Aging precisely: Precision medicine through the lens of an older adult.

Precision medicine presents an opportunity to use novel, data-driven strategies to improve patient care. The field of precision medicine has undergone many advancements over the past few years. It has moved beyond incorporation of individualized genetic risk into medical decision-making to include multiple other factors such as unique social, demographic, behavioral, and clinical characteristics. Geriatric medicine stands to benefit heavily from the integration of precision medicine into its standard practices. Older adults, compared with other populations, have high clinical and biological heterogeneity that can alter the risks and benefits of different approaches to patient care. These factors have not been routinely considered previously by geriatricians. Yet, geriatricians' ability to address older adults' baseline heterogeneity is increasingly recognized as a cornerstone of delivering quality care in a geriatric medical practice. Given the shared focus of individualized decision-making, precision medicine is a natural fit for geriatric medicine. This manuscript provides, via cases and discussion, examples that illustrate how precision medicine can improve the care of our older patients today. We will share specific and existing tools and evidence, and review the existing multilevel barriers to further incorporate and implement these tools into clinical practice. We propose methods to address these barriers and to help realize the full potential of precision medicine for the care of older adults. We conclude with a brief discussion of potential future directions of research of precision medicine in the care of older adults.

Cost of care for Alzheimer's disease and related dementias in the United States: 2016 to 2060.

Medical and long-term care for Alzheimer's disease and related dementias (ADRDs) can impose a large economic burden on individuals and societies. We estimated the per capita cost of ADRDs care in the in the United States in 2016 and projected future aggregate care costs during 2020-2060. Based on a previously published methodology, we used U.S. Health and Retirement Survey (2010-2016) longitudinal data to estimate formal and informal care costs. In 2016, the estimated per patient cost of formal care was $28,078 (95% confidence interval [CI]: $25,893-$30,433), and informal care cost valued in terms of replacement cost and forgone wages was $36,667 ($34,025-$39,473) and $15,792 ($12,980-$18,713), respectively. Aggregate formal care cost and formal plus informal care cost using replacement cost and forgone wage methods were $196 billion (95% uncertainty range [UR]: $179-$213 billion), $450 billion ($424-$478 billion), and $305 billion ($278-$333 billion), respectively, in 2020. These were projected to increase to $1.4 trillion ($837 billion-$2.2 trillion), $3.3 trillion ($1.9-$5.1 trillion), and $2.2 trillion ($1.3-$3.5 trillion), respectively, in 2060.

Construct validity of the electronic Veterans Affairs Frailty Index against clinician frailty assessment.

BACKGROUND: Electronic frailty indices (eFIs) can expand measurement of frailty in research and practice and have demonstrated predictive validity in associations with clinical outcomes. However, their construct validity is less well studied. We aimed to assess the construct validity of the VA-FI, an eFI developed for use in the U.S. Veterans Affairs Healthcare System. METHODS: Veterans who underwent comprehensive geriatric assessments between January 31, 2019 and June 6, 2022 at VA Boston and had sufficient data documented for a comprehensive geriatric assessment-frailty index (CGA-FI) were included. The VA-FI, based on diagnostic and procedural codes, and the CGA-FI, based on geriatrician-measured deficits, were calculated for each patient. Geriatricians also assessed the Clinical Frailty Scale (CFS), functional status (ADLs and IADLs), and 4-meter gait speed (4MGS). RESULTS: A total of 132 veterans were included, with median age 81.4 years (IQR 75.8-88.7). Across increasing levels of VA-FI (<0.2; 0.2-0.4; >0.4), mean CGA-FI increased (0.24; 0.30; 0.40). The VA-FI was moderately correlated with the CGA-FI (r 0.45, p < 0.001). Every 0.1-unit increase in the VA-FI was associated with an increase in the CGA-FI (linear regression beta 0.05; 95% confidence interval [CI] 0.03-0.06), higher CFS category (ordinal regression OR 1.69; 95% CI 1.24-2.30), higher odds of ADL dependency (logistic regression OR 1.59; 95% CI 1.20-2.11), IADL dependency (logistic regression OR 1.68; 95% CI 1.23-2.30), and a decrease in 4MGS (linear regression beta -0.07, 95% CI -0.12 to -0.02). All models were adjusted for age and race, and associations held after further adjustment for the Charlson Comorbidity Index. CONCLUSION: Our results demonstrate the construct validity of the VA-FI through its associations with clinical measures of frailty, including summary frailty measures, functional status, and objective physical performance. Our findings complement others' in showing that eFIs can capture functional and mobility domains of frailty beyond just comorbidity and may be useful to measure frailty among populations and individuals.

