WO2019234274A1 - Predictive model for predicting the development of type 2 diabetes mellitus using mirnas - Google Patents

Predictive model for predicting the development of type 2 diabetes mellitus using mirnas Download PDF

Info

Publication number
WO2019234274A1
WO2019234274A1 PCT/ES2019/070374 ES2019070374W WO2019234274A1 WO 2019234274 A1 WO2019234274 A1 WO 2019234274A1 ES 2019070374 W ES2019070374 W ES 2019070374W WO 2019234274 A1 WO2019234274 A1 WO 2019234274A1
Authority
WO
WIPO (PCT)
Prior art keywords
mir
pcr
seq
mirnas
dmt2
Prior art date
Application number
PCT/ES2019/070374
Other languages
Spanish (es)
French (fr)
Inventor
Oriol Alberto RANGEL ZÚÑIGA
Elena María YUBERO SERRANO
José LÓPEZ MIRANDA
Pablo PÉREZ MARTÍNEZ
Antonio CAMARGO GARCÍA
Rosa JIMÉNEZ LUCENA
Juan Francisco ALCALÁ DÍAZ
Javier DELGADO LISTA
Ana LEÓN ACUÑA
José David TORRES PEÑA
Antonio GARCÍA RÍOS
Francisco GÓMEZ DELGADO
Original Assignee
Servicio Andaluz De Salud
Universidad de Córdoba
Consorcio Centro de Investigación Biomédica en Red, M.P.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Servicio Andaluz De Salud, Universidad de Córdoba, Consorcio Centro de Investigación Biomédica en Red, M.P. filed Critical Servicio Andaluz De Salud
Publication of WO2019234274A1 publication Critical patent/WO2019234274A1/en

Links

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers

Definitions

  • the present invention belongs to the field of biomedicine, and more specifically it relates to markers to identify the individuals at greater risk of developing type 2 diabetes mellitus.
  • miRNAs as disease biomarkers was first introduced and demonstrated in cancer, but previous studies have suggested circulating miRNAs (in plasma or serum) as potential biomarkers for the diagnosis of type 2 diabetes mellitus (DMT2) .
  • DMT2 type 2 diabetes mellitus
  • Zhang et al [Biochem Biophys Res Commun. (2015) 463 (1-2)] through a cross-sectional study that included 40 subjects (20 diagnosed with pre-diabetes and 20 diagnosed with DMT2) observed differences in expression between groups in miR126 levels. This paper suggests the possibility of using miR126 as a diagnostic and prediction biomarker. Further findings by the same authors, after a 2-year follow-up study, suggest the use of miR126 to differentiate between DMT2 and normoglycemic subjects [Biomed Res Int. (2013) 761617]. Initially, Zhang et al is based on a cross-sectional study without monitoring the development of the disease.
  • Liu et al (Int J Mol Sci (2014) 15 10567-77), suggests the same differences in miR126 expression between these three groups of subjects. In this 6-month longitudinal study, the intervention with diet and physical exercise stands out. Liu et al suggests a model that allows differentiating between diabetics, pre-diabetics and normoglycemic subjects, which includes the miR126 added to conventional diabetes prediction variables (blood glucose, HbA1c, age, gender and BMI). However, variables such as diet and physical exercise are not included in the model, which were study variables with a six-month follow-up. The inclusion of these variables could significantly modify the sensitivity and specificity of the proposed model.
  • FIG. 1 The orthogonal partial least squares discriminant analysis (OPLS-DA) was used to compare the circulating levels of miRNAs at the start of the study between incident cases of DMT2 (gray circle) and subjects without DMT2 (black circle) during the period of follow up.
  • the quality of the models obtained by OPLS-DA was evaluated using the parameters of R 2 and Q 2 .
  • the miRNAs with VI P> 1 score were considered important for differentiating between groups (Table in Figure 1).
  • the data was processed using SIMCA-P + (version 14.0.0.1359; Umetrics, Umea, Sweden).
  • FIG. 1 Levels of circulating miRNAs at the start of the study according to the year in which patients were diagnosed with DMT2.
  • the data represent the mean ⁇ typical error of the mean and correspond to the ANOVA analysis of a factor.
  • subjects who developed DMT2 Incidents DMT2
  • subjects who did not develop DMT2 not DMT2.
  • Statistical significance was assessed using the Mann-Whitney U test and those p values ⁇ 0.05 were considered significant.
  • FIG. 3 ROC analysis and classification by average importance of the variables included in the ROC model based on miRNAs + HbA1c.
  • Panel A shows the comparison between the AUC of the ROC curves of three models: red line, model based on miRNAs + HbA1c; green line, model based on clinical parameters; and blue line, model based on FINDRISC.
  • panel B you can see the average importance classification of the variables included in the ROC curve.
  • FIG. 4 Disease-free probability analysis through a COX regression model based on six miRNAs.
  • the data represent circulating levels for each miRNA per tertiles, low levels (T1), medium levels (T2) and high levels (T3).
  • the analysis was carried out using SPPS (now PASW Statistic for Windows (version 21.0)) (IBM, Chicago, Illinois) and adjusted for diet, age, sex, BMI, TG, HDL and glycosylated hemoglobin (HbA1c).
  • FIG. 1 Disease-free probability analysis through a multi-miRNA regression model including miR-103, miR-28-3p, miR-29a, miR-9, miR-150 and miR-30a-5p.
  • a first aspect of the invention relates to the use of miR-9 miR-28-3p; miR-29a; miR-103; miR-223; miR-126; miR-375; miR-30a-5p and miR-150, or any combination thereof; to differentiate between subjects who develop DMT2 and those who do not develop it.
  • the profile of all 9 miRNAs is used together with glycosylated hemoglobin (HbA1c) simultaneously ( Figure 1 and 3).
  • the plasma levels of miR-9 miR-28-3p; miR-29a; miR-103; miR-223; miR-126; miR-375 are significantly lower in patients who develop DMT2, compared to those patients who do not develop it.
  • the levels of miR-30a-5p and miR-150 are significantly higher in patients who develop DMT2, compared to those patients who do not develop it.
  • miR-9s are also used; miR-28-3p; miR-29a; miR-103; miR-30a-5p and miR-150 or any combination thereof; to detect individuals with a higher risk of developing DMT2.
  • the profile of all 6 miRNAs are used simultaneously.
  • HbA1c glycosylated hemoglobin
  • the authors of the present invention have identified, for the first time, a signature of nine circulating miRNAs, which are powerful predictive biomarkers in the development of DMT2; being a more sensitive tool than those currently used in clinical practice to predict the development of the disease, as demonstrated in our study ( Figure 3).
  • they have identified a profile of circulating miRNAs which, added to HbA1c, have the ability to differentiate between individuals with greater or lesser risk of developing DMT2 (Figure 3).
  • the authors performed ROC curve analysis and developed a predictive model that includes 9 miRNAs (miR-9, miR-28-3p; miR-29a; miR-103, miR-15a; miR- 223; miR-126; miR-145; miR-375) which together with HbA1c, has a higher predictive value than using only HbA1c levels or only FINDRISC or Finnish Diabetes Risk Score (FINDRISC) (Lindstrom, J. Tuomilehto 2003. Diab Care, 26 (2003), pp 725-731).
  • the sequences of the miRNAs are as follows:
  • hsa-m ⁇ R103 AGCAGCAUUGUACAGGGCUAUGA hsa-m ⁇ R-103a-3p
  • hsa-m ⁇ R223 UGUCAGUUUGUCAAAUACCCCA hsa-m ⁇ R-223-3p
  • hsa-miR29a UAGCACCAUCUGAAAUCGGUU cgr-m ⁇ R-29a-3p
  • hsa-m ⁇ R28-3p CACUAGAUUGUGAGCUCCUGGA hsa-m ⁇ R-28-3p
  • hsa-m ⁇ R150 CUGGUACAGGCCUGGGGGACAG hsa-m ⁇ R-150-3p
  • hsa-miR30a-5p UGUAAACAUCCUCGACUGGAAG hsa-m ⁇ R-30a-5p
  • hsa-m ⁇ R375 UUUGUUCGUUCGGCUCGCGUGA hsa-m ⁇ R-375
  • hsa-m ⁇ R126 UCGUACCGUGAGUAAUAAUGCG mmu-m ⁇ R-126a-3p
  • Another aspect of the invention relates to an in vitro method for identifying individuals susceptible to developing DMT2, which comprises measuring the circulating levels of miR-9 miR-28-3p; miR-29a; mi R-103] miR-150 and miR-30a-5p in a biological sample isolated from said individual, where low levels of the first 4 and high levels of the last 2 indicate an increased risk in the individual of developing DMT2.
  • Another aspect of the invention relates to the in vitro model for the diagnosis, prediction and / or prognosis of DMT2 in an individual comprising measuring the circulating levels of the 9 miRNAs: miR-9; miR-28-3p; miR-29a; miR-103; miR-15a; miR-223; miR-126; miR-145; miR-375, in an isolated biological sample of said individual, and also includes assigning individuals who have levels lower than those described hereinbefore to the group of individuals with a predisposition to develop DMT2. More preferably it comprises adding to the miRNA levels, the plasma levels of glycosylated hemoglobin (HbA1c).
  • HbA1c glycosylated hemoglobin
  • BMI body mass index
  • HDL cholesterol-HDL
  • SCORE -12.897125 + hsam ⁇ R103 * 0.001463 + hsam ⁇ R223 * -0.009799 + hsam ⁇ R29a * - 0.011630 + hsam ⁇ R28-3p * -0.001136 + hsamR126 * -0.019230 + hsam ⁇ R150 * 0.559287 + hsam ⁇ R150 -5p * 0.033738 + hsam ⁇ R375 * -0.014507 + hsam ⁇ R9 * -0.072493 + Age * 0.006753 + Gender * 0.331970 + diet * 0.208350 + BMI * 0.038656 + Waist perimeter * 0.014300 + Triglycerides * 0.004072 + HDL * -0.002205 + HbA1c * 1.447587.
  • the biological sample is plasma.
  • the invention also relates to the in vitro model for the diagnosis, prediction and / or prognosis of DMT2 in an individual measuring the circulating levels of miR-30a-5p and miR-150 in a biological sample isolated from said individual and, in addition, includes assigning individuals who have levels greater than 1, 2 times, to the levels of the same marker in a reference sample, to the group of individuals, with the highest risk of developing DMT2.
  • the biological sample is plasma.
  • a “biological sample”, as defined herein, refers to samples of body fluids may be blood, plasma, serum, urine, sputum, cerebrospinal fluid, milk, sweat, tears, peritoneal fluid, sweat, tears and feces or samples of ductal fluid and can also be fresh, frozen or fixed.
  • the biological sample is selected from among biological samples including different types of tissue samples, as well as biological fluid samples, such as blood, plasma, serum, urine, sputum, cerebrospinal fluid, milk, sweat, tears, peritoneal fluid, sweat, tears and feces, or any combination thereof.
  • biological fluid samples such as blood, plasma, serum, urine, sputum, cerebrospinal fluid, milk, sweat, tears, peritoneal fluid, sweat, tears and feces, or any combination thereof.
  • said samples are biological samples of blood, serum or plasma.
  • the isolated biological sample is the plasma of said individual.
  • miRNA levels can be obtained by microarray expression profiles, PCR, reverse transcriptase PCR, real time reverse transcriptase PCR, quantitative real time PCR, point PCR final, endpoint multiplex PCR, coid PCR, ice coid PCR, mass spectrometry, in situ hybridization (ISH), in situ multiplex hybridization or nucleic acid sequencing.
  • microarray expression profiles PCR, reverse transcriptase PCR, real time reverse transcriptase PCR, quantitative real time PCR, point PCR final, endpoint multiplex PCR, coid PCR, ice coid PCR, mass spectrometry, in situ hybridization (ISH), in situ multiplex hybridization or nucleic acid sequencing.
  • the levels of miRNAs can be obtained by:
  • miRNA levels are obtained by real-time reverse transcriptase PCR (RT-qPCR).
  • RT-qPCR real-time reverse transcriptase PCR
