Biomarkers and method for diagnosing monogenic diabetes in young adults carrying deleterious HNF1a alleles

Abstract

The present invention relates to the in vitro use of the expression levels of SEQ ID NO.: 3 (miR-19a-3p), SEQ ID NO.: 6 (miR-16-5p) and ultrasensitive CRP (hs-CRP) as biomarkers for diagnosing non-autoimmune monogenic maturity-onset diabetes of the young in individuals carrying deleterious HNF1A alleles (HNF1A-MODY). The invention further relates to an associated diagnostic method and a kit for carrying out said method.

Description

[0001] BIOMARKERS AND DIAGNOSTIC METHOD FOR MONOGENIC DIABETES IN YOUNG ADULTS CARRIERS OF DELETERIOUS HNF1A ALLELES

[0002] DESCRIPTION

[0003] FIELD OF INVENTION

[0004] The invention relates to the field of medicine, more specifically to the field of medical prognosis and diagnosis, and even more specifically relates to the use of miRNAs and ultrasensitive PCR (hsPCR or usPCR) for the prognosis and diagnosis of monogenic onset diabetes in young adults carrying deleterious HNF1A alleles.

[0005] STATE OF THE ART

[0006] Although several genes are implicated in monogenic MODY (Maturity Onset Diabetes of the Young), HNF1A (hepatocyte nuclear factor-1 alpha) MODY is the most common form in adults and has a significant impact on management when properly diagnosed. Common clinical criteria for selecting individuals for MODY genetic testing include onset of diabetes before the age of 25 years, preserved endogenous insulin production, absence of pancreatic autoimmunity, and consecutive generations of diabetes. These criteria clearly overlap with the features of type 1 diabetes (T1DM) and type 2 diabetes (T2DM), so many individuals with HNF1A MODY remain unrecognized, especially if they do not meet classical MODY criteria.In this paper, we define MODY HNF1A as young adult-onset non-autoimmune diabetes in individuals carrying deleterious HNF1A alleles.

[0007] Due to its low frequency, monogenic diabetes is not usually the first diagnosis in a patient with hyperglycemia, and many countries have limited access to genetic testing for its diagnosis. Establishing a correct molecular diagnosis of MODY HNF1A allows for treatment transition to sulfonylureas or glinides, which can provide excellent diabetes control for decades. A correct diagnosis also facilitates the rapid identification of affected family members and, in the future, the planning of emerging gene therapy strategies.

[0008] Criteria such as the absence of beta-cell autoantibodies and the presence of C-peptide exclude most cases of autoimmune diabetes or T1DM. Work in this area has primarily focused on discriminating MODY presenting in childhood by selecting beta-cell antibody-negative children for further investigation. A recent study took a similar approach with young adults diagnosed before age 30, although most had a clinical label of T1DM.

[0009] Differentiating patients with type 2 diabetes, particularly in an older age group where the proportion of MODY is lower, is more complex. The addition of MODY-specific HNF1A biomarkers based on extrapancreatic manifestations of HNF1A could aid in differentiating this type of non-autoimmune diabetes.

[0010] The HNF1A gene encodes a transcription factor that regulates the expression of many genes. C-reactive protein (CRP) expression in the liver is regulated by HNF1A, and genome-wide association (GW / AS) studies showed that plasma HRP levels were associated with genetic variation close to HNF1A. A study by Owens' group (Juszczak et al., 2019) reported that hs-CRP levels were lower in subjects with HNF1A MODY compared to other forms of diabetes, and were particularly discriminatory from those with young-onset T2DM.

[0011] The use of biomarkers is increasing, and technical advances allow for a more precise identification of the processes involved in pathological progression. Recently, alternative biomarkers, such as circulating microRNAs (miRNAs), have been shown to be associated with T2DM along with many other diseases (La Sala, Micheloni, De Nigris, Prattichizzo, & Ceriello, 2018; Prattichizzo et al., 2016; Zampetaki et al., 2010). miRNAs are small endogenous non-coding RNAs, approximately 18–22 nucleotides in length, capable of modulating the expression of complementary messenger RNAs by pairing with the untranslated region (3-UTR) (DP, 2004). They are also known to be involved in the regulation of most molecular processes. There is consistent scientific evidence about the relationship of miRNAs with metabolic processes, including insulin metabolism and glucose homeostasis (Kato, Castro, & Natarajan, 2013).Several studies show how miRNAs alter gene expression in insulin-producing and insulin-sensitive tissues; examples include miR-375 in the pancreas (Poy et al., 2004) and miR-122 in the liver (Esau et al., 2006). Circulating miRNAs have been detected in the bloodstream, where they exhibit high stability and reproducibility. Circulating miRNAs can provide clinical information about pathophysiological conditions, suggesting their important role in the pathogenesis, early diagnosis, and progression of diabetes.

