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Chronic Disease Management Strategies / Diabetes, Cardiovascular Risks, and Lipoproteins · Journal article
Frontiers in Medicine · September 8, 2026
Early or partial results. Treat as a signal, not a conclusion.
This cross-sectional study uses hierarchical clustering on the Whitehall II cohort to derive four clinically distinct sub-phenotypes of co-occurring metabolic syndrome and pre-frailty in older adults, each with differing cardiometabolic, functional, and psychosocial profiles. The work is exploratory and hypothesis-generating, identifying associations between sub-phenotypes and lifestyle, mental health, and sociodemographic factors, but provides no prospective outcomes or evidence to change clinical management.
Cross-sectional cohort study with agglomerative hierarchical clustering. Older adults aged ≥60 years from the Whitehall II longitudinal cohort study; enrolled in the 2015–2016 wave with complete data on metabolic and frailty parameters.. n = 3,398. Whitehall II cohort; based in the United Kingdom (London civil service, historical occupational cohort)..
Four sub-phenotypes (SPs) identified with intra-sex concordance >87% SP1 (31.7%) showed most favorable cardiometabolic and functional parameters SP2 (28.7%) displayed cardiometabolic burden with hypertension but preserved function
No prospective clinical outcomes (e.g., incidence of frailty, hospitalization, mortality) reported to demonstrate predictive validity.
These sub-phenotypes provide a framework for stratifying heterogeneous presentations of metabolic syndrome and pre-frailty in older adults and suggest that intervention strategies might be tailored by profile. However, this is exploratory work; prospective validation and outcome studies are needed before clinical translation.
A hypothesis-generating, cross-sectional clustering study that identifies novel sub-phenotypes but lacks prospective validation, hard clinical outcomes, or causal evidence to guide clinical practice.
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Quoted from the source exactly as published.
These sub-phenotypes provide a framework for stratifying heterogeneous presentations of metabolic syndrome and pre-frailty in older adults and suggest that intervention strategies might be tailored by profile. However, this is exploratory work; prospective validation and outcome studies are needed before clinical translation.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Background There is emerging evidence that frailty, and its early stage, pre-frailty, are closely linked to metabolic changes, particularly in response to environmental factors such as lifestyle and diet. There is therefore a major interest in exploring these links, particularly with regard to the highly prevalent metabolic syndrome (MetS) and heterogeneity in phenotypes due to the multicriteria nature of both syndromes. In order to structure this heterogeneity, this study aimed to identify and characterize novel sub-phenotypes of MetS and (pre-)frailty co-occurrence in older adults, as well as to examine the associated clinical, psychosocial, and behavioral factors. Methods Data from the Whitehall II cohort (2015–2016 wave), including 3,398 participants aged ≥60 years, were used. A data-driven approach was applied using agglomerative hierarchical clustering based on selected clinical parameters defining MetS (harmonized consensus definition) and pre-frailty (Fried phenotype). After deriving and describing the resulting sub-phenotypes (SPs), their associated factors were examined using a multinomial logistic regression model that simultaneously included a broad set of biological, sociodemographic, lifestyle, and health variables. Results Four clinical SPs (intra-sex concordance >87%) emerged from this reclassification, each characterized by distinct patterns of cardiometabolic and functional parameters. The first profile (SP1 31.7%) showed the most favorable cardiometabolic parameters and superior functional performance, although handgrip strength and physical activity levels were slightly lower; it was associated with more favorable socio-demographic, lifestyle, and psychosocial characteristics. A second profile (SP2, 28.7%) displayed cardiometabolic burden, particularly hypertension, with still preserved physical functional characteristics. The two last profiles, SP3 (6.4%) characterized by slow gait, lower physical activity, and exhaustion despite largely preserved metabolic parameters, whereas SP4 (33.2%) showing abdominal obesity, hypertriglyceridemia, hyperglycemia, low HDL-cholesterol, and functional impairments, were associated with older age and male sex. Lower fruit and vegetable intake and poorer physical quality-of-life were associated to SP2 and SP4. Depression and poor mental health markedly increased the likelihood of belonging to SP3. Conclusions By structuring heterogeneity into clinically interpretable sub-phenotypes, these findings provide a framework for multidimensional assessment of complex age-related conditions and generate hypotheses regarding potential combinations of underlying metabolic, functional, and psychosocial mechanisms.
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