“…The combination of OH, cognitive impairment and MI has been labelled the Bermuda Triangle of Falls [ 21 , 22 ]. This study examines clustering of these three common geriatric syndromes, as well as the likelihood of falls related to these clusters, in a large, population-representative sample of community-dwelling older people.…”
Section: Discussionmentioning
confidence: 99%
“…A synergy between the factors can be explained by OH destabilising the underlying mobility impairment (MI), and cognitive impairment reducing prudential anticipation of the increased gait instability as well as impaired ability to appropriately react to the instability. This hypothesis was termed the ‘Bermuda Triangle’ of falls, representing physiological instability across three integrated systems, but this had yet to be demonstrated in a well-designed study [ 21 , 22 , 23 ]. The aim of this study was therefore to examine clustering of these three common geriatric syndromes—OH, cognitive and MI—and examine the longitudinal relationship between clusters of these syndromes and incident falls and fractures during an 8-year follow-up period.…”
Background
Orthostatic hypotension (OH), cognitive impairment (Cog) and mobility impairment (MI) frequently co-occur in older adults who fall. This study examines clustering of these three geriatric syndromes and ascertains their relationship with future falls/fractures in a large cohort of community-dwelling people ≥ 65 years during 8-year follow-up.
Methods
OH was defined as an orthostatic drop ≥ 20 mmHg in systolic blood pressure (from seated to standing) and/or reporting orthostatic unsteadiness. CI was defined as Mini Mental State Examination ≤ 24 and/or self-reporting memory as fair/poor. MI was defined as Timed Up and Go ≥12 s. Logistic regression models, including three-way interactions, assessed the longitudinal association with future falls (explained and unexplained) and fractures.
Results
Almost 10% (88/2,108) of participants had all three Bermuda syndromes. One-fifth of participants had an unexplained fall during follow-up, whereas 1/10 had a fracture. There was a graded relationship with incident unexplained falls and fracture as the number of Bermuda syndromes accumulated. In fully adjusted models, the cluster of OH, CI and MI was most strongly associated with unexplained falls (odds ratios (OR) 4.33 (2.59–7.24); P < 0.001) and incident fracture (OR 2.51 (1.26–4.98); P = 0.045). Other clusters significantly associated with unexplained falls included OH; CI and MI; MI and OH; CI and OH. No other clusters were associated with fracture.
Discussion
The ‘Bermuda Triangle’ of OH, CI and MI was independently associated with future unexplained falls and fractures amongst community-dwelling older people. This simple risk identification scheme may represent an ideal target for multifaceted falls prevention strategies in community-dwelling older adults.
“…The combination of OH, cognitive impairment and MI has been labelled the Bermuda Triangle of Falls [ 21 , 22 ]. This study examines clustering of these three common geriatric syndromes, as well as the likelihood of falls related to these clusters, in a large, population-representative sample of community-dwelling older people.…”
Section: Discussionmentioning
confidence: 99%
“…A synergy between the factors can be explained by OH destabilising the underlying mobility impairment (MI), and cognitive impairment reducing prudential anticipation of the increased gait instability as well as impaired ability to appropriately react to the instability. This hypothesis was termed the ‘Bermuda Triangle’ of falls, representing physiological instability across three integrated systems, but this had yet to be demonstrated in a well-designed study [ 21 , 22 , 23 ]. The aim of this study was therefore to examine clustering of these three common geriatric syndromes—OH, cognitive and MI—and examine the longitudinal relationship between clusters of these syndromes and incident falls and fractures during an 8-year follow-up period.…”
Background
Orthostatic hypotension (OH), cognitive impairment (Cog) and mobility impairment (MI) frequently co-occur in older adults who fall. This study examines clustering of these three geriatric syndromes and ascertains their relationship with future falls/fractures in a large cohort of community-dwelling people ≥ 65 years during 8-year follow-up.
Methods
OH was defined as an orthostatic drop ≥ 20 mmHg in systolic blood pressure (from seated to standing) and/or reporting orthostatic unsteadiness. CI was defined as Mini Mental State Examination ≤ 24 and/or self-reporting memory as fair/poor. MI was defined as Timed Up and Go ≥12 s. Logistic regression models, including three-way interactions, assessed the longitudinal association with future falls (explained and unexplained) and fractures.