Inflammatory Biomarkers Differ among Hospitalized Veterans Infected with Alpha, Delta, and Omicron SARS-CoV-2 Variants.

Mortality due to COVID-19 has been correlated with laboratory markers of inflammation, such as C-reactive protein (CRP). The lower mortality during Omicron variant infections could be explained by variant-specific immune responses or host factors, such as vaccination status. We hypothesized that infections due to Omicron variant cause less inflammation compared to Alpha and Delta, correlating with lower mortality. This was a retrospective cohort study of veterans hospitalized for COVID-19 at the Veterans Health Administration. We compared inflammatory markers among patients hospitalized during Omicron infection with those of Alpha and Delta. We reported the adjusted odds ratio (aOR) of the first laboratory results during hospitalization and in-hospital mortality, stratified by vaccination status. Of 2,075,564 Veterans tested for COVID-19, 29,075 Veterans met the criteria: Alpha (45.1%), Delta (23.9%), Omicron (31.0%). Odds of abnormal CRP in Delta (aOR = 1.85, 95% CI:1.64-2.09) and Alpha (aOR = 1.94, 95% CI:1.75-2.15) were significantly higher compared to Omicron. The same trend was observed for Ferritin, Alanine aminotransferase, Aspartate aminotransferase, Lactate dehydrogenase, and Albumin. The mortality in Delta (aOR = 1.92, 95% CI:1.73-2.12) and Alpha (aOR = 1.68, 95% CI:1.47-1.91) were higher than Omicron. The results remained significant after stratifying the outcomes based on vaccination status. Veterans infected with Omicron showed milder inflammatory responses and lower mortality than other variants.

Global and regional projections of the economic burden of Alzheimer's disease and related dementias from 2019 to 2050: A value of statistical life approach.

Background: The burden of Alzheimer's disease and related dementias (ADRDs) is expected to grow rapidly with population aging, especially in low- and middle-income countries, in the next few decades. We used a willingness-to-pay approach to project the global, regional, and national economic burden of ADRDs from 2019 to 2050 under status quo. Methods: We projected age group and country-specific disability-adjusted life years (DALYs) lost to ADRDs in future years based on historical growth in disease burden and available population projections. We used country-specific extrapolations of the value of a statistical life (VSL) year and its future projections based on historical income growth to estimate the economic burden - measured in terms of the value of lost DALYs - of ADRDs. A probabilistic uncertainty analysis was used to calculate point estimates and 95% uncertainty bounds of the economic burden. Findings: In 2019, the global VSL-based economic burden of ADRDs was an estimated $2.8 trillion. The burden was projected to increase to $4.7 trillion (95% uncertainty bound: $4 trillion-$5.5 trillion) in 2030, $8.5 trillion ($6.8 trillion-$10.8 trillion) in 2040, and $16.9 trillion ($11.3 trillion-$27.3 trillion) in 2050. Low- and middle-income countries (LMICs) would account for 65% of the global VSL-based economic burden in 2050, as compared with only 18% in 2019. Within LMICs, upper-middle income countries would carry the largest VSL-based economic burden by 2050 (92% of LMICs burden and 60% of global burden). Interpretation: ADRDs have a large and inequitable projected future VSL-based economic burden. Funding: The Davos Alzheimer's Collaborative.