  • Other techniques could be, but not limited to, combined RT with LAMP, or some new technique such as LASH (ligase-assisted sandwich hybridization (LASH).
  • LASH ligase-assisted sandwich hybridization
  • the expression of miRNA is normalized.
  • Another aspect of the invention relates to a method for classifying a human subject into one of two groups, in which group 1 comprises the subjects that can be identified by any of the methods described above and in which group 2 represents the subjects remaining.
  • Another aspect of the invention relates to a pharmaceutical composition
  • a pharmaceutical composition comprising a therapeutic agent suitable for treating a human subject of group 1 that can be identified by any of the methods described above.
  • kit or device of the invention comprising at least one oligonucleotide capable of hybridizing with (miR-9, SEQ ID: dme-miR-9a-5p; m ⁇ R-28-3p, SEQ ID: hsa-miR-28-3p; miR-29a, SEQ ID: oar-miR-29a; miR-103, SEQ ID: hsa-miR-103a-3p; miR-223 , SEQ ID: hsa-miR-223-3p; miR-126, SEQ ID: mmu-miR-126a-3p; miR-375, SEQ ID: hsa-miR-375; miR-30a-5p, SEQ ID: hsa -miR-30a-5p and miR-150, SEQ ID: hsa-miR-150-3p), and means for detecting said hybridization.
  • kit or device of the invention comprising at least one oligonu
  • kit or device to identify the individuals most at risk of developing DMT2, using any of the methods described above.
  • Another aspect of the invention relates to a computer program comprising program instructions to make a computer carry out the method according to the method of the invention.
  • the invention encompasses computer programs arranged on or within a carrier.
  • the carrier can be any entity or device capable of supporting the program.
  • the carrier may be constituted by said cable or other device or means.
  • the carrier could be an integrated circuit in which the program is included, the integrated circuit being adapted to execute, or to be used in the execution of, the corresponding processes.
  • the programs could be incorporated into a storage medium, such as a ROM, a CD ROM or a semiconductor ROM, a USB memory, or a magnetic recording medium, for example, a floppy disk or a disk hard.
  • a storage medium such as a ROM, a CD ROM or a semiconductor ROM, a USB memory, or a magnetic recording medium, for example, a floppy disk or a disk hard.
  • the programs could be supported on a transmissible carrier signal.
  • it could be an electrical or optical signal that could be transported through an electrical or optical cable, by radio or by any other means.
  • the invention also extends to computer programs adapted so that any processing means can implement the methods of the invention.
  • Such programs may have the form of source code, object code, an intermediate source of code and object code, for example, as in partially compiled form, or in any other form suitable for use in the implementation of the processes according to the invention .
  • Computer programs also cover cloud applications based on that procedure.
  • Another aspect of the invention relates to a computer-readable storage medium comprising program instructions capable of causing a computer to carry out the steps of the method of the invention.
  • Another aspect of the invention relates to a transmissible signal comprising program instructions capable of causing a computer to carry out the steps of the method of the invention.
  • CORDIOPREV study is a controlled, prospective, randomized, simple, blind dietary intervention study developed in 1002 Patients with CHD (high cardiovascular risk), aged between 20 and 75 years, who had their last coronary event within Six months before inclusion in the study, without serious illnesses and life expectancy of less than five years.
  • CHD high cardiovascular risk
  • the subjects were randomized into two different dietary models (Mediterranean diet and low-fat and high-carbohydrate diet).
  • the intervention phase is still in progress and will have a median follow-up of seven years.
  • the study was conducted with all 462 non-diabetic patients (n 462) at the start of the CORDIOPREV study. After a 60-month follow-up period, 43 subjects were diagnosed with DMT2 during the first year, 24 in the second year, 11 in the third year, 19 in the fourth year and 10 in the fifth year, for a total of 107 subjects who developed DMT2 (incidents-DMT2). Subjects were diagnosed based on an annual tolerance test for glucose (OGTT) and following the criteria established by the American Diabetes Association (ADA).
  • OGTT annual tolerance test for glucose
  • ADA American Diabetes Association
  • the remaining 355 subjects did not develop T2DMT2 during the study period and were used as a control group (non-DMT2).
  • the initial characteristics of the subjects in the study are shown in Table 1.
  • LDLc CT- (HDL + TG / 5).
  • Glucose measurements were made using the hexokinase method.
  • the hs-C reactive protein (hs-CRP) was determined by high sensitivity ELISA (BioCheck, Inc., Foster City, CA, USA). Plasma insulin concentrations were measured by enzymatic microparticle immunoassay (Abbott Diagnostics, Matsudo-shi, Japan). The concentrations of non-esterified fatty acid were measured by colorimetric enzymatic assay (Roche Diagnostics, Penzberg, Germany). ApoA-1 and ApoB concentrations were determined by immunoturbidimetry.
  • ISI 10,000 ⁇ V ([fasting plasma insulin X fasting plasma glucose] X [mean glucose in OGTT X average insulin in OGTT]).
  • IGI insulingenic index
  • IGI [30 min fasting insulin-insulin (pmol / 1)] / [30 min fasting glucose (mmol / 1)].
  • the function of beta cells was estimated by calculating the readiness index (DI) as follows:
  • DI ISI x [AUC30 min insulin / AUC30 min glucose],
  • AUC30 min is the area below the curve between the baseline and 30 min of the OGTT for insulin (pmol / I) and glucose (mmol / I), respectively, calculated by the trapezoidal method.
  • the indices used to determine specific tissue IR were the hepatic insulin resistance index (HIRI) and the insulin muscle sensitivity index (MISI), which were calculated as described in the previous work of our group and following the methods described by Matsuda and DeFronzo for HIRI and Abdul-Ghani and collaborators for MISI.
  • the FINDRISC index was calculated following the indications published by Lindstróm, et al, in 2003.
  • the miRNA expression study was carried out in 24 miRNAs, which, based on a literature search, were selected according to their association with insulin sensitivity, insulin secretion, inflammation and growth and proliferation of beta cells (Table 4).
  • the levels of circulating miRNAs were determined in RNA samples obtained from plasma samples and following the protocol of the miRNeasy Mini Kit (Qiagen, Hilden, Germany).
  • 200 pL of EDTA-plasma was mixed with 1 mL of Qiazol, incubated for 5 min at room temperature and subsequently mixed with 200 pL of chloroform.
  • 2 pg of MS2 RNA (Roche, Mannheim, Germany) was added before the chloroform step.
  • the organic and aqueous phase were then separated by centrifugation at 12,000 g for 15 minutes, at 4 ° C.
  • RNA was precipitated by the addition of 100% ethanol.
  • the mixture was applied to a miRNeasy Mini rotating column and centrifuged at 8,000 g for 2 min. TO Next, 700 ml of RWT buffer was added to the RNeasy MinElute centrifuge column at 8,000 g for 2 min. It was then washed again with 500 pL of RPE buffer and 500 pL of 80% ethanol.
  • RNA was eluted in 14 pL of RNase-free water. The purity and concentration of the RNA were evaluated by spectrophotometry using NanoDrop ND-2000 (ThermoFisher, Waltham, MA). RNA retrotranscription was carried out using the TaqMan Reverse Transcription Kit (Life Technologies, Carlsbad, CA, USA). The RT mix contains 2 pL RNA and 3 pL RT from the group of custom primers with a final volume of 7.5 pL.
  • the RT primer set was selected from specific primers of our set of target miRNAs in the database (https://www.thermofisher.com/en/en/home/life-science/pcr/real-time-pcr/ real-time-pcrassavs / mirna-ncrna-taqman-assays.html).
  • the plates were incubated in the iQ5 thermal cycler (Bio-Rad Laboratories, Inc., Hercules, CA, USA) at 16 ° C for 30 minutes, then 42 ° C for 30 minutes at, and finally at 85 ° C for 5 min; In this step, the cDNA was stored at -20 ° C for a maximum time of one week. Then, we prepare a mixture containing 10 ml of customized PreAmp primers, with a specific group for our set of target miRNAs, and 7.5 pL of RT mix and 20 pL of TaqMan PreAmp Master Mix (Life Technologies, Carlsbad, CA, USA). ) Up to a final volume of 40 pL.
  • the mixture was incubated in the Thermocycler iQ5 using the following steps: denaturation at 95 ° C for 10 min; then 55 ° C for 2 min and 72 ° C for 2 min; followed by 20 cycles of amplification (15 seconds at 95 ° C and 4 minutes at 60 ° C per cycle) and finally incubated 99.9 ° C for 10 minutes.
  • the preamplified products were then diluted with RNase-free water in a ratio of 1: 40 and used for real-time RT-PCR reactions.
  • miR-143 and miR-144 were selected as those with more stable CT values and used as reference (using the method Bestkeeper) to calculate the relative expression of the remaining 15 miRNAs.
  • Relative expression data was analyzed using OpenArray® Real-Time qPCR Analysis Software (Life Technologies, Carlsbad, CA, USA).
  • the orthogonal partial least squares discriminant analysis was used to compare the levels of miRNAs, in order to analyze the differences between incident patients-DMT2 and without DMT2 during follow-up.
  • the quality of the models obtained by OPLS-DA was evaluated by studying the parameters R 2 and Q 2 .
  • the miRNAs with a VIP> 1 were considered important to differentiate between groups.
  • the risk ratio (HR) was compared in the analysis between C1 versus C2 and C1 versus C3.
  • Linear regression and COX regression analyzes were adjusted for age, sex, diet, glycosylated hemoglobin (HbA1c), BMI, triglycerides, c-HDL and waist circumference. P values 0.05 were considered statistically significant.
  • ROC feature analysis Operative function
  • the models were corrected by those covariates that were allowed avoiding over-estimation of information, the set of covariates included: diet, age, sex, BMI, c-HDL, TG, HbA1c and waist circumference.
  • the degree of excess optimism was estimated by initial resampling of the original set (1000 randomized samples).
  • BMI body mass index
  • c-HDL high density lipoprotein
  • c-LDL low density lipoprotein
  • TG triglycerides
  • Apo A1 Apolipoprotein A1
  • Apo B apolipoprotein B
  • hs-CRP high sensitivity C-reactive protein
  • HbA1 c glycosylated hemoglobin
  • HIRI liver insulin resistance index
  • MISI muscle insulin sensitivity index
  • ISI insulin sensitivity index
  • IGI insulingenic index
  • DI readiness index
  • HOMA-IR insulin evaluation of resistance homeostasis model
  • HbA1c glycosylated hemoglobin
  • GLU glucose
  • HOMA-B homeostasis model evaluation - beta cell function
  • HOMA-IR homeostasis model insulin resistance evaluation
  • MIRI muscle insulin resistance index
  • IGI insulingenic index
  • ISI insulin sensitivity index
  • DI index provision
  • HIRI liver insulin resistance index. * p ⁇ 0.05. Correlation analysis performed by a linear regression model adjusted for age, body and gender mass index (BMI), triglycerides (TG) and high density lipoproteins (c-HDL), using SPSS (now PASW Statistic for Windows (version 21.0)) (IBM, Chicago, Illinois).

