CITO-PERIOPREDICTOR (Machine-translation by Google Translate, not legally binding)

专利摘要:
The present invention relates to the field of molecular methods of diagnosis and prognosis for pathologies. Specifically, it refers to a diagnostic/prognostic procedure based on multivariate predictive models generated from the levels of different combinations of proinflammatory cytokines (IL1alpha, IL1beta and IL17A) and anti-inflammatory (IFNgamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL3) in tissue or oral fluids for the diagnosis of periodontal diseases, their progression and their response to different therapeutic interventions. In a particular example, these combinations of cytokines showed an excellent ability to discriminate (> = 95%) the presence of chronic periodontitis with respect to a gingival/periodontal health situation, as well as precision percentages> = 92% in most of the combinations. (Machine-translation by Google Translate, not legally binding)
公开号:ES2698157A1
申请号:ES201730994
申请日:2017-07-31
公开日:2019-01-31
发明作者:Carmona Inmaculada Tomás；Castro José Carlos Balsa
申请人:Universidade de Santiago de Compostela；
IPC主号:

专利说明:

[0001] CITO-PERIOPREDICTOR
[0002] TECHNICAL SECTOR OF THE INVENTION
[0003] The present invention relates to the field of molecular methods of diagnosis and prognosis for pathologies. In particular, the invention relates to the use of multivariate predictive modeling techniques for the diagnosis and / or prognosis of periodontal diseases.
[0004] STATE OF THE ART
[0005] Periodontal diseases are among the most common conditions that affect humans (Dentino A et al, 2013). Periodontal diseases include gingivitis (which affects practically the entire population at some time in their life) (Offenbacher S et al, 2008) and several types of periodontitis (chronic and aggressive) (Henderson B et al, 2009) . In 2010, it was estimated that periodontitis represented the sixth most prevalent disease worldwide, affecting 743 million people and with a standardized incidence by age of 701 cases per 100,000 inhabitants / year (Kassebaum N et al, 2014). In the recent "11th European Workshop on Periodontology", experts confirmed that the prevalence of periodontitis in Europe continued to be very high, affecting more than 50% of adults, and in its severe forms, 11% of this population group (Tonetti M et al, 2015).
[0006] Periodontal diseases are not a silent problem. The patients suffering from these pathologies show a worse perception of their oral health status and a worse quality of life in relation to the subjects who do not suffer from these pathologies, representing the practice of a periodontal treatment a significant improvement of the perceptions on oral health and quality of life of these patients (Al-Harthi L et al, 2013; Shanbhag S et al, 2012). On the other hand, at present, periodontal diseases have acquired great importance in the field of general health, since there is solid scientific evidence that supports the bidirectional relationship of these oral pathologies with the pathogenesis of several systemic conditions, such as diabetes (Chapple ILC et al, 2013), coronary heart disease (Tonetti M et al, 2013), rheumatoid arthritis (by Pablo P et al, 2009), respiratory diseases (Bansal M et al, 2013) and Alzheimer's disease (Abbayya K et al, 2015).
[0007] In the dental field, certain clinical measures are used to evaluate the severity of periodontal diseases and the response to different therapies, and these include: the levels of bacterial plaque, the degree of gingival inflammation, the depth of the periodontal pockets, the level of clinical insertion and radiographic bone loss (Korte DL et al, 2016). However, this traditional clinical criteria associated with a high degree of subjectivity and imprecision are not useful for determining the activity of periodontal diseases or the future risk of bone loss (Zhang L et al, 2009; Giannobile WV et al, 2009). As a consequence, one of the main pending challenges in the field of periodontics is the development of diagnostic / prognostic tests based on biomarkers with proven sensitivity and specificity to predict the susceptibility to periodontal diseases from its initial stages, and to evaluate the activity of the pathology and efficacy of the different applied therapies (Zhang L et al, 2009; Buduneli N et al, 2011). The 'Biomarkers Definitions Working Group' defines "biomarker" as a characteristic that can be quantified and evaluated objectively, and that represents an indicator of a biological process, a pathological process or a pharmacological response to a therapeutic intervention (Biomarkers Definitions Working Group, 2001).
[0008] The main clinical characteristic of gingivitis is the presence of gingival bleeding, and periodontitis is the destruction of periodontal tissues, both characteristics are the result of acute (present in gingivitis) and chronic inflammation (present in periodontitis) caused by an immune response of the host to the presence of a polymicrobial dysbiosis (Yucel-Lindberg T & Bage T, 2013, Darveau RP, 2010). This immune response is characterized by: 1) the infiltration of immune cells (neutrophils, monocytes / macrophages and lymphocytes) into the gingival tissues; and 2) the production of high concentrations of mediators, including cytokines, chemokines, arachidonic acid metabolites, and proteolytic enzymes (Yucel-Lindberg T & Bage T, 2013; Preshaw PM & Taylor JJ, 2011). The nature and severity of this host immune response are fundamental factors in the susceptibility and progression of periodontal diseases (Jaedicke KM et al, 2016).
[0009] For the study of biomarkers associated with periodontal diseases, two oral fluids are used: the gingival crevicular fluid (FCG) and saliva. The analysis of these two fluids provides a better representation of the local pathological changes occurring in the oral cavity associated with the Periodontal diseases with respect to the findings obtained in serum samples (Jaedicke KM et al, 2016). FCG is a serum exudate originating in the gingival plexus of the blood vessels of the gingival connective tissue, near the epithelial lining of the dentogingival space (gingival sulcus). The levels of this fluid in the gingival sulcus increase significantly with the severity of the periodontal inflammation and its consistency transforms into an inflammatory exudate as it crosses the inflamed gingival tissues, collecting bacterial and host molecules (Griffiths GS, 2003; Champagne CME et al, 2003). Therefore, the FCG is a sample that reflects with high precision the set of inflammatory mediators and bone resorption associated with the pathogenesis of periodontal diseases and its collection represents a non-invasive method (Champagne CME et al, 2003). For its part, saliva is considered "the mirror of the body", comes from the exocrine secretions of the salivary glands and contains FCG, bacterial and host components, as well as food remains. Saliva is an easily collected sample that does not require complex clinical skills and can be obtained in high volumes (Jaedicke KM et al, 2016).
[0010]
[0011] With the development of immunoanalysis techniques (hereinafter, ELISA), interleukin (hereafter, IL) 1beta was the first cytokine to be quantified in the gingival tissues of patients with chronic periodontitis (Honig J et al, 1989). Since then, numerous authors have investigated the levels of cytokines in the FCG and in saliva, confirming that there is a distinctive profile of some of these inflammatory mediators in patients with periodontal diseases (Buduneli N et al, 2011; Boronat-Catalá M et al, 2014; Stadler AF et al, 2016) and even, the presence of a distinctive profile between different degrees of severity of these pathologies (Stadler AF et al, 2016). As an example, Stadler et al (Stadler et al, 2016) in their study of meta-analysis revealed the existence of significant differences between chronic periodontitis and periodontal health in the pro-inflammatory cytokines, IL1beta, IL6 and IL17; which presented higher levels in the disease with respect to health; and in the anti-inflammatory cytokines, interferon gamma (hereinafter, IFNgamma) and IL4, which presented higher levels of health with respect to the disease. There are also studies in the literature in which an increase in the expression of the genes of certain cytokines, such as those of IL1, IL6 or IL8, has been detected in patients with periodontitis (Zorina OA et al, 2016; Kobayashi T et al, 2016; Braosi AP et al 2012).
[0012]
[0013] On the other hand, smoking is considered a risk factor in the development of periodontal diseases (Genco RJ & Borgnakke WS, 2013; Johannesen A et al, 2014), which is why some authors have observed the influence of this habit on the levels of inflammatory mediators in the oral samples of periodontal patients (Tymkiw KD et al, 2011; Toker H et al, 2012). Specifically, in these studies, the authors demonstrated the possible immunosuppressive effect of tobacco in periodontal diseases, since smoking periodontal patients had lower concentrations of certain pro-inflammatory cytokines, such as IL1 alpha, IL1beta, IL6 and IL12 (p40), with compared to those detected in non-smoking periodontal patients (Tymkiw KD et al, 2011, Toker H et al, 2012).
[0014]
[0015] In relation to the possible prognostic value of inflammatory biomarkers in the progression of periodontal diseases, in some studies it has been observed that the practice of a periodontal treatment causes a significant alteration of the levels of the cytokines present in the FCG and in saliva of patients with periodontal diseases (Stadler AF et al, 2016; Jaedicke KM et al, 2016). However, it is important to point out that there are few cytokines in which this alteration of their levels has been observed after the practice of a periodontal treatment. Thus, in the FCG, it was shown that the practice of a periodontal treatment caused a decrease in the levels of IL1beta and IL17, while an increase in IL4 (Stadler AF et al, 2016); On the contrary, in saliva, only a reduction effect in IL1beta concentrations has been observed (Jaedicke KM et al, 2016). However, most studies in the literature focus on studying only certain pro-inflammatory cytokines, such as IL1beta or tumor necrosis factor alpha (hereafter, TFNalfa) (Nazar Majeed Z et al, 2016) or The results are contradictory on which cytokines, especially anti-inflammatory ones, are the most involved in the pathogenesis and evolution of periodontal diseases due to the absolute absence of uniformity in the methodological designs of the studies (Stadler AF et al, 2016). In addition, in most studies, the simultaneous determination of very few cytokines (less than 5 cytokines) was performed in oral fluids, which is far from reflecting the complex inflammatory response described in periodontal diseases (Thunell DH et al, 2010 ; Tymkiw k D et al, 2011; Shimada et al, 2013). Therefore, there is not enough evidence on the analysis of a broad spectrum of inflammatory biomarkers that accurately reflect the complex immunological reality associated with these pathologies.
[0016]
[0017] On the other hand, as previously described, in most of the studies published in the literature, the authors limit themselves to a comparative analysis of cytokine levels between different groups of patients (healthy controls versus periodontal patients, untreated periodontal patients versus treated periodontal patients, etc.). This type of analysis only reveals the relationship between the evaluated biomarkers and the biological condition, but it does not show at all its possible predictive or discriminatory capacity of said biological condition.
