A. TITLE

Method to identify patients who would respond favourably to hypolipidemic treatment.

B. TECHNICAL FIELD

The invention relates to a method to be used in the medical/pharmaceutical field; the method involves the calculation of a genetic risk score for adults with obesity and dyslipidaemia, which indicates the probability that they will respond to treatment with hypolipidemic medication.

C. BACKGROUND ART

The problem of the correlation of obesity and dyslipidaemia diseases with certain genetic factors has been the subject of previous inventions, which are included in the background art. A Method of epigenetic analysis for determining clinical genetic risk is known (WO 2016/112031 Al). This method determines the risk of a particular individual suffering from a metabolic disease, but without indicating its predisposition to respond or not to treatment.

Also known is a Method for determining a personalized nutrition and diet using nutrition and physiological data (US2010113892 Al). The authors of this patent have invented a personalized nutrition method, with a broader but more specific view of the proposed invention to be patented.

In addition, a Program for Regulating Health Conditions (United States Patent Application 20050095628 http://www.freepatentsonline.com/y2005/0095628.html) is known. In comparison to this patented invention, the proposed invention specifically addresses lipid metabolism and response to hypolipidemic treatment.

D. DISCLOSURE OF INVENTION The problem solved by the proposed invention is to identify those individuals for whom usual / classic hypolipidemic treatment will give the expected results, so that treatment will be administered to them. It is known that the usual / classic hypolipidemic treatment has adverse effects on the patients, so that knowing the patients on whom such treatment might be effective would reduce both the negative consequences on the health of the respective patients and the financial burden on the individual, health system and society in general.

The method set forth by the invention will enable healthcare professionals to identify those patients with obesity and dyslipidaemia who will benefit from treatment with hypolipidemic medication.

By applying the invention, the following objectives will be achieved:

- it will help healthcare professionals to avoid the use of classic hypolipidemic treatments for some patients with dyslipidaemia and obesity, treatments that would not only be ineffective in their case, but would also have adverse effects;

- will help people with dyslipidaemia and obesity receive effective, individualized treatment that will bring them health benefits;

- will help health professionals develop a personalized management plan for the treatment of dyslipidaemia, based on the risk that each individual has to respond or not to the treatment;

- will limit the costs incurred by the patients / the state for the purchase of hypolipidemic medication.

Background of the invention:

The prevalence of obesity is constantly increasing both on a global and national level. In Romania, the prevalence of overweight individuals in adults is 53.1% for men (of which 16.9% with obesity) and 49.1% for women (of which 21.2% with obesity). The general trend is one of worsening of the situation, the rate of growth of obesity in children in Romania being one of the highest in Europe (5% in eight years), according to the National Health Strategy 2014-2020. The economic impact of obesity worldwide is estimated at $ 2 trillion annually, in addition to the associated medical expenses and indirect economic costs (increased morbidity and mortality, reduced productivity and quality of life, etc.). In the United States, the Centres for Disease Control (CDC) estimates that $ 147 billion is used only for obesity-related medical treatments.

In the context of the continuous increase of the costs related to obesity and the increasing prevalence of this disease, including among the young population, it is necessary to include new therapeutic and preventive approaches, including using nutrigenomic methods for a personalized and effective treatment. Although research in this area has made significant progress, this knowledge has not yet been implemented into a public health service.

Although, in general, there is a broad consensus regarding the ways of monitoring and evaluating the pathology of lipid metabolism associated with obesity, the mechanism of these changes is far from being adequately explained. The risk of obesity, as well as the risk of incidence and evolution of dyslipidaemia, are partly dependent on the genetic and / or epigenetic profile of individuals. Lipid profile and response to treatment are variable, depending on gender, genetic profile, endocrine profile, and other unknown or incompletely characterized factors.

In the context of genetic profiling, polymorphisms are known in genes that regulate metabolic pathways in choline synthesis (PEMT gene), choline betaine synthesis (CHDH gene), methyl donation from 5-MTHF to homocysteine, and 5-MTHF synthesis. (MTHFD and MTHFR genes), intracellular transport of choline, as well as polymorphisms of the FADS2 gene, whose product (delta-6-desaturase) regulates the rate of desaturation of essential fatty acids; which have proven effects on lipid metabolism or are associated with the pathology of liver steatosis.

The pathology associated with obesity is complex, and includes both cardiovascular, hepatic, renal, pulmonary, locomotor, cerebral, immunological, as well as metabolic diseases (type 2 diabetes) and cancers. Changes in lipid metabolism associated with obesity represent an area of interest both in terms of the cardiovascular risk they carry, especially when other diseases are associated (diabetes, liver steatosis, atherosclerosis, etc.) and as a tool for monitoring during treatment. In the context of the proposed invention, it is important to note that obesity-associated dyslipidaemias, although complex and heterogeneous, are clinically monitored using biochemical variables obtained from blood including total cholesterol.

The proposed invention focuses on the identification of persons with obesity and dyslipidaemia who, as carriers of some combinations of genetic polymorphisms, will not respond to the treatment with hypolipemic medication.

E. MODES FOR CARRYING OUT THE INVENTION

The following definitions will be used:

Obesity is defined by BMI >30 kg / m2.

Dyslipidaemia at evaluation is defined by total serum cholesterol >200 mg / dl or DHL <50 mg / dl in women and <40 mg / dl in men or serum triglycerides >150 mg / dl.

Anamnestic dyslipidaemia is defined by the fact that the patient is aware of having dyslipidaemia, whether or not he is under treatment.

Hypolipemic treatment is defined as oral medication with statins, or statins plus fibrates, or statins plus fibrates plus ezetimibe, or fibrates, or omega3, or ezetimibe.