Landscapes

  • Chemical & Material Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Organic Chemistry (AREA)
  • Immunology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Molecular Biology (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Biomedical Technology (AREA)
  • Urology & Nephrology (AREA)
  • General Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Biotechnology (AREA)
  • Microbiology (AREA)
  • Hematology (AREA)
  • Medicinal Chemistry (AREA)
  • Food Science & Technology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • Genetics & Genomics (AREA)
  • Pathology (AREA)

Abstract

The invention relates to markers and a method for predicting or forecasting individuals with greater risk of developing diabetes mellitus, kit or device, and uses thereof.

Description

Modelo predictivo para predecir el desarrollo de diabetes mellitus tipo 2 usando miARNs  Predictive model to predict the development of type 2 diabetes mellitus using miRNAs
La presente invención pertenece al campo de la biomedicina, y más concretamente se refiere a marcadores para identificar los individuos con mayor riesgo de desarrollar diabetes mellitus tipo 2. The present invention belongs to the field of biomedicine, and more specifically it relates to markers to identify the individuals at greater risk of developing type 2 diabetes mellitus.
ESTADO DE LA TÉCNICA STATE OF THE TECHNIQUE
Según la organización mundial de salud (WHO), la diabetes ha alcanzado proporciones epidémicas en todo el mundo, ya que se estima que >420 millones de personas se ven afectadas por un trastorno metabólico de este tipo.  According to the World Health Organization (WHO), diabetes has reached epidemic proportions worldwide, since it is estimated that> 420 million people are affected by such a metabolic disorder.
El concepto del uso de los miARNs como biomarcadores de enfermedad fue introducido y demostrado por primera vez en cáncer, pero estudios previos han sugerido a los miARNs circulantes (en plasma o suero) como potenciales biomarcadores para el diagnostico de diabetes mellitus tipo 2 (DMT2). The concept of the use of miRNAs as disease biomarkers was first introduced and demonstrated in cancer, but previous studies have suggested circulating miRNAs (in plasma or serum) as potential biomarkers for the diagnosis of type 2 diabetes mellitus (DMT2) .
Así, Zampetaky, et al [ Circ Res (2010) 107 810-7] en un estudio longitudinal a 10 años, incluyendo 19 sujetos incidentes de DMT2 y 99 sujetos control, identificaron diferencias de expresión en los niveles circulantes de miR15a, miR29b, miR126, miR223 y miR28-3p, sugiriendo potenciales biomarcadores para el diagnostico y predicción de DMT2. Sin embargo, este estudio carece de valor predictivo, ya que se realizó en un número reducido de casos incidentes (n = 19) y el diagnóstico de DMT2 no se realizó de acuerdo con todos los criterios de diagnóstico de la Asociación Americana de la Diabetes (ADA). Thus, Zampetaky, et al [Circ Res (2010) 107 810-7] in a 10-year longitudinal study, including 19 incident subjects of DMT2 and 99 control subjects, identified differences in expression in the circulating levels of miR15a, miR29b, miR126 , miR223 and miR28-3p, suggesting potential biomarkers for the diagnosis and prediction of DMT2. However, this study lacks predictive value, since it was performed in a small number of incident cases (n = 19) and the diagnosis of DMT2 was not performed according to all the diagnostic criteria of the American Diabetes Association ( ADA).
Zhang et al [ Biochem Biophys Res Commun. (2015) 463(1-2)] mediante un estudio transversal en que se incluyeron 40 sujetos (20 diagnosticados de pre-diabetes y 20 diagnosticados de DMT2) observaron diferencias de expresión entre grupos en los niveles de miR126. En dicho trabajo se sugiere la posibilidad de la utilización de miR126 como biomarcador de diagnostico y predicción. Posteriores hallazgos de los mismos autores, tras un estudio de seguimiento a 2 años sugieren la utilización del miR126 para diferenciar entre sujetos DMT2 y normoglicémicos [ Biomed Res Int. (2013) 761617 ]. Inicialmente, Zhang et al se basa en un estudio transversal sin seguimiento al desarrollo de la enfermedad. Aunque en un posterior estudio longitudinal analizarán las diferencias de expresión del miR126 entre sujetos DMT2 y normoglicémicos, permanece latente la debilidad de una pequeña población lo que supone un bajo poder estadístico que permita sugerir un modelo de predicción. También en un estudio longitudinal a 3 meses de tratamiento con Metformina, en cual se incluyeron 48 sujetos DMT2 y 45 sujetos No-DMT2, Ortega et al [Diabetes Care. (2014) 37(5): 1375-83 ] estudiaron el perfil de expresión de miARNs. Ellos observaron en el grupo de sujetos diabéticos un marcado incremento en los niveles de expresión de miR140-5p, miR142- 3p, miR222, así como una disminución en los niveles de miR423-5p, miR125b, miR192, miR195, miR130b, miR532-5p, y miR126. En dicho estudio, observaron las diferencias de expresión de miARNs relacionados con DMT2, sin embargo, incluyen el tratamiento con metformina, que se debe tener en cuenta como co-variable en los estudios de predicción ya que podría modificar los perfiles de expresión de los miARNs y limita el uso de éstos como herramienta de predicción. Zhang et al [Biochem Biophys Res Commun. (2015) 463 (1-2)] through a cross-sectional study that included 40 subjects (20 diagnosed with pre-diabetes and 20 diagnosed with DMT2) observed differences in expression between groups in miR126 levels. This paper suggests the possibility of using miR126 as a diagnostic and prediction biomarker. Further findings by the same authors, after a 2-year follow-up study, suggest the use of miR126 to differentiate between DMT2 and normoglycemic subjects [Biomed Res Int. (2013) 761617]. Initially, Zhang et al is based on a cross-sectional study without monitoring the development of the disease. Although in a subsequent longitudinal study they will analyze the differences in miR126 expression between DMT2 and normoglycemic subjects, the weakness of a small population remains latent, which implies a low statistical power that suggests a prediction model. Also in a 3-month longitudinal study of Metformin treatment, which included 48 DMT2 subjects and 45 Non-DMT2 subjects, Ortega et al [Diabetes Care. (2014) 37 (5): 1375-83] studied the expression profile of miRNAs. They observed in the group of diabetic subjects a marked increase in the expression levels of miR140-5p, miR142-3p, miR222, as well as a decrease in the levels of miR423-5p, miR125b, miR192, miR195, miR130b, miR532-5p , and miR126. In this study, they observed the expression differences of miRNAs related to DMT2, however, they include metformin treatment, which should be taken into account as co-variable in the prediction studies since it could modify the expression profiles of the miRNAs and limits their use as a prediction tool.
Otros trabajos de Kong et al ( Acta Diabetol (2011) 48 61-9), mediante un estudio transversal demostraron las diferencias de expresión de 7 miARNs entre diabéticos, pre-diabéticos y sanos, 56 en total (18 nuevos diabéticos, 19 pre-diabéticos IFG ó IGT, y 19 sujetos sanos). Sin embargo, dicho estudio se limita a describir las diferencias de expresión de 7 miARNs ( miR9 , miR29a, miR30d, miR34a, miR124a, miR146a y miR375), entre diabéticos, pre-diabéticos y sanos, sin embargo, no se abordan modelos predictivos del desarrollo de DMT2. Other works by Kong et al (Acta Diabetol (2011) 48 61-9), through a cross-sectional study, demonstrated the differences in the expression of 7 miRNAs between diabetics, pre-diabetics and healthy, 56 in total (18 new diabetics, 19 pre IFG or IGT diabetics, and 19 healthy subjects). However, this study is limited to describing the differences in expression of 7 miRNAs (miR9, miR29a, miR30d, miR34a, miR124a, miR146a and miR375), between diabetics, pre-diabetic and healthy, however, predictive models of the DMT2 development.
Liu et al ( Int J Mol Sci (2014) 15 10567-77), sugiere las mismas diferencias de expresión del miR126 entre estos tres grupos de sujetos. En este estudio longitudinal a 6 meses resalta la intervención con dieta y ejercicio físico. Liu et al sugiere un modelo, que permite diferenciar entre diabéticos, pre-diabéticos y sujetos normoglucémicos, en el cual se incluye el miR126 añadido a variables convencionales de predicción de diabetes (glucosa en sangre, HbA1c, edad, genero e IMC). No obstante, dentro del modelo no se incluyen variables como la dieta y el ejercicio físico, los cuales fueron variables de estudio con un seguimiento a seis meses. La inclusión de estas variables podría modificar considerablemente la sensibilidad y especificidad del modelo propuesto. Liu et al (Int J Mol Sci (2014) 15 10567-77), suggests the same differences in miR126 expression between these three groups of subjects. In this 6-month longitudinal study, the intervention with diet and physical exercise stands out. Liu et al suggests a model that allows differentiating between diabetics, pre-diabetics and normoglycemic subjects, which includes the miR126 added to conventional diabetes prediction variables (blood glucose, HbA1c, age, gender and BMI). However, variables such as diet and physical exercise are not included in the model, which were study variables with a six-month follow-up. The inclusion of these variables could significantly modify the sensitivity and specificity of the proposed model.
Teniendo en cuenta lo anterior, en estudios previos el número de sujetos no es muy elevado, no pudiendo garantizar que el efecto biológico observado no es solo un fenómeno aislado y que se puede extrapolar a toda la población. Además, en general, el seguimiento de los pacientes ha sido limitado. Por tanto, son necesarios estudios de seguimiento a largo plazo en poblaciones más grandes con casos incidentes de DMT2 para identificar los miARNs que puedan utilizarse como biomarcadores para predecir o pronosticar el riesgo de desarrollo de DMT2, y desarrollar modelos predictivos de mayor sensibilidad y especificidad. DESCRIPCIÓN DE LAS FIGURAS Taking into account the above, in previous studies the number of subjects is not very high, not being able to guarantee that the observed biological effect is not only an isolated phenomenon and that the entire population can be extrapolated. In addition, in general, patient follow-up has been limited. Therefore, long-term follow-up studies are needed in larger populations with incident cases of DMT2 to identify miRNAs that can be used as biomarkers to predict or predict the risk of DMT2 development, and to develop predictive models of greater sensitivity and specificity. DESCRIPTION OF THE FIGURES
Figura 1. Se utilizó el análisis discriminante de mínimos cuadrados parciales ortogonales (OPLS-DA) para comparar los niveles circulantes de miARNs al inicio del estudio entre los casos incidentes de DMT2 (círculo gris) y sujetos sin DMT2 (círculo negro) durante el período de seguimiento. La calidad de los modelos obtenidos por OPLS-DA se evaluó mediante los parámetros de R2 y Q2. Los miARNs con puntaje VI P> 1 se consideraron importantes para diferenciar entre grupos (Tabla en la figura 1). Los datos se procesaron utilizando SIMCA-P + (versión 14.0.0.1359; Umetrics, Umea, Suecia). Figure 1. The orthogonal partial least squares discriminant analysis (OPLS-DA) was used to compare the circulating levels of miRNAs at the start of the study between incident cases of DMT2 (gray circle) and subjects without DMT2 (black circle) during the period of follow up. The quality of the models obtained by OPLS-DA was evaluated using the parameters of R 2 and Q 2 . The miRNAs with VI P> 1 score were considered important for differentiating between groups (Table in Figure 1). The data was processed using SIMCA-P + (version 14.0.0.1359; Umetrics, Umea, Sweden).
Figura 2. Niveles de miARNs circulantes al inicio del estudio de acuerdo con el año en el que los pacientes fueron diagnosticados con DMT2. Los datos representan la media ± error típico de la media y corresponden al análisis ANOVA de un factor. En gris, sujetos que desarrollaron DMT2 (Incidentes DMT2) y en blanco, sujetos que no desarrollaron DMT2 (no DMT2). La significancia estadística se evaluó mediante la prueba de U de Mann-Whitney y aquellos valores p < 0.05 se consideraron como significativos. Figure 2. Levels of circulating miRNAs at the start of the study according to the year in which patients were diagnosed with DMT2. The data represent the mean ± typical error of the mean and correspond to the ANOVA analysis of a factor. In gray, subjects who developed DMT2 (Incidents DMT2) and in white, subjects who did not develop DMT2 (not DMT2). Statistical significance was assessed using the Mann-Whitney U test and those p values <0.05 were considered significant.
Figura 3. Análisis ROC y clasificación por importancia promedio de las variables incluidas en el modelo ROC basado en miARNs+HbA1c. El panel A muestra la comparación entre las AUC de las curvas ROC de tres modelos: línea roja, modelo basado en miARNs+HbA1c; línea verde, modelo basado en parámetros clínicos; y línea azul, modelo basado en el FINDRISC. En el panel B se puede ver la clasificación de importancia promedio de las variables incluidas en la curva ROC. No-T2DM = 0; lncidente-T2DM = 1. El análisis fue llevado a cabo por Metaboanalyst 3.0. Figure 3. ROC analysis and classification by average importance of the variables included in the ROC model based on miRNAs + HbA1c. Panel A shows the comparison between the AUC of the ROC curves of three models: red line, model based on miRNAs + HbA1c; green line, model based on clinical parameters; and blue line, model based on FINDRISC. In panel B you can see the average importance classification of the variables included in the ROC curve. No-T2DM = 0; Incident-T2DM = 1. The analysis was carried out by Metaboanalyst 3.0.
Figura 4. Análisis de probabilidad libre de enfermedad a través de un modelo de regresión de COX basado en seis miARNs. Los datos representan niveles circulantes para cada miRNA por terciles, niveles bajos (T1), niveles medios (T2) y niveles altos (T3). El análisis se llevó a cabo utilizando SPPS (ahora PASW Statistic para Windows (versión 21.0)) (IBM, Chicago, Illinois) y ajustado por dieta, edad, sexo, IMC, TG, HDL y hemoglobina glicosilada (HbA1c). Figure 4. Disease-free probability analysis through a COX regression model based on six miRNAs. The data represent circulating levels for each miRNA per tertiles, low levels (T1), medium levels (T2) and high levels (T3). The analysis was carried out using SPPS (now PASW Statistic for Windows (version 21.0)) (IBM, Chicago, Illinois) and adjusted for diet, age, sex, BMI, TG, HDL and glycosylated hemoglobin (HbA1c).
