anxiety increases [8]. These findings highlight the disproportionate mental health burden on older women and the need for tailored interventions.

In India, compounded vulnerabilities—including chronic illnesses, sensory impairments, frailty, and immune decline—have led to elevated morbidity and mortality in individuals over 60 years, particularly women. Pandemic-related disruptions in routine healthcare, medication access, and social support, coupled with social isolation, stress, and digital illiteracy, further exacerbated these risks. Mitigating strategies include prioritized diagnostic testing, teleconsultations, community outreach, domiciliary care, and geriatric-focused healthcare training. Strengthening healthcare infrastructure and social safety measures is essential to reduce infection risk, improve clinical outcomes, and safeguard both physical and psychological well-being in elderly populations [9]. This underscores the need for integrated strategies addressing both biomedical and psychosocial vulnerabilities to optimize health outcomes in geriatric communities during the COVID-19 pandemic.

1.2 Rationale for Biomarker-Based Risk Prediction

The COVID-19 pandemic has disproportionately impacted older adults, who experience greater infection severity, hospitalization, and mortality due to immunosenescence and multiple comorbidities. Globally, millions of elderly individuals have contracted SARS-CoV-2, underscoring the urgency of vaccination as a protective measure. Various vaccine platforms—including mRNA, adenoviral vector, and inactivated formulations—have demonstrated acceptable safety and immunogenicity profiles in older populations. However, age-related immune decline and vaccine hesitancy can reduce efficacy. Ensuring timely vaccination coverage and monitoring immune responses in older adults are thus critical for minimizing severe disease and mortality. Systematic evaluation of vaccine performance in this demographic is essential to inform evidence-based vaccination strategies and optimize public health outcomes [10].

Protection against SARS-CoV-2 infection and severe COVID-19 diminishes over time, necessitating personalized booster vaccination strategies. Data from U.S. surveillance and seroprevalence studies indicate that frequent booster doses (every 6–12 months) significantly decrease the risk of severe disease among individuals aged ≥75 years and those who are immunocompromised. In contrast, the benefits of repeated boosting are modest in younger adults (18–49 years) and in those with prior infection. Customizing booster schedules based on age, immune status, and infection history can optimize long-term protection, prevent severe cases, and enhance resource allocation in public health systems [11].

The pandemic has also emphasized the need for comprehensive vaccine evaluation in older adults—a population characterized by wide variability in physiology, comorbidity burden, and frailty. Vaccine safety, efficacy, and immunogenicity must be interpreted within the context of age-related immune alterations, polypharmacy, and individual functional goals such as quality of life and independence. Clinical trials should include representative elderly cohorts, while post-marketing pharmacovigilance programs must monitor adverse events and geriatric syndromes. Regulatory decision-making should integrate both direct evidence from older participants and extrapolated data from younger populations, supported by continuous post-marketing surveillance to ensure vaccine safety and effectiveness in this high-risk group [12].

Furthermore, a structured biomarker-based risk assessment framework is essential for predicting and managing Long COVID in elderly patients. Recent studies have demonstrated a dose–response relationship between spike protein concentrations and symptom severity, including proinflammatory manifestations. Time-dependent modeling incorporating CXCL8 as a sensitive biomarker enables early identification of individuals at higher risk for persistent post-viral symptoms. Such predictive tools facilitate personalized monitoring, early therapeutic interventions, and targeted prevention of chronic sequelae. By addressing spike protein-mediated inflammatory pathways, future interventions may effectively reduce Long COVID burden. Expanding standardized biomarker-based frameworks to include additional mediators could enhance preparedness for post-viral syndromes and improve overall patient outcomes [13].

2. COVID-19 Booster Response and the Aging Immune System

The aging immune system, characterized by immunosenescence, critically influences COVID-19 vaccine efficacy in older adults. Age-related decline in immune competence leads to diminished primary vaccine responses and shorter durations of protective immunity, necessitating booster doses to sustain defense against SARS-CoV-2. Booster-induced immune reactivation enhances both humoral and cellular responses; however, the magnitude and persistence of these responses vary significantly among individuals due to factors such as chronological age, comorbidities, frailty, nutritional status, genetic predisposition, and prior vaccine exposure. Understanding these determinants is essential for optimizing booster strategies and mitigating severe COVID-19 outcomes in the elderly, emphasizing the need for tailored vaccination regimens that accommodate biological heterogeneity [14].