The positive response to the hypolipidemic treatment is the case when, after administration of the Treatment for Dyslipidaemia, total cholesterol, triglycerides and HDL are in normal values at evaluation.

Genetic polymorphism is a uninucleotide polymorphism (single-nucleotide polymorphism in English, abbreviated SNP, noted / coded "rs" followed by a few numbers) defined as a variation in the sequence of genomic DNA that concerns a single nucleotide - A, T, C or G - observed on the two homologous chromosomes of an individual.

Names of genes in which the studied SNPs are located:

ABCB4 — ATP binding cassette subfamily B member 4; CHDH — choline dehydrogenase;

FADS2 — fatty acid desaturase 2;

MTHFD1 — methylenetetrahydrofolate dehydrogenase; MTHFR — methylenetetrahydrofolate reductase;

SCD — stearoyl-CoA had desaturated; EMYP-phosphatidylethanolamine-methyltransferase;

PNPLA3 — patatin-like phospholipase domain containing 3.

The invention could be carried out in two modes, as follows: 1 ^st Mode for carrying out the invention:

Step 1 - Evaluation of the data needed for the calculation process

The first step is to evaluate the patient's medical history, in order to establish that the following premises are met: he suffers from obesity and dyslipidaemia and is between 18 and 70 years old; this step involves the analysis of the following data: sex, weight, height, medication history.

Furthermore, this step involves:

- Evaluation of biochemical blood tests (cholesterol, HLD, LDL, triglycerides).

- Evaluation of DNA blood tests - genotype involving molecular genetics techniques for several uninucleotide polymorphisms (single-nucleotide polymorphism in English, abbreviated SNP, noted / coded "rs" followed by several numbers) as follows: rs4846052 MTHFR gene, rs7849 SCD1 gene , rs6807783 gene CHDH, rsl801133 gene MTHFR, rs4244593 gene PEMT, rs9655950 gene ABCB4, rs8068641 gene PEMT, rs2526678 gene FADS2, rs7634578 gene CHDH, rs738409 gene rPL7133F gene PNPLA59

Technical conditions in Step 1 Usual biochemical test for cholesterol, HLD, LDL, triglycerides, using Ortho Clinical Vitros 350 Chemical System (Ortho Clinical Diagnostics Inc. New Jersey, USA), using standardized reagents, according to the manufacturer's protocol.

The genetic test for genotype evaluation includes a genetic test kit with a unique identification code, having a DNA collection device (EDTA vacutainer 2 ml).

Genomic DNA is isolated from whole blood using MagCore® Extractor System and MagCore® Genomic DNA Whole Blood Kit (RBC Bioscience, New Taipei City, Taiwan) following the manufacturer's protocol.

Molecular genetics techniques can be performed by Polymerase Chain Reaction (PCR) or by next generation sequencing (NGS).

NGS genotyping uses a MiSeq sequencer (Illumina, San Diego, USA) and a custom hotspot sequencing kit for 55 SNPs from 14 previously selected genes.

Step 2 - Mathematical modelling according to the gender of the person

Modelling is based on regression models for those who are known to have dyslipidaemia and undergoing treatment with hypolipidemic medication. Dependent variable (outcome) is the treatment response estimated by the value of cholesterol, and genetic polymorphisms are independent variables (predictors).

For women, the model uses the predictors values of rs4846052 MTHFR gene, rs7849 SCD gene, rs6807783 CHDH gene.

For men, the model uses as predictors the values of rsl801133 gene MTHFR, rs4244593 gene PEMT, rs9655950 gene ABCB4, rs8068641 gene PEMT, rs2526678 gene FADS2, rs7634578 gene CHDH, rs738409 gene PNPFA3, rsl0135928 gene MTHFD1.

The formulas for calculating the probability of response to treatment (total cholesterol - dependent variable) are:

Y (men) = 356.238+ (rsl801133 * -51.708) + (rs4244593 * -24.040) + (rs9655950 * - 53.208) + (rs8068641 * -43.422) + (rs2526678 * -47.798) + (rs7634578 * -5.850) rs738409 * -17.737) + (rsl0135928 * 47.207) Y (women) = 200.580 + (rs4846052 * -23.674) + (rs7849 * 34.205) + (rs6807783 * - 21.036)

Where: Y = value Total cholesterol estimated from genetic score (value indicating probability of response to treatment)

Rs (predictors) = the value of each SNP, which can be 0, 1 or 2 - according to Table 1 below); this value will be replaced as such in the formulas Y (men) and Y (women) above.

Table 1. Predictor coding - genetic polymorphisms: 0, 1, 2

Technical conditions step 2

Y value estimation can use computer-assisted methods (Excel program). Step 3 - Interpretation of the model result

If the value calculated from the model for variable Y is less than 200, the individual may be classified as having an 81% probability to respond to treatment with dyslipidaemia medication. If the calculated value for Y is greater than 200, the model cannot provide a sufficiently good probability for classification.

2 ^nd Mode for carrying out the invention:

Four categories are defined based on the effect of statins (0 - no effect, 1 - with effect) and cholesterol level (0 - low cholesterol, 1 - high cholesterol), such as S0C0, S0C1, S1C0, S1C1. The lower threshold for high cholesterol is considered 200 mg / dl.

For each category, a score is calculated according to the below formula: Where:

C represents the category, M represents the matrix of states, n represents the number of predictors (rs), SNP^ represents the current predictor and SS' represents the column selected based on category C.

In order to identify the StatineColesterol category (presented above), the category with the highest score will be selected as the defining one for the patient. The accuracy of identifying the correct category, for the male gender, is 92.5% and the accuracy is 70.6%, and for the female gender the accuracy is 76.25% and the accuracy of 92%.