# US 9,435,870 B2 · Methods, systems and computer programs for assessing CHD risk using adjusted HDL particle number measurements > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,435,870 B2 - Document ID: US9435870B2 - Status: Granted - Kind: B2 - Application number: 11/746,812 - Filed: 2007-05-10 - Published: 2007-11-15 - Granted: 2016-09-06 - Earliest claimed: 2006-05-10 - Assignees: LipoScience, Inc. - Applicants: James D. Otvos - Inventors: James D. Otvos - Web page: https://patentgrep.com/patent/US9435870B2 ## 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 coronary heart disease, comprising: conducting Nuclear Magnetic Resonance to obtain concentration measurements of small and large High Density Lipoprotein subclass particles from a single in vitro blood plasma or serum sample of a subject; and programmatically numerically adjusting at least one of the obtained small and large High Density Lipoprotein subclass particle concentration measurements of the single in vitro blood plasma and/or serum sample based on a predetermined mathematical model with at least one weighting factor that is multiplied to increase or decrease the small and/or large High Density Lipoprotein subclass particle concentration measurements, wherein the at least one weighting factor reflects predicted CHD risk for the weighted High Density Lipoprotein subclass, to yield a High Density Lipoprotein risk number that can be greater than a summation of an actual, unadjusted total High Density Lipoprotein particle concentration measurement, to thereby determine the subject's High Density Lipoprotein based risk of coronary heart disease. ### Claim 2 (depends on 1) A method according to claim 1, further comprising conducting Nuclear Magnetic Resonance to obtain concentration measurements of medium High Density Lipoprotein particles in the sample, and wherein the adjusting step is carried out to electronically adjust at least two of the small subclass particle measurements, the large High Density Lipoprotein subclass particle measurement and the medium High Density Lipoprotein particle measurement and calculating the High Density Lipoprotein risk number based on the at least two adjusted particle concentration measurements. ### Claim 3 (depends on 1) The method of claim 1, further comprising electronically generating a report with actual High Density Lipoprotein particle subclass concentrations and the High Density Lipoprotein risk number. ### Claim 4 A method for determining a subject's risk of coronary heart disease, comprising: conducting Nuclear Magnetic Resonance to obtain concentration measurements of small and large High Density Lipoprotein subclass particles in a biosample of interest; multiplying a weighting factor by at least one of the measured large and small High Density Lipoprotein particle concentrations; and calculating a High Density Lipoprotein risk number using the weighted High Density Lipoprotein particle concentration(s), wherein the High Density Lipoprotein risk number is a discrete parameter separate from actual High Density Lipoprotein subclass particle concentrations and that is different from a summation of the obtained actual small and large High Density Lipoprotein particle concentration measurements, wherein the obtaining, multiplying and calculating steps are carried out using at least one processor. ### Claim 5 (depends on 4) A method according to claim 4, wherein the High Density Lipoprotein risk number comprises a weighted large High Density Lipoprotein particle concentration summed with an actual unadjusted small High Density Lipoprotein particle concentration. ### Claim 6 (depends on 5) A method according to claim 5, wherein the obtaining step comprises: deconvolving at least one Nuclear Magnetic Resonance spectroscopic signal of the biosample to calculate the small and large High Density Lipoprotein particle subclass concentration measurements. ### Claim 7 (depends on 6) A method according to claim 6, wherein the obtaining step obtains a concentration measure of medium High Density Lipoprotein particles, and wherein the multiplying at least one weighting factor step is carried out to increase the medium High Density Lipoprotein particle measurement and the large High Density Lipoprotein particle measurement relative to the small High Density Lipoprotein particle measurement. ### Claim 8 (depends on 7) A method according to claim 7, wherein the biosample is an in vitro blood plasma and/or serum sample, and wherein the multiplying step is carried out to increase the medium High Density Lipoprotein particle measurement more than the large High Density Lipoprotein particle measurement. ### Claim 9 (depends on 4) A method according to claim 4, wherein the multiplying step is carried out to apply an increased weight to the large High Density Lipoprotein subclass concentration measurement relative to the small High Density Lipoprotein subclass concentration to calculate the High Density Lipoprotein risk number. ### Claim 10 A computer program product for adjusting measured in vitro concentrations of High Density Lipoprotein particles to assess coronary heart disease risk, the computer program product comprising: a non-transitory computer readable storage medium having embodied in said medium computer readable program code that: (i) deconvolutes