Comorbidity: From Bedside to Bench

1. Comorbidity: From Bedside to Bench Summary of the NIA/AGS R13 Conference ASG Annual Meetings, May 13, 2005, Orlando

2. Comorbidity, Multi-Morbidity • “any distinct clinical entity that has existed or may occur during the clinical course of a patient who has an index disease [or condition] under study.” (Feinstein, 1970) • …distinct clinical entities coexisting or likely to co-occur during a patient’s clinical course… ASG Annual Meetings, May 13, 2005, Orlando

3. Symposium Presenters • Rebecca Silliman, MD: The NIA Comorbidity Taskforce • Alison Moore, MD: Comorbidity in Relation to the Study and Treatment of Index Conditions • Christine Ritchie, MD: The Health and Social Burden of Multiple Morbidity • Stephanie Studenski, MD: The Research Agenda ASG Annual Meetings, May 13, 2005, Orlando

4. Multimorbidity: Concepts and Research Recommendations Thanks to Linda Fried for the use of some of her presentation and to the members of the “preclinical” break out session Stephanie Studenski MD MPH Professor, Department of Medicine (geriatrics) Staff Physician, VA Pittsburgh GRECC 3471 Fifth Avenue Suite 500 Pittsburgh Pa 15213 office 412 692 2360 fax 412 692 2370 email studenskis@msx.dept-med.pitt.edu ASG Annual Meetings, May 13, 2005, Orlando

5. Outline • Multimorbidity: the burden of illness • Clusters of diseases and conditions: causes and consequences Definitions Comorbidity: additional diseases beyond the index disease Multimorbidity: co-occurrence of diseases ASG Annual Meetings, May 13, 2005, Orlando

6. Multimorbidity • Often no index condition. • Systems serve as reserve capacity for each other’s losses. • Multimorbidity reflects total burden of illness and has implications for “reserve” and “tolerance to stress”. ASG Annual Meetings, May 13, 2005, Orlando

7. Measuring the Burden of Illness: Challenges • When burden is assessed by diagnoses, factors that influence the process of clinical diagnosis affect reports. • Eg: Clinical thresholds for the diagnosis of disease vary by provider recognition, shifts over time in definitions eg DM, hyperlipidemia, HBP • Eg: Severity measures may be affected by coexisting conditions eg treadmill testing and CAD. Subspecialists may ignore the effect of coexisting conditions. ASG Annual Meetings, May 13, 2005, Orlando

8. Physiological system indicators may eliminate variability due to clinical thresholds • When burden is assessed by diagnoses, factors that influence the process of clinical diagnosis affect reports. • Eg: Clinical thresholds for the diagnosis of disease vary by provider recognition, shifts over time in definitions eg DM, hyperlipidemia, HBP • Eg: Severity measures may be affected by coexisting conditions eg treadmill testing and CAD. Subspecialists may ignore the effect of coexisting conditions. ASG Annual Meetings, May 13, 2005, Orlando

9. Physiological system indicators may eliminate variability due to clinical thresholds Physiological System Dx Severity

10. Physiological system indicators may eliminate variability due to clinical thresholds Physiological System Dx Severity

11. Opportunities • Create a system of basic markers of physiological functions across key systems (a battery like “APACHE”) • Basic indicators by system (e.g., Hb, Creat). • Develop and evaluate using existing data. • Applications • Compare to measures based on diagnoses. • Might help ease barriers to including research on elders with comorbidity. • Use “battery” to examine interactions and demands between physiological systems. • Trials could look at subclinical adverse effects across subgroups

12. The Physiologic Battery as an indicator of burden of illness/multimorbidity • Expand battery to include “axes” within physiological systems/disease • Duration and pattern over time • Treatment effects • Adds detail but increases complexity and demand of measure

13. Next Steps • Modeling that accounts for time and patterns: the NIA longitudinal analysis RFA • Novel analytic methods • Training (K awards), methodological publications • Data sets (“core data”) with physiological indicators • Health ABC, InChianti, BLSA

14. Other Measures of Burden of Illness/Multimorbidity • Physical Performance measures can be thought of as summary measures of preclinical & clinical conditions; they are composites and are non-specific. • One kind of indicator; an integrative summary of multiple morbidity • Do not attribute symptoms and function only to index condition

15. Clusters: what can they tell us? • Cluster: system abnormalities that co-occur at a rate that is higher than expected by chance alone. • Types of clusters • single underlying common cause • secondary consequences of “index” condition • Clusters can provide insights into common causes and into combined effects on consequences like disability.

