WO2024256726A1 - Procédé mis en œuvre par ordinateur déterminant une valeur de charge allostatique d'un être humain - Google Patents

Procédé mis en œuvre par ordinateur déterminant une valeur de charge allostatique d'un être humain Download PDF

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WO2024256726A1
WO2024256726A1 PCT/EP2024/066814 EP2024066814W WO2024256726A1 WO 2024256726 A1 WO2024256726 A1 WO 2024256726A1 EP 2024066814 W EP2024066814 W EP 2024066814W WO 2024256726 A1 WO2024256726 A1 WO 2024256726A1
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biomarker
allostatic load
cpg
value
phenotype
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Sébastien NUSSLÉ
Semira GONSETH NUSSLÉ
Jonviea Danielle CHAMBERLAIN-CETTOU
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Genknowme SA
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Genknowme SA
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30—Detection of binding sites or motifs

Definitions

  • the present invention relates to a computer implemented method determining a value of allostatic load for a human being, especially a computer-based method.
  • Epigenetic modifications usually measured via DNA methylation (DNAm) or the addition of a methyl group at cytosine-guanine nucleotides - can be induced in response to external (i.e., environmental exposures) or internal stimuli, resulting in modifications to gene expression.
  • Epigenetic signatures summarize epigenetic modifications (termed CpGs) at different loci and regions of DNA. To this point, CRP measured via DNA methylation has been evidenced to have a superior temporal stability, reflecting rather cumulative exposure in comparison to serum CRP, and was more strongly associated with brain health outcomes.
  • an epigenetic signature of allostatic load helps to circumvent measurement issues and offer a standardized approach.
  • the present invention provides: (1) identification of relevant CpGs associated with a phenotypic measure of allostatic load using epigenomewide association analysis (EWAS) for a reference population; and (2) estimation and validation of an epigenetic signature of allostatic load for a human being.
  • EWAS epigenomewide association analysis
  • the human cells used to determine methylation levels for specific methylation sites can be solid tissue, blood, saliva sample that comprises genomic DNA.
  • the determination of methylation levels for specific methylation sites of the reference population can be done on the same or different types of human cells.
  • the methylation value of a CpG site in a population of human cells can be the average degree of methylation of said CpG methylation site in a population of a sample of cells, usually comprising hundreds up to and over hundreds of thousands of cells.
  • the method according to the invention allows starting from knowledge about a reference population to determine an allostatic load information of a subject human being only on the basis of the determination of some CpG levels of a cell sample of this human being, especially without having discussed with this human subject allostatic load issues or tested him about cardio values or his blood on diabetes, cholesterol issues etc. .
  • the invention uses a database of biomarkers.
  • a biomarker or biological marker is a measurable indicator of some biological state or condition.
  • the biomarkers can be arranged in a number of AL concept systems.
  • the invention now uses one or more AL phenotype variable concepts, which are the options: b1.) wherein a risk threshold value is provided for each biomarker and an allostatic load score for each member is based on the sum of risk values attributed for each biomarker depending on the distance of each biomarker value of said member beyond the corresponding threshold value of said biomarker, b2.) wherein an allostatic load score for each member is based on the sum of risk values attributed for each biomarker depending on the absolute z-score of each biomarker value of said member compared to the corresponding average value of said biomarker for the reference population, b3.) wherein, independently for a predetermined number of AL concept systems, an allostatic load score for each member is based on the sum of latent variable values for such an AL concept system with biomarkers attributed to this AL concept system. b1 .), b2.) and b3.) can be used alone or in combination of these options. It is noted that the number of AL phenotype variable concepts is
  • Each such AL phenotype variable concept is based on one or more of said different AL concept systems, especially at least three AL concept systems.
  • the different biomarkers of different AL concept systems are not differentiated, since in b1.) wherein a risk threshold value is provided for each biomarker and an allostatic load score for each member is based on the sum of risk values attributed for each biomarker depending on the distance of each biomarker value of said member beyond the corresponding threshold value of said biomarker, as well as in b2.) wherein an allostatic load score for each member is based on the sum of risk values attributed for each biomarker depending on the absolute z-score of each biomarker value of said member compared to the corresponding average value of said biomarker for the reference population, this calculation does not use the affiliation of a biomarker to such an AL concept system.
  • an allostatic load score for each member is based on the sum of latent variable values for such an AL concept system with biomarkers attributed to this concept system.
  • the biomarkers are arranged in especially three or more AL concept systems.