Figura 5. Análisis de probabilidad libre de enfermedad a través de un modelo de regresión de COX multi-miARNs incluyendo miR-103, miR-28-3p, miR-29a, miR-9, miR-150 y miR-30a-5p. Los datos representan los niveles circulantes de los seis miARNs juntos; por lo tanto, los sujetos se clasificaron en tres categorías: la categoría C1 está compuesta por pacientes con niveles bajos en al menos tres de los cuatro miARNs regulados negativamente (miR-9, miR-28- 3p, miR-29a , miR-103) y altos niveles en al menos uno de los 2 miARNs regulados positivamente ( miR-150 y miR-30a-5p ) (n = 46); La categoría C3 está compuesta por pacientes con niveles altos en al menos tres de los cuatro miARNs regulados negativamente y niveles bajos en al menos uno de los dos miARNs regulados positivamente (n = 32); y finalmente, C2 está compuesto por pacientes con un perfil de desregulación de miARN intermedio (n = 356). El análisis se llevó a cabo mediante SPPS (ahora PASW Statistic para Windows (versión 21.0)) (IBM, Chicago, Illinois) y se ajustó por edad, sexo, IMC, dieta, HbA1c, circunferencia de la cintura, TG, c-HDL, IGI, HOMA -IR y DI. Figure 5. Disease-free probability analysis through a multi-miRNA regression model including miR-103, miR-28-3p, miR-29a, miR-9, miR-150 and miR-30a-5p. The data represents the circulating levels of the six miRNAs together; therefore, the subjects were classified into three categories: category C1 is composed of patients with low levels in at least three of the four negatively regulated miRNAs (miR-9, miR-28- 3p, miR-29a, miR-103 ) and high levels in at least one of the 2 positively regulated miRNAs (miR-150 and miR-30a-5p) (n = 46); Category C3 is composed of patients with high levels in at least three of the four negatively regulated miRNAs and levels low in at least one of the two positively regulated miRNAs (n = 32); and finally, C2 is composed of patients with an intermediate miRNA deregulation profile (n = 356). The analysis was carried out using SPPS (now PASW Statistic for Windows (version 21.0)) (IBM, Chicago, Illinois) and adjusted for age, sex, BMI, diet, HbA1c, waist circumference, TG, c-HDL , IGI, HOMA -IR and DI.
DESCRIPCIÓN DE LA INVENCIÓN DESCRIPTION OF THE INVENTION
Se realizaron análisis para explorar en detalle el potencial de 24 miARNs circulantes como biomarcadores predictores de la DMT2. Para ello, se hizo el seguimiento de 462 pacientes no diabéticos (n = 462) durante 60 meses. Tras el periodo de seguimiento, 107 sujetos desarrollaron DMT2 (lncidentes-DMT2), mientras que los 355 sujetos restante no desarrollaron DMT2 y se consideraron como grupo control (no-DMT2). El diagnóstico de DMT2 se llevó a cabo según los criterios sugeridos por la Sociedad Americana de Diabetes (ADA), teniendo como base un test oral de glucosa (Test de Matsuda) llevado a cabo a todos los sujetos anualmente. Adicionalmente, se analizaron los niveles circulantes de 24 miARNs en muestras de plasma de todos los sujetos incluidos en los dos grupos. Mediante análisis estadísticos de predicción se identificaron 9 miARNs con capacidad para diferenciar entre los dos grupos de estudio, así como 6 miRNAs con potencial para evaluar el riesgo de DMT2. Analyzes were performed to explore in detail the potential of 24 circulating miRNAs as predictive biomarkers of DMT2. For this, 462 non-diabetic patients (n = 462) were followed for 60 months. After the follow-up period, 107 subjects developed DMT2 (incidents-DMT2), while the remaining 355 subjects did not develop DMT2 and were considered as a control group (non-DMT2). The diagnosis of DMT2 was carried out according to the criteria suggested by the American Diabetes Society (ADA), based on an oral glucose test (Matsuda Test) carried out on all subjects annually. Additionally, circulating levels of 24 miRNAs were analyzed in plasma samples of all subjects included in the two groups. By means of statistical prediction analyzes, 9 miRNAs with the capacity to differentiate between the two study groups were identified, as well as 6 miRNAs with the potential to assess the risk of DMT2.
Por tanto, un primer aspecto de la invención se refiere al uso de miR-9 miR-28-3p; miR-29a; miR-103; miR-223; miR-126; miR-375; miR-30a-5p y miR-150, o cualquiera de sus combinaciones; para diferenciar entre los sujetos que desarrollan DMT2 y aquellos que no la desarrollan. Preferiblemente se emplean el perfil de todos los 9 miARNs junto con la hemoglobina glicosilada (HbA1c) simultáneamente (Figura 1 y 3). Therefore, a first aspect of the invention relates to the use of miR-9 miR-28-3p; miR-29a; miR-103; miR-223; miR-126; miR-375; miR-30a-5p and miR-150, or any combination thereof; to differentiate between subjects who develop DMT2 and those who do not develop it. Preferably, the profile of all 9 miRNAs is used together with glycosylated hemoglobin (HbA1c) simultaneously (Figure 1 and 3).
En la presente invención se demuestra como los niveles plasmáticos de miR-9 miR-28-3p; miR- 29a; miR-103 ; miR-223; miR-126; miR-375 son significativamente inferiores en pacientes que desarrollan DMT2, en comparación con aquellos pacientes que no la desarrollan. Además, los niveles de los miR-30a-5p y miR-150 son significativamente superiores en pacientes que desarrollan DMT2, en comparación con aquellos pacientes que no la desarrollan. In the present invention it is demonstrated how the plasma levels of miR-9 miR-28-3p; miR-29a; miR-103; miR-223; miR-126; miR-375 are significantly lower in patients who develop DMT2, compared to those patients who do not develop it. In addition, the levels of miR-30a-5p and miR-150 are significantly higher in patients who develop DMT2, compared to those patients who do not develop it.
Tras un análisis de regresión de COX se concluye que los pacientes con bajos niveles de 4 miARNs ( miR-103 , miR-28-3p, miR-29a y miR-9) y altos niveles de 2 miARNs ( miR-30a-5p y miR-150) tienen 11 ,27 veces mayor riesgo de desarrollar DMT2 que aquellos que con cumplen dicha condición. Por lo tanto, esto seis miARNs ( miR-103 , miR-28-3p, miR-29a , miR-9, miR- 30a-5p y miR-150) en conjunto tienen la capacidad de evaluar el riesgo de desarrollo de DMT2 (Figura 5). After a COX regression analysis it is concluded that patients with low levels of 4 miRNAs (miR-103, miR-28-3p, miR-29a and miR-9) and high levels of 2 miRNAs (miR-30a-5p and miR-150) have 11, 27 times greater risk of developing DMT2 than those who meet that condition. Therefore, this six miRNAs (miR-103, miR-28-3p, miR-29a, miR-9, miR- 30a-5p and miR-150) together have the ability to assess the risk of DMT2 development (Figure 5).
Preferiblemente, también se usan los miR-9; miR-28-3p; miR-29a; miR-103; miR-30a-5p y miR- 150 o cualquiera de sus combinaciones; para detectar individuos con mayor riesgo de desarrollar DMT2. Preferiblemente se emplean el perfil de todos los 6 miARNs simultáneamente. Preferably, miR-9s are also used; miR-28-3p; miR-29a; miR-103; miR-30a-5p and miR-150 or any combination thereof; to detect individuals with a higher risk of developing DMT2. Preferably, the profile of all 6 miRNAs are used simultaneously.
Aún más preferiblemente se emplean los siguientes miARNs: Even more preferably the following miRNAs are used:
hsa-miR103 hsa-miR103
hsa-miR223 hsa-miR223
hsa-miR29a hsa-miR29a
hsa-miR28-3p hsa-miR28-3p
hsa-miR150 hsa-miR150
hsa-miR30a-5p hsa-miR30a-5p
hsa-miR375 hsa-miR375
hsa-miR9 hsa-miR126 hsa-miR9 hsa-miR126
Cabe destacar que las herramientas más comúnmente utilizados en la práctica clínica para el diagnóstico de pre-diabetes y DMT2, son el test de screening FINDRISC, el test de la ADA, los criterios sugeridos por la Organización Mundial de la Salud y los criterios sugeridos por la ADA incluyendo la hemoglobina glicosilada (HbA1c). Sin embargo, esto métodos no son capaces de identificar con precisión a los individuos en riesgo de desarrollar DMT2; además se ha demostrado que la HbA1c tiene baja especificidad y sensibilidad. It should be noted that the most commonly used tools in clinical practice for the diagnosis of pre-diabetes and DMT2 are the FINDRISC screening test, the ADA test, the criteria suggested by the World Health Organization and the criteria suggested by ADA including glycosylated hemoglobin (HbA1c). However, these methods are not able to accurately identify individuals at risk of developing DMT2; It has also been shown that HbA1c has low specificity and sensitivity.
Los autores de la presente invención han identificado, por primera vez, una firma de nueve miARNs circulantes, que son poderosos biomarcadores predictivos en el desarrollo de DMT2; siendo una herramienta más sensible que las utilizadas actualmente en la práctica clínica para predecir el desarrollo de la enfermedad, tal y como se demuestra en nuestro estudio (Figura 3). Además, han identificado un perfil de miARNs circulantes los cuales añadidos a la HbA1c, tienen la capacidad de diferenciar entre individuos con mayor o menor riesgo de desarrollar DMT2 (Figura 3). The authors of the present invention have identified, for the first time, a signature of nine circulating miRNAs, which are powerful predictive biomarkers in the development of DMT2; being a more sensitive tool than those currently used in clinical practice to predict the development of the disease, as demonstrated in our study (Figure 3). In addition, they have identified a profile of circulating miRNAs which, added to HbA1c, have the ability to differentiate between individuals with greater or lesser risk of developing DMT2 (Figure 3).
En la presente invención los autores realizaron análisis de curvas ROC y desarrollaron un modelo predictivo que incluye 9 miARNs (miR-9, miR-28-3p; miR-29a; miR-103, miR-15a; miR- 223; miR-126; miR-145; miR-375) que junto con la HbA1c, tiene mayor valor predictivo que utilizando únicamente los niveles de HbA1c o únicamente el FINDRISC o Finnish Diabetes Risk Score (FINDRISC) (Lindstrom, J. Tuomilehto 2003. Diab Care, 26 (2003), pp. 725- 731 ). Las secuencias de los miARNs son las siguientes: In the present invention, the authors performed ROC curve analysis and developed a predictive model that includes 9 miRNAs (miR-9, miR-28-3p; miR-29a; miR-103, miR-15a; miR- 223; miR-126; miR-145; miR-375) which together with HbA1c, has a higher predictive value than using only HbA1c levels or only FINDRISC or Finnish Diabetes Risk Score (FINDRISC) (Lindstrom, J. Tuomilehto 2003. Diab Care, 26 (2003), pp 725-731). The sequences of the miRNAs are as follows:
SEQ ID NO: 1 SEQ ID NO: 1
hsa-m¡R103 : AGCAGCAUUGUACAGGGCUAUGA hsa-m¡R-103a-3p hsa-m¡R103: AGCAGCAUUGUACAGGGCUAUGA hsa-m¡R-103a-3p
SEQ ID NO: 2  SEQ ID NO: 2
hsa-m¡R223 : UGUCAGUUUGUCAAAUACCCCA hsa-m¡R-223-3p hsa-m¡R223: UGUCAGUUUGUCAAAUACCCCA hsa-m¡R-223-3p
SEQ ID NO: 3 SEQ ID NO: 3
hsa-miR29a : UAGCACCAUCUGAAAUCGGUU cgr-m¡R-29a-3p hsa-miR29a: UAGCACCAUCUGAAAUCGGUU cgr-m¡R-29a-3p
SEQ ID NO: 4  SEQ ID NO: 4
hsa-m¡R28-3p : CACUAGAUUGUGAGCUCCUGGA hsa-m¡R-28-3p hsa-m¡R28-3p: CACUAGAUUGUGAGCUCCUGGA hsa-m¡R-28-3p
SEQ ID NO: 5  SEQ ID NO: 5
hsa-m¡R150 : CUGGUACAGGCCUGGGGGACAG hsa-m¡R-150-3p hsa-m¡R150: CUGGUACAGGCCUGGGGGACAG hsa-m¡R-150-3p
SEQ ID NO: 6  SEQ ID NO: 6
hsa-miR30a-5p : UGUAAACAUCCUCGACUGGAAG hsa-m¡R-30a-5p hsa-miR30a-5p: UGUAAACAUCCUCGACUGGAAG hsa-m¡R-30a-5p
SEQ ID NO: 7  SEQ ID NO: 7
hsa-m¡R375 : UUUGUUCGUUCGGCUCGCGUGA hsa-m¡R-375 hsa-m¡R375: UUUGUUCGUUCGGCUCGCGUGA hsa-m¡R-375
SEQ ID NO: 8 SEQ ID NO: 8
hsa-miR9 : UCUUUGGUUAUCUAGCUGUAUGA dme-m¡R-9a-5p hsa-miR9: UCUUUGGUUAUCUAGCUGUAUGA dme-m¡R-9a-5p
SEQ ID NO: 9 SEQ ID NO: 9
hsa-m¡R126 : UCGUACCGUGAGUAAUAAUGCG mmu-m¡R-126a-3p hsa-m¡R126: UCGUACCGUGAGUAAUAAUGCG mmu-m¡R-126a-3p
MÉTODOS DE LA INVENCIÓN METHODS OF THE INVENTION
Otro aspecto de la invención se refiere a un método in vitro para identificar a los individuos susceptibles de desarrollar DMT2, que comprende medir los niveles circulantes de miR-9 miR- 28-3p; miR-29a; mi R- 103] miR-150 y miR-30a-5p en una muestra biológica aislada de dicho individuo, donde bajos niveles de los 4 primeros y altos niveles de los 2 últimos indican un mayor riesgo en el individuo de desarrollar DMT2. Another aspect of the invention relates to an in vitro method for identifying individuals susceptible to developing DMT2, which comprises measuring the circulating levels of miR-9 miR-28-3p; miR-29a; mi R-103] miR-150 and miR-30a-5p in a biological sample isolated from said individual, where low levels of the first 4 and high levels of the last 2 indicate an increased risk in the individual of developing DMT2.
Otro aspecto de la invención se refiere al modelo in vitro para el diagnóstico, predicción y/o pronóstico de la DMT2 en un individuo que comprende medir los niveles circulantes de los 9 miARNs: miR-9; miR-28-3p; miR-29a; miR-103; miR-15a; miR-223; miR-126; miR-145; miR- 375, en una muestra biológica aislada de dicho individuo, y además comprende, asignar a los individuos que presentan unos niveles inferiores a los descritos anteriormente en esta memoria, al grupo de los individuos con predisposición de desarrollar DMT2. Más preferiblemente comprende añadir a los niveles de miRNA, los niveles plasmáticos de la hemoglobina glicosilada (HbA1c). Another aspect of the invention relates to the in vitro model for the diagnosis, prediction and / or prognosis of DMT2 in an individual comprising measuring the circulating levels of the 9 miRNAs: miR-9; miR-28-3p; miR-29a; miR-103; miR-15a; miR-223; miR-126; miR-145; miR-375, in an isolated biological sample of said individual, and also includes assigning individuals who have levels lower than those described hereinbefore to the group of individuals with a predisposition to develop DMT2. More preferably it comprises adding to the miRNA levels, the plasma levels of glycosylated hemoglobin (HbA1c).
Aún más preferiblemente comprende, además, determinar el tipo de dieta del individuo (es decir adherencia a dietas saludables como la dieta mediterránea o dieta baja en grasa sugerida por la ADA). Even more preferably it also includes determining the type of diet of the individual (ie adherence to healthy diets such as the Mediterranean diet or low-fat diet suggested by the ADA).
Aún más preferiblemente comprende, además, determinar IMC. En esta memoria se entiende por“IMC” Indice de masa corporal. Aún más preferiblemente comprende, además, determinar el perímetro de cintura. Aún más preferiblemente comprende, además, determinar los niveles de triglicéridos. Aún más preferiblemente comprende, además, determinar los niveles de HDL (colesterol-HDL). En una realización preferida de este aspecto, el modelo in vitro implica aplicar la fórmula: Even more preferably, it also includes determining BMI. In this report, “BMI” means body mass index. Even more preferably, it also includes determining the waist circumference. Even more preferably it also includes determining triglyceride levels. Even more preferably it also includes determining the levels of HDL (cholesterol-HDL). In a preferred embodiment of this aspect, the in vitro model involves applying the formula:
