Cardiovascular Burden of the V142I Transthyretin Variant

Senthil Selvaraj; Brian Claggett; Svati H Shah; Robert J Mentz; Michel G Khouri; Ani W Manichaikul; Sadiya S Khan; Stephen S Rich; Thomas H Mosley; Emily B Levitan; Pankaj Arora; Parag Goyal; Bernhard Haring; Charles B Eaton; Richard K Cheng; Gretchen L Wells; JoAnn E Manson; Marianna Fontana; Scott D Solomon

doi:10.1001/jama.2024.4467

. 2024 May 12;331(21):1824–1833. doi: 10.1001/jama.2024.4467

Cardiovascular Burden of the V142I Transthyretin Variant

Senthil Selvaraj ^1,², Brian Claggett ³, Svati H Shah ^1,², Robert J Mentz ¹, Michel G Khouri ¹, Ani W Manichaikul ⁴, Sadiya S Khan ⁵, Stephen S Rich ⁴, Thomas H Mosley ⁶, Emily B Levitan ⁷, Pankaj Arora ⁸, Parag Goyal ⁹, Bernhard Haring ^10,¹¹, Charles B Eaton ¹², Richard K Cheng ¹³, Gretchen L Wells ⁸, JoAnn E Manson ¹⁴, Marianna Fontana ¹⁵, Scott D Solomon ^3,^✉

¹Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, North Carolina

²Duke Molecular Physiology Institute, Durham, North Carolina

³Division of Cardiology, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts

⁴Center for Public Health Genomics, University of Virginia, Charlottesville

⁵Division of Cardiology, Department of Medicine and Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois

⁶The MIND Center, University of Mississippi Medical Center, Jackson

⁷Department of Epidemiology, University of Alabama at Birmingham

⁸Division of Cardiovascular Disease, University of Alabama at Birmingham

⁹Department of Medicine, Weill Cornell Medicine, New York, New York

¹⁰Department of Medicine III, Saarland University, Homburg, Saarland, Germany

¹¹Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York

¹²Center for Primary Care and Prevention, Department of Family Medicine, Department of Epidemiology, Warren Alpert Medical Scholl of Brown University, Brown University School of Public Health, Providence, Rhode Island

¹³Division of Cardiology, University of Washington, Seattle

¹⁴Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts

¹⁵University College London, London, United Kingdom

Accepted for Publication: February 20, 2024.

Published Online: May 12, 2024. doi:10.1001/jama.2024.4467

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Corresponding Author: Scott D. Solomon, MD, Brigham and Women’s Hospital, 75 Francis St, Boston, MA 02115 (ssolomon@rics.bwh.harvard.edu).

Author Contributions: Drs Selvaraj and Claggett had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Selvaraj, Haring, Solomon.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Selvaraj, Manichaikul, Haring, Cheng.

Critical review of the manuscript for important intellectual content: Claggett, Shah, Mentz, Khouri, Manichaikul, Khan, Rich, Mosley, Levitan, Arora, Goyal, Haring, Eaton, Cheng, Wells, Manson, Fontana, Solomon.

Statistical analysis: Selvaraj, Claggett, Manichaikul, Haring.

Obtained funding: Mosley, Levitan, Eaton, Manson, Solomon.

Administrative, technical, or material support: Manichaikul, Rich, Levitan, Arora, Goyal, Eaton, Manson, Fontana.

Supervision: Claggett, Shah, Mentz, Haring, Manson, Solomon.

