Project Summary
Cardiometabolic multimorbidity (CMM) in older adults represents the largest cause of
morbidity and mortality both in the United States. Over the years, an increasingly large amount of
evidence has demonstrated the importance of including physical function in the clinical
assessment of older adults. However, current risk-prediction models have primarily focused on
comorbidities, do not include physical function, and have shown inconsistent accuracy in older
adults. Recent multimorbidity research has demonstrated the need to include an examination of
physical and functional status on their influence on health outcomes, rather than relying on a mere
addition of diseases.
Gait speed and hand-grip strength are pragmatic and cost-effective screening tools as
these are simple, inexpensive, reliable, easy to perform, and can be integrated in the community
as screening tests with little training. Since the impact of multimorbidity on health outcomes is
notably influenced by physical function, examination of reliable functional biomarkers -gait speed
and hand-grip strength is critical. However, in older adults with CMM, the prognostic value of gait
speed and hand-grip strength has not been examined.
This study aims to 1) characterize risk profiles of older adults in the four pre-identified
mutually exclusive CMM clusters using sociodemographic characteristics, gait speed, hand-grip
strength, and health outcomes (hospitalizations, long-term placement, and mortality); 2) Identify
and compare rates of change in functional biomarkers between groups by examining trajectories
of gait speed and hand-grip strength within the four pre-identified mutually exclusive CMM clusters
over a period of 5 years and; 3) to establish gait speed and hand-grip strength cut-off points for
1) risk of hospitalization, 2) long-term care placement and 3) mortality in the four mutually
exclusive CMM clusters, and determine the sensitivity, specificity, and likelihood ratios of the
identified cut points.
To answer these questions, we link data from the National Health and Aging Trends Study
(NHATS) to Medicare claims data to longitudinally assess functional biomarkers and health
outcomes. Latent class growth curve modelling will be used to examine trajectories of gait speed
and hand-grip strength over the course of 10 years in older adults with CMM. A receiver operating
characteristic (ROC) curve and the area under the curve (AUC) will be calculated to identify the
cut-off points with optimal sensitivity, specificity, and likelihood ratios for defining gait speed and
hand-grip strength to predict hospitalization, long-term placement, and mortality.
