WO2015086913A1

WO2015086913A1 - Methods for detection of the risk of obesity, the metabolic syndrome and diabetes

Info

Publication number: WO2015086913A1
Application number: PCT/FI2014/050995
Authority: WO
Inventors: Jukka T. Salonen
Original assignee: Mas-Metabolic Analytical Services Oy
Priority date: 2013-12-12
Filing date: 2014-12-12
Publication date: 2015-06-18

Abstract

The present invention is directed to method of identifying risk of developing obesity, the metabolic syndrome and type 2 diabetes in a human individual, the method comprising: determining the presence or absence or the degree of heteroplasmy of mutations and other variants in the individual's mitochondrial DNA related to the risk of obesity, the metabolic syndrome and/or type 2 diabetes and determining the overall mutation level of the mitochondrial genome of said individual.

Description

Methods for detection of the risk of obesity, the metabolic syndrome and diabetes

FIELD OF THE INVENTION This invention is related to novel mitochondrial biomarkers and therapeutic targets of obesity, the metabolic syndrome and type 2 diabetes.

BACKGROUND OF THE INVENTION Obesity is excessive accumulation of energy in the form of body fat which impairs health. As the direct measurement of body fat is difficult, Body Mass Index (BMI), a simple ratio of weight to the square of height (kg/m²), is typically used to classify overweight (BMI > 25) and obese (BMI > 30) adults. Obesity is in part genetic, in part of behavioral origin. Genetic influences are either hereditary or due to somatic (acquired) mutations. Germ line mutations can be either in the nuclear DNA or in the mitochondrial DNA (mtDNA). We have previously shown that mtDNA variants predict human atherosclerosis (WO2011144818). We have also previously published nuclear genetic markers of obesity (EP10829581). The metabolic syndrome (MS), a concurrence of disturbed glucose and insulin metabolism, overweight and abdominal fat distribution, mild dyslipidemia, and hypertension, is characterized by insulin resistance and is also known as the insulin resistance syndrome (Lakka et al 2002). Diabetes mellitus type 2 (T2D, formerly noninsulin-dependent diabetes mellitus

(NIDDM) or adult-onset diabetes) is a metabolic disorder that is characterized by high blood glucose in the context of insulin resistance and relative insulin deficiency. Obesity is thought to be the primary cause of type 2 diabetes in people who are genetically predisposed to the disease. We have previously published risk genes for type 2 diabetes in the Eastern Finland founder population (Salonen et al 2007).

In several studies, mutations in the D-loop of mtDNA have been associated with MS and/or T2D. Weng et al (2005) reported an association between the mtDNA variant 16189 and the MS. No association with T2D was observed in either a large case- control study in the UK or in European case-control sets (Chinnery et al 2005). Ye et al (2013) reported an association between mtDNA OriB variant (16184-16193 polycytosine tract) with T2D in the large Norfolk Diabetes Case-Control Study.

Obesity, the metabolic syndrome and type 2 diabetes are intertwined in many ways and form a uniform disease entity. First, obesity is an etiologic precursor of the metabolic syndrome and type 2 diabetes, and a part of the definition of the metabolic syndrome. Secondly, these three conditions cluster in a population largely in the same persons. Thirdly, they have been observed to have mutual genetic background concerning the nuclear genes and their variants.

The high prevalence of obesity, the metabolic syndrome and type 2 diabetes, their significant contribution to morbidity and mortality of several common chronic diseases and lack of related biomarkers and risk assessment tests show unmet medical need both for obesity, the metabolic syndrome and type 2 diabetes related biomarkers as well as diagnostic methods and kits. The present invention provides a number of new correlations between various mitochondrial genetic variants and common obesity, the metabolic syndrome and type 2 diabetes. Obesity, the metabolic syndrome and type 2 diabetes associated biomarkers disclosed in this invention provide the basis for improved risk assessment, more detailed diagnosis and prognosis of obesity, the metabolic syndrome and type 2 diabetes.

The present invention concerns mitochondrial markers of overweight and obesity, the MS and T2D and related therapeutic targets.

SUMMARY OF THE INVENTION

This invention is directed to diagnosing and predicting obesity, the metabolic syndrome and type 2 diabetes and related conditions, selection of drugs, gene therapies and other therapies against obesity, the metabolic syndrome and type 2 diabetes and to methods of treatment of obesity, the metabolic syndrome and type 2 diabetes. The present invention relates to previously unknown associations between mitochondrial DNA variants and obesity, the metabolic syndrome and type 2 diabetes. These novel obesity, the metabolic syndrome and type 2 diabetes biomarkers provide basis for novel methods and kits for risk assessment and diagnosis of obesity, the metabolic syndrome and type 2 diabetes.

DETAILED DESCRIPTION OF THE INVENTION

A "biomarker" in the context of the present invention refers to a Mitochondrial DNA variant disclosed in Tables 1 through 10 or to a variant which is in linkage

disequilibrium with one or more disclosed Mitochondrial DNA variants, or to an organic biomolecule which is related to a Mitochondrial DNA variant set forth in Tables 1 through 10 and which is differentially present in samples taken from subjects (patients) being obese compared to comparable samples taken from subjects who are non-obese or non-diabetic. An "organic biomolecule" refers to an organic molecule of biological origin comprising steroids, amino acids, nucleotides, sugars, polypeptides, polynucleotides, complex carbohydrates and lipids. A biomarker is differentially present between two samples if the amount, structure, function or biological activity of the biomarker in one sample differs in a statistically significant way from the amount, structure, function or biological activity of the biomarker in the other sample.

A "haplotype," as described herein, refers to a combination of genetic markers ("alleles"). A haplotype can comprise two or more alleles and the length of a genome region comprising a haplotype may vary from few hundred bases up to hundreds of kilobases. The haplotypes described herein are differentially present in individuals with obesity, the metabolic syndrome and type 2 diabetes than in individuals without obesity, the metabolic syndrome and type 2 diabetes. Therefore, these haplotypes have diagnostic value for risk assessment, diagnosis and prognosis of obesity, the metabolic syndrome and type 2 diabetes in an individual. Detection of haplotypes can be accomplished by methods known in the art used for detecting nucleotides at polymorphic sites. Haplotypes found more frequently in obese individuals (risk increasing haplotypes) as well as haplotypes found more frequently in non-obese individuals (risk reducing haplotypes) have predictive value for predicting

susceptibility to obesity, the metabolic syndrome and type 2 diabetes in an individual.

A nucleotide position in genome at which more than one sequence is possible in a population, is referred to herein as a "polymorphic site" or "polymorphism". Where a polymorphic site is a single nucleotide in length, the site is referred to as a SNP. The term SNP is conventionally used to denote a known genetic variant with an RS-ID. Some but not all of the mitochondrial DNA variants to which we have disclosed novel associations with obesity, the metabolic syndrome and type 2 diabetes in Tables 1 through 10 of this invention have been known in prior art with their official reference SNP (rs) ID identification tags assigned to each unique SNP by the National Center for Biotechnological Information (NCBI). Some are novel, previously unknown variants, and we call them with reference to their contig position in the mtDNA.

Therefore, in the context of the present invention, SNP may be either previously known or unknown.

For example, if at a particular chromosomal location, one member of a population has an adenine and another member of the population has a thymine at the same position, then this position is a polymorphic site, and, more specifically, the polymorphic site is a SNP. Polymorphic sites may be several nucleotides in length due to insertions, deletions, conversions or translocations. Each version of the sequence with respect to the polymorphic site is referred to herein as an "allele" of the polymorphic site. Thus, in the previous example, the SNP allows for both an adenine allele and a thymine allele.

Typically, a reference nucleotide sequence is referred to for a particular gene e.g. in NCBI databases (www.ncbi.nlm.nih.gov). Alleles that differ from the reference are referred to as "variant" alleles. The polypeptide encoded by the reference nucleotide sequence is the "reference" polypeptide with a particular reference amino acid sequence, and polypeptides encoded by variant alleles are referred to as "variant" polypeptides with variant amino acid sequences. Nucleotide sequence variants can result in changes affecting properties of a polypeptide. These sequence differences, when compared to a reference nucleotide sequence, include insertions, deletions, conversions and substitutions: e.g. an insertion, a deletion or a conversion may result in a frame shift generating an altered polypeptide; a substitution of at least one nucleotide may result in a premature stop codon, amino acid change or abnormal mRNA splicing; the deletion of several nucleotides, resulting in a deletion of one or more amino acids encoded by the nucleotides; the insertion of several nucleotides, such as by unequal recombination or gene conversion, resulting in an interruption of the coding sequence of a reading frame; duplication of all or a part of a sequence; transposition; or a rearrangement of a nucleotide sequence, as described in detail above. Such sequence changes alter the polypeptide encoded by an obesity, the metabolic syndrome and type 2 diabetes susceptibility gene. For example, a nucleotide change resulting in a change in polypeptide sequence can alter the physiological properties of a polypeptide dramatically by resulting in altered activity, distribution and stability or otherwise affect on properties of a polypeptide.

Alternatively, nucleotide sequence variants can result in changes affecting transcription of a gene or translation of its mRNA. A polymorphic site located in a regulatory region of a gene may result in altered transcription of a gene e.g. due to altered tissue specificity, altered transcription rate or altered response to transcription factors. A polymorphic site located in a region corresponding to the mRNA of a gene may result in altered translation of the mRNA e.g. by inducing stable secondary structures to the mRNA and affecting the stability of the mRNA. Such sequence changes may alter the expression of an obesity, metabolic syndrome and type 2 diabetes susceptibility gene.