Immunosenescence encompasses progressive alterations in both innate and adaptive immunity, including decreased naïve T and B cell production, impaired antigen presentation, and dysregulated cytokine signaling, collectively compromising vaccine-induced immune memory. Efforts such as adjuvant optimization, dose adjustment, and inclusion of immune-stimulatory components have partially improved vaccine efficacy. Nonetheless, overcoming immunosenescence remains a major challenge. Future vaccine development should prioritize enhancing immunogenicity through innovative adjuvants, mucosal delivery systems, and heterologous booster regimens to strengthen protection and overall vaccine effectiveness in older adults [14].

At the cellular and molecular level, immunosenescence manifests as thymic involution, chronic low-grade inflammation (inflammaging), metabolic reprogramming, and hematopoietic changes that impair immune effector cell function. Thymic involution reduces naïve T cell output, limiting responses to novel antigens, while inflammaging promotes tissue damage and further suppresses adaptive immunity. Functional impairments in macrophages, neutrophils, dendritic cells, NK cells, and B cells lead to delayed pathogen clearance and reduced vaccine responsiveness. Comprehensive understanding of these molecular and cellular mechanisms is crucial for designing next-generation immunotherapies and vaccines capable of counteracting age-related immune decline and enhancing infectious disease protection in elderly populations [15].

3.  Biomarkers as Predictors of COVID-19 Outcomes

Biomarkers are critical tools for predicting COVID-19 outcomes, as they reflect disease severity, immune activation, and organ dysfunction. They can be categorized into inflammatory markers (C-reactive protein [CRP], interleukin-6 [IL-6], tumor necrosis factor-alpha [TNF-α], ferritin), hematological and coagulation indicators (D-dimer, lymphocyte count, platelet indices), metabolic and biochemical parameters (glucose, albumin, renal and hepatic enzymes), and genetic or molecular biomarkers (ACE2 polymorphisms, cytokine gene variants, microRNAs). By integrating these diverse biomarker classes, clinicians can more accurately identify high-risk patients, guide therapeutic decisions, and personalize clinical management, particularly for vulnerable populations such as the elderly [16].

Inflammation plays a central role in COVID-19 pathogenesis, although the predictive value of specific markers is variable. A retrospective study of 244 hospitalized patients revealed that serum ferritin, CRP, D-dimer, lactate dehydrogenase (LDH), and IL-6 were significantly associated with outcomes. Among these, CRP, D-dimer, and IL-6 independently predicted disease severity, while CRP, D-dimer, LDH, ferritin, and neutrophil-to-lymphocyte ratio (NLR) predicted mortality. D-dimer exhibited the highest sensitivity and specificity for severity, whereas LDH correlated most strongly with mortality, highlighting their prognostic utility [16].

Accurate prognostic assessment relies on combining clinical evaluation with serial biomarker monitoring to capture disease progression and multi-organ involvement. Hematological markers (lymphocyte count, NLR), inflammatory mediators (CRP, ESR, procalcitonin), immunological factors (IL-6), and biochemical indicators (D-dimer, troponin, creatine kinase, AST) are particularly informative. Emerging markers, including homocysteine and angiotensin II, show promise in refining risk prediction in severe COVID-19. Pediatric populations with multisystem inflammatory syndrome (MIS-C) also demonstrate characteristic biomarker alterations, underscoring the broad applicability of these tools. Although standardized guidelines for biomarker selection, timing, and interpretation are still evolving, integrating traditional and novel biomarkers can enhance diagnostic precision, stratify patient risk, and inform evidence-based therapeutic strategies [17][18].

4.  Predictive Biomarkers in Vaccine-Associated Outcomes

Predictive biomarkers are invaluable for evaluating COVID-19 vaccine outcomes, particularly in older adults who display highly variable immune responses. These biomarkers help distinguish individuals who develop strong vaccine-induced immunity from those who are weak or non-responders, while also identifying individuals at risk of vaccine-associated adverse events. Integrating biomarker data with clinical, demographic, and immunological profiles allows for precise prediction of morbidity and mortality risks following booster vaccination. This approach facilitates personalized vaccination strategies, optimizes immunization efficacy, minimizes complications, and improves public health outcomes by targeting high-risk populations for tailored interventions [18][19].

The ongoing emergence of SARS-CoV-2 and other persistent pathogens highlights the need for advanced predictive tools to assess vaccine effectiveness. Traditional evaluation methods often fail to capture the complexity of immune protection, making early biomarkers of immune response critical. Predictive biomarkers can accelerate vaccine development, support regulatory decision-making, and enable precision vaccinology by stratifying populations based on immune responsiveness. Systems biology approaches, combining high-throughput omics technologies, computational modeling, and immune profiling, have revealed molecular signatures predictive of vaccine-induced protection. These integrative analyses enhance understanding of immune correlates of protection and inform the rational design of next-generation vaccines with improved safety and efficacy profiles [19].