[0018] Consequently, there is no evidence on the development and validation of predictive models based on cytokine levels for the diagnosis and prognosis of periodontal diseases applying adequate multivariate predictive modeling techniques (Moons KGM et al, 2015); representing this a fundamental step in the creation of diagnosis / prognosis kits for periodontal diseases. The main difference of the present proposal with respect to what has been published up to now, lies in the development of numerous predictive models based on different combinations of proinflammatory and anti-inflammatory cytokines, which, by means of suitable multivariate predictive modeling techniques, showed an excellent capacity Predictive and discriminatory of periodontal diseases. This excellent predictive capacity is based on percentages of discrimination of the clinical condition of periodontitis with respect to periodontal health> 95%, as well as percentages of accuracy> 92% in most combinations. This predictive capacity is superior to that provided until now with other biomarkers with different biological functions, which does not reach 70%. These predictive models would represent different options of diagnostic / prognostic kits for periodontal diseases based on different combinations of pro-inflammatory and anti-inflammatory cytokines. DESCRIPTION OF THE INVENTION
[0019] The invention relates to a diagnostic / prognostic procedure based on multivariate predictive models generated from the levels of different combinations of pro-inflammatory and anti-inflammatory cytokines in gingival tissue, FCG and saliva for the prediction of periodontal diseases, their progression and its response to the different therapeutic interventions. In this regard, the inventors have shown that, unexpectedly, by means of multivariant predictive modeling techniques certain pro-inflammatory cytokines (IL1alpha, IL1beta, IL17A) combined with a series of anti-inflammatory (IFNgamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL3) show an excellent predictive capacity of periodontal diseases, with percentages of discrimination> 95%, precision percentages> 92%, as well as percentages of sensitivity and specificity> 90% in most combinations.
[0020] Therefore, this allows the use of different multivariate predictive models based on combinations of cytokines (pro-inflammatory and anti-inflammatory) and adjusted by the "smoking habit", not only as a diagnostic method of periodontal diseases, but also as a method to predict the future prognosis of these diseases as well as the clinical progression of them when a subject adopts a certain attitude or receives a certain therapeutic intervention.
[0021] Based on these findings, the inventors have developed the methods of the present invention in their different embodiments which are described in detail below.
[0022] FIRST METHOD. IN VITRO METHOD TO DIAGNOSE A PERIODONTAL DISEASE IN A SUBJECT
[0023] In a first aspect, the invention relates to an in vitro method for diagnosing periodontal disease in a subject, hereinafter, "the first method of the invention", comprising:
[0024] a) Determine the level of expression in a subject of at least two cytokines selected from the following: IL1alpha, IL1beta, IL17A, IFNgamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL3 in a sample of said subject.
[0025] b) Determine the condition of smoker, non-smoker or ex-smoker in said subject.
[0026] c) Obtain a probability value associated to the level of expression of cytokines, through multivariate predictive models, which refers to the probability of suffering from periodontal disease. The higher the probability value, the greater the certainty in the diagnosis of periodontal disease. The value 0.5, is indicative of the existence of the same probability of suffering or not a periodontal disease. This probability value is obtained either by using specific software for reading the test that provides this probability value or by providing said probability information to the user in any format, including oral, written, graphic or electronic to compare it with the values it has obtained.
[0027] The term "diagnosis" as used herein, refers both to the process of attempting to determine and / or identify a possible disease in a subject, i.e. the diagnostic procedure, as well as to the opinion reached through this process, that is, the diagnostic opinion. As such, it can also be considered as an attempt to classify an individual's status into separate and differentiated categories that allow medical decisions about treatment and prognosis to be made. As will be understood by the person skilled in the art, a diagnosis of this type may not be correct for 100% of the subjects to be diagnosed, although it is preferred. However, the term requires that a statistically significant proportion of subjects suffering from such pathologies (in this case, periodontal disease) can be identified.
[0028]
[0029] It can be determined whether a subject is statistically significant without further preamble by one skilled in the art using various well-known statistical evaluation tools such as, for example, confidence interval determination, p-value determination, cross-validation classification index and the like, etc.
[0030]
[0031] The term "periodontal disease" as used herein, refers to both gingival diseases and destructive periodontal diseases, encompassing numerous clinical entities that affect the gingival and periodontal structures of the teeth in the cavity. oral. Within the gingival diseases are differentiated:
[0032] - Gingival diseases induced by dental plaque: plaque-induced gingivitis (without other contributing local factors); Plaque-induced gingivitis with contributing local factors; Necrotizing ulcerative gingivitis; Gingivitis associated with puberty; Gingivitis associated with the menstrual cycle; Gingivitis associated with pregnancy; Pyogenic granuloma associated with pregnancy; Gingivitis associated with diabetes mellitus; Gingivitis associated with leukemia; Drug-induced gingival hyperplasia; Gingivitis associated with oral contraceptives; Gingivitis due to ascorbic acid deficiency. Gingival lesions not induced by plaque: Lesions associated with Neisseria gonorrhea; Lesions associated with Treponema Pallidum; Streptococcal injuries; Injuries associated with Mycobacterium Tuberculosis; Bacillary angiomatosis; Primary herpetic gingivostomatitis; Recurrent oral herpes; Varicella-zoster infections; Generalized gingival candidiasis; Gingival linear erythema; Histoplasmosis; Hereditary gingival fibromatosis.
[0033] - Gingival manifestations of: Lichen planus; Mucous membrane pemphigoid; Pemphigus vulgaris; Erythema multiforme; Lupus erythematosus; Linear IgA dermatosis; Wegener's granulomatosis; Psoriasis. - Allergic reactions of the gum: Restorative materials (mercury, nickel, acrylic); Toothpastes; Mouthwash; Additives from chewing gum or chewing gum; Food and food additives; Traumatic injuries of the gum; Chemical injuries; Physical injuries; Thermal injuries
[0034]
[0035] Within periodontal diseases of destructive type are differentiated:
[0036]
[0037] - Chronic periodontitis (localized / generalized).
[0038] - Aggressive periodontitis (localized / generalized).
[0039] - Periodontitis as a manifestation of systemic diseases: associated with hematological disorders; Acquired neutropenia; Leukemia; Associated with genetic disorders; Cyclic and familial neutropenia; Down's Syndrome; Leukocyte adhesion deficiency syndromes; Papillon-Lefévre syndrome; Chediak-Higashi syndrome; Disease of Langerhans cells (histocytosis syndromes); Glycogen storage disease; Chronic granulomatous disease; Infant genetic agranulocytosis; Cohen syndrome; Ehler-Danlos syndrome (types IV and VIII); Hypophosphatasia; Crohn's disease (inflammatory bowel disease); Marfan syndrome.
[0040] - Necrotizing ulcerative periodontitis.
[0041] - Abscesses of the periodontium.
[0042] - Apical periodontitis. In a particular embodiment, the first method of the invention relates to a method for diagnosing periodontal disease, wherein the periodontal disease is selected from the group consisting of gingivitis, chronic periodontitis or aggressive periodontitis. The term gingival / periodontal health is also included.
[0043]
[0044] The term "gingivitis", as used herein, refers to an inflammatory disease of the gingiva surrounding the teeth, of a reversible nature, characterized by the presence of gingival inflammation and bleeding.
[0045]
[0046] The term "chronic periodontitis", as used herein, refers to an inflammatory disease of the periodontium surrounding the teeth, characterized by the presence of gingival inflammation and bleeding, loss of adherence with periodontal pocket formation and reduction of alveolar bone.
[0047] The term "aggressive periodontitis", as used herein, refers to a form of periodontitis of rapid progression and great destructive capacity of the alveolar bone.
[0048] The term "gingival / periodontal health", as used herein, refers to an absolute absence of inflammatory disease and destructive processes of the gingival and periodontal tissues.
[0049] The first step of the first method of the invention comprises determining the level of expression in a subject of at least two cytokines selected from the following: IL1 alpha, IL1beta, IL17A, IFNgamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL3 in a sample of said subject.
[0050] The term "IL1alpha" or "interleukin 1alpha" as presented herein, refers to a protein that in humans is encoded by the IL1A gene (official symbol of the NCBI Gene database). The IL1A gene has also been designated as "interleukin 1 alpha" (official full name of the NCBI Gene database). IL-1alpha is a pro-inflammatory cytokine and plays a role in the immune system. In particular, the human IL1alpha protein sequence is provided in the Gene ID 3552 entry of the NCBI Gene database with updated annotation of June 4, 2017.
[0051] The term "IL1beta" or "interleukin 1beta" as presented herein, refers to a protein that in humans is encoded by the IL1B gene (official symbol of the NCBI Gene database). The IL1B gene has also been designated as "interleukin 1 beta" (official full name of the NCBI Gene database).). IL-1beta is a pro-inflammatory cytokine and plays a role in the immune system. In particular, the sequence of the human IL1beta protein is provided in the Gene ID 3553 entry of the NCBI Gene database with updated annotation of July 2, 2017.
[0052] The term "IL17A" or "interleukin 17A" as presented herein, refers to a protein that in humans is encoded by the IL17A gene (official symbol of the NCBI Gene database). The IL17A gene has also been designated as "interleukin 17A" (official full name of the NCBI Gene database). IL17A is a pro-inflammatory cytokine and plays a role in the immune system. In particular, the sequence of the human IL17A protein is provided in the Gene ID 3605 entry of the NCBI Gene database with updated annotation of July 2, 2017.
[0053] The term "IFNgamma" or "interferon gamma" as it is presented in this document, refers to a protein that in humans is encoded by the IFNG gene (official symbol of the NCBI Gene database). The IFNG gene has also been designated as "interferon gamma" (official full name of the NCBI Gene database). IFNgamma is an anti-inflammatory cytokine and plays a role in the immune system. In particular, the sequence of the human IFNgamma protein is provided in the Gene ID 3458 entry of the NCBI Gene database with updated annotation of July 1, 2017.
[0054] The term "IL2" or "interleukin 2" as it is presented herein, refers to a protein that in humans is encoded by the IL2 gene (official symbol of the NCBI Gene database). The IL2 gene has also been designated as "interleukin 2" (official full name of the NCBI Gene database). IL-2 is an anti-inflammatory cytokine and plays a role in the immune system. In particular, the sequence of the human IL-2 protein is provided in the Gene ID 3558 entry of the NCBI Gene database with updated annotation of July 2, 2017.
[0055] The term "IL12p70" or "interleukin 12p70" as it is presented herein, refers to a subunit obtained from the combination of the p35 subunit and the p40 subunit of a protein that is encoded by humans in humans. the IL12B gene (official symbol of the NCBI Gene database). The IL12B gene has also been designated as "interleukin 12B" (official full name of the NCBI Gene database). IL12p70 is an anti-inflammatory cytokine and plays a role in the immune system. In particular, the sequence of the human IL12p70 protein is provided in the Gene ID 3593 entry of the NCBI Gene database with updated annotation of June 6, 2017.
[0056] The term "IL3" or "interleukin 3" as presented herein, refers to a protein that in humans is encoded by the IL3 gene (official symbol of the NCBI Gene database). The Gen IL3 has also been designated as "interleukin 3" (official full name of the NCBI Gene database). IL3 is an anti-inflammatory cytokine and plays a role in the immune system. In particular, the sequence of the human IL3 protein is provided in the Gene ID 3562 entry of the NCBI Gene database with updated annotation of June 8, 2017.
[0057]
[0058] The term "IL4" or "interleukin 4" as it is presented herein, refers to a protein that in humans is encoded by the IL4 gene (official symbol of the NCBI Gene database). The IL4 gene has also been designated as "interleukin 4" (official full name of the NCBI Gene database). IL4 is an anti-inflammatory cytokine and plays a role in the immune system. In particular, the sequence of the human IL4 protein is provided in the Gene ID 3565 entry of the NCBI Gene database with updated annotation of June 25, 2017.