Although the numerical chromosomal position of a SNP may still change upon annotating the current human genome build the SNP identification information such as variable alleles and flanking nucleotide sequences assigned to a SNP will remain the same. Those skilled in the art will readily recognize that the analysis of the nucleotides present in one or more SNPs set forth in Tables 1 through 10 of this invention in an individual's nucleic acid can be done by any method or technique capable of determining nucleotides present in a polymorphic site using the sequence information assigned in prior art to the rs IDs of the SNPs listed in Tables 1 through 10 of this invention.

It is understood that the obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants described in Tables 1 through 10 of this invention may be associated with other polymorphisms. This is because the

Mitochondrial DNA variants listed in Tables 1 through 10 are flanking each other. Also, in our data set, all mtDNA variants correlated with each other. These other polymorphic sites that are associated with the Mitochondrial DNA variants listed in Tables 1 through 10 of this invention may be either equally useful as obesity, the metabolic syndrome and type 2 diabetes biomarkers or even more useful as causative variations explaining the observed obesity, the metabolic syndrome and type 2 diabetes association of Mitochondrial DNA variants of this invention. The term "gene," as used herein, refers to an entirety containing entire transcribed region and all regulatory regions of a gene. The transcribed region of a gene including all exon and intron sequences of a gene including alternatively spliced exons and introns so the transcribed region of a gene contains in addition to polypeptide encoding region of a gene also regulatory and 5' and 3' untranslated regions present in transcribed RNA. Each gene has been assigned a specific and unique nucleotide sequence by the scientific community. By using the name of a gene those skilled in the art will readily find the nucleotide sequences of the corresponding gene and it's encoded mRNAs as well as amino acid sequences of its encoded polypeptides although some genes may have been known with other name(s) in the art.

In certain methods described herein, an individual who has increased risk for developing obesity, the metabolic syndrome and type 2 diabetes is an individual in whom one or more obesity, the metabolic syndrome and type 2 diabetes associated genetic variants selected from Tables 1 through 10 of this invention are identified. In other embodiment also variants associated to one or more variants set forth in Tables 1 through 10 may be used in risk assessment of obesity, the metabolic syndrome and type 2 diabetes. The significance associated with an allele or a haplotype is measured by an odds ratio. In a further embodiment, the significance is measured by a percentage. In one embodiment, a significant risk is measured as odds ratio of 0.8 or less or at least about 1.2, including by not limited to: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.5, 3.0, 4.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0 and 40.0. In a further embodiment, a significant increase or reduction in risk is at least about 20%, including but not limited to about 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% and 98%. In a further embodiment, a significant increase in risk is at least about 50%. It is understood however, that identifying whether a risk is medically significant may also depend on a variety of factors such as subject's family history of obesity and diabetes, previously identified obesity, glucose intolerance, hypertriglyceridemia, hypercholesterolemia, elevated LDL cholesterol, low HDL cholesterol, elevated blood pressure (BP), hypertension, cigarette smoking, lack of physical activity, and inflammatory components as reflected by increased C- reactive protein levels or other inflammatory markers.

"Probes" or "primers" are oligonucleotides that hybridize in a base-specific manner to a complementary strand of nucleic acid molecules. By "base specific manner" is meant that the two sequences must have a degree of nucleotide complementarity sufficient for the primer or probe to hybridize to its specific target. Accordingly, the primer or probe sequence is not required to be perfectly complementary to the sequence of the template. Non-complementary bases or modified bases can be interspersed into the primer or probe, provided that base substitutions do not inhibit hybridization. The nucleic acid template may also include "non-specific priming sequences" or "nonspecific sequences" to which the primer or probe has varying degrees of complementarity. Probes and primers may include modified bases as in polypeptide nucleic acids (Nielsen PE et al, 1991). Probes or primers typically comprise about 15, to 30 consecutive nucleotides present e.g. in human genome and they may further comprise a detectable label, e.g., radioisotope, fluorescent compound, enzyme, or enzyme co-factor. Probes and primers to a Mitochondrial DNA variant disclosed in tables 3 to 43 are available in the art or can easily be designed using the flanking nucleotide sequences assigned to a SNP rs ID and standard probe and primer design tools. Primers and probes for Mitochondrial DNA variants disclosed in Tables 1 through 10 can be used in risk assessment as well as molecular diagnostic methods and kits of this invention.

The invention comprises polyclonal and monoclonal antibodies that bind to a polypeptide related to one or more obesity, metabolic syndrome and type 2 diabetes associated mitochondrial DNA variants set forth in Tables 1 through 10 of the invention. The term "antibody" as used herein refers to immunoglobulin molecules or their immunologically active portions that specifically bind to an epitope (antigen, antigenic determinant) present in a polypeptide or a fragment thereof, but does not substantially bind other molecules in a sample, e.g., a biological sample, which contains the polypeptide. Examples of immunologically active portions of

immunoglobulin molecules include F(ab) and F(ab') fragments which can be generated by treating the antibody with an enzyme such as pepsin. The term

"monoclonal antibody" as used herein refers to a population of antibody molecules that are directed against a specific epitope and are produced either by a single clone of B cells or a single hybridoma cell line. Polyclonal and monoclonal antibodies can be prepared by various methods known in the art. Additionally, recombinant antibodies, such as chimeric and humanized monoclonal antibodies, comprising both human and non-human portions, can be produced by recombinant DNA techniques known in the art. Antibodies can be coupled to various enzymes, prosthetic groups, fluorescent materials, luminescent materials, bioluminescent materials, or radioactive materials to enhance detection.

An antibody specific for a polypeptide related to one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants set forth in Tables 1 through 10 of the invention can be used to detect the polypeptide in a biological sample in order to evaluate the abundance and pattern of expression of the polypeptide. Antibodies can be used diagnostically to monitor protein levels in tissue such as blood as part of a test predicting the susceptibility to obesity, the metabolic syndrome and type 2 diabetes or as part of a clinical testing procedure, e.g., to, for example, determine the efficacy of a given treatment regimen. "An obesity related condition" in the context of this invention comprises type 2 diabetes, coronary artery disease, myocardial infarction, cerebrovascular stroke, hypertension, dyslipidaemias and the metabolic syndrome. Diagnostic methods and test kits

The present invention is directed to a method of identifying risk of developing obesity, the metabolic syndrome and/or type 2 diabetes in a human individual, the method comprising:

(a) providing a blood or adipose tissue sample taken from said individual;

(b) extracting genomic, both nuclear and mitochondrial DNA from said blood or adipose tissue sample; and

(c) determining the presence or absence of mutations, other variants and heteroplasmies in the genomic and mitochondrial DNA related to the risk of obesity, the metabolic syndrome and/or type 2 diabetes and determining the overall mutation level of mitochondrial genome of said individual, wherein the presence of risk polymorphisms and increased mutation level of mitochondrial genome compared to healthy population indicates the risk of obesity, the metabolic syndrome and/or type 2 diabetes in said individual, and wherein said the overall mutation level of

mitochondrial genome is the total number of mitochondrial mutations or other variations in the mitochondrial genome.

One major application of the current invention is diagnosing a susceptibility to obesity, the metabolic syndrome, type 2 diabetes and other obesity-related conditions. The risk assessment methods and test kits of this invention can be applied to any healthy person as a screening or predisposition test, although the methods and test kits are preferably applied to high-risk individuals (subjects who have e.g. family history of obesity, type 2 diabetes or hypertension, or previous glucose intolerance or elevated level of any other obesity, the metabolic syndrome and type 2 diabetes risk factor). Diagnostic tests that define genetic factors contributing to obesity, the metabolic syndrome and type 2 diabetes might be used together with or independent of the known clinical risk factors to define an individual's risk relative to the general population. Better means for identifying those individuals susceptible for obesity, the metabolic syndrome and type 2 diabetes should lead to better preventive and treatment regimens, including more aggressive management of the risk factors related to obesity, the metabolic syndrome and type 2 diabetes and related diseases e.g.

physicians may use the information on genetic risk factors to convince particular patients to adjust their life style e.g. to stop smoking, to reduce caloric intake and to increase exercise. Also, genetic predictive tests may be carried out already during pregnancy and infancy, enabling early premorbial prevention of obesity, the metabolic syndrome and type 2 diabetes.