30-day mortality following COVID-19 and influenza hospitalization among US veterans aged 65 and older.

BACKGROUND: COVID-19 and influenza are important sources of morbidity and mortality among older adults. Understanding how outcomes differ for older adults hospitalized with either infection is important for improving care. We compared outcomes from infection with COVID-19 and influenza among hospitalized older adults. METHODS: We conducted a retrospective study of 30-day mortality among veterans aged 65+ hospitalized with COVID-19 from March 1, 2020-December 31, 2020 or with influenza A/B from September 1, 2017 to August 31, 2019 in Veterans Affairs Health Care System (VAHCS). COVID-19 infection was determined by a positive PCR test and influenza by tests used in the VA system. Frailty was defined by the claims-based Veterans Affairs Frailty Index (VA-FI). Logistic regressions of mortality on frailty, age, and infection were adjusted for multiple confounders. RESULTS: A total of 15,474 veterans were admitted with COVID-19 and 7867 with influenza. Mean (SD) ages were 76.1 (7.8) and 75.8 (8.3) years, 97.7% and 97.4% were male, and 66.9% and 76.4% were white in the COVID-19 and influenza cohorts respectively. Crude 30-day mortality (95% CI) was 18.9% (18.3%-19.5%) for COVID-19 and 4.3% (3.8%-4.7%) for influenza. Combining cohorts, the odds ratio for 30-day mortality from COVID-19 (versus influenza) was 6.61 (5.74-7.65). There was a statistically significant interaction between infection with COVID-19 and frailty, but there was no significant interaction between COVID-19 and age. Separating cohorts, greater 30-day mortality was significantly associated with older age (p: COVID-19: <0.001, Influenza: <0.001) and for frail compared with robust individuals (p for trend: COVID-19: <0.001, Influenza: <0.001). CONCLUSION: Mortality from COVID-19 exceeded that from influenza among hospitalized older adults. However, odds of mortality were higher at every level of frailty among those admitted with influenza compared to COVID-19. Prevention will remain key to reducing mortality from viral illnesses among older adults.

Epigenetic Age Acceleration and Change in Frailty in MOBILIZE Boston.

Age-associated changes in DNA methylation have been implicated as 1 mechanism to explain the development of frailty; however, previous cross-sectional studies of epigenetic age acceleration (eAA) and frailty have had inconsistent findings. Few longitudinal studies have considered the association of eAA with change in frailty. We sought to determine the association between eAA and change in frailty in the MOBILIZE Boston cohort. Participants were assessed at 2 visits 12-18 months apart. Intrinsic, extrinsic, GrimAge, and PhenoAge eAA were assessed from whole-blood DNA methylation at baseline using the Infinium 450k array. Frailty was assessed by a continuous frailty score based on the frailty phenotype and by frailty index (FI). Analysis was by correlation and linear regression with adjustment for age, sex, smoking status, and body mass index. Three hundred and ninety-five participants with a frailty score and 431 with an FI had epigenetic and follow-up frailty measures. Mean (standard deviation) ages were 77.8 (5.49) and 77.9 (5.47) for the frailty score and the FI cohorts respectively, and 232 (58.7%) and 257 (59.6%) were female. All participants with epigenetic data identified as White. Baseline frailty score was not correlated with intrinsic or extrinsic eAA, but was correlated with PhenoAge and, even after adjustment for covariates, GrimAge. Baseline FI was correlated with extrinsic, GrimAge, and PhenoAge eAA with and without adjustment. No eAA measure was associated with change in frailty, with or without adjustment. Our results suggest that no eAA measure was associated with change in frailty. Further studies should consider longer periods of follow-up and repeated eAA measurement.

Predictors of COVID-19 outcomes: Interplay of frailty, comorbidity, and age in COVID-19 prognosis.