SCORE = -12.897125 + hsam¡R103 * 0.001463 + hsam¡R223 * -0.009799 + hsam¡R29a * - 0.011630 + hsam¡R28-3p * -0.001136 + hsam¡R126 * -0.019230 + hsam¡R150 * 0.559287 + hsam¡R30a-5p * 0.033738 + hsam¡R375 * -0.014507 + hsam¡R9 * -0.072493 + Edad * 0.006753 + Género * 0.331970 + dieta * 0.208350 + IMC * 0.038656 + Perímetro de cintura * 0.014300 + Triglicéridos * 0.004072 + HDL * -0.002205 + HbA1c * 1.447587. SCORE = -12.897125 + hsam¡R103 * 0.001463 + hsam¡R223 * -0.009799 + hsam¡R29a * - 0.011630 + hsam¡R28-3p * -0.001136 + hsamR126 * -0.019230 + hsam¡R150 * 0.559287 + hsam¡R150 -5p * 0.033738 + hsam¡R375 * -0.014507 + hsam¡R9 * -0.072493 + Age * 0.006753 + Gender * 0.331970 + diet * 0.208350 + BMI * 0.038656 + Waist perimeter * 0.014300 + Triglycerides * 0.004072 + HDL * -0.002205 + HbA1c * 1.447587.
Donde el área bajo la curva (AUC) = 0.8342, el Intervalo de Confianza 95%: 0.790 - 0.878; la sensibilidad = 0.776, la especificidad=0.809 y la exactitud (Accuracy)= 80% Where the area under the curve (AUC) = 0.8342, the 95% Confidence Interval: 0.790 - 0.878; sensitivity = 0.776, specificity = 0.809 and accuracy (Accuracy) = 80%
donde cuando el SCORE es mayor o igual a 0,20, más preferiblemente mayor o igual a 0,21 , preferiblemente mayor o igual a 0,2, preferiblemente mayor o igual a 0,23 preferiblemente mayor o igual a 0,24, y mucho más preferiblemente mayor o igual a 0,2499 se clasifican en el grupo de individuos con mayor probabilidad de desarrollar DMT2. En una realización preferida de este aspecto de la invención, la muestra biológica es el plasma. where when the SCORE is greater than or equal to 0.20, more preferably greater than or equal to 0.21, preferably greater than or equal to 0.2, preferably greater than or equal to 0.23 preferably greater than or equal to 0.24, and much more preferably greater than or equal to 0.2499 are classified in the group of individuals most likely to develop DMT2. In a preferred embodiment of this aspect of the invention, the biological sample is plasma.
Aún mucho más preferiblemente la invención se refiere además del modelo in vitro para el diagnóstico, predicción y/o pronóstico de la DMT2 en un individuo medir los niveles circulantes de de miR-30a-5p y miR-150 en una muestra biológica aislada de dicho individuo y, además, comprende, asignar a los individuos que presentan unos niveles mayores a 1 ,2 veces, a los niveles del mismo marcador en una muestra de referencia, al grupo de los individuos, con mayor riesgo de desarrollar DMT2. Even more preferably, the invention also relates to the in vitro model for the diagnosis, prediction and / or prognosis of DMT2 in an individual measuring the circulating levels of miR-30a-5p and miR-150 in a biological sample isolated from said individual and, in addition, includes assigning individuals who have levels greater than 1, 2 times, to the levels of the same marker in a reference sample, to the group of individuals, with the highest risk of developing DMT2.
En una realización preferida de este aspecto de la invención, la muestra biológica es el plasma. In a preferred embodiment of this aspect of the invention, the biological sample is plasma.
Una "muestra biológica", como se define aquí, se refiere a muestras de fluidos corporales puede ser sangre, plasma, suero, orina, esputo, líquido cefalorraquídeo, leche, sudor, lágrimas, fluido peritoneal, sudor, lágrimas y heces o muestras de fluido ductal y pueden asimismo ser frescos, congelados o fijadas. A "biological sample", as defined herein, refers to samples of body fluids may be blood, plasma, serum, urine, sputum, cerebrospinal fluid, milk, sweat, tears, peritoneal fluid, sweat, tears and feces or samples of ductal fluid and can also be fresh, frozen or fixed.
Por tanto, en una realización preferida de este aspecto de la invención, la muestra biológica se selecciona de entre muestras biológicas incluyen diferentes tipos de muestras de tejido, así como muestras de fluidos biológicos, tales como sangre, plasma, suero, orina, esputo, líquido cefalorraquídeo, leche, sudor, lágrimas, fluido peritoneal, sudor, lágrimas y heces, o cualquiera de sus combinaciones. Preferiblemente, dichas muestras son muestras biológicas de sangre, suero o plasma. Preferiblemente, la muestra biológica aislada es el plasma de dicho individuo. Therefore, in a preferred embodiment of this aspect of the invention, the biological sample is selected from among biological samples including different types of tissue samples, as well as biological fluid samples, such as blood, plasma, serum, urine, sputum, cerebrospinal fluid, milk, sweat, tears, peritoneal fluid, sweat, tears and feces, or any combination thereof. Preferably, said samples are biological samples of blood, serum or plasma. Preferably, the isolated biological sample is the plasma of said individual.
En otra realización preferida de este aspecto de la invención, los niveles de los miARNs se pueden obtener mediante perfiles de expresión de microarrays, PCR, PCR de transcriptasa inversa, PCR de tiempo real de transcriptasa inversa, PCR cuantitativa en tiempo real, PCR de punto final, PCR multiplex de punto final, coid PCR, ice coid PCR, espectrometría de masas, hibridación in situ (ISH), hibridación multiplex in situ o secuenciación de ácidos nucleicos. In another preferred embodiment of this aspect of the invention, miRNA levels can be obtained by microarray expression profiles, PCR, reverse transcriptase PCR, real time reverse transcriptase PCR, quantitative real time PCR, point PCR final, endpoint multiplex PCR, coid PCR, ice coid PCR, mass spectrometry, in situ hybridization (ISH), in situ multiplex hybridization or nucleic acid sequencing.
Preferiblemente los niveles de los miARNs se puede obtener por medio de: Preferably the levels of miRNAs can be obtained by:
(i) un método de pefilado genético, tal como un microarray; y/o  (i) a method of genetic peeling, such as a microarray; me
(ii) un método que comprende PCR, tal como la PCR en tiempo real; y/o  (ii) a method comprising PCR, such as real-time PCR; me
(iii) transferencia Northern.  (iii) Northern transfer.
Más preferiblemente los niveles de los miARNs se obtienen mediante PCR de tiempo real de transcriptasa inversa (RT-qPCR). Otras técnicas podrías ser, pero sin limitarnos, RT combinado con LAMP, o bien alguna técnica nueva como LASH (ligase-assisted sandwich hybridization (LASH). More preferably, miRNA levels are obtained by real-time reverse transcriptase PCR (RT-qPCR). Other techniques could be, but not limited to, combined RT with LAMP, or some new technique such as LASH (ligase-assisted sandwich hybridization (LASH).
En una realización preferida de este aspecto de la invención el individuo del que se obtiene la muestra biológica, y en el que al momento de tomar la muestra, no está siendo tratado por DMT2. In a preferred embodiment of this aspect of the invention the individual from whom the biological sample is obtained, and in which at the time of taking the sample, it is not being treated by DMT2.
En otra realización preferida de este aspecto de la invención la expresión de miARN está normalizada. In another preferred embodiment of this aspect of the invention the expression of miRNA is normalized.
Otro aspecto de la invención se refiere a un método para clasificar un sujeto humano en uno de dos grupos, en el que el grupo 1 comprende los sujetos que pueden identificarse mediante cualquiera de los métodos descritos anteriormente y en el que el grupo 2 representa los sujetos restantes. Another aspect of the invention relates to a method for classifying a human subject into one of two groups, in which group 1 comprises the subjects that can be identified by any of the methods described above and in which group 2 represents the subjects remaining.
Otro aspecto de la invención se refiere a una composición farmacéutica que comprende un agente terapéutico adecuado para tratar a un sujeto humano del grupo 1 que se puede identificar mediante cualquiera de los métodos descritos anteriormente. Another aspect of the invention relates to a pharmaceutical composition comprising a therapeutic agent suitable for treating a human subject of group 1 that can be identified by any of the methods described above.
KIT O DISPOSITIVO DE LA INVENCIÓN KIT OR DEVICE OF THE INVENTION
Otro aspecto de la invención se refiere a un kit o dispositivo, de ahora en adelante kit o dispositivo de la invención, que comprende al menos un oligonucleótido capaz de hibridar con (miR-9, SEQ ID: dme-miR-9a-5p; m¡R-28-3p, SEQ ID: hsa-miR-28-3p; miR-29a, SEQ ID: oar- miR-29a; miR-103, SEQ ID: hsa-miR-103a-3p; miR-223, SEQ ID: hsa-miR-223-3p; miR-126, SEQ ID: mmu-miR-126a-3p; miR-375, SEQ ID: hsa-miR-375; miR-30a-5p, SEQ ID: hsa-miR- 30a-5p y miR-150, SEQ ID: hsa-miR-150-3p ), y medios para detectar dicha hibridación. Another aspect of the invention relates to a kit or device, hereafter kit or device of the invention, comprising at least one oligonucleotide capable of hybridizing with (miR-9, SEQ ID: dme-miR-9a-5p; m¡R-28-3p, SEQ ID: hsa-miR-28-3p; miR-29a, SEQ ID: oar-miR-29a; miR-103, SEQ ID: hsa-miR-103a-3p; miR-223 , SEQ ID: hsa-miR-223-3p; miR-126, SEQ ID: mmu-miR-126a-3p; miR-375, SEQ ID: hsa-miR-375; miR-30a-5p, SEQ ID: hsa -miR-30a-5p and miR-150, SEQ ID: hsa-miR-150-3p), and means for detecting said hybridization.
El uso del kit o dispositivo para identificar los individuos con mayor riesgo de desarrollar DMT2, mediante cualquiera de los métodos descritos anteriormente. The use of the kit or device to identify the individuals most at risk of developing DMT2, using any of the methods described above.
Una secuencia totalmente complementaria (complementaria al 100%) de DNA, RNA o de cadenas de ácidos nucléicos modificados, (i.e. una sonda), capaz de hibridar con la secuencia de los miARNs miR-9, m¡R-28-3p, miR-29a, miR-103, miR-150, y miR-30a-5p. AUTOMATIZACIÓN DEL MÉTODO DE LA INVENCIÓN IMPLEMENTÁNDOLO EN UN PROGRAMA DE ORDENADOR A completely complementary (100% complementary) sequence of DNA, RNA or modified nucleic acid chains, (ie a probe), capable of hybridizing with the miRNA sequence miR-9, m¡R-28-3p, miR -29a, miR-103, miR-150, and miR-30a-5p. AUTOMATION OF THE METHOD OF THE INVENTION IMPLEMENTING IT IN A COMPUTER PROGRAM
Otro aspecto de la invención se refiere a un programa de ordenador que comprende instrucciones de programa para hacer que un ordenador lleve a la práctica el procedimiento de acuerdo con el método de la invención. Another aspect of the invention relates to a computer program comprising program instructions to make a computer carry out the method according to the method of the invention.
En particular, la invención abarca programas de ordenador dispuestos sobre o dentro de una portadora. La portadora puede ser cualquier entidad o dispositivo capaz de soportar el programa. Cuando el programa va incorporado en una señal que puede ser transportada directamente por un cable u otro dispositivo o medio, la portadora puede estar constituida por dicho cable u otro dispositivo o medio. Como variante, la portadora podría ser un circuito integrado en el que va incluido el programa, estando el circuito integrado adaptado para ejecutar, o para ser utilizado en la ejecución de, los procesos correspondientes. In particular, the invention encompasses computer programs arranged on or within a carrier. The carrier can be any entity or device capable of supporting the program. When the program is incorporated into a signal that can be directly transported by a cable or other device or medium, the carrier may be constituted by said cable or other device or means. As a variant, the carrier could be an integrated circuit in which the program is included, the integrated circuit being adapted to execute, or to be used in the execution of, the corresponding processes.
Por ejemplo, los programas podrían estar incorporados en un medio de almacenamiento, como una memoria ROM, una memoria CD ROM o una memoria ROM de semiconductor, una memoria USB, o un soporte de grabación magnética, por ejemplo, un disco flexible o un disco duro. Alternativamente, los programas podrían estar soportados en una señal portadora transmisible. Por ejemplo, podría tratarse de una señal eléctrica u óptica que podría transportarse a través de cable eléctrico u óptico, por radio o por cualesquiera otros medios. For example, the programs could be incorporated into a storage medium, such as a ROM, a CD ROM or a semiconductor ROM, a USB memory, or a magnetic recording medium, for example, a floppy disk or a disk hard. Alternatively, the programs could be supported on a transmissible carrier signal. For example, it could be an electrical or optical signal that could be transported through an electrical or optical cable, by radio or by any other means.
La invención se extiende también a programas de ordenador adaptados para que cualquier medio de procesamiento pueda llevar a la práctica los métodos de la invención. Tales programas pueden tener la forma de código fuente, código objeto, una fuente intermedia de código y código objeto, por ejemplo, como en forma parcialmente compilada, o en cualquier otra forma adecuada para uso en la puesta en práctica de los procesos según la invención. Los programas de ordenador también abarcan aplicaciones en la nube basadas en dicho procedimiento. The invention also extends to computer programs adapted so that any processing means can implement the methods of the invention. Such programs may have the form of source code, object code, an intermediate source of code and object code, for example, as in partially compiled form, or in any other form suitable for use in the implementation of the processes according to the invention . Computer programs also cover cloud applications based on that procedure.
Otro aspecto de la invención se refiere a un medio de almacenamiento legible por un ordenador que comprende instrucciones de programa capaces de hacer que un ordenador lleve a cabo los pasos del método de la invención. Another aspect of the invention relates to a computer-readable storage medium comprising program instructions capable of causing a computer to carry out the steps of the method of the invention.
Otro aspecto de la invención se refiere a una señal transmisible que comprende instrucciones de programa capaces de hacer que un ordenador lleve a cabo los pasos del método de la invención. Another aspect of the invention relates to a transmissible signal comprising program instructions capable of causing a computer to carry out the steps of the method of the invention.