[0059]
[0060] The term "IL5" or "interleukin 5" as it is presented in this document, refers to a protein that in humans is encoded by the IL5 gene (official symbol of the NCBI Gene database). The IL5 gene has also been designated as "interleukin 5" (official full name of the NCBI Gene database). IL5 is an anti-inflammatory cytokine and plays a role in the immune system. In particular, the sequence of the human IL5 protein is provided in the Gene ID 3567 entry of the NCBI Gene database with updated annotation of June 4, 2017.
[0061]
[0062] The term "IL10" or "interleukin 10" as presented herein, refers to a protein that in humans is encoded by the IL10 gene (official symbol of the NCBI Gene database). The IL10 gene has also been designated as "interleukin 10" (official full name of the NCBI Gene database). IL10 is an anti-inflammatory cytokine and plays a role in the immune system. In particular, the sequence of the human IL10 protein is provided in the Gene ID 3586 entry of the NCBI Gene database with updated annotation of July 2, 2017.
[0063]
[0064] The term "IL13" or "interleukin 13" as presented herein, refers to a protein that in humans is encoded by the iL13 gene (official symbol of the NCBI Gene database). The IL13 gene has also been designated as interleukin 13 (official full name of the NCBI Gene database). IL13 is an anti-inflammatory cytokine and plays a role in the immune system. In particular, the sequence of the human IL13 protein is provided in the Gene ID 3596 entry of the NCBI Gene database with updated annotation of June 11, 2017.
[0065]
[0066] The term "subject" as presented herein, refers to all animals classified as mammals and includes, but is not restricted to, domestic and farm animals, primates and humans, for example, humans, primates not humans, cows, horses, pigs, sheep, goats, dogs, cats or rodents. Preferably, the patient is a human being of male or female sex of any age or race.
[0067]
[0068] The term "sample" or "biological sample" as presented herein means biological material isolated from the mouth of a subject. The biological sample can contain any suitable biological material to detect the desired biomarker. In a preferred embodiment, the sample is an oral tissue sample, FCG or saliva. The sample can be isolated using any conventional method known in the art. In summary, samples of gingival tissue can be obtained by incising 1 mm of tissue, FCG samples by means of superabsorbent paper tips placed in the gingival sulcus or the use of micropipettes and saliva samples not stimulated or stimulated by external mechanisms.
[0069]
[0070] In a preferred embodiment, the first method of the invention comprises determining the expression level of the IL1beta Il12p70 cytokines in a sample of the subject to be diagnosed.
[0071] In another preferred embodiment, the first method of the invention comprises determining the level of expression of IL1beta IL2 in a sample of the subject to be diagnosed.
[0072] In another preferred embodiment, the first method of the invention comprises determining the expression level of IL1beta IFNgamma in a sample of the subject to be diagnosed.
[0073] In another preferred embodiment, the first method of the invention comprises determining the expression level of IL1alpha IFNgamma in a sample of the subject to be diagnosed.
[0074] In another preferred embodiment, the first method of the invention comprises determining the level of expression of IL1alpha IL13 in a sample of the subject to be diagnosed.
[0075] In another preferred embodiment, the first method of the invention comprises determining the expression level of ILalpha 12p70 a sample of the subject to be diagnosed.
[0076] In another preferred embodiment, the first method of the invention comprises determining the expression level of IL17A IL3 a sample of the subject to be diagnosed.
[0077] In another preferred embodiment, the first method of the invention comprises determining the expression level of IL17A IL12p70 a sample of the subject to be diagnosed
[0078] In another preferred embodiment, the first method of the invention comprises determining the expression level of IL17A IFNgamma a sample of the subject to be diagnosed.
[0079]
[0080] As used herein, the term "expression level" refers to the value of a parameter that measures the degree of expression of a specific gene or corresponding polypeptide. In a particular embodiment, said value can be determined by measuring the level of mRNA of the gene of interest or a fragment thereof or by measuring the amount of protein encoded by said gene of interest or a variant thereof. Therefore, in the context of the present invention, in a particular embodiment, said level of expression comprises determining the level of mRNA encoded by the genes of any of the combinations of two aforementioned cytokines or determining the protein levels of any of the combinations of two cytokines mentioned above.
[0081]
[0082] Any conventional method can be used to detect and quantify the level of expression of a specific gene within the framework of the present invention. By way of non-limiting illustration, the level of expression of a gene can be determined by means of quantification of the level of mRNA of said gene or by means of quantification of the level of protein encoded by said gene. Various methods for determining the amount of mRNA in the state of the art are known. For example, the nucleic acid contained in the sample, such as the sample from the study subject, is extracted according to conventional methods, for example, by the use of lytic enzymes, chemical solutions or fixing resins. The extracted mRNA can be detected by hybridization (for example, by means of Northern blot analysis or DNA or RNA alignments (microalignments) after converting the mRNA into labeled cDNA) and / or amplification by means of an enzymatic chain reaction. In general, quantitative or semiquantitative enzymatic amplification methods are preferred. The technique of polymerase chain reaction (PCR) or quantitative real-time (RT-PCR) or semiquantitative is particularly advantageous. Other methods of amplification include the ligase chain reaction (LCR), transcription mediated amplification (t Ma), strand displacement amplification (SDA) and amplification based on the nucleic acid sequence (NASBA). The amount of mRNA is preferably measured quantitatively or semiquantitatively.
[0083]
[0084] The determination of the amount of protein corresponding to the expression of a specific gene can also be carried out using any conventional method for the detection and quantification of proteins, for example, using immunoassay, etc. By way of non-limiting illustration, said determination can be carried out using antibodies with the ability to specifically bind to the protein to be determined (or fragments thereof with the antigenic determinants) and the subsequent quantification of the antigen-antibody complex derivatives. The antibodies can be, for example, polyclonal sera, supernatants of hybridomas, monoclonal antibodies, antibody fragments, etc. Said antibodies may or may not be labeled with a marker. Among the assays that can be used for the determination are, for example, Western, ELISA (enzyme-linked immunosorbent assay), RIA (radioimmunoassay) EIA (enzyme immunoassay), etc.
[0085]
[0086] In a particular embodiment, the determination of the level of expression of the cytokines can be carried out by flow cytometry. In summary, flow cytometry is a laser-based technology that allows the simultaneous analysis of physical and chemical characteristics of up to thousands of particles per second. It is based on the use of fluorophores that bind to an antibody that recognize a specific target in the cells.
[0087]
[0088] The second step of the first method of the invention comprises determining the condition of a smoker, non-smoker or ex-smoker of the subject. The term "smoker", as presented in this document, is someone who smokes every day, at least one cigarette (daily smoker) or who smokes today, but not every day, less than one cigarette. daily (occasional smoker or not daily) in the last 6 months. The term "non - smoking", as s presented in this document and the person who has never smoked or smoked less than 100 cigarettes in your entire life. The term "ex-smoker", as presented in this document, is the person who, having smoked, has maintained abstinence for at least the last 6 months.
[0089]
[0090] The determination of the status of smoker, non-smoker or ex-smoker is carried out through a structured questionnaire and / or through objective quantification, applying techniques such as high-performance liquid chromatography of tobacco components in the oral cavity.
[0091]
[0092] The third stage of the first method of the invention comprises obtaining a probability value, by means of a multivariate predictive model, associated to the level of expression of the cytokines obtained in the section a), which refers the probability of suffering a periodontal disease, obtained either by using specific software for reading the test that provides this probability value or by presenting said information presented in any format, including the forms oral, written, graphic or electronic. The higher the probability value, the greater the certainty in the diagnosis of periodontal disease. The value 0.5, is indicative of the existence of the same probability of suffering or not a periodontal disease.
[0093] The term "probability value" as used herein, refers to a measure of certainty associated with a future event or event and is usually expressed as a number between 0 and 1 (or between 0% and 100%) . The probability value is obtained by developing a mathematical model. In a particular embodiment of the first method of the invention, the objective is to diagnose a periodontal disease using the expression levels of certain combinations of pro-inflammatory cytokines with anti-inflammatory cytokines.
[0094] In another preferred embodiment of the first method of the invention, it is intended to diagnose that a patient suffers from gingivitis, in which case the higher the probability value, the greater certainty exists in the diagnosis of gingivitis. The value 0.5, is indicative of the existence of the same probability of suffering or not a gingivitis.
[0095] In another preferred embodiment of the first method of the invention, it is intended to diagnose that a patient suffers from chronic periodontitis in which case the higher the probability value, the greater certainty exists in the diagnosis of chronic periodontitis. The value 0.5, is indicative of the existence of the same probability of suffering or not chronic periodontitis.
[0096] In another preferred embodiment of the first method of the invention, the aim is to diagnose that a patient suffers from aggressive periodontitis, in which case the higher the probability value, the greater certainty exists in the diagnosis of aggressive periodontitis. The value 0.5, is indicative of the existence of the same probability of suffering or not an aggressive periodontitis.
[0097] SECOND METHOD. METHOD TO DETERMINE THE RISK OF DEVELOPING A PERIODONTAL DISEASE IN A SUBJECT
[0098] In a second aspect, the invention relates to an in vitro method for determining the risk of developing a periodontal disease in a subject, hereinafter "the second method of the invention" comprising:
[0099] a) Determine the level of expression of at least two cytokines selected from the following: IL1alpha, IL1beta, IL17A, IFN gamma, IL-2, IL12p70, IL-3, IL4, IL5, IL10 and IL13 in a sample of said subject.
[0100] b) Determine the condition of smoker, non-smoker or ex-smoker in said subject.
[0101] c) Obtain a probability value associated to the level of expression of cytokines, through multivariate predictive models, which refers the probability of risk of suffering a periodontal disease in the future. The higher the probability value, the higher the risk of suffering a future periodontal disease. The value 0.5 is indicative of the existence of the same probability of suffering or not the future periodontal disease. This probability value is obtained either by using specific software for reading the test that provides this probability value or by providing said probability information to the user in any format, including oral, written, graphic or electronic to compare it with the values it has obtained. The expression "determine risk" or "risk prediction", or the like, as used herein is synonymous with the expression "assess risk" or "risk assessment" means that the present invention makes it It is possible to predict, estimate or evaluate the risk of a subject developing a future periodontal disease clinically detectable. Predicting risk generally implies that the risk either increases or decreases. As will be understood by those skilled in the art, the prediction or the risk, although it is preferred that it be corrected, it is not necessary that it be done for 100% of the patients suffering from periodontal diseases to be evaluated. However, the term requires that a statistically significant part of subjects who have an increased likelihood of having future periodontal disease can be identified.
[0102] The terms "subject", "sample" and "periodontal disease" have been previously defined in the context of the first method of the invention and are equally applicable in the second method of the invention.
