Clustering Study Reveals Three Distinct Alzheimer’s Profiles in Hospitalized Patients

Alzheimer’s disease has long been portrayed as a single, relentless decline: memory dissolves first, then independence, and eventually the self. But a new real-world study of hospitalized patients suggests that this tidy narrative hides a far messier clinical reality. Researchers analyzing routine ward data have found that the relationship between how well patients function physically and the behavioral symptoms they display is surprisingly loose, and that when patients are grouped statistically, they fall into three distinct functional–behavioral profiles rather than one uniform trajectory of deterioration.

The study, conducted by a team from Shenzhen University Medical School and Shenzhen Kangning Hospital and published in BMC Geriatrics, took a deliberately practical approach. Rather than recruiting a carefully curated research cohort, the investigators turned to the everyday records of 118 hospitalized patients with clinically diagnosed Alzheimer’s disease. Their goal was to capture the heterogeneity that clinicians actually confront at the bedside, where a patient’s ability to feed themselves or walk to the toilet may tell a very different story from the agitation, apathy, or hallucinations documented in nursing observations.

To measure functional ability, the team used the Barthel Index, a widely employed clinical instrument that scores a patient’s independence across fundamental activities of daily living, including feeding, bathing, grooming, dressing, continence, toileting, transferring, mobility, and stair climbing. For behavioral symptoms, they used the Nurses’ Observation Scale for Inpatient Evaluation, known as NOSIE, a rating tool completed by nursing staff that captures the spectrum of ward-observed behaviors, from irritability and psychotic symptoms to social withdrawal and psychomotor slowing. Because these assessments are embedded in routine inpatient care, the resulting dataset reflects the textured, day-to-day clinical picture rather than a snapshot from a standardized research visit.

The first striking finding was statistical. When the researchers correlated overall functional scores with overall behavioral symptom burden using Spearman rank correlation, they found only a weak inverse relationship, with a correlation coefficient of −0.30. In plain terms, knowing how functionally impaired a patient is provides only limited information about how severe their behavioral symptoms are. Some patients with severe physical dependence show relatively modest behavioral disturbance, while others with middling function experience pronounced psychiatric symptoms. This statistical looseness is exactly what clinicians have long suspected: functional decline and behavioral symptomatology in Alzheimer’s disease are related, but they are not two faces of a single underlying dial.

That weak correlation set the stage for the study’s central move. Instead of treating Alzheimer’s patients as interchangeable points on a severity scale, the investigators applied k-means clustering, an unsupervised machine learning algorithm that partitions data points into groups based on similarity across multiple measured dimensions simultaneously. The algorithm does not know anything about diagnosis or prognosis; it simply finds natural groupings in the numbers. When fed the combined functional and behavioral data from the 118 patients, it returned three clusters, each representing a coherent clinical persona that the researchers labeled exploratory functional–behavioral profiles.

Profile 1 was defined by severe functional impairment accompanied by increased psychomotor retardation. Patients in this group were markedly dependent in basic self-care, and their behavioral signature leaned toward slowing, withdrawal, and reduced engagement rather than outward agitation. Profile 2, by contrast, represented the relatively favorable end of the spectrum: these patients retained comparatively preserved functional ability and carried a lower overall behavioral symptom burden. Profile 3 occupied an intermediate position on function but stood apart behaviorally, with more prominent psychotic symptoms such as hallucinations and delusions. In other words, the study found that a patient’s physical disability and their psychiatric symptom profile can dissociate, and that this dissociation creates recognizable, recurring clinical archetypes on the ward.

The differences between profiles were not confined to headline scores. The researchers observed significant differences across specific functional domains, including feeding, toileting, and walking, suggesting that the profiles capture granular differences in how independence erodes, not just how much. Behavioral dimensions also separated the groups meaningfully. What did not separate them was equally interesting: demographic characteristics such as age and sex, and comorbid medical conditions, showed no significant differences between the clusters. The profiles were not simply artifacts of who was older or sicker in a general medical sense; they reflected genuine variation in the neuropsychiatric expression of Alzheimer’s disease itself.