A lo largo de la descripción y las reivindicaciones la palabra "comprende" y sus variantes no pretenden excluir otras características técnicas, aditivos, componentes o pasos. Para los expertos en la materia, otros objetos, ventajas y características de la invención se desprenderán en parte de la descripción y en parte de la práctica de la invención. Los siguientes ejemplos y dibujos se proporcionan a modo de ilustración, y no se pretende que sean limitativos de la presente invención. Throughout the description and the claims the word "comprises" and its variants are not intended to exclude other technical characteristics, additives, components or steps. For the Those skilled in the art, other objects, advantages and features of the invention will emerge in part from the description and in part from the practice of the invention. The following examples and drawings are provided by way of illustration, and are not intended to be limiting of the present invention.
EJEMPLOS DE LA INVENCIÓN MATERIALES Y MÉTODOS EXAMPLES OF THE INVENTION MATERIALS AND METHODS
MATERIALES Y MÉTODOS Sujetos de estudio MATERIALS AND METHODS Subjects of study
Este trabajo se realizó en el marco del estudio CORDIOPREV. Los métodos y las características iniciales han sido reportados por Delgado-Lista., et al y registrado en Clinicaltrials.gov (NTC00924937). En resumen, el estudio CORDIOPREV, es un estudio de intervención dietética controlado, prospectivo, aleatorizado, simple, ciego, desarrollado en 1002 Pacientes con CHD (alto riesgo cardiovascular), con edades entre 20 y 75 años, que tuvieron su último evento coronario dentro de los seis meses antes de la inclusión en el estudio, sin enfermedades graves y esperanza de vida de menos de cinco años. Además del tratamiento convencional para CHD, los sujetos fueron aleatorizados en dos modelos dietéticos diferentes (dieta mediterránea y dieta baja en grasa y rica en carbohidratos). This work was carried out within the framework of the CORDIOPREV study. The initial methods and characteristics have been reported by Delgado-Lista., Et al and registered at Clinicaltrials.gov (NTC00924937). In summary, the CORDIOPREV study is a controlled, prospective, randomized, simple, blind dietary intervention study developed in 1002 Patients with CHD (high cardiovascular risk), aged between 20 and 75 years, who had their last coronary event within Six months before inclusion in the study, without serious illnesses and life expectancy of less than five years. In addition to conventional treatment for CHD, the subjects were randomized into two different dietary models (Mediterranean diet and low-fat and high-carbohydrate diet).
La fase de intervención aún está en progreso y tendrá una mediana de seguimiento de siete años. The intervention phase is still in progress and will have a median follow-up of seven years.
Los pacientes fueron reclutados desde noviembre de 2009 hasta febrero de 2012, principalmente en el Hospital Universitario Reina Sofía (Córdoba, España), también fueron admitidos pacientes de otros centros hospitalarios de las provincias de Córdoba y Jaén. Se obtuvo el consentimiento por escrito de todos los sujetos antes de la contratación; el protocolo de estudio y todas las enmiendas fueron aprobados por el Comité de Ética del Hospital Reina Sofía, el cual sigue las directrices de la Declaración de Helsinki y buenas prácticas clínicas. Patients were recruited from November 2009 to February 2012, mainly at the Reina Sofía University Hospital (Córdoba, Spain), patients from other hospitals in the provinces of Córdoba and Jaén were also admitted. Written consent was obtained from all subjects before hiring; The study protocol and all amendments were approved by the Ethics Committee of the Reina Sofía Hospital, which follows the guidelines of the Helsinki Declaration and good clinical practices.
El estudio se realizó con todos los 462 pacientes no diabéticos (n = 462) al inicio del estudio CORDIOPREV. Después de un período de seguimiento de 60 meses, 43 sujetos fueron diagnosticados con DMT2 durante el primer año, 24 en el segundo año, 11 en el tercer año, 19 en el cuarto año y 10 en el quinto año, para un total de 107 sujetos que desarrollaron DMT2 (lncidentes-DMT2). Los sujetos fueron diagnosticados basados en un test anual de tolerancia a la glucosa (OGTT) y siguiendo los criterios establecidos por la Asociación Americana de Diabetes (ADA). The study was conducted with all 462 non-diabetic patients (n = 462) at the start of the CORDIOPREV study. After a 60-month follow-up period, 43 subjects were diagnosed with DMT2 during the first year, 24 in the second year, 11 in the third year, 19 in the fourth year and 10 in the fifth year, for a total of 107 subjects who developed DMT2 (incidents-DMT2). Subjects were diagnosed based on an annual tolerance test for glucose (OGTT) and following the criteria established by the American Diabetes Association (ADA).
Los 355 sujetos restantes no desarrollaron T2DMT2 durante el período de estudio y se usaron como grupo de control (no-DMT2). Las características iniciales de los sujetos en el estudio se muestran en la Tabla 1. The remaining 355 subjects did not develop T2DMT2 during the study period and were used as a control group (non-DMT2). The initial characteristics of the subjects in the study are shown in Table 1.
Mediciones bioquímicas de parámetros metabólicos Biochemical measurements of metabolic parameters
La sangre venosa de los participantes se recogió en tubos que contenían EDTA después de 12 h de ayuno nocturno. Las variables lipídicas se evaluaron con el autoanalizador modular DDPPII Hitachi (Roche, Basilea, Suiza) utilizando reactivos específicos (Boehringer-Mannheim, Mannheim, Alemania). Las mediciones de los niveles de colesterol total (TC) y triglicéridos (TG) fueron realizados por métodos enzimáticos colorimétricos, se midió la lipoproteína-colesterol de alta densidad (HDL-c) por ensayo colorimétrico y la concentración de lipoproteína de baja densidad (LDL-C) se calculó mediante la ecuación de Friedewald, usando la siguiente fórmula: Participants' venous blood was collected in tubes containing EDTA after 12 hours of overnight fasting. Lipid variables were evaluated with the Hitachi DDPPII modular autoanalyzer (Roche, Basel, Switzerland) using specific reagents (Boehringer-Mannheim, Mannheim, Germany). Total cholesterol (CT) and triglyceride (TG) levels were measured by colorimetric enzymatic methods, high density lipoprotein-cholesterol (HDL-c) was measured by colorimetric assay and low density lipoprotein (LDL) concentration -C) was calculated using Friedewald's equation, using the following formula:
LDLc = CT- (HDL + TG / 5). LDLc = CT- (HDL + TG / 5).
Las mediciones de glucosa se realizaron utilizando el método de hexoquinasa. La proteína reactiva hs-C (hs-CRP) se determinó mediante ELISA de alta sensibilidad (BioCheck, Inc., Foster City, CA, EE. UU.). Las concentraciones plasmáticas de insulina se midieron por inmunoensayo enzimático por micropartículas (Abbott Diagnostics, Matsudo-shi, Japón). Las concentraciones de ácido graso no esterificado se midieron por ensayo enzimático colorimétrico (Roche Diagnostics, Penzberg, Alemania). Las concentraciones de ApoA-1 y ApoB fueron determinadas por inmunoturbidimetría. Glucose measurements were made using the hexokinase method. The hs-C reactive protein (hs-CRP) was determined by high sensitivity ELISA (BioCheck, Inc., Foster City, CA, USA). Plasma insulin concentrations were measured by enzymatic microparticle immunoassay (Abbott Diagnostics, Matsudo-shi, Japan). The concentrations of non-esterified fatty acid were measured by colorimetric enzymatic assay (Roche Diagnostics, Penzberg, Germany). ApoA-1 and ApoB concentrations were determined by immunoturbidimetry.
Estimación de IR, secreción de insulina, índices de función de células beta y FINDRISC. Estimation of IR, insulin secretion, beta cell function indexes and FINDRISC.
Antes de comenzar la prueba, los pacientes habían ayunado (de alimentos/drogas) durante 12 horas y se les preguntó sobre la abstención de fumar durante el período de ayuno y de la ingesta de alcohol durante los 7 días precedentes. También se les pidió que evitaran la actividad física extenuante el día anterior a la prueba.. A las 8:00 a.m., los pacientes ingresaron en el laboratorio para realizar la prueba de tolerancia a la glucosa (OGTT) (75 g de monohidrato de dextrosa en 250 mi de agua, NUTER. TEC GLUCOSA 50) y se realizó muestreo a los 0, 30, 60 y 120 min para establecer los niveles de glucosa e insulina en plasma. El índice de sensibilidad a la insulina Matsuda (ISI) se calculó a partir de la OGTT utilizando el siguiente fórmula: Before starting the test, patients had fasted (food / drugs) for 12 hours and were asked about abstaining from smoking during the fasting period and alcohol intake during the preceding 7 days. They were also asked to avoid strenuous physical activity the day before the test. At 8:00 am, patients entered the laboratory to perform the glucose tolerance test (OGTT) (75 g dextrose monohydrate in 250 ml of water, NUTER. TEC GLUCOSA 50) and sampling was carried out at 0, 30, 60 and 120 min to establish plasma glucose and insulin levels. The Matsuda insulin sensitivity index (ISI) was calculated from the OGTT using the following formula:
ISI = 10.000 ÷ V ([insulina plasmática en ayunas X glucosa plasmática en ayunas] X [glucosa media en OGTT X insulina media en OGTT]). ISI = 10,000 ÷ V ([fasting plasma insulin X fasting plasma glucose] X [mean glucose in OGTT X average insulin in OGTT]).
HOMA-IR se calculó como previamente ha sido descrito por Song, Y., et al. La secreción de insulina fue medida por el índice insulinogénico (IGI): HOMA-IR was calculated as previously described by Song, Y., et al. Insulin secretion was measured by the insulingenic index (IGI):
IGI = [30 min insulina-insulina en ayunas (pmol / 1)] / [30 min glucosa en ayunas (mmol / 1)]. IGI = [30 min fasting insulin-insulin (pmol / 1)] / [30 min fasting glucose (mmol / 1)].
La función de las células beta se estimó calculando el índice de disposición (DI) de la siguiente manera: The function of beta cells was estimated by calculating the readiness index (DI) as follows:
DI = ISI x [AUC30 min insulina / AUC30 min glucosa], DI = ISI x [AUC30 min insulin / AUC30 min glucose],
Donde AUC30 min es el área debajo de la curva entre la línea de base y 30 min de la OGTT para insulina (pmol / I) y glucosa (mmol / I), respectivamente, calculadas por el método trapezoidal. Los índices utilizados para determinar IR de tejido específico fueron la resistencia a la insulina hepática índice (HIRI) y el índice de sensibilidad muscular a la insulina (MISI), que se calcularon como se describió en el trabajo previo de nuestro grupo y siguiendo los métodos descritos por Matsuda y DeFronzo para HIRI y Abdul-Ghani y colaboradores para MISI. El índice FINDRISC se calculó siguiendo las indicaciones publicadas por Lindstróm, et al, en el año 2003. Where AUC30 min is the area below the curve between the baseline and 30 min of the OGTT for insulin (pmol / I) and glucose (mmol / I), respectively, calculated by the trapezoidal method. The indices used to determine specific tissue IR were the hepatic insulin resistance index (HIRI) and the insulin muscle sensitivity index (MISI), which were calculated as described in the previous work of our group and following the methods described by Matsuda and DeFronzo for HIRI and Abdul-Ghani and collaborators for MISI. The FINDRISC index was calculated following the indications published by Lindstróm, et al, in 2003.
El estudio de expresión de miARNs se llevó a cabo en 24 miARNs, que, basándose en un búsqueda bibliográfica, fueron seleccionados de acuerdo a su asociación con la sensibilidad a la insulina, secreción de insulina, inflamación y crecimiento y proliferación de células beta (Tabla 4). Los niveles de los miARNs circulantes se determinaron en muestras de ARN obtenido a partir de muestras de plasma y siguiendo el protocolo del miRNeasy Mini Kit (Qiagen, Hilden, Alemania). Así, se mezclaron 200 pL de plasma EDTA- con 1 mL de Qiazol, se incubaron durante 5 min a temperatura ambiente y posteriormente se mezcló con 200 pL de cloroformo. A continuación se añadieron 2 pg de ARN MS2 (Roche, Mannheim, Alemania) antes del paso de cloroformo. Seguidamente se separaron la fase orgánica y acuosa por centrifugación a 12,000 g durante 15 minutos, a 4°C. The miRNA expression study was carried out in 24 miRNAs, which, based on a literature search, were selected according to their association with insulin sensitivity, insulin secretion, inflammation and growth and proliferation of beta cells (Table 4). The levels of circulating miRNAs were determined in RNA samples obtained from plasma samples and following the protocol of the miRNeasy Mini Kit (Qiagen, Hilden, Germany). Thus, 200 pL of EDTA-plasma was mixed with 1 mL of Qiazol, incubated for 5 min at room temperature and subsequently mixed with 200 pL of chloroform. Then 2 pg of MS2 RNA (Roche, Mannheim, Germany) was added before the chloroform step. The organic and aqueous phase were then separated by centrifugation at 12,000 g for 15 minutes, at 4 ° C.
La fase acuosa se recogió y el ARN se precipitó mediante la adición de etanol al 100%. La mezcla se aplicó a una columna giratoria miRNeasy Mini y se centrifugó a 8,000 g por 2 min. A continuación, se añadieron 700 mI_ de tampón RWT a la columna de centrifugación RNeasy MinElute a 8,000 g por 2 min. Luego se lavó de nuevo con 500 pL de tampón RPE y 500 pL de etanol al 80%. The aqueous phase was collected and the RNA was precipitated by the addition of 100% ethanol. The mixture was applied to a miRNeasy Mini rotating column and centrifuged at 8,000 g for 2 min. TO Next, 700 ml of RWT buffer was added to the RNeasy MinElute centrifuge column at 8,000 g for 2 min. It was then washed again with 500 pL of RPE buffer and 500 pL of 80% ethanol.
El ARN se eluyó en 14 pL de agua libre de RNasa. La pureza y concentración del ARN fueron evaluadas por espectrofotometría usando NanoDrop ND-2000 (ThermoFisher, Waltham, MA). La retrotranscripción de ARN se llevó a cabo utilizando el TaqMan Kit de transcripción inversa (Life Technologies, Carlsbad, CA, EE. UU.). La mezcla RT contiene 2 pL ARN y 3 pL RT del grupo de cebadores personalizados con un volumen final de 7,5 pL. El conjunto de cebadores RT fue seleccionado de primers específicos de nuestro conjunto de miARNs objetivo en la base de datos (https://www.thermofisher.com/es/en/home/life-science/pcr/real-time-pcr/real- time-pcrassavs/mirna-ncrna-taqman-assays.html). The RNA was eluted in 14 pL of RNase-free water. The purity and concentration of the RNA were evaluated by spectrophotometry using NanoDrop ND-2000 (ThermoFisher, Waltham, MA). RNA retrotranscription was carried out using the TaqMan Reverse Transcription Kit (Life Technologies, Carlsbad, CA, USA). The RT mix contains 2 pL RNA and 3 pL RT from the group of custom primers with a final volume of 7.5 pL. The RT primer set was selected from specific primers of our set of target miRNAs in the database (https://www.thermofisher.com/en/en/home/life-science/pcr/real-time-pcr/ real-time-pcrassavs / mirna-ncrna-taqman-assays.html).
