Clusters of Physical Frailty and Cognitive Impairment and Their Associated Comorbidities in Older Primary Care Patients

Abstract

(1) Objectives: We aimed to identify clusters of physical frailty and cognitive impairment in a population of older primary care patients and correlate these clusters with their associated comorbidities. (2) Methods: We used a latent class analysis (LCA) as the clustering technique to separate different stages of mild cognitive impairment (MCI) and physical frailty into clusters; the differences were assessed by using a multinomial logistic regression model. (3) Results: Four clusters (latent classes) were identified: (1) highly functional (the mean and SD of the “frailty” test 0.58 ± 0.72 and the Mini-Mental State Examination (MMSE) test 27.42 ± 1.5), (2) cognitive impairment (0.97 ± 0.78 and 21.94 ± 1.95), (3) cognitive frailty (3.48 ± 1.12 and 19.14 ± 2.30), and (4) physical frailty (3.61 ± 0.77 and 24.89 ± 1.81). (4) Discussion: The comorbidity patterns distinguishing the clusters depend on the degree of development of cardiometabolic disorders in combination with advancing age. The physical frailty phenotype is likely to exist separately from the cognitive frailty phenotype and includes common musculoskeletal diseases.

Patterns of the physical, cognitive, and mental health status of older individuals in a real-life primary care setting and differences in coping styles

“…This is the first attempt in geriatric research to cluster older individuals according to the level of physical frailty, cognitive impairment, and symptoms of mental disorders, anxiety and depression. As evidenced by the recently published papers, and by our previous work, characteristics of patients in the sample, including age and gender structures, and the prevalence of particular chronic disorders, could have influenced the cluster creation (40,54). It has been shown e.g., that when physical frailty is fully developed, this allows for the cognitive frailty phenotype to form a cluster (8,55).…”

“…Our team was among the first authors who used the benefit of new methodology approaches such as the clustering techniques of machine learning methods, to provide an integrated view on associations between physical frailty and cognitive impairments, as critical intermediates of the aging process and risk stratification tools ( 8 , 39 , 40 ). In this study, the aim was to identify clusters of older primary care (PC) patients with particular aggregations of physical, cognitive, and mental dysfunctions.…”

Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians’ Clinical Reasoning by Reducing Patients’ Complexity

“…Regarding patients with DM2, the broken pieces of knowledge—how DM2 usually develops in obese individuals, how adipose tissue is a source of inflammation, how inflammatory mechanisms are implicated in the development of comorbidities, and how accumulated comorbidities exhaust homeostasis reserves and promotes frailty—need to be better integrated [ 9 , 19 ]. Thanks to the availability of new methodologies for data analysis, we have started to integrate this knowledge [ 16 , 17 , 65 ].…”

“…This is also the case for mental disorders, anxiety, and depression, which are known to often accompany DM2 and CVD, worsening the course of these conditions [ 15 ]. Recent evidence indicates that frailty, if associated with chronic health conditions, can modify CV risk factors and influence the outcomes [ 16 , 17 , 18 ]. The state of frailty is manifested by a reduction in muscle mass and strength, slow walking, and low activity and is considered reflective of exhausted homeostatic reserves in multiple organs and systems [ 19 ].…”

Cross-Talk between the Cytokine IL-37 and Thyroid Hormones in Modulating Chronic Inflammation Associated with Target Organ Damage in Age-Related Metabolic and Vascular Conditions

“…By keeping the aforementioned facts in mind and based on the recent evidence indicating that factors such as an individual’s age and the age of hypertension and T2D duration and the age of onset may influence the rates of CV risk factor accumulation and organ damage progression, we designed a simple cluster analysis for the group of older individuals diagnosed with T2D [ 136 , 137 ]. In creating this study design, we were guided by the recent trend in research on T2D, based on using clustering techniques in determining the phenotypic subgroups of these patients [ 138 , 139 ], as well as by our experience in using these techniques in geriatric studies [ 140 , 141 ]. As markers of target organ damage, we included the cytokines IL-17 and IL-37 [ 142 ].…”