Prior research has identified frailty, comorbidity, and age as predictors of outcomes for patients with coronavirus disease 2019 (COVID-19), including mortality. However, it remains unclear how these factors play different roles in COVID-19 prognosis. This study focused on correlations between frailty, comorbidity and age, and their correlations to discharge outcome and length-of-stay in hospitalized patients with COVID-19. Clinical data was collected from 56 patients who were ≥50 years old and admitted from March 2020 to June 2020 primarily for COVID-19. Frailty Risk Score (FRS) and the Charlson Comorbidity Index (CCI) were used for assessment of frailty and comorbidity burden, respectively. Age had significant positive correlation with FRS and CCI (P < .001, P < .001, respectively). There was also significant positive correlation between FRS and CCI (P < .001). For mortality, patients who died during their hospitalization had significantly higher FRS and CCI (P = .01 and P < .001, respectively) but were not significantly older than patients who did not. FRS, CCI, and age were all significantly associated when looking at overall adverse discharge outcome (transfer to other facility or death) (P < .001, P = .005, and P = .009, respectively). However, none of the 3 variables were significantly correlated with length-of-stay. Multivariate analysis showed FRS (P = .007) but not patient age (P = .967) was significantly associated with death. We find that frailty is associated with adverse outcomes from COVID-19 and supplants age in multivariable analysis. Frailty should be part of risk assessment of older adults with COVID-19.

Correction: Social determinants of mortality from COVID-19: A simulation study using NHANES.

[This corrects the article DOI: 10.1371/journal.pmed.1003490.].

Social determinants of mortality from COVID-19: A simulation study using NHANES.

BACKGROUND: The COVID-19 epidemic in the United States is widespread, with more than 200,000 deaths reported as of September 23, 2020. While ecological studies show higher burdens of COVID-19 mortality in areas with higher rates of poverty, little is known about social determinants of COVID-19 mortality at the individual level. METHODS AND FINDINGS: We estimated the proportions of COVID-19 deaths by age, sex, race/ethnicity, and comorbid conditions using their reported univariate proportions among COVID-19 deaths and correlations among these variables in the general population from the 2017-2018 National Health and Nutrition Examination Survey (NHANES). We used these proportions to randomly sample individuals from NHANES. We analyzed the distributions of COVID-19 deaths by race/ethnicity, income, education level, and veteran status. We analyzed the association of these characteristics with mortality by logistic regression. Summary demographics of deaths include mean age 71.6 years, 45.9% female, and 45.1% non-Hispanic white. We found that disproportionate deaths occurred among individuals with nonwhite race/ethnicity (54.8% of deaths, 95% CI 49.0%-59.6%, p < 0.001), individuals with income below the median (67.5%, 95% CI 63.4%-71.5%, p < 0.001), individuals with less than a high school level of education (25.6%, 95% CI 23.4% -27.9%, p < 0.001), and veterans (19.5%, 95% CI 15.8%-23.4%, p < 0.001). Except for veteran status, these characteristics are significantly associated with COVID-19 mortality in multiple logistic regression. Limitations include the lack of institutionalized people in the sample (e.g., nursing home residents and incarcerated persons), the need to use comorbidity data collected from outside the US, and the assumption of the same correlations among variables for the noninstitutionalized population and COVID-19 decedents. CONCLUSIONS: Substantial inequalities in COVID-19 mortality are likely, with disproportionate burdens falling on those who are of racial/ethnic minorities, are poor, have less education, and are veterans. Healthcare systems must ensure adequate access to these groups. Public health measures should specifically reach these groups, and data on social determinants should be systematically collected from people with COVID-19.

Machine learning approaches to the social determinants of health in the health and retirement study.

BACKGROUND: Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. METHODS: A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. RESULTS: All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data (R2 between 0.4-0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. DISCUSSION: Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

Acute coronary syndromes in low- and middle-income countries: Moving forward.