Las placas se incubaron en el termociclador iQ5 (Bio-Rad Laboratories, Inc. , Hercules, CA, EE. UU.) a 16°C durante 30 minutos, a continuación 42°C durente 30 minutos a , y finalmente a 85°C durante 5 min; en este paso, el cDNA se almacenó a -20 °C por un tiempo máximo de una semana. Luego, preparamos una mezcla que contiene 10 mI de cebadores PreAmp personalizados, con grupo específico para nuestro conjunto de miARNs objetivo, y 7.5 pL de mezcla RT y 20 pL de TaqMan PreAmp Master Mix (Life Technologies, Carlsbad, CA, EE. UU.) Hasta un volumen final de 40 pL. The plates were incubated in the iQ5 thermal cycler (Bio-Rad Laboratories, Inc., Hercules, CA, USA) at 16 ° C for 30 minutes, then 42 ° C for 30 minutes at, and finally at 85 ° C for 5 min; In this step, the cDNA was stored at -20 ° C for a maximum time of one week. Then, we prepare a mixture containing 10 ml of customized PreAmp primers, with a specific group for our set of target miRNAs, and 7.5 pL of RT mix and 20 pL of TaqMan PreAmp Master Mix (Life Technologies, Carlsbad, CA, USA). ) Up to a final volume of 40 pL.
A continuación, la mezcla se incubó en el Thermocycler iQ5 usando los siguientes pasos: desnaturalización a 95°C durante 10 min; a continuación 55°C durante 2 min y 72°C durante 2 min; seguido de 20 ciclos de amplificación (15 segundos a 95°C y 4 minutos a 60 ° C por ciclo) y finalmente se incuba 99.9 ° C para 10 minutos. Los productos preamplificados se diluyeron luego con agua libre de RNasa en una proporción de 1 :40 y se usa para las reacciones de RT- PCR en tiempo real. Next, the mixture was incubated in the Thermocycler iQ5 using the following steps: denaturation at 95 ° C for 10 min; then 55 ° C for 2 min and 72 ° C for 2 min; followed by 20 cycles of amplification (15 seconds at 95 ° C and 4 minutes at 60 ° C per cycle) and finally incubated 99.9 ° C for 10 minutes. The preamplified products were then diluted with RNase-free water in a ratio of 1: 40 and used for real-time RT-PCR reactions.
Medimos los niveles de miARNs circulantes con la plataforma OpenArray® (QuantStudio 12 k Flex) (Life Technologies, Carlsbad, CA, EUA) basada en PCR en tiempo real, siguiendo las instrucciones del fabricante. Se utilizó la herramienta bioinformática NormFinder (MOMA- Departamento de Medicina Molecular, Hospital Universitario de Aarhus, Dinamarca) para seleccionar los ensayos de miARN que eran más estables a la normalización de datos. We measure the levels of circulating miRNAs with the OpenArray® platform (QuantStudio 12k Flex) (Life Technologies, Carlsbad, CA, USA) based on real-time PCR, following the manufacturer's instructions. The NormFinder bioinformatics tool (MOMA- Department of Molecular Medicine, Aarhus University Hospital, Denmark) was used to select the miRNA trials that were most stable to data normalization.
A partir de los 17 miARNs incluidos en nuestro estudio, se seleccionaron miR-143 y miR-144 como aquellos con valores CT más estables y utilizados como referencia (utilizando el método Bestkeeper) para calcular la expresión relativa de los 15 miARNs restantes. Los datos de expresión relativa fueron analizados utilizando OpenArray® Real-Time qPCR Analysis Software (Life Technologies, Carlsbad, CA, EE. UU.). From the 17 miRNAs included in our study, miR-143 and miR-144 were selected as those with more stable CT values and used as reference (using the method Bestkeeper) to calculate the relative expression of the remaining 15 miRNAs. Relative expression data was analyzed using OpenArray® Real-Time qPCR Analysis Software (Life Technologies, Carlsbad, CA, USA).
Análisis discriminante de mínimos cuadrados parciales ortogonales (OPLS-DA). Discriminant analysis of orthogonal partial least squares (OPLS-DA).
Se utilizó el análisis discriminante de mínimos cuadrados parciales ortogonales (OPLS-DA) para comparar los niveles de miARNs, con el fin de analizar las diferencias entre los pacientes incidentes-DMT2 y sin DMT2 durante el seguimiento. La calidad de los modelos obtenidos por OPLS-DA se evaluó mediante el estudio de los parámetros R2 y Q2. A continuación, seleccionamos aquellos miARNs con mayor poder discriminatorio entre los grupos a partir de los resultados obtenidos de Proyección de importancia de variables (VI P) obtenidos con el modelo OPLS-DA. Los miARNs con un VIP> 1 se consideraron importantes para diferenciar entre grupos. The orthogonal partial least squares discriminant analysis (OPLS-DA) was used to compare the levels of miRNAs, in order to analyze the differences between incident patients-DMT2 and without DMT2 during follow-up. The quality of the models obtained by OPLS-DA was evaluated by studying the parameters R 2 and Q 2 . Next, we select those miRNAs with the highest discriminatory power among the groups based on the results obtained from Projection of importance of variables (VI P) obtained with the OPLS-DA model. The miRNAs with a VIP> 1 were considered important to differentiate between groups.
Análisis estadístico Statistic analysis
Los datos con distribución normal se evaluaron mediante la prueba de Kolmogorov-Smirnov, con el límite de distribución normal en p> 0,05. Para los datos que no se distribuyeron normalmente, usamos la prueba U de Mann-Whitney. Utilizamos el análisis de regresión de COX para probar el posible valor predictivo de los miRNA estudiados. Los valores de los niveles de cada miRNA se categorizaron por terciles: nivel bajo (T1), Nivel medio (T2) y nivel alto (T3). El Hazard ratio (HR) en el análisis de cada miRNA estudiado fue analizado comparando T1 vs T2 y T1 vs T3. Seis miARNs con HR T1 vs T3 ³ 2,5 se seleccionaron para el análisis de regresión de COX multi-miARNs. Por lo tanto, los sujetos se clasificaron en tres categorías: la categoría C1 ahora está compuesta por pacientes con niveles bajos en al menos tres de los cuatro miRNA regulados negativamente ( miR-9 , miR-28-3p, miR-29a , miR-103) y niveles elevados en al menos uno de los 2 miARNs regulados positivamente ( miR-150 y miR- 30a-5p) (n = 46); La categoría C3 está compuesta por pacientes con niveles altos en al menos tres de los cuatro miARNs regulados negativamente y niveles bajos en al menos uno de los dos miARNs regulados postivamente (n = 32); y finalmente, C2 está compuesto por pacientes con un perfil de desregulación de miARN intermedio (n = 356). Esta clasificación se llamó 6miARNs-variable y se incluyó en el análisis de regresión de COX multi-miARNs. Se comparó la relación de riesgo (HR) en el análisis entre C1 versus C2 y C1 frente a C3. La regresión lineal y los análisis de regresión de COX se ajustaron por edad, sexo, dieta, hemoglobina glicosilada (HbA1c), IMC, triglicéridos, c-HDL y circunferencia de la cintura. Los valores de p £ 0.05 se consideraron estadísticamente significativos. Utilizamos el análisis de la característica operativa del receptor (ROC) para estimar el área bajo la curva (AUC), la precisión, la especificidad y la sensibilidad de las variables para la diferenciación entre incidentes-DMT2 y pacientes que no desarrollaron DMT2. Los modelos fueron corregidos por aquellas covariables que fueron permitidas evitando la sobre estimación de información, el conjunto de covariables incluyó: dieta, edad, sexo, IMC, c-HDL, TG, HbA1c y circunferencia de la cintura. Para la validación interna del modelo, el grado de exceso de optimismo se estimó mediante el remuestreo inicial del conjunto original (1000 muestras aleatorizadas). Data with normal distribution were evaluated by the Kolmogorov-Smirnov test, with the normal distribution limit at p> 0.05. For data that was not normally distributed, we used the Mann-Whitney U test. We use COX regression analysis to test the possible predictive value of the miRNA studied. The values of the levels of each miRNA were categorized by tertiles: low level (T1), medium level (T2) and high level (T3). The Hazard ratio (HR) in the analysis of each miRNA studied was analyzed by comparing T1 vs T2 and T1 vs T3. Six miRNAs with HR T1 vs T3 ³ 2.5 were selected for regression analysis of multi-miRNA COXs. Therefore, the subjects were classified into three categories: category C1 is now composed of patients with low levels in at least three of the four negatively regulated miRNAs (miR-9, miR-28-3p, miR-29a, miR- 103) and elevated levels in at least one of the 2 positively regulated miRNAs (miR-150 and miR-30a-5p) (n = 46); Category C3 is composed of patients with high levels in at least three of the four negatively regulated miRNAs and low levels in at least one of the two positively regulated miRNAs (n = 32); and finally, C2 is composed of patients with an intermediate miRNA deregulation profile (n = 356). This classification was called 6miRNAs-variable and was included in the regression analysis of COX multi-miRNAs. The risk ratio (HR) was compared in the analysis between C1 versus C2 and C1 versus C3. Linear regression and COX regression analyzes were adjusted for age, sex, diet, glycosylated hemoglobin (HbA1c), BMI, triglycerides, c-HDL and waist circumference. P values 0.05 were considered statistically significant. We use feature analysis Operative function (ROC) to estimate the area under the curve (AUC), the accuracy, specificity and sensitivity of the variables for differentiation between DMT2 incidents and patients who did not develop DMT2. The models were corrected by those covariates that were allowed avoiding over-estimation of information, the set of covariates included: diet, age, sex, BMI, c-HDL, TG, HbA1c and waist circumference. For the internal validation of the model, the degree of excess optimism was estimated by initial resampling of the original set (1000 randomized samples).
Todos los análisis estadísticos se llevaron a cabo utilizando SPSS (ahora PASW Statistic para Windows (versión 21.0)) (IBM, Chicago, Illinois, EE. UU.). Además, utilizamos Metaboanalyst 3.0 para clasificar las variables incluidas en el modelo ROC según la importancia promedio de las variables. La normalización de datos se realizó utilizando el método de escalado automático, basado en la media centrada y dividido por la desviación estándar de cada variable. La prueba de DeLong se realizó para comparar los modelos ROC como se describe en DeLong et al. para curvas ROC emparejadas usando el software R. All statistical analyzes were carried out using SPSS (now PASW Statistic for Windows (version 21.0)) (IBM, Chicago, Illinois, USA). In addition, we use Metaboanalyst 3.0 to classify the variables included in the ROC model according to the average importance of the variables. Data normalization was performed using the automatic scaling method, based on the centered mean and divided by the standard deviation of each variable. The DeLong test was performed to compare the ROC models as described in DeLong et al. for ROC curves paired using the R software.
Figure imgf000017_0001
Figure imgf000017_0001
Valores expresados como media ± error estándar. IMC, índice de masa corporal; c-HDL, lipoproteína de alta densidad; c-LDL, lipoproteína de baja densidad; TG, triglicéridos; Apo A1 , Apolipoproteína A1 ; Apo B, apolipoproteína B; hs-CRP, proteína C-reactiva de alta sensibilidad; HbA1 c, hemoglobina glicosilada; HIRI, índice de resistencia a la insulina hepática; MISI, índice de sensibilidad a la insulina muscular; ISI, índice de sensibilidad a la insulina; IGI, índice insulinogénico; DI, índice de disposición; HOMA-IR, insulina de evaluación del modelo de homeostasis resistencia; * p <0.05. Las variables se calcularon utilizando el análisis ANOVA ONE-WAY a través de SPSS (ahora PASW Statistic para Windows (versión 21.0)) (IBM. Illinois) y la significación estadística se evaluó mediante la prueba de .Mann-Whitney U. Tabla 2; Relación entre los niveles circuíanles de míARlís y parámetros relacionados con DMT2 Values expressed as mean ± standard error. BMI, body mass index; c-HDL, high density lipoprotein; c-LDL, low density lipoprotein; TG, triglycerides; Apo A1, Apolipoprotein A1; Apo B, apolipoprotein B; hs-CRP, high sensitivity C-reactive protein; HbA1 c, glycosylated hemoglobin; HIRI, liver insulin resistance index; MISI, muscle insulin sensitivity index; ISI, insulin sensitivity index; IGI, insulingenic index; DI, readiness index; HOMA-IR, insulin evaluation of resistance homeostasis model; * p <0.05. Variables were calculated using ANOVA ONE-WAY analysis through SPSS (now PASW Statistic for Windows (version 21.0)) (IBM. Illinois) and statistical significance was assessed using the .Mann-Whitney U test. Table 2; Relationship between the circular levels of miARlís and parameters related to DMT2
Figure imgf000018_0001
Figure imgf000018_0001
HbA1c, hemoglobina glicosilada; GLU, glucosa; HOMA-B, evaluación del modelo de homeostasis - función de célula beta; HOMA-IR, modelo de homeostasis evaluación de resistencia a la insulina; MIRI, índice de resistencia a la insulina muscular; IGI, índice insulinogénico; ISI, índice de sensibilidad a la insulina; DI, disposición índice; HIRI, índice de resistencia a la insulina hepática. * p <0.05. Análisis de correlación realizado por un modelo de regresión lineal ajustado por edad, índice de masa corporal y de género (IMC), triglicéridos (TG) y lipoproteínas de alta densidad (c-HDL), usando SPSS (ahora PASW Statistic para Windows (versión 21.0)) (IBM, Chicago, Illinois). HbA1c, glycosylated hemoglobin; GLU, glucose; HOMA-B, homeostasis model evaluation - beta cell function; HOMA-IR, homeostasis model insulin resistance evaluation; MIRI, muscle insulin resistance index; IGI, insulingenic index; ISI, insulin sensitivity index; DI, index provision; HIRI, liver insulin resistance index. * p <0.05. Correlation analysis performed by a linear regression model adjusted for age, body and gender mass index (BMI), triglycerides (TG) and high density lipoproteins (c-HDL), using SPSS (now PASW Statistic for Windows (version 21.0)) (IBM, Chicago, Illinois).
Tabla 3. Indice de riesgo (HR) observado tras un análisis de regresión de COX para cada miARN incluido en el modelo de curva ROC. * miARNs seleccionados para el análisis de regresión múltiple COX con HR T1vsT3> 2.5 miRNA HR TlvsTl HR TlvsT2 HR TlvsT3 Table 3. Risk index (HR) observed after a COX regression analysis for each miRNA included in the ROC curve model. * miRNAs selected for COX multiple regression analysis with HR T1vsT3> 2.5 miRNA HR TlvsTl HR TlvsT2 HR TlvsT3
(95% CI) (95% CI) (95% CI)  (95% CI) (95% CI) (95% CI)
Figure imgf000018_0002
amplified in
Figure imgf000018_0002
amplified in
miRNA plasma samples Fluid, tissue or cell type Model References in our study miRNA plasma samples Fluid, tissue or cell type Model References in our study
miR-103  miR-103
and 107  and 107
miR-126 miR-143 miR-126 miR-143
miR-144 miR-145 miR-150  miR-144 miR-145 miR-150
miR-15a miR-15a
miR-182  miR-182
miR-192 miR-21 miR-223  miR-192 miR-21 miR-223
miR-28-3p miR-28-3p
miR-29a miR-29a
miR-30a5-p miR-30a5-p
miR-30d miR-320
Figure imgf000019_0001
Figure imgf000020_0001
miR-30d miR-320
Figure imgf000019_0001
Figure imgf000020_0001