[0012] Although the precise mechanisms of miRNA release into the bloodstream are only partially understood, miRNAs appear to reach the circulatory system through a complex mechanism of release from cells using extracellular vesicles (EVs) or carrier proteins (de Candía, Torn, Pagani, & Abrignani, 2014). Circulating miRNAs are candidates as novel biomarkers of insulin resistance and adiposity (Corona-Meraz et al., 2019), also for monitoring therapy response with respect to glycemic target (Catanzaro et al., 2018; Nunez Lopez, Retnakaran, Zinman, Pratley, & Seyhan, 2019; Yang et al., 2017) and for diabetic complications (Chien et al., 2015; Spinetti et al., 2013), but their involvement in the development of monogenic diabetes is unknown.

[0013] The objective of the present invention has been to develop a method based on circulating miRNAs that, together with hs-CRP, low levels of which have been described in MODY HNF1A, is useful in the differential diagnosis between patients with T2DM and MODY HNF1A. In particular, the value of different miRNAs that were selected from the literature for being involved in insulin secretion and regeneration of beta cells and pancreatic islets has been evaluated, of which mir-19 and mir-16-5p were finally selected together with hs-CRP as biomarkers, to develop an algorithm that will distinguish subjects with MODY HNF1A from subjects with T2DM, since it is the most frequent clinical dilemma situation in the care of diabetes with C peptide present and negative autoimmunity in adults.

[0014] DESCRIPTION OF THE INVENTION

[0015] A microRNA (abbreviated as “myRNA”) is a small non-coding RNA molecule found in both plants and animals, which frequently performs a function related to the post-transcriptional regulation of gene expression.

[0016] MicroRNAs contain approximately 22 nucleotides (approx. 18-25 nucleotides), are non-coding single-stranded RNAs with some secondary structure that negatively regulate (inhibit) gene expression either by inhibiting translation or cleaving messenger RNA (mRNA). Therefore, miRNAs are post-translational regulators that bind to complementary sequences of mRNA transcripts, generally resulting in translational repression or target degradation resulting in gene silencing. In a particular aspect of the invention, understanding the molecular details of the action of the miRNAs of the invention is not critical since the detected levels of the indicative miRNAs allow the methods of the invention to be carried out.

[0017] Under a standard nomenclature system, names are assigned to experimentally confirmed miRNAs as follows: the "mir" prefix is ​​followed by a hyphen and a number, so that the latter can indicate the order of the names. The uncapitalized "mir-" refers to the pre-miRNA, while an uppercase "miR-" refers to the mature form. miRNAs with almost identical sequences except for one or two nucleotides are annotated with an additional lowercase letter, e.g., miR-99a. Pre-miRNAs that lead to 100% identical mature miRNAs but are located at different loci in the genome are indicated with an additional hyphen-number suffix. Species of origin may be designated with a three-letter prefix, e.g., hsa-miR-223 is a human (Homo sapiens) miRNA. Since, in the context of this document, all individualized miRNAs are human miRNAs, the " / ?sa-" prefix is ​​sometimes omitted.When two mature miRNAs originate from opposite arms of the same pre-miRNA, they are denoted by a -3p or -5p suffix, such as m¡R-142-3p. When relative expression levels are known, an asterisk after the name indicates a miRNA expressed at low levels relative to the miRNA on the opposite arm of a hairpin. Most known miRNA genes are located in intergenic regions or oriented antisense to neighboring genes and are therefore thought to be transcribed as independent units. Their genes are usually transcribed by RNA polymerase II, and transcripts are processed, exported from the nucleus, and further processed by specific mechanisms, as is well known in the art (see, e.g., He et al., Nat. Rev. Genet. 2004 Jul;5(7):522-31). miRNA sequences can be accessed at http: / / www.mirbase.org.

[0018] Thus, the term "miRNA of the invention" will be used to refer to any miRNA selected from the group consisting of mir-181, mir-15, mir-19a-3p, mir-24, mir-375, mir-16-5p and mir124-3p.

[0019] Table 1: miRNA sequences of the invention.

[0020] A first aspect of the invention relates to the in vitro use of the expression levels of SEQ ID NO: 3 (m¡R-19a-3p), SEQ ID NO: 6 (m¡R-16-5p) and ultrasensitive POR (hs-PCR) as biomarkers to predict and / or diagnose young adult-onset non-autoimmune monogenic diabetes in individuals carrying deleterious HNF1A alleles (MODY HNF1A). In a preferred embodiment, the use of SEQ ID NO: 3 (m¡R-19a-3p), SEQ ID NO: 6 (m¡R-16-5p) and ultrasensitive POR (hs-PCR) is simultaneous. In another preferred embodiment, the expression levels of one or more of the following miRNAs are also used as biomarkers: SEQ ID NO: 1 (miR-181); SEQ ID NO: 2 (m¡R-15); SEQ ID NO: 4 (m¡R-24), SEQ ID.NO: 5 (miR-375) and SEQ ID NO: 7 (miR-124).

[0021] Another aspect of the invention relates to a biomarker expression profile suitable for diagnosing young adult-onset non-autoimmune monogenic diabetes in individuals carrying deleterious HNF1A alleles (MODY HNF1A), hereinafter referred to as the biomarker expression profile of the invention, comprising the expression levels of SEQ ID NO: 3 (m¡R-19a-3p), SEQ ID NO: 6 (m¡R-16-5p) and ultrasensitive PCR (hs-PCR). In another preferred embodiment, the biomarker expression profile of the invention further comprises the expression levels of one or more of the miRNAs as set forth in the following SEQ ID NO: SEQ ID NO: 1 (miR-181); SEQ ID NO: 2 (m¡R-15); SEQ ID NO: 4 (m¡R-24); SEQ ID NO: 5 (miR-375) and SEQ ID NO: 7 (miR-124).