Insights from other clinical contexts, such as ischemic stroke, further illustrate the utility of biomarkers. Immunological and inflammatory markers—including pro-inflammatory cytokines (IL-1β, IL-6, TNF-α), anti-inflammatory mediators (IL-10, TGF-β), and hematological indices like neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII)—correlate with disease severity and outcomes. Applying similar biomarker-guided strategies in vaccinology can refine risk-benefit assessment, improve stratification of vulnerable populations, and enable individualized vaccination strategies to maximize protection while minimizing adverse events [20].

5. Mortality Risk Factors in the Geriatric Population

The geriatric population exhibits disproportionately high COVID-19 mortality due to a combination of age-related physiological decline, immunosenescence, and prevalent comorbidities. Cardiometabolic conditions such as hypertension, diabetes, and coronary artery disease exacerbate disease severity, while frailty, chronic inflammation, and nutritional deficiencies further impair immune and organ function. Polypharmacy and potential drug–vaccine interactions add complexity to treatment and vaccination outcomes in older adults. Understanding these multifactorial influences is critical for designing tailored clinical care, optimizing therapeutic strategies, and improving patient monitoring to reduce mortality among elderly COVID-19 patients [21]. A systematic review of 20 studies (10 quantitative, 10 qualitative) identified male sex, age over 75 years, dementia, and dyspnoea as significant predictors of mortality, whereas diabetes and hypertension showed inconsistent associations. These findings emphasize the importance of personalized risk stratification and proactive interventions to manage non-traditional mortality predictors in geriatric populations. Early recognition of high-risk individuals allows for prioritized intensive care, enhanced monitoring, and preventive measures to mitigate fatal outcomes [21]. Epidemiological data further demonstrate that individuals aged above 76 years experience markedly increased rates of severe infection, hospitalization, and mortality due to the convergence of immunosenescence, organ dysfunction, and multiple comorbidities. Frailty, poor nutritional status, and limited physiological reserve exacerbate disease progression, underscoring the need for age-focused preventive strategies, geriatric-specific clinical management, and inclusion of older adults in vaccine trials [22]. A retrospective cohort study in Wuhan, China, involving 36,358 hospitalized patients confirmed that advanced age, male sex, and comorbidities including hypertension, diabetes, cancer, CKD, and intracranial hemorrhage were major mortality determinants. Lifestyle factors such as smoking and alcohol use also increased mortality risk. Age emerged as the most dominant predictor, influencing clinical presentation, complication patterns, and overall disease trajectory in elderly COVID-19 patients [23].

6. Role of Spike Protein in Long COVID-19 Patients

The SARS-CoV-2 spike protein plays a pivotal role in the onset and persistence of Long COVID, primarily through mechanisms involving sustained viral antigen exposure and immune dysregulation. The spike protein not only mediates viral entry via angiotensin-converting enzyme 2 (ACE2) receptors but also triggers complex inflammatory and immunopathological responses that persist beyond the acute infection phase.

Girdín Pérez et al. (2022) established a dose-dependent relationship between viral load and the severity of post-COVID-19 symptoms, demonstrating that higher viral exposure increases the likelihood and intensity of Long COVID manifestations [24]. Complementing these findings, Palestra et al. (2023) reported that the spike protein directly activates human lung macrophages, inducing robust secretion of pro-inflammatory cytokines and chemokines including CXCL8, IL-6, TNF-α, and IL-1β in a concentration-dependent manner [25]. This activation was accompanied by significant morphological and kinetic alterations in macrophages, indicating sustained immune activation and tissue-level inflammation.

Furthermore, Swank et al. (2023) identified persistent circulating spike protein in individuals with post-acute COVID-19 syndrome (PASC). Through temporal profiling and mathematical modeling, their study revealed that the duration of antigen persistence correlates with symptom longevity and severity, suggesting that chronic spike proteinemia may act as a driver of ongoing immune activation and systemic inflammation [26].

Collectively, these findings underscore the dual contribution of spike protein quantity and persistence in promoting chronic inflammation, immune dysregulation, and multi-organ sequelae characteristic of Long COVID. Understanding these mechanistic pathways is essential for developing biomarker-based diagnostic tools and targeted therapeutic interventions to mitigate long-term complications in affected individuals.