[0103] In a particular embodiment, the sample of said subject is obtained from tissue and oral fluids. Methods for obtaining tissue samples and oral fluids have been detailed in the first method of the invention. In another particular embodiment, the periodontal disease is selected from "gingivitis" and "chronic periodontitis" or "aggressive periodontitis" which have been previously defined.
[0104] The first step of the second method of the invention comprises determining the level of expression in a subject of at least two cytokines selected from the following: IL1alpha, IL1beta, IL17A, IFNgamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL3 in a sample of said subject. These terms have been previously defined in the first method of the invention.
[0105] In a preferred embodiment, the second method of the invention comprises determining the level of expression of the IL1beta Il12p70 cytokines in a sample of the subject in which the future risk is to be determined.
[0106] In another preferred embodiment, the second method of the invention comprises determining the level of expression of IL1beta IL2 in a sample of the subject in which the future risk is to be determined.
[0107] In another preferred embodiment, the second method of the invention comprises determining the expression level of IL1beta IFNgamma in a sample of the subject in which the future risk is to be determined.
[0108] In another preferred embodiment, the second method of the invention comprises determining the expression level of IL1alpha IFNgamma in a sample of the subject in which the future risk is to be determined. In another preferred embodiment, the second method of the invention comprises determining the level of expression of IL1alpha IL13 in a sample of the subject in which the future risk is to be determined. In another preferred embodiment, the second method of the invention comprises determining the expression level of ILalpha 12p70 a sample of the subject in which the future risk is to be determined.
[0109] In another preferred embodiment, the second method of the invention comprises determining the expression level of IL17A IL3 a sample of the subject in which the future risk is to be determined.
[0110] In another preferred embodiment, the second method of the invention comprises determining the expression level of IL17A IL12p70 a sample of the subject in which the future risk is to be determined.
[0111] In another preferred embodiment, the second method of the invention comprises determining the expression level of IL17A IFNgamma a sample of the subject in which the future risk is to be determined. The expression levels of cytokines have been previously defined. In a particular embodiment, said level of expression comprises determining the level of mRNA encoded by the genes of any of the combinations of two cytokines mentioned above or determining the protein levels of any of the combinations of two cytokines mentioned above.
[0112] Methods for determining the expression levels of IL1 alpha, IL1beta, IL17A, IFNgamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL3 in the context of the first method of the invention have been detailed and are equally applicable to the second method of the invention.
[0113] The second step of the second method of the invention comprises determining the condition of a smoker, non-smoker or ex-smoker of the subject. These terms have been previously defined in the context of the first method of the invention.
[0114] The third stage of the second method of the invention comprises obtaining a probability value, by means of a multivariate predictive model, associated to the level of expression of the cytokines obtained in section c) of risk of developing a periodontal disease in the future. In which case the higher the probability value, the greater the risk of developing a periodontal disease in the future. The value 0.5, is indicative of the existence of the same probability of risk of suffering or not the future periodontal disease. The term "probability value" has been previously defined in the context of the first method of the invention.
[0115] In a particular embodiment, the second method of the invention aims to determine the risk of developing gingivitis in the future in a subject who has been previously clinically diagnosed as gingival / periodontal.
[0116] In a particular embodiment, the second method of the invention aims to determine the risk of developing a periodontitis in a subject who has been previously clinically diagnosed as gingival / periodontal.
[0117] THIRD METHOD. METHOD TO DETERMINE THE RESPONSE TO THE TREATMENT OF A SUBJECT WHO SUFFERS A PERIODONTAL DISEASE
[0118]
[0119] In a third aspect, the invention relates to an in vitro method for determining the response to treatment of a subject suffering from periodontal disease, hereinafter "the third method of the invention" comprising:
[0120] a) Determine the level of expression of at least two cytokines selected from the following:
[0121] IL1alpha, IL1beta, IL17A, IFN gamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL13 in a sample of said subject;
[0122] b) Determine the condition of smoker, non-smoker or ex-smoker in said subject.
[0123] Obtain a probability value associated to the level of expression of cytokines, through multivariate predictive models, which refers the probability of a worse response to a given clinical treatment in a patient with periodontal disease in which case the higher the value of the probability, there is a worse response to a given clinical treatment. The value 0.5 is indicative of the existence of the same probability of a negative or positive response to a given clinical treatment. This probability value is obtained either by using specific software for reading the test that provides this probability value or by providing said probability information to the user in any format, including oral, written, graphic or electronic to compare it with the values it has obtained.
[0124] The term "determine the response" or "predict the response" is used in this document to refer to the possibility that a patient has a particular clinical response, either positive or negative. The predictive methods of the present invention can be used clinically to make treatment decisions by choosing the most appropriate treatment modalities for any particular patient. The predictive methods of the present invention are valuable tools in predicting whether a patient is likely to respond favorably or unfavorably to a treatment regimen with a therapy.
[0125] The term "therapy" as it is presented in this document, refers to a protocol of action for the treatment of a periodontal disease, which involves the use of drugs and antimicrobial agents and the practice of certain maneuvers.
[0126]
[0127] As will be understood by those skilled in the art, prediction, although it is preferred that it be corrected, is not necessarily done for 100% of the subjects to be evaluated. However, the term requires that it can be identified that a statistically significant part of subjects have an increased probability of having a given outcome.
[0128]
[0129] The terms "subject", "sample" and "periodontal disease" have been previously defined in the context of the first method of the invention and are equally applicable in the third method of the invention.
[0130]
[0131] In a particular embodiment, the sample of said subject is obtained from tissue and oral fluids. Methods for obtaining tissue samples and oral fluids have been detailed in the first method of the invention.
[0132] In another particular embodiment, the periodontal disease is selected from "gingivitis" and "chronic periodontitis" or "aggressive periodontitis" which have been previously defined.
[0133]
[0134] The first step of the second method of the invention comprises determining the level of expression in a subject of at least two cytokines selected from the following: IL1alpha, IL1beta, IL17A, IFNgamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL3 in a sample of said subject. These terms have been previously defined in the first method of the invention.
[0135] In a preferred embodiment, the second method of the invention comprises determining the level of expression of the IL1beta Il12p70 cytokines in a sample of the subject in which the response to a treatment is to be determined.
[0136] In another preferred embodiment, the second method of the invention comprises determining the level of expression of IL1beta IL2 in a sample of the subject in which the response to a treatment is to be determined.
[0137] In another preferred embodiment, the second method of the invention comprises determining the expression level of IL1beta IFNgamma in a sample of the subject in which the response to a treatment is to be determined.
[0138] In another preferred embodiment, the second method of the invention comprises determining the expression level of IL1alpha IFNgamma in a sample of the subject in which the response to a treatment is to be determined.
[0139] In another preferred embodiment, the second method of the invention comprises determining the level of expression of IL1alpha IL13 in a sample of the subject in which the response to a treatment is to be determined.
[0140] In another preferred embodiment, the second method of the invention comprises determining the expression level of ILalpha 12p70 a sample of the subject in which the response to a treatment is to be determined.
[0141] In another preferred embodiment, the second method of the invention comprises determining the expression level of IL17A IL3 a sample of the subject in which the response to a treatment is to be determined.
[0142] In another preferred embodiment, the second method of the invention comprises determining the expression level of IL17A IL12p70 a sample of the subject in which the response to a treatment is to be determined.
[0143] In another preferred embodiment, the second method of the invention comprises determining the expression level of IL17A IFNgamma a sample of the subject in which the response to a treatment is to be determined.
[0144] The expression levels of cytokines have been previously defined. In a particular embodiment, said level of expression comprises determining the level of mRNA encoded by the genes of any of the combinations of two cytokines mentioned above or determining the protein levels of any of the combinations of two cytokines mentioned above.
[0145] Methods for determining the expression levels of IL1 alpha, IL1beta, IL17A, IFNgamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL3 in the context of the first method of the invention have been detailed and are equally applicable to the third method of the invention.
[0146] The second step of the third method of the invention comprises determining the condition of a smoker, non-smoker or ex-smoker of the subject. The terms "smoker", "non-smoker" or "ex-smoker" have been previously defined in the context of the first method of the invention.
[0147] The third stage of the second method of the invention comprises obtaining a probability value, by means of a multivariate predictive model, associated to the level of expression of the cytokines obtained in section b), which refers the probability of a worse response to a given clinical treatment. of a patient with periodontal disease in which case the higher the probability value, there is a worse response to a given clinical treatment. The value 0.5 is indicative of the existence of the same probability of a negative or positive response to a given clinical treatment. The term "probability value" has been previously defined in the context of the first method of the invention.
[0148] Once the three stages of the method are performed, the third method of the invention allows treatment decisions to be made by choosing the most appropriate treatment modality for any patient with periodontal disease.
[0149] DESCRIPTION OF THE FIGURES
[0150] Figure 1. Calibration graph and ROC curve of the model based on IL1 alpha and "smoking habit".
[0151] Figure 2. Calibration graph and ROC curve of the model based on IL1 beta and "smoking habit".
[0152] Figure 3. Calibration graph and ROC curve of the model based on IL117A and "smoking habit".
[0153] Figure 4. Calibration graph and ROC curve of the model based on IL1alpha, IFNgamma and "smoking habit".
[0154] Figure 5. Calibration graph and ROC curve of the model based on IL1 alpha, IL2 and "smoking habit".
[0155] Figure 6. Calibration graph and ROC curve of the model based on IL1 alpha, IL3 and "smoking habit".
[0156] Figure 7. Calibration graph and ROC curve of the model based on IL1 alpha, IL4 and "smoking habit".
[0157] Figure 8. Calibration graph and ROC curve of the model based on IL1 alpha, IL5 and "smoking habit".
[0158] Figure 9. Calibration graph and ROC curve of the model based on IL1 alpha, IL10 and "smoking habit". Figure 10. Calibration graph and ROC curve of the model based on IL1alpha, IL12p70 and "smoking habit".
[0159] Figure 11. Calibration graph and ROC curve of the model based on IL1 alpha, IL13 and "smoking habit".
[0160] Figure 12 Calibration graph and ROC curve of the model based on ILIbeta, IFNgamma and "smoking habit".
[0161] Figure 13. Calibration graph and ROC curve of the model based on IL1 beta, IL2 and "smoking habit".
[0162] Figure 14. Calibration graph and ROC curve of the model based on IL1 beta, IL3 and "smoking habit".
[0163] Figure 15. Calibration graph and ROC curve of the model based on IL1 beta, IL4 and "smoking habit".
[0164] Figure 16. Calibration graph and ROC curve of the model based on IL1 beta, IL5 and "smoking habit".
[0165] Figure 17 Calibration graph and ROC curve of the model based on IL1beta, IL10 and "smoking habit". Figure 18. Calibration graph and ROC curve of the model based on IL1beta, IL12p70 and "smoking habit".