Conflict of Interest Disclosures: Dr Selvaraj reported receiving grants from the National Heart, Lung, and Blood Institute (NHLBI), American Heart Association, Doris Duke Foundation, Institute for Translational Medicine and Therapeutics, American Society of Nuclear Cardiology, Mandel Foundation, Duke Heart Center Leadership Council, and Foundation for Sarcoidosis Research and personal fees from AstraZeneca outside the submitted work. Dr Claggett reported receiving personal fees from Alnylam Pharmaceuticals, Cardurion, Corvia, Cytokinetics, Intellia, Rocket, and CVRx outside the submitted work. Dr Mentz reported receiving personal fees from Novartis and AstraZeneca during the conduct of the study; personal fees from Merck, Boehringer Ingelheim/Lilly, Bayer, Medtronic, Novo Nordisk, Pharmacosmos, and Rocket and grants from American Regent outside the submitted work. Dr Khouri reported receiving personal fees from Alnylam Pharmaceuticals, BridgeBio Pharma, and PRIME Education and grants from Alnylam Pharmaceuticals, BridgeBio Pharma, Ionis Pharmaceuticals, and Pfizer outside the submitted work. Dr Khan reported receiving grants from the NHLBI (U01HL160279 and R01HL159250) during the conduct of the study and grants from NHLBI (HL161514) outside the submitted work. Dr Levitan reported receiving grants from the NHLBI during the conduct of the study and grants from Amgen Inc and personal fees from the University of Pittsburgh outside the submitted work. Dr Arora reported receiving grants from Bristol Myers Squibb, Merck Sharp & Dohme LLC, and National Institutes of Health (NIH) and personal fees from Bristol Myers Squibb outside the submitted work. Dr Eaton reported receiving grants from the NIH during the conduct of the study. Dr Manson reported receiving grants from the NIH during the conduct of the study. Dr Fontana reported receiving grants from the British Heart Foundation, Pfizer, AstraZeneca, and BridgeBio and personal fees from Alnylam, AstraZeneca, Attralus, BridgeBio, Ionis, Intellia, Pfizer, Lexeo, Prothena, Janssen, and Akcea outside the submitted work. Dr Solomon reported receiving grants from Alexion, Actelion, Alnylam, Amgen, AstraZeneca, Bellerophon, Bayer, Bristol Myers Squibb, Boston Scientific, Celladon, Cytokinetics, Eidos, Gilead, GlaxoSmithKline, Ionis, Lilly, Mesoblast, MyoKardia, NIH/NHLBI, Neurotronik, Novartis, Novo Nordisk, Respicardia, Sanofi Pasteur, Theracos, Us2.AI, and Edgewise and personal fees from Abbott, Action, Akros, Alexion, Alnylam, Amgen, Arena, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardior, Cardurion, Corvia, Cytokinetics, Daiichi Sankyo, GlaxoSmithKline, Lilly, Merck, Myokardia, Novartis, Roche, Theracos, Quantum Genomics, Janssen, Cardiac Dimensions, Tenaya, Sanofi Pasteur, Dinaqor, Tremeau, CellProthera, Moderna, American Regent, Sarepta, Lexicon, Anacardio, Akros, and Valo outside the submitted work. No other disclosures were reported.

Funding/Support: The Atherosclerosis Risk in Communities (ARIC) study was funded in whole or in part with federal funds from the NHLBI, NIH, and US Department of Health and Human Services (contract No. HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, and HHSN268201700005I). The Reasons for Geographic and Racial Differences in Stroke Study (REGARDS) is supported by cooperative agreement U01 NS041588 co-funded by the National Institute of Neurological Disorders and Stroke (NINDS) and the National Institute on Aging (NIA), NIH, and Department of Health and Human Services. The Multi-Ethnic Study of Atherosclerosis (MESA) and the MESA SHARe Project were supported by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the NHLBI and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences. Funding for SHARe genotyping was provided by NHLBI contract N02-HL-64278. Genotyping was performed at Affymetrix and the Broad Institute of Harvard and MIT using the Affymetrix Genome-Wide Human SNP Array 6.0. Provision of exome chip genotyping was provided in part by support of NHLBI contract N02-HL-64278 and University of California, Los Angeles Clinical and Translational Science Institute (UL1-TR001881), and the Diabetes Research Center (DK063491). The Women’s Health Initiative (WHI) program is funded by the NHLBI, NIH, and Department of Health and Human Services through 75N92021D00001, 75N92021D00002, 75N92021D00003, 75N92021D00004, and 75N92021D00005. The WHI study was also supported by grant U01HG007376 from the NIH.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Representatives from each study cohort were involved in the review and approval of the manuscript.

Disclaimer: The opinions expressed in this article are those of the authors and do not necessarily reflect the views of the Department of Health and Human Services or NIH, NINDS, or NIA.

Meeting Presentation: This paper was presented at the Heart Failure 2024 Meeting of the European Society of Cardiology; May 12, 2024; Lisbon, Portugal.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We thank the staff and participants of each study for their important contributions. A list of ARIC investigators is available at https://aric.cscc.unc.edu/aric9/about/aric_structure. A list of participating MESA investigators and institutions can be found at https://www.mesa-nhlbi.org/. A list of participating REGARDS investigators and institutions can be found at https://www.uab.edu/soph/regardsstudy/. A list of WHI investigators is available at https://www.whi.org/doc/WHI-Investigator-Short-List.pdf.

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Corresponding author.

PMCID: PMC11089467 PMID: 38734952

Key Points

Question

What is the natural history and cardiovascular burden of the V142I variant of the transthyretin (TTR) gene among US Black carriers across mid to late life?