7. Regional Insights: The Assam Context

7.1 COVID-19 Booster Coverage and Public Health Strategies

The elderly population in Assam faces distinct challenges related to COVID-19 morbidity, mortality, and vaccination strategies. This demographic is characterized by a growing proportion of older adults with age-related comorbidities, limited healthcare access, and geographic barriers, which collectively heighten vulnerability to severe disease. Booster coverage among older adults in the region remains heterogeneous, influenced by public health initiatives, awareness campaigns, and vaccine accessibility. Additional region-specific factors, such as ethnic diversity and nutritional deficiencies, further modulate disease risk and outcomes. Despite increasing availability of epidemiological data, substantial gaps persist in understanding predictive biomarkers for COVID-19 in Northeast India, particularly regarding how immunological, inflammatory, and genetic markers correlate with disease severity and vaccine responsiveness. Enhancing regional research on biomarkers, coupled with tailored public health interventions, could improve risk stratification, optimize booster administration, and guide preventive strategies for older adults, ultimately reducing morbidity and mortality in this high-risk population.

In the pediatric population, parental decisions critically shape COVID-19 vaccine uptake. A mixed-method study conducted in five Indian states (March–September 2023) assessed parental willingness to vaccinate children aged 6–12 years. Among 2,017 participants, 76.4% expressed willingness to vaccinate. Lower acceptance was associated with factors such as parental literacy, smaller family size, lack of parental vaccination, and prior family COVID-19 infection. Barriers included concerns about vaccine safety, uncertainty regarding protection, and misconceptions, whereas facilitators encompassed trust in vaccines, perceived disease severity, and integration with routine immunizations. Implementation of targeted policies, school-based vaccination programs, and promotion of adult vaccination were identified as effective strategies to improve pediatric COVID-19 vaccine coverage [27].

This synthesis underscores the importance of age- and region-specific public health strategies, integrating both biomarker research and sociobehavioral factors, to enhance vaccination uptake and mitigate COVID-19 burden across vulnerable populations in Assam.

7.2 Epidemiological Data and Gaps in Predictive Biomarker Research in Northeast India

The emergence of SARS-CoV-2 in early 2020 generated global concern reminiscent of the 1918 Spanish flu pandemic; however, host factors such as age, gender, and comorbidities played a more decisive role in determining disease outcomes than the virus itself. Socioeconomic disparities further exacerbated morbidity and mortality, particularly in industrialized nations. In contrast, India’s dense and diverse population experienced a slower COVID-19 trajectory with comparatively lower mortality. This “naturally flattened” curve has been attributed to trained innate immunity and the interplay of biocultural, socioecological, and socioeconomic factors, which contribute to regional differences in COVID-19 morbidity and outcomes [28].

In Northeast India, existing epidemiological studies provide insights into infectious disease prevalence but reveal critical gaps in COVID-19 predictive biomarker research. A cross-sectional analysis of viral hepatitis (HBV, HCV, HDV, HAV, HEV) among tribal and non-tribal populations across eight northeastern states (2018–2022) highlighted age- and community-specific variations in infection rates. Younger tribal adults exhibited higher hepatitis prevalence, whereas older non-tribal adults were more affected. Tribes such as the Lushai of Mizoram showed elevated HDV, HCV, and HEV cases, while non-tribals had better hepatitis B vaccination coverage. Regional disparities were also evident in liver disease markers before and during the COVID-19 pandemic, suggesting that pandemic-related changes influenced disease patterns [29].

Despite these findings, predictive biomarker research for COVID-19 in Northeast India remains limited, particularly regarding how immunological, inflammatory, and genetic markers correlate with disease severity and vaccine responsiveness. Addressing these gaps through targeted research is critical to improving regional risk stratification, guiding vaccination strategies, and enhancing public health interventions for vulnerable populations.

8. Statistical and Predictive Models for COVID-19 Mortality

Predictive modeling has emerged as a critical tool for understanding COVID-19 mortality, particularly among high-risk populations such as the elderly. These models integrate epidemiological, clinical, and biomarker data to identify predictors of severe outcomes and death. Incorporating demographic and clinical parameters such as age, comorbidities, inflammatory markers, and vaccination status enhances model precision and enables personalized risk stratification.

Artificial intelligence (AI) and machine learning (ML) techniques further improve predictive accuracy by capturing complex, non-linear relationships within large datasets, facilitating real-time risk assessment and decision support [28][30]. Recent systematic reviews, including “COVID Mortality Prediction with Machine Learning Methods,” underscore the growing role of AI in clinical decision-making, resource allocation, and improving patient prognosis [29][30].