at least one Nuclear Magnetic Resonance signal of a patient biosample into measured concentrations of small and large High Density Lipoprotein particle subclasses; and (ii) adjusts concentration measurements of at least one of small and large High Density Lipoprotein particle subclasses of a biosample to generate a High Density Lipoprotein risk number to reflect a subject's risk of having or developing coronary heart disease, wherein the High Density Lipoprotein risk number is calculated based on a defined mathematical model to yield a numerical High Density Lipoprotein risk number that is different from a summation of the obtained actual small and large High Density Lipoprotein particle concentration measurements. ### Claim 11 (depends on 10) A computer program product according to claim 10, wherein the mathematical model is configured to increase the measured large High Density Lipoprotein particle concentration measurement relative to the small High Density Lipoprotein particle concentration to define the High Density Lipoprotein risk number. ### Claim 12 (depends on 11) A computer program product according to claim 11, wherein the mathematical model is configured to multiply a first weighting factor by the concentration measurement of small High Density Lipoprotein particles and multiply a second different weighting factor by the concentration measurement of large High Density Lipoprotein particles and sum the weighted small and large High Density Lipoprotein particles to generate the High Density Lipoprotein risk number. ### Claim 13 (depends on 12) A computer program product according to claim 12, wherein the computer readable program code of the mathematical model is configured to increase the actual large High Density Lipoprotein particle concentration measurement relative to the actual small High Density Lipoprotein particle concentration measurement. ### Claim 14 (depends on 10) A computer program product according to claim 10, wherein the computer readable program code that adjusts at least one of the measured small and large High Density Lipoprotein particle concentrations is configured to adjust at least two of small High Density Lipoprotein particle concentration, large High Density Lipoprotein particle concentration and medium High Density Lipoprotein particle concentration such that both the large High Density Lipoprotein and the medium High Density Lipoprotein particle measurements are increased relative to the small High Density Lipoprotein subclass measurement to generate the High Density Lipoprotein risk number. ### Claim 15 (depends on 14) A computer program product according to claim 14, further comprising computer readable program code that defines the High Density Lipoprotein risk number using a summation of the adjusted small and/or large High Density Lipoprotein particle concentrations and scales the summation to thereby define a corresponding High Density Lipoprotein-based coronary heart disease risk. ### Claim 16 (depends on 10) A computer program product according to claim 10, further comprising computer readable program code that calculates the High Density Lipoprotein particle risk number in units of concentration. ### Claim 17 (depends on 10) A computer program product according to claim 10, wherein the computer readable program code that adjusts the High Density Lipoprotein particle measurement(s) is configured to generate different weighting factors for small and large High Density Lipoprotein particles to predict coronary heart disease risk. ### Claim 18 (depends on 10) A computer program product according to claim 10, wherein the computer readable program code is configured to increase the large High Density Lipoprotein particle measurement relative to the small High Density Lipoprotein particle measurement to adjust the actual measurements from the in vitro blood plasma or serum sample. ### Claim 19 A system for obtaining data regarding lipoprotein constituents in a subject, comprising: a Nuclear Magnetic Resonance spectrometer for acquiring at least one Nuclear Magnetic Resonance spectrum of an in vitro blood, plasma or serum sample from a subject; and at least one processor in communication with the Nuclear Magnetic Resonance spectrometer, the at least one processor configured to: determine concentrations of small and large High Density Lipoprotein particle subclasses in the sample; and multiply at least one weighting factor by at least one of the determined small and large High Density Lipoprotein particle concentrations of the sample and sum weighted and unweighted concentrations to yield a numerical High Density Lipoprotein risk number for the sample that is different from a summation of the obtained actual small and large High Density Lipoprotein particle concentration measurements to determine a subject's risk of developing or having coronary heart disease. ### Claim 20 (depends on 19) A system according to claim 19, wherein the at least one Nuclear Magnetic Resonance spectrum is a composite spectrum, the processor further configured to execute computer program code defining a plurality of individual Nuclear Magnetic Resonance constituent spectra, each associated with a selected reference lipoprotein constituent signal lineshape, each constituent spectrum having associated spectra that contribute to the composite