[0166] Figure 19. Calibration graph and ROC curve of the model based on IL1 beta, IL3 and "smoking habit".
[0167] Figure 20 Calibration graph and ROC curve of the model based on IL17A, IFNgamma and "smoking habit".
[0168] Figure 21. Calibration graph and ROC curve of the model based on IL17A, IL2 and "smoking habit".
[0169] Figure 22. Calibration graph and ROC curve of the model based on IL17A, IL3 and "smoking habit".
[0170] Figure 23 Calibration graph and ROC curve of the model based on IL17A, IL4 and "smoking habit".
[0171] Figure 24. Calibration graph and ROC curve of the model based on IL17A, IL5 and "smoking habit".
[0172] Figure 25. Calibration graph and ROC curve of the model based on IL17A, IL10 and "smoking habit". Figure 26. Calibration graph and ROC curve of the model based on IL17A, IL12p70 and "smoking habit".
[0173] Figure 27 Calibration graph and ROC curve of the model based on IL17A, IL13 and "smoking habit". MODE FOR CARRYING OUT THE INVENTION
[0174] The present invention is suitably illustrated by the following examples, which do not intend to be limiting of its scope.
[0175] Example 1. Patients and control subjects
[0176] A convenience sample of 150 participants was selected, of which 75 were controls with periodontal health (control group) and 75 patients with moderate-severe generalized chronic periodontitis (perio group). These participants were selected from a group of 250 subjects from the general population who attended the Faculty of Medicine and Dentistry (University of Santiago de Compostela) for an evaluation of their oral health status between the years 2013-2015.
[0177] The exclusion criteria were: (i) medical history of cardiovascular, cerebrovascular, renal or hepatic disease or infection, diabetes mellitus or communicable diseases (ii) pregnancy or lactation, (iii) taking of systemic antibiotics in the previous 6 months or of anti-inflammatories in the previous 4 weeks, (iv) routine use of oral antiseptics. v) presence of implants or orthodontic appliances; vi) history of previous periodontal treatment; vii) ex-smoker status of less than 5 years; and viii) presence of at least 18 natural teeth.
[0178] An experienced and previously calibrated dentist made all the periodontal diagnoses. Clinical measurements were made on all teeth and in six areas per tooth using a PCP-UNC 15 probe and included: depth of periodontal probing (hereafter, PPD) and clinical insertion loss (hereafter, CAL = PPD gingival recession) ). The index of bleeding on probing (hereinafter, BOP) and the bacterial plaque index (hereinafter, BPL) were also recorded in the entire mouth and in six zones per tooth applying a binary scale (presence / absence). In each patient, a periapical radiographic series was performed in order to evaluate the status of the alveolar bone.
[0179] The diagnosis of chronic periodontitis was based on the clinical and radiographic information obtained. The control group included patients with periodontal health who presented BOP <25%, no location with PSP> 4 mm or radiographic evidence of alveolar bone loss. The perio group included patients who were diagnosed with moderate-severe generalized chronic periodontitis using criteria previously established clinics (Armitage GC, 1999; Page RC & Eke PI, 2007). The smoking habit was also evaluated through the application of a questionnaire, collecting information on the state of the habit (non-smoker, ex-smoker, current smoker), time spent as "ex-smoker", time spent as "smoker", and number of cigarettes per day .
[0180]
[0181] This study was carried out in accordance with the principles of the Declaration of Helsinki (revised in 2000) on studies in human experimentation (World Medical Association, 2013). The study protocol was approved by the Clinical Research Ethics Committee of Galicia (registration number 2015/006). The TRIPOD guidelines were applied for the development of multivariate predictive / predictive modeling techniques (Moons KGM et al, 2015).
[0182]
[0183] Example 2. Statistical analysis: Comparison of clinical characteristics between the two study groups (controls with periodontal health and patients with chronic periodontitis)
[0184]
[0185] To detect differences in clinical characteristics between the two study groups, a univariate analysis was carried out. The type of distribution of the quantitative variables was assessed by the Shapiro-Wilk test, obtaining absence of normality in all cases. Therefore, to compare clinical characteristics of a quantitative nature (age, number of teeth, clinical parameters of BPL, BOP, PPD and CAL in full mouth and in sampled locations) between both study groups, the Mann U test was applied. -Whitney. To analyze the association of qualitative clinical variables (gender and smoking habit) between both study groups, Fisher's exact test was applied. A level of significance of P <0.05 was established.
[0186]
[0187] Example 3. Results: Clinical characteristics of the patients of the study groups
[0188]
[0189] Of the 150 initial participants, there were 3 subjects (1 from the control group and 2 from the perio group) who were excluded for unexpected reasons, so that 147 subjects with an age of 48.37 ± 11.55 years (62 men) were finally evaluated. and 85 women). Of the 147 participants, 74 were from the control group and 73 from the perio group. The mean age of the patients in the control group was significantly lower than that in the perio group (45.65 ± 12.37 and 51.12 ± 10.01 years, respectively, P = 0.005). In relation to smoking, the number of smoking patients was significantly higher in the perio group with respect to control (41 and 13 patients, respectively, P <0.001). The analysis of the clinical variables associated with the oral health status showed that, compared to the control group, the patients of the perio group presented significantly higher values of BPL, BOP, PPD and CAL both in the entire mouth and in the selected oral areas. to obtain the FCG samples (P <0.001, Table 1).
[0190]
[0191] Table 1 Age, gender, smoking habit and clinical characteristics associated with periodontal status in patients of both study groups (control and perio). The values expressed are means (standard deviations) and number of subjects.
[0192]
[0193]
[0194]
[0195] BPL = bacterial plaque index; BOP = index of bleeding on probing; PPD = depth of periodontal probing; CAL = clinical insertion loss; NS = not significant. 1-Patients were defined as smokers if they smoked at present and had a smoking history of at least 8 years; Patients were defined as non-smokers if they had never smoked or had quit more than 5 years ago.
[0196]
[0197] Example 4. Methodology: Collection of oral samples of crevicular gingival fluid
[0198] From each patient, FCG samples were collected one week after the intraoral examination. These samples were always obtained at the same time of day (in the afternoon, approximately 5-7 hours after the last brushing). Before obtaining the sample, the dental pieces were isolated with cotton rolls, the supragingival plate was carefully removed and the selected location was dried slightly with the air syringe. Next, a strip of superabsorbent paper (Periopaper, Amityville, NY, USA) was inserted into the gingival sulcus or periodontal pocket for 30 seconds. In cases of contamination with blood, the paper strips were discarded and new locations were sampled.
[0199]
[0200] In the patients of the control group, FCG samples were collected from 20 subgingival locations of the teeth of quadrants 1 and 3 (incisor, canine, first premolar, second premolar and molar) and were mixed in the same tube. In patients of the perio group, FCG samples were collected from the subgingival sites with the highest PPD in each quadrant (a total of 20 non-adjacent subgingival sites) and mixed in the same tube. The 20 paper strips of each patient were placed in correctly labeled tubes containing: 300 ml of 0.01M PBS with a pH of 7.2 and a protease inhibitor (Complete Mini, protease inhibitor cocktail tablets, Roche Applied Science, Indianapolis , IN, USA). To prevent evaporative losses, the FCG volumes were determined by immediate measurements of the weight of the tubes and the paper strips before and after the collection of the samples using a high precision balance (Griffiths GS, 2003). After a stirring of 20 min, the paper strips were removed and the samples were centrifuged for 5 min at 5800 g in order to discard the cell pellet. The FCG samples were stored at -80 ° C until further biochemical analysis.
[0201]
[0202] Example 5. Methodology: Quantification of cytokine levels in the gingival crevicular fluid using a multiplexed immunoassay
[0203]
[0204] A single investigator blinded to the clinical data of the patients performed the analysis of cytokine quantification in the FCG samples. This quantification was carried out using a plex of 16 cytokines Procarta immunoassay (Affymetrix, Inc., Santa Clara, CA, USA), and the mediators evaluated were: 1) eight pro-inflammatory cytokines (granulocyte and macrophage colony stimulating factor -GMCSF -, IL1 alpha, ILIbeta, IL6, IL12p40, IL17A, IL17F and TNFalpha); and 2) eight anti-inflammatory cytokines (IFNgamma, IL2, IL3, IL4, IL5, IL10, IL12p70 and IL13).
[0205]
[0206] Immunoassays were performed on 96-well filter plates following the manufacturer's instructions. The filter plates were moistened with a wash buffer and said solution was aspirated from the wells using a manual magnetic separator block (Millipore Corporation, Billerica, ^{m A).} The microspheres coated with the monoclonal antibodies to the 16 target analytes were added to the wells. The standards and samples were injected into the wells and incubated overnight at 4 ° C. The wells were then washed, again using a manual magnetic separator block (Millipore Corporation), and a mixture of biotinylated secondary antibodies was added.
[0207] After incubation for 30 min, streptavidin conjugated to the fluorescent protein R-phycoerythrin (streptavidin-RPE) was added to the microspheres and incubated for 30 min. After a wash to remove the unbound reagents, a read buffer (Affymetrix, Inc.) was added to the wells and the microspheres were analyzed (minimum of 100 per analyte) using the Luminex 100TM instrument (Luminex Corporation, Austin, Texas, US). All samples were made in duplicate.
[0208] The Luminex 100 ™ analyzed the spectral properties of the microspheres to distinguish the different analytes while simultaneously measuring the amount of fluorescence associated with R-phycoerythrin, determining the median fluorescence intensity (MFI). The concentrations of the different analytes (antigens in the GCF samples) were: 1) estimated from their respective standard curves using a 5PL algorithm and the Luminex IS 2.3 and xPONENT 3.1 software (Luminex Software, Inc.); and 2) adjusted for the dilution factor and expressed as pg / ml. The concentration ranges for each biomarker analyzed were: GMC ^s F, 0.53-55.050 pg / ml; IFNgamma, 0.02-6.650 pg / ml; IL1alpha, 0.34-28,800 pg / ml; IL1beta, 0.09-23.150 pg / ml; IL2, 0.04-13,700 pg / ml; IL3, 0.19-26.500 pg / ml; IL4, 0.10 29.250 pg / ml; IL5, 0.04-17.800 pg / ml; IL6, 0.10-27.200 pg / ml; IL10, 0.04-10.050 pg / ml; IL12p40, 0.14 27.350 pg / ml; IL12p70, 0.26-18.050 pg / ml; IL13, 0.34-23,700 pg / ml; IL17A, 0.36-30.900 pg / ml; IL17F, O, 25-34,700 pg / ml; and TNFalpha, 0.21-16.800 pg / ml. FCG samples that showed analyte values below the detection limit (LD) of the assay were recorded as LD / 2 (Uh HW et al, 2008), while samples with values above the upper limit of quantification of The standard curves were assigned the highest value of the curve.