Findings

Across 4 cohort studies, carriers (754/23 338) faced a substantially increased risk for heart failure (by age 63 years) and death (by age 72 years), similarly in men and women, which was estimated to contribute to approximately 1 million years of life lost among US Black individuals aged ≥50 years.

Meaning

These data show the large, age-dependent burden of V142I, which may guide discussions regarding the initiation and results of genetic screening, provide clinicians with risk estimates to share with patients, and inform strategies for early targeted therapy.

Abstract

Importance

Individual cohort studies concur that the amyloidogenic V142I variant of the transthyretin (TTR) gene, present in 3% to 4% of US Black individuals, increases heart failure (HF) and mortality risk. Precisely defining carrier risk across relevant clinical outcomes and estimating population burden of disease are important given established and emerging targeted treatments.

Objectives

To better define the natural history of disease in carriers across mid to late life, assess variant modifiers, and estimate cardiovascular burden to the US population.

Design, Setting, and Participants

A total of 23 338 self-reported Black participants initially free from HF were included in 4 large observational studies across the US (mean [SD], 15.5 [8.2] years of follow-up). Data analysis was performed between May 2023 and February 2024.

Exposure

V142I carrier status (n = 754, 3.2%).

Main Outcomes and Measures

Hospitalizations for HF (including subtypes of reduced and preserved ejection fraction) and all-cause mortality. Outcomes were analyzed by generating 10-year hazard ratios for each age between 50 and 90 years. Using actuarial methods, mean survival by carrier status was estimated and applied to the 2022 US population using US Census data.

Results

Among the 23 338 participants, the mean (SD) age at baseline was 62 (9) years and 76.7% were women. Ten-year carrier risk increased for HF hospitalization by age 63 years, predominantly driven by HF with reduced ejection fraction, and 10-year all-cause mortality risk increased by age 72 years. Only age (but not sex or other select variables) modified risk with the variant, with estimated reductions in longevity ranging from 1.9 years (95% CI, 0.6-3.1) at age 50 to 2.8 years (95% CI, 2.0-3.6) at age 81. Based on these data, 435 851 estimated US Black carriers between ages 50 and 95 years are projected to cumulatively lose 957 505 years of life (95% CI, 534 475-1 380 535) due to the variant.

Conclusions and Relevance

Among self-reported Black individuals, male and female V142I carriers faced similar and substantial risk for HF hospitalization, predominantly with reduced ejection fraction, and death, with steep age-dependent penetrance. Delineating the individual contributions of, and complex interplay among, the V142I variant, ancestry, the social construct of race, and biological or social determinants of health to cardiovascular disease merits further investigation.

This study used data on Black participants in 4 large observational studies to better define the natural history of disease in V142I variant carriers across mid to late life, assess variant modifiers, and estimate cardiovascular burden to the US population.

Introduction

Transthyretin cardiac amyloidosis (ATTR-CA) is an increasingly recognized cardiomyopathy resulting from extracellular cardiac deposition of the misfolded transthyretin protein. ATTR-CA can occur in the presence of a detected genetic variant (ATTRv-CA, variant related) or its absence (ATTRwt-CA, wild-type related). The amyloidogenic V142I (legacy nomenclature V122I) variant of the transthyretin (TTR) gene has been reported to be present in a significant proportion of US Black individuals (3%-4%) (particularly in comparison with other ancestry groups),^1,2 is the most common cause of ATTRv-CA in the US,³ and demonstrates age-dependent anatomic penetrance on autopsy analysis.⁴ The American College of Medical Genetics recently classified the transthyretin variants as clinically actionable reportable secondary findings,⁵ and individual epidemiologic studies concur that carriers face increased risk for heart failure (HF) and all-cause mortality.^1,6,7,8,9,10 The availability of several targeted treatments, which may be more effective earlier in the disease process, has heightened interest in identifying at-risk individuals.^11,12

Because testing for the V142I variant is typically performed after a carrier presents with disease, precise data are needed regarding long-term, and modifiers of, risk in unselected carriers. To better define the natural history of disease in V142I carriers, data from Black participants in 4 large observational studies based in the US were combined. Pooling data from geographically diverse cohorts facilitated more precise and generalizable risk estimation across mid to late life, the ability to assess less-frequent outcomes (including subtypes of HF), analysis of effect modifiers (particularly sex, which is thought to influence disease manifestations),^2,6 and estimation of years of life lost among V142I carriers.

