# Early diagnosis of light chain amyloidosis

> **NIH NIH R01** · MEDICAL COLLEGE OF WISCONSIN · 2024 · $273,000

## Abstract

PROJECT SUMMARY
Light chain (AL) amyloidosis is a recalcitrant and deadly hematologic disease characterized by organ dysfunction
from insoluble fibril deposition derived from clonal free light chains arising from a monoclonal gammopathy. The
disease has a high early mortality of 40-45% at two years due to heart failure. Patients with advanced AL
amyloidosis have high morbidity and mortality in the initial period after diagnosis owing to cardiac dysfunction.
Despite experiencing multiple symptoms and demonstrating signs of the disease, many patients are diagnosed
late, sometimes by years, because these ‘precursor diagnoses’ are often non-specific. Observational data also
suggest that Black individuals are more likely to be underdiagnosed with cardiac amyloidosis. Monoclonal
gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (MGUS+) are more
common in Black individuals, as is the prevalence of hypertrophic cardiomyopathy and chronic kidney disease,
both of which also occur in AL amyloidosis. We hypothesize that patients can be diagnosed early by assessing
the patterns of precursor diagnoses that predate AL amyloidosis diagnosis. Our application seeks to create an
algorithm using Bayesian machine learning statistical methodology to create an alert system that can help guide
physicians toward early consideration of an AL amyloidosis diagnosis. We will execute the following specific
aims using nationally representative Medicare data: 1) Identify patterns of precursor diagnoses associated with
the occurrence of AL amyloidosis and develop a predictive algorithm using Bayesian machine learning
techniques in Medicare beneficiaries with MGUS+. Patterns will be examined longitudinally at one-year
timepoints over a five-year period preceding the AL amyloidosis diagnosis contrasting between MGUS+ with
known AL and MGUS+ with no known AL to identify patterns that might best predict disease. 2) Study the
performance of the predictive MGUS-AL algorithm. This will be assessed internally in the Medicare data set,
overall and by race groups (Aim 2A) and external validation using TriNetX multicenter EHR data for MGUS+
patients of all ages, races, and insurance coverage (Aim 2B). 3) Estimate the number of potentially undiagnosed
AL amyloidosis patients with MGUS+. Based on the patterns identified in Aim 1 and validated in Aim 2, we will
identify subjects at high risk for undiagnosed AL amyloidosis (Aim 3A) and estimate the excess 2-year mortality
and number of potential lives saved by our early warning system, overall and by racial group (Aim 3B). This
study provides an unprecedented opportunity to identify patterns of precursor diagnoses to diagnose AL
amyloidosis early. An important anticipated outcome is to improve health disparities by increasing AL amyloidosis
diagnosis in Black individuals who are already at higher risk for MGUS and other end-organ damage associated
with AL amyloidosis. The novel, rigorous and easy-to-implement early...

## Key facts

- **NIH application ID:** 10765694
- **Project number:** 5R01HL166339-02
- **Recipient organization:** MEDICAL COLLEGE OF WISCONSIN
- **Principal Investigator:** Anita D'Souza
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $273,000
- **Award type:** 5
- **Project period:** 2023-02-01 → 2028-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10765694

## Citation

> US National Institutes of Health, RePORTER application 10765694, Early diagnosis of light chain amyloidosis (5R01HL166339-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10765694. Licensed CC0.

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