In one embodiment of the invention, diagnosing a susceptibility to obesity, the metabolic syndrome and type 2 diabetes in a subject, is made by detecting one or more Mitochondrial DNA variants disclosed in Tables 1 through 10 of this invention in the subject's nucleic acid. The presence of obesity, the metabolic syndrome and type 2 diabetes associated alleles of the assessed Mitochondrial DNA variants (and haplotypes) in individual's genome indicates subject's increased risk for obesity, the metabolic syndrome and/or type 2 diabetes. The invention also pertains to methods of diagnosing a susceptibility to obesity, the metabolic syndrome and type 2 diabetes in an individual comprising detection of a haplotype in an obesity, the metabolic syndrome and type 2 diabetes risk gene that is more frequently present in an individual being obese (affected), compared to the frequency of its presence in a healthy non-obese individual (control), wherein the presence of the variant or haplotype is indicative of a susceptibility to obesity, the metabolic syndrome and type 2 diabetes. A variant or haplotype may be associated with a reduced rather than increased risk of obesity, the metabolic syndrome and type 2 diabetes, wherein the presence of the haplotype is indicative of a reduced risk of obesity, the metabolic syndrome and type 2 diabetes. In other embodiment of the invention, diagnosis of susceptibility to obesity, the metabolic syndrome and type 2 diabetes is done by detecting in the subject's nucleic acid one or more polymorphic sites being in linkage disequilibrium with one or more Mitochondrial DNA variants and disclosed in Tables 1 through 10 of this invention. Diagnostically the most useful polymorphic sites are those altering the biological activity of a polypeptide related to one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants set forth in Tables 1 through 10. Examples of such functional polymorphisms include, but are not limited to frame shifts, premature stop codons, amino acid changing polymorphisms and polymorphisms inducing abnormal mRNA splicing. Nucleotide changes resulting in a change in polypeptide sequence in many cases alter the physiological properties of a polypeptide by resulting in altered activity, distribution and stability or otherwise affect the properties of a polypeptide. Other diagnostically useful polymorphic sites are those affecting transcription of a gene or translation of it's mRNA due to altered tissue specificity, due to altered transcription rate, due to altered response to physiological status, due to altered translation efficiency of the mRNA and due to altered stability of the mRNA. Thus presence of nucleotide sequence variants altering the polypeptide structure and/or expression rate of a gene related to one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants set forth in Tables 1 through 10 of this invention in individual's nucleic acid is diagnostic for susceptibility to obesity, the metabolic syndrome and type 2 diabetes.

In diagnostic assays determination of the nucleotides present in one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants disclosed in this invention in an individual's nucleic acid can be done by any method or technique which can accurately determine nucleotides present in a polymorphic site. Numerous suitable methods have been described in the art (see e.g. Kwok P-Y, 2001), these methods include, but are not limited to, hybridization assays, ligation assays, primer extension assays, enzymatic cleavage assays, chemical cleavage assays and any combinations of these assays. The assays may or may not include PCR, solid phase step, a microarray, modified oligonucleotides, labeled probes or labeled nucleotides and the assay may be multiplex or singleplex. As it is obvious in the art the nucleotides present in a polymorphic site can be determined from either nucleic acid strand or from both strands.

In another embodiment of the invention, a susceptibility to obesity, the metabolic syndrome and type 2 diabetes is assessed from transcription products related to one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants set forth in Tables 1 through 10 of this invention. Qualitative or quantitative alterations in transcription products can be assessed by a variety of methods described in the art, including e.g. hybridization methods, enzymatic cleavage assays, RT-PCR assays and microarrays. A test sample from an individual is collected and the said transcription products are assessed from RNA molecules present in the test sample and the result of the test sample is compared with results from obese subjects (affected) and healthy non-obese subjects (control) to determine individual's susceptibility to obesity, the metabolic syndrome and type 2 diabetes. In another embodiment of the invention, diagnosis of a susceptibility to obesity, the metabolic syndrome and type 2 diabetes is made by examining expression, abundance, biological activities, structures and/or functions of polypeptides related to one or more obesity, the metabolic syndrome and type 2 diabetes associated

Mitochondrial DNA variants disclosed in Tables 1 through 10 of this invention. A test sample from an individual is assessed for the presence of alterations in the expression, biological activities, structures and/or functions of the polypeptides, or for the presence of a particular polypeptide variant (e.g., an isoform) related to one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants set forth in Tables 1 through 10 of this invention. An alteration can be, for example, quantitative (an alteration in the quantity of the expressed polypeptide, i.e., the amount of polypeptide produced) or qualitative (an alteration in the structure and/or function of a polypeptide i.e. expression of a mutant polypeptide or of a different splicing variant or isoform). Alterations in expression, abundance, biological activity, structure and/or function of a obesity, the metabolic syndrome and type 2 diabetes susceptibility polypeptide can be determined by various methods known in the art e.g. by assays based on chromatography, spectroscopy, colorimetry, electrophoresis, isoelectric focusing, specific cleavage, immunologic techniques and measurement of biological activity as well as combinations of different assays. An "alteration" in the polypeptide expression or composition, as used herein, refers to an alteration in expression or composition in a test sample, as compared with the expression or composition in a control sample and an alteration can be assessed either directly from the polypeptide itself or it's fragment or from substrates and reaction products of said polypeptide. A control sample is a sample that corresponds to the test sample (e.g., is from the same type of cells), and is from an individual who is not affected by obesity, the metabolic syndrome and type 2 diabetes. An alteration in the expression, abundance, biological activity, function or composition of a polypeptide related to one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants set forth in Tables 1 through 10 of this invention in the test sample, as compared with the control sample, is indicative of a susceptibility to obesity, the metabolic syndrome and type 2 diabetes. In another embodiment, assessment of the splicing variant or isoform(s) of a polypeptide encoded by a polymorphic or mutant gene related to one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants set forth in Tables 1 through 10 of this invention can be performed directly (e.g., by examining the polypeptide itself), or indirectly (e.g., by examining the mRNA encoding the polypeptide, such as through mRNA profiling).

Yet in another embodiment, a susceptibility to obesity, the metabolic syndrome and type 2 diabetes can be diagnosed by assessing the status and/or function of biological networks and/or metabolic pathways related to one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants disclosed in Tables 1 through 10. Status and/or function of a biological network and/or a metabolic pathway can be assessed e.g. by measuring amount or composition of one or several polypeptides or metabolites belonging to the biological network and/or to the metabolic pathway from a biological sample taken from a subject. Risk to develop obesity, the metabolic syndrome and type 2 diabetes is evaluated by comparing observed status and/or function of biological networks and or metabolic pathways of a subject to the status and/or function of biological networks and or metabolic pathways of healthy and obese subjects.

Another major application of the current invention is diagnosis of a molecular subtype of obesity, the metabolic syndrome and type 2 diabetes in a subject. Molecular diagnosis methods and kits of this invention can be applied to a person being obese. In one preferred embodiment, molecular subtype of obesity, the metabolic syndrome and type 2 diabetes in an individual is determined to provide information of the molecular etiology of obesity, the metabolic syndrome and type 2 diabetes. When the molecular etiology is known, better diagnosis and prognosis of obesity, the metabolic syndrome and type 2 diabetes can be made and efficient and safe therapy for treating obesity, the metabolic syndrome and type 2 diabetes in an individual can be selected on the basis of this subtype information. Physicians may use the information on genetic risk factors with or without known clinical risk factors to convince particular patients to adjust their life style and manage obesity, the metabolic syndrome and type 2 diabetes risk factors and select intensified preventive and curative interventions for them. In other embodiment, biomarker information obtained from methods and kits for determining molecular subtype of obesity, the metabolic syndrome and type 2 diabetes in an individual is for monitoring the effectiveness of their treatment. In one embodiment, methods and kits for determining molecular subtype of obesity, the metabolic syndrome and type 2 diabetes are used to select human subjects for clinical trials testing obesity, the metabolic syndrome and type 2 diabetes foods. The kits provided for diagnosing a molecular subtype of obesity, the metabolic syndrome and type 2 diabetes in an individual comprise wholly or in part protocol and reagents for detecting one or more biomarkers and interpretation software for data analysis and obesity, the metabolic syndrome and type 2 diabetes molecular subtype assessment.

The diagnostic assays and kits of the invention may further comprise a step of combining non-genetic information with the biomarker data to make risk assessment, diagnosis or prognosis of obesity, the metabolic syndrome and type 2 diabetes. Useful non-genetic information comprises age, gender, smoking status, physical activity, waist-to-hip circumference ratio (cm/cm), the subject family history of obesity, the metabolic syndrome and type 2 diabetes, previously identified glucose intolerance, hypertriglyceridemia, low HDL cholesterol, hypertension, elevated BP and dietary intakes of nutrients such as energy. The detection method of the invention may also further comprise a step determining blood, serum or plasma glucose, total cholesterol, HDL cholesterol, LDL cholesterol, triglyceride, apolipoprotein B and AI, fibrinogen, ferritin, transferrin receptor, C-reactive protein and insulin concentration.

The score that predicts the probability of developing obesity, the metabolic syndrome and type 2 diabetes may be calculated e.g. using a multivariate failure time model or a logistic regression equation. The results from the further steps of the method as described above render possible a step of calculating the probability of obesity, the metabolic syndrome and type 2 diabetes using a logistic regression equation as follows. Probability of obesity, the metabolic syndrome and type 2 diabetes = 1/[1 + e (-(-a +∑(bi*Xi))], where e is Napier's constant, Xi are variables related to the obesity, the metabolic syndrome and type 2 diabetes, bi are coefficients of these variables in the logistic function, and a is the constant term in the logistic function, and wherein a and bi are preferably determined in the population in which the method is to be used, and Xi are preferably selected among the variables that have been measured in the population in which the method is to be used. Preferable values for bi are between -20 and 20; and for i between 0 (none) and 100,000. A negative coefficient bi implies that the marker is risk-reducing and a positive that the marker is risk-increasing. Xi are binary variables that can have values or are coded as 0 (zero) or 1 (one) such as Mitochondrial DNA variants. The model may additionally include any interaction (product) or terms of any variables Xi, e.g. biXi. An algorithm is developed for combining the information to yield a simple prediction of obesity, the metabolic syndrome and type 2 diabetes as percentage of risk in one year, two years, five years, 10 years or 20 years. Alternative statistical models are failure-time models such as the Cox's proportional hazards' model, other iterative models and neural networking models.