Methods

We included 23 338 self-reported Black participants, including 754 carriers (3.2%) who were initially free of HF with available genotyping (Figure 1). This study involved self-reported Black participants in keeping with prior cohort studies included in this analysis that studied risk stratification with the variant, although race is a social construct and should not be used as a proxy for ancestry.^1,8,10 Participants provided written informed consent, and institutional review board approval was received from all participating institutions. This study followed the reporting guidelines of the Strengthening the Reporting of Genetic Association Studies (STREGA, an extension of the STROBE Statement).

Cohort Studies

Atherosclerosis Risk in Communities Study

The Atherosclerosis Risk in Communities Study is a prospective study in 4 communities across the US composed of 15 792 participants, aged 45 to 64 years, recruited between 1987 and 1989.¹³ A total of 3543 Black participants (including 112 participants who were heterozygous for V142I) were included for analysis.

Multi-Ethnic Study of Atherosclerosis

The Multi-Ethnic Study of Atherosclerosis is a cohort study of 6 communities and recruited 6814 participants free of clinical cardiovascular disease aged 45 to 84 years between July 2000 and August 2002.¹⁴ A total of 1584 Black participants, including 49 participants who were heterozygous for V142I, were included for analysis.

Reasons for Geographic and Racial Differences in Stroke

The Reasons for Geographic and Racial Differences in Stroke Study recruited 30 239 adults at least 45 years old between January 2003 and October 2007.¹⁵ Participants were randomly selected from a commercially available list to create a sample balance on race and sex across the stroke buckle, the stroke belt, and the rest of the continental US. A total of 8527 Black participants (including 259 heterozygous and 3 homozygous for V142I) met inclusion criteria for the study.¹⁰

Women’s Health Initiative

Women were eligible to participate in the Women’s Health Initiative if they were 50 to 79 years of age, generally healthy, and postmenopausal at the time of enrollment.¹⁶ A total of 161 808 participants were enrolled between 1993 and 1998 in the observational or clinical trial groups. A total of 9684 Black women (including 330 heterozygous and 1 homozygous for V142I) were included in this analysis.

The median African genetic ancestry percentage for each cohort was determined. Details regarding individual study cohorts, population descriptors, genotyping, ancestry analysis, comorbidities, and laboratory values are provided in the eMethods in Supplement 1.

Study Outcomes

Longitudinal outcomes included first HF hospitalization, first HF hospitalization subtypes (HF with reduced ejection fraction [HFrEF] and HF with preserved ejection fraction [HFpEF]), all-cause mortality, and a composite of HF hospitalization or all-cause mortality. HFpEF was defined by an EF of 50% or greater or qualitatively normal EF, while HFrEF was defined by an EF less than 50% or a qualitatively low EF (eMethods in Supplement 1). This EF dichotomization facilitated inclusion of qualitative reports of EF.

Statistical Analysis

Baseline characteristics were summarized using descriptive statistics, stratified by carrier status. We performed Cox proportional hazards models, stratifying by age, sex, and genotype platform, while adjusting for interactions between genotyping platform and the first 10 principal components,^8,10 using time from the baseline visit as the time scale to describe the relationship between the variant and adverse outcomes. Proportional hazards were assessed via Schoenfeld residuals.

We assessed for interactions between carrier status and several variables for HF hospitalization, all-cause mortality, and the combined outcome (HF hospitalization or all-cause mortality). Given strong effect modification by age, subsequent modeling focused on using sequences of 10-year age windows (eg, beginning age 50 through 59 years), generating 10-year adjusted hazard ratios using Cox regression, stratifying by sex and genotype platform while adjusting for the interactions between genotyping platform and principal components.⁹ To better visualize these resulting estimates, we applied locally weighted scatterplot smoothing (LOWESS).^9,17,18 For LOWESS, tricube weighting was used with bandwidth of 0.8 (80% of observations used at each point).¹⁹ This process was repeated for starting years between ages 50 and 90 years for HF hospitalization and death analyses, and between 58 and 90 years for HFpEF and HFrEF hospitalization (given fewer events at the lower age range). We reported age at first nominally statistically significant risk in each outcome. We performed post hoc sensitivity analyses using nominal statistical significance at α = .01 to acknowledge multiple age ranges tested. Participants were censored if they died (for HF hospitalization analyses) or were lost to follow-up, while participants were administratively censored at the time of last follow-up. For incident HFpEF hospitalization analyses, participants experiencing HF hospitalization with undetermined EF or reduced EF were censored (with parallel considerations for incident HFrEF hospitalization).