Cardiovascular disease remains the leading cause of death worldwide, particularly in low- and middle-income countries (LMICs), with substantial mortality from acute coronary syndromes. These deaths, when compared against high-income countries, occur at younger ages, and, beyond the lives lost, often result in economic privation for families deprived of a breadwinner and indebted by the oftentimes catastrophic cost of inpatient medical care. This burden will likely grow in scale in the years ahead as more countries pass through the epidemiologic transition. Billions around the world are beginning to experience the comforts that even modestly increased incomes can provide, including diets high in fats and sugars, more sedentary lifestyles, and tobacco and alcohol use and abuse. Health care systems in many of these countries are ill-equipped to prevent the harms caused by these lifestyles, as well as treat the acute coronary syndromes that result from them-including insufficient access to appropriate facilities and medications, difficulties with transport, and low awareness of the symptoms and need for emergent evaluation.

Equity and length of lifespan are not the same.

Efforts to understand the dramatic declines in mortality over the past century have focused on life expectancy. However, understanding changes in disparity in age of death is important to understanding mechanisms of mortality improvement and devising policy to promote health equity. We derive a novel decomposition of variance in age of death, a measure of inequality, and apply it to cause-specific contributions to the change in variance among the G7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) from 1950 to 2010. We find that the causes of death that contributed most to declines in the variance are different from those that contributed most to increase in life expectancy; in particular, they affect mortality at younger ages. We also find that, for two leading causes of death [cancers and cardiovascular disease (CVD)], there are no consistent relationships between changes in life expectancy and variance either within countries over time or between countries. These results show that promoting health at younger ages is critical for health equity and that policies to control cancer and CVD may have differing implications for equity.

Global perspective on acute coronary syndrome: a burden on the young and poor.

Ischemic heart disease (IHD) is the greatest single cause of mortality and loss of disability-adjusted life years worldwide, and a substantial portion of this burden falls on low- and middle-income countries (LMICs). Deaths from IHD and acute coronary syndrome (ACS) occur, on average, at younger ages in LMICs than in high-income countries, often at economically productive ages, and likewise frequently affect the poor within LMICs. Although data about ACS in LMICs are limited, there is a growing literature in this area and the research gaps are being steadily filled. In high-income countries, decades of investigation into the risk factors for ACS and development of behavioral programs, medications, interventional procedures, and guidelines have provided us with the tools to prevent and treat events. Although similar tools can be, and in fact have been, implemented in many LMICs, challenges remain in the development and implementation of cardiovascular health promotion activities across the entire life course, as well as in access to treatment for ACS and IHD. Intersectoral policy initiatives and global coordination are critical elements of ACS and IHD control strategies. Addressing the hurdles and scaling successful health promotion, clinical and policy efforts in LMICs are necessary to adequately address the global burden of ACS and IHD.

Multi-Country analysis of palm oil consumption and cardiovascular disease mortality for countries at different stages of economic development: 1980-1997.

BACKGROUND: Cardiovascular diseases represent an increasing share of the global disease burden. There is concern that increased consumption of palm oil could exacerbate mortality from ischemic heart disease (IHD) and stroke, particularly in developing countries where it represents a major nutritional source of saturated fat. METHODS: The study analyzed country-level data from 1980-1997 derived from the World Health Organization's Mortality Database, U.S. Department of Agriculture international estimates, and the World Bank (234 annual observations; 23 countries). Outcomes included mortality from IHD and stroke for adults aged 50 and older. Predictors included per-capita consumption of palm oil and cigarettes and per-capita Gross Domestic Product as well as time trends and an interaction between palm oil consumption and country economic development level. Analyses examined changes in country-level outcomes over time employing linear panel regressions with country-level fixed effects, population weighting, and robust standard errors clustered by country. Sensitivity analyses included further adjustment for other major dietary sources of saturated fat. RESULTS: In developing countries, for every additional kilogram of palm oil consumed per-capita annually, IHD mortality rates increased by 68 deaths per 100,000 (95% CI [21-115]), whereas, in similar settings, stroke mortality rates increased by 19 deaths per 100,000 (95% CI [-12-49]) but were not significant. For historically high-income countries, changes in IHD and stroke mortality rates from palm oil consumption were smaller (IHD: 17 deaths per 100,000 (95% CI [5.3-29]); stroke: 5.1 deaths per 100,000 (95% CI [-1.2-11.0])). Inclusion of other major saturated fat sources including beef, pork, chicken, coconut oil, milk cheese, and butter did not substantially change the differentially higher relationship between palm oil and IHD mortality in developing countries. CONCLUSIONS: Increased palm oil consumption is related to higher IHD mortality rates in developing countries. Palm oil consumption represents a saturated fat source relevant for policies aimed at reducing cardiovascular disease burdens.