Diagnostic test kits (e.g. reagent kits) of this invention comprise reagents, materials and protocols for assessing one or more biomarkers, and instructions and software for comparing the biomarker data from a subject to biomarker data from obese and non- obese people to make risk assessment, diagnosis or prognosis of obesity, the metabolic syndrome and type 2 diabetes. Useful reagents and materials for kits comprise PCR primers, hybridization probes and primers as described herein (e.g., labeled probes or primers), allele- specific oligonucleotides, reagents for genotyping Mitochondrial DNA variants, reagents for detection of labeled molecules, restriction enzymes (e.g., for RFLP analysis), DNA polymerases, RNA polymerases, DNA ligases, marker enzymes, antibodies which bind to polypeptides related to one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants disclosed in Tables 1 through 10, means for amplification and/or nucleic acid sequence analysis of nucleic acid fragments containing one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants set forth in Tables 1 through 10. In one embodiment, a kit for diagnosing susceptibility to obesity, the metabolic syndrome and type 2 diabetes comprises primers and reagents for detecting the nucleotides present in one or more Mitochondrial DNA variants selected from the Tables 1 through 10 of this invention in individual's nucleic acid.

Yet another application of the current invention is related to methods and test kits for monitoring the effectiveness of a treatment for obesity, the metabolic syndrome and type 2 diabetes. The disclosed methods and kits comprise taking a tissue sample (e.g. peripheral blood sample or adipose tissue biopsy) from a subject before starting a treatment, taking one or more comparable samples from the same tissue of the subject during the therapy, assessing expression (e.g., relative or absolute expression) of one or more genes related to one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants set forth in Tables 1 through 10 of this invention in the collected samples of the subject and detecting differences in expression related to the treatment. Differences in expression can be assessed from mRNAs and/or polypeptides related to one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants set forth in Tables 1 through 10 of this invention and an alteration in the expression towards the expression observed in the same tissue in healthy non-obese individuals indicates the treatment is efficient. In a preferred embodiment the differences in expression related to a treatment are detected by assessing biological activities of one or more polypeptides related to one or more obesity, the metabolic syndrome and type 2 diabetes associated Mitochondrial DNA variants set forth in Tables 1 through 10 of this invention.

Therapeutic targets, methods of prevention and treatment

The term "therapeutic agent" refers to an agent that can be used to ameliorate or prevent symptoms associated with obesity, the metabolic syndrome, T2D or obesity- or T2D-related condition.

Each mitochondrion contains 2-10 copies of circular DNA and a human cell has hundreds to thousands copies of the mitochondrial DNA. When a germ-line or somatic mutation takes place in a number of these, a mutated position becomes heteroplasmic. In the diseased cell, the mitochondrion possesses both mutant mtDNA and wild-type mtDNA. In the present invention we defined a mtDNA position variant, when the heteroplasmy reached a given threshold. We show in the experimental part of the present invention that excessive heteroplasmy i.e. mutations cause dysfunction of the mitochondria and consequently obesity, the metabolic syndrome and type 2 diabetes. These conditions can be prevented and treated by preventing or repairing the mitochondrial mutations or heteroplasmies. The suppression of mutant mtDNA or supplementation of wild-type mtDNA will reduce mitochondrial dysfunction and serve to both prevent and cure the disease. In another embodiment, the heteroplasmy percentage can be reduced by the addition of wild-type i.e. functional mtDNA to mitochondria. This can be done either by gene transfer or enzymatically, e.g. by DNA polymerase enzymes that replicate the wild-type DNA. DNA polymerases can be made specific to wild type mtDNA. Secondly, the transcription of exogenous DNA can be activated by mitochondrial transcription factors. Yet another aspect of the invention is to provide into the human body externally cultivated or even synthetic mitochondria. Also, the translation of the mitochondrial genes can be activated. Any of these approaches will improve the mitochondrial functions such as mitochondrial respiration, including the oxidative phosphorylation.

Gene transfer is defined as a technique to efficiently and stably introduce foreign genes into the genome of target cells. One approach to enhance the mitochondrial function is the expression of the 13 mtDNA-encoded proteins from nuclear transgenes (allotopic expression). Three approaches to mitochondrial gene therapy known in the art are: (a) Antisense-mediated inhibition of the replication of mutant mitochondrial (mt)DNA, (b) Introduction of replacement mtDNA into the mitochondria and (c) Introduction of modified replacement DNA into the nucleus, whose protein products would be imported into mitochondria (de Grey 2000).

Yet another aspect of the invention is to prevent or treat obesity, the MS and T2D by pharmacological inhibition of mTOR kinase by small-molecule agents such as rapamycin or by RNA interference (RNAi). RNA interference (RNAi), also called gene silencing, is based on using double-stranded RNA molecules (dsRNA) to turn off specific genes. In the cell, cytoplasmic double- stranded RNA molecules (dsRNA) are processed by cellular complexes into small interfering RNA (siRNA). The siRNA guide the targeting of a protein-RNA complex to specific sites on a target mRNA, leading to cleavage of the mRNA. The siRNA molecules are typically about 20, 21, 22 or 23 nucleotides in length. Thus, one aspect of the invention relates to isolated nucleic acid molecules, and the use of those molecules for RNA interference, i.e. as small interfering RNA molecules (siRNA). In one embodiment, the isolated nucleic acid molecules are 18-26 nucleotides in length, preferably 19-25 nucleotides in length, more preferably 20-24 nucleotides in length, and more preferably 21, 22 or 23 nucleotides in length.

Another pathway for RNAi-mediated gene silencing originates in endogenously encoded primary microRNA (pri-miRNA) transcripts, which are processed in the cell to generate precursor miRNA (pre-miRNA). These miRNA molecules are exported from the nucleus to the cytoplasm, where they undergo processing to generate mature miRNA molecules (miRNA), which direct translational inhibition by recognizing target sites in the 3' untranslated regions of mRNAs, and sobesity, the metabolic syndrome and type 2 diabetesequent mRNA degradation by processing P-bodies.

Clinical applications of RNAi include the incorporation of synthetic siRNA duplexes, which preferably are approximately 20-23 nucleotides in size, and preferably have 3' overlaps of 2 nucleotides. Knockdown of gene expression is established by sequence-specific design for the target mRNA. Several commercial sites for optimal design and synthesis of such molecules are known to those skilled in the art.

Other applications provide longer siRNA molecules (typically 25-30 nucleotides in length, preferably about 27 nucleotides), as well as small hairpin RNAs (shRNAs; typically about 29 nucleotides in length). The latter are naturally expressed.

Chemically synthetic siRNAs and shRNAs are sobesity, the metabolic syndrome and type 2 diabetestrates for in vivo processing, and in some cases provide more potent gene- silencing than shorter. In general siRNAs provide for transient silencing of gene expression, because their intracellular concentration is diluted by subsequent cell divisions. By contrast, expressed shRNAs mediate long- term, stable knockdown of target transcripts, for as long as transcription of the shRNA takes place.

Since RNAi molecules, including siRNA, miRNA and shRNA, act in a sequence- dependent manner, the variants presented herein can be used to design RNAi reagents that recognize specific nucleic acid molecules comprising specific alleles and/or haplotypes {e.g., the alleles and/or haplotypes of the present invention), while not recognizing nucleic acid molecules comprising other alleles or haplotypes. These RNAi reagents can thus recognize and destroy the target nucleic acid molecules. As with antisense reagents, RNAi reagents can be useful as therapeutic agents (i.e., for turning off disease-associated genes or disease-associated gene variants), but may also be useful for characterizing and validating gene function {e.g., by gene knock-out or gene knock-down experiments).

Delivery of RNAi may be performed by a range of methodologies known to those skilled in the art. Methods utilizing non- viral delivery include cholesterol, stable nucleic acid-lipid particle (SNALP), heavy-chain antibody fragment (Fab), aptamers and nanoparticles. Viral delivery methods include use of lentivirus, adenovirus and adeno-associated virus. The siRNA molecules are in some embodiments chemically modified to increase their stability. This can include modifications at the 2' position of the ribose, including 2'-0-methylpurines and 2'-fluoropyrimidines, which provide resistance to Rnase activity. Other chemical modifications are possible and known to those skilled in the art.

In the art, small molecule drugs that affects a protein encoded by a mitochondrial gene, cytochrome B, are known. They are atovaquone and proguanil, antimalarial drugs, which inhibit the parasite's CYTB (CYB) activity. The antibiotic azithromycin has a similar weaker effect. These compounds may increase the risk of obesity, MS and T2D.

Methods of assessing probability of response to therapeutic agents, methods of monitoring progress of treatment and methods of treatment As is known in the art, individuals can have differential responses to a particular therapy (e.g. , a therapeutic agent or therapeutic method). Pharmacogenomics addresses the issue of how genetic variations {e.g., the variants (markers and/or haplotypes) of the present invention) affect drug response, due to altered drug disposition and/or abnormal or altered action of the drug. Thus, the basis of the differential response is genetically determined in part. Clinical outcomes due to genetic variations affecting drug response may result in toxicity of the drug in certain individuals (e.g., carriers or non-carriers of the genetic variants of the present invention), or therapeutic failure of the drug. Therefore, the mtDNA variants of the present invention may determine the manner in which a therapeutic agent and/or method acts on the body, or the way in which the body metabolizes the therapeutic agent.