To estimate years of life lost among carriers compared with noncarriers, we used previously validated actuarial (age-based) methods to calculate nonparametric Kaplan-Meier estimates of overall survival and HF hospitalization–free survival at every year of age specified.^17,18 This method uses age (at a given starting age and at the time of death) as the time component. The area under the survival curve reflected projected event-free survival and overall survival. For each age between 50 and 95 years, we compared survival estimates of carriers and noncarriers. Estimates of survival gains were smoothed with LOWESS.

Finally, we applied these actuarial estimates of years of life lost at each age to the US population of Black individuals. For each age between 50 and 95 years, we extracted data on the estimated number of living Black individuals from the US Census Bureau’s American Community Survey 5-Year Estimates from 2022, which was then multiplied by the carrier frequency observed in the current analysis to estimate the number of carriers at each age in the US (accounting for decreasing frequency of the variant aging given increasing mortality risk). The years of life lost at each age among carriers was multiplied by the number of estimated carriers in the population to generate total years lost at each age, which was then summed to estimate the numbers of years lost among carriers at least age 50 years. Analyses were performed using Stata version 18 (StataCorp LLC). For interaction testing, a 2-sided P value <.05 divided by 9 (ie, number of subgroups) was considered significant. For other analyses, a 2-sided P value <.05 was considered significant.

Results

Baseline Characteristics

The mean (SD) age at visit 1 was 62 (9) years, 76.7% were women (due to the large sample size included from WHI), 62.9% had hypertension, and 21.9% had diabetes (Table 1). Carrier percentages were similar between men (3.0%) and women (3.3%). Characteristics were generally balanced across V142I carrier status.

Table 1. Clinical Characteristics of the Pooled Study Cohorts by V142I Carrier Status at Baseline Visit.

	V142I noncarriers (n = 22 584)	V142I carriers (n = 754)
Study cohort, No. (%)
Atherosclerosis Risk in Communities	3431 (15.2)	112 (14.9)
Multi-Ethnic Study of Atherosclerosis	1535 (6.8)	49 (6.5)
Reasons for Geographic and Racial Differences in Stroke	8265 (36.6)	262 (34.7)
Women’s Health Initiative	9353 (41.4)	331 (43.9)
Participant characteristics
Age, mean (SD), y	62 (9)	61 (9)
Sex, No. (%)
Female	17 308 (76.6)	591 (78.4)
Male	5276 (23.4)	163 (21.6)
Clinical characteristics
Physical examination
Systolic blood pressure, mean (SD), mm Hg [No.]	131 (18) [22 534]	131 (19) [752]
Diastolic blood pressure, mean (SD), mm Hg [No.]	78 (10) [22 527]	78 (11) [753]
Heart rate, mean (SD), beats/min [No.]	69 (19) [22 441]	68 (13) [746]
Body mass index, mean (SD) [No.]^a	30.7 (6.6) [22 435]	30.2 (6.7) [748]
Comorbidities, No./total (%)
Hypertension	13 965/22 174 (63.0)	460/744 (61.8)
Obesity	10 789/22 435 (48.1)	326/748 (43.6)
Diabetes	4909/22 349 (22.0)	156/748 (20.9)
Coronary heart disease	1570/22 544 (7.0)	51/753 (6.8)
Laboratory testing
Glucose, mg/dL
Mean (SD) [No.]	109 (45) [20 670]	108 (45) [684]
>125 mg/dL, No. (%)	3249 (15.7)	99 (14.5)
Creatinine, mg/dL
Mean (SD) [No.]	0.93 (0.59) [20 468]	0.89 (0.37) [677]
>1.5 mg/dL, No. (%)	692 (3.4)	21 (3.1)
Low-density lipoprotein cholesterol, mg/dL
Mean (SD) [No.]	131 (41) [20 216]	131 (40) [669]
≥190 mg/dL, No. (%)	1691 (8.4)	49 (7.3)

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SI conversion factors: To convert cholesterol to mmol/L, multiply by 0.0259; creatinine to μmol/L, multiply by 88.4; and glucose to mmol/L, multiply by 0.0555.

^{^a}

Calculated as weight in kilograms divided by height in meters squared.

