# US 10,504,610 B2 · Methods, systems and computer programs for assessing CHD risk using adjusted HDL article number measurements

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 10,504,610 B2
- Document ID: US10504610B2
- Status: Granted
- Kind: B2
- Application number: 15/223,237
- Filed: 2016-07-29
- Published: 2017-03-23
- Granted: 2019-12-10
- Earliest claimed: 2006-05-10
- Assignees: LipoScience, Inc.
- Applicants: LipoScience, Inc.
- Inventors: James D. Otvos
- Web page: https://patentgrep.com/patent/US10504610B2

## CPC

- G16B 5/00: ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
- G01N 15/0656: {using electric, e.g. electrostatic methods or magnetic methods (by investigating individual particles G01N15/1031, G01N15/12)}
- G01N 24/08: by using nuclear magnetic resonance (G01N24/12 takes precedence)
- G01N 33/4833: {of solid biological material, e.g. tissue samples, cell cultures (tissue in vivo A61B5/00; cell suspensions G01N33/48735)}
- G01R 33/465: applied to biological material, e.g. in vitro testing
- G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis
- G16H 20/00: ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/50: for simulation or modelling of medical disorders
- G01N 15/01: specially adapted for biological cells, e.g. blood cells (investigating sedimentation of particle suspensions in blood G01N15/05)
- G01N 2800/323: Arteriosclerosis, Stenosis
- G01N 2800/324: Coronary artery diseases, e.g. angina pectoris, myocardial infarction
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Methods, computer program products and apparatus determine a subject's risk of having or developing CHD using a calculated HDL particle risk number and/or a mathematical model of risk associated with HDL particles that adjusts concentrations of at least one of the subclasses of small, medium and large HDL particle measurements to reflect predicted CHD risk. A calculated LDL particle risk number may also be generated as well as a lipoprotein particle index derived from the ratio of RLDL/RHDL.

## Claims

### Claim 1

A method of determining a subject's risk of having and/or developing CHD, comprising:
performing Nuclear Magnetic Resonance on a biosample from a subject to obtain measurements of small and large HDL subclass particles in a blood plasma or serum sample;
determining NMR-derived concentrations for small and large HDL subclass particles based on the measurements of the biosample;
programmatically adjusting at least one of the small and large HDL subclass particle measurement values based on a predetermined mathematical model that comprises one or more weighting factors to generate a weighted HDL particle concentration value to predict a subject's cardiovascular risks; and
determining a subject's weighted risk of having and/or developing CHD based on the weighted HDL particle concentration value.

### Claim 2 (depends on 1)

The method of claim 1, wherein the predetermined mathematical model comprises a first weighting factor for the obtained concentration measurement of small HDL particles and a second different weighting factor for the obtained concentration measurement of large HDL particles.

### Claim 3 (depends on 2)

The method of claim 2, wherein the first and second weighting factors are multiplied to the respective small and large HDL subclass particle measurements and the results are added together to provide a weighted HDL particle risk factor number.

### Claim 4 (depends on 1)

The method of claim 1, wherein the adjusting step is carried out so that the large HDL particle measurement is increased relative to the small HDL particle concentration.

### Claim 5 (depends on 1)

The method of claim 1, wherein the obtaining step comprises obtaining the concentration of medium HDL particle concentration, and wherein the adjusting step is carried out so that the medium HDL particle measurement and the large HDL particle measurement are increased relative to the small HDL particle measurement.

### Claim 6 (depends on 1)

The method of claim 1, wherein the obtaining step comprises substantially concurrently obtaining NMR derived concentration measurements of small and large HDL subclass particles.

### Claim 7 (depends on 1)

The method of claim 1, wherein the obtaining step comprises: deconvolving at least one NMR spectroscopic signal of the biosample to calculate the small and large HDL particle subclass concentration measurements.

### Claim 8 (depends on 7)

The method of claim 7, wherein the obtaining step obtains a concentration measure of medium HDL particles, and wherein the applying at least one weighting factor step is carried out to increase the medium HDL particle measurement and the large HDL particle measurement relative to the small HDL subclass particle measurement.

### Claim 9

A computer program product for adjusting measured in vitro concentrations of HDL particles to assess CHD risk, the computer program product comprising:
a non-transitory computer readable storage medium having computer readable program code embodied in said medium, said computer-readable program code comprising:
computer readable program code that adjusts measured in vitro concentrations of at least one of small and large HDL particle subclasses based on a predetermined mathematical model that comprises one or more weighting factors to generate an HDL risk number to reflect a subject's risk of having or developing CHD.

### Claim 10 (depends on 9)

The computer program product of claim 9, wherein the computer-readable program code further comprises computer readable program code that generates a single risk predictor variable using a ratio of a weighted LDL particle number in units of concentration as a numerator and a weighted HDL particle number in units of concentration as a denominator.

### Claim 11

A system for obtaining data regarding lipoprotein constituents in a subject, comprising:
an NMR spectrometer for acquiring at least one composite NMR spectrum of an in vitro blood plasma or serum sample; and
at least one processor in communication with the NMR spectrometer, the at least one processor configured to determine concentrations of small and large HDL particle subclasses in the sample and adjust at least one of the determined small and large HDL particle concentrations based on a predetermined mathematical model that comprises one or more weighting factors to generate a weighted HDL particle concentration value to determine a risk of developing or having CHD.

### Claim 12 (depends on 11)

The system of claim 11, wherein the at least one processor is further configured to define a plurality of individual NMR constituent spectra, each associated with a selected reference lipoprotein constituent signal lineshape, each constituent spectrum having associated spectra that contribute to the composite NMR spectrum of the blood plasma or serum sample.

### Claim 13 (depends on 11)

The system of claim 11, wherein the at least one processor is configured to increase the measured large HDL particle concentration relative to the small HDL particle concentration.

### Claim 14 (depends on 11)

The system of claim 11, wherein the at least one processor is configured to apply the mathematical model defining a first weighting factor multiplied by the small HDL particle concentration and a second different weighting factor multiplied by the large HDL particle concentration, wherein the second weighting factor is larger than the first weighting factor.

### Claim 15 (depends on 11)

The system of claim 11, wherein the at least one processor is configured to increase a measured concentration of medium HDL particles and the measured concentration of large HDL particles relative to the small HDL particle measurement to determine CHD risk.

### Claim 16

A method of evaluating a person's risk of having or developing CHD, comprising:
calculating a HDL lipoprotein risk parameter number using at least two of small, medium and large HDL particle concentration values,
wherein at least one of the concentration values is electronically increased relative to the NMR spectrophotometer measured small HDL particle concentration, and
wherein both the medium HDL and large HDL particle concentrations are electronically increased relative to the small HDL particle concentration and combined with the small HDL particle NMR measurement to generate the HDL risk parameter number.

### Claim 17 (depends on 16)

The method of claim 16, further comprising calculating a LDL lipoprotein risk parameter number using at least two of small, medium and large LDL particle concentration values, wherein at least one of the concentration values is increased relative to the measured small LDL particle concentration.

### Claim 18 (depends on 17)

The method of claim 17, further comprising: generating a single risk predictor variable using a ratio of a weighted LDL particle number as a numerator and a weighted HDL particle number as a denominator.

### Claim 19 (depends on 16)

The method of claim 16, further comprising calculating a lipoprotein particle index from a ratio of the LDL risk parameter value to the HDL risk parameter value.

### Claim 20 (depends on 19)

The method of claim 19, further comprising generating a patient-specific report presenting the LDL risk parameter value and the HDL risk parameter value along with a patient's risk of CHD based on the presented values.