Accordingly, in one embodiment, the presence of a particular allele at a polymorphic site is indicative of a different response, e.g. a different response rate, to a particular treatment modality. This means that a patient diagnosed with obesity, MS and T2D, and carrying a certain allele at a polymorphic site described herein (e.g., the at-risk and protective alleles of the invention) would respond better to, or worse to, a specific therapeutic, drug and/or other therapy used to treat the disease. Therefore, the identity of a marker allele could aid in deciding what treatment should be used for a patient. For example, for a newly diagnosed patient, the presence of an at-risk marker allele of the present invention may be assessed (e.g., through testing DNA derived from a blood sample, as described herein). If the patient is positive for the marker allele, then the physician recommends one particular therapy, while if the patient is negative for the at least one allele of a marker, or a haplotype, then a different course of therapy may be recommended. Thus, the patient's carrier status could be used to help determine whether a particular treatment modality should be administered.

As described above, current clinical treatment options for obesity the MS and T2D include different surgical procedures, depending on the severity of the cases, e.g. whether the cancer is invasive into the muscle wall of the bladder. Treatment options also include radiation therapy, for which a proportion of patients experience adverse symptoms. The markers of the invention, as described herein, may be used to assess response to these therapeutic options, or to predict the progress of therapy using any one of these treatment options. Thus, genetic profiling can be used to select the appropriate treatment strategy based on the genetic status of the individual, or it may be used to predict the outcome of the particular treatment option, and thus be useful in the strategic selection of treatment options or a combination of available treatment options. Again, such profiling and classification of individuals is supported further by first analyzing known groups of patients for marker and/or haplotype status, as described further herein. By utilizing the variants of the present invention, one knowledgeable in the art may also predict the response of an individual to changes in the diet and exercise. Thus nutrigenetic tests can be constructed. The present invention also relates to methods of monitoring progress or effectiveness of a treatment for urinary bladder cancer. This can be done based on the genotype status of the markers described herein, i.e., by assessing the absence or presence of at least one allele of at least one polymorphic marker as disclosed herein, or by monitoring expression of genes that are associated with the variants (markers and haplotypes) described herein (e.g., the MT-ND5). The risk gene mRNA or the encoded polypeptide can be measured in a tissue sample (e.g., a peripheral blood sample, or a biopsy sample). Expression levels and/or mRNA levels can thus be determined before and during treatment to monitor its effectiveness. Alternatively, or concomitantly, the genotype status of at least one risk variant for obesity, the MS and T2D as presented herein is determined before and during treatment to monitor its effectiveness. In a further aspect, the markers of the present invention can be used to increase power and effectiveness of clinical trials. Thus, individuals who are carriers of at-risk variants described herein may be more likely to respond favorably to a particular treatment modality for obesity, the metabolic syndrome or type 2 diabetes. In one embodiment, individuals who carry an at-risk variant are more likely to be responders to the treatment. In another embodiment, individuals who carry at-risk variants of a gene, which expression and/or function is altered by the at-risk variant (e.g., the at-risk missense variants in the CYTB described herein), are more likely to be responders to a treatment modality targeting that gene, its expression or its gene product. This application can improve the safety of clinical trials, but can also enhance the chance that a clinical trial will demonstrate statistically significant efficacy, which may be limited to a certain sub-group of the population. Thus, one possible outcome of such a trial is that carriers of certain genetic variants, e.g., at-risk markers described herein, are statistically significantly likely to show positive response to the therapeutic agent, i.e. experience alleviation of symptoms associated with Bladder Cancer when taking the therapeutic agent or drug as prescribed.

EXPERIMENTAL SECTION

Example 1: Study population

EFFGE Study (East Finland Founder Population Genetics Study, "VERES") sample collections were carried out in the genetically homogenous founder population of Eastern Finland (five local cities and adjacent rural communities) in 2007. Ethics Committee of the Kuopio University Hospital approved the study protocol, and the study was carried out according to Helsinki Declaration. In signed written consents the participants gave the permission to Jukka T. Salonen to use their DNA samples for genetic analyses.

The study population consisted of hypertensive (50%) and non-hypertensive (50%) volunteers who were recruited by newspaper advertisements or in connection with their routine medical centre visits. The hypertensive cases were defined as follows: moderate-to- severe essential hypertension with prescription-documented

antihypertensive medication (diagnosed under 60 years of age) and positive family history of hypertension consisting of at least two affected first degree relatives among parents, siblings or off-spring. The non-hypertensive control subjects were defined as follows: systolic blood pressure <140 mmHg and diastolic blood pressure <90 mmHg, and no antihypertensive treatment, no hypertension in the family among parents, siblings or off-spring. In addition, all grandparents of the subjects had to be Finnish, and at least 2 of them born in Eastern Finland. No first degree relatives were allowed to participate in the study (checked afterwards by using genotype data). Cases and controls were matched for gender. The mean age of controls was 1.0 y older than that of cases. Secondary hypertension caused by 1) pathological conditions, such as renal dysfunction or adrenal malignancy, 2) pregnancy or 3) nutritional or behavioral factors, such as high salt intake or high alcohol consumption (visual habitus assessment), were exclusion criteria. The subjects were instructed not to use alcohol in the three days prior to the study visit, (see Kaikkonen et al 2013 for details).

Anthropometric data, such as height, weight, and waist and hip circumference, were measured during the study visit. Body mass index (BMI) and waist-to-hip

circumference ratio (WHR) were calculated from these variables. Serum high-density lipoprotein (HDL) cholesterol, plasma glucose, serum insulin, and apolipoprotein Al (apoAl), were measured by an auto-analyzer (Konelab 20, Thermo Fisher Scientific, Vantaa, Finland) with commercial kits (Thermo Fisher Scientific). Diabetes was defined as fasting plasma glucose of 7.0 mmol/L or more. In the present analysis, hypertension was defined as the mean diastolic blood pressure (the 2^nd and 3^rd measurements) of 100 mmHg or more. Obesity was defined as BMI of 27 kg/m² or more. The metabolic syndrome was defined as present if at least two of the following four conditions were met: the waist circumference >90 cm, plasma glucose >7 mmol/L, serum insulin >10 mU/L, diastolic blood pressure (mean of 2. and 3.

measurement) > 100 mmHg.

DNA was extracted from EDTA whole blood with QIAamp DNA Blood Midi Kit (Qiagen). The quantity and purity of each DNA sample was determined by absorbance measurements with a NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies). A sample was qualified for genetic analysis if the A260:A280 ratio was >1.7, the genotypic gender matched the phenotypic gender, and the DNA concentration of a sample could be set to 40-60 ng/ul (60 ng/μΐ. was the target). DNA samples were stored at -80°C until further processed.

Example 2: Sequencing of the mitochondrial genome Genomic DNA was extracted from leukocytes. Genomic DNA sample concentrations were measured with Qubit BR dsDNA kit (Life Technologies Ltd, Paisley, UK). Mitochondrial DNA was amplified from the genomic DNA using REPLI-g

Mitochondrial DNA kit (Qiagen, Hilden, Germany). After the enrichment the amplified mtDNA samples were processed to Illumina sequencing compatible libraries with Nextera DNA sample preparation kit (Illumina, San Diego, CA, USA). The mtDNA concentrations were measured with Qubit dsDNA kit for Nextera tagmentation reaction. The reaction volume in Nextera tagmentation and

amplification steps was 20μ1 and after both steps the libraries were purified with EdgeBio Performa V3 96-Well Short Plate (Edge BioSystems, Gaithersburg, MD, USA). After the amplification the libraries were first incubated with 4μ1 of EdgeBio SOPE Resin and then purified with EdgeBio Performa plates. After purification, 48 samples with different index tags were pooled together (2μ1 each) and concentrated with DNA Clean & Concentrator™-5 (Zymo Research, Irvine, CA, USA). The final volume of the concentrated pool was 15μ1. The sequencing ready libraries were quantified with Agilent 2100 Bioanalyzer High Sensitivity kit (Agilent, Santa Clara, CA, USA). The libraries were sequenced in Illumina MiSeq and HiSeq systems at the Finnish Institute of Molecular Medicine (FIMM, University of Helsinki). The data were analyzed using FIMM's in-house developed bioinformatics pipeline, VCP (Sulonen et al 2011). Single nucleotide variants were called using SAMtools pileup in VCP. The mitochondrial heteroplasmy was defined using variant calling threshold of 0.989. The variants with a quality value balanced signals below 1.1% were filtered out.

To estimate the total burden of mitochondrial mutations in a person, leading to mitochondrial dysfunction, we constructed a novel variable that was the number of mutations in the entire mtDNA in a person. This was done (a) as sum of all non- reference alleles, and (b) as sum of all minor alleles. Only for 11 of the 1164 variants, the minor allele was the reference allele. All results presented are based on the second approach, however the results were very similar.

Example 3: Mitochondrial markers of obesity, the metabolic syndrome and type 2 diabetes

The sum of mtDNA variants (total number of variants) had a mean of 93.8, SD of 91.7, minimum of 10 and maximum of 534, i.e. every subject had between 10 and 534 mtDNA variants. This sum variable predicted BMI strongly, as estimated by least- squares regression (standardized coefficient 0.11, t=3.84, p=0.00013). The association was even stronger when adjusted for age, gender and weekly hours of exercise (stand, coeff. 0.12, t=4.182, p=0.00003). Associations of the variant number with selected markers of obesity and the MS are shown in Table 1. E.g. 52% of the subjects with a high mtDNA mutation rate were obese (BMI >27 kg/m²), as compared with 37.5% of those who had low mtDNA mutation rate (odds ratio 1.81, 95% CI 1.41 to 2.33, p=0.000003, adjusted OR 1.89, 95% CI 1.46 to 2.44, p=0.0000013). We repeated the analyses by using the ratio of the said variant number to age. The findings were similar.