  • Such AL concept systems can be taken from the group encompassing the cardiovascular system, the neuroendocrine system, the metabolic system and/or the inflammatory system. It is also possible, with other cohorts, to use e.g. telomere length or functional age estimators as such AL concept system, depending which biomarkers are available.
  • Each of these AL concept systems comprise a number of biomarkers, usually more than one.
  • an AL score can be calculated based on the different biomarkers.
  • the score initially depends on the number of biomarkers of such an AL concept system and on the AL phenotype variable concept used. It can be the sum of dichotomized values, or it can be z-score. It can also be a latent variable.
  • the number of biomarkers available for a specific AL concept system can be very different, from very few as two or three biomarkers in a first AL concept system to e.g. ten or more biomarkers in a second AL concept system.
  • the simple use of addition of dichotomized values (0 or 1) and especially z-scores tend to emphasize the importance of one AL concept system having (by chance) a higher number of biomarkers.
  • a latent variable is defined as an AL score calculated based on the biomarker scores of a specific AL concept system. Therefore, the available biomarkers are defined to be part of a specific AL concept system as e.g. the cardiovascular system or the neuroendocrine system and provide a single score for all biomarkers of said specific system. This enables a more differentiated view on the AL score and prevents an excessive influence from biomarkers from a specific system.
  • the feature of calculating, independently for a predetermined number of AL concept systems, an allostatic load score for each member based on the sum of latent variable values for such an AL concept system with biomarkers attributed to this concept system relates to the feature of calculating, for a predetermined number of AL concept systems, especially at least two or three AL concept systems, e.g. the cardiovascular system or the neuroendocrine system, the independently, an allostatic load score for each member based on the sum of latent variable values for such an AL concept system with biomarkers attributed to this concept system.
  • the calculation of the method uses a database comprising a number of biomarkers and related risk values (dichotomized or as z-score etc.) and on the other side it provides the allostatic load score.
  • latent variables as intermediate allostatic load scores are determined for a number of predetermined AL concept systems as intermediate values which are then combined to the final allostatic load score, as a sum of the different concept values, dichotomized or modified with factors.
  • the biomarkers mean systolic blood pressure, mean diastolic blood pressure, and mean heart rate
  • one latent variable is calculated for the AL concept system "cardiovascular system".
  • AL-GP are the CpGs that were identified by EWAS to be specific to the allostatic load estimation with the GP method, i.e. the one described in b1).
  • AL-LBC CpGs are the one estimated with the z-threshold that was initially created using the LBC cohort, i.e. in b2). So methods developed in other cohorts were applied to the reference cohort mentioned in the present application, the SKIPOGH cohort.
  • An aim of the computer implemented method is to estimate allostatic load based on CpG only. But to build the method, biomarkers in the reference population have to be known, either directly, or to build latent variables.
  • the CpG to be used in the method was taken out of a pool of CpG. For this pool, all CpG at disposal can be used, the one linked to the latent variables, but also the one linked to the other metrics of allostatic load (AL-GP and AI-LBC).
  • the feature AL phenotype variable concept is related to the chosen biomarkers, e.g. either the list of biomarkers chosen from the database (i.e. all available or one or more different limited number of biomarkers from the list) or the latent variables, if the list is reduced to the Al concept system(s).
  • the method provides the connection between the measured CpGs and the nbf biomarkers, determined to be relevant for the AL score. Then the human user comes into play, or better, the result of the analysis of his probe of human cells, allowing to determine the methylation level of the same nbf CpGs as provided in the database for the reference population.
  • the method caculates the allostatic load for the n rp members of said reference population using elastic net regression and general population cut-off values of individual biomarkers, wherein the calculated coefficients for each CpG are used to calculate the allostatic load of the human being providing his sample of cells.
  • This provides the connex between the phenotype biomarkers available for the reference population and the CpG values just based on the analysis of some human cells of said human without e.g. asking him a questionnaire relating to the stress level or taking his blood pressure as a value for one part of a concept system or as the determinaton of this cardiovascular system value as latent variable.
  • the invention also provides a method for validating the phenotype-based method determining a value of allostatic load of a human being based on a sample of cells of this human as mentioned above.
  • steps a.) to d.) of claim 1 are executed to determine the CpGs followed by: separating the reference population into a first group being a randomised sample of n ri members of the reference population and a distinct second group of the remaining (n rp - n r i) members of the reference population.