[0209]
[0210] Example 6. Statistical analysis: Comparison of cytokine levels in gingival crevicular fluid between controls with periodontal health and patients with chronic periodontitis. Association of cytokine levels in the gingival crevicular fluid and the presence of chronic periodontitis
[0211] After applying the Shapiro-Wilk test, and because the concentrations of the cytokines presented non-normal distributions, these values were transformed into logarithmic values (log2). Despite the effect of the logarithmic transformation, the concentrations of most cytokines continued to show a non-normal distribution.
[0212]
[0213] Quantitative data on cytokine levels in the GCF were expressed as medians and interquartile ranges. The Mann-Whitney U test was applied to compare the levels of cytokines between the control group and the perio group. A binary logistic regression was performed with each variable to study the association of each cytokine with the presence of chronic periodontitis. Unadjusted odds ratios (ORna) and adjusted odds ratios (ORa) were calculated in relation to the covariates of age, gender (the male sex was established as a reference) and smoking (the "non-smoking" status was established as a reference ). The 95% confidence intervals (CI) were calculated for both OR and P values. The level of significance applied was P <0.05.
[0214]
[0215] Example 7. Results: Comparison of cytokine levels in the gingival crevicular fluid between controls with periodontal health and patients with chronic periodontitis. Association of cytokine levels in the gingival crevicular fluid and the presence of chronic periodontitis
[0216] The levels of all pro-inflammatory cytokines (GMCSF, IL1alpha, IL1beta, IL6, IL12p40, IL17A, IL17F and TNFalfa) were significantly higher in the perio group compared to the control group (P <0.001, for all comparisons). Regarding anti-inflammatory cytokines, only four mediators (IFNgamma, IL2, IL3 and IL4) showed significantly higher concentrations in the perio group (P <0.001, for all comparisons, Table 2).
[0217]
[0218] The levels of all the pro-inflammatory cytokines (GMCSF, IL1alpha, IL1beta, IL6, IL12p40, IL17A, IL17F and TNFalfa) showed significant positive associations with the presence of chronic periodontitis (range OR = 1,371-25,638). IL1alpha presented the highest OR value (25,368, 95% CI = 9,472-104,246) followed by ILbeta (OR = 9,474, 95% CI = 5,049-21,662), IL17A (OR = 6,946, 95% CI = 3,920-14,342). IL12p40 (OR = 4.307, 95% CI = 2.697-7.451). Regarding the anti-inflammatory cytokines, only four mediators showed significant positive associations with the presence of chronic periodontitis, this association being weaker than that detected in the pro-inflammatory cytokines (OR range = 1,413 1,735). IL2 showed the highest OR value (1,735, 95% CI = 1,241-2,549) followed by IL3 (OR = 1,719, 95% CI = 1,302-2,377) (Table 2).
[0219]
[0220] When ORs were adjusted for age, gender and smoking habit, we observed that these values were modified slightly (minus 20%), so it was not considered as evidence of confusion (Sanders AE et al, 2009).
[0221] Table 2 Concentrationes (log ² pg / ml) of the 16 cytokines in both study groups (U Mann-Whitney, P values) and estimated odds ratios (95% confidence intervals, P values ) of the models not adjusted by the "habit tobacco. "
[0222]
[0223]
[0224]
[0225] RIQ, interquartile range; OR, odds ratio; IC, confidence interval; FCG, gingival crevicular fluid; NS = not significant.
[0226] Concentration range for each analyzed cytokine: GMCSF, 0.53-55.050 pg / ml; IFNgamma, 0.02 6.650 pg / ml; IL1alpha, 0.34-28,800 pg / ml; IL1beta, 0.09-23.150 pg / ml; IL2, 0.04-13.700 pg / ml; IL3, 0.19 26,500 pg / ml; IL4, 0.10-29.250 pg / ml; IL5, 0.04-17,800 pg / ml; IL6, 0.10-27.200 pg / ml; IL10, 0.04-10.050 pg / ml; IL12p40, 0.14-27.350 pg / ml; IL12p70, 0.26-18.050 pg / ml; IL13, 0.34-23.700 pg / ml; IL17A, 0.36 30,900 pg / ml; IL17F, 0.25-34,700 pg / ml; TNFalpha, 0.21-16.800 pg / ml.
[0227]
[0228] Example 8. Statistical analysis: Multivariate modeling of cytokine levels in gingival crevicular fluid with predictive capacity: model selection and internal validation
[0229]
[0230] The Spearman correlations between the cytokines were calculated and used as an orientation for the construction of models, in order to avoid redundancies and possible colinearity between cytokines with similar biological effects. The models based on the levels of cytokines in the gingival crevicular fluid were selected for their biological significance, their ability to predict chronic periodontitis and their statistical validity. The biological criteria applied to select the predictive cytokines were based on their level of importance in the inflammatory process, and in particular, in the different etiopathogenic role exerted by the pro-inflammatory cytokines with respect to anti-inflammatory cytokines.
[0231] The models were constructed by initially selecting a pro-inflammatory cytokine as a predictor variable. In order to verify whether the predictive capacity of pro-inflammatory cytokines could be increased by the incorporation of cytokines with anti-inflammatory effects, models of two variables that combined these different mediators were analyzed. The resulting models were individually adjusted in relation to the "smoking status" (the condition of "non-smoker" was established as a reference).
[0232]
[0233] The statistical criterion applied for the selection of the model based on the levels of cytokines in the gingival crevicular fluid was its ability to discriminate the presence of chronic periodontitis, which was evaluated was evaluated by the ROC curve ("Receiver Operating Characteristic") applying the package Epi (Carstensen B et al, 2016). This curve is a graphic representation of the sensitivity versus specificity for a binary classifier system as the discrimination threshold of the respective model is varied. The area under the curve (hereinafter, ABC), which is the C statistic, was considered as an index to determine the discriminative capacity of the model and provides a scale of 0.5 to 1.0 (with 0.5 representing random probability) and 1.0 indicating perfect discrimination), with which to compare the ability of a biomarker to detect a positive result (Steyerberg EW et al, 2010). Note that models with an AUC value equal to or greater than 0.70 are considered acceptable predictive models (Hosmer DW et al, 2013). The calculation of the ABC values and their corresponding values of 95% CIs were made using bootstrap techniques using the pROC package (Robin X et al, 2011). Those models of a cytokine that had the highest ABC values were selected.
[0234]
[0235] From the models based on selected cytokines, using the pROC package and the bootstrap method, numerous classification measures were obtained such as precision (hereinafter, PREC), sensitivity (hereafter, SE), specificity (hereafter, SP) , the positive predictive value (hereafter, PPV) and the negative predictive value (hereafter, NPV), establishing an optimal threshold, as well as its corresponding IC of 95% (Robin X et al, 2011). The best cut value for each model was determined so that the percentage of correct predictions was the maximum.
[0236]
[0237] The Hosmer-Lemeshow test was applied to predictive models based on selected cytokines, applying the Resource Selection package (Lele SR et al, 2016). This is a calibration measure, which is significant for poorly calibrated models (Austin PC & Steyerberg EW, 2014). The calibration curves of these models were graphically constructed using the RMS package (Frank E & Harrell J, 2015) to evaluate the agreement between predicted probabilities and actual results. In a well-calibrated model, predictions must fall on a 45 ° diagonal line (Austin PC & Steyerberg EW, 2014).
[0238]
[0239] Example 9. Statistical analysis: Internal validation of the selected predictive models
[0240] In order to control the possible overfitting of the models, the average values of optimism were determined on the measures of calibration, discrimination and classification using "bootstrap" methods. Optimism is a measure of the internal validation of the model that indicates the absolute magnitude of error and is obtained from the difference between the respective parameters of the bootstrap model with the bootstrap sample data and the bootstrap model with the data from the original sample ( Efron B & Tibshirani RJ; Steyerberg EW et al, 2001). This analysis was replicated in 1000 different study samples with replacement of data of the same sample size as the original.
[0241]
[0242] The parameters corrected for optimism (co) of measures, discrimination (hereafter, co-ABC), classification (hereafter, co-PREC, co-SE, co-SP, co-VPP and co-VPN) were calculated. ) and calibration. These corrected parameters were obtained from their corresponding apparent measures derived from the original sample minus their respective optimism value (Efron B & Tibshirani RJ; Steyerberg EW et al, 2001).
[0243]
[0244] Example 10. Results: Multivariate modeling of cytokine levels in the gingival crevicular fluid with predictive capacity: model selection and internal validation
[0245]
[0246] For the multivariate predictive analysis, we had a total of 147 participants and 73 outcome events. As a first description of the relationship between cytokine levels, almost all correlations between them, both pro-inflammatory and anti-inflammatory, were positive. The interpretation is that when inflammation associated with periodontitis was present, all cytokines showed higher levels. In addition, higher correlations between pro-inflammatory cytokines were observed. Particularly, very high positive correlations were detected between some pro cytokines inflammatory factors very relevant in the pathogenesis of the disease, ILIalpha, ILIbeta and IL17A (rho> 0.85). Note that these cytokines showed the greatest differences between the two study groups.
[0247] Applying the established statistical criteria, 27 predictive models were obtained based on the levels of the cytokines, which are described in Table 3, as well as their corresponding values of apparent and corrected AUC.
[0248] Regarding the models of a cytokine adjusted by "snuff", the pro-inflammatory cytokines IL1alpha, IL1beta and IL17 were the predictor variables that showed higher values of ABC (0.973, 0.963, 0.937, respectively).
[0249] As for the models of two cytokines adjusted by "snuff", the incorporation of certain anti-inflammatory cytokines improved the values of ABC of the best models based on a proinflammatory cytokine, especially that of IL17A. The classification measures of the 27 predictive models are detailed in Table 4; while the calibration measures are detailed in Table 5.
[0250] Table 3. Description of the 27 predictive models based on the levels of the cytokines and their corresponding apparent and corrected discrimination values.
[0251]
[0252]
[0253]
[0254]
[0255] In the second column, the value corresponds to the apparent discrimination measures and in the third column, to the same measures of discrimination corrected by the optimism calculated by bootstrap techniques.
[0256] The IL1beta model presented the highest percentage of co-PREC (93.27%, co-SE = 92.64%, co-SP = 93.91%), followed by IL1alpha (91.82%, co-SE = 92). , 99%, co-SP = 90.81%), and IL17A (88.00%, co-SE = 88.25%, co-SP = 87.82%). The IL1alfa model was the best graphically calibrated, showing this predictor a linear effect on the result. The IL1 alpha model presented a corrected intercept of -0.024 and a corrected slope of 0.939. The value of the Hosmer-Lemeshow test was not significant (P = 0.504).