Results
Almost 10% (88/2,108) of participants had all three Bermuda syndromes. One-fifth of participants had an unexplained fall during follow-up, whereas 1/10 had a fracture. There was a graded relationship with incident unexplained falls and fracture as the number of Bermuda syndromes accumulated. In fully adjusted models, the cluster of OH, CI and MI was most strongly associated with unexplained falls (odds ratios (OR) 4.33 (2.59–7.24); P < 0.001) and incident fracture (OR 2.51 (1.26–4.98); P = 0.045). Other clusters significantly associated with unexplained falls included OH; CI and MI; MI and OH; CI and OH. No other clusters were associated with fracture.
Discussion
The ‘Bermuda Triangle’ of OH, CI and MI was independently associated with future unexplained falls and fractures amongst community-dwelling older people. This simple risk identification scheme may represent an ideal target for multifaceted falls prevention strategies in community-dwelling older adults.
“…From here, we went one step further and explored which FI deficits had the most important role in relation with neuroimaging biomarkers such as the cortex volume. The concept of network physiology emerged recently to highlight the fact that the functioning of different systems is interconnected ( Romero-Ortuño et al, 2021 ). For this reason, we decided to use a graph theory approach to explore the interconnection between cortex volume and each of the FI features.…”
BackgroundFrailty in older adults has been associated with reduced brain health. However, structural brain signatures of frailty remain understudied. Our aims were: (1) Explore associations between a frailty index (FI) and brain structure on magnetic resonance imaging (MRI). (2) Identify the most important FI features driving the associations.MethodsWe designed a cross-sectional observational study from a population-based study (The Irish Longitudinal Study on Aging: TILDA). Participants aged ≥50 years who underwent the wave 3 MRI sub-study were included. We measured cortex, basal ganglia, and each of the Desikan-Killiany regional volumes. Age-and sex-adjusted correlations were performed with a 32-item self-reported FI that included conditions commonly tested for frailty in research and clinical settings. A graph theory analysis of the network composed by each FI item and cortex volume was performed. White matter fiber integrity was quantified using diffusion tensor imaging (DTI).ResultsIn 523 participants (mean age 69, 49% men), lower cortex and thalamic volumes were independently associated with higher FI. Sensory and functional difficulties, diabetes, polypharmacy, knee pain, and self-reported health were the main FI associations with cortex volume. In the network analysis, cortex volume had a modest influence within the frailty network. Regionally, higher FI was significantly associated with lower volumes in both orbitofrontal and temporal cortices. DTI analyses revealed inverse associations between the FI and the integrity of some association bundles.ConclusionThe FI used had a recognizable but subtle structural brain signature in this sample. Only some FI deficits were directly associated with cortex volume, suggesting scope for developing FIs that include metrics more specifically related with brain health in future aging neuroscience studies.
“…11 As long as the concepts of longevity medicine are maturing and artificial intelligence (AI)-based precision oncology is at the inceptive state of entering the clinical routine, the concept of frailty versus fitness, the so-called ''Frailty syndrome'' , is gaining traction. 9,12,13 Identifying frail cancer patients, i.e., those with high susceptibility, low functional reserve and unstable homeostasis, leads to a better estimation of individual response to therapies than looking at chronological age alone, 9,13 therefore, careful consideration of each patient's status, including frailty in the treatment decision-making process is an integral part of oncological management. However, measuring and quantifying frailty in the real-world setting remains a challenge.…”
Section: Treatment Of Older Cancer Patientsmentioning
confidence: 99%
“…However, measuring and quantifying frailty in the real-world setting remains a challenge. 12 In addition to personal biological status, the optimal treatment in older cancer patients is challenged by the importance of maintaining quality of life (QoL). In routine clinical practice, the question often arises as to which treatment strategy is the right one for each patient, especially one that allows the patient to remain independent for as long as possible.…”
Section: Treatment Of Older Cancer Patientsmentioning
the population and the relatively high frequency of cancer in this age group, patients 65 years of age or older are significantly underrepresented in trials of cancer treatments. 18 There is limited information concerning the efficacy, tolerability, and toxicity of cancer therapies in senior adults, although the literature indicates that this trend is slowly changing. 19
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