  • the allostatic load of a member of the second group is based on his sample of cells through determining the methylation levels of human cells related to the allostatic load for the n ri members of the randomized sample using elastic net regression and general population cut-off values of individual biomarkers, wherein the calculated coefficients for each CpG are used to calculate the CPG-based allostatic load of the member of the second group providing his sample of cells. This is the same procedure as before.
  • the phenotype-based value of allostatic load of this member of the second group is calculated and this phenotype-based value of allostatic load is compared with the CpG-based allostatic load value of this member of the second group to determine the difference between the phenotype-based value and phenotypebased value of allostatic load.
  • the method is also capable of taking into account specificities when new cohorts of reference populations become available.
  • Fig. 1 shows a flow chart of the AL score generation for the reference population
  • Fig. 2 shows some entries of a risk threshold table for biomarkers
  • Fig. 3 shows some entries of allostatic load scores as risk categorization for the biomarkers shown in Fig. 2;
  • Fig. 4 shows some entries of allostatic load scores as risk categorization based on the Z-score for the reference population
  • Fig. 5 shows a flow chart of the overall principles of the method according to the invention.
  • Fig. 6 shows a flow chart of the overall principles of a specific embodiment of the method according to the invention.
  • Fig. 7 shows a flow chart of a validation method for the method according to an embodiment of the invention. DESCRIPTION OF PREFERRED EMBODIMENTS
  • Fig. 1 shows a flow chart of the AL score generation 100 for a reference population.
  • the method starts with providing a biomarker database 10 of a reference population comprising biomarker values of a plurality of biomarkers.
  • the methods described herein are computer implemented methods.
  • the databases are stored in memory accessible from a processor in which a software is loaded comprising instructions to execute the method steps. It provides a new approach to determine an allostatic load value of a person based on technical data which was until now not usable to obtain this information.
  • a “reference population” or “cohort” as used herein refers to sample of a larger population in which participants have been randomly sampled from population registries. It is hypothesized that the reference population is a representative sample of a population and therefore seeks to accurately reflect the characteristics of the larger population.
  • the larger population can be understood as an ethnicity, for example Caucasians, a sub-ethnicity such as Slavic people, a country with several ethnicities, for example Chinese people, a region, or even a continent, the African or South American population for example.
  • Examples of a reference population or cohort comprise the Swiss Kidney Project on Genes in Hypertension study (SKIPOGH).
  • Such a reference population can be the SKIPOGH group of people.
  • SKIPOGH stands for "Swiss Kidney Project on Genes in Hypertension" and comprises biomarkers for almost 1000 persons.
  • other cohorts can be used, and it is an aim of the present method to be applicable to any reference population.
  • Such other known databases are Framingham Heart Study, Generation Scotland, Lothian Birth Cohorts. If the person for whom the allostatic load is to be evaluated is from the same or similar reference group, especially similar ethnicity, then specifically adapted results are achieved.
  • methylation site refers to a CpG position that is potentially methylated. Methylation typically occurs in a CpG containing nucleic acid.
  • the CpG containing nucleic acid may be present in, e.g., in a CpG island, a CpG doublet, a promoter, an intron, or an exon of gene.
  • Hyper or hypo-methylation of the methylation sites can be assessed by detecting methylation status and comparing a value to a relevant reference level. For example, the methylation status of one or more markers can be indicated as a value.
  • the value can be one or more numerical values resulting from the assaying of one or more biological sample(s), and can be derived, e.g., by measuring methylation status of the marker(s) in the sample(s) by an assay, or from a dataset obtained from a provider such as a laboratory, or from a dataset stored on a server.
  • DNA methylation of the methylation markers can be measured using various approaches, which range from commercial array platforms (e.g. from IlluminaTM) to sequencing approaches of individual genes. This includes standard lab techniques or array platforms.
  • a variety of methods for detecting methylation status or patterns have been described in, for example U.S. Pat. Nos.
  • measuring methylation status comprises, performing methylation specific PCR (MSP), real-time methylation specific PCR, methylation-sensitive single-strand conformation analysis (MS-SSCA), quantitative methylation specific PCR (QMSP), PCR using a methylated DNA-specific binding protein, high resolution melting analysis (HRM), methylation-sensitive single-nucleotide primer extension (MS-SnuPE), base-specific cleavage/MALDI-TOF, PCR, real-time PCR, Combined Bisulfite Restriction Analysis (COBRA), methylated DNA, immunoprecipitation (MeDIP), a microarray-based method, pyrosequencing, or bisulfite sequencing.