A study applying multivariate and machine learning approaches to 250 hospitalized COVID-19 patients analyzed 108 clinical, comorbidity, and biochemical parameters. Using a modified SIMPLS-based model, the study achieved strong predictive accuracy (AUC > 0.85), identifying key mortality predictors: age > 65, coronary artery disease, diabetes, dementia, altered mental status, and biochemical markers including CRP, prothrombin, and lactate. Clustering analysis further classified patients into high- and low-risk groups, enabling more precise clinical decision-making [29][31].

Despite these advances, challenges persist, including variability in data quality, lack of standardized biomarker measurement, and limited external validation across diverse populations. Ensuring transparent model design, harmonized datasets, and robust validation frameworks is essential to enhance predictive reliability. The continued development of predictive models can support precision public health strategies and guide targeted interventions to reduce COVID-19–related mortality in vulnerable groups [28][30][31].

9. Ethical Challenges in Geriatric Research and Biomarker Studies

Geriatric research and biomarker studies pose distinct ethical challenges due to the vulnerability and heterogeneity of older adults. Key considerations include informed consent, cognitive impairment, autonomy, and careful assessment of the risk-benefit balance. Ensuring voluntary participation, privacy protection, and equitable access to research benefits is essential. Biomarker studies introduce additional complexities, such as data interpretation, incidental findings, and potential stigmatization. Ethical frameworks must prioritize respect, justice, and beneficence, implementing transparent communication, continuous oversight, and adaptive consent processes to protect elderly participants [29][32].

The COVID-19 pandemic further complicated geriatric research by causing protocol interruptions, recruitment delays, data inconsistencies, and workforce instability. Researchers faced challenges in ensuring participant safety, maintaining engagement, and adapting assessments to remote formats without compromising data validity. Disparities across age, race, and socioeconomic status were exacerbated, underscoring the need for inclusive approaches. Biomarker studies were additionally affected by limited access to shared equipment and sanitation constraints. Financial pressures and workforce losses hindered research continuity. Future geriatric studies must integrate disaster preparedness, remote technologies, and ethical inclusivity to maintain resilience, data integrity, and advancement in aging and dementia research during global crises [31][32].

10. Future Perspectives and Research Directions

Future research in geriatric health in India should emphasize longitudinal biomarker studies to elucidate aging trajectories and disease susceptibility in elderly populations. Integrating multi-omics approaches—including genomics, proteomics, and metabolomics—can facilitate personalized vaccination strategies and targeted interventions. Emphasis on predictive, preventive, and precision public health is critical for anticipating disease, optimizing preventive measures, and tailoring healthcare solutions to individual and population needs. These strategies will support early detection, improved vaccine responsiveness, and evidence-based public health policies, promoting healthier aging while advancing scientific understanding of age-related diseases and precision geriatric care across diverse Indian communities.

Specifically, future studies should focus on validating predictive biomarkers in larger geriatric cohorts following COVID-19 booster vaccination, incorporating genomics and immunological profiling. The development of personalized risk assessment tools may enhance clinical decision-making, optimize booster strategies, and improve outcomes in elderly populations, particularly in resource-limited regions such as Assam, India.

11. CONCLUSION

The COVID-19 pandemic has disproportionately impacted older adults due to immunosenescence, inflammaging, and reduced organ reserve, which increase susceptibility to severe disease, hospitalization, and mortality. Biomarkers including inflammatory, hematological, biochemical, and molecular indicators are pivotal for predicting disease severity, mortality risk, and vaccine responsiveness, enabling targeted clinical management and personalized interventions. Vaccination remains the primary preventive measure for geriatric populations, yet immune heterogeneity, waning immunity, and booster hesitancy present challenges. Tailored booster strategies guided by predictive biomarker profiles are crucial to sustain immunity and minimize adverse outcomes. Persistent SARS-CoV-2 antigen exposure, particularly via the spike protein, contributes to Long COVID pathophysiology, highlighting the need for biomarker-informed monitoring and therapeutic strategies. In regional contexts like Assam, India, integrating epidemiological, sociocultural, and healthcare accessibility factors is essential for public health planning. Limited biomarker research in Northeast India calls for longitudinal studies, multi-omics approaches, and AI-assisted predictive modeling to improve risk stratification and inform interventions. Future geriatric research must address ethical challenges in consent, autonomy, and equitable participation. Prioritizing precision public health, longitudinal biomarker analyses, and inclusive research frameworks can reduce COVID-19 morbidity and mortality while enhancing resilience and vaccine responsiveness in elderly populations. This integrated approach offers a roadmap for optimizing geriatric care and public health preparedness.

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