Almost all of the 1164 variants found correlated with each other and the above mentioned sum variable. This suggests that if mutation occurs in one mtDNA position, it is more likely to happen also in another, i.e. some universal factors influence the mutation rate in the entire mitochondrial genome. Therefore the total number of mtDNA variants describes the mitochondrial mutation rate i.e.

mutagenicity. For this reason it is reasonable that all mitochondrial proteins, both those encoded by mitochondrial and those encoded by nuclear genes, listed in Table 11, have a role in the etiology and regulation of obesity, the MS and T2D. The variant number (sum) had a strong association with fasting serum insulin

(unadjusted stand coeff 0.085, t=2.972, p=0.003, age-, gender- and exercise-adjusted stand coeff 0.096, t=3.393, p=0.001) and with MS (OR 2.99, 95% CI 1.79, 4.98, p=0.000012) and was also associated with T2D (OR 1.83, 95% CI 1.08, 3.09, p=0.023).

In the sequencing of the entire leukocyte mitochondrial genome in 1204 EFFGE participants from East Finland, 1164 different variants were found. Of these, 36 variants (in positions 67, 385, 2219, 2284, 2581, 2645, 2815, 3525, 3799, 3896, 4769, 5301, 5390, 8269, 8578, 8610, 8952, 10203, 11219, 11404, 11539, 11728, 12111, 12622, 12738, 12771, 13948, 14239, 14281, 15783, 16172, 16189, 16256, 16354, 16482 and 16519) were statistically significantly and consistently associated with BMI (Table 2). These included eight known variants (SNPs) and 28 novel variants, not previously reported in the literature. The variants were positioned in nine genes, ND1, ND2, ND3, ND4, ND5, ND6, C02, ATPase6 and CYTB and the RNR2 region. Two variants were flanking the gene TRNP. In univariate comparisons of means, the most significant differences in BMI between genotypes were for the variants in positions 16354, 2581, 8269, 13948 and 12622 (Table 2). The variant in the mitochondrial genome position 16354 was strongly associated with BMI (p=0.000007). The variant was common, 148 subjects had a non-reference (other than C) variant allele. The variant is in a non-coding mtDNA region.

The second strongest association was with the variant 2581 (p=0.000009). This variant is located in the MT-RNR2 region, encoded by nucleotides 1671-3229. Also four other variants (2219, 2284, 2645, 2815) in the mt-RNR2 region were associated with overweight (Tables 2, 3). Of these, the variants 2219 and 2284 were associated with 1.8-fold risk of obesity (95%CI 1.4 to 2.4, p=0.0000038, Table 3). The variant in the position 11219 had a strong association with overweight (Tables 2, 3). This variant is in the ND4 gene. The reference allele is A. There were 323 (26.8%) subjects with a variant allele. They had 1.82-fold probability of being overweight (95CI 1.4 to 2.4, p=0.000006) as compared with the reference allele carriers (Table 3). Also four other variants (contig pos 11404, 11539, 11728, 12111) in the ND4 gene were associated with overweight (Tables 2, 3).

Also the variant in the 12771 in the ND5 gene had a strong association with BMI (Table 2, p=0.0005). The 349 (29.0%) variant allele carriers had 1.8-fold risk of being overweight (95% CI 1.4 to 2.3, p=0.000014, Table 3). Also three other variants in the ND5 gene were associated with obesity (Tables 2, 3).

For the region RNR2 and genes ND4 and ND5 all variant allele carriers had elevated BMI and increased risk of overweight. If the variant alleles were loss-of-function alleles, theoretically the activation of these genes would reduce weight. This could be achieved, besides small molecule drugs, by biological medicines or gene transfer.

The variant in the 8952 position in the ATP6 had a weak association with BMI (Table 1) and a stronger association with WHR (mean 0.883 in 1195 ref allele carriers, 0.807 in 9 variant allele carriers, p=0.0063 for difference). For the genes ATP6 and TRNP, the variant alleles are associated with reduced BMI. Assuming that these alleles represent loss-of-function mutations, silencing these genes or inhibiting their products can be used to treat obesity.

In a linear least-squares step-up regression analysis adjusting for age and gender and predicting BMI, the variants 4769 (p=0.055), 11219 (p=0.011), 11728 (p=0.012), 16189 (p=0.017) and 16519 (p=0.035) had significant independent (partial) associations with BMI. When the variants shown in Table 3 were entered into a step- up logistic regression model (PIN=0.01, POUT=0.05), two models were iterated that included the variant 16189 and either the variant 2284 or 11219 (Table 5). When all variants in table 3 were tested for entry into a step-up logistic regression analysis, variants 67 (coefficient 0.506, p=0.001), 11404 (coeff. 0.563, p=0.035), 12622 (coeff. 0.875, p=0.055), 13948 (coeff. 0.684, p=0.005), 14281 (coeff, 0.963, p=0.013), 16819 (coeff. -0.317, p=0.012) and 16482 (coeff. 1.301, p=0.013) were entered (PIN=0.05, POUT 0.10), with constant -0.484.

The association of the variant 16189 with T2D and serum insulin is known in the art (Ye et all 2013). The variant is in the OriB region, also known as the polycytosine track (positions 16184 to 16193).

The variants 951, 3720, 6267, 6975, 8231, 8610, 10819, 11404, 12738, 13362, 14034, 14133, 14239, 14323, 15058, 15725, 15773 had strong associations with the probability of the metabolic syndrome (Table 6). These variants were tested for entry into a step-up logistic model (PIN=0.05, POUT=0.10). In the final model including also gender (coefficient -0.991) and hours of exercise (coeff -0.083), the following variants entered: 3720 (coeff 1.468, p=0.014), 8231 (coeff 1.068, p<0.001, 10819 (coeff 1.677, p=0.012), 12738 (coeff 2.477, p<0.001), 13362 (coeff 1.296, p=0.028), 14034 (coeff 1.594, p=0.053) and 15058 (coeff 1.289, p=0.037). The constant for the model was -1.537.

A high total number of variants was associated with 3.0-fold unadjusted risk (95% CI 1.8 to 5.0, p=0.000012) and 3.3-fold adjusted risk (95%CI 2.0 to 5.5, p=0.0000078, Table 6) of the metabolic syndrome.

The variants 385, 8292, 8610 and 10819 were associated with fasting serum insulin levels (Table 7). The association of variant 10819 (rs2835828), located in the ND4 gene, was very strong (p=0.0000001). The mean insulin among the variants was 2.3- fold as compared with the subjects with the reference allele.

The variants 8108, 9494, 11404 and 14239 were associated with plasma glucose concentration (Table 8). The variants 951, 6975, 10771, 12738, 14034, 14133, 14239, 14323 and 15725 were strongly associated with type 2 diabetes (Table 9).

Also the total number of variants had an association with the risk of diabetes. The associations of the said variants with type 2 diabetes became weaker but remained significant after statistical adjustment for age, gender, BMI and weekly exercise hours, indicating that these variants have an independent contribution to diabetes risk, while the effect is in part mediated through obesity.

In a multivariate logistic model in which the said nine variants were entered jointly in a step-up fashion, variants 951 (coefficient 1.112, p=0.002), 10771 (coeff 1.764, p=0.004), 14043 (coeff 2.013, p=0.002), 14133 (coeff 1.040, p<0.001), 14239 (coeff 1.874, p=0.001) and 14323 (coeff 1.541, p=0.013) had significant independent partial associations with diabetes risk (Table 10), with contant -3.628. In another model with age in years (coefficient 0.047), gender (coeff. -1.570) and BMI (coeff. 0.174) as covariates, the coefficients for the entered mtDNA variants were 1.006 for that in position 951, 2.062 for 10771, 1.111 for 14034, 1.005 for 14113, 1.259 for 14239 and 1.357 for variant 14323, with constant -9.147. Weekly hours of exercise had no significant association with T2D in this model including BMI as covariate.

A diagnostic model of type 2 diabetes can be constructed of these variables, possibly complemented by additional psychosocial, behavioral, biochemical and other measurements such as age, gender, the amount of exercise and measures of obesity and adiposity.

The variant 12738 in the MT-ND5 gene predicted strongly and independently both obesity, MS and T2D. It provided powerful additional prediction also in multivariate logistic models in which also a number of other variants, age, gender and weekly hours of exercise were allowed for. For example, the unadjusted OR for MS was 15.7 (95% CI 5.6, 43.6, p<0.0000001). This variant is located in the MT-ND5 gene.

Mutations in this gene have been shown to cause exercise intolerance (Sanaker and Bindoff 2013).