  • MSP methylation specific PCR
  • MS-SSCA methylation-sensitive single-strand conformation analysis
  • QMSP quantitative methylation specific PCR
  • PCR using a methylated DNA-specific binding protein
  • HRM high resolution melting analysis
  • MS-SnuPE methylation-sensitive
  • the methylation status will be expressed as a beta-value, i.e., the percentage of methylated DNA string at a given location. This is the percentage of methylation in a tissue. Each cell has only two DNA strings at one location, so the methylation is either 0%, 50% or 100%.
  • a tissue cell can be differentially methylated (typically in blood).
  • the Swiss Kidney Project on Genes in Hypertension is a Swiss family-based population cohort with two waves of cross-sectional data collection, for which participants were recruited between December 2009 and April 2013. Individuals were eligible for inclusion if they were (a) 18 years or older, (b) of Caucasian descent, (c) had at least one first-degree family member willing to participate, and (d) provided written consent for study inclusion. Clinical biomarkers were collected at one of three study centers, i.e. Lausanne, Bern or Geneva, while standardized questionnaires were filled-out by the participant at home. Whole blood samples were collected during the second follow-up survey (October 2012-December 2016).
  • DNA methylation was measured by first denaturing DNA ( ⁇ 1.2ug) with sodium bisulfate, and then amplifying the DNA by PCR.
  • DNA methylation was assayed using the Illumina Infinium HumanMethylation450 BeadChip, while 721 participants had DNA methylation assayed using the Illumina Infinium MethylationEPIC BeadChip. Blood cell counts were estimated using the Housemann method.
  • SKIPOGH data were divided into a training and validation set, also called being a first and a second group of the reference population in the database.
  • MethodAL epigenetic signature of allostatic load
  • CpGs confirmed in the validation EWAS were included in a stepwise regression model using the MASS package in RStudio. Relevant CpGs were selected based on the Akaike’s Information Criterion (AIC).
  • Fig. 3 shows a plot of the correlation between phenotypic AL and the epigenetic signature.
  • Fig. 4 shows a plot of odds ratios and 95% confidence intervals for probability of history of CVD corresponding to different measures of AL.
  • an AL score is generated for each member of the reference population.
  • three different ways to calculate such a score are shown, allowing to calculate six different AL scores, since the AL score generation 130 based on latent variables can be calculated for four different concept systems as explained below.
  • the biomarker database 10 can also comprise a risk threshold value table 111 or cut-off value table for each biomarker.
  • Fig. 2 shows some entries for such a risk threshold table 111 for biomarkers. Systolic blood pressure, Insulin and C-reactive protein are shown as three examples. A plurality of other biomarkers can be used.
  • the risk threshold values are usually cut-off values for the general population but can also be chosen specifically for a cohort.
  • Fig. 3 shows some entries of allostatic load scores as risk categorization for the biomarkers shown in Fig. 2.
  • the allostatic load is determined as 1 or another unity value and the allostatic load related to this biomarker is determined as 0.
  • the risk threshold is the lowest acceptable value of the biomarker and then the allostatic load is determined as 1 if the biomarker is lower (' ⁇ ') than this threshold.
  • the allostatic load score is calculated as a distance value and can thus have other values than natural numbers.
  • this distance value is calculated based on thresholds for higher risk values and can start from 0 or 1 if the threshold value is reached.
  • This first concept approach provides an allostatic load score for each member being based on the sum of risk values attributed for each biomarker depending on the distance of each biomarker value of said member beyond the corresponding threshold value of said biomarker.
  • the biomarker database 10 is used as a closed system and the biomarker values of the reference population are organized as Z-scores 121 for each different biomarker.
  • the biomarker database values of the database 10 are arranged as Z-scores, i.e. the average value for any biomarker receives the value 0 and value of plus or minus one standard deviation receives the value +1 and the value of plus or minus two standard deviations is +2 etc. .
  • the first concept uses scientifically approved values of the cut-off or threshold values, whereas the allostatic load values following the second concept are determined as deviation from the average value of the reference population.
  • the four different AL concept systems 131 are Inflammation, Neuroendocrine, Metabolic and Cardiovascular.
  • the biomarkers for the inflammation concept are: IL-6, CRP, IFN-y, TNF-a and IL-10
  • for the neuroendocrine concept are: Dehydroepiandtrosterone (24hr), Cortisol (24hr), Androsterone (24hr) and an assigned stress level from 1 to 10
  • for the Metabolic concept are: Ins2s, ALAT, GGT, Glucose, TG and Ura
  • for the cardiovascular concept are: Diastolic BP, Systolic BP and Heart Rate.