Carrier Status and Cardiovascular Outcomes

Total events and hazard ratios are shown in Table 2 and Kaplan-Meier event curves are displayed in eFigures 1 and 2 in Supplement 1. With a mean (SD) 15.5 (8.2) years of follow-up, carriers faced an increased risk for all study outcomes, though the association with HFpEF hospitalization did not reach statistical significance. Carrier risks for HF and death were comparable with several traditional cardiovascular risk factors (eTable 1 in Supplement 1). Risk for HF hospitalization or death was significantly increased among the 4 homozygotes compared with heterozygotes (hazard ratio, 7.75 [95% CI, 2.24-26.79]) (eTable 2 in Supplement 1). eTable 3 in Supplement 1 shows interaction P values between carrier status and select variables, including study cohort, age, sex, hypertension, diabetes, systolic blood pressure, heart rate, body mass index, and African ancestry with study outcomes. Of these, only age was identified as a significant modifier of variant risk.

Table 2. Events and Hazard Ratios for Adverse Cardiovascular Outcomes by V142I Carrier Status.

Outcomes	No./total (%)		Absolute difference, % (95% CI)	Hazard ratio (95% CI)^a^,^b	P value
Outcomes	V142I noncarrier events	V142I carrier events	Absolute difference, % (95% CI)	Hazard ratio (95% CI)^a^,^b	P value
HF hospitalization	2373/22 584 (10.5)	134/754 (17.8)	7.3 (4.5 to 10.0)	1.84 (1.54 to 2.20)	<.001
All-cause death	8744/22 584 (38.7)	329/754 (43.6)	4.9 (1.3 to 8.5)	1.27 (1.13 to 1.42)	<.001
HF hospitalization or all-cause death	9290/22 584 (41.1)	355/754 (47.1)	5.9 (2.3 to 9.6)	1.31 (1.18 to 1.46)	<.001
By HF type^c
HFrEF hospitalization	740/21 599 (3.4)	57/721 (7.9)	4.5 (2.5 to 6.5)	2.47 (1.87 to 3.26)	<.001
HFpEF hospitalization	722/21 599 (3.3)	32/721 (4.4)	1.1 (−0.4 to 2.6)	1.43 (0.99 to 2.05)	.054

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Abbreviations: HF, heart failure; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction.

^{^a}

Models adjusted for interactions between study genotyping platform and the first 10 principal components and stratified by age, sex, and the genotyping platform.

^{^b}

No proportional hazards violations were detected.

^{^c}

ARIC participants did not have HF events adjudicated (including ejection fraction) until 2005, leading to different participant numbers for HFrEF and HFpEF events compared with other events.

To further accommodate the age-related effects on outcomes, we used 10-year rolling hazard ratios (Figure 2; and eFigure 3 in Supplement 1). The risk for all outcomes generally increased between middle to late life. Statistically significant increased 10-year risk was first detected at age 63 years for HF, 65 for HF or death, and 72 for death. The increased HF risk was driven predominantly by an increased risk of HFrEF, with an elevated 10-year risk detected at age 65 years that strongly increased over time, in contrast to a later modest signal for HFpEF hospitalization emerging at age 76 years. Risk for HF hospitalization or death was similar between men and women (eFigure 4 in Supplement 1), in keeping with the lack of interaction reported by sex (eTable 3 in Supplement 1). Sensitivity analyses using 99% CIs were largely similar, noting modestly longer time to statistical significance (eFigures 5-6 in Supplement 1). Within analyses using 10-year intervals, there were still some indications of proportional hazards violations with respect to age.

Expected Overall and Event-Free Survival by Age

To describe the effect of the variant on years of life lost without relying on modeling assumptions, we provided several model-free estimates. Figure 3A shows expected overall survival at each age between 50 and 95 years by carrier status (additional details in eTable 4 in Supplement 1). Carriers lived 2 to 2.5 fewer years compared with noncarriers until approximately age 85 years, when differences in longevity began to attenuate (Figure 3B). Because the survival loss difference between carriers and noncarriers remained relatively constant across ages 50 to 85 years, while expected survival decreased, the relative (percentage) loss in expected remaining carrier longevity generally increased over time (eFigure 7 in Supplement 1). Specifically, carriers at age 50 years subsequently lived 1.9 fewer years (95% CI, 0.6-3.1) than noncarriers, representing a 6% reduction (95% CI, 2%-10%) in expected longevity. This longevity disparity increased to a maximum of 2.8 years (95% CI, 2.0-3.6) at age 81 years. The maximum relative reduction in longevity was 34% (95% CI, 11%-56%), achieved at age 90 years. Parallel findings were demonstrated using event-free survival (eTable 5 and eFigures 8-9 in Supplement 1).

Figure 3. — A, Estimated mean overall survival in carriers and noncarriers for every year between ages 50 and 95 years. B, The difference in mean years lost among carriers compared with noncarriers shown with the smoothed estimate (blue line), and 95% CI of the smoothed estimate (shaded area) after application of a locally weighted scatterplot smoothing procedure.