Comparative analysis of old-age mortality estimations in Africa.

BACKGROUND: Survival to old ages is increasing in many African countries. While demographic tools for estimating mortality up to age 60 have improved greatly, mortality patterns above age 60 rely on models based on little or no demographic data. These estimates are important for social planning and demographic projections. We provide direct estimations of older-age mortality using survey data. METHODS: Since 2005, nationally representative household surveys in ten sub-Saharan countries record counts of living and recently deceased household members: Burkina Faso, Côte d'Ivoire, Ethiopia, Namibia, Nigeria, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe. After accounting for age heaping using multiple imputation, we use this information to estimate probability of death in 5-year intervals ((5)q(x)). We then compare our (5)q(x) estimates to those provided by the World Health Organization (WHO) and the United Nations Population Division (UNPD) to estimate the differences in mortality estimates, especially among individuals older than 60 years old. FINDINGS: We obtained information on 505,827 individuals (18.4% over age 60, 1.64% deceased). WHO and UNPD mortality models match our estimates closely up to age 60 (mean difference in probability of death -1.1%). However, mortality probabilities above age 60 are lower using our estimations than either WHO or UNPD. The mean difference between our sample and the WHO is 5.9% (95% CI 3.8-7.9%) and between our sample is UNPD is 13.5% (95% CI 11.6-15.5%). Regardless of the comparator, the difference in mortality estimations rises monotonically above age 60. INTERPRETATION: Mortality estimations above age 60 in ten African countries exhibit large variations depending on the method of estimation. The observed patterns suggest the possibility that survival in some African countries among adults older than age 60 is better than previously thought. Improving the quality and coverage of vital information in developing countries will become increasingly important with future reductions in mortality.

Aging, transition, and estimating the global burden of disease.

BACKGROUND: The World Health Organization's Global Burden of Disease (GBD) reports are an important tool for global health policy makers, however the accuracy of estimates for countries undergoing an epidemiologic transition is unclear. We attempted to validate the life table model used to generate estimates for all-cause mortality in developing countries. METHODS AND RESULTS: Data were obtained for males and females from the Human Mortality Database for all countries with available data every ten years from 1900 to 2000. These provided inputs for the GBD life table model and served as comparison observed data. Above age sixty model estimates of survival for both sexes differed substantially from those observed. Prior to the year 1960 for males and 1930 for females, estimated survival tended to be greater than observed; following 1960 for both males and females estimated survival tended to be less than observed. Viewing observed and estimated survival separately, observed survival past sixty increased over the years considered. For males, the increase was from a mean (sd) probability of 0.22 (0.06) to 0.46 (0.1). For females, the increase was from 0.26 (0.06) to 0.65 (0.08). By contrast, estimated survival past sixty decreased over the same period. Among males, estimated survival probability declined from 0.54 (0.2) to 0.09 (0.06). Among females, the decline was from 0.36 (0.12) to 0.15 (0.08). CONCLUSIONS: These results show that the GBD mortality model did not accurately estimate survival at older ages as developed countries transitioned in the twentieth century and may be similarly flawed in developing countries now undergoing transition. Estimates of the size of older-age populations and their attributable disease burden should be reconsidered.