Longitudinal immune trajectories following SARS-CoV-2 vaccination in long-term care facility residents: an unsupervised analysis approach
10 affiliations
- 1Department of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Istituto Superiore di Sanità , ,
- 2Department of Infectious Diseases, Istituto Superiore di Sanità , ,
- 3Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center , ,
- 4ANASTE Humanitas Foundation , ,
- 5Research Unit of Internal Medicine, Department of Medicine and Surgery, Università Campus Bio-15 Medico , ,
- 6Department of Medical and Surgical Sciences, Hygiene Unit, Policlinico Foggia Hospital, University of Foggia , ,
- 7Unit of Hygiene and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public, University of Padua , ,
- 8Department of Medicine (DIMED), University of Padua , ,
- 9Microbiology and Virology Unit, “Pugliese-Ciaccio” Hospital , ,
- 10Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS , ,
Abstract
Background: Residents of Long-Term Care Facilities (LTCFs) represent a highly vulnerable population characterized by immunosenescence, multimorbidity, and heterogeneous immune responses to repeated SARS-CoV-2 vaccination and infection. Conventional analyses based on single clinical variables often fail to capture this complexity. We evaluated longitudinal humoral immune responses in institutionalized older adults to identify data-driven antibody profiles and characterize cluster-specific waning kinetics.
Methods: In a prospective multicenter cohort of 388 Italian LTCF residents followed across four time points (T0–T3 over 12 months), longitudinal anti-Spike (anti-S) and anti-Nucleocapsid (anti-N) IgG levels were measured alongside demographic and vaccination data. Principal Component Analysis (PCA) was used for dimensionality reduction, followed by K-means clustering. Optimal cluster number was determined using internal validation metrics (Silhouette, Calinski-Harabasz, and Davies-Bouldin indices) and bootstrap stability analysis. Linear mixed-effects models were applied to describe cluster-specific antibody waning trajectories.
Results: A longitudinal analysis was conducted on 388 LTCF residents. Three antibody trajectory profiles emerged: (i) a Vaccine-Driven profile (n = 130), characterized by moderate anti-S titers and progressive antibody decline; (ii) a Hybrid Immunity profile (n = 125), characterized by evidence of prior infection and remarkably stable antibody concentrations throughout follow-up despite limited uptake of the 2023/2024 booster; and (iii) a High-Peak Hybrid Responder profile (n = 133), exhibiting the highest baseline anti-S titers followed by the steepest rate of antibody waning. Across the cohort, 12 of 388 residents (3.1%) developed documented symptomatic SARS-CoV-2 infection during follow-up, with no COVID-19-related deaths and no significant difference in infection incidence among profiles. Bootstrap stability analysis showed a mean Adjusted Rand Index (ARI) of 0.74 (SD 0.20).
Conclusion: Unsupervised machine learning effectively resolves the complex heterogeneity of SARS-CoV-2 humoral responses in LTCF residents into data-driven trajectory patterns. These findings illustrate the utility of data-driven approach for describing humoral dynamics while distinguishing observational antibody kinetics from direct estimates of clinical protection.
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BibTeX
@article{palmieri2026longitudinal,
title = {{Longitudinal immune trajectories following SARS-CoV-2 vaccination in long-term care facility residents: an unsupervised analysis approach}},
author = {Annapina Palmieri and Cecilia Damiano and Pasqualina Leone and Caterina Trevisan and Alba Malara and Alberto Zucchelli and Raffaele Antonelli Incalzi and Rosa Prato and Francesca Fortunato and Tatjana Baldovin and Alessandra Coin and Pasquale Minchella and Stefania Giordano and Anna Teresa Palamara and Graziano Onder and Giorgio Fedele},
journal = {Frontiers in aging},
year = {2026},
month = oct,
doi = {10.3389/fragi.2026.1963384},
url = {https://europepmc.org/article/PMC/PMC13724463},
note = {CC BY},
}
RIS
TY - JOUR
TI - Longitudinal immune trajectories following SARS-CoV-2 vaccination in long-term care facility residents: an unsupervised analysis approach
AU - Annapina Palmieri
AU - Cecilia Damiano
AU - Pasqualina Leone
AU - Caterina Trevisan
AU - Alba Malara
AU - Alberto Zucchelli
AU - Raffaele Antonelli Incalzi
AU - Rosa Prato
AU - Francesca Fortunato
AU - Tatjana Baldovin
AU - Alessandra Coin
AU - Pasquale Minchella
AU - Stefania Giordano
AU - Anna Teresa Palamara
AU - Graziano Onder
AU - Giorgio Fedele
PY - 2026
DA - 2026/10/19
JO - Frontiers in aging
DO - 10.3389/fragi.2026.1963384
UR - https://europepmc.org/article/PMC/PMC13724463
AB - Background: Residents of Long-Term Care Facilities (LTCFs) represent a highly vulnerable population characterized by immunosenescence, multimorbidity, and heterogeneous immune responses to repeated SARS-CoV-2 vaccination and infection. Conventional analyses based on single clinical variables often fail to capture this complexity. We evaluated longitudinal humoral immune responses in institutionalized older adults to identify data-driven antibody profiles and characterize cluster-specific waning kinetics. Methods: In a prospective multicenter cohort of 388 Italian LTCF residents followed across four time points (T0–T3 over 12 months), longitudinal anti-Spike (anti-S) and anti-Nucleocapsid (anti-N) IgG levels were measured alongside demographic and vaccination data. Principal Component Analysis (PCA) was used for dimensionality reduction, followed by K-means clustering. Optimal cluster number was determined using internal validation metrics (Silhouette, Calinski-Harabasz, and Davies-Bouldin indices) and bootstrap stability analysis. Linear mixed-effects models were applied to describe cluster-specific antibody waning trajectories. Results: A longitudinal analysis was conducted on 388 LTCF residents. Three antibody trajectory profiles emerged: (i) a Vaccine-Driven profile (n = 130), characterized by moderate anti-S titers and progressive antibody decline; (ii) a Hybrid Immunity profile (n = 125), characterized by evidence of prior infection and remarkably stable antibody concentrations throughout follow-up despite limited uptake of the 2023/2024 booster; and (iii) a High-Peak Hybrid Responder profile (n = 133), exhibiting the highest baseline anti-S titers followed by the steepest rate of antibody waning. Across the cohort, 12 of 388 residents (3.1%) developed documented symptomatic SARS-CoV-2 infection during follow-up, with no COVID-19-related deaths and no significant difference in infection incidence among profiles. Bootstrap stability analysis showed a mean Adjusted Rand Index (ARI) of 0.74 (SD 0.20). Conclusion: Unsupervised machine learning effectively resolves the complex heterogeneity of SARS-CoV-2 humoral responses in LTCF residents into data-driven trajectory patterns. These findings illustrate the utility of data-driven approach for describing humoral dynamics while distinguishing observational antibody kinetics from direct estimates of clinical protection.
SN - 2673-6217
ER -