Complex I (NADH:ubiquinone oxidoreductase; EC 1.6.5.3) is the largest of the five mitochondrial respiratory chain complexes. It catalyzes oxidation of NADH by transfer of electrons to the lipid-soluble ubiquinone. Intact complex I can be resolved into four subcomplexes: Ια, Ιβ, Ιγ, and Ιλ. Mammalian complex I consists of at least 45 different subunits of which seven (NDl, ND2, ND3, ND4, ND4L, ND5, and ND6) are encoded by mtDNA. NDl, ND2, ND3, and ND4L reside in subcomplex Ιγ; ND4 and ND5 in subcomplex Ιβ. Subunits NDl and ND2 are grouped together, as are subunits ND4 and ND5. According to the present findings, all these subunits are implicated in obesity, the MS and T2D. By extrapolation, also the 38 subunits of

Complex I that are encoded by nuclear genes, are likely to be relevant with regard to obesity, MS and T2D. It is also likely that also the four other mitochondrial respiratory chain complexes have a role in the regulation and etiology of obesity, MS and T2D.

On the basis of the present results, mitochondrial mutagenicity and dysfunction are associated with obesity, MS and T2D. Consequently, therapies that enhance the mitochondrial function or inhibit mutations or dysfunction are expected to reduce obesity and treat or cure MS and T2D. These would be particularly effective and safest in individuals with mitochondrial dysfunction, high mitochondrial mutagenicity and a large mitochondrial mutation burden. Specifically, agents and therapies that reduce the mutation frequency in the mtDNA or repair occurred mutations are expected to function as therapies against obesity, MS and T2D. Suitable in Vitro and in Vivo models for the testing of compounds are known in the art, including chemicals, enhancing the mitochondrial metabolic rate etc. Experimental animals can be utilized to test the effects of candidate compounds on mitochondrial mutation rate by using repeat sequencing of mtDNA.

5

Table 1. Markers of obesity and the metabolic syndrome among persons with low and high number of mitochondrial DNA variants.

Marker of obesity High mtDNA Low mtDNA p-value mutation rate (>150), mutation rate (<150), n=340 n=864

Mean SD Mean SD

BMI (kg/m²) 27.8 5.05 26.6 4.27 <0.001

BMI >27 kg/m² 52.1% 37.5% <0.001

BMI >30 kg/m² 27.1% 18.8% 0.001

Self -reported obesity 18% 13% 0.046

Weight (kg) 76.4 15.2 73.8 13.9 0.005

Waist (cm) 91.2 13.8 89.7 12.5 0.066

Hip (cm) 102.5 9.44 100.9 8.52 0.006

Serum Insulin (mU/L) 8.85 7.12 7.62 7.19 0.007

Serum HDL cholesterol (mmol/L) 1.64 0.47 1.68 0.49 0.186

Serum apolipoprotein AI (g(L) 1.70 0.31 1.65 0.28 0.019

Table 2. Mitochondrial genome variants associated with BMI kg/m²)

* On the basis of Satterthwaite statistic assuming unequal variances. ** Upstream of TRNP.

*** 2-sided Table 3. Association of selected mitochondrial variants with obesity (BMI >27 kg/m² vs. BMI<27 kg/m²)

Table 4. Linear regression models predicting BMI.

Hours of exercise weekly

* Multivariate models included variants listed, hours of exercise, age (years) and gender (male vs. female).

OR denotes odds ratio, CI denotes confidence interval. Both models adjusted for gender, exercise and the other variant.

*Hours of exercise weekly

Table 6. Association of selected mitochondrial variants with the metabolic syndrome

*Defined as the presence of three or four of the following: the waist circumference >90 cm, plasma glucose >7 mmol/L, serum insulin >10 mU/L, diastolic blood pressure (mean of 2. and 3. measurement) > 100 mmHg. Table 7. Mitochondrial genome variants associated with serum insulin concentration

* On the basis of Satterthwaite statistic assuming unequal variances.

** Upstream of TRNP.

*** 2-sided

Table 8. Mitochondrial genome variants associated with plasma glucose concentration

* On the basis of Satterthwaite statistic assuming unequal variances.

** Upstream of TRNP.

*** 2-sided

Table 9. Association of selected mitochondrial variants with type 2 diabetes (Fasting plasma glucose >7 mmol/L vs. <7 mmol/L)

*Weekly hours of exercise.

** Number of variants >150 vs <150. Table 10. Association of selected mitochondrial variants with type 2 diabetes (Fasting plasma glucose >7 mmol/L vs. <7 mmol/L)

Each variant in separate model with 4 covariates.

* Age, per year, BMI per kg/m²

Gender coded: women 0, men 1

* Variants first in step-up model, then covariates in a second step-up.

Table 11. The most important mitochondrial proteins, their location in the cell and protein type.

ID Symbol Entrez Gene Name Location Type(s)

AC02 AC02 aconitase 2, mitochondrial Cytoplasm enzyme

AK4 AK4 adenylate kinase 4 Cytoplasm kinase

ALDH1B1 ALDH1B1 aldehyde dehydrogenase 1 family, member B1 Cytoplasm enzyme

ATP synthase, H+ transporting, mitochondrial F1 complex, alpha subunit 1,

ATP5A1 ATP5A1 cardiac muscle Cytoplasm transporter

ATP5L ATP5L ATP synthase, H+ transporting, mitochondrial Fo complex, subunit G Cytoplasm transporter

ATP50 ATP50 ATP synthase, H+ transporting, mitochondrial F1 complex, 0 subunit Cytoplasm transporter

ATPase ATPase Other group

ATPIF1 ATPIF1 ATPase inhibitory factor 1 Cytoplasm other carnitine O- carnitine O- octanoyl transferase octanoyltransferase Other group

CKMT1 A/CKMT1 B C MT1 A C MT1 B creatine kinase, mitochondrial 1B Cytoplasm kinase

COX5A COX5A cytochrome c oxidase subunit Va Cytoplasm enzyme

CPT1A CPT1A carnitine palmitoyltransferase 1 A (liver) Cytoplasm enzyme

CPT2 CPT2 carnitine palmitoyltransferase 2 Cytoplasm enzyme

Cytochrome bd Cytochrome bd Cytoplasm complex cytochrome-c

cytochrome-c oxidase oxidase Cytoplasm complex

DBT DBT dihydrolipoamide branched chain transacylase E2 Cytoplasm enzyme

ECH1 ECH1 enoyl CoA hydratase 1, peroxisomal Cytoplasm enzyme

ECHS1 ECHS1 enoyl CoA hydratase, short chain, 1, mitochondrial Cytoplasm enzyme

FANCI FANCI Fancont anemia, complementation group I Nucleus other

FH FH fumarate hydratase Cytoplasm enzyme translation

GFM1 GFM1 G elongation factor, mitochondrial 1 Cytoplasm regulator

H+-transporting two-sector

ATPase H+-transporting two-sector ATPase Other group hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/enoyl-CoA hydratase

HADHA HADHA (trifunctional protein), alpha subunit Cytoplasm enzyme

hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thioIase enoyl-CoA hydratase

HADHB HADHB (trifunctional protein), beta subunit Cytoplasm enzyme

HSPA9 HSPA9 heat shock 70kDa protein 9 (mortalin) Cytoplasm other

LARS2 LARS2 leucyt-tRNA synthetase 2, mitochondrial Cytoplasm enzyme

LIPT1 LIPT1 lipoyltransferase 1 Cytoplasm enzyme

LONP1 LONP1 Ion peptidase 1 , mitochondrial Cytoplasm peptidase

MDH2 DH2 malate dehydrogenase 2, NAD (mitochondrial) Cytoplasm enzyme IPEP MIPEP mitochondrial intermediate peptidase Cytoplasm peptidase

Mitochondrial

Mitochondrial complex 1 complex 1 Cytoplasm complex mitochondrial protein- transporting ATPase mitochondrial protein-transporting ATPase Other group RPL12 MRPL12 mitochondrial ribosomal protein L12 Cytoplasm other

MT-C03 MT-C03 cytochrome c oxidase III Cytoplasm enzyme

MT-COI MT-COI cytochrome c oxidase subunit I Cytoplasm enzyme

MT-CYB MT-CYB cytochrome b Cytoplasm enzyme

MT-ND2 MT-ND2 MTND2 Cytoplasm enzyme

MT-TA MT-TA tRNA Cytoplasm other T-TM MT-TM tRNA Cytoplasm other

MTCH2 MTCH2 mitochondrial carrier 2 Cytoplasm other

MTFMT MTFMT mitochondrial methionyl-tRNA formyltransferase Cytoplasm enzyme

MTHFD1 L MTHFD1L methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1-like Cytoplasm enzyme translation

MTIF2 MTIF2 mitochondrial translational initiation factor 2 Cytoplasm regulator

NADH dehydrogenase (ubiquinone) Fe-S protein 1, 75kDa (NADH-coenzyme Q

NDUFS1 NDUFS1 reductase) Cytoplasm enzyme

NDUFV1 NDUFV1 NADH dehydrogenase (ubiquinone) flavoprotein 1, 51kDa Cytoplasm enzyme

NDUFV3 NDUFV3 NADH dehydrogenase (ubiquinone) flavoprotein 3, 10kDa Cytoplasm enzyme

PMPCA PMPCA peptidase (mitochondrial processing) alpha Cytoplasm peptidase

POLRMT POLRMT polymerase (RNA) mitochondrial (DNA directed) Cytoplasm enzyme transcription

PRPF6 PRPF6 pre-m NA processing factor 6 Nucleus regulator pyruvate dehydrogenase pyruvate dehydrogenase (lipoamide) Other group

(llpoamicte)

Plasma

SLC25A1 SLC25A1 solute carrier family 25 (mitochondrial carrier; citrate transporter), member 1 Membrane transporter

SLC25A11 SLC25A11 solute carrier famfly 25 (mitochondrial carrier; oxoglutarate carrier), member 11 Cytoplasm transporter

SLC25A24 SLC25A24 solute carrier famiy 25 (mitochondrial carrier phosphate carrier), member 24 Cytoplasm other

solute carrierfamBy 25 (mitochondrial carrier; adenine nucleotide transiocator),

8LC25 SLC25A4 member 4 Cytoplasm transporter

SLC25A51 SLC25A51 solute carrierfamily 25, member 51 Cytoplasm other

SOD2 SOD2 superoxide dismutase 2, mitochondrial Cytoplasm enzyme

5SBP1 SSBP1 single-stranded DNA binding protein , mitochondrial Cytoplasm other

TI M13 TIMM13 translocase of Inner mitochondrial membrane 13 homolog (yeast) Cytoplasm transporter

TIMM17A TI M17A translocase of inner mitochondrial membrane 17 homolog A (yeast) Cytoplasm transporter transmembrane

TSPO TSPO transiocator protein (18kDa) Cytoplasm receptor

UCP2 UCP2 uncoupling protein 2 (mitochondrial, proton carrier) Cytoplasm transporter

UQC C2 UQCRC2 ublqulnol-cytochrome c reductase core protein II Cytoplasm enzyme

REFERENCES

De Grey A. D.N. J. Mitochondrial gene therapy: an arena for the biomedical use of inteins. Trends Biotech 2000; 18: 394-9.

