- Design
- Secondary analysis of a cohort with latent class analysis
- Population
- 16,095 community-dwelling adults aged 70+ in Australia
- Primary outcome
- All-cause mortality by multimorbidity cluster
- Effect
- Frailty-depression-osteoporosis cluster HR 1.56 (men), 1.68 (women)
A secondary analysis of the ASPREE cohort (Journals of Gerontology Series A, 1 October 2026) grouped 15 chronic conditions in 16,095 community-dwelling Australians aged 70 or over without dementia or cardiovascular disease, followed for a median of nearly 11 years with 4,217 deaths.
Multimorbidity was almost universal (88%). Of five clusters, the one combining frailty, depressive symptoms and osteoporosis was associated with higher mortality in both men (HR 1.56) and women (HR 1.68). A metabolic, gout and kidney cluster in men (HR 1.63) and a gout, anaemia and kidney cluster in women (HR 1.96) also carried higher risk.
Counting conditions treats all multimorbidity alike; these clusters suggest certain combinations matter more. The frailty-depression-osteoporosis grouping is one geriatricians often see together and may under-treat, especially depression.
- Screen for depression in older patients with frailty or osteoporosis
- Treat the combination of frailty, depression and bone fragility as a high-risk profile
- Look for anaemia and kidney disease together with gout, particularly in women
- Use clusters to prioritise review, not as a formal risk score yet
Why it matters
Which conditions co-occur may matter more for prognosis than how many there are.
Don't overread it
Hypothesis-generating associations in a relatively healthy trial cohort.
The statistics, in plain English
Hazard ratios of 1.6-2.0 mean roughly 60-100% higher risk of death compared with the reference cluster. Latent class analysis finds patterns in data, and different cohorts may produce different clusters.
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