Extrapolation of Years Lost Based on Carrier Prevalence in the US Population

We applied carrier rates derived from this analysis to US population data to estimate the burden of disease in the US Black population. Among the 13 284 819 estimated Black adults aged 50 to 95 years, there are an estimated 435 851 carriers (eTable 4 in Supplement 1). Based on the expected loss of longevity at each age among carriers compared with noncarriers, US Black individuals at least 50 years old are projected to cumulatively lose 957 505 years of life (95% CI, 534 475-1 380 535) compared with noncarriers.

Discussion

Leveraging individual-level participant data from 4 large observational cohort studies facilitated several analyses highlighting risk encountered by carriers of the amyloidogenic V142I variant of the TTR gene. First, this study provided more precise estimates of cardiovascular risk across mid to late life (>90 years old) than were previously available. These risks strongly related to age, but were not modified by sex, suggesting underdiagnosis of ATTRv-CA in women, as some clinical studies have reported male predominance of phenotypic penetration. Second, the earliest statistically significant increase in 10-year risk was identified at earlier ages than previously shown (age 63 years for HF hospitalization and age 72 years for mortality), likely due to the inclusion of a larger sample size.⁹ Third, the risk for HF hospitalization was driven by HFrEF, in contrast with its typical HFpEF association, a more modest signal that emerged in later life. Fourth, at each age from mid to late life, carriers died 2 to 2.5 years earlier than noncarriers until age 85 years (when differences began to attenuate), leading to an overall significant decrease in percentage expected longevity with increasing age. Fifth, accounting for the age-dependent frequency of the variant, Black carriers at least 50 years old are projected to live nearly 1 million fewer years than noncarriers. Given established and emerging treatment strategies, which may be more effective earlier in disease course,^11,12 early identification and treatment of V142I carriers with ATTRv-CA may have significant public health impact.

Population studies, including several included here, individually have shown significantly higher risk among V142I carriers for HF hospitalization and death, though the risk of homozygotes in these studies has remained unclear.^{1,7,8,9,10,20} The small group of homozygotes (n = 4) in this study was at substantially elevated risk even in comparison with heterozygotes (demonstrating allele dose dependence), albeit with wide confidence intervals. These findings were overall consistent with data from clinical populations.²¹ Pooling data importantly allowed for greater power to evaluate for interactions between clinical characteristics and variant status that might account for incomplete phenotypic penetrance. Only age was consistently identified as a strong modifier of risk. Clinical studies have demonstrated a greater risk for ATTRv-CA among male carriers,^2,6 which has been postulated to relate to less-aggressive disease trajectory in women.²² However, sex did not significantly modify risk in this study with very similar estimates of HF hospitalization or death in men and women, suggesting that previous reports may be explained by underdiagnosis of ATTRv-CA in women.^2,6,8,23,24 Women with ATTRv-CA present with thinner left ventricular walls compared with men, and not accounting for differences in body size may engender underdiagnosis.²³ Without clear alternative modifiers of risk, age may currently be the most reliable clinical marker to identify risk for progressive disease.

Additionally, this larger study enabled more precise risk estimation and earlier detection of phenotypic penetrance. Previous research in the Atherosclerosis Risk in Communities Study alone identified inflection points in 10-year risk around age 70 years for HF hospitalization and 75 years for mortality.⁹ The greater sample size in the current study showed that risk increased at even earlier ages (63 and 72 years, respectively). Defining these ages is clinically impactful and relevant when considering initiation of treatments such as stabilizers, silencers, or even in vivo gene editing, where treatment prior to the onset of symptoms might be most effective, though further data are needed to clarify the impact of early treatment.^11,25,26

Notably, the risk for HF hospitalization in this study was driven by incident HFrEF hospitalization. While ATTR-CA has traditionally been associated with HFpEF, V142I carriers are more likely to experience HFrEF events. Indeed, V142I-associated ATTRv-CA presents with greater disease severity (including lower left ventricular EF) and faster disease progression compared with both ATTRwt-CA and non-V142I ATTRv-CA.^27,28 Recent data from a clinical trial in ATTR-CA supported the presence of reduced EF in a significant proportion of patients with a tendency to progress in ATTR-CA.²⁹ Therefore, among Black hospitalized patients with HFrEF, suspicion for ATTR-CA should be heightened and not restricted to patients with preserved EF.^30,31,32,33 It is important to note that not all carriers with HFrEF have ATTRv-CA because carriers are at risk for other causes of cardiomyopathy as well (such as coronary artery disease), emphasizing the importance of further cardiovascular evaluation for etiology. Identification of ATTR-CA in the HFrEF population is also important because implementation of some guideline-directed medical therapies may not be well tolerated in ATTR-CA.³⁴ The more modest association with increased HFpEF hospitalization in late life may represent a survivor bias, whereby those carriers who survive to late life may have an inherently more benign form of the disease.