  • an allostatic load score for each member is based on the sum of latent variable values for the correspondingly chosen AL concept system with biomarkers attributed to this concept system, so for four AL concept systems, four additional ALs are generated. In total it is possible to generate up to six AL scores for each member of the reference population.
  • Fig. 5 shows a flow chart of the overall principles of the method according to the invention.
  • the following table 1 shows possible biomarkers to be used.
  • the biomarkers used depend on the level of data, the database 10 of the reference population contains.
  • the entry "Stress” is defined as "On a scale from 1-10, what is your level of daily stress?"
  • Table 2 provides a list of identified CpGs with the SKIPOGH cohort.
  • Fig. 6 shows a flow chart of the overall principles of a specific embodiment of the method according to the invention when such a list of identified CpGs is available as box 300.
  • the method then starts also with the AL score generation for a number of AL phenotypes variable concepts and then the AL score of the human being is calculated as above with the CpG taken as a sub-group from the above Table 2.
  • the thresholds for the general population are used to estimate the phenotype-based measure of allostatic load. These thresholds are used to define whether someone is “at risk” for a given biomarker.
  • the epigenetic signature for AL is derived by identifying the CpGs corresponding to the phenotype-based AL score, using elastic net regression, that result in the best fit. And therefore, the individual AL scores are the best ones given the reference population and the CpGs identified. It is a “local best fit”. The method is built on the insight that knowledge of methylation levels of identified CpGs is sufficient to determine a physical value relation to the corporal and mental situation of a human being normally related to specific phenotype-based measurement values.
  • Fig. 7 shows a flow chart of a validation method for the method according to an embodiment of the invention.
  • the reference population is separated into a first group being a randomized sample of n ri members of the reference population and a distinct second group of the remaining (n rp - n r i) members of the reference population as reflected in box 300.
  • the second validation group can be chosen smaller than making up the total number of the cohort.
  • the method for validating the phenotype-based method determining a value of allostatic load of a human being based on a sample of cells of this human being is starting with the steps 100 for each member of the first group, the steps 200 for every phenotype concept as checked in box 205 followed by compiling the identified CpGs into a list as in box 210. These steps can also be executed based on the entire reference population, but it is preferred to only use the first group. Then, this specific choice depending on available measurements and defining choice of applied concepts as well as thresholds is validated with and for the second group of the reference population.
  • the phenotype-based value of allostatic load of the member of the second group is calculated as shown in box 320 and compared this phenotype-based value of allostatic load with the CPG-based allostatic load of the member of the second group to determine the difference between the phenotype-based value and phenotype-based value of allostatic load.
  • steps 310 and 320 are repeated 325 for a predetermined number relating to the number of or a portion of the members of the second group and determine in Box 330 a negative validation result, if the determined differences between the phenotype-based value and phenotype-based value of allostatic load for the different members of the second group exceed a predetermined threshold level for a number of at least one of the predetermined number of members of the second group.
  • the method steps to determine the CpG value of allostatic load is conducted with different starting positions and choices. This can comprise a new distribution of the first and second groups as shown with the arrow going back to box 300, but it is also possible to maintain the same groups and make different choices for the steps as shown in boxes 100 or 200.
  • AL concept score generation 300 separate the population into AL score on risk threshold two distinct groups ' AL risk categorization 310 calculate AL score of a risk threshold database member of the second group
  • Z-Score of reference group population 320 calculate phenotype-based
  • AL score on latent variable value of allostatic load of the concept system biomarker member of the second group execute an EWAS identifying 325 reiterate for all second group CpGs members decision for creating a loop 330 determine validation result for further phenotypes comparing phenotype with compiling all identified CpGs CpG-based values of the into a single list second group members calculate AL score of a 335 if negative result go back human being based on 340 validation successful measured CpG levels of his

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Abstract

L'invention concerne un procédé qui détermine une valeur de charge allostatique pour un être humain sur la base de la détermination de certains niveaux CpG d'un échantillon de cellule de cet être humain avec des connaissances concernant des niveaux CpG d'une population de référence, en particulier, sans avoir discuté avec ce sujet humain des problèmes de charge allostatique ou sans l'avoir testé à propos de valeurs du cœur ou de son sang sur le diabète, des problèmes de cholestérol, etc.
PCT/EP2024/066814 2023-06-15 2024-06-17 Procédé mis en œuvre par ordinateur déterminant une valeur de charge allostatique d'un être humain Pending WO2024256726A1 (fr)

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