[0257] In relation to ILalfa and other anti-inflammatory cytokines, the three models that contributed the greatest increases in the values of co-PREC with respect to the one-variable model alpha (co-PREC = 91.82%) were: IL1alpha IFNgamma ( co-PREC = 93.67%, co-SE = 91.60%, co-SP = 95.75%), IL1alpha IL12p70 (co-PREC = 92.97%, co-SE = 92.92%, co -SP = 93.12%) and IL1alpha IL13 (co-PREC = 93.27%, co-SE = 88.02%, co-SP = 98.54%). The model IL1 alpha IL12p70 was the best calibrated graphically, presenting a corrected intercept of -0.013 and a corrected slope of 0.897. In all three models, the value of the Hosmer-Lemeshow test was not significant (P = 0.891, 0.733 and 0.938, respectively).
[0258] In relation to IL1beta and other anti-inflammatory cytokines, the three models that contributed the greatest increases in the values of co-PREC with respect to the one-variable IL1beta model (co-PREC = 93.27%) were: IL1beta IFNgamma (co -PREC = 93.77%, co-SE = 95.07%, co-SP = 92.55%), IL1beta IL2 (co-PREC = 93.92%, co-SE = 95.88%, co- SP = 92.03%) and IL1beta IL12p70 (co-PREC = 94.30%, co-SE = 96.20%, co-SP = 92.45%). None of these two-variable models showed a good graphic calligraphy and only the IL1beta IFNgamma model presented a non-significant Hosmer-Lemeshow test value (P = 0.051).
[0259] In relation to IL17A and other anti-inflammatory cytokines, the three models that contributed the greatest increases in the values of co-PREC with respect to the one-variable model IL17A were: IL17A IFNgamma (co-PREC = 91.11%; SE = 89.05%, co-SP = 93.21%), IL117A IL3 (co-PREC = 92.30%, co-SE = 89.36%, co-SP = 95.30%) and IL17A IL12p70 (co-PREC = 92.15%, co-SE = 88.80%, co-SP = 95.55%). The IL17A IFNgamma model was the best graphically calibrated, showing this predictor a linear effect on the result. The IL17A IFNgamma model presented a corrected intercept of -0.014 and a corrected slope of 0.884. In all three models, the value of the Hosmer-Lemeshow test was not significant (P = 0.522, 0.000 and 0.000, respectively).
[0260] Figures 1 and 2 show the ROC curves and the calibration graphs, including the measures corrected for the optimism of the models based on a pro-inflammatory cytokine and those of two cytokines (one, pro-inflammatory and the other, anti-inflammatory). ).
[0261] Table 4 Measures of discrimination and classification of the models based on the levels of cytokines in the gingival crevicular fluid adjusted by the variable "smoking habit".
[0262]
[0263]
[0264]
[0265] In each cell, the first value corresponds to the apparent discrimination and classification measures and the second value to the same measures corrected for the optimism calculated by bootstrap techniques.
[0266] Table 5 Calibration measurements of the models based on the levels of cytokines in the gingival crevicular fluid adjusted by the variable "smoking habit".
[0267]
[0268]
[0269] In each cell, the first value corresponds to the apparent calibration measurements and the second value to the same measurements corrected by the optimism calculated by bootstrapping techniques

权利要求:
Claims (39)
[1]
An in vitro method for diagnosing periodontal disease in a subject, comprising: a) Determining the level of expression of at least two cytokines selected from the following:
IL1alpha, IL1beta, IL17A, IFNgamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL13 in an oral sample of the subject;
b) Determine the condition of smoker, non-smoker or ex-smoker in said subject;
c) Obtain a probability value associated to the level of expression of cytokines, through multivariate predictive models, which refers to the probability of suffering from periodontal disease. The higher the probability value, the greater the certainty in the diagnosis of periodontal disease. The value 0.5, is indicative of the existence of the same probability of suffering or not a periodontal disease. This probability value is obtained either by using specific software for reading the test that provides this probability value or by providing said probability information to the user in any format, including oral, written, graphic or electronic to compare it with the values it has obtained.
[2]
2. In vitro method according to claim 1 comprising:
a) Determine the level of expression of two of the cytokines selected from the following:
IL1alpha, IL1beta, IL17A, IFNgamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL13 in an oral sample of the subject;
b) Determine the condition of smoker, non-smoker or ex-smoker in said subject;
c) Obtain a probability value associated to the level of expression of cytokines, through multivariate predictive models, which refers to the probability of suffering from periodontal disease. The higher the probability value, the greater the certainty in the diagnosis of periodontal disease. The value 0.5, is indicative of the existence of the same probability of suffering or not a periodontal disease. This probability value is obtained either by using specific software for reading the test that provides this probability value or by providing said probability information to the user in any format, including oral, written, graphic or electronic to compare it with the values it has obtained.
[3]
3. In vitro method according to claims 1 and 2 in which the multivariate predictive models used are, at least, one of the following:
-71.383 4,622x + 2,042x IL1alpha -1,146x IFNgamma snuff;
-62,495 4,149x IL1alpha -1,158x IL2 + 2,214x tobacco;
-56,949 3,847x IL1alpha -0.831x IL3 + 1.924x tobacco;
-60,877 3,899x IL1alpha -0.467x IL4 + 2.027x tobacco;
-50,152 3,141x IL1alpha -0,082 xIL5 + 1,782x tobacco;
-62.194 4.055x IL1alpha -0.999x IL10 + 1.617x tobacco;
-59.907 3,857x IL1alpha -0.661x IL12p70 + 1.977x tobacco;
-54,740 3.540x IL1alpha -0.515x IL13 + 2.010x tobacco;
-31.507 2,546x + 1,918x 1beta -0,648x IFNgamma snuff;
-28,347 2,297x IL1beta -0.430x IL2 1,943x tobacco;
-27,512 2,217x IL1beta -0.221x IL3 + 1.799x tobacco;
-27,994 2,172x IL1beta -0.058 xIL4 + 1.745x tobacco;
-28.997 1beta + 0,233x 2,172x + 1,686x IL5 snuff;
-28,811 2,331x IL1beta -0,505x IL10 + 1,701x tobacco
-27.856 -0.177 1beta 2,190x + 1,709x xIL12p70 snuff;
-27.274 2.204x IL1beta -0.329 xIL13 + 1.923x tobacco;
-12.376 5,024x + 2,984x 17A -3,167x IFNgamma snuff
-6,912 2,455x IL17A -0.906x IL2 + 2.208x tobacco;
-4,487 3,441x IL17A -1,636x IL3 + 2,661x tobacco;
-7,777 2.060x IL17A -0.218 xIL4 + 1.930x tobacco;
-6,889 2,015 IxL17A -0,403 xIL5 + 1,875x tobacco;
-7,533 2,766x IL17A -1,234x IL10 + 1,610x tobacco;
-7,999 2,536x IL17A -0,714x IL12p70 + 1.808x tobacco;
-8.087 3.023x IL17A -1.014x IL13 + 2.784x tobacco.
[4]
4. In vitro method according to claim 1 to 3 wherein said periodontal disease is selected from gingivitis or periodontitis.
[5]
The in vitro method of claim 4 wherein said disease is gingivitis, in which case the probability value corresponds to the expression levels of the cytokines measured in an oral sample of a subject with gingivitis.
[6]
6. The in vitro method of claim 4 wherein said disease is periodontitis, in which case the probability value corresponds to the expression levels of the measured cytokines in an oral sample of a subject with periodontitis.
[7]
An in vitro method according to claim 6 wherein said disease is chronic periodontitis, in which case the probability value corresponds to the expression levels of the measured cytokines in an oral sample of a subject with chronic periodontitis.
[8]
An in vitro method according to claim 6 wherein said disease is aggressive periodontitis, in which case the probability value corresponds to the expression levels of the measured cytokines in an oral sample of a subject with aggressive periodontitis.
[9]
In vitro method according to claim 1 to 8 wherein the oral sample is gingival tissue, gingival crevicular fluid or saliva.
[10]
10. In vitro method according to claim 9 wherein the oral sample is gingival crevicular fluid.
[11]
11. In vitro method according to claims 1 to 10 wherein said level of expression of the cytokine comprises determining the level of mRNA encoded by the cytokine gene or determining the level of the protein of the cytokines.
[12]
12. In vitro method according to claim 11 wherein said level of expression of the cytokine comprises determining the level of the protein.
[13]
13. The in vitro method of claim 11 and 12 wherein the determination of cytokine levels is performed by a method selected from immunohistochemistry, Western blotting, flow cytometry or by ELISA.
[14]
14. In vitro method to determine the risk of developing a periodontal disease in a patient with gingival / periodontal health that includes:
a) Determine the level of expression of at least two cytokines selected from the following: IL1alpha, IL1beta, IL17A, IFN gamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL13 in an oral sample of the subject;
b) Determine the condition of smoker, non-smoker or ex-smoker in said subject;
c) Obtain a probability value associated with the level of expression of cytokines, through multivariate predictive models, which refers to the probability of risk of suffering clinically detectable periodontal disease in the future. The higher the probability value, the higher the risk of suffering a future periodontal disease. The value 0.5 is indicative of the existence of the same probability of suffering or not the future periodontal disease. This probability value is obtained either by using specific software for reading the test that provides this probability value or by providing said probability information to the user in any format, including oral, written, graphic or electronic to compare it with the values it has obtained.
[15]
15. In vitro method according to claim 14 comprising:
d) Determine the level of expression of two cytokines selected from the following:
IL1alpha, IL1beta, IL17A, IFN gamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL13 in an oral sample of the subject;
e) Determine the condition of smoker, non-smoker or ex-smoker in said subject;
f) Obtain a probability value associated to the level of expression of cytokines, through multivariate predictive models, which refers to the probability of risk of suffering a periodontal disease in the future. The higher the probability value, the higher the risk of suffering a future periodontal disease. The value 0.5 is indicative of the existence of the same probability of suffering or not the future periodontal disease. This probability value is obtained either by using specific software for reading the test that provides this probability value or or by providing said probability information to the user in any format, including oral, written, graphic or electronic forms, so that he can compare it with the values he has obtained.
[16]
16. In vitro method according to claims 14 and 15 in which the multivariate predictive models used are, at least, one of the following:
-71.383 4,622x + 2,042x IL1alpha -1,146x IFNgamma snuff;
-62,495 4,149x IL1alpha -1,158x IL2 + 2,214x tobacco;
-56,949 3,847x IL1alpha -0.831x IL3 + 1.924x tobacco;
-60,877 3,899x IL1alpha -0.467x IL4 + 2.027x tobacco;
-50,152 3,141x IL1alpha -0,082 xIL5 + 1,782x tobacco;
-62.194 4.055x IL1alpha -0.999x IL10 + 1.617x tobacco;
-59.907 3,857x IL1alpha -0.661x IL12p70 + 1.977x tobacco;
-54,740 3.540x IL1alpha -0.515x IL13 + 2.010x tobacco;
-31.507 2,546x + 1,918x 1beta -0,648x IFNgamma snuff;
-28,347 2,297x IL1beta -0.430x IL2 1,943x tobacco;
-27,512 2,217x IL1beta -0.221x IL3 + 1.799x tobacco;
-27,994 2,172x IL1beta -0.058 xIL4 + 1.745x tobacco;
-28.997 1beta + 0,233x 2,172x + 1,686x IL5 snuff;
-28,811 2,331x IL1beta -0,505x IL10 + 1,701x tobacco
-27.856 -0.177 1beta 2,190x + 1,709x xIL12p70 snuff;
-27.274 2.204x IL1beta -0.329 xIL13 + 1.923x tobacco;
-12.376 5,024x + 2,984x 17A -3,167x IFNgamma snuff
-6,912 2,455x IL17A -0.906x IL2 + 2.208x tobacco;
-4,487 3,441x IL17A -1,636x IL3 + 2,661x tobacco;
-7,777 2.060x IL17A -0.218 xIL4 + 1.930x tobacco;
-6,889 2,015 IxL17A -0,403 xIL5 + 1,875x tobacco;
-7,533 2,766x IL17A -1,234x IL10 + 1,610x tobacco;
-7,999 2,536x IL17A -0,714x IL12p70 + 1.808x tobacco;
-8.087 3.023x IL17A -1.014x IL13 + 2.784x tobacco.