16. Clusters in late life: implications for causation • 24 year old woman with rash, arthritis and kidney disease • 84 year old woman with rash, arthritis and kidney disease • Since conditions are more rare in younger adult, a cluster is unlikely to be due to chance, and is likely to have a common cause. • Conversely, since conditions are more common in older adults, clusters are more likely to be due to chance and may not have a common cause.

17. Late Life Clusters • Unrecognized underlying process or condition precipitates multiple abnormalities; “inflammation” as cause of atherosclerosis, malnutrition, frailty, neurodegeneration: creates new target for intervention. (Ferrucci L et al A flame burning within. Aging Clin Exp Res. 2004) • A known condition precipitates others eg diabetes, atherosclerosis, renal failure: target precipitating condition for intervention(Volpato et al Diabetes Care 2002)

18. Primary clusters Clusters with no recognized underlying common cause are an opportunity for research into prevention and treatment of late life multimorbidity Disease B Potential underlying cause Disease C Disease D

19. Secondary Clusters:Diabetes and complications • Duration of diabetes associated with presence of: CHD, CHF, PAD, HTN, Depression • Diabetes associated with: • Peripheral neuropathy, CVD, visual impairment, obesity • Disability: mobility, ADL, IADL Volpato,Diabetes Care 2002

20. Consequences: combinations of diseases synergistically associated with disability

21. Two Diseases Present Concurrently have Joint Effects • Risk of Mobility Disability • Heart Disease Only: OR = 2.3 • Arthritis Only: OR = 4.3 • Both Heart Disease and Arthritis: OR = 13.6 NHANES III Ettinger et al;

22. Clusters and Consequences • “Much of the action is in the interaction” • The interactions between diseases contribute to disability, over and above the independent contribution of each disease. • Research questions: Interactions between specific disease pairs might have effects specific to different types of function. • Clinical implications: target preservation of specific functions by minimizing specific interactions?

23. Comorbidity in relation to study and treatment of index disease Alison A. Moore, MD, MPH David Geffen School of Medicine at UCLA Division of Geriatric Medicine

24. HIV/AIDS as a Chronic Disease: the Veterans Aging Cohort Study Amy C. Justice, MD, PhD PI, Veterans Aging Cohort Study GIM Section Chief, West Haven VAMC Yale University

25. Why Study HIV and Comorbidity? • Clinical Reasons: • Prevalence: People with HIV are living long enough to age • Incidence: As more people with HIV are aging, more older individuals will contract HIV • Toxicity: Difficult to determine what is due to treatment if we don’t understand underlying risk of comorbid disease • Research Reasons: • Bench: effect modification may lead to pathophysiologic insights • Outcomes: due to implications for survival: optimal management of HIV may differ by age; optimal management of comorbidity may differ by HIV status

26. Life Expectancy after HIV diagnosiswithandwithoutHAART Years Age 50 without Age 40 with without with without Age 30 with CD4 = 750 CD4 = 500 CD4 = 200

27. Non-AIDS DeathswithandwithoutHAART(Virtual Cohort) % Age 50 Age 40 with without without without with Age 30 with CD4 = 750 CD4 = 500 CD4 = 200

28. Conclusions • More HIV pts will die from non-HIV causes • Nearly half of patients with age>40 years • If mean age at HIV diagnosis remains 38, • Mean survival will approach 19.6 years • Mean age at death will approach 58 years • Guidelines for management of diseases occurring with complex chronic disease must account for • Shortened life expectancy • Increased risk due to primary disease and its treatment

29. Late Life Depression and Medical Comorbidity Ira R. Katz, MD, PhD Professor of Psychiatry University of Pennsylvania Director, MIRECC Philadelphia VA Medical Center

30. Depression amplifies morbidity • Disability • Cognitive impairment • Pain (and other symptoms) • Subnutrition • Decreased treatment adherence • Increased use of health services • Increased mortality • Suicide and non-Suicide

31. Associations between Depression and Frailty Proportion with CES-D > 10 by Frailty Status From Fried et al, J Gerontol Med Sci 56A: M146-M156, 2001 CHS data

32. Depressive Symptoms Confer VulnerabilityGlaser et al, Arch Gen Psych 60: 1009-1014, 2003 Changes in IL-6 after influenza vaccination in normal older individuals

33. Conclusions • Depression is a manifestation of morbidity and a source of vulnerability • … that arises from multiple comorbidities and paths • … and leads to multiple adverse health effects • Therefore, it can be considered a “frailty”

34. Cardiovascular DiseaseThe # 1 Comorbidity in Aging Patients Anne B. Newman, MD, MPH Professor of Epidemiology and Medicine University of Pittsburgh