The implications for care could be substantial. Ward teams currently tailor much of Alzheimer’s care around global severity staging, which lumps together functional and cognitive decline. A three-way functional–behavioral typology invites a different logic. A patient in the severe-dependency, psychomotor-retardation group may need fundamentally different nursing attention, rehabilitation goals, and environmental design than a patient whose main challenge is managing psychotic symptoms while retaining considerable physical function. Behavioral symptoms such as agitation and psychosis are among the most burdensome aspects of dementia care, driving caregiver distress, hospitalization, and pharmacological treatment with its attendant risks. Grouping patients by combined profiles could help staff anticipate problems, communicate more precisely during handovers, and design individualized care plans that address the specific symptom cluster a patient actually carries.

The study’s method also carries broader lessons for how real-world clinical data can be mined. Unsupervised clustering does not require pre-existing labels or hypotheses about disease subtypes; it lets the data reveal structure. Applied to routinely collected instruments like the Barthel Index and NOSIE, it can extract clinically interpretable groupings from information that hospitals already generate, without additional testing burden on patients. This fits a growing movement in geriatric medicine toward data-driven phenotyping, in which statistical patterns in electronic records are used to organize patient heterogeneity that traditional diagnostic categories obscure. For a disease as heterogeneous as Alzheimer’s, such approaches may eventually inform risk stratification, resource allocation, and even trial design, by ensuring that patients with similar profiles are analyzed together.

Caution is warranted, however, and the authors are explicit about the limits of their work. This was a retrospective, cross-sectional analysis of 118 patients at a single hospital, and the profiles are described as exploratory. Clustering algorithms will always return groupings, and it remains to be shown whether these particular profiles are stable across time, reproducible in other institutions and health systems, and genuinely predictive of outcomes such as length of stay, discharge destination, readmission, or mortality. Longitudinal studies are needed to determine whether patients migrate between profiles as their disease evolves, and whether profile membership at admission can meaningfully guide treatment decisions. The researchers themselves state that external and longitudinal validation is required before these profiles can be considered a clinical tool rather than a descriptive insight.

Even so, the study adds a valuable piece to the emerging picture of Alzheimer’s disease as a syndrome of many faces. The weak correlation between function and behavior is a quiet but important statistic: it tells clinicians and caregivers that observing one dimension tells them little about the other, and that both must be assessed in their own right. The three profiles offer a vocabulary for that dual assessment, turning what looks like random variation into organized, discussable clinical patterns. As populations age and dementia-related hospitalizations climb, tools that convert messy ward data into actionable patient groupings will only grow in value. This analysis is an early step, but it points toward a more individualized future for dementia care, in which the label Alzheimer’s disease is only the beginning of the story, and the functional–behavioral profile helps determine how that story is managed, ward by ward, patient by patient.

Subject of Research: Functional–behavioral heterogeneity and clustering profiles in hospitalized patients with Alzheimer's disease

Article Title: Functional–behavioral profiles and clinical characteristics among hospitalized patients with Alzheimer’s disease: a real-world clustering analysis

Article References: Jie, W., Yu, X., Wu, J., Xu, Y., He, C., Fan, Y., Wang, Y., & Wu, H. (2026). Functional–behavioral profiles and clinical characteristics among hospitalized patients with Alzheimer’s disease: a real-world clustering analysis. BMC Geriatrics. https://doi.org/10.1186/s12877-026-08444-0

Image Credits: AI Generated

DOI: 10.1186/s12877-026-08444-0

Keywords: Alzheimer's disease, k-means clustering, Barthel Index, NOSIE, behavioral symptoms, functional ability, psychotic symptoms, hospitalized patients, individualized care, geriatric psychiatry, real-world data, dementia care