[0022] METHOD OF DIAGNOSIS / PROGNOSIS OF THE INVENTION

[0023] Another aspect of the invention relates to an in vitro method for diagnosing and / or prognosing young adult onset non-autoimmune monogenic diabetes in individuals carrying deleterious HNF1A alleles comprising: a) measuring the expression levels of SEQ ID NO: 3 (m¡R-19a-3p), SEQ ID NO: 6 (m¡R-16-5p) and ultrasensitive PCR (hs-PCR) in a biological sample, b) applying the formula where the variables have been calculated as follows: to approximate them to a normal distribution, the log was applied 10 and the Z-score (Z ~ N(0,1)) to standardize them and be able to compare them with each other.

[0024] PCR-hs = Z score log 10 PCR-hs m¡R-19a-3p= Z score log 10 2delta Ct miR- 19a - 3p m¡R-16-5p=Z score log 10 2delta Ct miR- 16 ' 5pc) classify the individual in the group of individuals suffering from MODY HNF1A when they present a P greater than or equal to 0.311, more preferably greater than or equal to 0.40 and much more preferably, greater than or equal to 0.83.

[0025] The method of the present invention allows to classify individuals with values ​​of P< 0.05 in the group of individuals who do not suffer from MODY HNF1A with certainty.

[0026] The first method of the invention involves comparing said miRNA levels with the levels of said miRNAs in a reference sample or with a median value. In the context of the present invention, "reference sample" is understood to mean the sample used to determine the variation in the expression levels of the miRNAs of the present invention. In a preferred embodiment, the reference value is obtained from the expression values ​​obtained from a sample of individuals who do not have the disease, i.e., who do not suffer from MODY HNF1A.

[0027] Preferably, reference samples are taken from several individuals who do not have the disease and combined, such that the reference value reflects the average value of said molecules in the population of individuals who do not suffer from MODY HNF1A. "Reference value" is the level of expression of a miRNA of the invention in a reference sample.

[0028] In a preferred embodiment, expression levels are normalized with respect to the control miRNA, in order to mitigate expression variations of non-biological origin. In a more preferred embodiment, the control miRNA is the so-called spike-in template Un¡Sp6, a synthetic miRNA molecule that we introduce when we perform the RT with the Qiagen miRCURY LNATM Universal RT kit (ref 339340) following the manufacturer's protocol and recommendations. As a standardization method, RTs were also performed from total RNA concentrations of 25 ngr (nanodrop measurement).

[0029] Table 2: Spike-in control miRNA Un¡Sp6.

[0030] In a preferred embodiment of this aspect of the invention, the biological sample is selected from blood, plasma, and serum. In an even more preferred embodiment, the biological sample is serum.

[0031] In another preferred embodiment of this aspect of the invention, the result can be obtained by any of the following techniques: (i) a miRNA profiling method, such as a microarray, and / or (ii) a method comprising PCR (polymerase chain reaction), such as real-time PCR and / or digital PCR, (iii) immunoassay.

[0032] In the invention, the method for determining the result, that is, the expression level of the miRNA, need not be particularly limited, and may be selected by a miRNA profiling method such as a microarray, and / or a method comprising PCR (polymerase chain reaction), such as PCR time and / or immunoassay; Northern Blot.

[0033] Real-time quantitative PCR (polymerase chain reaction) (usually abbreviated as RQ-PCR, RT-qPCR, rt-PCR or qPCR) is a sensitive and reproducible miRNA expression quantification technique that can be used particularly for profiling miRNA expression in cells and tissues. Any method can be used to evaluate the RT-PCR results, and the ACt method and the AACt method may be preferred. The AACt method is described in detail by Livak et al. (Methods 2001, 25: 402-408). (Ct = Cycle threshold values). In practicing the present invention, the AACt method described by Livak et al. (Methods 2001, 25: 402-408) will be preferably used. The AACt-method will include a 'control sample' and a 'subject sample'. The 'subject sample' is a sample of the subject to be analyzed.For each sample, a target miRNA (here: miRNA of interest) and a synthetic and / or endogenous control miRNA (as described below) are included for PCR amplification from aliquots (typically in series). Typically, several replicates for each diluted concentration are used to derive the amplification efficiency. PCR amplification efficiency can be defined as percentage amplification (from 0 to 1). During the qPCR reaction, software typically measures for each sample the cycle number at which fluorescence (indicator of PCR amplification) crosses an arbitrary line, the threshold. This crossing point is the Ct value.A microarray is an array on a solid substrate (usually a glass slide or a silicon thin film cell) that analyzes large amounts of biological material, in the present case a large number of different miRNAs or, preferably, their reverse DNA transcripts, which are detectable by specific probes immobilized on the solid substrate.

[0034] A Northern blot involves the use of electrophoresis to separate RNA samples by size and subsequent detection with a hybridization probe complementary to (part of) the RNA target sequence of interest.