Our actuarial analyses demonstrated that carriers die approximately 2 to 2.5 years earlier than noncarriers. Because these findings were consistent until approximately 85 years of age, the relative reduction in longevity of carriers compared with noncarriers significantly increased with aging, further reflecting the steep age-dependent penetrance of the variant. Because the variant is relatively common (3%-4% among US Black individuals), these years of life lost at the individual level translate to a substantial burden at the population level. The approximately 435 000 living carriers between ages 50 and 95 years will collectively lose nearly 1 million years of life. Because targeted therapies are either available, emerging, or promising in ATTR-CA,^11,25,26,35 these results suggest that identification of disease and implementation of efficacious therapies might extend longevity in this large population.¹¹ These results may be increasingly relevant with greater access to genetic testing in the population, whether accomplished through biobanks, direct-to-consumer testing, or broad population assessments (including the All of Us Research Program³⁶). Additionally, these results support the inclusion of the variant as a clinically actionable secondary finding.⁵

Limitations

There are several limitations of this study. First, specific phenotypic markers or diagnoses of cardiac amyloidosis were not broadly available in these studies, and therefore understanding which individual carriers have ATTR-CA (as opposed to other causes of HF) is limited. However, at a population level, the absolute difference estimates of HF events provide understanding of the burden of ATTRv-CA. Second, despite pooling studies, interaction analyses may still be underpowered to detect modifiers of variant risk. Third, extrapolation of the current estimates to the US population assumes that the cohorts studied here are broadly representative, and these data may not accurately inform estimates in other parts of the world where variant prevalence and event rates may vary.² Fourth, participants who self-reported as Black were included, although race does not capture the significant genetic diversity within Black individuals in the US and worldwide. Delineating the individual contributions of, and complex interplay between, the V142I variant, ancestry, race as a social construct, and biological or social determinants of health to cardiovascular disease merits further investigation. Fifth, adjudication of HFrEF and HFpEF events varied by study and were obtained through available medical record review.

Conclusions

Among self-reported Black individuals, male and female V142I carriers faced similar and substantial risk for HF hospitalization, predominantly with reduced ejection fraction, and all-cause death later in life, with steep age-dependent penetrance. Delineating the individual contributions of, and complex interplay among, the V142I variant, ancestry, the social construct of race, and biological or social determinants of health to cardiovascular disease merits further investigation.

Supplement 1.

eMethods

eFigure 1. Kaplan-Meier Curves for Heart Failure and Mortality Events by V142I Carrier Status

eFigure 2. Kaplan-Meier Curves for Heart Failure Events by V142I Carrier Status

eFigure 3. V142I Hazard Ratio for Heart Failure Events by Age

eFigure 4. V142I Hazard Ratio for Heart Failure Hospitalization or Death by Age and Sex

eFigure 5. V142I Hazard Ratio for Adverse Cardiovascular Events by Age Using 99%

eFigure 6. V142I Hazard Ratio for Heart Failure Events by Age Using 99% Confidence

eFigure 7. Relative Effect of V142I on Remaining Years of Life Compared With Noncarriers

eFigure 8. Effect of V142I on HFH-free Survival Compared With Noncarriers

eFigure 9. Relative Effect of V142I on Remaining Years of Life Free From HFH Compared With Noncarriers

eTable 1. Hazard Ratios for Heart Failure Hospitalization and Death by Carrier Status and Cardiovascular Risk Factors

eTable 2. Events and Hazard Ratios for Adverse Cardiovascular Outcomes by Number of Alleles

eTable 3. Interaction Analysis of the Variant With Select Variables for Study Outcomes

eTable 4. Estimated Survival Time Comparing Carriers and Noncarriers by Age for All-Cause Mortality With Extrapolation to the United State Population

eTable 5. Estimated Survival Time Comparing Carriers and Noncarriers by Age for Heart Failure Hospitalization or Death

eReferences

jama-e244467-s001.pdf^{(922.7KB, pdf)}

Supplement 2.

Data Sharing Statement

jama-e244467-s002.pdf^{(14.5KB, pdf)}

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Supplementary Materials