[17]
17. In vitro method according to claims 14 to 16 wherein said periodontal disease is selected from gingivitis or periodontitis.
[18]
18. In vitro method according to claims 14 to 17, wherein the sample of said subject is selected from gingival tissue, gingival crevicular fluid or saliva.
[19]
19. In vitro method according to claim 18 wherein the sample is gingival crevicular fluid.
[20]
20. In vitro method according to claims 14 to 19, wherein said level of expression comprises determining the level of mRNA encoded by the cytokine gene or determining the level of the cytokine protein.
[21]
21. In vitro method according to claim 20 wherein said level of expression of the cytokine comprises determining the level of the protein.
[22]
22. In vitro method according to claim 20 and 21 wherein the determination of cytokine levels is performed by a method selected from immunohistochemistry, Western blotting, flow cytometry or by ELISA.
[23]
23. In vitro method to determine the response to a specific treatment of a patient with periodontal disease that includes:
g) Determine the level of expression of, at least, two cytokines selected from the following: IL1alpha, IL1beta, IL17A, IFN gamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL13 in an oral sample of the subject;
h) Determine the condition of smoker, non-smoker or ex-smoker in said subject; i) Obtain a probability value associated with the level of expression of cytokines, through multivariate predictive models, which refers the probability of a worse response to a given clinical treatment of a patient with periodontal disease. In this case, the higher the Probability value, there is a worse response to a given clinical treatment. The value 0.5 is indicative of the existence of the same probability of a negative or positive response to a given clinical treatment.
[24]
24. In vitro method according to claim 23 comprising:
j) Determine the level of expression of two cytokines selected from the following:
IL1alpha, IL1beta, IL17A, IFN gamma, IL2, IL12p70, IL3, IL4, IL5, IL10 and IL13 in an oral sample of the subject;
k) Determine the condition of smoker, non-smoker or ex-smoker in said subject;
l) Obtain a probability value associated to the level of expression of cytokines, through multivariate predictive models, which refers the probability of a worse response to a given clinical treatment of a patient with periodontal disease. In this case, the higher the Probability value, there is a worse response to a given clinical treatment. The value 0.5 is indicative of the existence of the same probability of a negative or positive response to a given clinical treatment. This probability value is obtained either by using specific software for reading the test that provides this probability value or by providing said probability information to the user in any format, including oral, written, graphic or electronic to compare it with the values it has obtained.
[25]
25. In vitro method according to claims 23 and 24 in which the multivariate predictive models used are, at least, one of the following:
-71.383 4,622x + 2,042x IL1alpha -1,146x IFNgamma snuff;
-62,495 4,149x IL1alpha -1,158x IL2 + 2,214x tobacco;
-56,949 3,847x IL1alpha -0.831x IL3 + 1.924x tobacco;
-60,877 3,899x IL1alpha -0.467x IL4 + 2.027x tobacco;
-50,152 3,141x IL1alpha -0,082 xIL5 + 1,782x tobacco;
-62.194 4.055x IL1alpha -0.999x IL10 + 1.617x tobacco;
-59.907 3,857x IL1alpha -0.661x IL12p70 + 1.977x tobacco;
-54,740 3.540x IL1alpha -0.515x IL13 + 2.010x tobacco;
-31.507 2,546x + 1,918x 1beta -0,648x IFNgamma snuff;
-28,347 2,297x IL1beta -0.430x IL2 1,943x tobacco;
-27,512 2,217x IL1beta -0.221x IL3 + 1.799x tobacco;
-27,994 2,172x IL1beta -0.058 xIL4 + 1.745x tobacco;
-28.997 1beta + 0,233x 2,172x + 1,686x IL5 snuff;
-28,811 2,331x IL1beta -0,505x IL10 + 1,701x tobacco
-27.856 -0.177 1beta 2,190x + 1,709x xIL12p70 snuff;
-27.274 2.204x IL1beta -0.329 xIL13 + 1.923x tobacco;
-12.376 5,024x + 2,984x 17A -3,167x IFNgamma snuff
-6,912 2,455x IL17A -0.906x IL2 + 2.208x tobacco;
-4,487 3,441x IL17A -1,636x IL3 + 2,661x tobacco;
-7,777 2.060x IL17A -0.218 xIL4 + 1.930x tobacco;
-6,889 2,015 IxL17A -0,403 xIL5 + 1,875x tobacco;
-7,533 2,766x IL17A -1,234x IL10 + 1,610x tobacco;
-7,999 2,536x IL17A -0,714x IL12p70 + 1.808x tobacco;
-8.087 3.023x IL17A -1.014x IL13 + 2.784x tobacco.
[26]
26. In vitro method according to claims 23 to 25, wherein said periodontal disease is selected from gingivitis or periodontitis.
[27]
27. The in vitro method of claim 26 wherein said disease is gingivitis, in which case the probability value corresponds to the expression levels of the cytokines measured in a sample of a subject with gingivitis.
[28]
28. In vitro method according to claim 26 wherein said disease is periodontitis, in which case the probability value corresponds to the expression levels of the measured cytokines in a sample of a subject with periodontitis.
[29]
29. In vitro method according to claim 28 wherein said disease is chronic periodontitis, in which case the probability value corresponds to the expression levels of the measured cytokines in a sample from a subject with chronic periodontitis.
[30]
30. In vitro method according to claim 28 wherein said disease is aggressive periodontitis, in which case the probability value corresponds to the expression levels of the measured cytokines in a sample of a subject with aggressive periodontitis.
[31]
31. In vitro method according to claims 23 to 30, wherein the sample of said subject is selected from gingival tissue, gingival crevicular fluid or saliva.
[32]
32. In vitro method according to claim 31 wherein the sample is gingival crevicular fluid.
[33]
33. In vitro method according to claims 23 to 31, wherein said level of expression comprises determining the level of mRNA encoded by the cytokine gene or determining the level of the cytokine protein.
[34]
34. An in vitro method according to claim 33 wherein said level of expression of the cytokine comprises determining the level of the protein.
[35]
35. An in vitro method according to claim 33 and 34 in which the determination of cytokine levels is performed by a method selected from immunohistochemistry, Western blotting, flow cytometry or by ELISA.
[36]
36. Kit for diagnosing the disease, the risk of suffering from the disease and / or the effectiveness of a treatment in a periodontal disease that includes:
a) an antibody, a polypeptide, a primer and / or a probe that specifically binds to the cytokine.
b) Some instructions
c) A positive control
d) A negative control
[37]
37. Kit according to claim 36, which also includes information for calculating the diagnosis of the disease, the risk of suffering from the disease and / or the effectiveness of the treatment, in the form of a multivariate predictive model, based on the levels of expression of the disease. the cytokines.
[38]
38. Kit according to claim 37 in which the information for calculating the diagnosis of the disease, the risk of suffering from the disease and / or the effectiveness of the treatment is obtained by applying, at least one, the following models multivariate predictors: -71.383 4,622x + 2,042x IL1alpha -1,146x IFNgamma snuff;
-62,495 4,149x IL1alpha -1,158x IL2 + 2,214x tobacco;
-56,949 3,847x IL1alpha -0.831x IL3 + 1.924x tobacco;
-60,877 3,899x IL1alpha -0.467x IL4 + 2.027x tobacco;
-50,152 3,141x IL1alpha -0,082 xIL5 + 1,782x tobacco;
-62.194 4.055x IL1alpha -0.999x IL10 + 1.617x tobacco;
-59.907 3,857x IL1alpha -0.661x IL12p70 + 1.977x tobacco;
-54,740 3.540x IL1alpha -0.515x IL13 + 2.010x tobacco;
-31.507 2,546x + 1,918x 1beta -0,648x IFNgamma snuff;
-28,347 2,297x IL1beta -0.430x IL2 1,943x tobacco;
-27,512 2,217x IL1beta -0.221x IL3 + 1.799x tobacco;
-27,994 2,172x IL1beta -0.058 xIL4 + 1.745x tobacco;
-28.997 1beta + 0,233x 2,172x + 1,686x IL5 snuff;
-28,811 2,331x IL1beta -0,505x IL10 + 1,701x tobacco
-27.856 -0.177 1beta 2,190x + 1,709x xIL12p70 snuff;
-27.274 2.204x IL1beta -0.329 xIL13 + 1.923x tobacco;
-12.376 5,024x + 2,984x 17A -3,167x IFNgamma snuff
-6,912 2,455x IL17A -0.906x IL2 + 2.208x tobacco;
-4,487 3,441x IL17A -1,636x IL3 + 2,661x tobacco;
-7,777 2.060x IL17A -0.218 xIL4 + 1.930x tobacco;
-6,889 2,015 IxL17A -0,403 xIL5 + 1,875x tobacco;
-7,533 2,766x IL17A -1,234x IL10 + 1,610x tobacco;
-7,999 2,536x IL17A -0,714x IL12p70 + 1.808x tobacco;
-8.087 3.023x IL17A -1.014x IL13 + 2.784x tobacco.
[39]
39. Kit according to claim 37 and 38 in which the information for the calculation is provided to the user in any format, including oral, written, graphic, electronic or through software

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同族专利:

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公开号 | 公开日

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ES2698157B2|2020-11-04|

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WO2019025652A1|2019-02-07|

引用文献:

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公开号 | 申请日 | 公开日 | 申请人 | 专利标题

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US5328829A|1990-07-05|1994-07-12|Forsyth Dental Infirmary For Children|Method of determining sites of active periodontal disease|

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申请号 | 申请日 | 专利标题

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ES201730994A|ES2698157B2|2017-07-31|2017-07-31|CITO-PERIOPREDICTOR|ES201730994A| ES2698157B2|2017-07-31|2017-07-31|CITO-PERIOPREDICTOR|

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PCT/ES2018/070521| WO2019025652A1|2017-07-31|2018-07-23|Cyto-periodontal prediction|