Claims

REIVINDICACIONES
1.- Un método in vitro para identificar los individuos con mayor probabilidad de desarrollar DMT2, que comprende: a) medir los niveles circulantes de miR-9 miR-28-3p; miR-29a; miR-103, miR-15a; miR-223; miR-126; miR-145; miR-375; miR-30a-5p y miR-150 en una muestra biológica aislada de dicho individuo. b) determinar score de dieta, índice de masa corporal, perímetro de cintura, triglicéridos, colesterol HDL y hemoglobina glicosilada. aplicar la fórmula: 1.- An in vitro method to identify the individuals most likely to develop DMT2, which comprises: a) measuring the circulating levels of miR-9 miR-28-3p; miR-29a; miR-103, miR-15a; miR-223; miR-126; miR-145; miR-375; miR-30a-5p and miR-150 in an isolated biological sample of said individual. b) determine diet score, body mass index, waist circumference, triglycerides, HDL cholesterol and glycosylated hemoglobin. apply the formula:
SCORE = -12.897125 + hsam¡R103 * 0.001463 + hsam¡R223 * -0.009799 + hsam¡R29a * - 0.011630 + hsam¡R28-3p * -0.001136 + hsam¡R126 * -0.019230 + hsam¡R150 * 0.559287 + hsamiR30a-5p * 0.033738 + hsam¡R375 * -0.014507 + hsam¡R9 * -0.072493 + Edad * 0.006753 + Género * 0.331970 + dieta * 0.208350 + IMC * 0.038656 + Perímetro de cintura * 0.014300 + Triglicéridos * 0.004072 + HDL * -0.002205 + HbA1c * 1.447587. donde cuando el SCORE es mayor o igual a 0,20, más preferiblemente mayor o igual a 0,21 , preferiblemente mayor o igual a 0,2, preferiblemente mayor o igual a 0,23 preferiblemente mayor o igual a 0,24, y mucho más preferiblemente mayor o igual a 0,2499 se clasifican en el grupo de individuos con mayor probabilidad de desarrollar DMT2.  SCORE = -12.897125 + hsam¡R103 * 0.001463 + hsam¡R223 * -0.009799 + hsam¡R29a * - 0.011630 + hsam¡R28-3p * -0.001136 + hsam ¡R126 * -0.019230 + hsam¡R150 * 0.559307-hsami * 0.033738 + hsam¡R375 * -0.014507 + hsam¡R9 * -0.072493 + Age * 0.006753 + Gender * 0.331970 + diet * 0.208350 + BMI * 0.038656 + Waist perimeter * 0.014300 + Triglycerides * 0.004072 + HDL * -0.002205 + HbA1c * HbA1c * 1.447587. where when the SCORE is greater than or equal to 0.20, more preferably greater than or equal to 0.21, preferably greater than or equal to 0.2, preferably greater than or equal to 0.23 preferably greater than or equal to 0.24, and much more preferably greater than or equal to 0.2499 are classified in the group of individuals most likely to develop DMT2.
2 - El método según la reivindicación anterior donde la muestra biológica aislada es el plasma de dicho individuo. 2 - The method according to the preceding claim wherein the isolated biological sample is the plasma of said individual.
3.- El método según cualquiera de las reivindicaciones 1-2 donde los niveles de los miARNs se pueden obtener mediante perfiles de expresión de microarrays, PCR, PCR de transcriptasa inversa, PCR de tiempo real de transcriptasa inversa, PCR cuantitativa en tiempo real, PCR de punto final, PCR multiplex de punto final, coid PCR, ice coid PCR, espectrometría de masas, hibridación in situ (ISH), hibridación multiplex in situ o secuenciación de ácidos nucleicos. 3. The method according to any of claims 1-2 wherein the levels of miRNAs can be obtained by microarray expression profiles, PCR, reverse transcriptase PCR, real time reverse transcriptase PCR, quantitative real time PCR, Endpoint PCR, endpoint multiplex PCR, coid PCR, ice coid PCR, mass spectrometry, in situ hybridization (ISH), in situ multiplex hybridization or nucleic acid sequencing.
4 - El método según cualquiera de las reivindicaciones 1-3, donde los niveles de los miARNs se puede obtener por medio de: 4 - The method according to any of claims 1-3, wherein the levels of the miRNAs can be obtained by means of:
(i) un método de pefilado genético, tal como un microarray; y/o  (i) a method of genetic peeling, such as a microarray; me
(ii) un método que comprende PCR, tal como la PCR en tiempo real; y/o (iii) transferencia Northern. (ii) a method comprising PCR, such as real-time PCR; me (iii) Northern transfer.
5.- El método según cualquiera de las reivindicaciones 1-4, donde los niveles de los miARNs se obtienen mediante PCR de tiempo real de transcriptasa inversa (RT-qPCR). 5. The method according to any of claims 1-4, wherein the levels of miRNAs are obtained by real time reverse transcriptase PCR (RT-qPCR).
6.- El método según cualquiera de las reivindicaciones 1-5, donde el individuo del que se obtiene la muestra biológica, y en el que al momento de tomar la muestra, no está siendo tratado por DMT2. 6. The method according to any of claims 1-5, wherein the individual from whom the biological sample is obtained, and in which at the time of taking the sample, it is not being treated by DMT2.
7.- El método según cualquiera de las reivindicaciones 1-6, donde la expresión de miARN está normalizada. 7. The method according to any of claims 1-6, wherein miRNA expression is normalized.
8.- Un método para clasificar un sujeto humano en uno de dos grupos, en el que el grupo 1 comprende los sujetos que pueden identificarse por medio del método de acuerdo con cualquiera de las reivindicaciones 1-7, y en el que el grupo 2 representa los sujetos restantes. 8. A method for classifying a human subject into one of two groups, in which group 1 comprises the subjects that can be identified by means of the method according to any of claims 1-7, and in which group 2 Represents the remaining subjects.
9.- Una composición farmacéutica que comprende un agente terapéutico adecuado para tratar a un sujeto humano del grupo 1 que se puede identificar mediante el método de la reivindicación 8. 9. A pharmaceutical composition comprising a therapeutic agent suitable for treating a human subject of group 1 that can be identified by the method of claim 8.
10.- Un kit o dispositivo, de ahora en adelante kit o dispositivo de la invención, que comprende al menos un oligonucleótido capaz de hibridar con ( miR-9 , SEQ ID: dme-miR-9a-5p; miR-28-3p, SEQ ID: hsa-miR-28-3p; miR-29a, SEQ ID: oar-miR-29a; miR-103, SEQ ID: hsa-miR-103a-3p; miR-223, SEQ ID: hsa-miR-223-3p; miR-126, SEQ ID: mmu-miR-126a-3p; miR-375, SEQ ID: hsa-miR-375; miR-30a-5p, SEQ ID: hsa-miR-30a-5p y miR-150, SEQ ID: hsa-miR-150-3p ), y medios para detectar dicha hibridación. 10. A kit or device, hereafter kit or device of the invention, comprising at least one oligonucleotide capable of hybridizing with (miR-9, SEQ ID: dme-miR-9a-5p; miR-28-3p , SEQ ID: hsa-miR-28-3p; miR-29a, SEQ ID: oar-miR-29a; miR-103, SEQ ID: hsa-miR-103a-3p; miR-223, SEQ ID: hsa-miR -223-3p; miR-126, SEQ ID: mmu-miR-126a-3p; miR-375, SEQ ID: hsa-miR-375; miR-30a-5p, SEQ ID: hsa-miR-30a-5p and miR-150, SEQ ID: hsa-miR-150-3p), and means for detecting said hybridization.
11.- El uso del kit o dispositivo según la reivindicación 10, para identificar los individuos con mayor riesgo de desarrollar DMT2. 11. The use of the kit or device according to claim 10, to identify the individuals most at risk of developing DMT2.
12.- Una secuencia totalmente complementaria (complementaria al 100%) de DNA, RNA o de cadenas de ácidos nucléicos modificados, (i.e. una sonda), capaz de hibridar con la secuencia de los miARNs miR-9, m¡R-28-3p, miR-29a, miR-103, miR-150, y miR-30a-5p. 12.- A completely complementary sequence (100% complementary) of DNA, RNA or modified nucleic acid chains, (ie a probe), capable of hybridizing with the miRNA sequence miR-9, m¡R-28- 3p, miR-29a, miR-103, miR-150, and miR-30a-5p.
PCT/ES2019/070374 2018-06-04 2019-06-03 Predictive model for predicting the development of type 2 diabetes mellitus using mirnas WO2019234274A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
ES201830540 2018-06-04
ESP201830540 2018-06-04

Publications (1)

Publication Number Publication Date
WO2019234274A1 true WO2019234274A1 (en) 2019-12-12

Family

ID=68770736

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/ES2019/070374 WO2019234274A1 (en) 2018-06-04 2019-06-03 Predictive model for predicting the development of type 2 diabetes mellitus using mirnas

Country Status (1)

Country Link
WO (1) WO2019234274A1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11553759B2 (en) 2018-06-08 2023-01-17 Decathlon Method for producing a shoe and shoe that can be obtained by said method

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
CAMARGO A ET AL.: "Circulating miRNAs as predictive biomarkers of type 2 diabetes mellitus development: from the CORDIOPREV study", EUROPEAN JOURNAL OF CLINICAL INVESTIGATION 20180501, vol. 48, no. 1, 5 January 2018 (2018-01-05), pages 205, XP055665476, ISSN: 1365-2362 *
CHIEN HUNG- YU ET AL.: "Circulating microRNA as a diagnostic marker in populations with type 2 diabetes mellitus and diabetic complications", JOURNAL OF THE CHINESE MEDICAL ASSOCIATION: JCMA NETHERLANDS, vol. 78, no. 4, 31 March 2015 (2015-03-31), pages 204 - 211, ISSN: 1728-7731 *
JIMENEZ-LUCENA ROSA ET AL.: "Circulating miRNAs as Predictive Biomarkers of Type 2 Diabetes Mellitus Development in Coronary Heart Disease Patients from the CORDIOPREV Study", MOLECULAR THERAPY. NUCLEIC ACIDS UNITED STATES, vol. 12, 9 July 2018 (2018-07-09), pages 146 - 157, XP055665479, ISSN: 2162-2531, Retrieved from the Internet <URL:https://www.sciencedirect.com/science/article/pii/S2162253118300994?via%3Dihub> [retrieved on 20190805] *
ZHU HONGMEI ET AL.: "Identification of microRNA biomarkers in type 2 diabetes: a meta-analysis of controlled profiling studies", DIABETOLOGIA, 20150213, vol. 58, no. 5, 13 February 2015 (2015-02-13), BERLIN, DE, pages 900 - 911, XP035479131, ISSN: 0012-186X, DOI: 10.1007/s00125-015-3510-2 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11553759B2 (en) 2018-06-08 2023-01-17 Decathlon Method for producing a shoe and shoe that can be obtained by said method

Similar Documents

Publication Publication Date Title
Huo et al. Brain and blood metabolome for Alzheimer's dementia: findings from a targeted metabolomics analysis
Villard et al. Diagnostic value of cell-free circulating microRNAs for obesity and type 2 diabetes: a meta-analysis
Hooten et al. The accelerated aging phenotype: the role of race and social determinants of health on aging
Ashar et al. Association of mitochondrial DNA levels with frailty and all-cause mortality
TWI816927B (en) Media, methods, and systems for calculating ancestry-specific genetic risk scores
Hromadnikova et al. Diabetes mellitus and cardiovascular risk assessment in mothers with a history of gestational diabetes mellitus based on postpartal expression profile of micrornas associated with diabetes mellitus and cardiovascular and cerebrovascular diseases
Hernández-Alonso et al. Chronic pistachio intake modulates circulating microRNAs related to glucose metabolism and insulin resistance in prediabetic subjects
Iida et al. A functional variant in ZNF512B is associated with susceptibility to amyotrophic lateral sclerosis in Japanese
EP3600472B1 (en) Analysis and prediction of traumatic brain injury and concusion symptoms
US11149313B2 (en) Methods of using miRNAs from bodily fluids for detection and differentiation of neurodegenerative diseases
Han et al. Analysis of genetic mutations in Chinese patients with systemic primary carnitine deficiency
Qiu et al. Identification of differentially expressed genes in omental adipose tissues of obese patients by suppression subtractive hybridization
Divoux et al. MicroRNA‐196 regulates HOX gene expression in human gluteal adipose tissue
Jiang et al. Hyperhomocysteinemia and related genetic polymorphisms correlate with ulcerative colitis in Southeast China
Sakai et al. Development of novel diagnostic system for pancreatic cancer, including early stages, measuring mRNA of whole blood cells
Jørsboe et al. An LDLR missense variant poses high risk of familial hypercholesterolemia in 30% of Greenlanders and offers potential of early cardiovascular disease intervention
WO2019234274A1 (en) Predictive model for predicting the development of type 2 diabetes mellitus using mirnas
Liu et al. Physiological adjustments and circulating MicroRNA reprogramming are involved in early acclimatization to high altitude in Chinese Han Males
Cao et al. A genome-wide methylation study of body fat traits in the Norfolk Island isolate
Friedenberg et al. Use of RNA-seq to identify cardiac genes and gene pathways differentially expressed between dogs with and without dilated cardiomyopathy
US20210404006A1 (en) Salivary Extracellular Rna Biomarkers for Gingivitis
Yu et al. Transcriptomic changes in human renal proximal tubular cells revealed under hypoxic conditions by RNA sequencing
Norwood et al. Transcriptomic and bioinformatic analysis of Clcn7-dependent autosomal dominant osteopetrosis type 2. Preclinical and clinical implications
Tuzlukaya et al. PLASMA miR-103 AND miR-107 LEVELS IN OBESE CHILDREN: THE SIGN OF INSULIN RESISTANCE.
Borai et al. Association of MTHFR C677T and ABCA1 G656A polymorphisms with obesity among Egyptian children

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19815765

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19815765

Country of ref document: EP

Kind code of ref document: A1