Hadley KEY SLIDES 9-5 7:45-8:15 PM

endocrinologist

Research Approaches to Multidimensional Aging Problems: Its and Thems Evan Hadley, M.D. Geriatrics and Clinical Gerontology Program National Institute on Aging

Two Perspectives on Contributors to Conditions in Old Age Multiple contributors to a condition A B C D Shared contributor to multiple conditions B C D A

Searching for common contributors: Why bother? B C D For the goal of improving interventions, finding common contributors can help to: Identify better therapeutic targets Decrease polypharmacy (different treatments for B, C, and D) and its potential adverse consequences Increase efficiency or power of clinical trials against B,C, and D: If B+C+D can be considered as an “it”, e.g., a “syndrome” caused by A, this can avoid losses in power due to multiple-comparison adjustments when considering B, C, and D as separate conditions (“them”).

Searching for common causes or contributors One approach: B C D Looking for clustering: Determine whether B, C, and D occur together more often than would be expected by chance. If they do, there is evidence that there may be an “A” Can be done even in absence of “candidate” for A Clustering could occur without any “A”, e.g., B C D

Searching for common causes or contributors B C D ? If evidence of clustering is found, can test relationship of “candidates” for A to outcomes B, C,and D.

Searching for common causes or contributors Another approach: A Candidate etiology: Identify a putative contributor to a group of outcomes.

Searching for common causes or contributors B C D Candidate etiology approach can be done even in absence of a priori evidence for clustering of B, C, and D

Different types of clustering of conditions that could share a common cause or contributory factor Two extremes: Consistent co-occurrence of conditions that are rare individually, e.g., syndromes due to single-gene mutations (Example: Wilson’s Disease) Prima facie case for shared cause is strong Inconsistent co-occurrence of conditions that are common individually, e.g., thinking, moving, feeling problems in old people. Co-occurrence could be due to chance Statistical approaches needed to determine clustering Large number of common conditions in older persons makes this a typical situation in old age

Comorbidities Affect Ascertainment of Clustering Example: Combination of four conditions: Evidence for common underlying cause in 35-year old men? Libido | Muscle Atrophy | Depressive Symptoms | Mild Cognitive Deficits

 Testosterone  Libido | Muscle Atrophy | Depressive Symptoms | Mild Cognitive Deficits Evidence of strong association of the combination of four conditions with a single common causal factor is basis for considering the combination as one condition (hypogonadism), with a single therapeutic target (testosterone level).

Comorbidities Affect Ascertainment of Clustering Example: Combination of these four conditions: Evidence for underlying common cause in 85-year old men?  Libido | Muscle Atrophy | Depressive Symptoms | Mild Cognitive Deficits

Mild Cognitive Deficits Physical inactivity Malnutrition Peripheral vascular disease Et al. Sleep Disorders Social factors Hypothyroidism Early Alzheimer’s Anticholinergic drugs  Testosterone Muscle Atrophy Depressive Symptoms Mild Cognitive Deficits  Libido Depression Some SSRIs Chronic kidney disease Et al. Conditions are common individually, so co-occurrence not rare Each condition has other common potential contributory factors besides low testosterone Only partial overlap of other potential contributory factors to each condition

(pb∩c/pbpc)F+ vs. (pb∩c/pbpc)F- Strategies to Identify Clustering of Conditions and Shared Contributory Factors in the Presence of Multiple Morbidity Statistical techniques to determine clustering – the extent to which a combination of conditions occurs more often than expected by chance, e.g.,using observed/expected ratios (pb∩c/pbpc). If clustering is confined to a small subgroup that has the contributory factor, it may be missed in the overall population. However, role of a “candidate” common contributory factor in a subgroup can be probed by comparing the degree of clustering in the subgroup with the factor with the degree of clustering in the rest of the population, e.g., (pb∩c/pbpc)F+ vs. (pb∩c/pbpc)F-

Candidate etiology approach to identify shared contributory factors: B C D ? ? ?

Candidate etiology approach to identify shared contributory factors: B C D Potential error from observational studies: A is common effect, not common cause. Possible example: cytokines.

Candidate etiology approach to identify shared contributory factors B C D E F Bad effect Good effect Potential problem with pleiotropic factors: Contributory factors to multiple conditions can also protect against other conditions Examples: cytokines, oxygen radicals, various hormones Therapeutic implications, particularly in presence of multiple morbidities or risk factors Therapeutic role for tissue-specific modulators of pleiotropic processes (SERMs, SARMs, et al.)

Contributions of an aging process to multiple conditions can be masked by disease progression and complications Age Primary aging process Early disease process Late stage disease and complications Aging mechanisms may be responsible for starting disease processes whose progression causes new pathologies. The new pathologies can contribute to further progression through mechanisms independent of the original aging mechanism that started the process.

Contributions of an aging process to multiple conditions can be masked by disease progression and complications Age Primary aging process Early disease process Late stage disease and complications Thus, an aging process leading to multiple pathologies in old age may be undetectable in old age if its role in the pathologies is confined to their initiation earlier in life. Implication: Interventions against such an aging process may be ineffective against these pathologies, or even harmful, if started late in life, but effective if started earlier. Some laboratory animal evidence for this phenomenon: caloric restriction, growth hormone.

Life-course approaches to elucidating effects of an aging process on multiple pathologies in old age: Epidemiologic data on relationships of phenotypes and exposures in early and mid-life to co-occurrence of conditions in old age Genetic epidemiologic studies on relationships of genotypes to progression of multiple pathologies over the life span. At what stage(s) does a genotype have its effects? Animal models of effects of genetic factors on co-occurrence of pathologies in old age Animal models of effects of interventions on aging processes in early and mid-life on co-occurrence of conditions in late life