[0035] The method of the present invention can be applied with samples from individuals of either sex, that is, men or women, and at any age.

[0036] In the method of the present invention, miRNA expression can be normalized, preferably relative to the expression of another RNA molecule. Normalization methods are well known in the state of the art, although to date there is no consensus or universal method for standardizing miRNAs in serum.

[0037] For the development of the algorithm, the control miRNA called spike-in template Un¡Sp6 has been used, as it is a method that allows us to use it in other groups of subjects and ensures the replication of the measurements in different populations.

[0038] The invention provides a method for assigning a human subject into one of two groups: group 2, comprising subjects identifiable by the method of the invention, and group 2, representing the remaining subjects.

[0039] It is possible to provide a personalized therapy to an individual depending on whether the individual is assigned to group 1 or group 2. Group 1 comprises subjects identifiable by the method of the invention as individuals suffering from MODY HNF1A and group 2 represents the remaining subjects.

[0040] KIT OR DEVICE OF THE INVENTION

[0041] Another aspect of the invention relates to a kit or device comprising at least one or more oligonucleotides capable of hybridizing with miRNAs established as SEQ ID NO: 3 and 6 under stringent conditions.

[0042] It is preferred that said oligonucleotide(s) be capable of doing so under stringency conditions. In a preferred embodiment, one or more of said one or more oligonucleotides (preferably DNA) are further defined by the following probes or primers: hsa-m¡RXX-m¡RCURY LNA miRNA measured with syber, LNA™ PCR specific primers.

[0043] More preferably the kit also comprises oligonucleotide(s) capable of hybridizing with miRNAs set forth as SEQ ID NO: 1, 2, 4, 5 and / or 7 under stringent conditions.

[0044] Stringency is a term used in hybridization experiments. Stringency reflects the degree of complementarity between the oligonucleotide and the nucleic acid (which in this case is the miRNA to be detected); the higher the stringency, the higher the percentage of homology between the probe and the nucleic acid bound to the filter. Those skilled in the art are well aware that temperature and salt concentrations have a direct effect on the results obtained. It is recognized that hybridization results are related to the number of degrees below the Tm (melting temperature) of the DNA in which the experiment is performed. Stringent conditions are often defined as a wash with 0.1X SSC (saline-sodium citrate (SSC) buffer at 65 °C. (SSC is usually provided as a 20X stock solution, consisting of 3 M sodium chloride and 300 mM trisodium citrate (adjusted to pH 7.0 with HCl)).

[0045] In particular embodiments, the kit is selected from (a) a kit suitable for PCR, (b) a kit suitable for Northern Blot (c) Immunoassay, and (d) a kit suitable for microarray analysis. Two or more of these embodiments may also be combined, such that the kit may comprise, for example, both (a) and (c).

[0046] In the case of (a) a kit suitable for PCR, this PCR is typically a real-time quantitative PCR (RQ-PCR), a sensitive and reproducible gene expression quantification technique. In this case, it is desired that the kit additionally comprises a polyT oligonucleotide primer in addition to the oligonucleotide(s) of the kit. The polyT oligonucleotide primer can be used together with the oligonucleotide(s) of the invention for priming PCR, after polyadenylation of the isolated miRNAs by methods known to those skilled in the art, such as the use of poly-(A) polymerase and ATP. These reagents may optionally be included in the kit.

[0047] A Northern blot (b) involves the use of electrophoresis to separate RNA samples by size and subsequent detection with an oligonucleotide(s) (hybridization probe) complementary to (part of) the RNA target sequence of interest.

[0048] The kit can also be of the immunoassay type (c), with controls, calibrators and a standard curve to measure the expression of miRNAs in a spectrophotometer. It is also possible for the oligonucleotide(s) to be immobilized in spots on a (preferably solid) surface. In one embodiment thereof, the kit comprises a microarray (d). An RNA microarray is an array on a solid substrate (generally a glass slide or a silicon thin film cell) that analyzes large quantities of different RNAs (in this case miRNA), which can be detected by specific probes immobilized on spots of the solid substrate. Each spot contains a specific nucleic acid sequence, typically a DNA sequence, known as probes (or reporters). Although the number of spots is not limited, there is a preferred embodiment in which the microarray is adapted to the methods of the invention.In one embodiment, such a custom microarray comprises fifty spots or fewer, such as thirty spots or fewer, including twenty spots or fewer.

[0049] The kit or device of the invention may be used and the use is not particularly limited, although use in the method of the invention in any of its embodiments is preferred.

[0050] In another preferred embodiment of this aspect of the invention, the oligonucleotides, primers, probes or antibodies are modified or labeled, for example, but not limited to, by radioactive or immunological labeling. Thus, preferably, the oligonucleotides have modifications in some of their nucleotides, such as, for example, but not limited to, nucleotides that have one of their atoms with a radioactive isotope, normally 32P or tritium, immunolabeled nucleotides, such as with a digoxigenin molecule, and / or immobilized on a membrane. Various possibilities are known in the state of the art.