35. Comorbidity - CVD and other diseases • CVD and Osteoarthritis • Most common combination • CVD and Depression • Numerous studies show depression increases risk of CVD • Also possible that there is a “vascular depression” • CVD and Dementia • Vascular dementia vs. AD with vascular disease? • CVD and Cancer • CVD and Chronic Lung Disease

36. Multivariate Analysis of Subclinical Cardiovascular Disease for 1st MI CHS; n=4,946; follow-up: 4.8 yrs. * Adjusted for age, race, gender, SBP, glucose, AAI, ICA-IMT, and EF. Variables that did not make into final model: LV mass by ECG, FVC, HDL-C, smoking, and fibrinogen. Psaty BM, Furberg CD, Kuller LH, Bild DE, Rautaharju PM, Polak JF, Bovill E Gottniener JS. Traditional risk factors and subclinical disease measures as predictors of first myocardial infarction in older adults: The Cardiovascular Health Study. Arch Intern Med. 1999; 159:1339-1347.

37. Probability of “Successful Aging” by Age, Gender, and Subclinical Cardiovascular Disease 65-69 70-74 75-79 80+ 65-69 70-74 75-79 80+ Men Women Newman AB, Arnold AM, Naydeck BL, Fried LP, Burke GL, Enright P, Gottdiener J, Hirsch C, O’Leary D, Tracy R. Successful Aging: Effects of Subclinical Cardiovascular Disease. Arch Intern Med. 2003;163:2315-2322.

38. Summary • CVD is so common that it will - more often than not - be “comorbid” with something else • Clinically diagnosed CVD represents less than half of the total burden of CVD • An equal proportion have subclinical CVD • Subclinical CVD is related to • Physical performance • Frailty • Cognitive decline • Dementia

39. Diabetes and Comorbidity in Older Adults Caroline S. Blaum University of Michigan Ann Arbor VA Medical Center March, 2005

40. Research questions and hypotheses • In type 2 diabetes, do frailty and disability result from accumulating comorbidities or is it the underlying pathophysiological disruption that causes comorbidity accumulation, frailty and disability development? • Is there a stepwise relationship between the MS, Diabetes, and Diabetes+comorbidities, and frailty and disability?

41. Percent change in mobility score associated with metabolic syndrome group

42. Summary • Comorbidity prevalent in older adults with diabetes • Increases with age • Stepwise progression from MS to new diabetes to longstanding diabetes • MS related to worsening in mobility disability but diabetes has much stronger association • Obesity and diabetes are independently related to prevalent frailty. • MS is related to incident frailty and may maintain association in the presence of incident diabetes • Diabetes and many comorbidities are related to incident frailty

43. Clinical Epidemiology of Comorbidity in Aging Patients: Findings and Insights from Geriatric Oncology William A. Satariano, Ph.D, MPH School of Public Health University of California, Berkeley

44. Reasons for Research on Comorbidity and Cancer • There are age-associated patterns of cancer incidence, stage, treatment, and survival (both duration and quality of life). • It is hypothesized that age-associated patterns of comorbidity may help to account for those age-associated differences in cancer outcomes.

45. Reasons for Research on Comorbidity and Cancer • There is an extensive network of hospital-based and, more important, population-based cancer registries and surveillance systems. • Assessment of large number of cancer cases by cancer site, stage, histology, first-course of treatment. • System of linkage with other sources of health data that include records of diagnosis and treatment for other health conditions. • Affords opportunity to conduct detail analysis of cancer outcomes.

46. Reasons for Research on Comorbidity and Cancer • There is a significant area of clinical and epidemiological research on multiple primary cancers, a history of two or more primary cancers dx in a single individual.

47. Benefits and Risks of Alcohol Use among Older Persons Alison A. Moore, MD, MPH Division of Geriatric Medicine

48. All-cause mortality Coronary heart disease Congestive heart failure Cerebrovascular disease Ischemic stroke Diabetes Cholelithiasis Dementia ?Falls Conditions which may be prevented by light to moderate alcohol use

49. Lip and oropharyngeal cancer Esophageal varices and cancer Laryngeal cancer Liver cirrhosis and cancer Gastro-esophageal hemorrhage Acute and chronic pancreatitis Female breast cancer Epilepsy Hypertension Cardiac arrhythmias Hemorrhagic stroke Psoriasis Depression Gout Alcohol use disorders Conditions that may be caused or worsened by alcohol use

50. What is the effect of moderate drinking if you have comorbidities for which alcohol is beneficial? • Evidence that moderate alcohol use is beneficial among those persons having: • CHD • Stroke • Diabetes