Chinnery PF et al. Role of the mitochondrial DNA 16184-16193 poly-C tract in type 2 diabetes. The Lancet 2005; 366: 1650-1.

Kaikkonen JE, Vilppo T, Asikainen J, Voutilainen S, Kurl S, Salonen JT. Fatty acids as determinants of in-vivo lipid peroxidation: the EFFGE study in Eastern Finnish hypertensive and non-hypertensive subjects. Ann Med 2013; 45: 455-64.

Kwok PY. Methods for genotyping single nucleotide polymorphisms.

Annu Rev Genomics Hum Genet 2001; 2: 235-58.

Lakka H-M, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, Salonen JT. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA 2002; 21: 2709-2716.

Salonen JT, Uimari P, Aalto JM, Pirskanen M, Kaikkonen J, Todorova B, Hypponen J, Korhonen VP, Asikainen J, Devine C, Tuomainen TP, Luedemann J, Nauck M, Kerner W, Stephens RH, New JP, Oilier WE, Gibson JM, Payton A, Horan MA, Pendleton N, Mahoney W, Meyre D, Delplanque J, Froguel P, Luzzatto O, Yakir B, Darvasi A. Type 2 diabetes whole-genome association study in four populations: the DiaGen consortium. Am J Hum Genet 2007; 81: 338-45.

Sanaker PS, Bindoff LA. MT-ND5 mutation causing exercise intolerance displays intercellular heteroplasmy and rapid shifts between generations. Hum Mutat 2013; 34: 292-5.

Sulonen A-M et al: Comparison of solution-based exome capture methods for next generation sequencing. Genome Biol 2011, 12:R94. Ye Z, Gillson C, Sims M, Khaw K-T, Plotka M, Poulton J, Langenberg C, Wareham NJ. The association of the mitochondrial DNA oriB variant (16184-16193 polycytosine tract) with type 2 diabetes in Europid populations. Diabetologia 2013; 56: 1907-13.

Weng S-W, et al. Association of mitochondrial deoxiribonucleis acid 16189 variant (T-C transition) with metabolic syndrome in Chinese adults. J Clin Endocrin Metab 2005; 90: 5037-40

Claims

1. A method of identifying risk of developing obesity, the metabolic syndrome and/or type 2 diabetes in a human individual, the method comprising:

(a) providing a blood or adipose tissue sample taken from said individual;

(c) determining the presence or absence of mutations, other variants and heteroplasmies in the genomic and mitochondrial DNA related to the risk of obesity, the metabolic syndrome and/or type 2 diabetes and determining the overall mutation level of mitochondrial genome of said individual, wherein the presence of risk polymorphisms and increased mutation level of mitochondrial genome compared to healthy population indicates the risk of obesity, the metabolic syndrome and/or type 2 diabetes in said individual.

2. The method according to claim 1, wherein the number of mitochondrial

genome variants is determined by sequencing of the mitochondrial DNA.

3. The method according to claim 1 or 2, wherein DNA mutations and

polymorphisms are determined in step e) by a DNA microarray or DNA chip.

4. The method according to any one of claims 1 - 3, wherein the mutations and other variants in the genomic DNA related to the risk of obesity are determined from the mitochondrial genome.

5. The method according to claim 4, wherein said mutations, other variants and heteroplasmies are determined from a gene or mtDNA region selected from the group consisting of: MT-ND5, MT-ND4, MT-NDl, MT-ND2, MT-C02, MT-ATP6, MT-ND3, MT-ND6, MT-CYTB, MT-RNR2 and MT-TRNP.

6. The method according to claim 4 or 5, wherein the variant determined is in the position 8269, 16354 or 11404 of the mitochondrial genome.

7. The method according to claim 4 or 5, wherein the variant determined is in the position 67, 385, 2219, 2284, 2581, 2645, 2815, 3525, 3799, 3896, 4769, 5301, 5390, 8269, 8578, 8610, 8952, 10203, 11219, 11404, 11539, 11728, 12111, 12622, 12738, 12771, 13948, 14239, 14281, 15783, 16172, 16256, 16354, 16482 or 16519 of the mitochondrial genome.

8. The method according to claim 7, wherein a linear or logistic function value based on the variants in positions 67, 2219, 2284, 4769, 11219, 11404, 11728, 12622, 13948, 14281, 16189, 16482, and 16519 is determined.

9. The method according to any one of claims 1 - 8, wherein the mutations and other variants in the genomic DNA related to the risk of the metabolic syndrome are determined from the mitochondrial genome.

10. The method according to claim 9, wherein said mutations, other variants and heteroplasmies are determined from a gene selected from the group consisting of: MT-ND5, MT-ND1, MT-COX1, MT-C02, MT-ATP6, MT-ND3, MT- ND4, MT-ND6, MT-CYTB and MT-RNR1..

11. The method according to claim 9 or 10, wherein the variant determined is in the position 12738, 11404, 951, 3720, 6267, 6975, 8231, 8610, 10819, 13362, 14034, 14133, 14239, 14323, 15058, 15725, or 15773 of the mitochondrial genome.

12. The method according to any one of claims 9-11, wherein a linear or logistic function value based on the variants in positions 12738, 3720, 8231, 10819, 13362, 14034 and 15058 is determined.

13. The method according to any one of claims 1 - 12, wherein the mutations and other variants in the genomic DNA related to the risk of type 2 diabetes are determined from the mitochondrial genome.

14. The method according to claim 13, wherein said mutations, other variants and heteroplasmies are determined from a gene selected from the group consisting of: MT-ND5, MT-COX1, MT-ND4, MT-ND6, MT-CYTB and the MT-RNR1 region.

15. The method according to claims 13 and 14, wherein the variant determined is in the position 11404, 14133, 951, 6975, 10771, 12738, 14034, 14239, 14323 or 15725 of the mitochondrial genome.

16. The method according to claim 15, wherein a linear or logistic function value based on the variants in positions 11404, 14133, 951, 6975, 10771, , 12738, 14034, 14239, 14323 and 15725 of the mitochondrial genome is determined.

17. The method according to any one of claims 1-16, wherein the risk of obesity, the metabolic syndrome and/or type 2 diabetes is calculated using a logistic regression equation as follows:

Probability of obesity, the metabolic syndrome and/or type 2 diabetes = [1 + e (^{"(_a +∑(bl*Xl}W] ^_1 ₅ where e is the Napier's constant, X; are variables related to obesity, the metabolic syndrome and type 2 diabetes, bi are coefficients of these variables in the logistic function, and a is the constant term in the logistic function.

18. The method according to claim 17, wherein a and bi are determined in the population in which the method is to be used.

19. The method according to claim 17, wherein Xi are selected among the

variables that have been measured in the population in which the method is to be used.

20. The method according to claim 17, wherein bi are between the values of -20.0 and 20.0.

21. The method according to claim 17 - 20, wherein X; are binary variables that can have values or are coded as 0 (zero) or 1 (one).

22. The method according to claim 17, wherein i is between the values 0 (none) and 100,000.

23. A method of identifying risk of developing obesity, the metabolic syndrome and/or type 2 diabetes in a human individual, the method comprising:

(a) providing a blood or adipose tissue sample taken from said individual;

(c) determining the overall mutation level of mitochondrial genome of said individual, wherein increased mutation level of mitochondrial genome compared to healthy population indicates the risk of obesity, the metabolic syndrome and/or type 2 diabetes in said individual.

24. The method according to claim 23, wherein said the overall mutation level of mitochondrial genome is the total number of mitochondrial mutations or other variations in the mitochondrial genome.

25. A test kit comprising means for carrying out the method according to any one of claims 1-22.

26. The test kit according to claim 23 comprising software, wherein the output of the software is the probability to become obese, the probability to develop a disease due to obesity, the metabolic syndrome and/or type 2 diabetes, and the probability of the outcome of a treatment of obesity, the metabolic syndrome and/or type 2 diabetes and recommendation for the choice of a drug, gene therapy or other therapy for the treatment of obesity, the metabolic syndrome and/or type 2 diabetes.