[0051] The kit or device of the invention may comprise controls, program instructions, and information necessary to carry out any of the methods of the invention. Another aspect of the invention relates to the use of the kit according to any of the embodiments described above to carry out any of the embodiments of the method of the invention.

[0052] AUTOMATION OF THE METHODS OF THE INVENTION

[0053] The invention also extends to computer programs adapted to any processing medium capable of carrying out the methods of the invention. Such programs may be in the form of source code, object code, intermediate source code, and object code, for example, in partially compiled form, or in any other form suitable for use in carrying out the processes according to the invention. Computer programs also encompass cloud applications based on such a method.

[0054] In particular, the invention encompasses computer programs arranged on or within a carrier. The carrier may be any entity or device capable of supporting the program. When the program is embodied in a signal that can be transported directly by a cable or other device or medium, the carrier may be constituted by said cable or other device or medium. As a carrier, the carrier could be an integrated circuit on which the program is embedded, the integrated circuit being adapted to execute, or to be used in the execution of, the corresponding processes.

[0055] For example, programs could be embedded in a storage medium, such as a ROM, CD-ROM, or semiconductor ROM, a USB flash drive, or a magnetic recording medium, such as a floppy disk or hard disk. Alternatively, programs could be supported by a transmissible carrier signal. For example, this could be an electrical or optical signal that could be transported via electrical or optical cable, by radio, or by any other means.

[0056] The invention also extends to computer programs adapted to any processing medium capable of carrying out the methods of the invention. Such programs may be in the form of source code, object code, intermediate source code, and object code, for example, in partially compiled form, or in any other form suitable for use in carrying out the processes according to the invention. Computer programs also encompass cloud applications based on such a method.

[0057] Thus, another aspect of the invention relates to a computer-readable storage medium comprising program instructions capable of causing a computer to perform the steps of any of the methods of the invention.

[0058] Another aspect of the invention relates to a transmissible signal comprising program instructions capable of causing a computer to carry out the steps of any of the methods of the invention.

[0059] Specifically, a calculator has been developed with the formula of the algorithm of the invention to facilitate the calculation of probability, using a programming code and a user-friendly interface

[0060] The diagnostic calculator was created using the Python programming language and the Tkinter library. This library provides various tools for creating and developing a graphical user interface and allows the interface to be compatible with most operating systems. The interface consists of an application window in which the various widgets that make up the calculator are arranged. It includes:

[0061] - 4 text boxes in which the values ​​of the characteristics that make up the algorithm are entered.

[0062] -1 button which executes the function for the calculation in the equation obtained in the development of the algorithm.

[0063] -1 text box showing the calculation result and the probability of having the disease at four levels: negative, possible positive, probable positive, and very probable positive; depending on whether the p value is less than 0.311 (negative), greater than or equal to 0.311 (possible positive), greater than or equal to 0.40 (probable positive), or greater than or equal to 0.83 (very probable positive).

[0064] An “isolated biological sample” includes, but is not limited to, cells, tissues, and / or biological fluids from an organism, obtained by any method known to a person skilled in the art. Preferably, the isolated biological sample is peripheral venous blood.

[0065] The term “individual,” as used in the description, refers to animals, preferably mammals, and more preferably, humans. The terms “individual,” “human subject,” and “subject” are used interchangeably herein and are synonymous with “patient,” and are not intended to be limiting in any way, and may be of any age, sex, and physical condition. The terms “one or more” or “at least one” or “several,” as used herein, include one and the individualized specification of any number that is more than one, such as two, three, four, five, six, etc. The term “comprises” may also be interpreted, in a particular embodiment, as “consists of.” The term “comprises” and its variants are not intended to exclude other technical characteristics, additives, components, or steps.For those skilled in the art, other objects, advantages and features of the invention will be apparent in part from the description and in part from the practice of the invention.

[0066] DESCRIPTION OF THE FIGURES

[0067] Figure 1: ROC curve of the algorithm design model. Statistical method for determining the diagnostic accuracy of the method of the invention, specifically its sensitivity and specificity and its discriminatory capacity, the area under the curve of the model, the probability that given a pair of individuals, one sick and the other healthy, the test will classify them correctly.

[0068] Figure 2: ROC curve of the algorithm model with samples for validation.

[0069] EXAMPLES OF THE INVENTION

[0070] Study participants for algorithm design.

[0071] MODY HNF1A subjects were recruited from the Diabetes Unit of the Regional University Hospital of Málaga (HRUM), and subjects with T2DM were selected from the di@bet.es study. They were matched by age, BMI, and sex. The number of MODY HNF1A patients with a previous Sanger diagnosis was 17. The number of subjects with T2DM was 22.

[0072] Study participants for algorithm validation.

[0073] MODY HNF1A subjects were recruited from the Diabetes Unit of the Regional University Hospital of Málaga (HRUM), and subjects with T2DM were selected from the di@bet.es study. They were matched by age, BMI, and sex. The number of MODY HNF1A patients with a previous Sanger diagnosis was 29. The number of subjects with T2DM was 75.

[0074] DNA sequencing and evaluation of HNF1A alleles.