Nuclear Magnetic Resonance spectrum of the blood plasma or serum sample. ### Claim 21 (depends on 19) A system according to claim 19, wherein the at least one processor is configured to increase the actual measured large High Density Lipoprotein particle concentration relative to the actual small High Density Lipoprotein particle concentration of the respective single samples. ### Claim 22 (depends on 21) A system according to claim 21, wherein the at least one processor is configured to define a first weighting factor that is multiplied by the small High Density Lipoprotein particle concentration and a second different weighting factor that is multiplied by the large High Density Lipoprotein particle concentration, wherein the second weighting factor is larger than the first weighting factor. ### Claim 23 (depends on 19) A system according to claim 19, wherein the at least one processor is configured to multiply at least one weighting factor to at least one of the determined small and large High Density Lipoprotein particle concentrations of the sample to increase the measured concentration of medium High Density Lipoprotein particles and the measured concentration of large High Density Lipoprotein particles but not to the small High Density Lipoprotein particle measurement to determine coronary heart disease risk. ### Claim 24 (depends on 23) A system according to claim 23, wherein the at least one processor is configured to generate a High Density Lipoprotein risk number using the adjusted High Density Lipoprotein particle concentrations. ### Claim 25 A method of evaluating a person's risk of having or developing coronary heart disease, comprising: electronically calculating a High Density Lipoprotein risk number for a person using a summation of at least two of small, medium and large High Density Lipoprotein particle concentration values of a biosample from the person, wherein at least one of the concentration values for the summation for the sample is electronically increased, using a processor, relative to the measured small High Density Lipoprotein particle concentration value using a predefined weighting factor, wherein the High Density Lipoprotein risk number can have a concentration value that is greater than a total actual High Density Lipoprotein subclass concentration measurement. ### Claim 26 (depends on 25) A method according to claim 25, further comprising electronically, using a processor, defining a risk of developing or having coronary heart disease associated with the High Density Lipoprotein risk number. ### Claim 27 (depends on 25) A method according to claim 25, further comprising using a processor to mathematically scale the calculated risk number. ### Claim 28 (depends on 25) A method according to claim 25, further comprising using a processor to increase both the medium High Density Lipoprotein and large High Density Lipoprotein particle concentration values relative to actual small High Density Lipoprotein particle concentration value and summing these increased values with the actual small High Density Lipoprotein particle concentration measurement value to generate the High Density Lipoprotein risk number. ### Claim 29 (depends on 28) A method according to claim 28, further comprising using a processor to calculate a Low Density Lipoprotein risk number using at least two of small, medium and large Low Density Lipoprotein particle concentration values, wherein at least one of the concentration values is increased using a defined weighting factor relative to the measured small Low Density Lipoprotein particle concentration value. ### Claim 30 (depends on 29) A method according to claim 29, further comprising using a processor to calculate a lipoprotein particle index from a ratio of the Low Density Lipoprotein risk number to the High Density Lipoprotein risk number. ### Claim 31 (depends on 30) A method according to claim 30, further comprising using a processor to generate a patient-specific report presenting the lipoprotein particle index, the Low Density Lipoprotein risk number and the High Density Lipoprotein risk number. ### Claim 32 (depends on 25) A method according to claim 25, wherein the calculating step is carried out using at least one processor. ### Claim 33 A method of assessing coronary heart disease risk in a patient, comprising: generating a single risk predictor variable using a ratio of a Low Density Lipoprotein particle number as a numerator and a High Density Lipoprotein particle number as a denominator, wherein the generating step is carried out using at least one processor, and wherein the Low Density Lipoprotein and High Density Lipoprotein particle numbers are calculated using at least one predefined weighting factor that is multiplied by an actual concentration measurement of at least one subclass of respective Low Density Lipoprotein and High Density Lipoprotein particle subclasses, wherein the multiplied concentration of at least one subclass of respective Low Density Lipoprotein and High Density Lipoprotein particle subclasses increases or decreases actual concentrations of the Low Density Lipoprotein particle number and the High Density Lipoprotein particle number and are different from a summation of total Low Density Lipoprotein particle number and total High Density Lipoprotein particle number.