[0075] DNA was extracted, amplified, and sequenced using the Sanger method. Mutation Surveyor version 5.0.1 (SoftGenetics, Cambridge, UK) was used for missense detection against the reference sequence (NM_000545.5). Rare HNF1A alleles were then systematically assessed (minor allele frequency (MAF), 1%) and aligned with the American College of Medical Genetics (ACMG) classification. This included clinical features, co-segregation of the allele with diabetes in the family, and in silico analysis of missense variants using the intolerant-to-tolerant classification (SIFT), polymorphism phenotyping version 2 (PolyPhen2), and protein variation effect analyzer (PROVEAN). The potential effect on splicing was examined using the Human Splicing Finder (HSF).

[0076] Biochemical and immunological assays.

[0077] hsCRP was measured using a broad-range latex-enhanced high-sensitivity immunoturbidimetric assay on an ADVIA 2400 analyzer (Siemens Healthcare Diagnostics, Erlangen, Germany) with a limit of quantification of 0.01 mg / L. hsCRP measurements for MODY HNF1A samples were performed using the Abbott hsCRP method, and the lowest quantifiable level was 0.1 mg / L.

[0078] The methods were reproducible, with a coefficient of variation for both methods below 10.5% across the entire concentration range tested. Comparison of clinical samples measured by both methods using Passing and Bablock regression showed concordance for the Abbott method = 0.26 + 0.99 (Siemens method).

[0079] Methodology for determining miRNAs.

[0080] We selected 10 candidate miRNAs from the literature as potential biomarkers for the diagnosis of MODY HNF1A based on criteria related to insulin synthesis and regeneration processes of beta cells and pancreatic islets. We then performed a pilot study with a group of patient serum samples where these 10 candidate miRNAs were measured, and after this test we obtained consistent results regarding their determination in serum of the following 7: m¡R181, m¡R15, m¡R19-3p, m¡R124-3p, m¡R24, m¡R375 and m¡R16-5p.

[0081] miRNA extraction from serum samples was performed by automated methods on a Promega Maxwell 16 (serial number 23627808) with the Maxwell® 16 miRNA Tissue Kit (Promega Biotech Ibérica SL, Madrid, Spain) and converted to microcDNA by reverse transcription with the Qiagen miRCURY LNA™ Universal RT Kit (ref 339340) following the manufacturer’s recommendations for each kit. Measurements of miRNA expression levels were performed by real-time qPCR on 384 plates in a Light Cycler 480 (Roche Diagnostics, SL, Barcelona, ​​Spain) on the IBIMA Genomics platform. The master mix was prepared following Exiqon guidelines with GoTaq® qPCR Master Mix (Promega Biotech Ibérica SL, Madrid, Spain) and LNA™ PCR specific primer sets (Qiagen). miRNA analysis was performed using the delta Ct method.For data normalization, negative controls and a specific calibrator were included on all plates for plate-to-plate normalization. miRNA determinations with Ct > 38 were considered below the detection limit and removed from the analysis.

[0082] Statistical analysis.

[0083] Subject characteristics and experimental results were analyzed using SPSS 23 software. Continuous data are presented as medians with interquartile ranges (IQR), and the Kruskal-Wallis test was used to compare groups. A p-value of 0.05 was considered statistically significant.

[0084] Diagnostic efficacy was analyzed by comparing the two patient groups using stepwise logistic regression analysis, with the best diagnostic model selected. The predicted probabilities of the model were dichotomized based on the best cut-off calculated using the ROC curve. Predictive values ​​were then calculated based on the cut-off.

[0085] Results.

[0086] The miRNAs miR-181, m¡R-15, m¡R-19-3p, m¡R-24, m¡R 124-3p, miR-375, and m¡R-16-5p were measured along with hs-CRP in individuals with T2DM and MODY HNF1A. All showed significant differences when adjusted for age, sex, and IMG.

[0087] At a practical level, for adequate phenotyping and activation of the MODY HNF1A genetic study, we are interested in differentiating between diabetes caused by the MODY HNF1A mutation and T2DM; therefore, the diagnostic efficacy analysis was performed between these two groups using a stepwise logistic regression analysis.

[0088] The final model includes the following variables: m¡R-19-3p, m¡R-16-5p and hs-PCR, and is defined with the following algorithm:

[0089] Therefore, this logarithm yields the probability of presenting MODY HNF1A (MODY 3) based on the 3 variables m¡R-19-3p, m¡R-16-5p and PCR-hs, where the values ​​of the variables analyzed were those indicated below: Table 3: Description of the variables used for algorithm design.

[0090] Predicted probabilities are dichotomized based on the best cut-off calculated based on the best sensitivity and specificity using the ROC curve = 0.311. Predictive values ​​were calculated using this cut-off. The Nagelkerke R2 of the model is 0.777, indicating that 77.7% of the variability of the dependent variable is explained by the model.

[0091] P values ​​> 0.837 indicate MODY HNF1A with certainty.

[0092] P values ​​< 0.056 confidently rule out MODY HNF1A. Table 4: Contrast outcome variables: Predicted probability of MODY HNF1A vs DM2 with the algorithm model. a. Under the non-parametric assumption b. Null hypothesis: true area = 0.5

[0093] Table 5: Results of the diagnostic test with the samples for algorithm design. Positive = MODY 3; Negative = T2DM Of the 16 subjects diagnosed with MODY HNF1a, the algorithm correctly classified 15 and of the 23 subjects with T2DM, 21 were correctly classified within their group by applying the algorithm.

[0094] Table 6: Characteristics of the diagnostic algorithm based on the samples used for its development.

[0095] Table 7: Change in predictive values ​​depending on the prevalence of the disease in the population in which the differential diagnosis is made.

[0096] According to calculations, this algorithm allows a patient with MODY HNF1A to be diagnosed with a positive predictive value of 93.4% in a specialized monogenic diabetes clinic where appropriate phenotyping is performed, increasing the pre-test probability of monogenic diabetes.

[0097] Validation

[0098] The predicted probabilities were then calculated using the new subjects selected for validation, dichotomizing based on the same cut-off used in the development of the algorithm (0.311), calculated from the best specificity and sensitivity values ​​obtained from the ROC curve obtained from the model. Table 8: Description of the variables used for validation.

[0099] Table 9. Predicted probability of algorithm model with samples for validation. a. Under the non-parametric assumption b. Null hypothesis: true area = 0.5 Table 10: Result of the diagnostic test with the samples for validation.

[0100] Positive= MODY 3; Negative = DM2

[0101] Of the 26 subjects diagnosed with HNF1a selected for validation, the algorithm correctly classified 25, and of the 75 subjects with T2DM selected for validation, 39 were correctly classified within their group by applying the algorithm.

[0102] Table 11: Characteristics of the diagnostic algorithm based on the samples used for validation.

[0103] MODY HNF1A is diagnosed using sequencing techniques such as SANGER technology, which are gene-by-gene studies, or next-generation sequencing (NGS) technology, which allows many genes to be studied at once. Clinicians often have limited access to these tests, but in other countries they do have greater access. Biomarkers can still be useful as an estimate of risk prior to MODY HNF1A genetic testing and can also provide information in the context of result interpretation. In other countries, including Spain, very few genetic tests are routinely available, so biomarkers may still play a role in identifying people at higher risk of MODY HNF1A for single-gene Sanger sequencing approaches.

[0104] The combination of circulating biomarkers such as hs-CRP and miRNAs opens a new avenue of study for developing more affordable diagnostic tests that are a cost-effective alternative to the genetic tests required for diseases such as monogenic diabetes. In situations where the availability of genetic tests is more limited in routine clinical practice, these biomarkers can be used for pre-screening to identify subjects at high risk of developing a type of MODY. Currently, genetic testing is the first-line diagnostic test; however, in these cases, the use of these biomarkers can guide us regarding the pathogenicity of new, undescribed variants or those of uncertain significance.

[0105] In conclusion, our study found that the biomarkers mi-19-3p, mir-16-5p, and hs-CRP could differentiate individuals with pathological HNF1A alleles from those with T2DM. A diagnostic protocol that combines clinical characteristics with biomarkers could improve subject selection for genetic testing of MODY HNF1A, the most common form of monogenic diabetes in adults. The application of this algorithm could optimize resources for healthcare systems as it would allow for more accurate information when prescribing genetic testing.

[0106] Table 12: Price-execution time comparison between techniques.

[0107] It is also interesting to note that both Sanger sequencing and massively automated sequencing (NGS) require much more expensive equipment than that required for miRNA determination and / or hs-PCR. Measurement of miRNA can be performed in a traditional thermocycler for real-time PCR, and determination of hs-PCR only requires an absorbance reader, which can be found in any basic clinical analysis laboratory. Therefore, and as previously discussed in this document, performing biomarker determinations for the application of this MODY HFN1a diagnostic algorithm would be more feasible in centers with more limited resources and therefore much more discrete equipment.

[0108] References

[0109] 1. Catanzaro, G., Besharat, Z. M., Chiacchiarini, M., Abballe, L., Sabato, C., Vacca, A., Ferretti, E. (2018). Circulating MicroRNAs in elderly type 2 diabetic patients. International Journal of Endocrinology, 2018.

[0110] 2. Chien, H. Y., Lee, T. P., Chen, C. Y., Chiu, Y. H., Un, Y. C., Lee, L. S., & Li, W. C. (2015). Circulating microRNA as a diagnostic marker in populations with type 2 diabetes mellitus and diabetic complications. Journal of the Chinese Medical Association, Vol. 78.

[0111] 3. Corona-Meraz, F.I., Vázquez-Del Mercado, M., Ortega, F.J., Ruiz-Quezada, S.L., Guzmán-Ornelas, M.O., & Navarro-Hernández, R.E. (2019). Aging influences the relationship of circulating miR-33a and miR-33b levels with insulin resistance and adiposity. Diabetes and Vascular Disease Research, 16(3), 244-253.

[0112] 4. de Candía, P., Torn, A., Pagani, M., & Abrignani, S. (2014). Serum microRNAs as biomarkers of human lymphocyte activation in health and disease. Frontiers in Immunology, Vol. 5. 5. DP, B. (2004). MicroRNAs : Genomics, Biogenesis, Mechanism, and Function.pdf. Cell.

[0113] 6. Esau, C., Davis, S., Murray, S. F., Yu, X. X., Pandey, S. K., Pear, M., ... Monia, B. P. (2006). miR-122 regulation of lipid metabolism revealed by in vivo antisense targeting. Cell Metabolism, 3(2), 87-98.

[0114] 7. Juszczak, A., Pavic, T., Vuckovic, F., Bennett, A. J., Shah, N., Medvidovic, E. P., ... Gornik, O. (2019). Plasma fucosylated glycans and C-reactive protein as biomarkers of HNF1A-MODY in young adult-onset nonautoimmune diabetes. Diabetes Care, 42(1), 17-26.

[0115] 8. Kato, M., Castro, N. E., & Natarajan, R. (2013). MicroRNAs: Potential mediators and biomarkers of diabetic complications. Free Radical Biology and Medicine, Vol. 64, pp. 85-94.

[0116] 9. La Sala, L., Micheloni, S., De Nigris, V., Prattichizzo, F., & Ceriello, A. (2018, October 1). Novel insights into the regulation of miRNA transcriptional control: implications for T2D and related complications. Acta Diabetologica, Vol. 55, pp. 989-998.

[0117] 10. Nunez Lopez, Y. O., Retnakaran, R., Zinman, B., Pratley, R. E., & Seyhan, A. A. (2019). Predicting and understanding the response to short-term intensive insulin therapy in people with early type 2 diabetes. Molecular Metabolism, 20, 63-78.

[0118] 11. Poy, M. N., Eliasson, L., Krutzfeldt, J., Kuwajima, S., Ma, X., MacDonald, P. E., ... Stoffel, M. (2004). A pancreatic islet-specific microRNA regulates insulin secretion. Nature, 432(7014), 226-230.

[0119] 12. Prattichizzo, F., Giuliani, A., De Nigris, V., Pujadas, G., Ceka, A., La Sala, L., ... Ceriello, A. (2016, September 1). Extracellular microRNAs and endothelial hyperglycaemic memory: a therapeutic opportunity? Diabetes, Obesity and Metabolism, Vol. 18, pp. 855-867.

[0120] 13. Spinetti, G., Fortunato, O., Caporali, A., Shantikumar, S., Marchetti, M., Meloni, M., ... Emanueli, C. (2013). MicroRNA-15a and MicroRNA-16 impair human circulating proangiogenic cell functions and are increased in the proangiogenic cells and serum of patients with critical limb ischemia. Circulation Research, 112(2), 335-346.

[0121] 14. Yang, Z. M., Chen, L. H., Hong, M., Chen, Y. Y., Yang, X. R., Tang, S. M., ... Chen, W. W. (2017). Serum microRNA profiling and bioinformatics analysis of patients with type 2 diabetes mellitus in a Chinese population. Molecular Medicine Reports, 15(4).

[0122] 15. Zampetaki, A., Kiechl, S., Drozdov, I., Willeit, P., Mayr, U., Prokopi, M., ... Mayr, M. (2010). Plasma MicroRNA profiling reveals loss of endothelial MiR-126 and other MicroRNAs in type 2 diabetes. Circulation Research, 107(0), 810-817.

Claims

CLAIMS 1. In vitro use of the expression levels of SEQ ID NO: 3 (m¡R-19a-3p), SEQ ID NO: 6 (m¡R-16-5p) and ultrasensitive POR (hs-PCR) as biomarkers to diagnose young adult-onset non-autoimmune monogenic diabetes in individuals carrying deleterious HNF1A alleles (MODY HNF1A).

2. The use according to claim 1, which further comprises using as biomarkers the expression levels of one or more miRNAs to be selected from: SEQ ID NO: 1 (miR-181); SEQ ID NO: 2 (m¡R-15); SEQ ID NO: 5 (miR-375) and SEQ ID NO: 4 (m¡R-24).

3. An in vitro method for diagnosing young adult-onset non-autoimmune monogenic diabetes in individuals carrying deleterious HNF1A alleles comprising: a) measuring the expression levels of SEQ ID NO: 3 (m¡R-19a-3p), SEQ ID NO: 6 (m¡R-16-5p) and ultrasensitive PCR (hs-PCR) in a biological sample, c) classify the individual in the group of individuals suffering from MODY HNF1A when they present a P greater than or equal to 0.311, more preferably greater than or equal to 0.40, and much more preferably greater than or equal to 0.

83.

4. The method according to the preceding claim wherein the biological sample is selected from blood, plasma and serum.

5. The method according to any of claims 3-4, wherein the biological sample is serum.

6. The method according to any of claims 3-5, wherein said result can be obtained by: (i) a method for generating miRNA profiles, such as a microarray, and / or (i) a method comprising PCR (polymerase chain reaction), such as real-time PCR; and / or (iii) immunoassay.

7. Use of a kit or device comprising at least one or more oligonucleotides capable of hybridizing with the miRNAs established as SEQ ID NO: 3 and 6 under stringent conditions in a method according to any of claims 3-6.

8. The use according to the preceding claim, characterized in that the kit also comprises oligonucleotide(s) capable of hybridizing with the miRNAs established as SEQ ID NO: 1, 2, 5, and / or 4 under stringent conditions.

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